Lesion-based grading system using clinicopathological and MRI features for predicting positive surgical margins in prostate cancer.

Objectives: To develop and validate a lesion-based grading system using clinicopathological and MRI features for predicting positive surgical margin (PSM) following robotic-assisted laparoscopic prostatectomy (RALP) among prostate cancer (PCa) patients.

Methods: Consecutive MRI examinations of patients undergoing RALP for PCa were retrospectively collected from two medical institutions. Patients from center 1 undergoing RALP between January 2020 and December 2021 were included in the derivation cohort and those between January 2022 and December 2022 were allocated to the validation cohort. Patients from center 2 were assigned to the test cohort. PSM associated imaging and clinicopathological predictors were assessed. A grading system was developed through fixed effect logistic regression and classification and regression tree analysis. The area under the curve (AUC), sensitivity and specificity were calculated and compared by Delong test and McNemar test.

Results: A total 489 lesions from 396 patients were included and 82 (29.1%), 32 (35.6%) and 42 (35.9%) of lesions were observed PSM after RALP in the derivation, validation and test cohorts, respectively. The grading system comprised tumor morphology, tumor location, anatomical feature and clinical risk stratification. The grading system demonstrated good prediction performance for PSM in the derivation (AUC 0.82 [95% CI: 0.77, 0.86]), validation (AUC 0.76 [95% CI: 0.66, 0.85]) and test (AUC 0.81 [95% CI: 0.72, 0.88]) cohorts. When compared with Park's model (AUC: 0.73 [95% CI: 0.64, 0.81]) in the test cohort, our grading system demonstrated significantly higher AUC and specificity (P < 0.05).

Conclusion: The lesion-based grading system can assess the likelihood of PSM after RALP, assisting surgeons in minimizing the occurrence rate of PSM while optimizing functional preservation.