Schwartz, Lawrence H.

New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)

Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer

Medical Imaging Techniques and ApplicationsStatistical Methods in Clinical Trials

Cited by 29,534

The multimodal brain tumor image segmentation benchmark (BRATS)

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 6,781

Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification

Cheson, B. D., Fisher, R. I., Barrington, S. F., Cavalli, F., Schwartz, L. H., Zucca, E., & Lister, T. A. (2014) · Journal of Clinical Oncology

Lymphoma Diagnosis and TreatmentHematological disorders and diagnostics

Cited by 5,715

Artificial intelligence in radiology

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 3,803

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

Armato, S. G., McLennan, G., Bidaut, L., McNitt‐Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., et al. (2011) · Medical Physics

Lung Cancer Diagnosis and TreatmentAI in cancer detection

Cited by 2,887

iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics

Seymour, L., Bogaerts, J., Perrone, A., Ford, R., Schwartz, L. H., Mandrekar, S., Lin, N. U., Litière, S., Dancey, J., Chen, A., et al. (2017) · The Lancet Oncology

Cancer Immunotherapy and BiomarkersMedical Imaging Techniques and Applications

Cited by 2,472

RECIST 1.1—Update and clarification: From the RECIST committee

Schwartz, L. H., Litière, S., de Vries, E., Ford, R., Gwyther, S., Mandrekar, S., Shankar, L., Bogaerts, J., Chen, A., Dancey, J., et al. (2016) · European Journal of Cancer

Medical Imaging Techniques and ApplicationsStatistical Methods in Clinical Trials

Cited by 1,906

Artificial intelligence in cancer imaging: Clinical challenges and applications

Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 1,846

Phase II Multicenter Study of Abiraterone Acetate Plus Prednisone Therapy in Patients With Docetaxel-Treated Castration-Resistant Prostate Cancer

Danila, D. C., Morris, M. J., de Bono, J. S., Ryan, C. J., Denmeade, S. R., Smith, M. R., Taplin, M.-E., Bubley, G. J., Kheoh, T., Haqq, C., et al. (2010) · Journal of Clinical Oncology

Prostate Cancer Treatment and ResearchProstate Cancer Diagnosis and Treatment

Cited by 410

Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics

Dercle, L., Fronheiser, M., Lu, L., Du, S., Hayes, W., Leung, D. K., Roy, A., Wilkerson, J., Guo, P., Fojo, A. T., et al. (2020) · Clinical Cancer Research

Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and Treatment

Cited by 177

Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification

Eche, T., Schwartz, L. H., Mokrane, F.-Z., & Dercle, L. (2021) · Radiology: Artificial Intelligence

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Cited by 163

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review

Kenner, B., Chari, S. T., Kelsen, D., Klimstra, D. S., Pandol, S. J., Rosenthal, M., Rustgi, A. K., Taylor, J. A., Yala, A., Abul-Husn, N., et al. (2021) · Pancreas

AI in cancer detectionPancreatic and Hepatic Oncology Research

Cited by 158

Uncontrolled confounders may lead to false or overvalued radiomics signature: a proof of concept using survival analysis in a multicenter cohort of kidney cancer

Lu, L., Ahmed, F. S., Akin, O., Luk, L., Guo, X., Yang, H., Yoon, J., Hakimi, A. A., Schwartz, L. H., & Zhao, B. (2021) · Frontiers in Oncology

Radiomics and Machine Learning in Medical ImagingRenal cell carcinoma treatment

Cited by 31