Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists

A review of big data technology and its application in cancer care

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.

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.

Watson for oncology decision system for treatment consistency study in breast cancer

The Watson for Oncology (WFO) decision system has been rolled out in many cancers. However, the consistency of treatment for breast cancer is still unclear in relatively economically disadvantaged areas. Patients with postoperative adjuvant stage (January 2017 to December 2017) and advanced-stage breast cancer (January 2014 to December 2018) in northwest of China were included in this study. Patient information was imported to make treatment decisions using Watson version 19.20 analysis and subsequently compared with clinician decisions and analyzed for influencing factors. A total of 263 patients with postoperative adjuvant breast cancer and 200 with advanced breast cancer were included in this study. The overall treatment modality for WFO was in 80.2% and 50.5% agreement with clinicians in the adjuvant and advanced-stage population, respectively. In adjuvant treatment after breast cancer surgery, menopausal status (odds ratio (OR) = 2.89, P = 0.012, 95% CI, 1.260-6.630), histological grade (OR = 0.22, P = 0.019, 95% CI, 0.061-0.781) and tumor stage (OR = 0.22, P = 0.042, 95% CI, 0.050-0.943) were independent factors affecting the concordance between the two stages. In the first-line treatment of advanced breast cancer, hormone receptor status was a factor influencing the consistency of treatment (χ2 = 14.728, P < 0.001). There was good agreement between the WFOs and clinicians' treatment decisions in postoperative adjuvant breast cancer, but poor agreement was observed in patients with advanced breast cancer.

Real-world Evidence of Diagnostic Testing and Treatment Patterns in US Patients With Breast Cancer With Implications for Treatment Biomarkers From RNA Sequencing Data

OBJECTIVE/BACKGROUND

We performed a retrospective analysis of longitudinal real-world data (RWD) from patients with breast cancer to replicate results from clinical studies and demonstrate the feasibility of generating real-world evidence. We also assessed the value of transcriptome profiling as a complementary tool for determining molecular subtypes.

METHODS

De-identified, longitudinal data were analyzed after abstraction from records of patients with breast cancer in the United States (US) structured and stored in the Tempus database. Demographics, clinical characteristics, molecular subtype, treatment history, and survival outcomes were assessed according to strict qualitative criteria. RNA sequencing and clinical data were used to predict molecular subtypes and signaling pathway enrichment.

RESULTS

The clinical abstraction cohort (n = 4000) mirrored the demographics and clinical characteristics of patients with breast cancer in the US, indicating feasibility for RWE generation. Among patients who were human epidermal growth factor receptor 2-positive (HER2+), 74.2% received anti-HER2 therapy, with ∼70% starting within 3 months of a positive test result. Most non-treated patients were early stage. In this RWD set, 31.7% of patients with HER2+ immunohistochemistry (IHC) had discordant fluorescence in situ hybridization results recorded. Among patients with multiple HER2 IHC results at diagnosis, 18.6% exhibited intra-test discordance. Through development of a whole-transcriptome model to predict IHC receptor status in the molecular sequenced cohort (n = 400), molecular subtypes were resolved for all patients (n = 36) with equivocal HER2 statuses from abstracted test results. Receptor-related signaling pathways were differentially enriched between clinical molecular subtypes.

CONCLUSIONS

RWD in the Tempus database mirrors the overall population of patients with breast cancer in the US. These results suggest that real-time, RWD analyses are feasible in a large, highly heterogeneous database. Furthermore, molecular data may aid deficiencies and discrepancies observed from breast cancer RWD.

Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review

BACKGROUND

Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the development and validation of health care AI, only few applications have been actually implemented at the frontlines of clinical practice.

OBJECTIVE

The objective of this study was to systematically review AI applications that have been implemented in real-life clinical practice.

METHODS

We conducted a literature search in PubMed, Embase, Cochrane Central, and CINAHL to identify relevant articles published between January 2010 and May 2020. We also hand searched premier computer science journals and conferences as well as registered clinical trials. Studies were included if they reported AI applications that had been implemented in real-world clinical settings.

RESULTS

We identified 51 relevant studies that reported the implementation and evaluation of AI applications in clinical practice, of which 13 adopted a randomized controlled trial design and eight adopted an experimental design. The AI applications targeted various clinical tasks, such as screening or triage (n=16), disease diagnosis (n=16), risk analysis (n=14), and treatment (n=7). The most commonly addressed diseases and conditions were sepsis (n=6), breast cancer (n=5), diabetic retinopathy (n=4), and polyp and adenoma (n=4). Regarding the evaluation outcomes, we found that 26 studies examined the performance of AI applications in clinical settings, 33 studies examined the effect of AI applications on clinician outcomes, 14 studies examined the effect on patient outcomes, and one study examined the economic impact associated with AI implementation.

CONCLUSIONS

This review indicates that research on the clinical implementation of AI applications is still at an early stage despite the great potential. More research needs to assess the benefits and challenges associated with clinical AI applications through a more rigorous methodology.

Precision Medicine, AI, and the Future of Personalized Health Care

The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.

Updates to the ASCO Patient-Centered Oncology Payment Model

The past decade has seen considerable innovation in the delivery of care and payment in oncology. Key initiatives have included the development of oncology medical home care delivery standards, the Medicare Oncology Care Model, and multiple commercial payer initiatives. Looking forward, our next challenge is to reflect on lessons learned from these limited-scale demonstration projects and work toward models that are scalable and sustainable and reflect true collaboration between payers and providers sharing common objectives and methods to advance cancer care delivery. To this end, ASCO continues its work on care delivery standards, quality measurement, and alternative payment models. Over the past year, ASCO has received input from physicians, administrators, payers, and employers to update its Patient-Centered Oncology Payment (PCOP) model. PCOP incorporates current work on provider-payer collaboration, the oncology medical home, and the value of clinical pathways and recognizes the need for common quality measurement, performance methodology, and payment structure across multiple sources of payment. The following represents a summary of the entire model. The model includes chapters on PCOP communities, clinical practice transformation, payment methodology, consolidated payments for oncology care, performance methodology, and implementation considerations. In future work, ASCO will continue its support of the PCOP model, including further development of care delivery standards, quality measures, and technology solutions (eg, CancerLinQ).

Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists

INTRODUCTION

Cognitive computing point-of-care decision support tools which ingest patient attributes from electronic health records and display treatment options based on expert training and medical literature, supplemented by real world evidence (RWE), might prove useful to expert and novice oncologists. The concordance of augmented intelligence systems with best medical practices and potential influences on physician behavior remain unknown.

METHODS

Electronic health records from 88 breast cancer patients evaluated at a USA tertiary care center were presented to subspecialist experts and oncologists focusing on other disease states with and without reviewing the IBM Watson for Oncology with Cota RWE platform.

RESULTS

The cognitive computing "recommended" option was concordant with selection by breast cancer experts in 78.5% and "for consideration" option was selected in 9.4%, yielding agreements in 87.9%. Fifty-nine percent of non-concordant responses were generated from 8% of cases. In the Cota observational database 69.3% of matched controls were treated with "recommended," 11.4% "for consideration", and 19.3% "not recommended." Without guidance from Watson for Oncology (WfO)/Cota RWE, novice oncologists chose 75.5% recommended/for consideration treatments which improved to 95.3% with WfO/Cota RWE. The novices were more likely than experts to choose a non-recommended option (P < .01) without WfO/Cota RWE and changed decisions in 39% cases.

CONCLUSIONS

Watson for Oncology with Cota RWE options were largely concordant with disease expert judged best oncology practices, and was able to improve treatment decisions among breast cancer novices. The observation that nearly a fifth of patients with similar disease characteristics received non-recommended options in a real world database highlights a need for decision support.