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Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy

Institution:	1. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. No. 1, Shuaifuyuan, Dongcheng District, Bejing 100730, P.R.China;2. CT Collaboration, Siemens Healthineers Ltd. Shenyang, P.R.China;3. Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. No. 1, Shuaifuyuan, Dongcheng District, Bejing 100730, P.R.China;1. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi District, Tianjin 300060, China;2. National Clinical Research Center for Cancer, Tianjin, China;3. Tianjin''s Clinical Research Center for Cancer, Tianjin, China;4. The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China;5. Department of General Surgery, Weifang People''s Hospital, Weifang City, Shandong Province, China;6. Department of Gastrointestinal Cancer Biology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China;7. Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China

Abstract:	BackgroundTo investigate the prognostic value of dual-energy CT (DECT) based radiomics to predict disease-free survival (DFS) and overall survival (OS) for patients with advanced gastric cancer (AGC) after neoadjuvant chemotherapy (NAC).MethodsFrom January 2014 to December 2018, a total of 156 AGC patients were enrolled and randomly allocated into a training cohort and a testing cohort at a ratio of 2:1. Volume of interest of primary tumor was delineated on eight image series. Four feature sets derived from pre-NAC and delta radiomics were generated for each survival arm. Random survival forest was used for generating the optimal radiomics signature (RS). Statistical metrics for model evaluation included Harrell's concordance index (C-index) and the average cumulative/dynamic AUC throughout follow-up. A clinical model and a combined Rad-clinical model were built for comparison.ResultsThe pre-IU (derived from iodine uptake images before NAC) RS performed best for DFS and OS in the testing cohort (C-indices, 0.784 and 0.698; the average cumulative/dynamic AUCs, 0.80 and 0.77). When compared with the clinical model, the radiomics model had significantly higher C-index to predict DFS in the testing cohort (0.784 vs. 0.635, p < 0.001), but no statistical difference was found for OS (0.698 vs. 0.680, p = 0.473). The combined Rad-clinical models showed improved performance in the testing cohort, with C-indices of 0.810 and 0.710 for DFS and OS, respectively.ConclusionDECT-derived radiomics serves as a promising non-invasive biomarker to predict survival for AGC patients after NAC, providing an opportunity for transforming proper treatment.

Keywords:	Stomach neoplasms Tomography X-ray computed Neoadjuvant treatment Image processing Computer-assisted AGC"} {"#name":"keyword" "$":{"id":"kwrd0045"} "$$":[{"#name":"text" "_":"advanced gastric cancer AIC"} {"#name":"keyword" "$":{"id":"kwrd0055"} "$$":[{"#name":"text" "_":"akaike information criterion CA125"} {"#name":"keyword" "$":{"id":"kwrd0065"} "$$":[{"#name":"text" "_":"carbohydrate antigen 125 CA199"} {"#name":"keyword" "$":{"id":"kwrd0075"} "$$":[{"#name":"text" "_":"carbohydrate antigen 199 CEA"} {"#name":"keyword" "$":{"id":"kwrd0085"} "$$":[{"#name":"text" "_":"carcinoembryonic antigen C-index"} {"#name":"keyword" "$":{"id":"kwrd0095"} "$$":[{"#name":"text" "_":"concordance index DCA"} {"#name":"keyword" "$":{"id":"kwrd0105"} "$$":[{"#name":"text" "_":"decision curve analysis DECT"} {"#name":"keyword" "$":{"id":"kwrd0115"} "$$":[{"#name":"text" "_":"dual-energy CT DFS"} {"#name":"keyword" "$":{"id":"kwrd0125"} "$$":[{"#name":"text" "_":"disease-free survival GC"} {"#name":"keyword" "$":{"id":"kwrd0135"} "$$":[{"#name":"text" "_":"gastric cancer IQR"} {"#name":"keyword" "$":{"id":"kwrd0145"} "$$":[{"#name":"text" "_":"interquartile range IU"} {"#name":"keyword" "$":{"id":"kwrd0155"} "$$":[{"#name":"text" "_":"iodine uptake KM"} {"#name":"keyword" "$":{"id":"kwrd0165"} "$$":[{"#name":"text" "_":"Kaplan-Meier LASSO"} {"#name":"keyword" "$":{"id":"kwrd0175"} "$$":[{"#name":"text" "_":"least absolute shrinkage and selection operator M"} {"#name":"keyword" "$":{"id":"kwrd0185"} "$$":[{"#name":"text" "_":"120-kV equivalent mixed images NAC"} {"#name":"keyword" "$":{"id":"kwrd0195"} "$$":[{"#name":"text" "_":"neoadjuvant chemotherapy OS"} {"#name":"keyword" "$":{"id":"kwrd0205"} "$$":[{"#name":"text" "_":"overall survival RQS"} {"#name":"keyword" "$":{"id":"kwrd0215"} "$$":[{"#name":"text" "_":"radiomics quality score RS"} {"#name":"keyword" "$":{"id":"kwrd0225"} "$$":[{"#name":"text" "_":"radiomics signature RSF"} {"#name":"keyword" "$":{"id":"kwrd0235"} "$$":[{"#name":"text" "_":"random survival forest TRG"} {"#name":"keyword" "$":{"id":"kwrd0245"} "$$":[{"#name":"text" "_":"tumor regression grade VOI"} {"#name":"keyword" "$":{"id":"kwrd0255"} "$$":[{"#name":"text" "_":"volume of interest
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