Personalized Pancreatic Cancer Management: A Systematic Review of How Machine Learning Is Supporting Decision-making

Clinical decision support systems for multidisciplinary team decision-making in patients with solid cancer: Composition of an implementation model based on a scoping review

Generating guideline-based recommendations during multidisciplinary team (MDT) meetings in solid cancers is getting more complex due to increasing amount of information needed to follow the guidelines. Usage of clinical decision support systems (CDSSs) can simplify and optimize decision-making. However, CDSS implementation is lagging behind. Therefore, we aim to compose a CDSS implementation model. By performing a scoping review of the currently reported CDSSs for MDT decision-making we determined 102 barriers and 86 facilitators for CDSS implementation out of 44 papers describing 20 different CDSSs. The most frequently reported barriers and facilitators for CDSS implementation supporting MDT decision-making concerned CDSS maintenance (e.g. incorporating guideline updates), validity of recommendations and interoperability with electronic health records. Based on the identified barriers and facilitators, we composed a CDSS implementation model describing clinical utility, analytic validity and clinical validity to guide CDSS integration more successfully in the clinical workflow to support MDTs in the future.

Machine learning in pancreas surgery, what is new? literature review

Background

Machine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery.

Methods

We integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included.

Results

A search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022.

Conclusion

The integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes.

The Oesophageal Cancer Multidisciplinary Team: Can Machine Learning Assist Decision-Making?

BACKGROUND

The complexity of the upper gastrointestinal (UGI) multidisciplinary team (MDT) is continually growing, leading to rising clinician workload, time pressures, and demands. This increases heterogeneity or 'noise' within decision-making for patients with oesophageal cancer (OC) and may lead to inconsistent treatment decisions. In recent decades, the application of artificial intelligence (AI) and more specifically the branch of machine learning (ML) has led to a paradigm shift in the perceived utility of statistical modelling within healthcare. Within oesophageal cancer (OC) care, ML techniques have already been applied with early success to the analyses of histological samples and radiology imaging; however, it has not yet been applied to the MDT itself where such models are likely to benefit from incorporating information-rich, diverse datasets to increase predictive model accuracy.

METHODS

This review discusses the current role the MDT plays in modern UGI cancer care as well as the utilisation of ML techniques to date using histological and radiological data to predict treatment response, prognostication, nodal disease evaluation, and even resectability within OC.

RESULTS

The review finds that an emerging body of evidence is growing in support of ML tools within multiple domains relevant to decision-making within OC including automated histological analysis and radiomics. However, to date, no specific application has been directed to the MDT itself which routinely assimilates this information.

CONCLUSIONS

The authors feel the UGI MDT offers an information-rich, diverse array of data from which ML offers the potential to standardise, automate, and produce more consistent, data-driven MDT decisions.

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review (Preprint).. [DOI: 10.2196/preprints.44248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.

OBJECTIVE

This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.

METHODS

A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.

RESULTS

Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.

CONCLUSIONS

This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

A Comparison of Machine Learning Methods and Conventional Logistic Regression for the Prediction of In-Hospital Mortality in Acute Biliary Pancreatitis

OBJECTIVES

For population databases, multivariable regressions are established analytical standards. The utilization of machine learning (ML) in population databases is novel. We compared conventional statistical methods and ML for predicting mortality in biliary acute pancreatitis (biliary AP).

METHODS

Using the Nationwide Readmission Database (2010-2014), we identified patients (age ≥18 years) with admissions for biliary AP. These data were randomly divided into a training (70%) and test set (30%), stratified by the outcome of mortality. The accuracy of ML and logistic regression models in predicting mortality was compared using 3 different assessments.

RESULTS

Among 97,027 hospitalizations for biliary AP, mortality rate was 0.97% (n = 944). Predictors of mortality included severe AP, sepsis, increasing age, and nonperformance of cholecystectomy. Assessment metrics for predicting the outcome of mortality, the scaled Brier score (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.16-0.33 vs 0.18; 95% CI, 0.09-0.27), F-measure (OR, 43.4; 95% CI, 38.3-48.6 vs 40.6; 95% CI, 35.7-45.5), and the area under the receiver operating characteristic (OR, 0.96; 95% CI, 0.94-0.97 vs 0.95; 95% CI, 0.94-0.96) were comparable between the ML and logistic regression models, respectively.

CONCLUSIONS

For population databases, traditional multivariable analysis is noninferior to ML-based algorithms in predictive modeling of hospital outcomes for biliary AP.

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

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An overview of systematic reviews on the application of AI including 129 studies.

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AI use is prominent in Universal Health Coverage, featuring image analysis in neoplasms.

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Half of the reviews did not evaluate validation procedures nor reporting guidelines.

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Risk of bias was only included un a third of the reviews.

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There is not sufficient evidence to transfer AI to actual healthcare delivery.

Background

Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people’s health. It is necessary to assess the current status on the application of AI towards the improvement of people’s health in the domains defined by WHO’s Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges.

Objective

To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people’s health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality.

Methods

A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO’s PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist.

Results

The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews.

Conclusion

Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

The role of artificial intelligence in pancreatic surgery: a systematic review

Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for patients with pancreatic cancer. Increasing amounts of data are available in medicine: AI and ML can help incorporate large amounts of information in clinical practice. We conducted a systematic review, based on PRISMA criteria, of studies that explored the use of AI or ML algorithms in pancreatic surgery. To our knowledge, this is the first systematic review on this topic. Twenty-five eligible studies were included in this review; 12 studies with implications in the preoperative diagnosis, while 13 studies had implications in patient evolution. Preoperative diagnosis, such as predicting the malignancy of IPMNs, differential diagnosis between pancreatic cystic lesions, classification of different pancreatic tumours, and establishment of the correct management for each of these lesions, can be facilitated through different AI or ML algorithms. Postoperative evolution can also be predicted, and some studies reported prediction models for complications, including postoperative pancreatic fistula, while other studies have analysed the implications for prognosis evaluation (from predicting a textbook outcome, the risk of metastasis or relapse, or the mortality rate and survival). One study discussed the possibility of predicting an intraoperative complication-massive intraoperative bleeding. Artificial intelligence and machine learning models have promising applications in pancreatic surgery, in the preoperative period (high-accuracy diagnosis) and postoperative setting (prognosis evaluation and complication prediction), and the intraoperative applications have been less explored.

Appraisal of multivariable prognostic models for post-operative liver decompensation following partial hepatectomy: a systematic review

BACKGROUND

Few reports have evaluated prognostic modelling studies of tools used for surgical decision-making. This systematic review aimed to describe and critically appraise studies that have developed or validated multivariable prognostic models for post-operative liver decompensation following partial hepatectomy.

METHODS

This study was designed using the CHARMS checklist. Following a comprehensive literature search, two reviewers independently screened candidate references for inclusion and abstracted relevant study details. Qualitative assessment was performed using the PROBAST tool.

RESULTS

We identified 36 prognostic modelling studies; 25 focused on development only, 3 developed and validated models, and 8 validated pre-existing models. None compared routine use of a prognostic model against standard clinical practice. Most studies used single-institution, retrospective cohort designs, conducted in Eastern populations. In total, 15 different outcome definitions for post-operative liver decompensation events were used. Statistical concerns surrounding model overfitting, performance assessment, and internal validation led to high risk of bias for all studies.

CONCLUSIONS

Current prognostic models for post-operative liver decompensation following partial hepatectomy may not be valid for routine clinical use due to design and methodologic concerns. Landmark resources and reporting guidelines such as the TRIPOD statement may assist researchers, and additionally, model impact assessment studies represent opportunities for future research.

Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol

INTRODUCTION

Machine learning (ML) has been used in bio-medical research, and recently in clinical and public health research. However, much of the available evidence comes from high-income countries, where different health profiles challenge the application of this research to low/middle-income countries (LMICs). It is largely unknown what ML applications are available for LMICs that can support and advance clinical medicine and public health. We aim to address this gap by conducting a scoping review of health-related ML applications in LMICs.

METHODS AND ANALYSIS

This scoping review will follow the methodology proposed by Levac et al. The search strategy is informed by recent systematic reviews of ML health-related applications. We will search Embase, Medline and Global Health (through Ovid), Cochrane and Google Scholar; we will present the date of our searches in the final review. Titles and abstracts will be screened by two reviewers independently; selected reports will be studied by two reviewers independently. Reports will be included if they are primary research where data have been analysed, ML techniques have been used on data from LMICs and they aimed to improve health-related outcomes. We will synthesise the information following evidence mapping recommendations.

ETHICS AND DISSEMINATION

The review will provide a comprehensive list of health-related ML applications in LMICs. The results will be disseminated through scientific publications. We also plan to launch a website where ML models can be hosted so that researchers, policymakers and the general public can readily access them.

A systematic review of methodological quality of model development studies predicting prognostic outcome for resectable pancreatic cancer

OBJECTIVES

To assess the methodological quality of prognostic model development studies pertaining to post resection prognosis of pancreatic ductal adenocarcinoma (PDAC).

DESIGN/SETTING

A narrative systematic review of international peer reviewed journals DATA SOURCE: Searches were conducted of: MEDLINE, Embase, PubMed, Cochrane database and Google Scholar for predictive modelling studies applied to the outcome of prognosis for patients with PDAC post resection. Predictive modelling studies in this context included prediction model development studies with and without external validation and external validation studies with model updating. Data was extracted following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) checklist.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcomes were all components of the CHARMS checklist. Secondary outcomes included frequency of variables included across predictive models.

RESULTS

263 studies underwent full text review. 15 studies met the inclusion criteria. 3 studies underwent external validation. Multivariable Cox proportional hazard regression was the most commonly employed modelling method (n=13). 10 studies were based on single centre databases. Five used prospective databases, seven used retrospective databases and three used cancer data registry. The mean number of candidate predictors was 19.47 (range 7 to 50). The most commonly included variables were tumour grade (n=9), age (n=8), tumour stage (n=7) and tumour size (n=5). Mean sample size was 1367 (range 50 to 6400). 5 studies reached statistical power. None of the studies reported blinding of outcome measurement for predictor values. The most common form of presentation was nomograms (n=5) and prognostic scores (n=5) followed by prognostic calculators (n=3) and prognostic index (n=2).

CONCLUSIONS

Areas for improvement in future predictive model development have been highlighted relating to: general aspects of model development and reporting, applicability of models and sources of bias.

TRIAL REGISTRATION NUMBER

CRD42018105942.