Extendable and explainable deep learning for pan-cancer radiogenomics research

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Hallmarks of Cancer: The Next Generation

Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590 · see citing works

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works

Textural Features for Image Classification

Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works

Tensor Decompositions and Applications

Kolda, T. G., & Bader, B. W. (2009) · SIAM Review · doi:10.1137/07070111x · Cited by 10,595 · see citing works

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works

Radiomics: Images Are More than Pictures, They Are Data

Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421 · see citing works

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01228-7 · Cited by 6,909 · see citing works

“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682 · see citing works

Computational Radiomics System to Decode the Radiographic Phenotype

(2017) · doi:10.1158/0008-5472.can-17-0339 · Cited by 6,593 · see citing works

Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position

Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422 · see citing works

Random decision forests

Ho, T. K. (2002) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.1995.598994 · Cited by 6,311 · see citing works

Radiomics: extracting more information from medical images using advanced feature analysis

Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084 · see citing works

Mediation Analysis

MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085542 · Cited by 5,688 · see citing works

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works

DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

Moosavi-Dezfooli, S.-M., Fawzi, A., & Frossard, P. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.282 · Cited by 5,317 · see citing works

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599 · see citing works

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology · doi:10.1148/radiol.2020191145 · Cited by 4,177 · see citing works

Axiomatic attribution for deep networks

Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087 · see citing works

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works

Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge

Tomczak, K. J., Czerwińska, P., & Wiznerowicz, M. (2015) · Współczesna Onkologia · doi:10.5114/wo.2014.47136 · Cited by 3,503 · see citing works

Striving for Simplicity: The All Convolutional Net

Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447 · see citing works

The ethics of algorithms: Mapping the debate

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 3,432 · see citing works

Deep learning for computer vision: A brief review

Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/7068349 · Cited by 3,403 · see citing works

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Nguyen, A., Yosinski, J., & Clune, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298640 · Cited by 3,258 · see citing works

Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas

The Cancer Genome Atlas Research Network (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1402121 · Cited by 3,148 · see citing works

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921 · see citing works

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W. F., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206848 · Cited by 2,714 · see citing works

Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic

Chan, T. A., Yarchoan, M., Jaffee, E., Swanton, C., Quezada, S. A., Stenzinger, A., & Peters, S. (2019) · Annals of Oncology · doi:10.1093/annonc/mdy495 · Cited by 2,683 · see citing works

Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent

Simon, N., Friedman, J. H., Hastie, T. J., & Tibshirani, R. J. (2011) · Journal of Statistical Software · doi:10.18637/jss.v039.i05 · Cited by 2,524 · see citing works

One Pixel Attack for Fooling Deep Neural Networks

Su, J., Vargas, D. V., & Sakurai, K. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2890858 · Cited by 2,406 · see citing works

Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers

Goodman, A. M., Kato, S., Bazhenova, L., Patel, S. P., Frampton, G. M., Miller, V., Stephens, P. J., Daniels, G. A., & Kurzrock, R. (2017) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-17-0386 · Cited by 2,264 · see citing works

Metagenes and molecular pattern discovery using matrix factorization

Brunet, J.-P., Tamayo, P., Golub, T. R., & Mesirov, J. P. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308531101 · Cited by 2,183 · see citing works

A universal SNP and small-indel variant caller using deep neural networks

Poplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4235 · Cited by 1,840 · see citing works

Data augmentation for improving deep learning in image classification problem

Mikołajczyk, A., & Grochowski, M. (2018) · International Interdisciplinary PhD Workshop (IIPhDW) · doi:10.1109/iiphdw.2018.8388338 · Cited by 1,737 · see citing works

Understanding Neural Networks Through Deep VisualizationPreprint

Yosinski, J., Clune, J., Nguyen, A. M., Fuchs, T. J., & Lipson, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.06579 · Cited by 1,687 · see citing works

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1994) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1994.323798 · Cited by 1,490 · see citing works

SmoothGrad: removing noise by adding noise

Smilkov, D., Thorat, N., Kim, B., Viégas, F., & Wattenberg, M. (2017) · arXiv · doi:10.48550/arxiv.1706.03825 · Cited by 1,467 · see citing works

An Empirical Exploration of Recurrent Network Architectures

Józefowicz, R., Zaremba, W., & Sutskever, I. (2015) · Cited by 1,459 · see citing works

High-Resolution Mapping and Characterization of Open Chromatin across the Genome

Boyle, A. P., Davis, S., Shulha, H. P., Meltzer, P., Margulies, E. H., Weng, Z., Furey, T. S., & Crawford, G. E. (2008) · Cell · doi:10.1016/j.cell.2007.12.014 · Cited by 1,415 · see citing works

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

(2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-28954-6 · Cited by 1,377 · see citing works

Explaining nonlinear classification decisions with deep taylor decomposition

Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.008 · Cited by 1,357 · see citing works

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., & Sayres, R. (2017) · arXiv · doi:10.48550/arxiv.1711.11279 · Cited by 1,240 · see citing works

Textural features corresponding to textural properties

Amadasun, M., & King, R. A. (1989) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.44046 · Cited by 1,163 · see citing works

Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080 · see citing works

DANN: a deep learning approach for annotating the pathogenicity of genetic variants

Quang, D. X., Chen, Y., & Xie, X. S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu703 · Cited by 1,073 · see citing works

Stem cell transcriptome profiling via massive-scale mRNA sequencing

Cloonan, N., Forrest, A. R. R., Kolle, G., Gardiner, B. B. A., Faulkner, G. J., Brown, M. K., Taylor, D. F., Steptoe, A. L., Wani, S., Bethel, G., et al. (2008) · Nature Methods · doi:10.1038/nmeth.1223 · Cited by 1,035 · see citing works

A primer on deep learning in genomics

Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., & Telenti, A. (2019) · Nature Genetics · doi:10.1038/s41588-018-0295-5 · Cited by 891 · see citing works

Artificial Intelligence and Black‐Box Medical Decisions: <i>Accuracy versus Explainability</i>

London, A. J. (2019) · Hastings Center Report · doi:10.1002/hast.973 · Cited by 801 · see citing works

Measuring Skewness: A Forgotten Statistic?

Doane, D. P., & Seward, L. W. (2011) · Journal of Statistics Education · doi:10.1080/10691898.2011.11889611 · Cited by 769 · see citing works

Explainable Deep Learning Models in Medical Image Analysis

Singh, A., Sengupta, S., & Lakshminarayanan, V. (2020) · Journal of Imaging · doi:10.3390/jimaging6060052 · Cited by 718 · see citing works

Residual Networks Behave Like Ensembles of Relatively Shallow Networks

Veit, A., Wilber, M., & Belongie, S. (2016) · arXiv · doi:10.48550/arxiv.1605.06431 · Cited by 705 · see citing works

More Is Better: Recent Progress in Multi-Omics Data Integration Methods

Huang, S., Chaudhary, K., & Garmire, L. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00084 · Cited by 637 · see citing works

Towards better understanding of gradient-based attribution methods for Deep Neural NetworksPreprint

Ancona, M., Ceolini, E., Öztireli, C., & Groß, M. H. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.3929/ethz-b-000249929 · Cited by 634 · see citing works

The emerging clinical relevance of genomics in cancer medicine

Berger, M. F., & Mardis, E. R. (2018) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-018-0002-6 · Cited by 627 · see citing works

RIP-Chip: the isolation and identification of mRNAs, microRNAs and protein components of ribonucleoprotein complexes from cell extracts

Keene, J. D., Komisarow, J. M., & Friedersdorf, M. B. (2006) · Nature Protocols · doi:10.1038/nprot.2006.47 · Cited by 612 · see citing works

The CPTAC Data Portal: A Resource for Cancer Proteomics Research

Edwards, N. J., Oberti, M., Thangudu, R. R., Cai, S., McGarvey, P. B., Jacob, S., Madhavan, S., & Ketchum, K. A. (2015) · Journal of Proteome Research · doi:10.1021/pr501254j · Cited by 565 · see citing works

Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data

Witten, D., & Tibshirani, R. J. (2009) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1470 · Cited by 551 · see citing works

Interpretability of machine learning-based prediction models in healthcare

Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1379 · Cited by 541 · see citing works

Metrics for explainable AI: challenges and prospects

Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018) · arXiv · doi:10.48550/arxiv.1812.04608 · Cited by 518 · see citing works

Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study

Liu, Z., Li, Z., Qu, J., Zhang, R., Zhou, X., Li, L., Sun, K., Tang, Z., Jiang, H., Li, H., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-3190 · Cited by 494 · see citing works

Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability

Coeckelbergh, M. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00146-8 · Cited by 475 · see citing works

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

Chang, P. D., Grinband, J., Weinberg, B. D., Bardis, M. D., Khy, M., Cadena, G., Su, M., Cha, S., Filippi, C. G., Bota, D. A., et al. (2018) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5667 · Cited by 466 · see citing works

integrOmics: an R package to unravel relationships between two omics datasets

Cao, K. L., González, I., & Dejean, S. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp515 · Cited by 459 · see citing works

Deep Learning-Based Image Segmentation on Multimodal Medical Imaging

Guo, Z., Li, X., Huang, H., Guo, N., & Li, Q. (2019) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2018.2890359 · Cited by 445 · see citing works

Using sensitivity analysis and visualization techniques to open black box data mining models

Cortez, P. C., & Embrechts, M. J. (2012) · Information Sciences · doi:10.1016/j.ins.2012.10.039 · Cited by 444 · see citing works

T2–FLAIR Mismatch, an Imaging Biomarker for IDH and 1p/19q Status in Lower-grade Gliomas: A TCGA/TCIA Project

Patel, S. H., Poisson, L., Brat, D. J., Zhou, Y., Cooper, L., Snuderl, M., Thomas, C., Franceschi, A. M., Griffith, B. D., Flanders, A. E., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-0560 · Cited by 426 · see citing works

Kurtosis: A Critical Review

Balanda, K. P., & MacGillivray, H. (1988) · The American Statistician · doi:10.1080/00031305.1988.10475539 · Cited by 419 · see citing works

MADE4: an R package for multivariate analysis of gene expression data

Culhane, A. C., Thioulouse, J., Perrière, G., & Higgins, D. G. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti394 · Cited by 396 · see citing works

Non–Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results

Gevaert, O., Xu, J., Hoang, C. D., Leung, A. N. C., Xu, Y., Quon, A. S. W., Rubin, D. L., Napel, S. A., & Plevritis, S. K. (2012) · Radiology · doi:10.1148/radiol.12111607 · Cited by 395 · see citing works

Regularized Generalized Canonical Correlation Analysis

Tenenhaus, A., & Tenenhaus, M. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.01.008 · Cited by 392 · see citing works

Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging

Chang, K., Bai, H. X., Zhou, H., Su, C., Bi, W. L., Agbodza, E., Kavouridis, V. K., Senders, J. T., Boaro, A., Beers, A. L., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-2236 · Cited by 392 · see citing works

Random Decision Forests

Sammut, C., & Webb, G. I. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_100391 · Cited by 387 · see citing works

CCA: AnRPackage to Extend Canonical Correlation Analysis

Gonzalez, I., Dejean, S., Martin, P. G. P., & Baccini, A. (2008) · Journal of Statistical Software · doi:10.18637/jss.v023.i12 · Cited by 379 · see citing works

Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging

Sala, E., Mema, E., Himoto, Y., Veeraraghavan, H., Brenton, J. D., Snyder, A., Weigelt, B., & Vargas, H. A. (2016) · Clinical Radiology · doi:10.1016/j.crad.2016.09.013 · Cited by 369 · see citing works

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired DataPreprint

Almahairi, A., Rajeswar, S., Sordoni, A., Bachman, P., & Courville, A. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.10151 · Cited by 356 · see citing works

MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

Zhang, Z., Xie, Y., Xing, F., McGough, M., & Yang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.378 · Cited by 354 · see citing works

Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

Wang, H., Cruz-Roa, Á., Basavanhally, A. N., Gilmore, H. L., Shih, N. N. C., Feldman, M., Tomaszewski, J., Gonzalez, F. A., & Madabhushi, A. (2014) · Journal of medical imaging · doi:10.1117/1.jmi.1.3.034003 · Cited by 349 · see citing works

Onco-Multi-OMICS Approach: A New Frontier in Cancer Research

Chakraborty, S., Hosen, M. I., Ahmed, M., & Shekhar, H. U. (2018) · BioMed Research International · doi:10.1155/2018/9836256 · Cited by 341 · see citing works

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

Buda, M., Saha, A., & Mazurowski, M. A. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.05.002 · Cited by 339 · see citing works

Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma

Li, Z., Wang, Y., Yu, J., Guo, Y., & Cao, W. (2017) · Scientific Reports · doi:10.1038/s41598-017-05848-2 · Cited by 332 · see citing works

Differential Data Augmentation Techniques for Medical Imaging Classification Tasks.

Hussain, Z., Gimenez, F., Yi, D., & Rubin, D. L. (2017) · PubMed Central · Cited by 323 · see citing works

A radiogenomic dataset of non-small cell lung cancer

Bakr, S. H., Gevaert, O., Echegaray, S., Ayers, K., Zhou, M., Shafiq, M., Zheng, H., Benson, J. A., Zhang, W., Leung, A. N. C., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.202 · Cited by 319 · see citing works

Radiogenomics: bridging imaging and genomics

Bodalal, Z., Trebeschi, S., Nguyen-Kim, T. D. L., Schats, W., & Beets-Tan, R. (2019) · Abdominal Radiology · doi:10.1007/s00261-019-02028-w · Cited by 310 · see citing works

Random survival forests for competing risks

Ishwaran, H., Gerds, T. A., Kogalur, U. B., Moore, R. D., Gange, S. J., & Lau, B. M. (2014) · Biostatistics · doi:10.1093/biostatistics/kxu010 · Cited by 298 · see citing works

Radiogenomics: What It Is and Why It Is Important

Mazurowski, M. A. (2015) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2015.04.019 · Cited by 289 · see citing works

3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients

Nie, D., Zhang, H., Adeli, E., Liu, L., & Shen, D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_25 · Cited by 282 · see citing works

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

Zhang, B., Chang, K., Ramkissoon, S. H., Tanguturi, S. K., Bi, W. L., Reardon, D. A., Ligon, K. L., Alexander, B. M., Wen, P. Y., & Huang, R. Y. (2016) · Neuro-Oncology · doi:10.1093/neuonc/now121 · Cited by 262 · see citing works

Deep learning and radiomics in precision medicine

Parekh, V. S., & Jacobs, M. A. (2019) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2019.1585805 · Cited by 245 · see citing works

Breast Mass Classification from Mammograms using Deep Convolutional Neural NetworksPreprint

Lévy, D., & Jain, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00542 · Cited by 229 · see citing works

Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence

Akkus, Z., Ali, I. M., Sedlář, J., Agrawal, J. P., Parney, I. F., Giannini, C., & Erickson, B. J. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9984-3 · Cited by 227 · see citing works

Deep CT to MR Synthesis Using Paired and Unpaired Data

Jin, C., Kim, H., Liu, M., Jung, W., Joo, S., Park, E., Ahn, Y., Han, I. H., Lee, J., & Cui, X. (2019) · Sensors · doi:10.3390/s19102361 · Cited by 226 · see citing works

Understanding the Mechanisms of Deep Transfer Learning for Medical Images

Ravishankar, H., Sudhakar, P., Venkataramani, R., Thiruvenkadam, S. R., Annangi, P., Babu, N., & Vaidya, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_20 · Cited by 203 · see citing works

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status

Korfiatis, P. D., Kline, T. L., Lachance, D. H., Parney, I. F., Buckner, J. C., & Erickson, B. J. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-0009-z · Cited by 200 · see citing works

A multi-task convolutional deep neural network for variant calling in single molecule sequencing

Luo, R., Sedlazeck, F. J., Lam, T., & Schatz, M. C. (2019) · Nature Communications · doi:10.1038/s41467-019-09025-z · Cited by 188 · see citing works

Background, current role, and potential applications of radiogenomics

Pinker, K., Shitano, F., Sala, E., Do, R. K., Young, R. J., Wibmer, A. G., Hricak, H., Sutton, E. J., & Morris, E. A. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25870 · Cited by 184 · see citing works

Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics

Zhang, J., Saha, A., Zhu, Z., & Mazurowski, M. A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2865671 · Cited by 175 · see citing works

SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation

Sun, J., Darbehani, F., Zaidi, M., & Wang, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59719-1_77 · Cited by 169 · see citing works

Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications

Visvikis, D., Rest, C. C. L., Jaouen, V., & Hatt, M. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04373-w · Cited by 168 · see citing works

Deep learning for identifying radiogenomic associations in breast cancer

Zhu, Z., AlBadawy, E. A., Saha, A., Zhang, J., Harowicz, M. R., & Mazurowski, M. A. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.018 · Cited by 167 · see citing works

Epidermal Growth Factor Receptor Extracellular Domain Mutations in Glioblastoma Present Opportunities for Clinical Imaging and Therapeutic Development

Binder, Z. A., Thorne, A. H., Bakas, S., Wileyto, E. P., Bilello, M., Akbari, H., Rathore, S., Ha, S. M., Zhang, L., Ferguson, C., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.06.006 · Cited by 166 · see citing works

Predictive radiogenomics modeling of EGFR mutation status in lung cancer

Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Jensen, K. C., Berry, G. J., Guo, H. H., Lau, C., Plevritis, S. K., et al. (2017) · Scientific Reports · doi:10.1038/srep41674 · Cited by 160 · see citing works

Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study

Li, Z., Bai, H., Sun, Q., Li, Q., Liu, L., Zou, Y., Chen, Y., Liang, C., & Zheng, H. (2018) · European Radiology · doi:10.1007/s00330-017-5302-1 · Cited by 157 · see citing works

Radiographic prediction of meningioma grade by semantic and radiomic features

Coroller, T. P., Bi, W. L., Huynh, E., Abedalthagafi, M., Aizer, A. A., Greenwald, N. F., Parmar, C., Narayan, V., Wu, W. W., de Moura, S. M., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187908 · Cited by 152 · see citing works

Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability

Kim, H., Park, C. M., Lee, M., Park, S. J., Song, Y., Lee, J. H., Hwang, E. J., & Goo, J. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0164924 · Cited by 143 · see citing works

Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97

(1997) · doi:10.1145/258734 · Cited by 136 · see citing works

Multi-Modality Medical Image Fusion using Discrete Wavelet Transform

Bhavana, V., & Krishnappa, H. K. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.10.057 · Cited by 133 · see citing works

Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas

Liang, S., Zhang, R., Liang, D., Song, T., Ai, T., Chen, X., Xia, L., & Wang, Y. (2018) · Genes · doi:10.3390/genes9080382 · Cited by 133 · see citing works

What Are Omics Sciences?

Vailati-Riboni, M., Palombo, V., & Loor, J. J. (2017) · Springer eBooks · doi:10.1007/978-3-319-43033-1_1 · Cited by 129 · see citing works

Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm

Ha, R. S., Mutasa, S., Karcich, J., Gupta, N., Sant, E. P. V., Nemer, J. S., Sun, M., Chang, P., Liu, M. Z., & Jambawalikar, S. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00179-2 · Cited by 124 · see citing works

Sparse integrative clustering of multiple omics data sets

Shen, R., Wang, S., & Mo, Q. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas578 · Cited by 121 · see citing works

A deep auto-encoder model for gene expression prediction

Xie, R., Wen, J., Quitadamo, A., Cheng, J. J., & Shi, X. M. (2017) · BMC Genomics · doi:10.1186/s12864-017-4226-0 · Cited by 120 · see citing works

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Yip, S. S. F., Liu, Y., Parmar, C., Li, Q., Liu, S., Qu, F., Ye, Z., Gillies, R. J., & Aerts, H. J. W. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-02425-5 · Cited by 119 · see citing works

CNAmet: an R package for integrating copy number, methylation and expression data

Louhimo, R., & Hautaniemi, S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr019 · Cited by 116 · see citing works

In vivoevaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature

Akbari, H., Bakas, S., Pisapia, J. M., Nasrallah, M. P., Rozycki, M., Martinez‐Lage, M., Morrissette, J. J. D., Dahmane, N., O’Rourke, D. M., & Davatzikos, C. A. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy033 · Cited by 113 · see citing works

Imaging genomics in cancer research: limitations and promises

Bai, H. X., Lee, A. M., Yang, L., Zhang, P. J., Davatzikos, C. A., Maris, J. M., & Diskin, S. J. (2016) · British Journal of Radiology · doi:10.1259/bjr.20151030 · Cited by 112 · see citing works

Interpretable Deep Learning under FirePreprint

Zhang, X., Wang, N., Shen, H., Ji, S., Luo, X., & Wang, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00891 · Cited by 108 · see citing works

Software for the Integration of Multiomics Experiments in Bioconductor

Ramos, M., Schiffer, L., Re, A., Azhar, R., Basunia, A., Cabrera, C. R., Chan, T., Chapman, P. J., Davis, S. R., Gómez-Cabrero, D., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-0344 · Cited by 107 · see citing works

Bringing radiomics into a multi-omics framework for a comprehensive genotype–phenotype characterization of oncological diseases

Zanfardino, M., Franzese, M., Pane, K., Cavaliere, C., Monti, S., Esposito, G., Salvatore, M., & Aiello, M. (2019) · Journal of Translational Medicine · doi:10.1186/s12967-019-2073-2 · Cited by 97 · see citing works

MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks

Han, L., & Kamdar, M. R. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0031 · Cited by 96 · see citing works

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

Trivizakis, E., Papadakis, G. Z., Souglakos, I., Papanikolaou, N. K., Koumakis, L., Spandidos, D. A., Tsatsakis, A. M., Karantanas, A. H., & Kostas Marias, A. (2020) · International Journal of Oncology · doi:10.3892/ijo.2020.5063 · Cited by 93 · see citing works

Regression Concept Vectors for Bidirectional Explanations in Histopathology

Graziani, M., Andrearczyk, V., & Müller, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02628-8_14 · Cited by 89 · see citing works

Understanding and Interpreting Machine Learning in Medical Image Computing Applications

Pereira, S., Meier, R., Alves, V., Reyes, M., & Silva, C. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02628-8 · Cited by 85 · see citing works

Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI

Papanastasopoulos, Z., Samala, R. K., Chan, H., Hadjiiski, L. M., Paramagul, C., Helvie, M. A., & Neal, C. H. (2020) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2549298 · Cited by 78 · see citing works

Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas

Eichinger, P., Alberts, E., Delbridge, C., Trebeschi, S., Valentinitsch, A., Bette, S. J., Huber, T. S., Gempt, J., Meyer, B., Schlegel, J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13679-4 · Cited by 76 · see citing works

TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review

Zanfardino, M., Pane, K., Mirabelli, P., Salvatore, M., & Franzese, M. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20236033 · Cited by 75 · see citing works

Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images

Tsehay, Y. K., Lay, N. S., Roth, H. R., Wang, X., Kwak, J. T., Turkbey, B. I., Pinto, P. A., Wood, B. J., & Summers, R. M. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254423 · Cited by 74 · see citing works

Introduction to Genomics

Giacco, L. D., & Cattaneo, C. (2011) · Methods in molecular biology · doi:10.1007/978-1-60327-216-2_6 · Cited by 73 · see citing works

Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage

Burnside, E. S., Drukker, K., Li, H., Bonaccio, E., Zuley, M., Ganott, M., Net, J. M., Sutton, E. J., Brandt, K. R., Whitman, G. J., et al. (2016) · Cancer · doi:10.1002/cncr.29791 · Cited by 71 · see citing works

Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score

Ha, R. S., Chang, P., Mutasa, S., Karcich, J., Goodman, S., Blum, E. S., Kalinsky, K. M., Liu, M. Z., & Jambawalikar, S. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26244 · Cited by 71 · see citing works

Immunoprecipitation of chromatin

O’Neill, L. P., & Turner, B. M. (1996) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(96)74017-x · Cited by 68 · see citing works

Classification of breast lesions using cross-modal deep learning

Hadad, O., Bakalo, R., Ben‐Ari, R., Hashoul, S., & Amit, G. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950480 · Cited by 66 · see citing works

A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging

Giannini, V., Mazzetti, S., Vignati, A., Russo, F., Bollito, E., Porpiglia, F., Stasi, M., & Regge, D. (2015) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.09.001 · Cited by 65 · see citing works

Non-Invasive Tumor Genotyping Using Radiogenomic Biomarkers, a Systematic Review and Oncology-Wide Pathway Analysis

Jansen, R. W., van Amstel, P., Martens, R. M., Kooi, I. E., Wesseling, P., de Langen, A. J., Menke-Van der Houven van Oordt, C. W., Jansen, B. H. E., Moll, A. C., Dorsman, J. C., et al. (2018) · Oncotarget · doi:10.18632/oncotarget.24893 · Cited by 63 · see citing works

Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas

Korfiatis, P. D., & Erickson, B. J. (2019) · Clinical Radiology · doi:10.1016/j.crad.2019.01.028 · Cited by 57 · see citing works

Integrating diverse genomic data using gene sets

Tyekucheva, S., Marchionni, L., Karchin, R., & Parmigiani, G. (2011) · Genome biology · doi:10.1186/gb-2011-12-10-r105 · Cited by 54 · see citing works

Towards Complementary Explanations Using Deep Neural Networks

Silva, W., Fernandes, K., Cardoso, M. J., & Cardoso, J. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02628-8_15 · Cited by 53 · see citing works

Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images

Codella, N. C. F., Lin, C.-C., Halpern, A., Hind, M., Feris, R., & Smith, J. R. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-02628-8_11 · Cited by 52 · see citing works

Multimodal Medical Image Fusion based on Deep Learning Neural Network for Clinical Treatment Analysis

(2018) · International Journal of ChemTech Research · doi:10.20902/ijctr.2018.110621 · Cited by 49 · see citing works

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

(2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315131 · Cited by 47 · see citing works

Algorithmic Transparency via Quantitative Input Influence

Datta, A., Sen, S., & Zick, Y. (2017) · Studies in big data · doi:10.1007/978-3-319-54024-5_4 · Cited by 43 · see citing works

Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: Preliminary results

Parekh, V. S., Macura, K. J., Harvey, S. C., Kamel, I. R., Khouli, R. H. E., Bluemke, D. A., & Jacobs, M. A. (2019) · Medical Physics · doi:10.1002/mp.13849 · Cited by 38 · see citing works

Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images

Buda, M., AlBadawy, E. A., Saha, A., & Mazurowski, M. A. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019180050 · Cited by 27 · see citing works

Genomic profiling of lower-grade gliomas uncovers cohesive disease groups: implications for diagnosis and treatment

Zhang, C., & Brat, D. J. (2016) · 癌症：英文版 · doi:10.1186/s40880-015-0071-1 · Cited by 24 · see citing works

MultiDataSet: an R package for encapsulating multiple data sets with application to omic data integration

Hernandéz-Ferrer, C., Ruiz-Arenas, C., Beltran-Gomila, A., & González, J. R. (2017) · BMC Bioinformatics · doi:10.1186/s12859-016-1455-1 · Cited by 23 · see citing works

Predictive and discriminative localization of IDH genotype in high grade gliomas using deep convolutional neural nets

Ahmad, A., Sarkar, S., Shah, A., Gore, S. A., Santosh, V., Saini, J. S., & Ingalhalikar, M. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759313 · Cited by 22 · see citing works

Brain tumour detection using discrete wavelet transform based medical image fusion

Udhaya Suriya, T. S., & Rangarajan, P. (2017) · Biomedical Research-tokyo · Cited by 18 · see citing works

Convolutional neural networks for predicting molecular profiles of non-small cell lung cancer

Yu, D., Zhou, M., Yang, F., Dong, D., Gevaert, O., Liu, Z., Shi, J., & Tian, J. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950585 · Cited by 18 · see citing works

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application—Part 1: From Methodology to Clinical Implementation

Shaikh, F., Franc, B. L., Allen, E., Sala, E., Awan, O. A. R., Hendrata, K., Halabi, S. S., Mohiuddin, S. A., Malik, S., Hadley, D., et al. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.008 · Cited by 14 · see citing works

A Novel Radiogenomics Framework for Genomic and Image Feature Correlation using Deep Learning

Li, S., Han, H., Sui, D., Hao, A., & Qin, H. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621432 · Cited by 13 · see citing works

Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network AblationsPreprint

Meyes, R., de Puiseau, C. W., Posada-Moreno, A. F., & Meisen, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.01254 · Cited by 13 · see citing works

Discovering and interpreting transcriptomic drivers of imaging traits using neural networks

Smedley, N. F., El-Saden, S. M., & Hsu, W. W. Y. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa126 · Cited by 12 · see citing works

tensorBF: an R package for Bayesian tensor factorizationPreprint

Khan, S. A., & Ammad-ud-din, M. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/097048 · Cited by 10 · see citing works

Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer

Yoon, H., Ramanathan, A., Alamudun, F., & Tourassi, G. D. (2018) · International Workshop on Breast Imaging (IWBI) · doi:10.1117/12.2318508 · Cited by 10 · see citing works

Quantitative image feature variability amongst CT scanners with a controlled scan protocol

Ger, R. B., Zhou, S., Chi, P.-C. M., Goff, D. L., Zhang, L., Lee, H. J., Fuller, C. D., Howell, R. M., Li, H., Stafford, J., et al. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2293701 · Cited by 5 · see citing works

Interpretable deep learning for guided structure-property explorations in photovoltaicsPreprint

Pokuri, B. S. S., Ghosal, S., Kokate, A., Ganapathysubramanian, B., & Sarkar, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.06067 · Cited by 3 · see citing works

Deep Domain Adaptation Learning Framework for Associating Image Features to Tumour Gene Profile

Xia, T. (2018) · The Sydney eScholarship Repository (The University of Sydney) · Cited by 1 · see citing works

Editor’s Pick: Regulatory and Ethical Issues in the New Era of Radiomics and Radiogenomics

Pesapane, F. (2020) · EMJ Radiology · doi:10.33590/emjradiol/19-00165 · Cited by 1 · see citing works

Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning

Şahin, E., Arslan, N. N., & Özdemir, D. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10437-2 · Cited by 323 · see citing works

Identification and validation of an anoikis-associated gene signature to predict clinical character, stemness, IDH mutation, and immune filtration in glioblastoma

Sun, Z., Zhao, Y., Yan, W., Ding, X., Tan, C., & Wang, C. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.939523 · Cited by 104 · see citing works

Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology

Shao, J., Ma, J., Zhang, Q., Li, W., & Wang, C. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.006 · Cited by 81 · see citing works

Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis

Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics &amp; Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works

Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis

Abdullakutty, F. C., Akbari, Y., Al-Maadeed, S. A., Bouridane, A., Talaat, I. M., & Hamoudi, R. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1450103 · Cited by 65 · see citing works

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

He, W., Huang, W., Zhang, L., Wu, X.-W., Zhang, S.-X., & Zhang, B. (2024) · MedComm · doi:10.1002/mco2.722 · Cited by 65 · see citing works

The impact of AI on modern oncology from early detection to personalized cancer treatment

Li, J., Zhang, L., Yu, Z., Bao, Z., Li, D., & Wang, L. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01276-6 · Cited by 24 · see citing works

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

Ming, W., Zhu, Y., Bai, Y., Gu, W., Li, F., Hu, Z., Xia, T., Dai, Z., Yu, X., Li, H., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.943326 · Cited by 21 · see citing works

Towards interpretable imaging genomics analysis: Methodological developments and applications

Cen, X., Dong, W., Lv, W., Zhao, Y. C., Dubee, F., Mentis, A. A., Jovic, D., Yang, H., & Li, Y. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102032 · Cited by 21 · see citing works

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Demetriou, D., Lockhat, Z. I., Brzozowski, L., Saini, K. S., Dlamini, Z., & Hull, R. (2024) · Cancers · doi:10.3390/cancers16051076 · Cited by 19 · see citing works

Recent advances in data-driven fusion of multi-modal imaging and genomics for precision medicine

Wang, S., Liu, M., Li, Y., Zhang, X., Sun, M., Wang, Z., Li, R., Li, Q., Li, Q., He, Y., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102738 · Cited by 17 · see citing works

Current methods in explainable artificial intelligence and future prospects for integrative physiology

Finzel, B. (2025) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-025-03067-7 · Cited by 15 · see citing works

A review on explainable artificial intelligence in radiomics: State-of-the-art tools, prospective use cases, challenges and future directions

Ray, P. P. (2025) · European Journal of Radiology Artificial Intelligence · doi:10.1016/j.ejrai.2025.100069 · Cited by 11 · see citing works

An Architecture and a New Deep Learning Method for Head and Neck Cancer Prognosis by Analyzing Serial Positron Emission Tomography Images

Hurtado, R., Guzmán, S., & Muñoz, A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-40942-4_10 · Cited by 11 · see citing works

A novel deep convolutional neural network and its application to fault diagnosis of the squirrel-cage asynchronous motor under noisy environment

Chen, L., Ma, Y., Wang, H., Wen, S., & Guo, L. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acea9b · Cited by 9 · see citing works

AI-Enhanced Digital Pathology and Radiogenomics in Precision Oncology

Demetriou, D., Hull, R., Kgoebane-Maseko, M., Lockhat, Z. I., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_5 · Cited by 6 · see citing works

Ultrasound-based radiogenomics: status, applications, and future direction

Wang, S.-R., Shen, Y.-T., Huang, B., & Xu, H. (2025) · ULTRASONOGRAPHY · doi:10.14366/usg.24152 · Cited by 5 · see citing works

Molecular hallmarks of breast multiparametric magnetic resonance imaging during neoadjuvant chemotherapy

Lin, P., Wan, W.-J., Kang, T., Qin, L.-F., Meng, Q.-X., Wu, X., Qin, H., Lin, Y.-Q., He, Y., & Yang, H. (2023) · La radiologia medica · doi:10.1007/s11547-023-01595-9 · Cited by 4 · see citing works

Trustworthy Applications of ML Algorithms in Medicine - Discussion and Preliminary Results for a Problem of Small Vessels Disease Diagnosis

Ferlin, M. A., Klawikowska, Z., Niemierko, J., Grzywińska, M., Kwasigroch, A., Szurowska, E., & Grochowski, M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-16159-9_1 · Cited by 2 · see citing works

Breast cancer radiogenomics analysis via computational perturbation on AI-driven multi-omics guided image synthesis

Zhang, X.-Y., & Liu, Q. (2026) · bioRxiv · doi:10.1109/ccece68150.2026.11610465 · Cited by 1 · see citing works

An introduction to the application of deep learning in the biomedical field

Ruisong, W. A. N. G., Shengnan, W. A. N. G., & Tieliu, S. H. I. (2024) · Scientia Sinica Vitae · doi:10.1360/ssv-2023-0297

A Trustworthy Decision-Making Algorithm Based on Vote Trees of Random Forest for Abdominal Aortic Aneurysm Diagnostic

Liang, Y., Chen, S., Dong, X. L., & Liao, H. (2025) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda64898.2025.11030410

Deep Learning Techniques–Based Medical Image Segmentation in Cervical Cancer

Saranya, A., Ravi, S., Latha, P. H., & Kalaichelvi, T. (2025) · doi:10.1002/9781394270613.ch25

MYCN-Amplified Neuroblastoma Detection Radiomics Vs. Trainable Features

Malafaia, M., Silva, F., Carvalho, D. C., Martins, R., Dias, S. C., Torrão, H., Oliveira, H. P., & Pereira, T. (2025) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe66822.2025.00034

Fusing imaging and metabolic modeling via multimodal deep learning in ovarian cancer

Eftekhari, N., Verma, S., Saha, A., Zampieri, G., Sawan, S., Occhipinti, A., & Angione, C. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101594

Swin Transformer-Based Adaptive Framework for Radiogenomic Prediction of MGMT Promoter Methylation in Glioblastoma

Mohammed, Q. H., Namburu, A., & Modalavalasa, S. S. T. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-25568-6_34

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

Trivizakis, E., Papadakis, G. Z., Souglakos, I., Papanikolaou, N. K., Koumakis, L., Spandidos, D. A., Tsatsakis, A. M., Karantanas, A. H., & Kostas Marias, A. (2020) · International Journal of Oncology · doi:10.3892/ijo.2020.5063 · Cited by 93 · see citing works

Cancer Genotypes Prediction and Associations Analysis From Imaging Phenotypes: A Survey on Radiogenomics

Wang, Y., Wang, Y., Guo, C., Xie, X., Liang, S., Zhang, R., Pang, W., & Huang, L. (2020) · Biomarkers in Medicine · doi:10.2217/bmm-2020-0248 · Cited by 4 · see citing works

Deep Learning of radiology-genomics integration for computational oncology: A mini review

Wang, F., Li, Y., & Zeng, T. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.06.019 · Cited by 21 · see citing works

Radiomics and Radiogenomics with Deep Learning in Neuro-oncology

Patel, J., Gidwani, M., Chang, K., & Kalpathy–Cramer, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66843-3_20 · Cited by 1 · see citing works

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

Shui, L., Ren, H., Yang, X., Li, J., Chen, Z., Cheng, Y., Zhu, H., & Shui, P. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.570465 · Cited by 141 · see citing works