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Dual-layer spectral detector computed tomography multiparameter machine learning model for prediction of invasive
Jiayu Wan1,2,3, Xue Lin1,2,3, Zhaokai Wang4
1Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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.
