Hist-Immune signature: a prognostic factor in colorectal cancer using immunohistochemical slide image analysis

Zhao, Ke; Li, Zhenhui; Yong, Li; Yao, Su; Huang, Yanqi; Wang, Yingyi; Zhang, Fang; Wu, Lin; Chen, Xin; Liang, Changhong; Liu, Zaiyi

doi:10.1080/2162402x.2020.1841935

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Certain quantitative signature of tumor-infiltrating immune cells is increasingly recognized as a predictive biomarker to enable personalized treatment selection and improve patient management ( 9 ). Although the potential clinical relevance of density and ratios of infiltrating cells has already been evaluated in previous studies ( 6 , 10 – 12 ), with the recent availability of high-throughput quantitative image features extracting algorithm ( 13 ), there is now an opportunity for the systematic analysis of immunohistochemistry (IHC) staining digital image to identify previously unrecognized features that correlate with patients’ prognoses ( 14 ). To the best of our knowledge, there were no prognostic models based on IHC-stained digital image analysis being developed to individually predict the survival outcomes in patients with resected NSCLC.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of an Immune-Based Prognostic Risk Score for Patients With Resected Non-Small Cell Lung Cancer

et al. 2022

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BackgroundDespite the well-known role of immunoscore, as a prognostic tool, that appeared to be superior to tumor–node–metastasis (TNM) staging system, no prognostic scoring system based on immunohistochemistry (IHC) staining digital image analysis has been established in non-small cell lung cancer (NSCLC). Hence, we aimed to develop and validate an immune-based prognostic risk score (IMPRS) that could markedly improve individualized prediction of postsurgical survival in patients with resected NSCLC.MethodsIn this retrospective study, complete resection of NSCLC (stage I–IIIA) was performed for two independent patient cohorts (discovery cohort, n=168; validation cohort, n=115). Initially, paraffin-embedded resected specimens were stained by immunohistochemistry (IHC) of three immune cell types (CD3+, CD4+, and CD8+ T cells), and a total of 5,580 IHC-immune features were extracted from IHC digital images for each patient by using fully automated pipeline. Then, an IHC-immune signature was constructed with selected features using the LASSO Cox analysis, and the association of signature with patients’ overall survival (OS) was analyzed by Kaplan–Meier method. Finally, IMPRS was established by incorporating IHC-immune signature and independent clinicopathological variables in multivariable Cox regression analysis. Furthermore, an external validation cohort was included to validate this prognostic risk score.ResultsEight key IHC-immune features were selected for the construction of IHC-immune signature, which showed significant associations with OS in all cohorts [discovery: hazard ratio (HR)=11.518, 95%CI, 5.444–24.368; validation: HR=2.664, 95%CI, 1.029–6.896]. Multivariate analyses revealed IHC-immune signature as an independent prognostic factor, and age, T stage, and N stage were also identified and entered into IMPRS (all p<0.001). IMPRS had good discrimination ability for predicting OS (C-index, 0.869; 95%CI, 0.861–0.877), confirmed using external validation cohort (0.731, 0.717–0.745). Interestingly, IMPRS had better prognostic value than clinicopathological-based model and TNM staging system termed as C-index (clinicopathological-based model: 0.674; TNM staging: 0.646, all p<0.05). More importantly, decision curve analysis showed that IMPRS had adequate performance for predicting OS in resected NSCLC patients.ConclusionsOur findings indicate that the IMPRS that we constructed can provide more accurate prognosis for individual prediction of OS for patients with resected NSCLC, which can help in guiding personalized therapy and improving outcomes for patients.

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Section: Introductionmentioning

confidence: 99%

Development and Validation of an Immune-Based Prognostic Risk Score for Patients With Resected Non-Small Cell Lung Cancer

et al. 2022

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“…4,5 Therefore, alternative approaches have been explored to improve the prognostic accuracy of patients with colorectal cancer. 6,7 Tumor deposits (TDs) were incorporated into the American Joint Committee on Cancer (AJCC) TNM classification (eighth edition) and classified as pN1c (stage III) in the absence of lymph node metastasis. 5 However, recent studies have shown that TDs are associated with worse prognosis regardless of lymph node status.…”

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confidence: 99%

“…4,5 Therefore, alternative approaches have been explored to improve the prognostic accuracy of patients with colorectal cancer. 6,7…”

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Pathological-Features-Modified TNM Staging System Improves Prognostic Accuracy for Rectal Cancer

Yang,

Lyu

et al. 2023

Diseases of the Colon &Amp; Rectum

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BACKGROUND: Variations in survival outcomes are observed in the American Joint Committee on Cancer’s 8th edition TNM staging system. OBJECTIVE: Machine learning ensemble methods were used to develop and evaluate the effectiveness of a pathological-features-modified tumor node metastasis staging system in predicting survival for patients with rectal cancer by using commonly reported pathological features, such as histological grade, tumor deposits, and perineural invasion, to improve the prognostic accuracy. DESIGN: This was a retrospective population-based study. SETTINGS: Data were assessed from the database of the Surveillance, Epidemiology, and End Results Program. PATIENTS: The study cohort comprised 14,468 rectal cancer patients diagnosed between 2010 and 2015. The development cohort included those who underwent surgery as the primary treatment while patients who received neoadjuvant therapy were assigned to the validation cohort. MAIN OUTCOME MEASURES: The primary outcome measures included cumulative rectal cancer survival, adjusted hazard ratios, and both calibration and discrimination statistics to evaluate model performance and internal validation. RESULTS: Multivariable Cox regression analysis identified all three pathological features as prognostic factors, following which patients were categorized into four pathological groups based on the number of pathological features (i.e., 0, 1, 2, and 3). Distinct survival differences were observed among the groups, especially with stage III patients. The proposed pathological-features-modified tumor-node-metastasis staging outperformed the TNM staging in both the development and validation cohorts. LIMITATIONS: Retrospective in design and lack of external validation. CONCLUSIONS: The proposed pathological-features-modified tumor-node-metastasis staging could complement the current TNM staging by improving the accuracy of rectal cancer patients’ survival estimation. See Video Abstract.

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

Zhao

Huang

et al. 2021

Precision Clinical Medicine

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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 amount of adenocarcinoma, 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 patients 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 by the Cox proportional hazard model. Result Patients were stratified to mucus-low and mucus-high groups by 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-low groups, respectively. The results were confirmed in the validation cohort (2.09, 1.21-3.60, 0.008; 79.8% vs. 62.8%). 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.

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Hist-Immune signature: a prognostic factor in colorectal cancer using immunohistochemical slide image analysis

Cited by 5 publications

References 30 publications

Development and Validation of an Immune-Based Prognostic Risk Score for Patients With Resected Non-Small Cell Lung Cancer

Development and Validation of an Immune-Based Prognostic Risk Score for Patients With Resected Non-Small Cell Lung Cancer

Pathological-Features-Modified TNM Staging System Improves Prognostic Accuracy for Rectal Cancer

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

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