Personalised Medicine for Colorectal Cancer Using Mechanism-Based Machine Learning Models

A Systems Biology Approach to Understand the Racial Disparities in Colorectal Cancer

Racial disparities between Black/African Americans (AA) and White patients in colorectal cancer are an ever-growing area of concern. Black/AA show the highest incidence and have the highest mortality among major U.S. racial groups. There is no definite cause other than possible sociodemographic, socioeconomic, education, nutrition, delivery of healthcare, screening, and cultural factors. A primary limitation in this field is the lack of and small sample size of Black/AA studies. Thus, this study aimed to investigate whether differences in gene expression contribute to this ongoing unanswered racial disparity issue. In this study, we examined transcriptomic data of Black/AA and White patient cohorts using a bioinformatic and systems biology approach. We performed a Kaplan-Meier overall survival analysis between both patient cohorts across critical colorectal cancer signal transduction networks (STN), to determine the differences in significant genes across each cohort. Other bioinformatic analyses performed included PROGENy (pathway responsive genes for activity inference), RNA sequencing differential expression using DESeq2, multivariable-adjusted regression, and other associated Kaplan-Meier analyses. These analyses identified novel prognostic genes independent from each cohort, 176 differentially expressed genes, and specific patient cohort STN survival associations. Despite the overarching limitation, the results revealed several novel differences in gene expression between the colorectal cancer Black/AA and White patient cohorts, which allows one to dive deeper into and understand the behavior on a systems level of what could be driving this racial difference across colorectal cancer. Concretely, this information can guide precision medicine approaches tailored specifically for colorectal cancer racial disparities.

SIGNIFICANCE

The purpose of this work is to investigate the racial disparities in colorectal cancer between Black/AA and White patient cohorts using a systems biology and bioinformatic approach. Our study investigates the underlying biology of each patient cohort. Concretely, the findings of this study include disparity-associated genes and pathways, which provide a tangible starting point to guide precision medicine approaches tailored specifically for colorectal cancer racial disparities.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer

OBJECTIVE

To predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches.

METHODS

Data were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for validation cohorts. Clinical information (i.e., demographics; initial laboratory tests; vital signs; outcomes) were collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and a logistic regression were applied for the prediction of 28-day mortality.

RESULTS

Overall, 693 patients were included from the eICU cohort, 181 patients from the MIMIC-IV cohort and 95 from the Wuhan Union cohort. Among the six machine learning models, the ensemble model exhibited the best predictive ability (AUC, 0.86), followed by random forest (AUC, 0.83) and LightGBM (AUC, 0.82) in the training cohort. The models also obtained the good predictive performance for the 28-day mortality in the validation cohorts.

CONCLUSIONS

We showed that machine learning algorithms can be used for the 28-day mortality prediction in critically ill, elderly patients with CRC.

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

Blood Biomarkers Panels for Screening of Colorectal Cancer and Adenoma on a Machine Learning-Assisted Detection Platform

OBJECTIVE

A mini-invasive and good-compliance program is critical to broaden colorectal cancer (CRC) screening and reduce CRC-related mortality. Blood testing combined with imaging examination has been proved to be feasible on screen for multicancer and guide intervention. The study aims to construct a machine learning-assisted detection platform with available multi-targets for CRC and colorectal adenoma (CRA) screening.

METHODS

This was a retrospective study that the blood test data from 204 CRCs, 384 CRAs, and 229 healthy controls was extracted. The classified models were constructed with 4 machine learning (ML) algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), and eXtreme Gradient Boosting (XGB) based on the candidate biomarkers. The importance index was used by SHapely Adaptive exPlanations (SHAP) analysis to identify the dominant characteristics. The performance of classified models was evaluated. The most dominating features from the proposed panel were developed by logistic regression (LR) for identification CRC from control.

RESULTS

The candidate biomarkers consisted of 26 multi-targets panel including CEA, AFP, and so on. Among the 4 models, the SVM classifier for CRA yields the best predictive performance (the area under the receiver operating curve, AUC: .925, sensitivity: .904, and specificity: .771). As for CRC classification, the RF model with 26 candidate biomarkers provided the best predictive parameters (AUC: .941, sensitivity: .902, and specificity: .912). Compared with CEA and CA199, the predictive performance was significantly improved. The streamlined model with 6 biomarkers for CRC also obtained a good performance (AUC: .946, sensitivity: .885, and specificity: .913).

CONCLUSIONS

The predictive models consisting of 26 multi-targets panel would be used as a non-invasive, economical, and effective risk stratification platform, which was expected to be applied for auxiliary screening of CRA and CRC in clinical practice.