Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

中文翻译：

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用于管理不确定肺结节的综合生物标志物。


理由：具有癌症风险的不确定性肺结节 (IPN) 患者接受高比例的侵入性、昂贵且病态的手术。目标：训练和外部验证结合临床、血液和影像生物标志物的风险预测模型，以改善 IPN 的无创管理。方法：在这项前瞻性收集、回顾性盲法评估研究中，使用 Mayo Clinic 模型计算了 456 名患者结节的癌症概率，并将患者分为低、中、高风险组。组合生物标志物模型 (CBM) 包括临床变量、血清高灵敏度 CYFRA 21-1 水平和放射组学特征，在队列 1 (n = 170) 中进行训练，并在队列 2-4 (总 n = 286) 中进行验证。汇集所有患者以重新校准模型以进行临床实施。与当前临床护理相比，CBM 的临床效用在 2 个队列中进行了评估。测量和主要结果：与 Mayo Clinic 模型相比，CBM 提供了更高的诊断准确性，曲线下面积提高了 0.124（95% bootstrap 置信区间，0.091-0.156；P < 2 × 10-16）。应用 10% 和 70% 风险阈值导致病例和对照受试者的偏倚校正临床重新分类指数分别为 0.15 和 0.12。对患者病历的临床效用分析估计，CBM 指导策略可将中危良性人群中的侵入性手术从 62.9% 减少至 50.6%，并将中危良性人群中诊断癌症的中位时间从 60 天缩短至 21 天。患癌症的风险。 结论：临床、血液和图像生物标志物的整合改善了 IPN 患者的无创诊断，有可能降低不必要的侵入性操作的发生率，同时缩短诊断时间。