Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules : a prospective cohort study
© 2024. The Author(s)..
BACKGROUND: Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy.
METHODS: This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes.
RESULTS: The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies.
CONCLUSIONS: This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
BMC medicine - 22(2024), 1 vom: 02. Apr., Seite 147 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Feihang [VerfasserIn] |
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| Links: |
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| Themen: |
Benign thyroid nodule |
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| Anmerkungen: |
Date Completed 03.04.2024 Date Revised 04.04.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12916-024-03368-1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370512634 |
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| 520 | |a © 2024. The Author(s). | ||
| 520 | |a BACKGROUND: Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy | ||
| 520 | |a METHODS: This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes | ||
| 520 | |a RESULTS: The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies | ||
| 520 | |a CONCLUSIONS: This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Benign thyroid nodule | |
| 650 | 4 | |a Blood leukocyte | |
| 650 | 4 | |a DNA methylation | |
| 650 | 4 | |a Malignant thyroid nodule | |
| 650 | 4 | |a Methylation model | |
| 700 | 1 | |a Zhao, Danyang |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Wang-Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yiying |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Huiyi |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Shanshan |e verfasserin |4 aut | |
| 700 | 1 | |a Ji, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Jingjing |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a He, Qiye |e verfasserin |4 aut | |
| 700 | 1 | |a Gong, Chengxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Su, Zhixi |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Zhiping |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Lingxiao |e verfasserin |4 aut | |
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