利用手术数据构建放射组学模型预测立体定向体部放疗治疗的临床 I 期非小细胞肺癌的局部区域复发和预后
Predicting Regional Recurrence and Prognosis in Stereotactic Body Radiation Therapy-Treated Clinical Stage I Non-small Cell Lung Cancer Using a Radiomics Model Constructed With Surgical Data.
机构信息
Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China.
Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
出版信息
Int J Radiat Oncol Biol Phys. 2024 Nov 15;120(4):1096-1106. doi: 10.1016/j.ijrobp.2024.06.010. Epub 2024 Jun 25.
PURPOSE
Risk stratification of regional recurrence (RR) is clinically important in the design of adjuvant treatment and surveillance strategies in patients with clinical stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). This study aimed to develop a radiomics model predicting occult lymph node metastasis (OLNM) using surgical data and apply it to the prediction of RR in SBRT-treated early-stage NSCLC patients.
METHODS AND MATERIALS
Patients with clinical stage I NSCLC who underwent curative surgery with systematic lymph node dissection from January 2013 to December 2018 (the training cohort) and from January 2019 to December 2020 (the validation cohort) were included. A preoperative computed tomography-based radiomics model, a clinical feature model, and a fusion model predicting OLNM were constructed. The performance of the 3 models was quantified and compared in the training and validation cohorts. Subsequently, the radiomics model was used to predict RR in a cohort of consecutive SBRT-treated early-stage NSCLC patients from 2 academic medical centers.
RESULTS
A total of 769 patients were included. Eight computed tomography features were identified in the radiomics model, achieving areas under the curves of 0.85 (95% CI, 0.81-0.89) and 0.83 (95% CI, 0.80-0.88) in the training and validation cohorts, respectively. Nevertheless, adding clinical features did not improve the performance of the radiomics model. With a median follow-up of 40.0 (95% CI, 35.2-44.8) months, 32 of the 213 patients in the SBRT cohort developed RR and those in the high-risk group based on the radiomics model had a higher cumulative incidence of RR (P < .001) and shorter regional recurrence-free survival (P = .02), progression-free survival (P = .004) and overall survival (P = .006) than those in the low-risk group.
CONCLUSIONS
The radiomics model based on pathologically confirmed data effectively identified patients with OLNM, which may be useful in the risk stratification among SBRT-treated patients with clinical stage I NSCLC.
目的
在接受立体定向体部放射治疗（SBRT）的 I 期非小细胞肺癌（NSCLC）患者中，对区域复发（RR）的风险分层在辅助治疗和监测策略的设计中具有重要的临床意义。本研究旨在使用手术数据开发一种预测隐匿性淋巴结转移（OLNM）的放射组学模型，并将其应用于预测接受 SBRT 治疗的早期 NSCLC 患者的 RR。
方法和材料
本研究纳入了 2013 年 1 月至 2018 年 12 月（训练队列）和 2019 年 1 月至 2020 年 12 月（验证队列）期间接受根治性手术和系统淋巴结清扫术的临床 I 期 NSCLC 患者。构建了一个基于术前 CT 的放射组学模型、临床特征模型和预测 OLNM 的融合模型。在训练和验证队列中对这 3 种模型的性能进行了量化和比较。随后，该放射组学模型被用于预测来自 2 家学术医疗中心的连续接受 SBRT 治疗的早期 NSCLC 患者的 RR。
结果
本研究共纳入了 769 例患者。在放射组学模型中确定了 8 个 CT 特征，在训练和验证队列中的曲线下面积分别为 0.85（95%置信区间，0.81-0.89）和 0.83（95%置信区间，0.80-0.88）。然而，添加临床特征并没有提高放射组学模型的性能。在中位随访 40.0（95%置信区间，35.2-44.8）个月后，在 SBRT 队列的 213 例患者中有 32 例发生 RR，基于放射组学模型的高危组患者的 RR 累积发生率更高（P<0.001），区域无复发生存率（P=0.02）、无进展生存率（P=0.004）和总生存率（P=0.006）更短。
结论
基于病理证实数据的放射组学模型有效地识别出了有 OLNM 的患者，这可能有助于对接受 SBRT 治疗的 I 期 NSCLC 患者进行风险分层。
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