Evidence map›Paper›PMID 35852678›Full record

ArticleEndocrine pathology2022

Metastatic Risk Stratification of 2526 Medullary Thyroid Carcinoma Patients: A Study Based on Surveillance, Epidemiology, and End Results Database.

Minh-Khang Le, Masataka Kawai, Toru Odate, Huy Gia Vuong, Naoki Oishi, Tetsuo Kondo

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Article in Endocrine pathology, 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
–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

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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.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Minh-Khang LeDepartment of Pathology, University of Yamanashi, Yamanashi, 409-3898, Japan.
Masataka KawaiDepartment of Pathology, University of Yamanashi, Yamanashi, 409-3898, Japan.
Toru OdateDepartment of Pathology, University of Yamanashi, Yamanashi, 409-3898, Japan.
Huy Gia VuongDepartment of Pathology, Oklahoma University Health Sciences Center, Oklahoma City, OK, 73104, USA.
Naoki OishiDepartment of Pathology, University of Yamanashi, Yamanashi, 409-3898, Japan.
Tetsuo KondoDepartment of Pathology, University of Yamanashi, Yamanashi, 409-3898, Japan. ktetsuo@yamanashi.ac.jp.ORCID http://orcid.org/0000-0003-2268-0302

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The risk of distant metastasis in medullary thyroid carcinoma (MTC) has not been well studied. Additional evaluation of MTC metastatic risk can be helpful for improving the quality of medical management. Therefore, we conducted a large population study to develop a method to stratify the risk of metastasis at the initial presentation of MTC patients. We collected 3612 MTC patients from the Surveillance, Epidemiology, and End Results (SEER) database, and included 2526 MTC patients in the study after applying exclusion criteria. We selected the most informative variables from a learning cohort of 2019 patients to obtain 1000 models by repetitive random data splicing into training and regularization cohorts. We selected the optimal model and developed a risk table from that model. Our risk table variables consist of age, gender, tumor size, extrathyroidal extension, and lymph node metastasis. The final model showed good calibration when metastatic risk was < 25% and good performance with areas under the curve (AUCs) of 0.81, 0.84, and 0.84 in the training, regularization, and test cohorts, respectively. We performed K-means clustering analysis on the model's metastatic estimation and determined three risk groups of patients with significant survival differences (p < 0.001). Low-risk patients had 0.88%, 1.3%, and 0.5% while high-risk patients had 19.7%, 15.8%, and 17.8% risk of metastasis in the three cohorts, respectively. The incorporation of our table into the International MTC Grading System (IMTCGS) requires more comprehensive clinicopathological studies.

Indexed as

Carcinoma, NeuroendocrineThyroid NeoplasmsHumansPrognosisRisk AssessmentThyroidectomyDistant metastasisMedullary thyroid carcinomaNomogramRisk stratificationSEERThyroid

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Registered trials

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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 OpenQuestion graph.