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基于临床、剂量学和炎症相关参数的立体定向体部放射治疗肺癌患者局部控制预测列线图的开发与验证

Development and validation of a nomogram for local control prediction in lung cancer patients treated with stereotactic body radiation therapy based on clinical, dosimetric, and inflammation-related parameters.

作者信息

Huang Bao-Tian, Lin Pei-Xian, Wang Ying, Luo Li-Mei

机构信息

Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.

Department of Nosocomial Infection Management, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China.

出版信息

BMC Pulm Med. 2025 Jul 10;25(1):332. doi: 10.1186/s12890-025-03800-z.

Abstract

BACKGROUND

The incidence of local recurrence remains noteworthy among lung cancer patients treated with stereotactic body radiation therapy (SBRT). The aim of the study is to identify the risk factors and develop a nomogram for local control (LC) prediction.

METHODS

One hundred fifty-eight primary or metastatic lung cancer patients treated with SBRT were retrospectively analyzed. The clinical, dosimetric and inflammation-related parameters were collected. The Cox regression analysis was performed to determine the independent prognostic factors. A nomogram based on the prognostic factors was established and internally validated using a bootstrap resampling method.

RESULTS

The median follow-up time for the whole cohort was 40 months (95% CI: 34-46) and 35.4% of the patients (56/158) experienced local recurrence. The 1-year, 3-year and 5-year LC rates were 97.4%, 85.8% and 76.1%. Multivariate Cox regression analysis revealed that six independent factors were associated with LC, including age, clinical stage, planning target volume (PTV) volume, BED of the prescription dose (BEDPD), lymphocyte count, and neutrocyte count. The bootstrap-corrected C-index of the developed nomogram was 0.745 (95% CI, 0.663-0.793). The time-dependent AUC indicated the nomogram exhibited strong discriminatory capability. Calibration curves demonstrated a good concordance between the predicted and the observed probabilities. The results of decision curve analysis highlighted the clinical utility of the model. Additionally, the high- and low-risk patients were stratified based on the cut-off point from the nomogram (P < 0.0001).

CONCLUSIONS

A nomogram based on the clinical, dosimetric, and inflammation-related predictors is developed for LC prediction in lung cancer patients treated with SBRT. External validation is required for further confirm its validity.

摘要

背景

在接受立体定向体部放射治疗(SBRT)的肺癌患者中,局部复发的发生率仍然值得关注。本研究的目的是确定危险因素并开发一种用于预测局部控制(LC)的列线图。

方法

回顾性分析了158例接受SBRT治疗的原发性或转移性肺癌患者。收集了临床、剂量学和炎症相关参数。进行Cox回归分析以确定独立的预后因素。基于预后因素建立了列线图,并使用自举重采样方法进行内部验证。

结果

整个队列的中位随访时间为40个月(95%CI:34 - 46),35.4%的患者(56/158)出现局部复发。1年、3年和5年的LC率分别为97.4%、85.8%和76.1%。多因素Cox回归分析显示,六个独立因素与LC相关,包括年龄、临床分期、计划靶体积(PTV)体积、处方剂量的生物等效剂量(BEDPD)、淋巴细胞计数和中性粒细胞计数。所开发列线图的自举校正C指数为0.745(95%CI,0.663 - 0.793)。时间依赖性AUC表明列线图具有很强的鉴别能力。校准曲线显示预测概率与观察概率之间具有良好的一致性。决策曲线分析结果突出了该模型的临床实用性。此外,根据列线图的截断点对高风险和低风险患者进行了分层(P < 0.0001)。

结论

开发了一种基于临床、剂量学和炎症相关预测因素的列线图,用于预测接受SBRT治疗的肺癌患者的LC。需要进行外部验证以进一步确认其有效性。

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