Purpose: To discriminate between patients with high-risk (HR) and low-risk (LR) prostate cancer (PCa) in order to support radiologists in deciding on the most proper therapy strategy. Materials and Methods: 42 patients with a clinical suspicion of PCa were consecutively selected from the database of our institution. All patients underwent 3T-mpMRI and TRUS biopsy, and based on their Gleason Scores (GS) were assigned to HR (GS≥3+4) or LR class, the latter including, besides patients with GS=3+3, patients with a negative biopsy, whether they have positive or negative mpMRI. 84 radiomic features were extracted on DWI sequences and related ROC curves computed. The feature showing the lowest p-value in discriminating HR from LR was selected. Results: The mean of local coefficient of variation (CVL-m), representing local DWI variance, performed the best (p~10-6) and discriminates HR from LR with AUC=0.91 (95% CI, 0.75-0.97), specificity=85%, sensitivity=87% (4 FP and 2 FN), and all FPs are GS=3+3. These results yielded the probability of FDR=24% of the overtreatment for LR patients and probability of FOR=8% that a HR patient is not treated. Conclusions: One of our radiomic features derived from DWI sequences was enough to differentiate HR- from LR- PCa. Since the level of restriction to the motion of water molecules in the extracellular compartment affects tumour behaviour, radiomic features extracted from DWI sequences result in the best candidate to quantify relevant properties of tumour habitats needed to characterize the different tumour heterogeneities. Limitations: Patients were not enough to reliably include clinical parameters in the PCa risk assessment. Although they could be crucial to help to improve the radiomic model, a higher number of parameters requires a number of patients growing exponentially to have a representative sample size. Ethics committee approval: IRB approval, written informed consent was waived. Funding: No funding was received for this work.

Diffusion weighted imaging in prostate cancer: a descriptor of tumour habitat differentiates high-risk and low-risk lesions

Alessandro Bevilacqua;Margherita Mottola;
2020

Abstract

Purpose: To discriminate between patients with high-risk (HR) and low-risk (LR) prostate cancer (PCa) in order to support radiologists in deciding on the most proper therapy strategy. Materials and Methods: 42 patients with a clinical suspicion of PCa were consecutively selected from the database of our institution. All patients underwent 3T-mpMRI and TRUS biopsy, and based on their Gleason Scores (GS) were assigned to HR (GS≥3+4) or LR class, the latter including, besides patients with GS=3+3, patients with a negative biopsy, whether they have positive or negative mpMRI. 84 radiomic features were extracted on DWI sequences and related ROC curves computed. The feature showing the lowest p-value in discriminating HR from LR was selected. Results: The mean of local coefficient of variation (CVL-m), representing local DWI variance, performed the best (p~10-6) and discriminates HR from LR with AUC=0.91 (95% CI, 0.75-0.97), specificity=85%, sensitivity=87% (4 FP and 2 FN), and all FPs are GS=3+3. These results yielded the probability of FDR=24% of the overtreatment for LR patients and probability of FOR=8% that a HR patient is not treated. Conclusions: One of our radiomic features derived from DWI sequences was enough to differentiate HR- from LR- PCa. Since the level of restriction to the motion of water molecules in the extracellular compartment affects tumour behaviour, radiomic features extracted from DWI sequences result in the best candidate to quantify relevant properties of tumour habitats needed to characterize the different tumour heterogeneities. Limitations: Patients were not enough to reliably include clinical parameters in the PCa risk assessment. Although they could be crucial to help to improve the radiomic model, a higher number of parameters requires a number of patients growing exponentially to have a representative sample size. Ethics committee approval: IRB approval, written informed consent was waived. Funding: No funding was received for this work.
Alessandro Bevilacqua, Margherita Mottola, Fabio Ferroni, Domenico Barone, Giampaolo Gavelli
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/759086
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