Abstract #1519
Comparison of whole-prostate radiomics models of disease severity derived from expert and AI based prostate segmentations
Paul E Summers1, Lars Johannes Isaksson2, Matteo Johannes Pepa2, Mattia Zaffaroni2, Maria Giulia Vincini2, Giulia Corrao2,3, Giovanni Carlo Mazzola2,3, Marco Rotondi2,3, Sara Raimondi4, Sara Gandini4, Stefania Volpe2,3, Zaharudin Haron5, Sarah Alessi1, Paola Pricolo1, Francesco Alessandro Mistretta6, Stefano Luzzago6, Federico Cattani7, Gennaro Musi3,6, Ottavio De Cobelli3,6, Marta Cremonesi8, Roberto Orecchia9, Giulia Marvaso2,3, Barbara Alicja Jereczek-Fossa2,3, and Giuseppe Petralia3,10
1Division of Radiology, IEO, European Institute of Oncology IRCCS, Milano, Italy, 2Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milano, Italy, 3Department of Oncology and Hemato-oncology, University of Milan, Milano, Italy, 4Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milano, Italy, 5Radiology Department, National Cancer Institute, Putrajaya, Malaysia, 6Division of Urology, IEO, European Institute of Oncology IRCCS, Milano, Italy, 7Unit of Medical Physics, IEO, European Institute of Oncology IRCCS, Milano, Italy, 8Radiation Research Unit, IEO, European Institute of Oncology IRCCS, Milano, Italy, 9Scientific Directorate, IEO, European Institute of Oncology IRCCS, Milano, Italy, 10Precision Imaging and Research Unit, IEO, European Institute of Oncology IRCCS, Milano, Italy
Synopsis
A persisting concern is that downstream models of clinical endpoints may depend on whether the contours were drawn by an expert or an AI. Prediction models for surgical margin status, and pathology-based lymph nodes, tumor stage and ISUP grade group were formed using clinical and radiological features along with whole-prostate radiomic features based on manual and AI segmentations of the prostate in 100 patients who proceeded to prostatectomy after multiparametric-MRI. The models based on AI segmented prostates differed from those based on manual segmentation, but with similar if not better performance. Further testing of generalizability of the models is required.
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