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Abstract：
BACKGROUND: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data.
METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV).
RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients.
CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.
METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV).
RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients.
CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.
摘要：
背景：本研究旨在探讨临床T1-2N0M0（cT1-2N0M0）小细胞肺癌（SCLC）患者隐匿性淋巴结转移（OLM）的发生率，并使用术前瘤内和瘤周对比增强CT影像数据开发机器学习预测模型。
方法：通过对4个百分点的242名合格患者进行回顾性分析，我们确定了cT1-2N0M0SCLC患者OLM的发生率。对于每个病变，使用肿瘤总体积(GTV)和肿瘤周围15mm的瘤周体积(PTV)定义两个ROI。通过从GTV和PTV中分别提取一组完整的1595个增强的基于CT的放射学特征，构建了五个模型，我们使用各种指标严格评估了模型性能，包括曲线下面积（AUC），准确度,灵敏度,特异性，校正曲线,和决策曲线分析(DCA)。为了增强临床适用性，我们制定了一个结合临床参数和rad_score(GTV和PTV)的列线图.
结果：初步调查显示cT1-2N0M0SCLC患者的OLM阳性率为33.9%。我们的组合模型，结合了GTV和PTV的三个放射学特征，以及两个临床参数（吸烟状况和形状），表现出强大的预测能力。外部验证队列的AUC峰值为0.772，该模型的性能优于替代模型。列线图显着提高了放射科医师的诊断精度，并为cT1-2N0M0SCLC患者的临床决策过程增加了实质价值。
结论：SCLC患者OLM的发生率超过非小细胞肺癌患者。组合模型表现出显著的泛化效应，以无创方式有效区分阳性和阴性OLM，从而指导cT1-2N0M0SCLC患者的个体化临床决策。
方法：通过对4个百分点的242名合格患者进行回顾性分析，我们确定了cT1-2N0M0SCLC患者OLM的发生率。对于每个病变，使用肿瘤总体积(GTV)和肿瘤周围15mm的瘤周体积(PTV)定义两个ROI。通过从GTV和PTV中分别提取一组完整的1595个增强的基于CT的放射学特征，构建了五个模型，我们使用各种指标严格评估了模型性能，包括曲线下面积（AUC），准确度,灵敏度,特异性，校正曲线,和决策曲线分析(DCA)。为了增强临床适用性，我们制定了一个结合临床参数和rad_score(GTV和PTV)的列线图.
结果：初步调查显示cT1-2N0M0SCLC患者的OLM阳性率为33.9%。我们的组合模型，结合了GTV和PTV的三个放射学特征，以及两个临床参数（吸烟状况和形状），表现出强大的预测能力。外部验证队列的AUC峰值为0.772，该模型的性能优于替代模型。列线图显着提高了放射科医师的诊断精度，并为cT1-2N0M0SCLC患者的临床决策过程增加了实质价值。
结论：SCLC患者OLM的发生率超过非小细胞肺癌患者。组合模型表现出显著的泛化效应，以无创方式有效区分阳性和阴性OLM，从而指导cT1-2N0M0SCLC患者的个体化临床决策。
