Evidence map›Paper›PMID 40232415›Full record

ArticleAbdominal radiology (New York)2025

Development and validation of ADC-based nomogram model for predicting the prognostic factors in preoperative clinical early-stage cervical cancer patients.

Xiaoliang Ma, Lu Zhang, Jingjing Lu, Pengju Xu, Liheng Liu, Mengsu Zeng, Jianjun Zhou, Songqi Cai, Minhua Shen

Abstract readValidation StudyMulticenter Study
In one paragraph

Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Xiaoliang MaDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Lu ZhangDepartment of Radiology, People's Hospital of Jiaozuo City, Jiaozuo, China.
Jingjing LuDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Pengju XuDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Liheng LiuDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Mengsu ZengDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Jianjun ZhouDepartment of Radiology, Zhongshan Hospital (Xiamen Branch), Fudan University, Xiamen, China.
Songqi CaiDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China. Songqi_ZS@163.com.
Minhua ShenDepartment of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China. shenminhua860213@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo investigate the feasibility of ADC-based nomogram models for predicting cervical cancer (CC) subtype, lymphovascular space invasion (LVSI) and lymph node metastases (LNM) status in preoperative clinical early-stage CC patients. MATERIALS AND

methodsA total of 535 CC patients from three independent centers [center A (n = 251) for model training, and centers B (n = 193) and C (n = 91) for external validation] were included. Volumetric ADC histogram metrics (volume, minADC, meanADC, maxADC, skewness, kurtosis, entropy, P10_ADC, P25_ADC, P50_ADC, P75_ADC, and P90_ADC) derived the whole-tumor were calculated. Univariate and multivariate analyses were used to screen the independent predictors and develop nomogram models, with the area under the receiver operating characteristic curve (AUC) for predicting performance estimation.

resultsIn differentiating adenosquamous carcinoma (ASC)/adenocarcinoma (AC) from squamous cell carcinoma (SCC), the independent predictors of P25_ADC, SCC antigen (SCC-Ag), and CA199 constructed the nomogram_1 model, with AUCs of 0.900 and 0.873 in training and validation sets, respectively. In differentiating AC from ASC, the independent predictors of P50_ADC and SCC-Ag constructed the nomogram_2 model, with AUCs of 0.837 and 0.829 in training and validation sets, respectively. Tumor volume is the only independent predictor of LVSI(+) and LNM(+), with AUCs of 0.608 and 0.694 in the training set, and 0.553 and 0.656 in the validation set, respectively.

conclusionThe ADC-based nomogram models can effectively predict the CC subtypes, but might be insufficient in predicting the LVSI and LNM status in preoperative clinical early-stage patients.

Indexed as

Diffusion Magnetic Resonance ImagingNomogramsUterine Cervical NeoplasmsAdultAgedFeasibility StudiesFemaleHumansLymphatic MetastasisMiddle AgedNeoplasm InvasivenessNeoplasm StagingPredictive Value of TestsPrognosisRetrospective StudiesApparent diffusion coefficientDiffusion weighted imagingHistogramMagnetic resonance imagingUterine cervical neoplasms

Identifiers

PMID40232415
PMCPMC12568852

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