Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules

Abstract

Objective To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for preoperative differentiation of pulmonary nodular mucinous adenocarcinoma (PNMA) from pulmonary tuberculoma (PTB). Method A total of 124 and 53 patients with PNMA and PTB, respectively, were retrospectively analyzed from January 2017 to November 2022 in The Fourth Affiliated Hospital of Hebei Medical University (Ligang et al., A machine learning model based on CT and clinical features to distinguish pulmonary nodular mucinous adenocarcinoma from tuberculoma, 2023). A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into a training group and a test group at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (LR), support vector machine (SVM) and random forest (RF). The best performing model was adopted, and the radiomics score (Radscore) was then computed. The clinical model was developed using logistic regression. Finally, a combined model was established based on clinical factors and radiomics features. We externally validated the three models in a group of 68 patients (46 and 22 patients with PNMA and PTB, respectively) from Xing Tai People’s Hospital (30 and 14 patients with PNMA and PTB, respectively) and The First Hospital of Xing Tai (16 and 8 patients with PNMA and PTB, respectively). The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the predictive value of the developed models. Results The combined model established by the logistic regression method had the best performance. The ROC-AUC (also a decision curve analysis) of the combined model was 0.940, 0.990 and 0.960 in the training group, test group and external validation group, respectively, and the combined model showed good predictive performance for the differentiation of PNMA from PTB. The Brier scores of the combined model were 0.132 and 0.068 in the training group and test group, respectively. Conclusion The combined model incorporating radiomics features and clinical parameters may have potential value for the preoperative differentiation of PNMA from PTB.

Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules

“…Several studies have found that radiomics models perform well in classifying malignant from benign solid pulmonary nodules; however, these studies only focused on specific pathological types, i.e. , lung adenocarcinoma and tuberculoma with nodule diameters less than 3 or 4 cm [ 11 , 13 , 15 , 16 ]. Zhang et al [ 16 ] proposed a diagnostic model combining CT and radiomic features and achieved an AUC of 0.85 (95% CI, 0.78–0.91) in the validation cohort, but they focused on solid nodules ranging from 5 to 20 mm.…”

“…Radiomics can extract a large number of high-dimensional imaging features and convert this imaging information into quantitative parameters for analysis and modeling [ 14 ], which may serve as a noninvasive method to support personalized clinical decision-making. Previous studies have revealed that radiomics has great potential to support radiologists in identifying benign and malignant solid pulmonary nodules [ 11 , 15 – 18 ], but few studies have investigated the performance of enhanced CT radiomics in differentiating malignant from benign SSPNs. We aimed to explore the value of enhanced CT-based radiomics in discriminating malignant from benign SSPNs, to develop a combined model based on clinical and radiomics features for the differential diagnosis of SSPNs in the clinic.…”

Pulmonary mucinous adenocarcinoma: An overview of pathophysiology and advancements in treatment

“…Pulmonary mucinous adenocarcinoma (PMA) stands out as a distinctive subtype among lung adenocarcinomas, falling within the broad category of non-small cell lung cancer (NSCLC) [ 1 , 2 ]. Characterized by the excessive production of mucin, a viscous, gel-like substance crucial for pathological identification, PMA significantly differs from other lung adenocarcinomas [ 3 , 4 ]. Mucin abundance serves as a diagnostic indicator and influences tumor biology, including invasiveness and metastatic potential.…”