A predictive model and rapid multi-dynamic algorithm developed based on tumor-stroma percentage in gastric cancer:a retrospective,observational study  

作　　者：Yitian Xu Yan Yang Feichi Cheng Zai Luo Yuan Zhang Pengshan Zhang Jiahui Qiu Zhengjun Qiu Chen Huang 

机构地区：[1]Department of Gastrointestinal Surgery,Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine,Hongkou District,Shanghai,P.R.China [2]Department of Gastrointestinal Surgery,First Affiliated Hospital of Bengbu Medical College,Bengbu,Anhui,P.R.China

出　　处：《Gastroenterology Report》2024年第1期413-423,共11页胃肠病学报道(英文)

基　　金：supported by the National Natural Science Foundation of China[grant number 82072662 and 82203751];Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support[grant number 2019142];Shanghai Three-year Action Plan to Promote Clinical Skills and Clinical Innovation in Municipal Hospitals[grant number SHDC2020CR4022];the 2021 Shanghai“Rising Stars of Medical Talent”Youth Development Program:Outstanding Youth Medical Talents。

摘　　要：Background:Tumor-stroma percentage(TSP)is a prognostic risk factor in numerous solid tumors.Despite this,the prognostic significance of TSP in gastric cancer(GC)remains underexplored.Through the development of a personalized predictive model and a semi-automatic identification system,our study aimed to fully unlock the predictive potential of TSP in GC.Methods:We screened GC patients from Shanghai General Hospital(SGH)between 2012 and 2019 to develop and validate a nomogram.Univariate and multivariate Cox proportional hazards regression analyses were employed to identify independent prognostic factors influencing the prognosis for GC patients.The nomogram was further validated externally by using a cohort from Bengbu Medical College(BMC).All patients underwent radical gastrectomy,with those diagnosed with locally advanced GC receiving adjuvant chemotherapy.The primary outcome measured was overall survival(OS).The semi-automatic identification of the TSP was achieved through a computer-aided detection(CAD)system,denoted as TSP-cad,while TSP identified by pathologists was labeled as TSP-visual.Results:A total of 813 GC patients from SGH and 59 from BMC were enrolled in our study.TSP-visual was identified as an adverse prognostic factor for OS in GC and was found to be associated with pathological Tumor Node Metastasis staging system(pTNM)stage,T stage,N stage,perineural invasion(PNI),lymphovascular invasion(LVI),TSP-visual,tumor size,and other factors.Multivariate Cox regression using the training cohort revealed that TSP-visual(hazard ratio[HR],2.042;95%confidential interval[CI],1.485-2.806;P<0.001),N stage(HR,2.136;95%CI,1.343-3.397;P=0.010),PNI(HR,1.791;95%CI,1.270-2.526;P=0.001),and LVI(HR,1.482;95%CI,1.021-2.152;P=0.039)were independent predictors.These factors were incorporated into a novel nomogram,which exhibited strong predictive accuracy for 5-year OS in the training,internal validation,and external validation cohorts(area under the curve?0.744,0.759,and 0.854,respectively).The decision curve analysis of

关 键 词：gastric cancer tumor-stroma percentage NOMOGRAM algorithm 

分 类 号：R73[医药卫生—肿瘤]