Preoperative prediction of the degree of differentiation of lung adenocarcinoma presenting as sub-solid or solid nodules with a radiomics nomogram. Issue 9 (September 2022)

• Record Type: Journal Article
• Authors: Yang, Y.; Tan, M.; Ma, W.; Duan, S.; Huang, X.; Jin, L.; Tang, L.; Li, M.
• Is Part Of: Clinical radiology. Volume 77:Issue 9(2022)
• Page Start: e680
• Page End: e688
• Publication Date: 2022-09
• 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.2022.05.015
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : AIM: To develop and validate a radiomics nomogram for prediction of degree of differentiation in lung adenocarcinoma presenting as sub-solid or solid nodules. MATERIALS AND METHODS: A total of 438 patients with histopathologically confirmed adenocarcinoma (248 non-poorly differentiated and 190 poorly differentiated) were divided into training cohort ( n= 235) and internal validation cohort ( n= 203) according to surgery sequence. Sixty patients form public TCIA dataset were selected for external validation. One thousand, two hundred and eighteen radiomics features were extracted from each volumetric region of interest and a least absolute shrinkage and selection operator logistic regression was applied to select meaningful radiomic features for building a radiomics score (Rad-score) model. A nomogram model incorporating the Rad-score and type was established after multivariable logistic regression. The discrimination efficiency, calibration efficacy, and clinical utility value of the nomogram were evaluated. RESULTS: The Rad-score model could predict the differentiation degree of lung adenocarcinoma with an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78–0.89) in the internal validation cohort. The AUC of the nomogram and radiographic model was 0.86 (95% CI: 0.80–0.91), 0.78 (95% CI: 0.72–0.84) in the internal validation cohort respectively. The AUC of the nomogram in the external validation cohort was 0.73 (95% CI: 0.58–0.88). Delong's test showed that the nomogram performed better than radiographic features alone ( p= 0.001). CONCLUSIONS: The proposed radiomics nomogram has the potential to predict the differentiation degree of lung adenocarcinoma preoperatively. Highlights: Evaluating the degree of differentiation continues to be a major challenge. A nomogram based on radiomics was developed to predict degree of differentiation. The nomogram performed better than radiographic model. Potentially reliable tool for preoperatively predict the differentiation degree.