Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis
Abstract BackgroundSurgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly small tumors, until diagnostic surgery is performed. ObjectiveThis study aimed to identify the size-specific predictor...
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JMIR Publications
2025-07-01
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| Series: | JMIR Cancer |
| Online Access: | https://cancer.jmir.org/2025/1/e73069 |
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| author | Xin Li Wen-yu Yang Fan Zhang Rui Shan Fang Mei Shi-Bing Song Bang-Kai Sun Jing Chen Run-ze Hu Yang Yang Yi-hang Yang Jing-yao Liu Chun-Hui Yuan Zheng Liu |
| author_facet | Xin Li Wen-yu Yang Fan Zhang Rui Shan Fang Mei Shi-Bing Song Bang-Kai Sun Jing Chen Run-ze Hu Yang Yang Yi-hang Yang Jing-yao Liu Chun-Hui Yuan Zheng Liu |
| author_sort | Xin Li |
| collection | DOAJ |
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Abstract
BackgroundSurgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly small tumors, until diagnostic surgery is performed.
ObjectiveThis study aimed to identify the size-specific predictors for the malignancy risk of FTNs preoperatively.
MethodsA 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.
ResultsAltogether, 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.
ConclusionThis study identified size-specific predictors for malignancy risk in FTNs, highlighting the importance of stratified prediction based on tumor size. |
| format | Article |
| id | doaj-art-c7f8a06205dc48d4816dee314b88b479 |
| institution | DOAJ |
| issn | 2369-1999 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | JMIR Publications |
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| series | JMIR Cancer |
| spelling | doaj-art-c7f8a06205dc48d4816dee314b88b4792025-08-20T02:41:10ZengJMIR PublicationsJMIR Cancer2369-19992025-07-0111e73069e7306910.2196/73069Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning AnalysisXin Lihttp://orcid.org/0009-0002-8445-5354Wen-yu Yanghttp://orcid.org/0009-0000-1479-468XFan Zhanghttp://orcid.org/0009-0006-1165-7093Rui Shanhttp://orcid.org/0009-0006-7603-337XFang Meihttp://orcid.org/0000-0001-5690-674XShi-Bing Songhttp://orcid.org/0009-0007-6981-1142Bang-Kai Sunhttp://orcid.org/0009-0004-6452-9082Jing Chenhttp://orcid.org/0000-0002-6640-7140Run-ze Huhttp://orcid.org/0009-0003-2968-555XYang Yanghttp://orcid.org/0009-0006-8429-9573Yi-hang Yanghttp://orcid.org/0009-0002-3926-8226Jing-yao Liuhttp://orcid.org/0009-0003-1032-9221Chun-Hui Yuanhttp://orcid.org/0000-0003-1427-1023Zheng Liuhttp://orcid.org/0000-0002-0405-2348 Abstract BackgroundSurgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly small tumors, until diagnostic surgery is performed. ObjectiveThis study aimed to identify the size-specific predictors for the malignancy risk of FTNs preoperatively. MethodsA 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. ResultsAltogether, 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. ConclusionThis study identified size-specific predictors for malignancy risk in FTNs, highlighting the importance of stratified prediction based on tumor size.https://cancer.jmir.org/2025/1/e73069 |
| spellingShingle | Xin Li Wen-yu Yang Fan Zhang Rui Shan Fang Mei Shi-Bing Song Bang-Kai Sun Jing Chen Run-ze Hu Yang Yang Yi-hang Yang Jing-yao Liu Chun-Hui Yuan Zheng Liu Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis JMIR Cancer |
| title | Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis |
| title_full | Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis |
| title_fullStr | Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis |
| title_full_unstemmed | Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis |
| title_short | Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis |
| title_sort | size specific predictors for malignancy risk in follicular thyroid neoplasms machine learning analysis |
| url | https://cancer.jmir.org/2025/1/e73069 |
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