Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis.

Background: For therapy planning in cancer patients multidisciplinary team meetings (MDM) are mandatory. Due to the high number of cases being discussed and significant workload of clinicians, Clinical Decision Support System (CDSS) may improve the clinical workflow. Methods: This review and meta-analysis aims to provide an overview of the systems utilized and evaluate the correlation between a CDSS and MDM. Results: A total of 31 studies were identified for final analysis. Analysis of different cancers shows a concordance rate (CR) of 72.7% for stage I-II and 73.4% for III-IV. For breast carcinoma, CR for stage I-II was 72.8% and for III-IV 84.1%, P≤ 0.00001. CR for colorectal carcinoma is 63% for stage I-II and 67% for III-IV, for gastric carcinoma 55% and 45%, and for lung carcinoma 85% and 83% respectively, all P>0.05. Analysis of SCLC and NSCLC yields a CR of 94,3% and 82,7%, P=0.004 and for adenocarcinoma and squamous cell carcinoma in lung cancer a CR of 90% and 86%, P=0.02. Conclusion: CDSS has already been implemented in clinical practice, and while the findings suggest that its use is feasible for some cancers, further research is needed to fully evaluate its effectiveness.

Concordance of a decision algorithm and multidisciplinary team meetings for patients with liver cancer-a study protocol for a randomized controlled trial.

INTRODUCTION: Multidisciplinary team meetings (MDMs), also known as tumor conferences, are a cornerstone of cancer treatments. However, barriers such as incomplete patient information or logistical challenges can postpone tumor board decisions and delay patient treatment, potentially affecting clinical outcomes. Therapeutic Assistance and Decision algorithms for hepatobiliary tumor Boards (ADBoard) aims to reduce this delay by providing automated data extraction and high-quality, evidence-based treatment recommendations. METHODS AND ANALYSIS: With the help of natural language processing, relevant patient information will be automatically extracted from electronic medical records and used to complete a classic tumor conference protocol. A machine learning model is trained on retrospective MDM data and clinical guidelines to recommend treatment options for patients in our inclusion criteria. Study participants will be randomized to either MDM with ADBoard (Arm A: MDM-AB) or conventional MDM (Arm B: MDM-C). The concordance of recommendations of both groups will be compared using interrater reliability. We hypothesize that the therapy recommendations of ADBoard would be in high agreement with those of the MDM-C, with a Cohen's kappa value of ≥ 0.75. Furthermore, our secondary hypotheses state that the completeness of patient information presented in MDM is higher when using ADBoard than without, and the explainability of tumor board protocols in MDM-AB is higher compared to MDM-C as measured by the System Causability Scale. DISCUSSION: The implementation of ADBoard aims to improve the quality and completeness of the data required for MDM decision-making and to propose therapeutic recommendations that consider current medical evidence and guidelines in a transparent and reproducible manner. ETHICS AND DISSEMINATION: The project was approved by the Ethics Committee of the Charité - Universitätsmedizin Berlin. REGISTRATION DETAILS: The study was registered on ClinicalTrials.gov (trial identifying number: NCT05681949; https://clinicaltrials.gov/study/NCT05681949 ) on 12 January 2023.

Cardiorespiratory and metabolic responses to exercise testing during lower-body positive pressure running.

Exercise reduces the future cardiometabolic disease risk. However, not everyone can participate in routine physical activity because of obesity or orthopedic impairments. Body weight-supported (BWS) exercise may be an option for these individuals. Unfortunately, very little data are available with regard to BWS running in untrained healthy individuals. Yet, this information is important to assess the potential use of lower-body positive pressure (LBPP) treadmill running for the prevention of cardiometabolic disease. Twenty healthy but untrained participants (10 females, mean age 31.5 yr) were included in this study. Participants completed two exercise tests (one with 100% and one with 60% body wt) in randomized order on a LBPP treadmill. Expired gas data and heart rate (HR) were collected continuously. Blood lactate, blood pressure (BP), pulse wave velocity (PWV), and rating of perceived exertion (RPE) were measured during a 2-min break after each stage. Oxygen uptake increased significantly independent of BWS but was lower with BWS. Furthermore, we identified a significant correlation between HR and RPE independent of BWS. BP and PWV showed a large heterogeneity in response to BWS. The lower O2 requirement when running with BWS may help untrained individuals to adapt to an exercise regimen. Future research needs to explore the heterogenetic response of blood pressure and pulse wave velocity to LBPP BWS between individuals.NEW & NOTEWORTHY Lower-body positive pressure body weight-supported exercise has a lower metabolic and cardiovascular demand. Furthermore, heart rate and rating of perceived exertion are highly correlated independent of body weight support. Our data support the further examination of lower-body positive pressure exercise training for cardiovascular disease risk groups.