Evidence map›Paper›PMID 39713568›Full record

ArticleCancer management and research2024

Development and Validation of Nomograms to Predict the Overall Survival and Progression-Free Survival in Patients with Advanced Unresectable Intrahepatic Cholangiocarcinoma.

Feng Xian, Xuewu Song, Jun Bie, Guohui Xu

Abstract read
In one paragraph

Article in Cancer management and research, 2024. 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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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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Feng XianSichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, People's Republic of China.ORCID 0000-0002-9060-4910
Xuewu SongDepartment of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, People's Republic of China.
Jun BieDepartment of Oncology, Nanchong Central Hospital, The Second Clinical College of North Sichuan Medical College, Nanchong, Sichuan, People's Republic of China.
Guohui XuDepartment of Interventional Therapy, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, Sichuan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to develop and validate clinical nomograms for predicting progression-free survival (PFS) and overall survival (OS) in unresectable ICC patients. Patients and Methods: Patients with ICC between 1 January 2018 and 31 May 2023 were selected and randomized into a training set and an internal validation set as a 7:3 ratio. Data analysis and modeling were conducted through R software. The univariate and multivariate Cox regression models were used to analyze the prognosis factors affecting OS and PFS. Survival analysis was conducted using the Kaplan-Meier (KM) method, and comparisons were made using the Log rank test. Then, two nomogram models were constructed to predict OS and PFS, respectively. The nomogram was evaluated and calibrated using the Harrell's C-index, receiver operating characteristic curve (ROC), and calibration plots, and the decision curve analysis (DCA) was conducted to assess its clinical utility. Results: A total of 110 patients were enrolled in this study, with 77 to the training set and 33 to the validation set. In the entire population, the OS rates at 6 and 12 months were 75.5% and 35.5%, respectively, while the PFS rates at 6 and 12 months were 47.3% and 20%, respectively. Cox regression analyses showed that ECOG, Tumor volume, HBsAg and AFP were the prognosis factors of OS, and the predictors in the model of PFS included Gender, Stage of tumor, CDC20 expression and AFP. The nomograms were constructed based on the predictors above. The C-index for predicting OS was 0.802 (0.755, 0.849) in the training set, 0.813 (0.764, 0.862) in the internal validation set; the C-index for predicting PFS was 0.658 (0.568, 0.748) in the training set, and 0.795 (0.705, 0.885) in the internal validation set. Finally, calibration curves and DCA indicated that two nomograms showed favorable performance. Conclusion: Two practical and effective prognostic nomograms were developed to assist clinicians in evaluating OS and PFS in patients with unresectable ICC.

Indexed as

intrahepatic cholangiocarcinomanomogramoverall survivalprognostic modelprogression-free survival

Identifiers

PMID39713568
PMCPMC11663373

What Socratic holds

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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 Socratic graph.