Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.

Background: Machine learning (ML) methods have shown great potential in predicting colorectal cancer (CRC) survival. However, the ML models introduced thus far have mainly focused on binary outcomes and have not considered the time-to-event nature of this type of modeling. Objective: This study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival. Methods: The data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance. Results: A total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival. Conclusions: This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.

Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer

The prevalence of postoperative low anterior resection syndrome (LARS) in patients with colorectal cancer is high, which seriously affects the quality of life after operation. The purpose of this meta-analysis is to systematically evaluate the related factors of LARS in patients with colorectal cancer and provide reference for clinicians when making reasoned decisions. A systematic electronic search of PubMed, Embase, The Cochrane Library, WANFANG and CNKI was performed from 2012 to Dec 2020. We analyzed the risk factors of LARS by extracting baseline data and clinical results. The odds ratio (OR) was used to analyze binary variables. A total of 5102 patients were included in 21 literatures, of which the prevalence of LARS was 49.7% (2538/5102). Meta-analysis showed that there was no significant difference in the influence of age (P = 0.48) and sex (P = 0.68) on LARS, but low tumor height (P < 0.001), low anastomotic height (P < 0.001), radiotherapy and chemotherapy (P < 0.001) and postoperative anastomotic leakage (P < 0.001), disfunction stoma (P < 0.001) are high risk factors for the prevalence of LARS. Low tumor height, low anastomotic height, radiotherapy and chemotherapy, anastomotic leakage and disfunction stoma are risk factors for postoperative LARS in patients with colorectal cancer. Clinicians should pay attention to these indicators of patients to provide better clinical intervention for patients and improve their quality of life after operation.

Background: Patients with colorectal cancer (CRC) often present with multiple comorbidities, and many of these can affect treatment and survival. However, previous comorbidity studies primarily focused on diseases in commonly used comorbidity indices. The comorbid status of CRC patients with respect to the entire spectrum of chronic diseases has not yet been investigated. Objective: This study aimed to systematically analyze all chronic diagnoses and diseases co-occurring, using a network-based approach and large-scale administrative health data, and provide a complete picture of the comorbidity pattern in patients newly diagnosed with CRC from southwest China. Methods: In this retrospective observational study, the hospital discharge records of 678 hospitals from 2015 to 2020 in Sichuan Province, China were used to identify new CRC cases in 2020 and their history of diseases. We examined all chronic diagnoses using ICD-10 (International Classification of Diseases, 10th Revision) codes at 3 digits and focused on chronic diseases with >1% prevalence in at least one subgroup (1-sided test, P<.025), which resulted in a total of 66 chronic diseases. Phenotypic comorbidity networks were constructed across all CRC patients and different subgroups by sex, age (18-59, 60-69, 70-79, and ≥80 years), area (urban and rural), and cancer site (colon and rectum), with comorbidity as a node and linkages representing significant correlations between multiple comorbidities. Results: A total of 29,610 new CRC cases occurred in Sichuan, China in 2020. The mean patient age at diagnosis was 65.6 (SD 12.9) years, and 75.5% (22,369/29,610) had at least one comorbidity. The most prevalent comorbidities were hypertension (8581/29,610, 29.0%; 95% CI 28.5%-29.5%), hyperplasia of the prostate (3816/17,426, 21.9%; 95% CI 21.3%-22.5%), and chronic obstructive pulmonary disease (COPD; 4199/29,610, 14.2%; 95% CI 13.8%-14.6%). The prevalence of single comorbidities was different in each subgroup in most cases. Comorbidities were closely associated, with disorders of lipoprotein metabolism and hyperplasia of the prostate mediating correlations between other comorbidities. Males and females shared 58.3% (141/242) of disease pairs, whereas male-female disparities occurred primarily in diseases coexisting with COPD, cerebrovascular diseases, atherosclerosis, heart failure, or renal failure among males and with osteoporosis or gonarthrosis among females. Urban patients generally had more comorbidities with higher prevalence and more complex disease coexistence relationships, whereas rural patients were more likely to have co-existing severe diseases, such as heart failure comorbid with the sequelae of cerebrovascular disease or COPD. Conclusions: Male-female and urban-rural disparities in the prevalence of single comorbidities and their complex coexistence relationships in new CRC cases were not due to simple coincidence. The results reflect clinical practice in CRC patients and emphasize the importance of measuring comorbidity patterns in terms of individual and coexisting diseases in order to better understand comorbidity patterns.

Cervical cancer is the third most commonly diagnosed malignancy and the fourth leading cause of cancer-related deaths in women worldwide. MicroRNA-296 (miR-296) is aberrantly expressed in a variety of human cancer types. However, the expression levels, biological roles, and underlying molecular mechanisms of miR-296 in cervical cancer remain unclear. This study aimed to detect miR-296 expression in cervical cancer and evaluate its roles and underlying mechanisms in cervical cancer. This study demonstrated that miR-296 was significantly downregulated in cervical cancer tissues and cell lines. Restoring the expression of miR-296 inhibited the proliferation and invasion of cervical cancer cells. Moreover, miR-296 directly targeted the 3′-untranslated regions of specificity protein 1 (SP1) and decreased its endogenous expression at both the mRNA and protein levels. Similar to induced miR-296 expression, SP1 knockdown suppressed the proliferation and invasion of cervical cancer cells. Besides, resumption expression of SP1 rescued the tumor-suppressing roles of miR-296 in cervical cancer. These results indicated that miR-296 may act as a tumor suppressor in cervical cancer by directly targeting SP1. Therefore, SP1 may be developed as a therapeutic target for the treatment of patients with this malignancy.