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About this Research Topic

Abstract Submission Deadline 18 December 2022
Manuscript Submission Deadline 18 February 2023

Medical imaging has been embedded in all phases of drug development, including discovery, preclinical, clinical, and manufacturing. Quantitative analysis of these images has provided evidence for the assessment of drug efficacy and safety properties to support critical go/no-go decisions.

In recent years, the complexity, scale, and quantity of medical images obtained in drug development have been increasing at a fast pace. As a result, major challenges are posed in the analysis of these images, such as handling images from large-scale multi-center clinical trials, extracting features as robust imaging biomarkers, and combining multiple imaging modalities to make predictions about patient outcomes. Meanwhile, the fast development of machine learning approaches has enabled rapid analysis and accurate quantification of medical images.

The objectives of this Research Topic are to advance the research on medical imaging and image analysis and as well as to facilitate the application of novel machine learning approaches in drug development, especially in clinical and translational studies. We hope the Research Topic will inspire image analysis practitioners in drug development to confidently apply advanced machine learning methods in their work.

This Research Topic welcomes the submission of manuscripts covering the role of radiology and machine learning in clinical drug development. As such, although it is not limited to these, we are particularly interested in the following topics:
• Radiation genomics and imaging genomics applied to drug discovery
• Unsupervised, semi-supervised, and self-supervised machine learning in drug development
• Machine learning on small-size samples
• Federated learning, active learning, and representation learning in clinical drug development
• Statistics and causal inference to discover disease mechanisms and/or to predict the effectiveness of treatments based upon image data
• Translational animal studies and reverse translation
• Multi-modal data fusion – learning novel methods to combine data from multiple imaging modalities and/or non-imaging data, e.g. genomics, to maximize the capabilities to describe patient properties or predict clinical outcomes
• Imaging biomarkers based on images in clinical trials, e.g. CT, MRI, PET, that can be used to strategy patient population and/or predict clinical outcomes
• Integration of radiology and digital pathology in drug discovery
• Radiomics on novel therapeutics and/or large-scale clinical trials
• Novel approaches for segmentation, registration, classification, synthesis, and object detection in medical images

We would like to acknowledge Mr. Zhou as the Topic Coordinator and Dr. Chen who has contributed to the preparation of the proposal for this Research Topic. Mr. Zhou's research interests include AI in medical image reconstruction, medical computer vision, and trustworthy AI in biomedical imaging. He is a medical imaging researcher at Yale. Previously, he obtained his MS in computer vision at Carnegie Mellon University and MS in biomedical engineering at Case Western Reserve University. He has authored 40+ peer-reviewed articles, 4 patents, and served as the reviewer for 25+ journals/conferences in the AI field. He is also a broad member of the MICCAI student board.

Drs. Chen, Goldmacher, and Tomaszewski are affiliated with Merck & Co., and Dr. Mortazi with Volastra Therapeutics. All other Topic Editors declare no competing interests regarding the Research Topic subject.

Keywords: drug development, radiology, machine learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Medical imaging has been embedded in all phases of drug development, including discovery, preclinical, clinical, and manufacturing. Quantitative analysis of these images has provided evidence for the assessment of drug efficacy and safety properties to support critical go/no-go decisions.

In recent years, the complexity, scale, and quantity of medical images obtained in drug development have been increasing at a fast pace. As a result, major challenges are posed in the analysis of these images, such as handling images from large-scale multi-center clinical trials, extracting features as robust imaging biomarkers, and combining multiple imaging modalities to make predictions about patient outcomes. Meanwhile, the fast development of machine learning approaches has enabled rapid analysis and accurate quantification of medical images.

The objectives of this Research Topic are to advance the research on medical imaging and image analysis and as well as to facilitate the application of novel machine learning approaches in drug development, especially in clinical and translational studies. We hope the Research Topic will inspire image analysis practitioners in drug development to confidently apply advanced machine learning methods in their work.

This Research Topic welcomes the submission of manuscripts covering the role of radiology and machine learning in clinical drug development. As such, although it is not limited to these, we are particularly interested in the following topics:
• Radiation genomics and imaging genomics applied to drug discovery
• Unsupervised, semi-supervised, and self-supervised machine learning in drug development
• Machine learning on small-size samples
• Federated learning, active learning, and representation learning in clinical drug development
• Statistics and causal inference to discover disease mechanisms and/or to predict the effectiveness of treatments based upon image data
• Translational animal studies and reverse translation
• Multi-modal data fusion – learning novel methods to combine data from multiple imaging modalities and/or non-imaging data, e.g. genomics, to maximize the capabilities to describe patient properties or predict clinical outcomes
• Imaging biomarkers based on images in clinical trials, e.g. CT, MRI, PET, that can be used to strategy patient population and/or predict clinical outcomes
• Integration of radiology and digital pathology in drug discovery
• Radiomics on novel therapeutics and/or large-scale clinical trials
• Novel approaches for segmentation, registration, classification, synthesis, and object detection in medical images

We would like to acknowledge Mr. Zhou as the Topic Coordinator and Dr. Chen who has contributed to the preparation of the proposal for this Research Topic. Mr. Zhou's research interests include AI in medical image reconstruction, medical computer vision, and trustworthy AI in biomedical imaging. He is a medical imaging researcher at Yale. Previously, he obtained his MS in computer vision at Carnegie Mellon University and MS in biomedical engineering at Case Western Reserve University. He has authored 40+ peer-reviewed articles, 4 patents, and served as the reviewer for 25+ journals/conferences in the AI field. He is also a broad member of the MICCAI student board.

Drs. Chen, Goldmacher, and Tomaszewski are affiliated with Merck & Co., and Dr. Mortazi with Volastra Therapeutics. All other Topic Editors declare no competing interests regarding the Research Topic subject.

Keywords: drug development, radiology, machine learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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