Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models.

OBJECTIVE

Prostate cancer is prevalent among older men. Although this malignancy has a relatively low mortality rate, its aggressiveness is critical in determining patient prognosis and treatment options. This study therefore aimed to evaluate the effectiveness of a 2.5D deep learning model based on prostate MRI to assess prostate cancer aggressiveness.

MATERIALS AND METHODS

This study included 335 patients with pathologically-confirmed prostate cancer from a tertiary medical center between January 2022 and December 2023. Of these, 266 cases were classified as aggressive and 69 as non-aggressive, using a Gleason score ≥7 as the cutoff. The subjects were automatically divided into a test set and validation set in a 7:3 ratio. Before pathological biopsy, all patients underwent biparametric MRI, including T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient scans. Two radiologists, blinded to pathology results, segmented the lesions using ITK-SNAP software, extracting the minimal bounding rectangle of the largest ROI layer, along with the corresponding ROIs from adjacent layers above and below it. Subsequently, radiomic features were extracted using pyradiomics tool, while deep learning features from each cross-section were derived using the Inception_v3 neural network. To ensure consistency in feature extraction, intraclass correlation coefficient (ICC) analysis was performed on features extracted by radiologists, followed by feature normalization using the mean and standard deviation of the training set. Highly correlated features were removed using t-tests and Pearson correlation tests, and redundant features were ultimately screened with least absolute shrinkage and selection operator (Lasso). Models were constructed using the LightGBM algorithm: a radiomic feature model, a deep learning feature model, and a combined model integrating radiomic and deep learning features. Further, a clinical feature model (Clinic-LightGBM) was constructed using LightGBM to include clinical information. The optimal feature model was then combined with Clinic-LightGBM to establish a nomogram. The Grad-CAM technique was employed to explain the deep learning feature extraction process, supported by tree model visualization techniques to illustrate the decision-making process of the LightGBM model. Model classification performance in the test set was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

In the test set, the nomogram demonstrated the highest predictive ability for prostate cancer aggressiveness (AUC = 0.919, 95% CI: 0.8107-1.0000), with a sensitivity of 0.966 and specificity of 0.833. The DLR-LightGBM model (AUC = 0.872) outperformed the DL-LightGBM (AUC = 0.818) and Rad-LightGBM (AUC = 0.758) models, indicating the benefit of combining deep learning and radiomic features.

CONCLUSION

Our 2.5D deep learning model based on prostate MRI showed efficacy in identifying clinically significant prostate cancer, providing valuable references for clinical treatment and enhancing patient net benefit.