预测早期磨玻璃不透明肺腺癌的侵袭:基于放射组学的机器学习方法

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-09-13 DOI:10.1186/s12880-024-01421-2
Junjie Bin, Mei Wu, Meiyun Huang, Yuguang Liao, Yuli Yang, Xianqiong Shi, Siqi Tao
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引用次数: 0

摘要

设计一种基于计算机断层扫描(CT)放射组学和机器学习的肺磨玻璃结节(GGN)分类方法,用于预测早期磨玻璃不透明(GGO)肺腺癌的侵袭情况。这项回顾性研究纳入了2020年至2023年经组织学证实为原位腺癌(AIS)、微侵袭性腺癌(MIA)或侵袭性腺癌(IAC)的肺GGN患者。对所有患者的 CT 图像进行了自动分割,并获得了每位患者的 107 个放射学特征。利用随机森林(RF)和交叉验证建立了分类模型,其中包括三个 "单对单 "模型和一个 "三类 "模型。对于每个模型,根据归一化基尼重要性对特征进行排序,并选择累计重要性超过 0.9 的最小子集。这些选定的特征随后被用于训练最终模型。计算模型的性能指标,包括曲线下面积(AUC)、准确率、灵敏度和特异性。对 AUC 和准确性进行比较,以确定最终的最佳方法。研究对象包括 193 名患者(平均年龄 54 ± 11 岁,65 名男性),其中包括 65 名 AIS 患者、54 名 MIA 患者和 74 名 IAC 患者,分为一个训练队列(N = 154)和一个测试队列(N = 39)。最终的三类 RF 模型在区分每一类和其他两类方面优于三个单独的 "单对单 "模型。就多级分类模型而言,AIS 的 AUC、准确性、灵敏度和特异性分别为 0.87、0.79、0.62 和 0.88;MIA 的 AUC、准确性、灵敏度和特异性分别为 0.90、0.79、0.54 和 0.89;IAC 的 AUC、准确性、灵敏度和特异性分别为 0.87、0.69、0.73 和 0.67。基于放射组学的多分类射频模型可有效区分三种类型的肺GGN,从而实现GGO肺腺癌的早期诊断。
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Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach
To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma. This retrospective study included pulmonary GGN patients who were histologically confirmed to have adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma cancer (IAC) from 2020 to 2023. CT images of all patients were automatically segmented and 107 radiomic features were obtained for each patient. Classification models were developed using random forest (RF) and cross-validation, including three one-versus-others models and one three-class model. For each model, features were ranked by normalized Gini importance, and a minimal subset was selected with a cumulative importance exceeding 0.9. These selected features were then used to train the final models. The models’ performance metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity, were computed. AUC and accuracy were compared to determine the final optimal method. The study comprised 193 patients (mean age 54 ± 11 years, 65 men), including 65 AIS, 54 MIA, and 74 IAC, divided into one training cohort (N = 154) and one test cohort (N = 39). The final three-class RF model outperformed three individual one-versus-others models in distinguishing each class from the other two. For the multiclass classification model, the AUC, accuracy, sensitivity, and specificity were 0.87, 0.79, 0.62, and 0.88 for AIS; 0.90, 0.79, 0.54, and 0.89 for MIA; and 0.87, 0.69, 0.73, and 0.67 for IAC, respectively. A radiomics-based multiclass RF model could effectively differentiate three types of pulmonary GGN, which enabled early diagnosis of GGO pulmonary adenocarcinoma.
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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