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Magnetic resonance imaging has evolved as a key component for prostate cancer(PCa) detection, substantially increasing the radiologist workload. Artificialintelligence (AI) systems can support radiological assessment by segmenting andclassifying lesions in clinically significant (csPCa) and non-clinicallysignificant (ncsPCa). Commonly, AI systems for PCa detection involve anautomatic prostate segmentation followed by the lesion detection using theextracted prostate. However, evaluation reports are typically presented interms of detection under the assumption of the availability of a highlyaccurate segmentation and an idealistic scenario, omitting the propagation oferrors between modules. For that purpose, we evaluate the effect of twodifferent segmentation networks (s1 and s2) with heterogeneous performances inthe detection stage and compare it with an idealistic setting (s1:89.90+-2.23vs 88.97+-3.06 ncsPCa, P<.001, 89.30+-4.07 and 88.12+-2.71 csPCa, P<.001). Ourresults depict the relevance of a holistic evaluation, accounting for all thesub-modules involved in the system.

Reconsidering evaluation practices in modular systems: On the propagation of errors in MRI prostate cancer detection