Combined CT morphological and histogram features improve differentiation of PIMA from PINMA compared to separate modelsCombining CT features improved lung cancer subtype prediction accuracy in a small review

ObjectiveTo assess the value of combining computed tomography (CT) morphological and histogram features for the differentiation of solitary pulmonary invasive mucinous adenocarcinoma (PIMA) from pulmonary invasive non-mucinous adenocarcinoma (PINMA).MethodsThis retrospective study analyzed the CT images and clinical data of 58 and 105 patients with PIMA and PINMA, respectively. CT histogram features were extracted after delineating regions of interest using 3D Slicer software. CT morphological and histogram features were compared between the PIMA and PINMA groups, and those that differed significantly were included in multivariate logistic regression models. The independent predictive factors identified were used to create CT morphological, CT histogram-based, and combined prediction models. The best-performing model was visualized and evaluated by constructing a nomogram.ResultsThe CT morphological prediction model included nodule type, vacuole sign, and tumor location as factors predictive of PIMA and had an area under the curve of 0.754. The CT histogram-based prediction model included kurtosis and the 90th percentile as factors predictive of PIMA and had an area under the curve of 0.820. The combined prediction model, which included tumor location, vacuole sign, kurtosis, and the 90th percentile, had an area under the curve of 0.845, suggesting greater diagnostic accuracy than the separate models. The combined prediction model also exhibited good calibration and high clinical applicability.ConclusionIntegrating CT morphological features and histogram analysis improves the accuracy of differentiating PIMA from PINMA. The nomogram provides a practical and effective tool for the non-invasive diagnosis of lung cancer subtypes.