Evidence mapPaperPMID 41553642Full record

ArticleBreast cancer (Tokyo, Japan)2026

Validation of the postoperative prognostication tool PREDICT version 2.2 and 3.0 using data from the National cancer center hospital in Japan.

Hiromi Hashiguchi, Nobuji Kouno, Masaaki Komatsu, Sho Shiino, Naoto Takehara, Katsuji Takeda, Satoshi Takahashi, Yi-Wen Hsiao, Takeshi Murata, Kazutaka Obama and 3 more

Abstract readValidation Study
In one paragraph

Article in Breast cancer (Tokyo, Japan), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Hiromi Hashiguchi *Department of Breast Surgery, National Cancer Center Hospital, Tokyo, Japan.
Nobuji Kouno *Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan. nkouno@ncc.go.jp.ORCID http://orcid.org/0000-0003-2061-2111
Masaaki KomatsuDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
Sho ShiinoDepartment of Breast Surgery, Shin-Matsudo Central General Hospital, Chiba, Japan.
Naoto TakeharaDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
Katsuji TakedaAI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
Satoshi TakahashiDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
Yi-Wen HsiaoDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Takeshi MurataDepartment of Breast Surgery, National Cancer Center Hospital, Tokyo, Japan.
Kazutaka ObamaDepartment of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Shin TakayamaDepartment of Breast Surgery, National Cancer Center Hospital, Tokyo, Japan.
Paul D P PharoahDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Ryuji HamamotoDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan. rhamamot@ncc.go.jp.ORCID http://orcid.org/0000-0002-2632-1334

Funding

Cabinet Office, Government of Japan BRIDGE
6 · The paper itself

Abstract

backgroundPREDICT is a prognostic tool developed in the United Kingdom to estimate postoperative overall survival (OS) and the additional benefits of adjuvant therapies in patients with breast cancer. It has been validated in various international cohorts and continuously updated with the inclusion of new variables and model retraining. However, their efficacy in the Japanese population remains unclear. We aimed to evaluate the generalizability of PREDICT versions 2.2 (v2.2) and 3.0 (v3.0) using data from the National Cancer Center Hospital in Japan, a high-volume cancer center.

methodsWe analyzed a retrospective cohort (2006-2016) including 2,980 cases of postoperative breast cancer. We calculated survival predictions using both v2.2 and v3.0, and compared them with the Kaplan-Meier-estimated survival probabilities using a calibration plot. Additionally, we performed a time-dependent receiver operating characteristic (ROC) curve analysis for v2.2 and v3.0.

resultsBoth models tended to underestimate survival in our cohort, whereas v3.0 showed improved calibration compared to v2.2, for 5- and 10-year OS. Both v2.2 and v3.0 maintained good discriminative performance throughout 10 years, with values under the ROC curve generally above 0.80.

conclusionsDespite these differences, both versions demonstrated satisfactory performance, suggesting that they can be generalized for Japanese patients with postoperative breast cancer. Notably, v3.0, given its improved calibration, might be more suitable for supporting shared decision making. The model's performance in predicting 5-year OS supports its generalizability, whereas 10-year projections warrant caution due to limited follow-up. Overall, this study demonstrates that PREDICT is a valuable prognostic tool for Japanese patients with breast cancer.

Indexed as

Breast NeoplasmsAdultAgedFemaleHumansJapanKaplan-Meier EstimateMastectomyMiddle AgedPostoperative PeriodPrognosisRetrospective StudiesROC CurveCalibrationDiscriminationJapanese breast cancerPREDICTValidation

Identifiers

PMID41553642
PMCPMC12960381

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