Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China.
Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, China.
Biomed Res Int. 2022 Aug 30;2022:3203965. doi: 10.1155/2022/3203965. eCollection 2022.
The purpose was to compare the accuracy of extraprostatic extension (EPE) grade on MRI predicting EPE with Partin tables, Memorial Sloan Kettering Cancer Center nomogram (MSKCCn), and combined models and to analyze the clinical incremental value of EPE grade.
105 prostate cancer patients confirmed by pathology after radical prostatectomy in our hospital from 2017 to 2021 were selected. The clinical stage, PSA, Gleason score, number of positive biopsy cores, and percentage of positive biopsy cores were recorded. Evaluate EPE grade according to EPE grade criteria, and calculate the probability of predicting EPE with Partin tables and MSKCCn. EPE grade is combined with Partin tables and MSKCCn to construct EPE grade+Partin tables and EPE grade+MSKCCn models. Calculate the area under the curve (AUC), sensitivity, and specificity of EPE grade, Partin tables, MSKCCn, EPE grade+Partin tables, and EPE grade+MSKCCn and compare their diagnostic efficacy. The clinical decision curve was used to analyze the clinical net income of each prediction scheme.
The AUC of EPE grade was 0.79, Partin tables was 0.50, MSKCCn was 0.78, the EPE grade+Partin table model was 0.79, and the EPE grade+MSKCCn model was 0.83. After EPE grade was combined with Partin tables and MSKCCn, the diagnostic efficiency of clinical model was significantly improved ( < 0.05). There was no significant difference in the diagnostic efficacy of the combined model compared with the single EPE grade ( > 0.05). The calibration curve of the combined model shows that it has a good calibration degree for EPE. In the analysis of the decision curve, the net income of the EPE grade is higher than that of Partin tables and MSKCCn and is equal to the EPE grade+Partin tables and is slightly lower than that of EPE grade+MSKCCn. The clinical net income of the combined model is obviously higher than that of individual clinical models.
The accuracy of EPE classification in predicting prostate cancer EPE is high, and combined with the clinical model, it can significantly improve the diagnostic efficiency of the clinical model and increase the clinical benefit.
比较 MRI 上前列腺癌外扩程度（EPE）分级预测 EPE 的准确性，与 Partin 表、 Memorial Sloan Kettering 癌症中心列线图（MSKCCn）和联合模型进行比较，并分析 EPE 分级的临床增量价值。
选取我院 2017 年至 2021 年经根治性前列腺切除术后病理证实的 105 例前列腺癌患者，记录临床分期、PSA、Gleason 评分、阳性活检核心数及阳性活检核心百分比。按 EPE 分级标准评估 EPE 分级，并计算 Partin 表和 MSKCCn 预测 EPE 的概率。将 EPE 分级与 Partin 表和 MSKCCn 相结合，构建 EPE 分级+Partin 表和 EPE 分级+MSKCCn 模型。计算 EPE 分级、Partin 表、MSKCCn、EPE 分级+Partin 表和 EPE 分级+MSKCCn 的曲线下面积（AUC）、敏感度和特异度，并比较其诊断效能。采用临床决策曲线分析各预测方案的临床净收益。
EPE 分级的 AUC 为 0.79，Partin 表为 0.50，MSKCCn 为 0.78，EPE 分级+Partin 表模型为 0.79，EPE 分级+MSKCCn 模型为 0.83。EPE 分级与 Partin 表和 MSKCCn 结合后，临床模型的诊断效能明显提高（<0.05）。与单一 EPE 分级相比，联合模型的诊断效能差异无统计学意义（>0.05）。联合模型的校准曲线表明，其对 EPE 具有良好的校准程度。在决策曲线分析中，EPE 分级的净收益高于 Partin 表和 MSKCCn，与 EPE 分级+Partin 表相当，略低于 EPE 分级+MSKCCn。联合模型的临床净收益明显高于单一临床模型。
EPE 分级预测前列腺癌 EPE 的准确性较高，与临床模型相结合，可显著提高临床模型的诊断效能，增加临床获益。
