Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology

The aim of the present study was to develop a non-invasive method based on histological imaging and clinical features for predicting the preoperative status of visceral pleural invasion (VPI) in patients with lung adenocarcinoma (LUAD) located near the pleura. VPI is associated with a worse prognosis of LUAD; therefore, early and accurate detection is critical for effective treatment planning. A total of 112 patients with preoperative computed tomography presentation of adjacent pleura and postoperative pathological findings confirmed as invasive LUAD were retrospectively enrolled. Clinical and histological imaging features were combined to develop a preoperative VPI prediction model and validate the model's efficacy. Finally, a nomogram for predicting LUAD was established and validated using a logistic regression algorithm. Both the clinical signature and radiomics signature (Rad signature) exhibited a perfect fit in the training cohort. The clinical signature was overfitted in the testing cohort, whereas the Rad signature showed a good fit. To combine clinical and radiomics signatures for optimal performance, a nomogram was created using the logistic regression algorithm. The results indicated that this approach had the highest predictive performance, with an area under the curve of 0.957 for the clinical signature and 0.900 for the Rad signature. In conclusion, histological imaging and clinical features can be combined in columnar maps to predict the preoperative VPI status of patients with adjacent pleural infiltrative lung carcinoma.