NITRC: The Cancer Imaging Archive (TCIA): PubMed Mentions: Display Document

PubMed Mentions

The links below are to publications on PubMed referring to The Cancer Imaging Archive (TCIA). This list is gathered weekly from PubMed automatically.

Publication/References
A simple tool for neuroimaging data sharing. Description: Haselgrove, Christian, et al. A simple tool for neuroimaging data sharing. ''Front Neuroinform''. 2014; '''8''': 52
Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models. Description: Chaddad, Ahmad. Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models. ''Int J Biomed Imaging''. 2015; '''2015''': 868031
Comparing nonrigid registration techniques for motion corrected MR prostate diffusion imaging. Description: Buerger, C, et al. Comparing nonrigid registration techniques for motion corrected MR prostate diffusion imaging. ''Med Phys''. 2015 Jan; '''42''' (1):69-80
High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Description: Chaddad, Ahmad, et al. High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. ''Adv Bioinformatics''. 2015; '''2015''': 728164
The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Description: Tomczak, Katarzyna, et al. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. ''Contemp Oncol (Pozn)''. 2015; '''19''' (1A):A68-77
Large scale validation of the M5L lung CAD on heterogeneous CT datasets. Description: Torres, E Lopez, et al. Large scale validation of the M5L lung CAD on heterogeneous CT datasets. ''Med Phys''. 2015 Apr; '''42''' (4):1477-89
LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. Description: Armato, Samuel G 3rd, et al. LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. ''J Med Imaging (Bellingham)''. 2015 Apr; '''2''' (2):020103
DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer. Description: Saha, Abhijoy, et al. DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer. ''Neuroimage Clin''. 2016; '''12''': 132-43
Publishing descriptions of non-public clinical datasets: proposed guidance for researchers, repositories, editors and funding organisations. Description: Hrynaszkiewicz, Iain, et al. Publishing descriptions of non-public clinical datasets: proposed guidance for researchers, repositories, editors and funding organisations. ''Res Integr Peer Rev''. 2016; '''1''': 6
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. Description: Li, Hui, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. ''NPJ Breast Cancer''. 2016; '''2''':
Radiomics: Images Are More than Pictures, They Are Data. Description: Gillies, Robert J, et al. Radiomics: Images Are More than Pictures, They Are Data. ''Radiology''. 2016 Feb; '''278''' (2):563-77
Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images. Description: Chaddad, Ahmad, et al. Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images. ''Brain Inform''. 2016 Mar; '''3''' (1):53-61
Differential localization of glioblastoma subtype: implications on glioblastoma pathogenesis. Description: Steed, Tyler C, et al. Differential localization of glioblastoma subtype: implications on glioblastoma pathogenesis. ''Oncotarget''. 2016 May 3; '''7''' (18):24899-907
Exploring cancer genomic data from the cancer genome atlas project. Description: Lee, Ju-Seog. Exploring cancer genomic data from the cancer genome atlas project. ''BMB Rep''. 2016 Nov; '''49''' (11):607-611
An Approach Toward Automatic Classification of Tumor Histopathology of Non-Small Cell Lung Cancer Based on Radiomic Features. Description: Patil, Ravindra, et al. An Approach Toward Automatic Classification of Tumor Histopathology of Non-Small Cell Lung Cancer Based on Radiomic Features. ''Tomography''. 2016 Dec; '''2''' (4):374-377
Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network. Description: Farahani, Keyvan, et al. Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network. ''Tomography''. 2016 Dec; '''2''' (4):242-249
A Comprehensive Infrastructure for Big Data in Cancer Research: Accelerating Cancer Research and Precision Medicine. Description: Hinkson, Izumi V, et al. A Comprehensive Infrastructure for Big Data in Cancer Research: Accelerating Cancer Research and Precision Medicine. ''Front Cell Dev Biol''. 2017; '''5''': 83
Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes. Description: Sutton, Elizabeth J, et al. Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes. ''Eur Radiol Exp''. 2017; '''1''' (1):22
Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI. Description: Hsieh, Kevin Li-Chun, et al. Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI. ''PLoS One''. 2017; '''12''' (2):e0171342
A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer. Description: Hugo, Geoffrey D, et al. A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer. ''Med Phys''. 2017 Feb; '''44''' (2):762-771
A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. Description: Semmineh, Natenael B, et al. A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. ''Tomography''. 2017 Mar; '''3''' (1):41-49
Revealing cancer subtypes with higher-order correlations applied to imaging and omics data. Description: Graim, Kiley, et al. Revealing cancer subtypes with higher-order correlations applied to imaging and omics data. ''BMC Med Genomics''. 2017 Mar 31; '''10''' (1):20
Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software. Description: Lee, Myungeun, et al. Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software. ''Korean J Radiol''. 2017 May-Jun; '''18''' (3):498-509
Association between tumor architecture derived from generalized Q-space MRI and survival in glioblastoma. Description: Taylor, Erik N, et al. Association between tumor architecture derived from generalized Q-space MRI and survival in glioblastoma. ''Oncotarget''. 2017 Jun 27; '''8''' (26):41815-41826
Associations between gene expression profiles of invasive breast cancer and Breast Imaging Reporting and Data System MRI lexicon. Description: Kim, Ga Ram, et al. Associations between gene expression profiles of invasive breast cancer and Breast Imaging Reporting and Data System MRI lexicon. ''Ann Surg Treat Res''. 2017 Jul; '''93''' (1):18-26
Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas. Description: Hsieh, Kevin Li-Chun, et al. Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas. ''Oncotarget''. 2017 Jul 11; '''8''' (28):45888-45897
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Description: Vallieres, Martin, et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. ''Sci Rep''. 2017 Aug 31; '''7''' (1):10117
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Description: Bakas, Spyridon, et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. ''Sci Data''. 2017 Sep 5; '''4''': 170117
Comparison of novel multi-level Otsu (MO-PET) and conventional PET segmentation methods for measuring FDG metabolic tumor volume in patients with soft tissue sarcoma. Description: Lee, Inki, et al. Comparison of novel multi-level Otsu (MO-PET) and conventional PET segmentation methods for measuring FDG metabolic tumor volume in patients with soft tissue sarcoma. ''EJNMMI Phys''. 2017 Sep 18; '''4''' (1):22
Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas. Description: Eichinger, Paul, et al. Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas. ''Sci Rep''. 2017 Oct 17; '''7''' (1):13396
Predicting survival time of lung cancer patients using radiomic analysis. Description: Chaddad, Ahmad, et al. Predicting survival time of lung cancer patients using radiomic analysis. ''Oncotarget''. 2017 Nov 28; '''8''' (61):104393-104407
2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer. Description: Shen, Chen, et al. 2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer. ''Transl Oncol''. 2017 Dec; '''10''' (6):886-894
A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. Description: Liu, Xing, et al. A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. ''Neuroimage Clin''. 2018; '''20''': 1070-1077
Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways. Description: Halani, Sameer H, et al. Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways. ''NPJ Precis Oncol''. 2018; '''2''': 24
Quantification of glioblastoma mass effect by lateral ventricle displacement. Description: Steed, Tyler C, et al. Quantification of glioblastoma mass effect by lateral ventricle displacement. ''Sci Rep''. 2018 Feb 12; '''8''' (1):2827
Radiomics and radiogenomics for precision radiotherapy. Description: Wu, Jia, et al. Radiomics and radiogenomics for precision radiotherapy. ''J Radiat Res''. 2018 Mar 1; '''59''' (suppl_1):i25-i31
Challenges and Technological Trends Toward Improved Medical Imaging-based Predictive Data-mining. Description: Zaritsky, Assaf. Challenges and Technological Trends Toward Improved Medical Imaging-based Predictive Data-mining. ''EBioMedicine''. 2018 Sep; '''35''': 20-21
Molecular profiles of tumor contrast enhancement: A radiogenomic analysis in anaplastic gliomas. Description: Liu, Xing, et al. Molecular profiles of tumor contrast enhancement: A radiogenomic analysis in anaplastic gliomas. ''Cancer Med''. 2018 Sep; '''7''' (9):4273-4283
Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer. Description: Wu, Jia, et al. Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer. ''Breast Cancer Res''. 2018 Sep 3; '''20''' (1):101
Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction. Description: Qian, Zenghui, et al. Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction. ''Aging (Albany NY)''. 2018 Oct 22; '''10''' (10):2884-2899
Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial. Description: Cherezov, Dmitry, et al. Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial. ''Cancer Med''. 2018 Dec; '''7''' (12):6340-6356
Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma. Description: Li, Zhi-Cheng, et al. Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma. ''Cancer Med''. 2018 Dec; '''7''' (12):5999-6009
A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis. Description: Zou, Lian, et al. A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis. ''Comput Math Methods Med''. 2019; '''2019''': 6509357
Immune Cytolytic Activity Is Associated With Genetic and Clinical Properties of Glioma. Description: Wang, Zhi-Liang, et al. Immune Cytolytic Activity Is Associated With Genetic and Clinical Properties of Glioma. ''Front Immunol''. 2019; '''10''': 1756
Performance of sparse-view CT reconstruction with multi-directional gradient operators. Description: Hsieh, Chia-Jui, et al. Performance of sparse-view CT reconstruction with multi-directional gradient operators. ''PLoS One''. 2019; '''14''' (1):e0209674
Discovery of pre-therapy 2-deoxy-2-(18)F-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients. Description: Arshad, Mubarik A, et al. Discovery of pre-therapy 2-deoxy-2-(18)F-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients. ''Eur J Nucl Med Mol Imaging''. 2019 Feb; '''46''' (2):455-466
A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. Description: Lu, Haonan, et al. A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. ''Nat Commun''. 2019 Feb 15; '''10''' (1):764
QIN Benchmarks for Clinical Translation of Quantitative Imaging Tools. Description: Farahani, Keyvan, et al. QIN Benchmarks for Clinical Translation of Quantitative Imaging Tools. ''Tomography''. 2019 Mar; '''5''' (1):1-6
Acute Tumor Transition Angle on Computed Tomography Predicts Chromosomal Instability Status of Primary Gastric Cancer: Radiogenomics Analysis from TCGA and Independent Validation. Description: Lai, Ying-Chieh, et al. Acute Tumor Transition Angle on Computed Tomography Predicts Chromosomal Instability Status of Primary Gastric Cancer: Radiogenomics Analysis from TCGA and Independent Validation. ''Cancers (Basel)''. 2019 May 9; '''11''' (5):
Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. Description: Qian, Zenghui, et al. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. ''Cancer Lett''. 2019 Jun 1; '''451''': 128-135
A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data. Description: Zhang, Zhuo, et al. A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data. ''Brief Bioinform''. 2019 Jul 19; '''20''' (4):1524-1541
Advanced 3D printed model of middle cerebral artery aneurysms for neurosurgery simulation. Description: Nagassa, Ruth G, et al. Advanced 3D printed model of middle cerebral artery aneurysms for neurosurgery simulation. ''3D Print Med''. 2019 Aug 1; '''5''' (1):11
Tumor Transcriptome Reveals High Expression of IL-8 in Non-Small Cell Lung Cancer Patients with Low Pectoralis Muscle Area and Reduced Survival. Description: Cury, Sarah Santiloni, et al. Tumor Transcriptome Reveals High Expression of IL-8 in Non-Small Cell Lung Cancer Patients with Low Pectoralis Muscle Area and Reduced Survival. ''Cancers (Basel)''. 2019 Aug 26; '''11''' (9):
Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. Description: Fan, Ming, et al. Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. ''Breast Cancer Res''. 2019 Oct 17; '''21''' (1):112
Distributed radiomics as a signature validation study using the Personal Health Train infrastructure. Description: Shi, Zhenwei, et al. Distributed radiomics as a signature validation study using the Personal Health Train infrastructure. ''Sci Data''. 2019 Oct 22; '''6''' (1):218
Fusion Radiomics Features from Conventional MRI Predict MGMT Promoter Methylation Status in Lower Grade Gliomas. Description: Jiang, Chendan, et al. Fusion Radiomics Features from Conventional MRI Predict MGMT Promoter Methylation Status in Lower Grade Gliomas. ''Eur J Radiol''. 2019 Dec; '''121''': 108714
Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma. Description: Zeng, Hao, et al. Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma. ''Front Cell Dev Biol''. 2020; '''8''': 553099
Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma. Description: Pan, Zi-Qi, et al. Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma. ''Behav Neurol''. 2020; '''2020''': 1712604
Stemness Related Genes Revealed by Network Analysis Associated With Tumor Immune Microenvironment and the Clinical Outcome in Lung Adenocarcinoma. Description: Zeng, Hao, et al. Stemness Related Genes Revealed by Network Analysis Associated With Tumor Immune Microenvironment and the Clinical Outcome in Lung Adenocarcinoma. ''Front Genet''. 2020; '''11''': 549213
Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis. Description: Chen, Xin, et al. Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis. ''Biomed Res Int''. 2020; '''2020''': 9258649
Deciphering the tumor microenvironment through radiomics in non-small cell lung cancer: Correlation with immune profiles. Description: Yoon, Hyun Jung, et al. Deciphering the tumor microenvironment through radiomics in non-small cell lung cancer: Correlation with immune profiles. ''PLoS One''. 2020; '''15''' (4):e0231227
Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional Study. Description: Wu, Ming-Yu, et al. Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional Study. ''Cancer Manag Res''. 2020; '''12''': 3503-3512
Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings. Description: Feng, Zhan, et al. Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings. ''Front Oncol''. 2020; '''10''': 279
Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma. Description: Zhang, Luyuan, et al. Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma. ''Front Oncol''. 2020; '''10''': 937
Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches. Description: Kurc, Tahsin, et al. Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches. ''Front Neurosci''. 2020; '''14''': 27
Computer-aided diagnosis of isocitrate dehydrogenase genotypes in glioblastomas from radiomic patterns. Description: Lo, Chung-Ming, et al. Computer-aided diagnosis of isocitrate dehydrogenase genotypes in glioblastomas from radiomic patterns. ''Medicine (Baltimore)''. 2020 Feb; '''99''' (8):e19123
SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization. Description: Zhang, Fan, et al. SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization. ''JCO Clin Cancer Inform''. 2020 Mar; '''4''': 299-309
From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health. Description: Capobianco, Enrico, et al. From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health. ''J Pers Med''. 2020 Mar 2; '''10''' (1):
Data-driven translational prostate cancer research: from biomarker discovery to clinical decision. Description: Lin, Yuxin, et al. Data-driven translational prostate cancer research: from biomarker discovery to clinical decision. ''J Transl Med''. 2020 Mar 7; '''18''' (1):119
Artificial intelligence in oncology. Description: Shimizu, Hideyuki, et al. Artificial intelligence in oncology. ''Cancer Sci''. 2020 May; '''111''' (5):1452-1460
Weakly-supervised learning for lung carcinoma classification using deep learning. Description: Kanavati, Fahdi, et al. Weakly-supervised learning for lung carcinoma classification using deep learning. ''Sci Rep''. 2020 Jun 9; '''10''' (1):9297
AI in Medical Imaging Informatics: Current Challenges and Future Directions. Description: Panayides, Andreas S, et al. AI in Medical Imaging Informatics: Current Challenges and Future Directions. ''IEEE J Biomed Health Inform''. 2020 Jul; '''24''' (7):1837-1857
Surgical spectral imaging. Description: Clancy, Neil T, et al. Surgical spectral imaging. ''Med Image Anal''. 2020 Jul; '''63''': 101699
Open Data Revolution in Clinical Research: Opportunities and Challenges. Description: Shahin, Mohamed H, et al. Open Data Revolution in Clinical Research: Opportunities and Challenges. ''Clin Transl Sci''. 2020 Jul; '''13''' (4):665-674
Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction. Description: Sun, Yingli, et al. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction. ''Eur Radiol''. 2020 Jul; '''30''' (7):3650-3659
Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment (18)F-PET/CT. Description: Liu, Zheran, et al. Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment (18)F-PET/CT. ''Aging (Albany NY)''. 2020 Jul 16; '''12''' (14):14593-14619
Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation. Description: Dong, Xianling, et al. Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation. ''Cancer Imaging''. 2020 Aug 1; '''20''' (1):53
Single-Cell Spatial Analysis of Tumor and Immune Microenvironment on Whole-Slide Image Reveals Hepatocellular Carcinoma Subtypes. Description: Wang, Haiyue, et al. Single-Cell Spatial Analysis of Tumor and Immune Microenvironment on Whole-Slide Image Reveals Hepatocellular Carcinoma Subtypes. ''Cancers (Basel)''. 2020 Nov 28; '''12''' (12):
Age-related copy number variations and expression levels of F-box protein FBXL20 predict ovarian cancer prognosis. Description: Zheng, Shuhua, et al. Age-related copy number variations and expression levels of F-box protein FBXL20 predict ovarian cancer prognosis. ''Transl Oncol''. 2020 Dec; '''13''' (12):100863
Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients. Description: Le, Quoc Cuong, et al. Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients. ''Sci Rep''. 2020 Dec 4; '''10''' (1):21301
Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Description: Noorbakhsh, Javad, et al. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. ''Nat Commun''. 2020 Dec 11; '''11''' (1):6367
A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology. Description: Li, Jian, et al. A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology. ''BMC Med Inform Decis Mak''. 2020 Dec 15; '''20''' (Suppl 14):297
Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives. Description: Huang, Shigao, et al. Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives. ''Int J Biol Sci''. 2021; '''17''' (6):1581-1587
A Voxel-Based Radiographic Analysis Reveals the Biological Character of Proneural-Mesenchymal Transition in Glioblastoma. Description: Qi, Tengfei, et al. A Voxel-Based Radiographic Analysis Reveals the Biological Character of Proneural-Mesenchymal Transition in Glioblastoma. ''Front Oncol''. 2021; '''11''': 595259
DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data. Description: van der Voort, Sebastian R, et al. DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data. ''Neuroinformatics''. 2021 Jan; '''19''' (1):159-184
Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. Description: Tufail, Ahsan Bin, et al. Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. ''Comput Math Methods Med''. 2021; '''2021''': 9025470
Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma. Description: Chen, Hongyu, et al. Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma. ''Front Oncol''. 2021; '''11''': 734433
Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma. Description: Gao, Ruizhi, et al. Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma. ''Front Oncol''. 2021; '''11''': 613668
Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma. Description: Huang, Yeqian, et al. Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma. ''Front Oncol''. 2021; '''11''': 640881
Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures. Description: Liu, Dongming, et al. Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures. ''Front Oncol''. 2021; '''11''': 699265
Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma. Description: Li, Hui, et al. Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma. ''Front Oncol''. 2021; '''11''': 636451
Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis. Description: Zhao, Binghao, et al. Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis. ''Front Immunol''. 2021; '''12''': 557994
Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas. Description: Mandal, Ayan S, et al. Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas. ''Brain Commun''. 2021; '''3''' (4):fcab289
Multimodal Deep Learning for Prognosis Prediction in Renal Cancer. Description: Schulz, Stefan, et al. Multimodal Deep Learning for Prognosis Prediction in Renal Cancer. ''Front Oncol''. 2021; '''11''': 788740
Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis. Description: Xiao, Zheng, et al. Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis. ''Front Oncol''. 2021; '''11''': 663451
Radiomics and Digital Image Texture Analysis in Oncology (Review). Description: Litvin, A A, et al. Radiomics and Digital Image Texture Analysis in Oncology (Review). ''Sovrem Tekhnologii Med''. 2021; '''13''' (2):97-104
The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy. Description: Tang, Xin, et al. The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy. ''Front Oncol''. 2021; '''11''': 591502
Uncovering a Distinct Gene Signature in Endothelial Cells Associated With Contrast Enhancement in Glioblastoma. Description: Yang, Fan, et al. Uncovering a Distinct Gene Signature in Endothelial Cells Associated With Contrast Enhancement in Glioblastoma. ''Front Oncol''. 2021; '''11''': 683367
A Systematic Approach of Data Collection and Analysis in Medical Imaging Research. Description: K N, Manjunath, et al. A Systematic Approach of Data Collection and Analysis in Medical Imaging Research. ''Asian Pac J Cancer Prev''. 2021 Feb 1; '''22''' (2):537-546
Genetic alterations associated with (18)F-fluorodeoxyglucose positron emission tomography/computed tomography in head and neck squamous cell carcinoma. Description: Han, Sangwon, et al. Genetic alterations associated with (18)F-fluorodeoxyglucose positron emission tomography/computed tomography in head and neck squamous cell carcinoma. ''Transl Oncol''. 2021 Feb; '''14''' (2):100988
Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features. Description: Ning, Zhenyuan, et al. Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features. ''Ann Transl Med''. 2021 Feb; '''9''' (4):298
Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging. Description: Lopez-Cabrera, Jose Daniel, et al. Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging. ''Health Technol (Berl)''. 2021 Feb 5; 1-14
Generalized chest CT and lab curves throughout the course of COVID-19. Description: Kassin, Michael T, et al. Generalized chest CT and lab curves throughout the course of COVID-19. ''Sci Rep''. 2021 Mar 25; '''11''' (1):6940
Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma. Description: Zeng, Hao, et al. Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma. ''Aging (Albany NY)''. 2021 Mar 26; '''13''' (7):9960-9975
Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Description: Ibrahim, A, et al. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. ''Methods''. 2021 Apr; '''188''': 20-29
The next horizon in precision oncology: Proteogenomics to inform cancer diagnosis and treatment. Description: Rodriguez, Henry, et al. The next horizon in precision oncology: Proteogenomics to inform cancer diagnosis and treatment. ''Cell''. 2021 Apr 1; '''184''' (7):1661-1670
Radiomic profiling of clear cell renal cell carcinoma reveals subtypes with distinct prognoses and molecular pathways. Description: Lin, Peng, et al. Radiomic profiling of clear cell renal cell carcinoma reveals subtypes with distinct prognoses and molecular pathways. ''Transl Oncol''. 2021 Apr 13; '''14''' (7):101078
CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. Description: Gitto, Salvatore, et al. CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. ''Insights Imaging''. 2021 Jun 2; '''12''' (1):68
Geometric and dosimetric impact of 3D generative adversarial network-based metal artifact reduction algorithm on VMAT and IMPT for the head and neck region. Description: Nakamura, Mitsuhiro, et al. Geometric and dosimetric impact of 3D generative adversarial network-based metal artifact reduction algorithm on VMAT and IMPT for the head and neck region. ''Radiat Oncol''. 2021 Jun 6; '''16''' (1):96
Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography. Description: Uhm, Kwang-Hyun, et al. Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography. ''NPJ Precis Oncol''. 2021 Jun 18; '''5''' (1):54
Deep learning for semi-automated unidirectional measurement of lung tumor size in CT. Description: Woo, MinJae, et al. Deep learning for semi-automated unidirectional measurement of lung tumor size in CT. ''Cancer Imaging''. 2021 Jun 23; '''21''' (1):43
Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma. Description: Chen, Linyan, et al. Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma. ''Cancer Med''. 2021 Jul; '''10''' (13):4615-4628
Radiomic assessment as a method for predicting tumor mutation burden (TMB) of bladder cancer patients: a feasibility study. Description: Tang, Xin, et al. Radiomic assessment as a method for predicting tumor mutation burden (TMB) of bladder cancer patients: a feasibility study. ''BMC Cancer''. 2021 Jul 16; '''21''' (1):823
Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography. Description: Usui, Keisuke, et al. Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography. ''Vis Comput Ind Biomed Art''. 2021 Jul 25; '''4''' (1):21
A Generalized Linear modeling approach to bootstrapping multi-frame PET image data. Description: O'Sullivan, Finbarr, et al. A Generalized Linear modeling approach to bootstrapping multi-frame PET image data. ''Med Image Anal''. 2021 Aug; '''72''': 102132
Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT. Description: Rossi, Matteo, et al. Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT. ''Diagnostics (Basel)''. 2021 Aug 9; '''11''' (8):
Text-based multi-dimensional medical images retrieval according to the features-usage correlation. Description: Safaei, AliAsghar. Text-based multi-dimensional medical images retrieval according to the features-usage correlation. ''Med Biol Eng Comput''. 2021 Aug 20;
Radiological tumor classification across imaging modality and histology. Description: Wu, Jia, et al. Radiological tumor classification across imaging modality and histology. ''Nat Mach Intell''. 2021 Sep; '''3''': 787-798
Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. Description: Zeng, Hao, et al. Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. ''Gynecol Oncol''. 2021 Oct; '''163''' (1):171-180
Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information. Description: Khodabakhshi, Zahra, et al. Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information. ''J Digit Imaging''. 2021 Oct; '''34''' (5):1086-1098
A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset. Description: Donisi, Leandro, et al. A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset. ''J Imaging''. 2021 Oct 18; '''7''' (10):
Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning. Description: Rossi, Matteo, et al. Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning. ''Med Phys''. 2021 Nov; '''48''' (11):7112-7126
Machine intelligence in non-invasive endocrine cancer diagnostics. Description: Thomasian, Nicole M, et al. Machine intelligence in non-invasive endocrine cancer diagnostics. ''Nat Rev Endocrinol''. 2021 Nov 9;
Explainable Artificial Intelligence for Human-Machine Interaction in Brain Tumor Localization. Description: Esmaeili, Morteza, et al. Explainable Artificial Intelligence for Human-Machine Interaction in Brain Tumor Localization. ''J Pers Med''. 2021 Nov 16; '''11''' (11):
Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine. Description: Fathi Kazerooni, Anahita, et al. Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine. ''Cancers (Basel)''. 2021 Nov 25; '''13''' (23):
Development and Validation of an MRI Radiomics-Based Signature to Predict Histological Grade in Patients with Invasive Breast Cancer. Description: Wang, Shihui, et al. Development and Validation of an MRI Radiomics-Based Signature to Predict Histological Grade in Patients with Invasive Breast Cancer. ''Breast Cancer (Dove Med Press)''. 2022; '''14''': 335-342
Multimodal analysis suggests differential immuno-metabolic crosstalk in lung squamous cell carcinoma and adenocarcinoma. Description: Leitner, Brooks P, et al. Multimodal analysis suggests differential immuno-metabolic crosstalk in lung squamous cell carcinoma and adenocarcinoma. ''NPJ Precis Oncol''. 2022 Jan 27; '''6''' (1):8
An artificial intelligence method to assess the tumor microenvironment with treatment outcomes for gastric cancer patients after gastrectomy. Description: Chen, Tao, et al. An artificial intelligence method to assess the tumor microenvironment with treatment outcomes for gastric cancer patients after gastrectomy. ''J Transl Med''. 2022 Feb 21; '''20''' (1):100
A deep learning pipeline to simulate fluorodeoxyglucose (FDG) uptake in head and neck cancers using non-contrast CT images without the administration of radioactive tracer. Description: Chandrashekar, Anirudh, et al. A deep learning pipeline to simulate fluorodeoxyglucose (FDG) uptake in head and neck cancers using non-contrast CT images without the administration of radioactive tracer. ''Insights Imaging''. 2022 Mar 14; '''13''' (1):45
Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study. Description: Wang, Zixing, et al. Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study. ''Insights Imaging''. 2022 Apr 28; '''13''' (1):82
HFCF-Net: A hybrid-feature cross fusion network for COVID-19 lesion segmentation from CT volumetric images. Description: Wang, Yanting, et al. HFCF-Net: A hybrid-feature cross fusion network for COVID-19 lesion segmentation from CT volumetric images. ''Med Phys''. 2022 Jun; '''49''' (6):3797-3815
Deep learning features encode interpretable morphologies within histological images. Description: Foroughi Pour, Ali, et al. Deep learning features encode interpretable morphologies within histological images. ''Sci Rep''. 2022 Jun 8; '''12''' (1):9428
Prognostic impact of an integrative analysis of [(18)F]FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma. Description: Choi, Jinyeong, et al. Prognostic impact of an integrative analysis of [(18)F]FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma. ''EJNMMI Res''. 2022 Jun 27; '''12''' (1):38
Association testing for binary trees-A Markov branching process approach. Description: Wu, Xiaowei, et al. Association testing for binary trees-A Markov branching process approach. ''Stat Med''. 2022 Jun 30; '''41''' (14):2557-2573
Development and verification of radiomics framework for computed tomography image segmentation. Description: Gu, Jiabing, et al. Development and verification of radiomics framework for computed tomography image segmentation. ''Med Phys''. 2022 Oct; '''49''' (10):6527-6537
Patient-Specific Magnetic Catheters for Atraumatic Autonomous Endoscopy. Description: Pittiglio, Giovanni, et al. Patient-Specific Magnetic Catheters for Atraumatic Autonomous Endoscopy. ''Soft Robot''. 2022 Dec; '''9''' (6):1120-1133
The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. Description: Crombe, Amandine, et al. The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. ''Cancer Commun (Lond)''. 2022 Dec; '''42''' (12):1288-1313
An intravenous pancreatic cancer therapeutic: Characterization of CRISPR/Cas9n-modified Clostridium novyi-Non Toxic. Description: Dailey, Kaitlin M, et al. An intravenous pancreatic cancer therapeutic: Characterization of CRISPR/Cas9n-modified Clostridium novyi-Non Toxic. ''PLoS One''. 2023; '''18''' (11):e0289183
Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme. Description: Zhang, Di, et al. Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme. ''Front Med (Lausanne)''. 2023; '''10''': 1271687
CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma. Description: He, Zenglei, et al. CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma. ''PLoS One''. 2023; '''18''' (9):e0290900
Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients. Description: Dammu, Hongyi, et al. Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients. ''PLoS One''. 2023; '''18''' (1):e0280148
SIFT-GVF-based lung edge correction method for correcting the lung region in CT images. Description: Li, Xin, et al. SIFT-GVF-based lung edge correction method for correcting the lung region in CT images. ''PLoS One''. 2023; '''18''' (2):e0282107
The involvement of brain regions associated with lower KPS and shorter survival time predicts a poor prognosis in glioma. Description: Bao, Hongbo, et al. The involvement of brain regions associated with lower KPS and shorter survival time predicts a poor prognosis in glioma. ''Front Neurol''. 2023; '''14''': 1264322
Translating Data Science Results into Precision Oncology Decisions: A Mini Review. Description: Capobianco, Enrico, et al. Translating Data Science Results into Precision Oncology Decisions: A Mini Review. ''J Clin Med''. 2023 Jan 5; '''12''' (2):
Molecular hallmarks of breast multiparametric magnetic resonance imaging during neoadjuvant chemotherapy. Description: Lin, Peng, et al. Molecular hallmarks of breast multiparametric magnetic resonance imaging during neoadjuvant chemotherapy. ''Radiol Med''. 2023 Jan 21; 1-13
Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer. Description: Liu, Qian, et al. Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer. ''Biomark Res''. 2023 Jan 24; '''11''' (1):9
Prognostic value of (18)F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer. Description: Wang, Bingzhen, et al. Prognostic value of (18)F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer. ''EJNMMI Res''. 2023 Feb 13; '''13''' (1):14
Computed tomography-based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma. Description: He, Hongchao, et al. Computed tomography-based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma. ''Cancer Med''. 2023 Mar; '''12''' (6):7627-7638
New insights into glioma frequency maps: From genetic and transcriptomic correlate to survival prediction. Description: Bao, Hongbo, et al. New insights into glioma frequency maps: From genetic and transcriptomic correlate to survival prediction. ''Int J Cancer''. 2023 Mar 1; '''152''' (5):998-1012
Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline. Description: Ye, Zezhong, et al. Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline. ''medRxiv''. 2023 Mar 6;
An Online Mammography Database with Biopsy Confirmed Types. Description: Cai, Hongmin, et al. An Online Mammography Database with Biopsy Confirmed Types. ''Sci Data''. 2023 Mar 7; '''10''' (1):123
Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study. Description: Lavinia Loeffler, Chiara Maria, et al. Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study. ''medRxiv''. 2023 Mar 10;
An immune indicator based on BTK and DPEP2 identifies hot and cold tumors and clinical treatment outcomes in lung adenocarcinoma. Description: Han, Tao, et al. An immune indicator based on BTK and DPEP2 identifies hot and cold tumors and clinical treatment outcomes in lung adenocarcinoma. ''Sci Rep''. 2023 Mar 29; '''13''' (1):5153
MTF1 has the potential as a diagnostic and prognostic marker for gastric cancer and is associated with good prognosis. Description: He, Jin, et al. MTF1 has the potential as a diagnostic and prognostic marker for gastric cancer and is associated with good prognosis. ''Clin Transl Oncol''. 2023 Apr 24; 1-11
Clinical applications of artificial intelligence in radiology. Description: Mello-Thoms, Claudia, et al. Clinical applications of artificial intelligence in radiology. ''Br J Radiol''. 2023 Apr 26; '''96''' (1150):20221031
Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features. Description: Wang, Fen, et al. Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features. ''Sci Rep''. 2023 Jun 8; '''13''' (1):9302
Assessment of brain cancer atlas maps with multimodal imaging features. Description: Capobianco, Enrico, et al. Assessment of brain cancer atlas maps with multimodal imaging features. ''J Transl Med''. 2023 Jun 12; '''21''' (1):385
Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning. Description: Boyd, Aidan, et al. Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning. ''medRxiv''. 2023 Jun 30;
HLA-DQA1 expression is associated with prognosis and predictable with radiomics in breast cancer. Description: Zhou, JingYu, et al. HLA-DQA1 expression is associated with prognosis and predictable with radiomics in breast cancer. ''Radiat Oncol''. 2023 Jul 11; '''18''' (1):117
Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer. Description: Lu, Haonan, et al. Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer. ''Cell Rep Med''. 2023 Jul 18; '''4''' (7):101092
Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System. Description: Lee, So Jeong, et al. Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System. ''Korean J Radiol''. 2023 Aug; '''24''' (8):772-783
SAMPLER: Empirical distribution representations for rapid analysis of whole slide tissue images. Description: Mukashyaka, Patience, et al. SAMPLER: Empirical distribution representations for rapid analysis of whole slide tissue images. ''bioRxiv''. 2023 Aug 3;
SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers. Description: Al-Tashi, Qasem, et al. SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers. ''Patterns (N Y)''. 2023 Aug 11; '''4''' (8):100777
CT radiomics prediction of CXCL9 expression and survival in ovarian cancer. Description: Gu, Rui, et al. CT radiomics prediction of CXCL9 expression and survival in ovarian cancer. ''J Ovarian Res''. 2023 Aug 30; '''16''' (1):180
A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information. Description: Ramakrishnan, Divya, et al. A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information. ''ArXiv''. 2023 Sep 12;
Prediction of cancer treatment response from histopathology images through imputed transcriptomics. Description: Hoang, Danh-Tai, et al. Prediction of cancer treatment response from histopathology images through imputed transcriptomics. ''Res Sq''. 2023 Sep 15;
A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer. Description: Zhan, Feng, et al. A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer. ''Sci Rep''. 2023 Sep 29; '''13''' (1):16397
Radiological Study of Atlas Arch Defects with Meta-Analysis and a Proposed New Classification. Description: Suphamungmee, Worawit, et al. Radiological Study of Atlas Arch Defects with Meta-Analysis and a Proposed New Classification. ''Asian Spine J''. 2023 Oct; '''17''' (5):975-984
ARPC5 acts as a potential prognostic biomarker that is associated with cell proliferation, migration and immune infiltrate in gliomas. Description: Ming, Yue, et al. ARPC5 acts as a potential prognostic biomarker that is associated with cell proliferation, migration and immune infiltrate in gliomas. ''BMC Cancer''. 2023 Oct 3; '''23''' (1):937
Noninvasive radiomic analysis of enhanced CT predicts CTLA4 expression and prognosis in head and neck squamous cell carcinoma. Description: Zhu, Yeping, et al. Noninvasive radiomic analysis of enhanced CT predicts CTLA4 expression and prognosis in head and neck squamous cell carcinoma. ''Sci Rep''. 2023 Oct 5; '''13''' (1):16782
Association of graph-based spatial features with overall survival status of glioblastoma patients. Description: Lee, Joonsang, et al. Association of graph-based spatial features with overall survival status of glioblastoma patients. ''Sci Rep''. 2023 Oct 9; '''13''' (1):17046
CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations. Description: Xia, Tian, et al. CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations. ''J Digit Imaging''. 2023 Dec; '''36''' (6):2356-2366
Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics. Description: Jiang, Wenying, et al. Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics. ''Cancer Med''. 2023 Dec; '''12''' (24):21861-21872
Deep learning-based segmentation of multisite disease in ovarian cancer. Description: Buddenkotte, Thomas, et al. Deep learning-based segmentation of multisite disease in ovarian cancer. ''Eur Radiol Exp''. 2023 Dec 7; '''7''' (1):77
Lesion detection in women breast's dynamic contrast-enhanced magnetic resonance imaging using deep learning. Description: Saikia, Sudarshan, et al. Lesion detection in women breast's dynamic contrast-enhanced magnetic resonance imaging using deep learning. ''Sci Rep''. 2023 Dec 18; '''13''' (1):22555
Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital. Description: Kulkarni, Chaitanya, et al. Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital. ''BJR Open''. 2024 Jan; '''6''' (1):tzad008
Intensity scaling of conventional brain magnetic resonance images avoiding cerebral reference regions: A systematic review. Description: Wiltgen, Tun, et al. Intensity scaling of conventional brain magnetic resonance images avoiding cerebral reference regions: A systematic review. ''PLoS One''. 2024; '''19''' (3):e0298642
Use of fractals in determining the malignancy degree of lung nodules. Description: Amador-Legon, Noel Victor, et al. Use of fractals in determining the malignancy degree of lung nodules. ''Front Med Technol''. 2024; '''6''': 1362688
Focus stacking single-event particle radiography for high spatial resolution images and 3D feature localization. Description: Volz, Lennart, et al. Focus stacking single-event particle radiography for high spatial resolution images and 3D feature localization. ''Phys Med Biol''. 2024 Jan 10; '''69''' (2):
A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images. Description: Sampath, Kanimozhi, et al. A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images. ''Sci Rep''. 2024 Jan 25; '''14''' (1):2144
Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme. Description: Luan, Jixin, et al. Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme. ''J Transl Med''. 2024 Jan 26; '''22''' (1):107
MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma. Description: Chen, Di, et al. MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma. ''Biomark Res''. 2024 Jan 31; '''12''' (1):14
A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring. Description: Connor, Kate, et al. A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring. ''Sci Rep''. 2024 Feb 1; '''14''' (1):2720
Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation. Description: Santinha, Joao, et al. Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation. ''J Imaging Inform Med''. 2024 Feb; '''37''' (1):31-44
A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI. Description: Wei, Ruili, et al. A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI. ''J Cancer Res Clin Oncol''. 2024 Feb 2; '''150''' (2):73
Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis. Description: Boubnovski Martell, Marc, et al. Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis. ''NPJ Precis Oncol''. 2024 Feb 3; '''8''' (1):28
AI applications in musculoskeletal imaging: a narrative review. Description: Gitto, Salvatore, et al. AI applications in musculoskeletal imaging: a narrative review. ''Eur Radiol Exp''. 2024 Feb 15; '''8''' (1):22
CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer. Description: Wang, Jiexiao, et al. CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer. ''BMC Med Imaging''. 2024 Feb 15; '''24''' (1):45
Reducing image artifacts in sparse projection CT using conditional generative adversarial networks. Description: Usui, Keisuke, et al. Reducing image artifacts in sparse projection CT using conditional generative adversarial networks. ''Sci Rep''. 2024 Feb 16; '''14''' (1):3917
CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies. Description: Gitto, Salvatore, et al. CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies. ''Insights Imaging''. 2024 Feb 27; '''15''' (1):54
A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information. Description: Ramakrishnan, Divya, et al. A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information. ''Sci Data''. 2024 Feb 29; '''11''' (1):254
Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer. Description: Huang, Zi Huai, et al. Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer. ''J Transl Med''. 2024 Mar 2; '''22''' (1):226
Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept. Description: Salehjahromi, Morteza, et al. Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept. ''Cell Rep Med''. 2024 Mar 19; '''5''' (3):101463
Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response. Description: Chen, Ziqiang, et al. Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response. ''NPJ Precis Oncol''. 2024 Mar 22; '''8''' (1):73
A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study. Description: Lai, Jianguo, et al. A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study. ''Int J Surg''. 2024 Apr 1; '''110''' (4):2162-2177