The value of CT radiomics features to predict visceral pleural invasion in ≤3 cm peripheral type early non-small cell lung cancer

Abstract

OBJECTIVE: To investigate predictive value of CT-based radiomics features on visceral pleural invasion (VPI) in ≤3.0 cm peripheral type early non-small cell lung cancer (NSCLC). METHODS: A total of 221 NSCLC cases were collected. Among them, 115 are VPI-positive and 106 are VPI-negative. Using a stratified random sampling method, 70% cases were assigned to training dataset (n = 155) and 30% cases (n = 66) were assigned to validation dataset. First, CT findings, imaging features, clinical data and pathological findings were retrospectively analyzed, the size, location and density characteristics of nodules and lymph node status, the relationship between lesions and pleura (RAP) were assessed, and their mean CT value and the shortest distance between lesions and pleura (DLP) were measured. Next, the minimum redundancy-maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) features were extracted from the imaging features. Then, CT imaging prediction model, texture feature prediction model and joint prediction model were built using multifactorial logistic regression analysis method, and the area under the ROC curve (AUC) was applied to evaluate model performance in predicting VPI. RESULTS: Mean diameter, density, fractal relationship with pleura, and presence of lymph node metastasis were all independent predictors of VPI. When applying to the validation dataset, the CT imaging model, texture feature model, and joint prediction model yielded AUC = 0.882, 0.824 and 0.894, respectively, indicating that AUC of the joint prediction model was the highest (p < 0.05). CONCLUSION: The study demonstrates that the joint prediction model containing CT morphological features and texture features enables to predict the presence of VPI in early NSCLC preoperatively at the highest level.

Predicting visceral pleural invasion in lung adenocarcinoma presenting as part‐solid density utilizing a nomogram model combined with radiomics and clinical features

“…Eventually, 10 optimal quantitative radiomic features were extracted. This study covered first‐ to high‐order texture features which is partially consistent with a previous study by Wei et al, 28 suggesting some similarities between the two studies regarding texture features, but not in agreement with the study by Zha et al 15 According to our study, the model combination with radiomic and clinical features is more effective. Due to the difficulty in delineating peritumoral ROI for the reason of the proximity of the tumor to the pleura, we only annotated the interior VOI.…”

Research progress in predicting visceral pleural invasion of lung cancer: a narrative review

“…The prediction of VPI in lung cancer based on traditional CT features has a certain value at the expense of strong subjectivity, therefore, some studies have explored the efficacy of image-based one-dimensional histogram features and texture analysis in predicting VPI of lung cancer ( 34 , 35 ). Zuo et al ( 34 ) studied the preoperative prediction of VPI of cT1N0M0 lung adenocarcinoma based on CT texture characteristics, a total of 313 patients enrolled from two independent institutions.…”

“…The model reported a good discriminant ability and goodness of fit. Wei et al ( 35 ) built a comprehensive model based on CT texture features and pathology-image features and included 221 patients with NSCLC ≤3 cm. Results displayed that mean tumor diameter, density type, classification of tumor-pleural relationship, and pathological lymph node metastasis status in the clinical model were independent risk factors for predicting VPI.…”

Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma

“…Recent studies have demonstrated that incorporating radiomics features from the peritumoral region into modeling analysis can enhance predictive accuracy in the preoperative assessment of pathological invasiveness [17], lymphovascular invasion [18], lymph node metastasis [25], and spread through air space in lung cancer patients, compared to models relying solely on intratumoral features [26]. While previous investigations have successfully applied radiomics for assessing VPI status in early lung cancer [27][28][29][30][31][32], their primary focus has been on intratumoral features, with limited exploration of the potential contribution of peritumoral radiomics features. Additionally, the reliability and reproducibility of these models have not been validated in external sets.…”