Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

• Published in: Cancer imaging Vol. 20; no. 1; p. 83
• Main Authors: Wang, Xiao-Xiao, Ding, Yi, Wang, Si-Wen, Dong, Di, Li, Hai-Lin, Chen, Jian, Hu, Hui, Lu, Chao, Tian, Jie, Shan, Xiu-Hong
• Format: Journal Article
• Language: English
• Published: England BioMed Central 23.11.2020
• Subjects: Aged; Algorithms; Cancer; Cancer therapies; Classification; Computed tomography; Confidence intervals; Evaluation; Feature selection; Female; Gastric cancer; Humans; Magnetic resonance imaging; Male; Metastasis; Middle Aged; Neoplasm Staging; Nomograms; Patients; Radiomics; Regression analysis; Standard deviation; Statistical analysis; Stomach Neoplasms - classification; Stomach Neoplasms - diagnostic imaging; Stomach Neoplasms - pathology; Surgery; Tomography, X-Ray Computed - methods; Training; Tumors; Lauren classification; Computed tomography; Peritumoral analysis; Gastric cancer; Radiomics
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-020-00358-3

Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC. A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness. The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663-0.767) in the training cohort and 0.714 (95% CI, 0.636-0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696-0.795) and a validation AUC of 0.758 (95% CI, 0.685-0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy.