Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics

Abstract

Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based radiomics model. It aims to distinguish between the histological subtypes of lung adenocarcinoma and squamous cell carcinoma, employing four different machine learning techniques. A total of 255 Non-Small Cell Lung Cancer (NSCLC) patients were retrospectively analyzed and randomly divided into the training (n = 177) and validation (n = 78) sets, respectively. Radiomics features were extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was employed for feature selection. Subsequently, models were constructed using four distinct machine learning techniques, with the top-performing algorithm determined by evaluating metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC). The efficacy of the various models was appraised and compared using the DeLong test. A nomogram was developed based on the model with the best predictive efficiency and clinical utility, and it was validated using calibration curves. Results indicated that the logistic regression classifier had better predictive power in the validation cohort of the radiomic model. The combined model (AUC 0.870) exhibited superior predictive power compared to the clinical model (AUC 0.848) and the radiomics model (AUC 0.774). In this study, we discovered that the combined model, refined by the logistic regression classifier, exhibited the most effective performance in classifying the histological subtypes of NSCLC.

Fig 1. The workflow of this study.

Fig 2. Number and ratio of handcrafted features.

A show CT features, B show PET features.

Fig 3. Radiomic features selected using a LASSO regression model for subgroups.

A-C The coefficients of each feature in the most predictive feature subset. The abscissa is the coefficient, and the ordinate shows the reserved features. The larger the coefficient is, the more predictive effect of the feature is. A shows feature selected in the clinic model, B shows feature selected in the RS model, C shows feature selected in the combined model, D MSE of 10 fold cross validation. E Coefficients of 10 fold cross validation.

Fig 4. Comparison of receiver operating characteristic (ROC) curves for predicting subtype of pathology.

A shows the ROC curve of LR in the training cohort; B shows the ROC curve of LR in the validation cohort.

Fig 5. Clinical utility of prediction models.

A shows Nomogram of a clinical radiomics model developed based on a logistic regression model for the training cohort. gender 1:male 2:female. B,C show that Decision curve analysis (DCA) was conducted for the prediction model based on the logistic regression model in the training (B) and validation cohorts (C). D,E show Calibration curves of the nomogram based on the logistic regression model in the training (D) and validation cohorts (E).