Predicting cancer outcomes with radiomics and artificial intelligence in radiologyKaustav Bera, Anant Madabhushi, Nathaniel Braman et al.|Nature Reviews Clinical Oncology|2021Cited by 810
Integrated, High-Throughput, Multiomics Platform Enables Data-Driven Construction of Cellular Responses and Reveals Global Drug Mechanisms of ActionJeremy L. Norris, Richard M. Caprioli, Melissa A. Farrow et al.|Journal of Proteome Research|2017Cited by 44
Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple CancersNathaniel Braman, Anant Madabhushi, Amit Gupta et al.|Clinical Cancer Research|2022Cited by 41
Radiomic predicts early response to CDK4/6 inhibitors in hormone receptor positive metastatic breast cancerMohammadhadi Khorrami, Anant Madabhushi, Vidya Sakar Viswanathan et al.|npj Breast Cancer|2023Cited by 13
Abstract 912: CheckpointPx, an interpretable radiology AI tool, predicts checkpoint blockade benefit independent of PDL1 status in non-small cell lung cancers (NSCLC): A multi-institutional validation studyAmogh Hiremath, Young Kwang Chae, Seyoung Lee et al.|Cancer Research|2024Cited by 2