CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer.

IF 3.5 2区 医学 Q2 ONCOLOGY
Xiaoyan Lu, Fan Liu, Jiahui E, Xiaoting Cai, Jingyi Yang, Xueqi Wang, Yuwei Zhang, Bingsheng Sun, Ying Liu
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引用次数: 0

Abstract

Background: Accurate preoperative assessment of occult lymph node metastasis (OLNM) plays a crucial role in informing therapeutic decision-making for lung cancer patients. Computed tomography (CT) is the most widely used imaging modality for preoperative work-up. The aim of this study was to develop and validate a CT-based machine learning model integrating intra-and peri-tumoral features to predict OLNM in lung cancer patients.

Methods: Eligible patients with peripheral lung cancer confirmed by radical surgical excision with systematic lymphadenectomy were retrospectively recruited from January 2019 to December 2021. 1688 radiomics features were obtained from each manually segmented VOI which was composed of gross tumor volume (GTV) covering the boundary of entire tumor and three peritumoral volumes (PTV3, PTV6 and PTV9) that capture the region outside the tumor. A clinical-radiomics model incorporating radiomics signature, independent clinical factors and CT semantic features was established via multivariable logistic regression analysis and presented as a nomogram. Model performance was evaluated by discrimination, calibration, and clinical utility.

Results: Overall, 591 patients were recruited in the training cohort and 253 in the validation cohort. The radiomics signature of PTV9 showed superior diagnostic performance compared to PTV3 and PTV6 models. Integrating GPTV radiomics signature (incorporating Rad-score of GTV and PTV9) with clinical risk factor of serum CEA levels and CT imaging features of lobulation sign and tumor-pleura relationship demonstrated favorable accuracy in predicting OLNM in the training cohort (AUC, 0.819; 95% CI: 0.780-0.857) and validation cohort (AUC, 0.801; 95% CI: 0.741-0.860). The predictive performance of the clinical-radiomics model demonstrated statistically significant superiority over that of the clinical model in both cohorts (all p < 0.05).

Conclusions: The clinical-radiomics model was able to serve as a noninvasive preoperative prediction tool for personalized risk assessment of OLNM in peripheral lung cancer patients.

基于ct的机器学习模型,整合肿瘤内和肿瘤周围放射组学特征,用于预测周围性肺癌的隐性淋巴结转移。
背景:准确的术前评估隐性淋巴结转移(OLNM)对肺癌患者的治疗决策具有重要意义。计算机断层扫描(CT)是术前检查中使用最广泛的成像方式。本研究的目的是开发和验证基于ct的机器学习模型,整合肿瘤内和肿瘤周围特征来预测肺癌患者的OLNM。方法:回顾性招募2019年1月至2021年12月经根治性手术切除并系统性淋巴结切除术证实的符合条件的周围性肺癌患者。每个人工分割的VOI由覆盖整个肿瘤边界的总肿瘤体积(GTV)和捕获肿瘤外区域的三个肿瘤周围体积(PTV3, PTV6和PTV9)组成,共获得1688个放射组学特征。通过多变量logistic回归分析,建立了包含放射组学特征、独立临床因素和CT语义特征的临床-放射组学模型,并以nomogram表示。通过鉴别、校准和临床应用来评估模型的性能。结果:总的来说,591名患者被纳入训练组,253名患者被纳入验证组。与PTV3和PTV6模型相比,PTV9的放射组学特征显示出更好的诊断性能。将GPTV放射组学特征(结合GTV和PTV9的ad评分)与血清CEA水平的临床危险因素、分叶征的CT影像特征以及肿瘤与胸膜的关系相结合,在训练队列(AUC, 0.819; 95% CI: 0.780-0.857)和验证队列(AUC, 0.801; 95% CI: 0.741-0.860)中预测OLNM具有良好的准确性。临床放射组学模型的预测性能在两个队列中均优于临床模型(均为p)。结论:临床放射组学模型可作为周围性肺癌患者OLNM个性化风险评估的无创术前预测工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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