Parmar, Chintan

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 5,132

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

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine

Radiomics and Machine Learning in Medical ImagingAI in cancer detection

Cited by 579

Defining the biological basis of radiomic phenotypes in lung cancer

Grossmann, P., Stringfield, O., El-Hachem, N., Bui, M. M., Rios Velazquez, E., Parmar, C., Leijenaar, R. T., Haibe-Kains, B., Lambin, P., Gillies, R. J., et al. (2017) · eLife

Radiomics and Machine Learning in Medical ImagingCancer Immunotherapy and Biomarkers

Cited by 365

Deep convolutional neural networks to predict cardiovascular risk from computed tomography

Zeleznik, R., Foldyna, B., Eslami, P., Weiss, J., Alexander, I., Taron, J., Parmar, C., Alvi, R. M., Banerji, D., Uno, M., et al. (2021) · Nature Communications

Cardiac Imaging and DiagnosticsArtificial Intelligence in Healthcare and Education

Cited by 240