Evaluating the Microsatellite Instability of Colorectal Cancer Based on Multimodal Deep Learning Integrating Histopathological and Molecular Data

Precision prognosis of colorectal cancer: a multi-tiered model integrating microsatellite instability genes and clinical parameters

Background

Prognostic assessment for colorectal cancer (CRC) displays substantial heterogeneity, as reliance solely on traditional TNM staging falls short of achieving precise individualized predictions. The integration of diverse biological information sources holds the potential to enhance prognostic accuracy.

Objective

To establish a comprehensive multi-tiered precision prognostic evaluation system for CRC by amalgamating gene expression profiles, clinical characteristics, and tumor microsatellite instability (MSI) status in CRC patients.

Methods

We integrated genomic data, clinical information, and survival follow-up data from 483 CRC patients obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. MSI-related gene modules were identified using differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Three prognostic models were constructed: MSI-Related Gene Prognostic Model (Model I), Clinical Prognostic Model (Model II), and Integrated Multi-Layered Prognostic Model (Model III) by combining clinical features. Model performance was assessed and compared using Receiver Operating Characteristic (ROC) curves, Kaplan-Meier analysis, and other methods.

Results

Six MSI-related genes were selected for constructing Model I (AUC = 0.724); Model II used two clinical features (AUC = 0.684). Compared to individual models, the integrated Model III exhibited superior performance (AUC = 0.825) and demonstrated good stability in an independent dataset (AUC = 0.767).

Conclusion

This study successfully developed and validated a comprehensive multi-tiered precision prognostic assessment model for CRC, providing an effective tool for personalized medical management of CRC.

Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning

Objective

The objective of this study was to provide a multi-modal deep learning framework for forecasting the survival of rectal cancer patients by utilizing both digital pathological images data and non-imaging clinical data.

Materials and methods

The research included patients diagnosed with rectal cancer by pathological confirmation from January 2015 to December 2016. Patients were allocated to training and testing sets in a randomized manner, with a ratio of 4:1. The tissue microarrays (TMAs) and clinical indicators were obtained. Subsequently, we selected distinct deep learning models to individually forecast patient survival. We conducted a scanning procedure on the TMAs in order to transform them into digital pathology pictures. Additionally, we performed pre-processing on the clinical data of the patients. Subsequently, we selected distinct deep learning algorithms to conduct survival prediction analysis using patients' pathological images and clinical data, respectively.

Results

A total of 292 patients with rectal cancer were randomly allocated into two groups: a training set consisting of 234 cases, and a testing set consisting of 58 instances. Initially, we make direct predictions about the survival status by using pre-processed Hematoxylin and Eosin (H&E) pathological images of rectal cancer. We utilized the ResNest model to extract data from histopathological images of patients, resulting in a survival status prediction with an AUC (Area Under the Curve) of 0.797. Furthermore, we employ a multi-head attention fusion (MHAF) model to combine image features and clinical features in order to accurately forecast the survival rate of rectal cancer patients. The findings of our experiment show that the multi-modal structure works better than directly predicting from histopathological images. It achieves an AUC of 0.837 in predicting overall survival (OS).

Conclusions

Our study highlights the potential of multi-modal deep learning models in predicting survival status from histopathological images and clinical information, thus offering valuable insights for clinical applications.

Detecting microsatellite instability in colorectal cancer using Transformer-based colonoscopy image classification and retrieval

Colorectal cancer (CRC) is a major global health concern, with microsatellite instability-high (MSI-H) being a defining characteristic of hereditary nonpolyposis colorectal cancer syndrome and affecting 15% of sporadic CRCs. Tumors with MSI-H have unique features and better prognosis compared to MSI-L and microsatellite stable (MSS) tumors. This study proposed establishing a MSI prediction model using more available and low-cost colonoscopy images instead of histopathology. The experiment utilized a database of 427 MSI-H and 1590 MSS colonoscopy images and vision Transformer (ViT) with different feature training approaches to establish the MSI prediction model. The accuracy of combining pre-trained ViT features was 84% with an area under the receiver operating characteristic curve of 0.86, which was better than that of DenseNet201 (80%, 0.80) in the experiment with support vector machine. The content-based image retrieval (CBIR) approach showed that ViT features can obtain a mean average precision of 0.81 compared to 0.79 of DenseNet201. ViT reduced the issues that occur in convolutional neural networks, including limited receptive field and gradient disappearance, and may be better at interpreting diagnostic information around tumors and surrounding tissues. By using CBIR, the presentation of similar images with the same MSI status would provide more convincing deep learning suggestions for clinical use.

Long Non-Coding RNAs in Colorectal Cancer: Navigating the Intersections of Immunity, Intercellular Communication, and Therapeutic Potential

This comprehensive review elucidates the intricate roles of long non-coding RNAs (lncRNAs) within the colorectal cancer (CRC) microenvironment, intersecting the domains of immunity, intercellular communication, and therapeutic potential. lncRNAs, which are significantly involved in the pathogenesis of CRC, immune evasion, and the treatment response to CRC, have crucial implications in inflammation and serve as promising candidates for novel therapeutic strategies and biomarkers. This review scrutinizes the interaction of lncRNAs with the Consensus Molecular Subtypes (CMSs) of CRC, their complex interplay with the tumor stroma affecting immunity and inflammation, and their conveyance via extracellular vesicles, particularly exosomes. Furthermore, we delve into the intricate relationship between lncRNAs and other non-coding RNAs, including microRNAs and circular RNAs, in mediating cell-to-cell communication within the CRC microenvironment. Lastly, we propose potential strategies to manipulate lncRNAs to enhance anti-tumor immunity, thereby underlining the significance of lncRNAs in devising innovative therapeutic interventions in CRC.

The role of deep learning in diagnosing colorectal cancer

Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.

Long non-coding RNA signature in colorectal cancer: research progression and clinical application

Colorectal cancer is one of the top-ranked human malignancies. The development and progression of colorectal cancer are associated with aberrant expression of multiple coding and non-coding genes. Long non-coding RNAs (lncRNAs) have an important role in regulating gene stability as well as gene expression. Numerous current studies have shown that lncRNAs are promising biomarkers and therapeutic targets for colorectal cancer. In this review, we have searched the available literature to list lncRNAs involved in the pathogenesis and regulation of colorectal cancer. We focus on the role of lncRNAs in cancer promotion or suppression, their value in tumor diagnosis, and their role in treatment response and prognosis prediction. In addition, we will discuss the signaling pathways that these lncRNAs are mainly associated with in colorectal cancer. We also summarize the role of lncRNAs in colorectal precancerous lesions and colorectal cancer consensus molecular subgroups. We hope this review article will bring you the latest research progress and outlook on lncRNAs in colorectal cancer.