Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment 18F-FDG PET/CT Using Deep Learning

Zhao, Hongyue; Su, Yexin; Lyu, Zhehao; Tian, Lin; Xu, Peng; Lin, Lin; Wang, Han; Fu, Peng

doi:10.1016/j.acra.2023.03.032

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…To date, published studies have focused on distinguishing ADC from SCC, or SCLC. A study by Zhao et al (30) established a Mobilenet v2 model for discriminating ADC and SCC, yielding an AUC value of 0.767, slightly lower than ours. In this study, we innovatively constructed 3 models to predict ADC, SCC, and LCC, respectively, whereas very few prediction models involving LCC subtype have been previously developed.…”

Section: Discussioncontrasting

confidence: 82%

Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach

Kuang,

Zhang,

Zhang

et al. 2024

Front. Med.

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ObjectiveThis research aims to develop and assess the performance of interpretable machine learning models for diagnosing three histological subtypes of non-small cell lung cancer (NSCLC) utilizing CT imaging data.MethodsA retrospective cohort of 317 patients diagnosed with NSCLC was included in the study. These individuals were randomly segregated into two groups: a training set comprising 222 patients and a validation set with 95 patients, adhering to a 7:3 ratio. A comprehensive extraction yielded 1,834 radiomic features. For feature selection, statistical methodologies such as the Mann–Whitney U test, Spearman’s rank correlation, and one-way logistic regression were employed. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized. The study designed three distinct models to predict adenocarcinoma (ADC), squamous cell carcinoma (SCC), and large cell carcinoma (LCC). Six different classifiers, namely Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, eXtreme Gradient Boosting (XGB), and LightGBM, were deployed for model training. Model performance was gauged through accuracy metrics and the area under the receiver operating characteristic (ROC) curves (AUC). To interpret the diagnostic process, the Shapley Additive Explanations (SHAP) approach was applied.ResultsFor the ADC, SCC, and LCC groups, 9, 12, and 8 key radiomic features were selected, respectively. In terms of model performance, the XGB model demonstrated superior performance in predicting SCC and LCC, with AUC values of 0.789 and 0.848, respectively. For ADC prediction, the Random Forest model excelled, showcasing an AUC of 0.748.ConclusionThe constructed machine learning models, leveraging CT imaging, exhibited robust predictive capabilities for SCC, LCC, and ADC subtypes of NSCLC. These interpretable models serve as substantial support for clinical decision-making processes.

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Section: Discussioncontrasting

confidence: 82%