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Objectives <p>To develop a grading system integrating MRI and clinicopathological features for predicting positive surgical margin (PSM) following robotic-assisted laparoscopic prostatectomy (RALP) among patients with prostate cancer.</p> Methods <p>Patients undergoing RALP were retrospectively included with consecutive MRI examinations collected from two centers (center 1 and center 2). The train cohort included patients at center 1 between January 2020 and December 2021, and the validation cohort comprised those between January 2022 and December 2022. Patients from center 2 were assigned to the test cohort. MRI and clinicopathological features associated with PSM were assessed. A logistic regression model was used to develop the grading system. The prediction and calibration performance were evaluated by area under the receiver operating characteristic curves (AUCs) and Hosmer-Lemeshow goodness-of-fit test. AUC values were compared by Delong test.</p> Results <p>A total of 396 patients and 29.2% (64/219), 37.6% (26/69) and 38.0% (41/108) of patients in the train, validation and test cohorts exhibited PSMs, respectively. The grading system comprised clinical risk stratification (ESMO-risk) and MRI features. The PSM grading system demonstrated good prediction performance (AUC 0.79, 95% CI: 0.70, 0.86) and showed good calibration (<i>P</i> = 0.82) in the test cohorts. When compared with ESMO-risk (AUC: 0.70, 95% CI: 0.60, 0.78), Park’s model (AUC: 0.72, 95% CI: 0.63, 0.81) and Xu’s model (AUC: 0.70, 95% CI: 0.60, 0.78) in the test cohort, our PSM grading system demonstrated higher AUC value (<i>P</i> &lt; 0.05).</p> Conclusion <p>The PSM grading system integrating MRI and clinicopathological features can assess the likelihood of PSM after RALP.</p>

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Development and validation of a preoperative grading system incorporating MRI and clinicopathological features to predict positive surgical margins in robot-assisted laparoscopic prostatectomy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Honghao Xu,
  • Yuanhao Ma,
  • Xueyi Ning,
  • Baichuan Liu,
  • Xu Bai,
  • Di Chen,
  • Xiaohui Ding,
  • Yun Zhang,
  • Zhe Dong,
  • Mengqiu Cui,
  • Xiaojing Zhang,
  • Aitao Guo,
  • Xuetao Mu,
  • Huiyi Ye,
  • Baojun Wang,
  • Haiyi Wang

摘要

Objectives

To develop a grading system integrating MRI and clinicopathological features for predicting positive surgical margin (PSM) following robotic-assisted laparoscopic prostatectomy (RALP) among patients with prostate cancer.

Methods

Patients undergoing RALP were retrospectively included with consecutive MRI examinations collected from two centers (center 1 and center 2). The train cohort included patients at center 1 between January 2020 and December 2021, and the validation cohort comprised those between January 2022 and December 2022. Patients from center 2 were assigned to the test cohort. MRI and clinicopathological features associated with PSM were assessed. A logistic regression model was used to develop the grading system. The prediction and calibration performance were evaluated by area under the receiver operating characteristic curves (AUCs) and Hosmer-Lemeshow goodness-of-fit test. AUC values were compared by Delong test.

Results

A total of 396 patients and 29.2% (64/219), 37.6% (26/69) and 38.0% (41/108) of patients in the train, validation and test cohorts exhibited PSMs, respectively. The grading system comprised clinical risk stratification (ESMO-risk) and MRI features. The PSM grading system demonstrated good prediction performance (AUC 0.79, 95% CI: 0.70, 0.86) and showed good calibration (P = 0.82) in the test cohorts. When compared with ESMO-risk (AUC: 0.70, 95% CI: 0.60, 0.78), Park’s model (AUC: 0.72, 95% CI: 0.63, 0.81) and Xu’s model (AUC: 0.70, 95% CI: 0.60, 0.78) in the test cohort, our PSM grading system demonstrated higher AUC value (P < 0.05).

Conclusion

The PSM grading system integrating MRI and clinicopathological features can assess the likelihood of PSM after RALP.