Habitat analysis based on magnetic resonance imaging for the prediction of prostate cancer: a dual-center study.

Background: The application of habitat analysis is anticipated to enhance the diagnostic efficacy of magnetic resonance imaging (MRI) in prostate cancer (PCa) by providing a more accurate reflection of the microenvironmental characteristics within the lesion. The objective of this study was to investigate the feasibility of multisequence and multiregional MRI-based habitat analysis in the differentiation of PCa and benign prostatic hyperplasia (BPH).

Methods: We retrospectively evaluated the data of 673 cases from The Second Affiliated Hospital of Nanchang University and The First Hospital of Xiushui who received MRI examination of the prostate and pathologically confirmed diagnosis of PCa or BPH. Habitat features and classical radiomic features from the regions of lesions and prostate gland (PG) were extracted for model construction. Receiver operating characteristic analysis was used to assess the performance of the models. An integrated nomogram combining dominant models and clinical variables was ultimately constructed. In addition, we further assessed the performance of the nomogram in a subgroup of early-stage lesions without capsular invasion (CIV). The Delong test was used to compare the differences in the area under receiver operating characteristic curve (AUC) between models.

Results: The AUCs of the habitat radiomics score (rad-score) based on the lesion (LHrad-score) in both the internal (0.898) and external validation (0.878) sets were higher than those of the rad-score based on the lesion (0.860 and 0.854, respectively). The AUCs of the classical rad-score based on PG (PCrad-score; 0.883 and 0.865 in the internal and external sets, respectively) were higher than those of the habitat rad-score based on PG (0.871 and 0.773, respectively). By combining the PCrad-score and LHrad-score with clinically independent predictors, the nomogram yielded AUCs of 0.899 and 0.963 in the internal and external sets, respectively. Discrimination between early-stage PCa and BPH in the overall validation set yielded an AUC of 0.802.

Conclusions: The habitat analysis may serve as a means to noninvasively and preoperatively identifying PCa from BPH, even in the early stages of PCa.