CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: an external validation cohort study. Issue 3 (March 2022)

• Record Type: Journal Article
• Authors: Zuo, Z.; Li, Y.; Peng, K.; Li, X.; Tan, Q.; Mo, Y.; Lan, Y.; Zeng, W.; Qi, W.
• Is Part Of: Clinical radiology. Volume 77:Issue 3(2022)
• Page Start: e215
• Page End: e221
• Publication Date: 2022-03
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2021.11.008
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : AIM: To develop a nomogram based on computed tomography (CT) texture analysis for the preoperative prediction of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma. MATERIALS AND METHODS: A dataset of chest CT containing lung nodules was collected from two institutions, and all surgically resected nodules were classified pathologically based on the presence of visceral pleural invasion. Each nodule on the CT image was segmented automatically by artificial-intelligence software and its CT texture features were extracted. The dataset was divided into training and external validation cohorts according to the institution, and a nomogram for predicting visceral pleural invasion was developed and validated. RESULTS: Of a total of 313 patients enrolled from two independent institutions, 63 were diagnosed with visceral pleural invasion. Three-dimensional (3D) CT long diameter, skewness, and sphericity, and chronic obstructive pulmonary disease were identified as independent predictors for visceral pleural invasion by multivariable logistic regression. The nomogram based on multivariable logistic regression showed great discriminative ability, as indicated by a C-index of 0.890 (95% confidence interval [CI]: 0.867–0.914) and 0.864 (95% CI: 0.817–0.911) for the training and external validation cohorts, respectively. Additionally, calibration of the nomogram revealed good predictive ability, as indicated by the Brier score (0.108 and 0.100 for the training and external validation cohorts, respectively). CONCLUSIONS: A nomogram was developed that could compute the probability of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma with good calibration and discrimination. The nomogram has potential as a reliable tool for clinical evaluation and decision-making. Highlights: Determining the visceral pleural invasion (VPI) status can be a challenging task. A reliable nomogram based on CT texture features was developed to predict VPI. The nomogram was validated with good calibration and discrimination. Potentially reliable tool for individual evaluations and clinical decision-making.