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

Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review

Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly revealed molecular mechanisms diversify therapeutic approaches for colorectal cancer. However, the selection of personalized treatment among multiple treatment options has become another challenge in the era of precision medicine. Artificial intelligence has recently been increasingly investigated in the treatment of colorectal cancer. This narrative review mainly discusses the applications of artificial intelligence in the treatment of colorectal cancer patients. A comprehensive literature search was conducted in MEDLINE, EMBASE, and Web of Science to identify relevant papers, resulting in 49 articles being included. The results showed that, based on different categories of data, artificial intelligence can predict treatment outcomes and essential guidance information of traditional and novel therapies, thus enabling individualized treatment strategy selection for colorectal cancer patients. Some frequently implemented machine learning algorithms and deep learning frameworks have also been employed for long-term prognosis prediction in patients with colorectal cancer. Overall, artificial intelligence shows encouraging results in treatment strategy selection and prognosis evaluation for colorectal cancer patients.

Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning

Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction. Therefore, we established survival curve-based CRC survival predictors and benchmarked them against standard binary survival predictors, comparing their performance extensively on the clinical high and low risk subsets of one internal and three external cohorts. Survival curve-based risk prediction achieved a very similar risk stratification to binary risk prediction for this task. Exchanging other components of the pipeline, namely input tissue and feature extractor, had largely identical effects on model performance independently of the type of risk prediction. An ensemble of all survival curve-based models exhibited a more robust performance, as did a similar ensemble based on binary risk prediction. Patients could be further stratified within clinical risk groups. However, performance still varied across cohorts, indicating limited generalization of all investigated image analysis pipelines, whereas models using clinical data performed robustly on all cohorts.

Development and validation of a scoring system incorporating tumor growth pattern and perineural invasion for risk stratification in colorectal cancer

Tumor growth pattern (TGP) and perineural invasion (PNI) at the invasive margin have been recognized as indicators of tumor invasiveness and prognostic events in colorectal cancer (CRC). This study aims to develop a scoring system incorporating TGP and PNI, and further investigate its prognostic significance for CRC risk stratification. A scoring system, termed tumor-invasion score, was established by summing TGP and PNI scores. The discovery cohort (N = 444) and the validation cohort (N = 339) were used to explore the prognostic significance of the tumor-invasion score. The endpoints of the event were disease-free survival (DFS) and overall survival (OS) which were analyzed by the Cox proportional hazard model. In the discovery cohort, Cox regression analysis showed that DFS and OS were inferior for score 4 group compared with score 1 group (DFS, hazard ratio (HR) 4.44, 95% confidence interval (CI) 2.49-7.92, p < 0.001; OS, 4.41, 2.37-8.19,p < 0.001). The validation cohort showed similar results (DFS, 4.73, 2.39-9.37, p < 0.001; OS, 5.52, 2.55-12.0, p < 0.001). The model combining tumor-invasion score and clinicopathologic information showed good discrimination performance than single predictors. TGP and PNI were associated with tumor invasiveness and survival in CRC. The tumor-invasion score generated by TGP and PNI scores served as an independent prognostic parameter of DFS and OS for CRC patients.

Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis

This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review's findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.

Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology

Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.

The Effect of the Mucinous Component Presence on the Clinical Outcomes of Colorectal Cancer

Background. The effect of colorectal cancer (CRC) histological subtypes on the prognosis is still a controversial issue. We aimed to compare clinical findings, histopathologic data, and survival outcomes in CRC patients with classical and mucinous subtypes. Methods. Patients who were operated on for CRC between 2010 and 2017 were included in the study. Patients were classified into two groups according to the presence of a mucinous component: mucinous adenocarcinoma (MAC) - mucinous component > 50% and classical adenocarcinoma (CAC). Clinical and histopathologic findings, recurrence, metastasis, and survival rates were compared. Results. Data of the 484 CRC patients were documented. Sixty-nine patients (14.3%) were in the MAC group and 415 (85.7%) patients were in the CAC group. The mean age of patients with MAC and CAC was 63.4 ± 13.5 and 68.5 ± 12.7 years, respectively (p = 0.002). Proximal colon localization was found in 30 (43.5%) MAC patients and 123 (29.6%) CAC patients (p = 0.029). The number of patients with metastatic lymph nodes was higher in the MAC group (58% vs. 41.2%, p = 0.03). Nevertheless, there was no significant difference between the CAC and MAC groups in terms of disease-free survival (63.1% vs. 69.6%, p = 0.37) and disease-related mortality (23.6% vs. 23.2%, p = 0.94) over the follow-up period. Multivariate analysis showed that the presence of perineural invasion, patient’s age, and disease stage were associated with mortality in CRC patients. Conclusions. MACs occurred at a younger age than CACs and were more likely localized in the proximal colon as compared to CACs. Despite increased lymph node metastasis in MAC patients, no statistical significance was detected in overall survival or disease-free survival. Multivariate analysis revealed that age, perineural invasion, and disease stage were relevant to mortality in CRC patients.