The Crohn's-like lymphoid reaction density: a new artificial intelligence quantified prognostic immune index in colon cancer

Automated classification of tertiary lymphoid structures in colorectal cancer using TLS-PAT artificial intelligence tool

Colorectal cancer (CRC) ranks as the third most common and second deadliest cancer worldwide. The immune system, particularly tertiary lymphoid structures (TLS), significantly influences CRC progression and prognosis. TLS maturation, especially in the presence of germinal centers, correlates with improved patient outcomes; however, consistent and objective TLS assessment is hindered by varying histological definitions and limitations of traditional staining methods. This study involved 656 patients with colorectal adenocarcinoma from CHU Brest, France. We employed dual immunohistochemistry staining for CD21 and CD23 to classify TLS maturation stages in whole-slide images and implemented a fivefold cross-validation. Using ResNet50 and Vision Transformer models, we compared various aggregation methods, architectures, and pretraining techniques. Our automated system, TLS-PAT, achieved high accuracy (0.845) and robustness (kappa = 0.761) in classifying TLS maturation, particularly with the Vision Transformer pretrained on ImageNet using Max Confidence aggregation. This AI-driven approach offers a standardized method for automated TLS classification, complementing existing detection techniques. Our open-source tools are designed for easy integration with current methods, paving the way for further research in external datasets and other cancer types.

Deep-learning enabled combined measurement of tumour cell density and tumour infiltrating lymphocyte density as a prognostic biomarker in colorectal cancer

BACKGROUND

Within the colorectal cancer (CRC) tumour microenvironment, tumour infiltrating lymphocytes (TILs) and tumour cell density (TCD) are recognised prognostic markers. Measurement of TILs and TCD using deep-learning (DL) on haematoxylin and eosin (HE) whole slide images (WSIs) could aid management.

METHODS

HE WSIs from the primary tumours of 127 CRC patients were included. DL was used to quantify TILs across different regions of the tumour and TCD at the luminal surface. The relationship between TILs, TCD, and cancer-specific survival was analysed.

RESULTS

Median TIL density was higher at the invasive margin than the luminal surface (963 vs 795 TILs/mm2, P = 0.010). TILs and TCD were independently prognostic in multivariate analyses (HR 4.28, 95% CI 1.87-11.71, P = 0.004; HR 2.72, 95% CI 1.19-6.17, P = 0.017, respectively). Patients with both low TCD and low TILs had the poorest survival (HR 10.0, 95% CI 2.51-39.78, P = 0.001), when compared to those with a high TCD and TILs score.

CONCLUSIONS

DL derived TIL and TCD score were independently prognostic in CRC. Patients with low TILs and TCD are at the highest risk of cancer-specific death. DL quantification of TILs and TCD could be used in combination alongside other validated prognostic biomarkers in routine clinical practice.

Comparative analysis of tertiary lymphoid structures for predicting survival of colorectal cancer: a whole-slide images-based study

Background

Tertiary lymphoid structures (TLS) are major components in the immune microenvironment, correlating with a favorable prognosis in colorectal cancer. However, the methods used to define and characterize TLS were not united, hindering its clinical application. This study aims to seek a more stable method to characterize TLS and clarify their prognostic value in larger multicenter cohorts.

Methods

A total of 1609 patients from four hospitals and The Cancer Genome Atlas database were analyzed. We quantified the number and maximum length of TLS along the invasive margin of tumor using hematoxylin and eosin-stained whole-slide images (WSIs). Additionally, the length of the invasive margin was determined to calculate the TLS density. The prognostic value of TLS for overall survival was evaluated. In addition, we examined the association between TLS density and immune cell infiltration using immunohistochemistry-stained WSIs. The performance for predicting overall survival was measured using hazard ratios (HR) with 95% confidence intervals (CI).

Results

Among the three TLS quantification methods, TLS density has the strongest discriminative performance. Survival analysis indicated that higher TLS density correlated with better overall survival [HR for high vs. low 0.57 (95% CI 0.42-0.78) in the primary cohort; 0.49 (0.35-0.69) in the validation cohort; 0.35 (0.18-0.67) in TCGA cohort]. A high TLS density was associated with a high level of CD3+ T cell infiltration.

Conclusions

Based on this comparative multicenter analysis, TLS density was identified as a simple, robust, and effective immune prognostic index for colorectal cancer.

Tertiary lymphoid structures in colorectal cancer

BACKGROUND

Tertiary lymphoid structures (TLS) are ectopic clusters of immune cells found in non-lymphoid tissues, particularly within the tumor microenvironment (TME). These structures resemble secondary lymphoid organs and have been identified in various solid tumors, including colorectal cancer (CRC), where they are associated with favorable prognosis. The role of TLS in modulating the immune response within the TME and their impact on cancer prognosis has garnered increasing attention in recent years.

OBJECTIVE

This review aims to summarize the current understanding of TLS in CRC, focusing on their formation, function, and potential as prognostic markers and therapeutic targets. We explore the mechanisms by which TLS influence the immune response within the TME and their correlation with clinical outcomes in CRC patients.

METHODS

We conducted a comprehensive review of recent studies that investigated the presence and role of TLS in CRC. The review includes data from histopathological analyses, immunohistochemical studies, and clinical trials, examining the association between TLS density, composition, and CRC prognosis. Additionally, we explored emerging therapeutic strategies targeting TLS formation and function within the TME.

RESULTS

The presence of TLS in CRC is generally associated with an improved prognosis, particularly in early-stage disease. TLS formation is driven by chronic inflammation and is characterized by the organization of B and T cell zones, high endothelial venules (HEVs), and follicular dendritic cells (FDCs). The density and maturity of TLS are linked to better patient outcomes, including reduced recurrence rates and increased survival. Furthermore, the interplay between TLS and immune checkpoint inhibitors (ICIs) suggests potential therapeutic implications for enhancing anti-tumor immunity in CRC.

CONCLUSIONS

TLS represent a significant prognostic marker in CRC, with their presence correlating with favorable clinical outcomes. Ongoing research is required to fully understand the mechanisms by which TLS modulate the immune response within the TME and to develop effective therapies that harness their potential. The integration of TLS-focused strategies in CRC treatment could lead to improved patient management and outcomes.

Development of high-quality artificial intelligence for computer-aided diagnosis in determining subtypes of colorectal cancer

BACKGROUND AND AIM

There are no previous studies in which computer-aided diagnosis (CAD) diagnosed colorectal cancer (CRC) subtypes correctly. In this study, we developed an original CAD for the diagnosis of CRC subtypes.

METHODS

Pretraining for the CAD based on ResNet was performed using ImageNet and five open histopathological pretraining image datasets (HiPreD) containing 3 million images. In addition, sparse attention was introduced to improve the CAD compared to other attention networks. One thousand and seventy-two histopathological images from 29 early CRC cases at Kyoto Prefectural University of Medicine from 2019 to 2022 were collected (857 images for training and validation, 215 images for test). All images were annotated by a qualified histopathologist for segmentation of normal mucosa, adenoma, pure well-differentiated adenocarcinoma (PWDA), and moderately/poorly differentiated adenocarcinoma (MPDA). Diagnostic ability including dice sufficient coefficient (DSC) and diagnostic accuracy were evaluated.

RESULTS

Our original CAD, named Colon-seg, with the pretraining of both HiPreD and ImageNET showed a better DSC (88.4%) compared to CAD without both pretraining (76.8%). Regarding the attentional mechanism, Colon-seg with sparse attention showed a better DSC (88.4%) compared to other attentional mechanisms (dual: 79.7%, ECA: 80.7%, shuffle: 84.7%, SK: 86.9%). In addition, the DSC of Colon-seg (88.4%) was better than other types of CADs (TransUNet: 84.7%, MultiResUnet: 86.1%, Unet++: 86.7%). The diagnostic accuracy of Colon-seg for each histopathological type was 94.3% for adenoma, 91.8% for PWDA, and 92.8% for MPDA.

CONCLUSION

A deep learning-based CAD for CRC subtype differentiation was developed with pretraining and fine-tuning of abundant histopathological images.

Necrosis score as a prognostic factor in stage I-III colorectal cancer: a retrospective multicenter study

BACKGROUND

Tumor necrosis results from failure to meet the requirement for rapid proliferation of tumor, related to unfavorable prognosis in colorectal cancer (CRC). However, previous studies used traditional microscopes to evaluate necrosis on slides, lacking a simultaneous phase and panoramic view for assessment. Therefore, we proposed a whole-slide images (WSIs)-based method to develop a necrosis score and validated its prognostic value in multicenter cohorts.

METHODS

Necrosis score was defined as the proportion of necrosis in the tumor area, semi-quantitatively classified into 3-level score groups by the cut-off of 10% and 30% on HE-stained WSIs. 768 patients from two centers were enrolled in this study, divided into a discovery (N = 445) and a validation (N = 323) cohort. The prognostic value of necrosis score was evaluated by Kaplan-Meier curves and the Cox model.

RESULT

Necrosis score was associated with overall survival, with hazard ratio for high vs. low in discovery and validation cohorts being 2.62 (95% confidence interval 1.59-4.32) and 2.51 (1.39-4.52), respectively. The 3-year disease free survival rates of necrosis-low, middle, and high were 83.6%, 80.2%, and 59.8% in discovery cohort, and 86.5%, 84.2%, and 66.5% in validation cohort. In necrosis middle plus high subgroup, there was a trend but no significant difference in overall survival between surgery alone and adjuvant chemotherapy group in stage II CRC (P = .075).

CONCLUSION

As a stable prognostic factor, high-level necrosis evaluated by the proposed method on WSIs was associated with unfavorable outcomes. Additionally, adjuvant chemotherapy provide survival benefits for patients with high necrosis in stage II CRC.

Artificial intelligence for quantifying Crohn's-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer

Crohn's-like lymphoid reaction (CLR) and tumor-infiltrating lymphocytes (TILs) are crucial for the host antitumor immune response. We proposed an artificial intelligence (AI)-based model to quantify the density of TILs and CLR in immunohistochemical (IHC)-stained whole-slide images (WSIs) and further constructed the CLR-I (immune) score, a tissue level- and cell level-based immune factor, to predict the overall survival (OS) of patients with colorectal cancer (CRC). The TILs score and CLR score were obtained according to the related density. And the CLR-I score was calculated by summing two scores. The development (Hospital 1, N = 370) and validation (Hospital 2 & 3, N = 144) cohorts were used to evaluate the prognostic value of the CLR-I score. The C-index and integrated area under the curve were used to assess the discrimination ability. A higher CLR-I score was associated with a better prognosis, which was identified by multivariable analysis in the development (hazard ratio for score 3 vs score 0 = 0.22, 95% confidence interval 0.12-0.40, P < 0.001) and validation cohort (0.21, 0.05-0.78, P = 0.020). The AI-based CLR-I score outperforms the single predictor in predicting OS which is objective and more prone to be deployed in clinical practice.

Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer

BACKGROUND

We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer.

METHODS

A total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor stroma proportion and the infiltration of immune cells in the stroma region. We further analyzed the correlation between the score and CD3+ T cells density in the stroma region using immunohistochemistry-stained whole-slide images. Survival analysis was performed using the Cox proportional hazard model, and the endpoint of the event was the overall survival.

RESULT

Patients were classified into 4-level score groups (score 1-4). A high Deep-immune score was associated with a high level of CD3+ T cells infiltration in the stroma region. In the primary cohort, survival analysis showed a significant difference in 5-year survival rates between score 4 and score 1 groups: 87.4% vs. 58.2% (Hazard ratio for score 4 vs. score 1 0.27, 95% confidence interval 0.15-0.48, P < 0.001). Similar trends were observed in the validation cohort (89.8% vs. 67.0%; 0.31, 0.15-0.62, < 0.001). Stratified analysis showed that the Deep-immune score could distinguish high-risk and low-risk patients in stage II colorectal cancer (P = 0.018).

CONCLUSION

The proposed Deep-immune score quantified by artificial intelligence can reflect the immune status of patients with colorectal cancer and is associate with favorable survival. This digital pathology-based finding might advocate change in risk stratification and consequent precision medicine.

Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review

Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.