scholarly journals Concordance between a clinical decision-support system and treatments selected by clinicians as a function of cancer type or stage.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 95-95
Author(s):  
Suthida Suwanvecho ◽  
Harit Suwanrusme ◽  
Surasit Issarachai ◽  
Tanawat Jirakulaporn ◽  
Nimit Taechakraichana ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Colon Cancer ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Treatment Options ◽  
Stage Iv ◽  
Clinical Decision ◽  
Cancer Type ◽  
Colon And Rectal Cancer ◽  
Cancer Types

95 Background: Watson for Oncology (WFO) is an artificial intelligence (AI) based clinical decision-support tool trained by Memorial Sloan Kettering. This retrospective observational study of breast, lung, colon and rectal cancer examined the concordance of treatment options provided by WFO to treatments selected by clinicians at Bumrungrad International Hospital (BIH) as a function of stage or cancer type. Methods: Concordance between WFO treatment options and treatments selected by BIH clinicians (WFO-BIH concordance) was defined as identical or equally acceptable treatments, as determined by a panel of experts blinded to the source of treatment. Relationships between stage or type of cancer and WFO-BIH concordant treatments were evaluated by Chi-squared analysis. Results: Analysis revealed a statistically significant association ( P = 0.02) between cancer stage and concordance. For all 4 cancer types combined, stages I-III demonstrated higher concordance than stage IV. A highly significant association ( P < 0.001) between concordance and cancer type was identified. Colon cancer demonstrated the highest concordance, followed by rectal, lung and breast cancer. Reasons for discordance, when given, related to oncologist or patient preferences, and treatment availability. Conclusions: BIH clinicians tended to agree more with WFO therapeutic options for stage I-III cancers and colon cancer in general, as compared to relatively less agreement for stage IV cancers and breast cancer in general, suggesting the need to understand reasons for discordance among all cancer types and stages. An AI tool, trained by experts in the U.S., provides treatment options consistent with some therapies selected in international settings, but preferences and treatment availability may affect choices made in practice. [Table: see text]

scholarly journals Comparison of an oncology clinical decision-support system’s recommendations with actual treatment decisions

2021 ◽  
Author(s):  
Suthida Suwanvecho ◽  
Harit Suwanrusme ◽  
Tanawat Jirakulaporn ◽  
Surasit Issarachai ◽  
Nimit Taechakraichana ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Treatment Options ◽  
Stage Iv ◽  
Clinical Decision ◽  
Treatment Decisions ◽  
Therapeutic Options ◽  
Cancer Type ◽  
The Us ◽  
Rectal Cancers

Abstract Objective IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice. Methods This study included patients treated between 1/2017 and 7/2018 for breast, colon, lung, and rectal cancers at Bumrungrad International Hospital (BIH), Thailand. Treatments selected by clinicians were paired with therapeutic options presented by the CDSS and coded to mask the origin of options presented. The panel rated the acceptability of each treatment in the pair by consensus, with acceptability defined as compliant with BIH’s institutional practices. Descriptive statistics characterized the study population and treatment-decision evaluations by cancer type and stage. Results Nearly 60% (187) of 313 treatment pairs for breast, lung, colon, and rectal cancers were identical or equally acceptable, with 70% (219) of WfO therapeutic options identical to, or acceptable alternatives to, BIH therapy. In 30% of cases (94), 1 or both treatment options were rated as unacceptable. Of 32 cases where both WfO and BIH options were acceptable, WfO was preferred in 18 cases and BIH in 14 cases. Colorectal cancers exhibited the highest proportion of identical or equally acceptable treatments; stage IV cancers demonstrated the lowest. Conclusion This study demonstrates that a system designed in the US to support, rather than replace, cancer-treating clinicians provides therapeutic options which are generally consistent with recommendations from oncologists outside the US.

scholarly journals Application of Clinical Decision Support System to Assist Breast Cancer Patients with Lifestyle Modifications during the COVID-19 Pandemic: A Randomised Controlled Trial

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 2115
Author(s):  
Panos Papandreou ◽  
Aristea Gioxari ◽  
Frantzeska Nimee ◽  
Maria Skouroliakou
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Control Group ◽  
Healthy Weight ◽  
Lifestyle Modifications ◽  
Randomised Controlled

Clinical decision support systems (CDSS) are data aggregation tools based on computer technology that assist clinicians to promote healthy weight management and prevention of cardiovascular diseases. We carried out a randomised controlled 3-month trial to implement lifestyle modifications in breast cancer (BC) patients by means of CDSS during the COVID-19 pandemic. In total, 55 BC women at stages I-IIIA were enrolled. They were randomly assigned either to Control group, receiving general lifestyle advice (n = 28) or the CDSS group (n = 27), to whom the CDSS provided personalised dietary plans based on the Mediterranean diet (MD) together with physical activity guidelines. Food data, anthropometry, blood markers and quality of life were evaluated. At 3 months, higher adherence to MD was recorded in the CDSS group, accompanied by lower body weight (kg) and body fat mass percentage compared to control (p < 0.001). In the CDSS arm, global health/quality of life was significantly improved at the trial endpoint (p < 0.05). Fasting blood glucose and lipid levels (i.e., cholesterol, LDL, triacylglycerols) of the CDSS arm remained unchanged (p > 0.05) but were elevated in the control arm at 3 months (p < 0.05). In conclusion, CDSS could be a promising tool to assist BC patients with lifestyle modifications during the COVID-19 pandemic.

Clinical Decision Support and Individualized Prediction of Survival in Colon Cancer: Bayesian Belief Network Model

2012 ◽  
Vol 20 (1) ◽  
pp. 161-174 ◽  
Author(s):  
Alexander Stojadinovic ◽  
Anton Bilchik ◽  
David Smith ◽  
John S. Eberhardt ◽  
Elizabeth Ben Ward ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Decision Support ◽  
Network Model ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Bayesian Belief Network ◽  
Belief Network ◽  
Prediction Of Survival ◽  
Individualized Prediction

scholarly journals Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 369 ◽  
Author(s):  
Claudia Mazo ◽  
Cathriona Kearns ◽  
Catherine Mooney ◽  
William M. Gallagher
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Major Advance

Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.

Clinical Decision Support for High-Risk Stage II Colon Cancer: A Real-World Study of Treatment Concordance and Survival

2020 ◽  
Vol 63 (10) ◽  
pp. 1383-1392 ◽  
Author(s):  
Peng-ju Chen ◽  
Tian-le Li ◽  
Ting-ting Sun ◽  
Van C. Willis ◽  
M. Christopher Roebuck ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Decision Support ◽  
High Risk ◽  
Clinical Decision Support ◽  
Real World ◽  
Clinical Decision ◽  
Stage Ii ◽  
Stage Ii Colon Cancer

Time-dependent Estimates of Recurrence and Survival in Colon Cancer: Clinical Decision Support System Tool Development for Adjuvant Therapy and Oncological Outcome Assessment

2014 ◽  
Vol 80 (5) ◽  
pp. 441-453 ◽  
Author(s):  
Scott R. Steele ◽  
Anton Bilchik ◽  
Eric K. Johnson ◽  
Aviram Nissan ◽  
George E. Peoples ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Decision Support ◽  
High Risk ◽  
Adjuvant Therapy ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Predictive Values

Unanswered questions remain in determining which high-risk node-negative colon cancer (CC) cohorts benefit from adjuvant therapy and how it may differ in an equal access population. Machine-learned Bayesian Belief Networks (ml-BBNs) accurately estimate outcomes in CC, providing clinicians with Clinical Decision Support System (CDSS) tools to facilitate treatment planning. We evaluated ml-BBNs ability to estimate survival and recurrence in CC. We performed a retrospective analysis of registry data of patients with CC to train–test–crossvalidate ml-BBNs using the Department of Defense Automated Central Tumor Registry (January 1993 to December 2004). Cases with events or follow-up that passed quality control were stratified into 1-, 2-, 3-, and 5-year survival cohorts. ml-BBNs were trained using machine-learning algorithms and k-fold crossvalidation and receiver operating characteristic curve analysis used for validation. BBNs were comprised of 5301 patients and areas under the curve ranged from 0.85 to 0.90. Positive predictive values for recurrence and mortality ranged from 78 to 84 per cent and negative predictive values from 74 to 90 per cent by survival cohort. In the 12-month model alone, 1,132,462,080 unique rule sets allow physicians to predict individual recurrence/mortality estimates. Patients with Stage II (N0M0) CC benefit from chemotherapy at different rates. At one year, all patients older than 73 years of age with T2–4 tumors and abnormal carcinoembryonic antigen levels benefited, whereas at five years, all had relative reduction in mortality with the largest benefit amongst elderly, highest T-stage patients. ml-BBN can readily predict which high-risk patients benefit from adjuvant therapy. CDSS tools yield individualized, clinically relevant estimates of outcomes to assist clinicians in treatment planning.

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