Evidence map›Paper›PMID 42591366›Full record

ArticleTranslational cancer research2026

Development and validation of a nomogram for predicting chemotherapy-induced liver injury in breast cancer patients.

Meizhen Liang, Dixin Xue, Rusi Su, Weili Wu, Chengliang Chen

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Article in Translational cancer research, 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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5 · Who and what money

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5 authors.

Meizhen LiangDepartment of Thyroid and Breast Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Dixin XueDepartment of Thyroid and Breast Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Rusi SuDepartment of Thyroid and Breast Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Weili WuDepartment of Thyroid and Breast Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Chengliang ChenDepartment of Thyroid and Breast Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chemotherapy‑induced liver injury (CILI) is a common complication in breast cancer patients, which may lead to treatment interruption and poor prognosis. An effective tool for individualized risk prediction is lacking. This study aimed to develop and validate a nomogram for predicting CILI risk in breast cancer patients undergoing chemotherapy. Methods: A retrospective cohort of breast cancer patients receiving chemotherapy between May 2022 and May 2025 in The Third Affiliated Hospital of Wenzhou Medical University was enrolled. Demographic, clinical, tumor‑related, treatment, and laboratory data were collected. An increase in alanine aminotransferase (ALT) or aspartate aminotransferase (AST) exceeding the normal upper limit is defined as liver dysfunction. Logistic regression with least absolute shrinkage and selection operator (LASSO) variable selection was used to identify independent predictors. A nomogram was constructed. Model performance was assessed by the concordance index (C‑index), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Internal validation was performed using 1,000‑bootstrap resampling. Results: A total of 422 patients were included. Age and chemotherapy regimen (neoadjuvant Conclusions: The proposed nomogram provides an individualized, visual tool for predicting CILI risk in breast cancer patients undergoing chemotherapy. It may assist clinicians in early identification of high‑risk patients, optimizing chemotherapy regimens, and implementing timely liver function protective strategies.

Indexed as

Breast cancerchemotherapy‑induced liver injury (CILI)nomogramprediction modelrisk assessment

Identifiers

PMID42591366
PMCPMC13461848

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