Development and validation of a Web-based malignancy risk-stratification system of thyroid nodules
© 2020 John Wiley & Sons Ltd..
OBJECTIVES: Previous publications on risk-stratification systems for malignant thyroid nodules were based on conventional ultrasound only. We aimed to develop a practical and simplified prediction model for categorizing the malignancy risk of thyroid nodules based on clinical data, biochemical data, conventional ultrasound and real-time elastography.
DESIGN: Retrospective cohort study.
PATIENTS: A total of 2818 patients (1890 female, mean age, 45.5 ± 13.2 years) with 2850 thyroid nodules were retrospectively evaluated between April 2011 and October 2016. 26.8% nodules were malignant.
MEASUREMENTS: We used a randomly divided sample of 80% of the nodules to perform a multivariate logistic regression analysis. Cut-points were determined to create a risk-stratification scoring system. Patients were classified as having low, moderate and high probability of malignancy according to their scores. We validated the models to the remaining 20% of the nodules. The area under the curve (AUC) was used to evaluate the discrimination ability of the systems.
RESULTS: Ten variables were selected as predictors of malignancy. The point-based scoring systems with and without elasticity score achieved similar AUCs of 0.916 (95% confidence interval [CI]: 0.885-0.948) and 0.906 (95% CI: 0.872-0.941) when validated. Malignancy risk was segmented from 0% to 100.0% and was positively associated with an increase in risk scores. We then developed a Web-based risk-stratification system of thyroid nodules (http: thynodscore.com).
CONCLUSION: A simple and reliable Web-based risk-stratification system could be practically used in stratifying the risk of malignancy in thyroid nodules.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:93 |
|---|---|
| Enthalten in: |
Clinical endocrinology - 93(2020), 6 vom: 08. Dez., Seite 729-738 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zhang, Bin [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Journal Article |
|---|
| Anmerkungen: |
Date Completed 18.08.2021 Date Revised 18.08.2021 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1111/cen.14255 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM310133602 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM310133602 | ||
| 003 | DE-627 | ||
| 005 | 20231225135429.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1111/cen.14255 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1033.xml |
| 035 | |a (DE-627)NLM310133602 | ||
| 035 | |a (NLM)32430931 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Zhang, Bin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Development and validation of a Web-based malignancy risk-stratification system of thyroid nodules |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 18.08.2021 | ||
| 500 | |a Date Revised 18.08.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2020 John Wiley & Sons Ltd. | ||
| 520 | |a OBJECTIVES: Previous publications on risk-stratification systems for malignant thyroid nodules were based on conventional ultrasound only. We aimed to develop a practical and simplified prediction model for categorizing the malignancy risk of thyroid nodules based on clinical data, biochemical data, conventional ultrasound and real-time elastography | ||
| 520 | |a DESIGN: Retrospective cohort study | ||
| 520 | |a PATIENTS: A total of 2818 patients (1890 female, mean age, 45.5 ± 13.2 years) with 2850 thyroid nodules were retrospectively evaluated between April 2011 and October 2016. 26.8% nodules were malignant | ||
| 520 | |a MEASUREMENTS: We used a randomly divided sample of 80% of the nodules to perform a multivariate logistic regression analysis. Cut-points were determined to create a risk-stratification scoring system. Patients were classified as having low, moderate and high probability of malignancy according to their scores. We validated the models to the remaining 20% of the nodules. The area under the curve (AUC) was used to evaluate the discrimination ability of the systems | ||
| 520 | |a RESULTS: Ten variables were selected as predictors of malignancy. The point-based scoring systems with and without elasticity score achieved similar AUCs of 0.916 (95% confidence interval [CI]: 0.885-0.948) and 0.906 (95% CI: 0.872-0.941) when validated. Malignancy risk was segmented from 0% to 100.0% and was positively associated with an increase in risk scores. We then developed a Web-based risk-stratification system of thyroid nodules (http: thynodscore.com) | ||
| 520 | |a CONCLUSION: A simple and reliable Web-based risk-stratification system could be practically used in stratifying the risk of malignancy in thyroid nodules | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a real-time elastography | |
| 650 | 4 | |a risk stratification | |
| 650 | 4 | |a thyroid nodules | |
| 650 | 4 | |a ultrasonography | |
| 700 | 1 | |a Pei, Shufang |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Qiuying |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Yuhao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Lu |e verfasserin |4 aut | |
| 700 | 1 | |a Mo, Xiaokai |e verfasserin |4 aut | |
| 700 | 1 | |a Cong, Shuzhen |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shuixing |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Clinical endocrinology |d 1972 |g 93(2020), 6 vom: 08. Dez., Seite 729-738 |w (DE-627)NLM000089117 |x 1365-2265 |7 nnns |
| 773 | 1 | 8 | |g volume:93 |g year:2020 |g number:6 |g day:08 |g month:12 |g pages:729-738 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1111/cen.14255 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 93 |j 2020 |e 6 |b 08 |c 12 |h 729-738 | ||