Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images

Background:In colorectal cancer(CRC),mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype,morphology,and prognosis.However,mucinous components are present in a large number of adenocarcinomas,and the prognostic value of mucus proportion has not been investigated.Artificial intelligence provides a way to quantify mucus proportion on whole-slide images(WSIs)accurately.We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts.Methods:Deep learning was used to segment WSIs stained with hematoxylin and eosin.Mucus-tumor ratio(MTR)was defined as the proportion of mucinous component in the tumor area.A training cohort(N=419)and a validation cohort(N=315)were used to evaluate the prognostic value of MTR.Survival analysis was performed using the Cox proportional hazard model.Result:Patients were stratified tomucus-low andmucus-high groups,with 24.1%as the threshold.In the training cohort,patients with mucus-high had unfavorable outcomes(hazard ratio for high vs.low 1.88,95%confidence interval 1.18–2.99,P=0.008),with 5-year overall survival rates of 54.8%and 73.7%in mucus-high and mucus-lowgroups,respectively.The resultswere confirmed in the validation cohort(2.09,1.21–3.60,0.008;62.8%vs.79.8%).The prognostic value of MTR was maintained in multivariate analysis for both cohorts.Conclusion:The deep learning quantified MTR was an independent prognostic factor in CRC.With the advantages of advanced efficiency and high consistency,our method is suitable for clinical application and promotes precision medicine development.