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

Naito, Yoichi; Sunami, Kuniko; Kage, Hidenori; Komine, Keigo; Amano, Toraji; Imai, Michiko; Koyama, Takafumi; Ennishi, Daisuke; Kanai, Masashi; Kenmotsu, Hirotsugu; Maeda, Takahiro; Morita, Sachi; Sakai, Daisuke; Watanabe, Kousuke; Shirota, Hidekazu; Kinoshita, Ichiro; Yoshioka, Masashiro; Mamesaya, Nobuaki; Ito, Mamoru; Saigusa, Yusuke; Yamamoto, Keiji; Hirata, Makoto; Tsuchihara, Katsuya; Yoshino, Takayuki

doi:10.1001/jamanetworkopen.2022.45081

Cited by 10 publications

(20 citation statements)

References 20 publications

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“…Many institutions that carry out CGP testing have MTBs available, 13 and most have reported on MTBs done at single institutions 15–20 . The MTBs encompassing multiple institutions are reported less frequently, 21 and reports on multiple MTBs are rare 22–24 . We have for the first time, to our knowledge, quantified the time spent on each administrative step necessary to undergo CGP testing in Japan, excluding the clinical steps.…”

Section: Discussionmentioning

confidence: 99%

Human resources for administrative work to carry out a comprehensive genomic profiling test in Japan

et al. 2023

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Comprehensive genomic profiling (CGP) tests have been nationally reimbursed in Japan since June 2019 under strict restrictions, and over 46,000 patients have taken the test. Core Hospitals and Designated Hospitals host molecular tumor boards, which is more time‐consuming than simply participating in them. We sent a questionnaire to government‐designated Cancer Genomic Medicine Hospitals, including all 12 Core Hospitals, all 33 Designated Hospitals, and 117 of 188 Cooperative Hospitals. The questionnaire asked how much time physicians and nonphysicians spent on administrative work for cancer genomic medicine. For every CGP test, 7.6 h of administrative work was needed. Physicians spent 2.7 h/patient, while nonphysicians spent 4.9 h/patient. Time spent preparing for molecular tumor boards, called Expert Panels, was the longest, followed by time spent participating in Expert Panels. Assuming an hourly wage of ¥24,000/h for physicians and ¥2800/h for nonphysicians, mean labor cost was ¥78,071/patient. On a monthly basis, more time was spent on administrative work at Core Hospitals compared with Designated Hospitals and Cooperative Hospitals (385 vs. 166 vs. 51 h/month, respectively, p < 0.001). Consequently, labor cost per month was higher at Core Hospitals than at Designated Hospitals and Cooperative Hospitals (¥3,951,854 vs. ¥1,687,167 vs. ¥487,279/month, respectively, p < 0.001). Completing a CGP test for a cancer patient in Japan is associated with significant labor at each hospital, especially at Core Hospitals. Streamlining the exchange of information and simplifying Expert Panels will likely alleviate this burden.

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Section: Discussionmentioning

confidence: 99%

Human resources for administrative work to carry out a comprehensive genomic profiling test in Japan

et al. 2023

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“…The existence or absence of TP53 (OMIM 191170) changes, which were identified as characteristics linked with the concordance of treatment recommendations by multivariate analysis in the earlier study, 13 and the amount of evidence (high vs low) were used to stratify 50 simulated cases into prelearning and postlearning evaluation groups (Figure 1A). The cancer types and genomic alterations present in each case in the prelearning and postlearning assessment groups are displayed in eTable 1 in Supplement 1.…”

Section: Methodsmentioning

confidence: 99%

“…As in the previous study, 13 evidence levels were established according to the clinical practice guidance for nextgeneration sequencing in cancer diagnosis and treatment 10 (eTable 2 in Supplement 1). Evidence levels A, B, and R, common biomarkers for routine clinical practice, were defined as high, whereas evidence levels C, D, and E were defined as low.…”

Section: Evidence Levelsmentioning

confidence: 99%

“…While some studies have evaluated the differences in treatment recommendations across MTBs, 11,12 the sample sizes were small and the factors affecting the discrepancies were not fully investigated. Naito et al 13 previously observed that treatment recommendations differed significantly among MTBs, notably in genomic alterations with low evidence levels for which appropriate treatments are still being examined. Because most treatment recommendations for biomarkers with poor evidence levels were investigational new drug (IND) trials, sharing information on IND trials with low evidence levels across MTBs may be useful in improving MTB quality.…”

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confidence: 99%

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

Sunami,

Naito,

Saigusa

et al. 2024

JAMA Oncol

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ImportanceSubstantial heterogeneity exists in treatment recommendations across molecular tumor boards (MTBs), especially for biomarkers with low evidence levels; therefore, the learning program is essential.ObjectiveTo 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 ParticipantsThis 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.ExposuresThe learning program shared the methodology of making appropriate treatment recommendations, especially for biomarkers with low evidence levels.Main Outcomes and MeasuresThe 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.ResultsOf the 47 participants who applied, 42 were eligible. The accreditation rate of the MTBs was 55.6% (95% CI, 35.3%-74.5%; P &lt; .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 RelevanceThe 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.

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“…CGP testing was performed using hybrid capture-based targeted DNA sequencing with FoundationOne CDx (Foundation Medicine), the OncoGuide NCC Oncopanel System (Sysmex Corporation), in-house NGS testing, or in blood-derived cell-free DNA with FoundationOne Liquid CDx (Foundation Medicine) or Guardant360 CDx (Guardant Health) . All patients were referred to the NCCH following discussion by regional molecular tumor boards (MTB) comprising medical oncologists, pediatric oncologists, pathologists, bioinformaticians, genome researchers, and genetic counselors . In addition to reports from the inspection companies, the MTBs used reports containing clinical annotation and information regarding genotype-matched clinical trials from the Center for Cancer Genomics and Advanced Therapeutics national database …”

Section: Methodsmentioning

confidence: 99%

Travel Time and Distance and Participation in Precision Oncology Trials at the National Cancer Center Hospital

Uehara,

Koyama,

Katsuya

et al. 2023

JAMA Netw Open

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ImportanceGenotype-matched trials, which are becoming increasingly important in the precision oncology era, require referrals from institutions providing comprehensive genomic profiling (CGP) testing to those conducting these trials, and the travel burden for trial participation is significant. However, it remains unknown whether travel time or distance are associated with genotype-matched trial participation.ObjectiveTo assess whether travel time or distance are associated with disparities in genotype-matched trial participation following CGP testing.Design, Setting, and ParticipantsThis retrospective cohort study from June 2020 to June 2022 included patients with advanced or metastatic solid tumors referred to the National Cancer Center Hospital for participation in genotype-matched trials following CGP testing and discussion by molecular tumor boards. Data were analyzed from June to October 2022.ExposuresTravel time and distance.Main Outcomes and MeasuresThe primary and secondary outcomes were enrollment in genotype-matched trials and all-cancer clinical trials, respectively.ResultsOf 1127 patients (mean [range] age, 62 [16-85] years; 584 women [52%]; all residents of Japan), 127 (11%) and 241 (21%) were enrolled in genotype-matched trials and all-cancer clinical trials, respectively. The overall median (IQR) travel distance and time were 38 (21-107) km and 55 (35-110) minutes, respectively. On multivariable regression with 23 covariates, travel distance (≥100 km vs &lt;100 km) was not associated with the likelihood of genotype-matched trial participation (26 of 310 patients [8%] vs 101 of 807 patients [12%]; odds ratio [OR], 0.64; 95% CI, 0.40-1.02), whereas in patients with travel time of 120 minutes or more, the likelihood of genotype-matched trial participation was significantly lower than those with travel time less than 120 minutes (19 of 276 patients [7%] vs 108 of 851 patients [13%]; OR, 0.51; 95% CI, 0.29-0.84). The likelihood of genotype-matched trial participation decreased as travel time increased from less than 40 (38 of 283 patients [13%]) to 40 to 120 (70 of 568 patients [12%]) and 120 or more (19 of 276 patients [7%]) minutes (OR, 0.74; 95% CI, 0.48-1.17; OR, 0.41; 95% CI, 0.22-0.74, respectively). Neither travel time nor distance were associated with the likelihood of all-cancer clinical trial participation.Conclusions and RelevanceIn this cohort study of patients undergoing CGP testing, an increased travel time was associated with a decreased likelihood of genotype-matched trial participation. This warrants further research on interventions, such as decentralization of clinical trials to mitigate travel burden.

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

Cited by 10 publications

References 20 publications

Human resources for administrative work to carry out a comprehensive genomic profiling test in Japan

Human resources for administrative work to carry out a comprehensive genomic profiling test in Japan

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

Travel Time and Distance and Participation in Precision Oncology Trials at the National Cancer Center Hospital

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