Impact of uncertainty quantification through conformal prediction on volume assessment from deep learning-based MRI...

To estimate the uncertainty of a deep learning (DL)-based prostate segmentation algorithm through conformal prediction (CP) and to assess its effect on the calculation of the prostate volume (PV) in patients at risk of prostate cancer (PC).

Three-hundred seventy-seven multi-center 3-Tesla axial T2-weighted exams from biopsied males (66.64  $$  7.47 years) at risk of PC were retrospectively included in the study. Assessment of PV based on PI-RADS 2.1 ellipsoid formula ( $$ ) was available for included patients. Prostate segmentations were obtained from a DL model and used to calculate the PV ( $$ ). CP was applied at a confidence level of 85% to flag unreliable pixel segmentations of the DL model. Subsequently, the PV ( $$ ) was calculated when disregarding uncertain pixel segmentations. Agreement between $$ and $$ was evaluated against the reference standard $$ . Intraclass correlation coefficient (ICC) and Bland-Altman plots were used to assess the agreement. The relative volume difference (RVD) was used to evaluate the PV calculation accuracy, and the Wilcoxon Signed-Rank Test was used to assess statistical differences. A p-value < 0.05 was considered statistically significant.

Conformal prediction significantly reduced RVD when compared to the DL algorithm (RVD = - 2.81  $$  8.85 and RVD = -8.01  $$  11.50). $$ showed a significantly larger agreement than $$ when using the reference standard $$ (mean difference (95% limits of agreement) $$ : 1.27 mL (- 13.64; 16.17 mL) $$ : 6.07 mL (- 14.29; 26.42 mL)), with an excellent ICC ( $$ : 0.97 (95% CI: 0.97 to 0.98)).

Uncertainty quantification through CP increases the accuracy and reliability of DL-based PV assessment in patients at risk of PC.

Conformal prediction can flag uncertain pixel predictions of DL-based prostate MRI segmentation at a desired confidence level, increasing the reliability and safety of prostate volume assessment in patients at risk of prostate cancer.

Conformal prediction can flag uncertain pixel predictions of prostate segmentations at a user-defined confidence level. Deep learning with conformal prediction shows high accuracy in prostate volumetric assessment. Agreement between automatic and ellipsoid-derived volume was significantly larger with conformal prediction.