Department of Medical Imaging, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan City, Taiwan, R.O.C.
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan, R.O.C.
Radiol Med. 2024 Jan;129(1):56-69. doi: 10.1007/s11547-023-01730-6. Epub 2023 Nov 16.
The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological subtypes and Lung Imaging Reporting and Data System (Lung-RADS) scores.
The proposed model was trained, validated, and tested using three datasets: one public dataset, the Lung Nodule Analysis 2016 (LUNA16) Grand challenge dataset (n = 1004), and two private datasets, the Lung Nodule Received Operation (LNOP) dataset (n = 1027) and the Lung Nodule in Health Examination (LNHE) dataset (n = 1525). The proposed model used a stacked ensemble model by employing a machine learning (ML) approach with an AutoGluon-Tabular classifier. The input variables were modified 3D convolutional neural network (CNN) features, radiomics features, and clinical features. Three classification tasks were performed: Task 1: Classification of lung nodules into benign or malignant in the LUNA16 dataset; Task 2: Classification of lung nodules into different pathological subtypes; and Task 3: Classification of Lung-RADS score. Classification performance was determined based on accuracy, recall, precision, and F1-score. Ten-fold cross-validation was applied to each task.
The proposed model achieved high accuracy in classifying lung nodules into benign or malignant categories in LUNA 16 with an accuracy of 92.8%, as well as in classifying lung nodules into different pathological subtypes with an F1-score of 75.5% and Lung-RADS scores with an F1-score of 80.4%.
Our proposed model provides an accurate classification of lung nodules based on the benign/malignant, different pathological subtypes, and Lung-RADS system.
本研究旨在开发一种结合深度学习（DL）、放射组学和临床数据的综合模型，将肺结节分为良性或恶性类别，并进一步将肺结节分为不同的病理亚型和肺成像报告和数据系统（Lung-RADS）评分。
该模型使用三个数据集进行训练、验证和测试：一个公共数据集、LUNA16 大挑战数据集（n=1004）和两个私有数据集，即肺结节接受手术（LNOP）数据集（n=1027）和健康体检肺结节（LNHE）数据集（n=1525）。所提出的模型使用堆叠集成模型，采用机器学习（ML）方法和 AutoGluon-Tabular 分类器。输入变量为修改后的 3D 卷积神经网络（CNN）特征、放射组学特征和临床特征。进行了三项分类任务：任务 1：在 LUNA16 数据集中对肺结节进行良性或恶性分类；任务 2：对肺结节进行不同的病理亚型分类；任务 3：对 Lung-RADS 评分进行分类。分类性能基于准确性、召回率、精度和 F1 得分来确定。每个任务都应用了 10 倍交叉验证。
所提出的模型在 LUNA16 中将肺结节分为良性或恶性类别的准确率达到 92.8%，在将肺结节分为不同的病理亚型的 F1 得分为 75.5%，以及 Lung-RADS 评分的 F1 得分为 80.4%，均取得了较高的准确率。
我们提出的模型能够基于良性/恶性、不同的病理亚型和 Lung-RADS 系统对肺结节进行准确分类。
