Evidence map›Paper›PMID 39714567›Full record

ReviewInternational journal of clinical oncology2025

A prospective study comparing highly qualified Molecular Tumor Boards with AI-powered software as a medical device.

Hideaki Bando, Yoichi Naito, Tomoyuki Yamada, Takao Fujisawa, Mitsuho Imai, Yasutoshi Sakamoto, Yusuke Saigusa, Kouji Yamamoto, Yutaka Tomioka, Nobuyoshi Takeshita and 6 more

Abstract readComparative StudyReview
PubMed Full text at PMC Publisher
In one paragraph

Review in International journal of clinical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Hideaki Bando *Translational Research Support Office, Division of Drug and Diagnostic Development Promotion, Department for the Promotion of Drug and Diagnostic Development, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.ORCID http://orcid.org/0000-0001-5041-2765
Yoichi Naito *Department of General Internal Medicine, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Tomoyuki Yamada *Genomedia Inc, 4-1-4 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Takao FujisawaTranslational Research Support Office, Division of Drug and Diagnostic Development Promotion, Department for the Promotion of Drug and Diagnostic Development, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Mitsuho ImaiTranslational Research Support Office, Division of Drug and Diagnostic Development Promotion, Department for the Promotion of Drug and Diagnostic Development, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Yasutoshi SakamotoTranslational Research Support Office, Division of Drug and Diagnostic Development Promotion, Department for the Promotion of Drug and Diagnostic Development, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Yusuke SaigusaDepartment of Biostatistics, Yokohama City University, 3-9 Fukuura, Kanazawa-Ku, Yokohama, Kanagawa, 236-0004, Japan.
Kouji YamamotoDepartment of Biostatistics, Yokohama City University, 3-9 Fukuura, Kanazawa-Ku, Yokohama, Kanagawa, 236-0004, Japan.
Yutaka TomiokaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6- 5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Nobuyoshi TakeshitaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6- 5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Kuniko SunamiDepartment of Laboratory Medicine, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Megumi FutamuraTranslational Research Support Office, Division of Drug and Diagnostic Development Promotion, Department for the Promotion of Drug and Diagnostic Development, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Chiemi NotakeTranslational Research Support Office, Division of Drug and Diagnostic Development Promotion, Department for the Promotion of Drug and Diagnostic Development, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Satoko AokiGenomedia Inc, 4-1-4 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Kazunori OkanoGenomedia Inc, 4-1-4 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Takayuki YoshinoTranslational Research Support Office, Division of Drug and Diagnostic Development Promotion, Department for the Promotion of Drug and Diagnostic Development, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. tyoshino@east.ncc.go.jp.

Funding

Japan Agency for Medical Research and Development JP23he0122034New Energy and Industrial Technology Development Organization JPNP14012
6 · The paper itself

Abstract

backgroundThe implementation of cancer precision medicine in Japan is deeply intertwined with insurance reimbursement policies and requires case-by-case reviews by Molecular Tumor Boards (MTBs), which impose considerable operational burdens on healthcare facilities. The extensive preparation and review times required by MTBs hinder their ability to efficiently assess comprehensive genomic profiling (CGP) test results. Despite attempts to optimize MTB operations, significant challenges remain. This study aims to evaluate the effectiveness of QA Commons, an artificial intelligence-driven system designed to improve treatment planning using CGP analysis. QA Commons utilizes a comprehensive knowledge base of drugs, regulatory approvals, and clinical trials linked to genetic biomarkers, thereby enabling the delivery of consistent and standardized treatment recommendations. Initial assessments revealed that the QA Commons' recommendations closely matched the ideal treatment recommendations (consensus annotations), outperforming the average results of MTBs at Cancer Genomic Medicine Core Hospitals.

methodsA clinical performance evaluation study will be conducted by comparing the QA Commons' treatment recommendations with those of the Academia Assembly, which includes medical professionals from the Cancer Genomic Medicine Core and Hub Hospitals. One hundred cases selected from the "Registry of the Academia Assembly," based on defined inclusion and exclusion criteria, will be analyzed to assess the concordance of recommendations.

conclusionThe expected outcomes suggest that QA Commons could reduce the workload of MTB members, standardize the quality of MTB discussions, and provide consistent outcomes in repeated patient consultations. In addition, the global expansion of QA Commons could promote worldwide adoption of Japan's pioneering precision oncology system.

Indexed as

Artificial IntelligenceNeoplasmsPrecision MedicineSoftwareHumansJapanProspective StudiesAI-powered diagnostic toolsCancer precision medicineComprehensive genomic profilingMolecular Tumor Board

Identifiers

PMID39714567
PMCPMC11785689

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.