Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer. (September 2020)

• Record Type: Journal Article
• Authors: Yang, Jing; Wu, Qingyao; Xu, Lei; Wang, Zijie; Su, Kefan; Liu, Ruiqing; Yen, Eric Alexander; Liu, Shunli; Qin, Jiale; Rong, Yi; Lu, Yun; Niu, Tianye
• Is Part Of: Radiotherapy and oncology. Volume 150(2020)
• Page Start: 89
• Page End: 96
• Publication Date: 2020-09
• Subjects: Lymph node metastasis -- Gastric cancer -- Radiomics -- Preoperative prediction; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.06.004
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: Built a novel model integrating tumor and lymph node radiomics. High performance in lymph node metastasis prediction for gastric cancer. Multi-step selection and clinicopathologic information improved prediction power. Demonstrated superior capability in aiding decision making for gastroenterologists. Abstract: Background: To develop and validate a radiomics method via integrating tumor and lymph node radiomics for the preoperative prediction of lymph node (LN) status in gastric cancer (GC). Materials and methods: We retrospectively collected 170 contrast-enhanced abdominal CT images from GC patients. Five times repeated random hold-out experiment was employed. Tumor and nodal radiomics features were extracted from each individual tumor and LN respectively, and then multi-step feature selection was performed. The optimal tumor and nodal features were selected using Pearson correlation analysis and sequential forward floating selection (SFFS) algorithm. After feature fusion, the SFFS algorithm was used to develop radiomics signatures. The performance of the radiomics signatures developed based on logistic regression classifier was further analyzed and compared using the area under the receiver operating characteristic curve (AUC). Results: The AUC values, reported as mean ± standard deviation, were 0.9319 ± 0.0129 and 0.8546 ± 0.0261 for the training and validation cohorts respectively. The radiomic signatures could predict LN status, especially in T2-stage, diffuse-type and moderately/well differentiated GC. After integrating clinicopathologic information, the radiomic-clinicopathologic model (training cohort, 0.9432 ± 0.0129; validation cohort, 0.8764 ± 0.0322) showed a better discrimination capability than other radiomics models and clinicopathologic model. The radiomic-clinicopathologic model also showed superior performance to the gastroenterologist' decision in all experiments, and outperformed the radiologist in some experiments. Conclusion: Our proposed method presented good predictive performance and great potential for predicting LNM in GC. As a noninvasive preoperative prediction tool, it can be helpful for guiding the prognosis and treatment decision-making in GC patients.