Evidence map›Paper›PMID 31714685›Full record

ArticleCancer medicine2020

A novel nomogram to predict the local tumor progression after microwave ablation in patients with early-stage hepatocellular carcinoma: A tool in prediction of successful ablation.

Chao An, Songsong Wu, Zhimei Huang, Jiayan Ni, Mengxuan Zuo, Yangkui Gu, Tianqi Zhang, Jinhua Huang

Abstract readValidation Study
In one paragraph

Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Chao AnState Key Laboratory of Oncology in South China, Department of Minimal Invasive Intervention, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.ORCID 0000-0002-1096-4183
Songsong WuDepartment of Ultrasonography, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, PR China.
Zhimei HuangState Key Laboratory of Oncology in South China, Department of Minimal Invasive Intervention, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Jiayan NiState Key Laboratory of Oncology in South China, Department of Minimal Invasive Intervention, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Mengxuan ZuoState Key Laboratory of Oncology in South China, Department of Minimal Invasive Intervention, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Yangkui GuState Key Laboratory of Oncology in South China, Department of Minimal Invasive Intervention, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Tianqi ZhangState Key Laboratory of Oncology in South China, Department of Minimal Invasive Intervention, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Jinhua HuangState Key Laboratory of Oncology in South China, Department of Minimal Invasive Intervention, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.ORCID 0000-0003-2960-6148

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop a nomogram for the local tumor progression (LTP) in patients with early-stage hepatocellular carcinoma (HCC) after computed tomography-guided percutaneous microwave ablation (CT-PMWA) and to assess clinical-pathologic risk factors for individual LTP estimation. Furthermore, we compared the prognostic predictive ability for LTP between the nomogram and the traditional staging systems.

methodsThis retrospective study was approved by the institutional review board. Five hundred and forty treatment-naïve patients with HCC according to the Milan criteria, who subsequently underwent CT-PMWA were reviewed from 2009 to 2019. Baseline characteristics were collected to identify the risk factors for the determination of LTP after CT-PMWA. The multivariate Cox proportional-hazards model based on significant prognostic factors of LTP was used to construct the nomogram, which was then assessed for its predictive accuracy using mainly the Harrell's C-index and time-dependent area under the curve (tAUC).

resultsAfter a median follow-up time of 28.7 months, 6.5% (35/540) patients had LTP. The nomogram was developed based on the tumor size, tumor number, Child-Turcotte-Pugh (CTP) grade, platelet, and alanine aminotransferase (ALT). The nomogram had good calibration and discriminatory abilities in the training set, with C-indexes of 0.799 (95% confidence interval (CI): 0.738, 0.860), and tAUCs of 0.844 (CI: 0.728, 0.895), that were greater than those of traditional staging systems. Internal validation with 1000 bootstrap resamples had a good C-index of 0.735 (CI: 0.648, 0.816).

conclusionsThe nomogram model can be used to predict accurately LTP after CT-PMWA for early-stage HCC, as well as to assist physicians during the therapeutic decision-making process.

Indexed as

NomogramsAdultAgedAged, 80 and overCarcinoma, HepatocellularCatheter AblationClinical Decision-MakingDisease ProgressionFemaleFollow-Up StudiesHumansKaplan-Meier EstimateLiverLiver NeoplasmsMaleMicrowavesablation techniqueshepatocellular carcinomaneoplasm stagingnomogramrecurrence

Identifiers

PMID31714685
PMCPMC6943142

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.