Evidence map›Paper›PMID 40644624›Full record

ArticleJMIR cancer2025

Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis.

Xin Li, Wen-Yu Yang, Fan Zhang, Rui Shan, Fang Mei, Shi-Bing Song, Bang-Kai Sun, Jing Chen, Run-Ze Hu, Yang Yang and 4 more

Abstract read
In one paragraph

Article in JMIR cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Xin Li *Department of General Surgery, Peking University Third Hospital, Beijing, China.ORCID 0009-0002-8445-5354
Wen-Yu Yang *China Center for Health Development Studies, Peking University, Beijing, China.ORCID 0009-0000-1479-468X
Fan Zhang *Department of Ultrasound, Peking University Third Hospital, Beijing, China.ORCID 0009-0006-1165-7093
Rui ShanDepartment of Maternal and Child Health, School of Public Health, Peking University, 38 Huayuan Road, Haidian District, Beijing, Beijing, 100191, China, 86 82801222.ORCID 0009-0006-7603-337X
Fang MeiDepartment of Pathology, Peking University Third Hospital, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.ORCID 0000-0001-5690-674X
Shi-Bing SongDepartment of General Surgery, Peking University Third Hospital, Beijing, China.ORCID 0009-0007-6981-1142
Bang-Kai SunInformation Management and Big Data Center, Peking University Third Hosptial, Beijing, China.ORCID 0009-0004-6452-9082
Jing ChenDepartment of Maternal and Child Health, School of Public Health, Peking University, 38 Huayuan Road, Haidian District, Beijing, Beijing, 100191, China, 86 82801222.ORCID 0000-0002-6640-7140
Run-Ze HuDepartment of Maternal and Child Health, School of Public Health, Peking University, 38 Huayuan Road, Haidian District, Beijing, Beijing, 100191, China, 86 82801222.ORCID 0009-0003-2968-555X
Yang YangDepartment of Maternal and Child Health, School of Public Health, Peking University, 38 Huayuan Road, Haidian District, Beijing, Beijing, 100191, China, 86 82801222.ORCID 0009-0006-8429-9573
Yi-Hang YangDepartment of Maternal and Child Health, School of Public Health, Peking University, 38 Huayuan Road, Haidian District, Beijing, Beijing, 100191, China, 86 82801222.ORCID 0009-0002-3926-8226
Jing-Yao LiuDepartment of Maternal and Child Health, School of Public Health, Peking University, 38 Huayuan Road, Haidian District, Beijing, Beijing, 100191, China, 86 82801222.ORCID 0009-0003-1032-9221
Chun-Hui YuanDepartment of General Surgery, Peking University Third Hospital, Beijing, China.ORCID 0000-0003-1427-1023
Zheng LiuDepartment of Maternal and Child Health, School of Public Health, Peking University, 38 Huayuan Road, Haidian District, Beijing, Beijing, 100191, China, 86 82801222.ORCID 0000-0002-0405-2348

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Surgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly small tumors, until diagnostic surgery is performed. Objective: This study aimed to identify the size-specific predictors for the malignancy risk of FTNs preoperatively. Methods: A retrospective cohort study was conducted at Peking University Third Hospital in Beijing, China, from 2012 to 2023. Patients with a postoperative pathological diagnosis of follicular thyroid adenoma (FTA) or follicular thyroid carcinoma (FTC) were included. FTNs were classified into small- and large-sized categories based on the cutoff value of the tumor diameter derived from spline regression, which indicated the turning point of malignancy risk. We identified the 5 most important predictors from 22 variables including demography, sonography, and hormones, using machine learning methods. We also calculated the odds ratios (OR) with 95% CI for these predictors in both small- and large-sized FTNs. Results: Altogether, we included 1494 FTNs, comprising 1266 FTAs and 228 FTCs. FTNs with a maximum diameter less than 3.0 cm were grouped as small-sized tumors (n=715), while those with larger diameters were categorized as large-sized tumors (n=779). In the small-sized group, tumors with macrocalcification (OR 2.90, 95% CI 1.50-5.60), those with peripheral calcification (OR 4.50, 95% CI 1.50-13.00), and those in younger patients (OR 1.33, 95% CI 1.05-1.69) showed a higher malignancy risk. In the large-sized group, tumors presenting with a nodule-in-nodule appearance (OR 3.30, 95% CI 1.30-7.90) exhibited a higher malignancy risk. In both groups, lower thyroid-stimulating hormone levels (OR 1.49, 95% CI 1.20-1.85 for small-sized FTNs; OR 1.61, 95% CI 1.37-1.96 for large-sized FTNs) and a larger mean diameter (OR 1.40, 95% CI 1.10-1.70 for small-sized FTNs; OR 1.50 95% CI 1.20-1.70 for large-sized FTNs) were associated with the malignancy risk of FTNs. Conclusions: This study identified size-specific predictors for malignancy risk in FTNs, highlighting the importance of stratified prediction based on tumor size.

Indexed as

Adenocarcinoma, FollicularMachine LearningThyroid NeoplasmsAdultAgedChinaFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsTumor BurdenUltrasonographyfollicular thyroid adenomafollicular thyroid cancerfollicular thyroid neoplasmmachine learningmalignancyrandom foresttumor sizeXGBoost

Identifiers

PMID40644624
PMCPMC12274017

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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.