The role of the tumor peripheral microenvironment to establish prostate cancer invasiveness is gaining interest. Radiomics is a rapidly growing research field, however there are still many methodological challenges to guarantee robustness and reproducibility of the models. We aimed to verify the feasibility of a semi-automated segmentation strategy for periprostatic tissue on axial T2-weighted images from 30 magnetic resonance imaging scans, test stability of hand-crafted radiomics features to multiple segmentation and their potential value in identification of extracapsular tumor extension using a machine learning approach. 1274 radiomics features were extracted from each volume of interest, with less than half (40 %) resulting stable at the ICC analysis. The trained Naïve Bayesian model correctly classified 63 % of instances aggregating the cross-validation data (AUC = 0.68). Although the performance of our machine learning model did not reach optimal results, the proposed segmentation approach could represent a facilitator for future research in the field.

Semi-Automated Image Segmentation of Peri-Prostatic Tissue on MRI and Radiomics Features Stability: A Feasibility Study for Locally Advanced Prostate Cancer Detection

Cuocolo, Renato;
2022-01-01

Abstract

The role of the tumor peripheral microenvironment to establish prostate cancer invasiveness is gaining interest. Radiomics is a rapidly growing research field, however there are still many methodological challenges to guarantee robustness and reproducibility of the models. We aimed to verify the feasibility of a semi-automated segmentation strategy for periprostatic tissue on axial T2-weighted images from 30 magnetic resonance imaging scans, test stability of hand-crafted radiomics features to multiple segmentation and their potential value in identification of extracapsular tumor extension using a machine learning approach. 1274 radiomics features were extracted from each volume of interest, with less than half (40 %) resulting stable at the ICC analysis. The trained Naïve Bayesian model correctly classified 63 % of instances aggregating the cross-validation data (AUC = 0.68). Although the performance of our machine learning model did not reach optimal results, the proposed segmentation approach could represent a facilitator for future research in the field.
978-1-6654-8574-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4811891
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