Department of Radiology, The First Affiliated Hospital of Nanjing Medical University; Nanjing, Jiangsu Province, PR China.
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou 215006, PR China.
EBioMedicine. 2021 Jun;68:103395. doi: 10.1016/j.ebiom.2021.103395. Epub 2021 May 25.
Accurate identification of pelvic lymph node metastasis (PLNM) in patients with prostate cancer (PCa) is crucial for determining appropriate treatment options. Here, we built a PLNM-Risk calculator to obtain a precisely informed decision about whether to perform extended pelvic lymph node dissection (ePLND).
The PLNM-Risk calculator was developed in 280 patients and verified internally in 71 patients and externally in 50 patients by integrating a set of radiologists' interpretations, clinicopathological factors and newly refined imaging indicators from MR images with radiomics machine learning and deep transfer learning algorithms. Its clinical applicability was compared with Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms.
The PLNM-Risk achieved good diagnostic discrimination with areas under the receiver operating characteristic curve (AUCs) of 0.93 (95% CI, 0.90-0.96), 0.92 (95% CI, 0.84-0.97) and 0.76 (95% CI, 0.62-0.87) in the training/validation, internal test and external test cohorts, respectively. If the number of ePLNDs missed was controlled at < 2%, PLNM-Risk provided both a higher number of ePLNDs spared (PLNM-Risk 59.6% vs MSKCC 44.9% vs Briganti 38.9%) and a lower number of false positives (PLNM-Risk 59.3% vs MSKCC 70.1% and Briganti 72.7%). In follow-up, patients stratified by the PLNM-Risk calculator showed significantly different biochemical recurrence rates after surgery.
The PLNM-Risk calculator offers a noninvasive clinical biomarker to predict PLNM for patients with PCa. It shows improved accuracy of diagnosis support and reduced overtreatment burdens for patients with findings suggestive of PCa.
This work was supported by the Key Research and Development Program of Jiangsu Province (BE2017756) and the Suzhou Science and Technology Bureau-Science and Technology Demonstration Project (SS201808).
准确识别前列腺癌（PCa）患者的盆腔淋巴结转移（PLNM）对于确定合适的治疗方案至关重要。在这里，我们构建了一个 PLNM-Risk 计算器，以便能够准确地做出是否进行广泛盆腔淋巴结清扫术（ePLND）的决策。
该 PLNM-Risk 计算器是在 280 名患者中开发的，并通过将一组放射科医生的解读、临床病理因素和从 MR 图像中提取的新的精细成像指标与放射组学机器学习和深度转移学习算法相结合，在 71 名内部患者和 50 名外部患者中进行了内部验证。并将其临床适用性与 Briganti 和 Memorial Sloan Kettering Cancer Center（MSKCC）列线图进行了比较。
PLNM-Risk 在训练/验证、内部测试和外部测试队列中的受试者工作特征曲线（AUC）下面积分别为 0.93（95%CI，0.90-0.96）、0.92（95%CI，0.84-0.97）和 0.76（95%CI，0.62-0.87），具有良好的诊断区分度。如果控制错过的 ePLND 数量<2%，PLNM-Risk 可以提供更多的 ePLND 保留（PLNM-Risk 为 59.6%，MSKCC 为 44.9%，Briganti 为 38.9%）和更少的假阳性（PLNM-Risk 为 59.3%，MSKCC 为 70.1%，Briganti 为 72.7%）。在随访中，根据 PLNM-Risk 计算器分层的患者在手术后显示出明显不同的生化复发率。
PLNM-Risk 计算器提供了一种非侵入性的临床生物标志物，用于预测 PCa 患者的 PLNM。它显示出了对诊断支持的准确性提高，并降低了对有 PCa 迹象患者的过度治疗负担。
这项工作得到了江苏省重点研发计划（BE2017756）和苏州市科技局科技示范项目（SS201808）的支持。
