Dual-layer spectral detector computed tomography multiparameter machine learning model for prediction of invasive lung adenocarcinoma.

IF 4 2区医学 Q2 ONCOLOGY

Translational lung cancer research Pub Date : 2025-02-28 Epub Date: 2025-02-27 DOI:10.21037/tlcr-24-822

Jiayu Wan, Xue Lin, Zhaokai Wang, Peng Sun, Shen Gui, Tianhe Ye, Qianqian Fan, Weiwei Liu, Feng Pan, Bo Yang, Xiaotong Geng, Zhen Quan, Lian Yang

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引用次数: 0

Abstract

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths. High-resolution computed tomography (HRCT) has improved the detection of ground glass nodules (GGNs), which are early indicators of lung cancer. Accurate assessment of GGN invasiveness is crucial for determining the appropriate surgical approach. Dual-layer spectral detector computed tomography (DLCT) offers advanced imaging capabilities, including electron density and iodine density, which enhance the evaluation of GGN invasiveness. This study aims to develop a machine learning (ML) model that integrates DLCT parameters and clinical features to predict the invasiveness of GGNs in LUAD, aiding in surgical decision-making and prognosis improvement.

Methods: The retrospective study encompassed 272 patients who were diagnosed with LUAD, comprising 154 cases of invasive adenocarcinomas (IA) and 118 cases of pre-invasive minimally invasive adenocarcinoma (MIA) which were then randomly allocated into a training set and a test set. Six ML models were developed based on five DLCT parameters (conventional, iodine density, virtual noncontrast, electron density, and effective atomic number). Subsequently, a nomogram was constructed using multi-factor logistic regression, incorporating radiomic characteristics and clinicopathological risk factors.

Results: The ML model based on conventional plus electron density performed better than the models with other DLCT parameters, with the area under the curves (AUCs) of 0.945 and 0.964 in the training and test sets, respectively. The clinical model and radiomics score (Rad-score) were combined in the logistic regression to construct a joint model, of which the AUCs were 0.974 in the training sets and 0.949 in the test sets. The ML model effectively differentiated between IA and pre-invasive MIA, and further classified patients into high and medium risk categories for invasion using waterfall plots.

Conclusions: The ML model based on DLCT parameters helps predict the invasiveness of GGNs and classifies the GGNs into different risk grades.

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双层光谱检测器计算机断层扫描多参数机器学习模型预测浸润性肺腺癌。

背景：肺腺癌（LUAD）是癌症相关死亡的主要原因。高分辨率计算机断层扫描（HRCT）提高了磨砂玻璃结节（ggn）的检测，这是肺癌的早期指标。准确评估GGN侵袭性对于确定合适的手术入路至关重要。双层光谱探测器计算机断层扫描（dct）提供了先进的成像能力，包括电子密度和碘密度，增强了GGN侵袭性的评估。本研究旨在建立一种整合dct参数和临床特征的机器学习（ML）模型，以预测LUAD中ggn的侵袭性，帮助手术决策和改善预后。方法：回顾性研究纳入272例诊断为LUAD的患者，其中侵袭性腺癌（IA） 154例，侵袭前微创腺癌（MIA） 118例，随机分为训练集和测试集。基于5个dct参数（常规、碘密度、虚拟非对比、电子密度和有效原子序数）建立了6个ML模型。随后，使用多因素逻辑回归构建了放射学特征和临床病理危险因素的nomogram。结果：基于常规正电子密度的ML模型优于其他dct参数的模型，训练集和测试集的曲线下面积（auc）分别为0.945和0.964。将临床模型与放射组学评分（Rad-score）进行logistic回归，构建联合模型，其中训练集的auc为0.974，测试集的auc为0.949。ML模型有效区分IA和侵袭前MIA，并利用瀑布图将患者进一步划分为侵袭高危和中危类别。结论：基于dct参数的ML模型有助于预测ggn的侵袭性，并将ggn划分为不同的风险等级。

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

Translational lung cancer research Medicine-Oncology

CiteScore

7.20

自引率

2.50%

发文量

137

期刊介绍： Translational Lung Cancer Research(TLCR, Transl Lung Cancer Res, Print ISSN 2218-6751; Online ISSN 2226-4477) is an international, peer-reviewed, open-access journal, which was founded in March 2012. TLCR is indexed by PubMed/PubMed Central and the Chemical Abstracts Service (CAS) Databases. It is published quarterly the first year, and published bimonthly since February 2013. It provides practical up-to-date information on prevention, early detection, diagnosis, and treatment of lung cancer. Specific areas of its interest include, but not limited to, multimodality therapy, markers, imaging, tumor biology, pathology, chemoprevention, and technical advances related to lung cancer.