Bio

Dr. Hassan Jahanandish is a Postdoctoral Fellow at Stanford School of Medicine, where his research focuses on the intersection of multimodal AI and medical imaging with the overarching objective of advancing care paradigms for cancer patients. Before joining Stanford, he completed his PhD in Bioengineering at the University of Texas at Dallas and the University of Texas Southwestern Medical Center (2022). Beyond his research pursuits, Hassan is an Instructor and Team Mentor at Stanford Center for Biodesign, where he helps shape the future of medical innovation and healthcare entrepreneurship. Hassan's scholarly contributions have been published in numerous journals and conferences, including Lancet Oncology, NeurIPS, and ICRA, and his work in collaboration with Nokia Bell Labs has been awarded a United States patent. Hassan's achievements have been recognized by awards such as the Jonsson Family Graduate Fellowship in Bioengineering, the Doctoral Dissertation Research Award, and being an International RehabWeek paper award finalist (2019).

Stanford Advisors

Contact

  • Academic
    hjahan@stanford.edu
    University - Scholar Department: Urology - Divisions Position: Postdoctoral Scholar

Additional Info

  • Mail Code: 5118

Links

Lab Affiliations

All Publications

  • Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. The Lancet. Oncology Saha, A., Bosma, J. S., Twilt, J. J., van Ginneken, B., Bjartell, A., Padhani, A. R., Bonekamp, D., Villeirs, G., Salomon, G., Giannarini, G., Kalpathy-Cramer, J., Barentsz, J., Maier-Hein, K. H., Rusu, M., Rouvière, O., van den Bergh, R., Panebianco, V., Kasivisvanathan, V., Obuchowski, N. A., Yakar, D., Elschot, M., Veltman, J., Fütterer, J. J., de Rooij, M., Huisman, H. 2024

    Abstract

    Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale.In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5-10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4-6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov, NCT05489341.Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87-0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83-0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6-63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3-92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3-72·4] vs 69·0% [65·5-72·5]) at the same sensitivity (96·1%, 94·0-98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (-0·04) was greater than the non-inferiority margin (-0·05) and a p value below the significance threshold was reached (p<0·001).An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicability of this system.Health~Holland and EU Horizon 2020.

    View details for DOI 10.1016/S1470-2045(24)00220-1

    View details for PubMedID 38876123

  • AI VS. UROLOGISTS: A COMPARATIVE ANALYSIS FOR PROSTATE CANCER DETECTION ON TRANSRECTAL B-MODE ULTRASOUND Vesal, S., Bhattacharya, I., Jahanandish, H., Choi, M., Zhou, S., Kornberg, Z., Sommer, E., Fan, R. E., Rusu, M., Sonn, G. A. LIPPINCOTT WILLIAMS & WILKINS. 2024: E1056

    View details for Web of Science ID 001263885303367

  • ARTIFICIAL INTELLIGENCE-ASSISTED PROSTATE CANCER DETECTION ON B-MODE TRANSRECTAL ULTRASOUND IMAGES Bhattacharya, I., Vesal, S., Jahanandish, H., Choi, M., Zhou, S., Kornberg, Z., Sommer, E., Fan, R. E., Brooks, J. D., Rusu, M., Sonn, G. A. LIPPINCOTT WILLIAMS & WILKINS. 2024: E511

    View details for Web of Science ID 001263885301468

  • SwinTransformer-Based Affine Registration of MRI and Ultrasound Images of the Prostate Sang, S., Jahanandish, H., Li, X., Vesal, S., Bhattacharya, I., Zhang, L., Fan, R. E., Sonn, G., Rusu, M., Boehm, B., Bottenus, N. SPIE-INT SOC OPTICAL ENGINEERING. 2024

    View details for DOI 10.1117/12.3008797

    View details for Web of Science ID 001223524400006

  • A deep learning framework to assess the feasibility of localizing prostate cancer on b-mode transrectal ultrasound images Jahanandish, H., Vesal, S., Bhattacharya, I., Li, C., Fan, R. E., Sonn, G. A., Rusu, M., Boehm, B., Bottenus, N. SPIE-INT SOC OPTICAL ENGINEERING. 2024

    View details for DOI 10.1117/12.3008819

    View details for Web of Science ID 001223524400023

  • MIC-CUSP: Multimodal Image Correlations for Ultrasound-Based Prostate Cancer Detection Bhattacharya, I., Vesal, S., Jahanandish, H., Choi, M., Zhou, S., Kornberg, Z., Sommer, E., Fan, R., Brooks, J., Sonn, G., Rusu, M., Kainz, B., Noble, A., Schnabel, J., Khanal, B., Muller, J. P., Day, T. SPRINGER INTERNATIONAL PUBLISHING AG. 2023: 121-131

    View details for DOI 10.1007/978-3-031-44521-7_12

    View details for Web of Science ID 001115849400012