Evidence map›Paper›PMID 42163883›Full record

ArticleAmerican journal of cancer research2026

Development and internal-external validation of a nomogram for predicting postoperative 30-day malnutrition risk in cervical cancer patients: a retrospective cohort study.

Fanyan Wei, Yuxia Du, Haifeng Hu, Wenxia Li, Beiyu Tuo

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Article in American journal of 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.

Fanyan WeiThe 3rd Department of Gynecology, Northwest Women's and Children's Hospital No. 1616, Yanxiang Road, Yanta District, Xi'an 710000, Shaanxi, China.
Yuxia DuDepartment of Obstetrics and Gynecology, Xi'an International Medical Center No. 777, Xitai Road, Gaoxin District, Xi'an 710000, Shaanxi, China.
Haifeng HuDepartment of Oncology, Yan'an Traditional Chinese Medicine Hospital No. 26, Xuanyuan Avenue, New District, Yan'an 716000, Shaanxi, China.
Wenxia LiDepartment of Oncology, Yan'an Traditional Chinese Medicine Hospital No. 26, Xuanyuan Avenue, New District, Yan'an 716000, Shaanxi, China.
Beiyu TuoDepartment of Oncology, Yan'an Traditional Chinese Medicine Hospital No. 26, Xuanyuan Avenue, New District, Yan'an 716000, Shaanxi, China.

Funding

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6 · The paper itself

Abstract

Malnutrition at 30 days after surgery is common in cervical cancer patients and may adversely affect long-term outcomes. This retrospective study developed and validated an interpretable predictive nomogram for early identification of postoperative malnutrition (NRS-2002 ≥3) in patients with FIGO stage IB-IIA cervical cancer undergoing radical surgery. A total of 784 patients were included and divided into a training cohort (n=431), an internal validation cohort (n=180), and an independent external cohort (n=173). Clinical, sociodemographic, treatment-related, and laboratory variables were collected, and predictors were screened using univariate and multivariate logistic regression. Model discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), Brier score, calibration analysis, decision curve analysis, and DeLong testing against albumin (ALB) alone. The final model incorporated eight independent predictors: age, body mass index (BMI), marital status, presence of a caregiver, parenteral nutrition support, lymph node metastasis, FIGO stage, and ALB. The nomogram achieved AUCs of 0.756, 0.742, and 0.807 in the training, internal validation, and external validation cohorts, respectively, with Brier scores ranging from 0.1859 to 0.2143, and showed stable net benefit across a wide threshold probability range (0.04-0.99). In the pooled sample, the nomogram significantly outperformed ALB alone (P<0.001). SHapley Additive exPlanations (SHAP) analysis enhanced interpretability and identified ALB, age, and lymph node metastasis as the most influential features driving predictions. Among 725 patients (92.5%) with follow-up data, 77 deaths (10.6%) occurred, and survival analyses demonstrated that unmarried status (HR=2.21), lymph node metastasis (HR=4.74), higher FIGO stage (HR=5.15), poor differentiation (HR=2.12), and higher risk scores (HR=1.88) were independently associated with worse overall survival, whereas human papillomavirus positivity was protective (HR=0.63). These findings suggest that the proposed nomogram provides accurate and explainable prediction of postoperative malnutrition and may support early risk stratification as well as long-term prognostic assessment in cervical cancer patients.

Indexed as

Cervical cancerexternal validationmachine learningmalnutritionnomogramoverall survivalSHAP

Identifiers

PMID42163883
PMCPMC13184753

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