Objective: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer.However, there is still room for improvement in this scoring method to achieve further advances in its clini...Objective: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer.However, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore(IS-mod) system for predicting overall survival(OS) in patients with stage Ⅰ-Ⅲ colon cancer.Methods: The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort(N=212) and validation cohort(N=103)from two centers were used to evaluate the prognostic value of the IS-mod.Results: In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS[adjusted hazard ratio(HR)=0.36, 95% confidence interval(95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like(IS-like) system(C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25%(C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation cohort.Conclusions: Our method simplifies the annotation and accelerates the calculation of Immunoscore method,thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set(N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer.展开更多
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,a...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.展开更多
基金supported by the National Key Research and Development Program of China(No.2017YFC1309102)National Natural Science Foundation of China(No.81771912,No.82001986,No.82071892)+1 种基金National Science Fund for Distinguished Young Scholars(No.81925023)High-level Hospital Construction Project(No.DFJH201805 and No.DFJH201914)。
文摘Objective: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer.However, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore(IS-mod) system for predicting overall survival(OS) in patients with stage Ⅰ-Ⅲ colon cancer.Methods: The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort(N=212) and validation cohort(N=103)from two centers were used to evaluate the prognostic value of the IS-mod.Results: In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS[adjusted hazard ratio(HR)=0.36, 95% confidence interval(95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like(IS-like) system(C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25%(C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation cohort.Conclusions: Our method simplifies the annotation and accelerates the calculation of Immunoscore method,thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set(N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer.
基金This work was supported by the National Key Research and Development Program of China(grant No.2017YFC1309102)the National Science Fund for Distinguished Young Scholars(grant No.81925023)+1 种基金the National Natural Science Foundation of China(grant Nos.81771912,81701782,81702322,82001986,and 82071892)the High-level Hospital Construction Project(grant Nos.DFJH201805 and DFJH201914).
文摘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.
