Background

Currently, no tool exists for risk stratification in patients undergoing segmentectomy for non–small cell lung cancer (NSCLC).

Purpose

To develop and validate a deep learning (DL) prognostic model using preoperative CT scans and clinical and radiologic information for risk stratification in patients with clinical stage IA NSCLC undergoing segmentectomy.

Materials and Methods

In this single-center retrospective study, transfer learning of a pretrained model was performed for survival prediction in patients with clinical stage IA NSCLC who underwent lobectomy from January 2008 to March 2017. The internal set was divided into training, validation, and testing sets based on the assignments from the pretraining set. The model was tested on an independent test set of patients with clinical stage IA NSCLC who underwent segmentectomy from January 2010 to December 2017. Its prognostic performance was analyzed using the time-dependent area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for freedom from recurrence (FFR) at 2 and 4 years and lung cancer–specific survival and overall survival at 4 and 6 years. The model sensitivity and specificity were compared with those of the Japan Clinical Oncology Group (JCOG) eligibility criteria for sublobar resection.

Results

The pretraining set included 1756 patients. Transfer learning was performed in an internal set of 730 patients (median age, 63 years [IQR, 56–70 years]; 366 male), and the segmentectomy test set included 222 patients (median age, 65 years [IQR, 58–71 years]; 114 male). The model performance for 2-year FFR was as follows: AUC, 0.86 (95% CI: 0.76, 0.96); sensitivity, 87.4% (7.17 of 8.21 patients; 95% CI: 59.4, 100); and specificity, 66.7% (136 of 204 patients; 95% CI: 60.2, 72.8). The model showed higher sensitivity for FFR than the JCOG criteria (87.4% vs 37.6% [3.08 of 8.21 patients], P = .02), with similar specificity.

Conclusion

The CT-based DL model identified patients at high risk among those with clinical stage IA NSCLC who underwent segmentectomy, outperforming the JCOG criteria.

© RSNA, 2024

Supplemental material is available for this article.

中文翻译：

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基于 CT 的 AI 预后模型在非小细胞肺癌肺段切除术中的临床应用

 背景

目前，尚无工具可对因非小细胞肺癌 (NSCLC) 接受肺段切除术的患者进行风险分层。

 目的

利用术前 CT 扫描以及临床和放射学信息开发和验证深度学习 (DL) 预后模型，对接受肺段切除术的临床 IA 期 NSCLC 患者进行风险分层。

 材料和方法

在这项单中心回顾性研究中，对 2008 年 1 月至 2017 年 3 月接受肺叶切除术的临床 IA 期 NSCLC 患者进行了预训练模型的迁移学习，用于生存预测。内部集分为训练集、验证集和测试集。预训练集中的作业。该模型在 2010 年 1 月至 2017 年 12 月接受肺段切除术的临床 IA 期 NSCLC 患者的独立测试集上进行了测试。使用受试者工作特征曲线 (AUC) 下的时间依赖性面积、灵敏度和2 年和 4 年无复发 (FFR) 的特异性以及 4 年和 6 年肺癌特异性生存率和总生存率。将模型的敏感性和特异性与日本临床肿瘤学组（JCOG）亚肺叶切除资格标准进行比较。

 结果

预训练集包括 1756 名患者。在 730 名患者的内部组中进行转移学习（中位年龄，63 岁 [IQR，56-70 岁]；366 名男性），肺段切除测试组包括 222 名患者（中位年龄，65 岁 [IQR，58-71]）年]；114 男）。 2 年 FFR 的模型性能如下：AUC，0.86（95% CI：0.76，0.96）；敏感性，87.4%（8.21 名患者中的 7.17 名；95% CI：59.4，100）；特异性为 66.7%（204 名患者中的 136 名；95% CI：60.2，72.8）。该模型对 FFR 的敏感性高于 JCOG 标准（87.4% vs 37.6% [8.21 名患者中的 3.08 名患者]， P = 0.02），具有相似的特异性。

 结论

基于 CT 的 DL 模型识别出接受肺段切除术的临床 IA 期 NSCLC 患者中的高风险患者，其表现优于 JCOG 标准。

 © 北美放射学会，2024

本文提供了补充材料。