Data Visualization Support for Tumor Boards and Clinical Oncology: Protocol for a Scoping Review

BACKGROUND

Complex and expanding data sets in clinical oncology applications require flexible and interactive visualization of patient data to provide the maximum amount of information to physicians and other medical practitioners. Interdisciplinary tumor conferences in particular profit from customized tools to integrate, link, and visualize relevant data from all professions involved.

OBJECTIVE

The scoping review proposed in this protocol aims to identify and present currently available data visualization tools for tumor boards and related areas. The objective of the review will be to provide not only an overview of digital tools currently used in tumor board settings, but also the data included, the respective visualization solutions, and their integration into hospital processes.

METHODS

The planned scoping review process is based on the Arksey and O'Malley scoping study framework. The following electronic databases will be searched for articles published in English: PubMed, Web of Knowledge, and SCOPUS. Eligible articles will first undergo a deduplication step, followed by the screening of titles and abstracts. Second, a full-text screening will be used to reach the final decision about article selection. At least 2 reviewers will independently screen titles, abstracts, and full-text reports. Conflicting inclusion decisions will be resolved by a third reviewer. The remaining literature will be analyzed using a data extraction template proposed in this protocol. The template includes a variety of meta information as well as specific questions aiming to answer the research question: "What are the key features of data visualization solutions used in molecular and organ tumor boards, and how are these elements integrated and used within the clinical setting?" The findings will be compiled, charted, and presented as specified in the scoping study framework. Data for included tools may be supplemented with additional manual literature searches. The entire review process will be documented in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart.

RESULTS

The results of this scoping review will be reported per the expanded PRISMA-ScR guidelines. A preliminary search using PubMed, Web of Knowledge, and Scopus resulted in 1320 articles after deduplication that will be included in the further review process. We expect the results to be published during the second quarter of 2024.

CONCLUSIONS

Visualization is a key process in leveraging a data set's potentially available information and enabling its use in an interdisciplinary setting. The scoping review described in this protocol aims to present the status quo of visualization solutions for tumor board and clinical oncology applications and their integration into hospital processes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/53627.

A Learning Program for Treatment Recommendations by Molecular Tumor Boards and Artificial Intelligence

Importance

Substantial heterogeneity exists in treatment recommendations across molecular tumor boards (MTBs), especially for biomarkers with low evidence levels; therefore, the learning program is essential.

Objective

To determine whether a learning program sharing treatment recommendations for biomarkers with low evidence levels contributes to the standardization of MTBs and to investigate the efficacy of an artificial intelligence (AI)-based annotation system.

Design, Setting, and Participants

This prospective quality improvement study used 50 simulated cases to assess concordance of treatment recommendations between a central committee and participants. Forty-seven participants applied from April 7 to May 13, 2021. Fifty simulated cases were randomly divided into prelearning and postlearning evaluation groups to assess similar concordance based on previous investigations. Participants included MTBs at hub hospitals, treating physicians at core hospitals, and AI systems. Each participant made treatment recommendations for each prelearning case from registration to June 30, 2021; participated in the learning program on July 18, 2021; and made treatment recommendations for each postlearning case from August 3 to September 30, 2021. Data were analyzed from September 2 to December 10, 2021.

Exposures

The learning program shared the methodology of making appropriate treatment recommendations, especially for biomarkers with low evidence levels.

Main Outcomes and Measures

The primary end point was the proportion of MTBs that met prespecified accreditation criteria for postlearning evaluations (approximately 90% concordance with high evidence levels and approximately 40% with low evidence levels). Key secondary end points were chronological enhancements in the concordance of treatment recommendations on postlearning evaluations from prelearning evaluations. Concordance of treatment recommendations by an AI system was an exploratory end point.

Results

Of the 47 participants who applied, 42 were eligible. The accreditation rate of the MTBs was 55.6% (95% CI, 35.3%-74.5%; P < .001). Concordance in MTBs increased from 58.7% (95% CI, 52.8%-64.4%) to 67.9% (95% CI, 61.0%-74.1%) (odds ratio, 1.40 [95% CI, 1.06-1.86]; P = .02). In postlearning evaluations, the concordance of treatment recommendations by the AI system was significantly higher than that of MTBs (88.0% [95% CI, 68.7%-96.1%]; P = .03).

Conclusions and Relevance

The findings of this quality improvement study suggest that use of a learning program improved the concordance of treatment recommendations provided by MTBs to central ones. Treatment recommendations made by an AI system showed higher concordance than that for MTBs, indicating the potential clinical utility of the AI system.

Leveraging Large Language Models for Decision Support in Personalized Oncology

Importance

Clinical interpretation of complex biomarkers for precision oncology currently requires manual investigations of previous studies and databases. Conversational large language models (LLMs) might be beneficial as automated tools for assisting clinical decision-making.

Objective

To assess performance and define their role using 4 recent LLMs as support tools for precision oncology.

Design, Setting, and Participants

This diagnostic study examined 10 fictional cases of patients with advanced cancer with genetic alterations. Each case was submitted to 4 different LLMs (ChatGPT, Galactica, Perplexity, and BioMedLM) and 1 expert physician to identify personalized treatment options in 2023. Treatment options were masked and presented to a molecular tumor board (MTB), whose members rated the likelihood of a treatment option coming from an LLM on a scale from 0 to 10 (0, extremely unlikely; 10, extremely likely) and decided whether the treatment option was clinically useful.

Main Outcomes and Measures

Number of treatment options, precision, recall, F1 score of LLMs compared with human experts, recognizability, and usefulness of recommendations.

Results

For 10 fictional cancer patients (4 with lung cancer, 6 with other; median [IQR] 3.5 [3.0-4.8] molecular alterations per patient), a median (IQR) number of 4.0 (4.0-4.0) compared with 3.0 (3.0-5.0), 7.5 (4.3-9.8), 11.5 (7.8-13.0), and 13.0 (11.3-21.5) treatment options each was identified by the human expert and 4 LLMs, respectively. When considering the expert as a criterion standard, LLM-proposed treatment options reached F1 scores of 0.04, 0.17, 0.14, and 0.19 across all patients combined. Combining treatment options from different LLMs allowed a precision of 0.29 and a recall of 0.29 for an F1 score of 0.29. LLM-generated treatment options were recognized as AI-generated with a median (IQR) 7.5 (5.3-9.0) points in contrast to 2.0 (1.0-3.0) points for manually annotated cases. A crucial reason for identifying AI-generated treatment options was insufficient accompanying evidence. For each patient, at least 1 LLM generated a treatment option that was considered helpful by MTB members. Two unique useful treatment options (including 1 unique treatment strategy) were identified only by LLM.

Conclusions and Relevance

In this diagnostic study, treatment options of LLMs in precision oncology did not reach the quality and credibility of human experts; however, they generated helpful ideas that might have complemented established procedures. Considering technological progress, LLMs could play an increasingly important role in assisting with screening and selecting relevant biomedical literature to support evidence-based, personalized treatment decisions.

Concordance Between Recommendations From Multidisciplinary Molecular Tumor Boards and Central Consensus for Cancer Treatment in Japan

IMPORTANCE

Quality assurance of molecular tumor boards (MTBs) is crucial in cancer genome medicine.

OBJECTIVE

To evaluate the concordance of recommendations by MTBs and centrally developed consensus treatment recommendations at all 12 leading institutions for cancer genomic medicine in Japan using 50 simulated cases.

DESIGN, SETTING, AND PARTICIPANTS

This was a prospective quality improvement study of 50 simulated cancer cases. Molecular tumor boards from 12 core hospitals independently recommended treatment for 50 cases blinded to the centrally developed consensus treatment recommendations. The study's central committee consisted of representatives from all 12 core hospitals in Japan who selected the 50 simulated cases from The Cancer Genome Atlas database, including frequently observed genomic alterations. The central committee recommended centrally developed consensus treatment. The concordance rate for genomically matched treatments between MTBs and centrally developed consensus treatment recommendations was evaluated. Data analysis was conducted from January 22 to March 3, 2021.

EXPOSURES

Simulated cases of cancer.

MAIN OUTCOMES AND MEASURES

The primary outcome was concordance, defined as the proportion of recommendations by MTBs concordant with centrally developed consensus treatment recommendations. A mixed-effects logistic regression model, adjusted for institutes as a random intercept, was applied. High evidence levels were defined as established biomarkers for which the treatment was ready for routine use in clinical practice, and low evidence levels were defined as biomarkers for genomically matched treatment that were under investigation.

RESULTS

The Clinical Practice Guidance for Next-Generation Sequencing in Cancer Diagnosis and Treatment (edition 2.1) was used for evidence-level definition. The mean concordance between MTBs and centrally developed consensus treatment recommendations was 62% (95% CI, 57%-65%). Each MTB concordance varied from 48% to 86%. The concordance rate was higher in the subset of patients with colorectal cancer (100%; 95% CI, 94.0%-100%), ROS1 fusion (100%; 95% CI, 85.5%-100%), and high evidence level A/R (A: 88%; 95% CI, 81.8%-93.0%; R:100%; 95% CI, 92.6%-100%). Conversely, the concordance rate was lower in cases of cervical cancer (11%; 95% CI, 3.1%-26.1%), TP53 mutation (16%; 95% CI, 12.5%-19.9%), and low evidence level C/D/E (C: 30%; 95% CI, 24.7%-35.9%; D: 25%; 95% CI, 5.5%-57.2%; and E: 18%; 95% CI, 13.8%-23.0%). Multivariate analysis showed that evidence level (high [A/R] vs low [C/D/E]: odds ratio, 4.4; 95% CI, 1.8-10.8) and TP53 alteration (yes vs no: odds ratio, 0.06; 95% CI, 0.03-0.10) were significantly associated with concordance.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that genomically matched treatment recommendations differ among MTBs, particularly in genomic alterations with low evidence levels wherein treatment is being investigated. Sharing information on matched therapy for low evidence levels may be needed to improve the quality of MTBs.

Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards

BACKGROUND

Structured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards.

METHODS

High-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0-1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically.

RESULTS

A median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician's choice.

CONCLUSIONS

Reproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts.

Lessons learned: the first consecutive 1000 patients of the CCCMunichLMU Molecular Tumor Board

Purpose

In 2016, the University of Munich Molecular Tumor Board (MTB) was implemented to initiate a precision oncology program. This review of cases was conducted to assess clinical implications and functionality of the program, to identify current limitations and to inform future directions of these efforts.

Methods

Charts, molecular profiles, and tumor board decisions of the first 1000 consecutive cases (01/2016–03/2020) were reviewed. Descriptive statistics were applied to describe relevant findings.

Results

Of the first 1000 patients presented to the MTB; 914 patients received comprehensive genomic profiling. Median age of patients was 56 years and 58% were female. The most prevalent diagnoses were breast (16%) and colorectal cancer (10%). Different types of targeted or genome-wide sequencing assays were used; most of them offered by the local department of pathology. Testing was technically successful in 88%. In 41% of cases, a genomic alteration triggered a therapeutic recommendation. The fraction of patients receiving a tumor board recommendation differed significantly between malignancies ranging from over 50% in breast or biliary tract to less than 30% in pancreatic cancers. Based on a retrospective chart review, 17% of patients with an MTB recommendation received appropriate treatment.

Conclusion

Based on these retrospective analyses, patients with certain malignancies (breast and biliary tract cancer) tend to be more likely to have actionable variants. The low rate of therapeutic implementation (17% of patients receiving a tumor board recommendation) underscores the importance of meticulous follow-up for these patients and ensuring broad access to innovative therapies for patients receiving molecular tumor profiling.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-022-04165-0.

Transitioning the Molecular Tumor Board from Proof of Concept to Clinical Routine: A German Single-Center Analysis

Molecular precision oncology faces two major challenges: first, to identify relevant and actionable molecular variants in a rapidly changing field and second, to provide access to a broad patient population. Here, we report a four-year experience of the Molecular Tumor Board (MTB) of the Comprehensive Cancer Center Freiburg (Germany) including workflows and process optimizations. This retrospective single-center study includes data on 488 patients enrolled in the MTB from February 2015 through December 2018. Recommendations include individual molecular diagnostics, molecular stratified therapies, assessment of treatment adherence and patient outcomes including overall survival. The majority of MTB patients presented with stage IV oncologic malignancies (90.6%) and underwent an average of 2.1 previous lines of therapy. Individual diagnostic recommendations were given to 487 patients (99.8%). A treatment recommendation was given in 264 of all cases (54.1%) which included a molecularly matched treatment in 212 patients (43.4%). The 264 treatment recommendations were implemented in 76 patients (28.8%). Stable disease was observed in 19 patients (25.0%), 17 had partial response (22.4%) and five showed a complete remission (6.6%). An objective response was achieved in 28.9% of cases with implemented recommendations and for 4.5% of the total population (22 of 488 patients). By optimizing the MTB workflow, case-discussions per session increased significantly while treatment adherence and outcome remained stable over time. Our data demonstrate the feasibility and effectiveness of molecular-guided personalized therapy for cancer patients in a clinical routine setting showing a low but robust and durable disease control rate over time.

Support of a molecular tumour board by an evidence-based decision management system for precision oncology

BACKGROUND

Reliable and reproducible interpretation of molecular aberrations constitutes a bottleneck of precision medicine. Evidence-based decision management systems may improve rational therapy recommendations. To cope with an increasing amount of complex molecular data in the clinical care of patients with cancer, we established a workflow for the interpretation of molecular analyses.

METHODS

A specialized physician screened results from molecular analyses for potential biomarkers, irrespective of the diagnostic modality. Best available evidence was retrieved and categorized through establishment of an in-house database and interrogation of publicly available databases. Annotated biomarkers were ranked using predefined evidence levels and subsequently discussed at a molecular tumour board (MTB), which generated treatment recommendations. Subsequent translation into patient treatment and clinical outcomes were followed up.

RESULTS

One hundred patients were discussed in the MTB between January 2016 and May 2017. Molecular data were obtained for 70 of 100 patients (50 whole exome/RNA sequencing, 18 panel sequencing, 2 immunohistochemistry (IHC)/microsatellite instability analysis). The MTB generated a median of two treatment recommendations each for 63 patients. Thirty-nine patients were treated: 6 partial responses and 12 stable diseases were achieved as best responses. Genetic counselling for germline events was recommended for seven patients.

CONCLUSION

The development of an evidence-based workflow allowed for the clinical interpretation of complex molecular data and facilitated the translation of personalized treatment strategies into routine clinical care. The high number of treatment recommendations in patients with comprehensive genomic data and promising responses in patients treated with combination therapy warrant larger clinical studies.