Abstract #0040
Predicting pathological status of prostate cancer patients – Examining the value and leading features in MRI Radiomics
Lars Johannes Isaksson1, Paul E Summers2, Matteo Johannes Pepa1, Mattia Zaffaroni1, Maria Giulia Vincini1, Giulia Corrao1,3, Giovanni Carlo Mazzola1,3, Marco Rotondi1,3, Sara Raimondi4, Sara Gandini4, Stefania Volpe1,3, Zaharudin Haron5, Sarah Alessi2, Paola Pricolo2, Francesco Alessandro Mistretta6, Stefano Luzzago6, Federico Cattani7, Gennaro Musi3,6, Ottavio De Cobelli3,6, Marta Cremonesi8, Roberto Orecchia9, Giulia Marvaso1,3, Barbara Alicja Jereczek-Fossa1,3, and Giuseppe Petralia3,10
1Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milano, Italy, 2Division of Radiology, 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
The risk of patients being under- or overtreated during radiotherapy depends heavily on the pre-treatment assessment. Prediction models for surgical margin status, pathological lymph nodes, pathological tumor stage and ISUP grade group were formed using clinical and radiological features alone and together with whole-prostate radiomic features in 100 patients who proceeded to prostatectomy after multiparametric-MRI. The addition of radiomics features significantly improved AUC for the prediction models. The leading radiomic features differed between the different models.
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