Evidence map›Paper›PMID 35937812›Full record

ArticleFrontiers in endocrinology2022

Development of an Active Surveillance or Surgery Model to Predict Lymph Node Metastasis in cN0 Papillary Thyroid Microcarcinoma.

Huan Zhang, Xiangqian Zheng, Juntian Liu, Ming Gao, Biyun Qian

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.4field-weighted citation impact, top 36% of its field
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

6 citing papers in PubMed, 3 citations in OpenAlex.

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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

5 authors at 2 institutions in 1 country.

Huan ZhangCancer Prevention Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Xiangqian ZhengDepartment of Head and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Juntian LiuCancer Prevention Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Ming GaoDepartment of Head and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Biyun QianHongqiao International Institute of Medicine, Shanghai Tongren Hospital and Faculty of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Tianjin Medical University Cancer Institute and Hospital · CNTongren Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Involvement of multiple lymph node (LN) metastasis in papillary thyroid microcarcinoma (PTMC) may indicate a progressive disease. To assist treatment decision, we conducted a clinical study to develop and validate a prediction model for the preoperative evaluation of LN metastasis involving more than five lymph nodes in patients with clinical N0 (cN0) PTMC. Material and Methods: Using data from 6,337 patients with cN0 PTMCs at Tianjin Medical University Cancer Institute and Hospital from 2013 to 2017, we identified and integrated risk factors for the prediction of multiple LN metastasis to build a nomogram. The predictive accuracy and discriminative ability of the nomogram were evaluated by the concordance index (C-index) and calibration curve. The model was validated using bootstrap resampling of the training cohort and an independent temporal validation cohort at the same institution. Results: In the training cohort (n = 3,209 patients), six independent risk factors were identified and included the prediction model (PTMC Active Surveillance or Surgery (ASOS) Model), including age, gender, multifocality, tumor size, calcification, and aspect ratio. The PTMC ASOS model was validated both internally and through the temporal validation cohort (n = 3,128 patients) from the same institute. The C-indexes of the prediction model in the training cohort were 0.768 (95% CI, 0.698-0.838), 0.768 and 0.771 in the internal validation and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) was 0.7068 and 0.6799. The calibration curve for probability of large-LN metastasis showed good agreement between prediction by nomogram and actual observation. DCA curves were used for comparison with another model, and IDI and NRI were also calculated. The cutoff value of our model was obtained by the ROC curve. Based on this model and cut point, a web-based dynamic nomogram was developed (https://tjmuch-thyroid.shinyapps.io/PTMCASOSM/). Conclusion: We established a novel nomogram that can help to distinguish preoperatively cN0 PTMC patients with or without metastasis of multiple lymph nodes. This clinical prediction model may be used in decision making for both active surveillance and surgery.

Indexed as

Models, StatisticalWatchful WaitingCarcinoma, PapillaryHumansLymphatic MetastasisPrognosisRetrospective StudiesThyroid Neoplasmslymph node metastasispapillary thyroid microcarcinomaprediction modelrisk factorsultrasonic feature

Identifiers

PMID35937812
PMCPMC9353015
OpenAlexW4286447897

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.