학술논문
Therapeutic Decision Making in Prevascular Mediastinal Tumors Using CT Radiomics and Clinical Features: Upfront Surgery or Pretreatment Needle Biopsy?
Document Type
Academic Journal
Author
Source
Cancers. February 2024, Vol. 16 Issue 4
Subject
Language
English
ISSN
2072-6694
Abstract
Author(s): Chao-Chun Chang [1,†]; Chia-Ying Lin [2,†]; Yi-Sheng Liu [2]; Ying-Yuan Chen [1]; Wei-Li Huang [1]; Wu-Wei Lai [1,3]; Yi-Ting Yen (corresponding author) [1,4,*]; Mi-Chia Ma (corresponding author) [5,*]; Yau-Lin [...]
Initial management approaches for prevascular mediastinal tumors (PMTs) can be divided into two categories: direct surgery and core needle biopsy (CNB). Although the gold standard diagnostic method is histopathological examination, the selection of the initial management between direct surgery and CNB is more urgent for patients with PMTs, compared with the definite diagnosis of PMT subtypes. The study aimed to develop clinical–radiomics machine learning (ML) classification models to differentiate patients who needed direct surgery from patients who needed CNB, among the patients with PMTs. An ensemble learning model, combining five ML models, had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (86.1%; p < 0.05), which may be used as clinical decision support system to facilitate the selection of the initial management of PMT. The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from patients who needed core needle biopsy among patients with prevascular mediastinal tumor (PMT). Patients with PMT who received a contrast-enhanced computed tomography (CECT) scan and initial management for PMT between January 2010 and December 2020 were included in this retrospective study. Fourteen ML algorithms were used to construct candidate classification models via the voting ensemble approach, based on preoperative clinical data and radiomic features extracted from the CECT. The classification accuracy of clinical diagnosis was 86.1%. The first ensemble learning model was built by randomly choosing seven ML models from a set of fourteen ML models and had a classification accuracy of 88.0% (95% CI = 85.8 to 90.3%). The second ensemble learning model was the combination of five ML models, including NeuralNetFastAI, NeuralNetTorch, RandomForest with Entropy, RandomForest with Gini, and XGBoost, and had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (p < 0.05). Due to the superior performance, the voting ensemble learning clinical–radiomic classification model may be used as a clinical decision support system to facilitate the selection of the initial management of PMT.
Initial management approaches for prevascular mediastinal tumors (PMTs) can be divided into two categories: direct surgery and core needle biopsy (CNB). Although the gold standard diagnostic method is histopathological examination, the selection of the initial management between direct surgery and CNB is more urgent for patients with PMTs, compared with the definite diagnosis of PMT subtypes. The study aimed to develop clinical–radiomics machine learning (ML) classification models to differentiate patients who needed direct surgery from patients who needed CNB, among the patients with PMTs. An ensemble learning model, combining five ML models, had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (86.1%; p < 0.05), which may be used as clinical decision support system to facilitate the selection of the initial management of PMT. The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from patients who needed core needle biopsy among patients with prevascular mediastinal tumor (PMT). Patients with PMT who received a contrast-enhanced computed tomography (CECT) scan and initial management for PMT between January 2010 and December 2020 were included in this retrospective study. Fourteen ML algorithms were used to construct candidate classification models via the voting ensemble approach, based on preoperative clinical data and radiomic features extracted from the CECT. The classification accuracy of clinical diagnosis was 86.1%. The first ensemble learning model was built by randomly choosing seven ML models from a set of fourteen ML models and had a classification accuracy of 88.0% (95% CI = 85.8 to 90.3%). The second ensemble learning model was the combination of five ML models, including NeuralNetFastAI, NeuralNetTorch, RandomForest with Entropy, RandomForest with Gini, and XGBoost, and had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (p < 0.05). Due to the superior performance, the voting ensemble learning clinical–radiomic classification model may be used as a clinical decision support system to facilitate the selection of the initial management of PMT.