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Abstract #4118

Sensitivity of radiomics to inter-reader variations in prostate cancer delineation on MRI should be considered to improve generalizability 

Rakesh Shiradkar1, Michael Sobota1, Leonardo Kayat Bittencourt2, Sreeharsha Tirumani2, Justin Ream3, Ryan Ward3, Amogh Hiremath1, Ansh Roge1, Amr Mahran1, Andrei Purysko3, Lee Ponsky2, and Anant Madabhushi1
1Case Western Reserve University, Cleveland, OH, United States, 2University Hospitals Cleveland Medical Center, Cleveland, OH, United States, 3Cleveland Clinic, Cleveland, OH, United States

Radiomic approaches for prostate cancer risk stratification largely depend on radiologist delineation of prostate cancer regions of interest (ROI) on MRI. In this study, we acquired multi-reader delineations of ROIs, derived radiomic features within the ROIs trained and evaluated machine learning classifiers. We observed that variation in delineations did not affect the classification performance within a cohort but it did affect when evaluated on an independent validation set. We observed that a more conservative approach in delineations may ensure better generalizability and classification performance of machine learning models.

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