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 / Stanzione, Arnaldo; Cuocolo, Renato; Califano, Gianluigi; Ponsiglione, Andrea; Ruvolo, Claudia Colla; Spadarella, Gaia; Giorgi, Marco De; Nessuno, Francesca; Longo, Nicola; Imbriaco, Massimo. - (2022), pp. 641-645. (Intervento presentato al convegno 2022 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering) [10.1109/MetroXRAINE54828.2022.9967607].
Semi-Automated Image Segmentation of Peri-Prostatic Tissue on MRI and Radiomics Features Stability: A Feasibility Study for Locally Advanced Prostate Cancer Detection
Stanzione, Arnaldo;Califano, Gianluigi;Ponsiglione, Andrea;Ruvolo, Claudia Colla;Spadarella, Gaia;Giorgi, Marco De;Nessuno, Francesca;Longo, Nicola;Imbriaco, Massimo
2022
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.