Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data

Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review

The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Artificial neural network-based gene screening and immune cell infiltration analysis of osteosarcoma feature

BACKGROUND

The present study aimed to construct an artificial neural network (ANN) model that leverages characteristic genes associated with osteosarcoma (OS) to enable accurate prognostication for OS patients.

METHODS

Our research revealed 467 differentially expressed genes (DEGs) via gene expression contrast analysis, consisting of 345 downregulated genes and 122 upregulated genes. Gene Ontology (GO) enrichment analysis illuminated functions primarily encompassing T-cell activation, secretory granule lumen and antioxidant activity, among others. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we discovered significant correlations between the DEGs and certain pathways, including phagosome, Staphylococcus aureus infection and human T-cell leukemia virus 1 infection. We then screened out 30 characteristic DEGs (CDEGs) based on random forest analysis and constructed the ANN model using the gene score matrix. To verify the credibility and accuracy of the ANN model, we performed internal and external validation processes, which affirmed our model's predictive capabilities.

RESULTS

The study further delved into the analysis of immune cell infiltration and its correlation with the target CDEGs, revealing disparities in the infiltration of 22 types of immune cells across different groups and their interrelationships. Moreover, we probed the expression of the two foremost CDEGs (YES1 and MFNG) in OS and normal tissues. We noted a positive relationship between the expression of YES1 and MFNG in OS tissues and the clinicopathological characteristics of OS patients.

CONCLUSIONS

Collectively, the findings of the present study validate the effectiveness of the CDEGs-based ANN model in predicting OS patients, which might facilitate early diagnosis and treatment of OS.

The leading global health challenges in the artificial intelligence era

Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.

Risk Stratification for Early-onset Colorectal Cancer Screening: Are We Ready for Implementation?

Early-onset colorectal cancer (EOCRC) is increasing at alarming rates and identifying risk factors is a high priority. There is a need to develop risk stratification approaches for colorectal cancer screening among younger populations. Although there is a growing body of evidence identifying risk factors for EOCRC, including the report by Imperiale and colleagues in this issue, risk stratification for EOCRC screening has not been implemented into practice. This publication highlights how essential it is to bring research findings into practice and bridge the gaps between developing risk prediction modeling in epidemiology and implementation science. While encouraging, we are still a long way off from having a clinically applicable risk prediction tool. See related article by Imperiale et al., p. 513.

Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach

Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review

OBJECTIVES

In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction.

STUDY DESIGN AND SETTING

We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices.

RESULTS

We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion.

CONCLUSION

The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.

Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review

The inclusion of machine-learning-derived models in systematic reviews of risk prediction models for colorectal cancer is rare. Whilst such reviews have highlighted methodological issues and limited performance of the models included, it is unclear why machine-learning-derived models are absent and whether such models suffer similar methodological problems. This scoping review aims to identify machine-learning models, assess their methodology, and compare their performance with that found in previous reviews. A literature search of four databases was performed for colorectal cancer prediction and prognosis model publications that included at least one machine-learning model. A total of 14 publications were identified for inclusion in the scoping review. Data was extracted using an adapted CHARM checklist against which the models were benchmarked. The review found similar methodological problems with machine-learning models to that observed in systematic reviews for non-machine-learning models, although model performance was better. The inclusion of machine-learning models in systematic reviews is required, as they offer improved performance despite similar methodological omissions; however, to achieve this the methodological issues that affect many prediction models need to be addressed.

A scoping review of risk-stratified bowel screening: current evidence, future directions

PURPOSE

In this scoping review, we examined the international literature on risk-stratified bowel screening to develop recommendations for future research, practice and policy.

METHODS

Six electronic databases were searched from inception to 18 October 2021: Medline, Embase, PsycINFO, CINAHL, Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials. Forward and backwards citation searches were also undertaken. All relevant literature were included.

RESULTS

After de-deduplication, 3,629 records remained. 3,416 were excluded at the title/abstract screening stage. A further 111 were excluded at full-text screening stage. In total, 102 unique studies were included. Results showed that risk-stratified bowel screening programmes can potentially improve diagnostic performance, but there is a lack of information on longer-term outcomes. Risk models do appear to show promise in refining existing risk stratification guidelines but most were not externally validated and less than half achieved good discriminatory power. Risk assessment tools in primary care have the potential for high levels of acceptability and uptake, and therefore, could form an important component of future risk-stratified bowel screening programmes, but sometimes the screening recommendations were not adhered to by the patient or healthcare provider. The review identified important knowledge gaps, most notably in the area of organisation of screening services due to few pilots, and what risk stratification might mean for inequalities.

CONCLUSION

We recommend that future research focuses on what organisational challenges risk-stratified bowel screening may face and a consideration of inequalities in any changes to organised bowel screening programmes.

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

BACKGROUND

Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.

METHODS

We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models.

RESULTS

Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available.

CONCLUSIONS

The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.

An overview of artificial intelligence in oncology

Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes - prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.

Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records

BACKGROUND AND AIMS

The incidence of colorectal cancer (CRC) is increasing in adults younger than 50, and early screening remains challenging due to cost and under-utilization. To identify individuals aged 35-50 years who may benefit from early screening, we developed a prediction model using machine learning and electronic health record (EHR)-derived factors.

METHODS

We enrolled 3,116 adults aged 35-50 at average-risk for CRC and underwent colonoscopy between 2017-2020 at a single center. Prediction outcomes were (1) CRC and (2) CRC or high-risk polyps. We derived our predictors from EHRs (e.g., demographics, obesity, laboratory values, medications, and zip code-derived factors). We constructed four machine learning-based models using a training set (random sample of 70% of participants): regularized discriminant analysis, random forest, neural network, and gradient boosting decision tree. In the testing set (remaining 30% of participants), we measured predictive performance by comparing C-statistics to a reference model (logistic regression).

RESULTS

The study sample was 55.1% female, 32.8% non-white, and included 16 (0.05%) CRC cases and 478 (15.3%) cases of CRC or high-risk polyps. All machine learning models predicted CRC with higher discriminative ability compared to the reference model [e.g., C-statistics (95%CI); neural network: 0.75 (0.48-1.00) vs. reference: 0.43 (0.18-0.67); P = 0.07] Furthermore, all machine learning approaches, except for gradient boosting, predicted CRC or high-risk polyps significantly better than the reference model [e.g., C-statistics (95%CI); regularized discriminant analysis: 0.64 (0.59-0.69) vs. reference: 0.55 (0.50-0.59); P<0.0015]. The most important predictive variables in the regularized discriminant analysis model for CRC or high-risk polyps were income per zip code, the colonoscopy indication, and body mass index quartiles.

DISCUSSION

Machine learning can predict CRC risk in adults aged 35-50 using EHR with improved discrimination. Further development of our model is needed, followed by validation in a primary-care setting, before clinical application.

Statistical biopsy: An emerging screening approach for early detection of cancers

Despite large investment cancer continues to be a major source of mortality and morbidity throughout the world. Traditional methods of detection and diagnosis such as biopsy and imaging, tend to be expensive and have risks of complications. As data becomes more abundant and machine learning continues advancing, it is natural to ask how they can help solve some of these problems. In this paper we show that using a person's personal health data it is possible to predict their risk for a wide variety of cancers. We dub this process a "statistical biopsy." Specifically, we train two neural networks, one predicting risk for 16 different cancer types in females and the other predicting risk for 15 different cancer types in males. The networks were trained as binary classifiers identifying individuals that were diagnosed with the different cancer types within 5 years of joining the PLOC trial. However, rather than use the binary output of the classifiers we show that the continuous output can instead be used as a cancer risk allowing a holistic look at an individual's cancer risks. We tested our multi-cancer model on the UK Biobank dataset showing that for most cancers the predictions generalized well and that looking at multiple cancer risks at once from personal health data is a possibility. While the statistical biopsy will not be able to replace traditional biopsies for diagnosing cancers, we hope there can be a shift of paradigm in how statistical models are used in cancer detection moving to something more powerful and more personalized than general population screening guidelines.

A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning

Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier’s accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms.

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

Magnetic resonance imaging-based artificial intelligence model in rectal cancer

Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize the progress of research on the use of artificial intelligence (AI) in rectal cancer in recent years. AI, represented by machine learning, is being increasingly used in the medical field. The application of AI models based on high-resolution MRI in rectal cancer has been increasingly reported. In addition to staging the diagnosis and localizing radiotherapy, an increasing number of studies have reported that AI models based on high-resolution MRI can be used to predict the response to chemotherapy and prognosis of patients.

Risk Stratification Strategies for Colorectal Cancer Screening: From Logistic Regression to Artificial Intelligence

Risk stratification is a system by which clinically meaningful separation of risk is achieved in a group of otherwise similar persons. Although parametric logistic regression dominates risk prediction, use of nonparametric and semiparametric methods, including artificial neural networks, is increasing. These statistical-learning and machine-learning methods, along with simple rules, are collectively referred to as "artificial intelligence" (AI). AI requires knowledge of study validity, understanding of model metrics, and determination of whether and to what extent the model can and should be applied to the patient or population under consideration. Further investigation is needed, especially in model validation and impact assessment.

Evidenced-Based Screening Strategies for a Positive Family History

The most commonly recognized high-risk group for colorectal cancer (CRC) is individuals with a positive family history. It is generally recognized that those with a first-degree relative (FDR) with CRC are at a 2-fold or higher risk of CRC or advanced neoplasia. FDRs of patients with advanced adenomas have a similarly increased risk. Accordingly, all major US guidelines recommend starting CRC screening by age 40 in these groups. Barriers to screening this group include patient lack of knowledge on family and polyp history, provider limitations in collecting family history, and insufficient application of guidelines.

A risk-stratified approach to colorectal cancer prevention and diagnosis

Population screening and endoscopic surveillance are used widely to prevent the development of and death from colorectal cancer (CRC). However, CRC remains a major cause of cancer mortality and the increasing burden of endoscopic investigations threatens to overwhelm some health services. This Perspective describes the rationale for and approach to improved risk stratification and decision-making for CRC prevention and diagnosis. Limitations of current approaches will be discussed using the UK as an example of the challenges faced by a particular health-care system, followed by discussion of novel risk biomarker utilization. We explore how risk stratification will be advantageous to current health-care providers and users, enabling more efficient use of limited colonoscopy resources. We discuss risk stratification in the setting of population screening as well as the surveillance of high-risk groups and investigation of symptomatic patients. We also address challenges in the development and validation of risk stratification tools and identify key research priorities.