利用放射学特征和临床参数鉴别可切除的III期外周小细胞肺癌和非小细胞肺癌的预测模型的发展和验证。

IF 2.8 4区 医学 Q3 ONCOLOGY
Technology in Cancer Research & Treatment Pub Date : 2025-01-01 Epub Date: 2025-08-21 DOI:10.1177/15330338251368956
Junjie Zhang, Ligang Hao, Qiuxu Zhang, Lina Zheng, Qian Xu, Fengxiao Gao
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引用次数: 0

摘要

肺癌主要分为小细胞肺癌(SCLC)和非小细胞肺癌(NSCLC),每一种都有不同的治疗方法和预后结果,特别是在III期外周病例中。本研究旨在利用临床和放射学数据建立预测模型,以便术前区分III期外周小细胞肺癌和非小细胞肺癌。方法回顾性分析2016年1月至2024年7月在我院收治的33例III期外周小细胞肺癌和99例III期外周非小细胞肺癌。从增强CT扫描中提取了1037个放射学特征。将队列分为训练集(n = 92)和测试集(n = 40)。使用LASSO算法进行放射学特征选择,并对9个机器学习模型进行评估。采用最优模型计算放射组学评分(Rad-score),构建临床模型。结合临床因素和放射学特征,通过受试者工作特征(ROC)曲线分析(曲线下面积,AUC)、KS统计和决策曲线分析(DCA)评估临床效用。我们在另一家医院的84名患者组中外部验证了联合模型。结果基于logistic回归的联合模型在训练组、临床组和放射组学组的AUC分别为0.956、0.775和0.841,在测试组的AUC分别为0.905、0.864和0.732。在外部验证队列中,联合模型的AUC为0.843。KS统计和DCA表明联合模型的临床实用性,Brier评分为0.115。结论在联合模型中整合临床参数和放射组学特征可能对III期外周小细胞肺癌和非小细胞肺癌的术前鉴别具有重要潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Development and Validation of Predictive Models for Differentiating Resectable Stage III Peripheral SCLC from NSCLC Using Radiomic Features and Clinical Parameters.

Development and Validation of Predictive Models for Differentiating Resectable Stage III Peripheral SCLC from NSCLC Using Radiomic Features and Clinical Parameters.

Development and Validation of Predictive Models for Differentiating Resectable Stage III Peripheral SCLC from NSCLC Using Radiomic Features and Clinical Parameters.

Development and Validation of Predictive Models for Differentiating Resectable Stage III Peripheral SCLC from NSCLC Using Radiomic Features and Clinical Parameters.

ObjectiveLung cancer is primarily categorized into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), each characterized by distinct therapeutic approaches and prognostic outcomes, particularly in stage III peripheral cases. This study aimed to develop predictive models utilizing clinical and radiomic data to preoperatively differentiate stage III peripheral SCLC from NSCLC.MethodWe conducted a retrospective analysis of 33 stage III peripheral SCLC cases and 99 stage III peripheral NSCLC cases treated at our hospital between January 2016 and July 2024. A total of 1037 radiomic features were extracted from contrast-enhanced CT scans. The cohort was divided into a training set (n = 92) and a test set (n = 40). Radiomic feature selection was performed using the LASSO algorithm, and nine machine learning models were evaluated. The optimal model was employed to compute the radiomics score (Rad-score) and construct a clinical model. A combined model, integrating clinical factors and radiomic features, was assessed for clinical utility through receiver operating characteristic (ROC) curve analysis (area under the curve, AUC), KS statistics and decision curve analysis (DCA). We externally validated the combined model in a group of 84 patients from another hospital.ResultsThe logistic regression-based combined model exhibited superior performance, achieving AUC values of 0.956, 0.775, and 0.841 for the combined, clinical, and radiomics models, respectively, within the training cohort, and 0.905, 0.864, and 0.732 in the test cohort. AUC for the combined model was 0.843 in the external validation cohort. The KS statistics and DCA indicated the clinical utility of the combined model, as evidenced by a Brier score of 0.115.ConclusionThe integration of clinical parameters and radiomics features within the combined model may hold significant potential for the preoperative differentiation of stage III peripheral SCLC from NSCLC.

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来源期刊
CiteScore
4.40
自引率
0.00%
发文量
202
审稿时长
2 months
期刊介绍: Technology in Cancer Research & Treatment (TCRT) is a JCR-ranked, broad-spectrum, open access, peer-reviewed publication whose aim is to provide researchers and clinicians with a platform to share and discuss developments in the prevention, diagnosis, treatment, and monitoring of cancer.
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