The TNAPP web-based algorithm improves thyroid nodule management in clinical practice: A retrospective validation study
BackgroundThe detection of thyroid nodules has been increasing over time, resulting in an extensive use of fine-needle aspiration (FNA) and cytology. Tailored methods are required to improve the management of thyroid nodules, including algorithms and web-based tools.Study aimsTo assess the performan...
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| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.1080159/full |
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| author | Vincenzo Triggiani Giuseppe Lisco Giuseppina Renzulli Andrea Frasoldati Rinaldo Guglielmi Jeffrey Garber Enrico Papini |
| author_facet | Vincenzo Triggiani Giuseppe Lisco Giuseppina Renzulli Andrea Frasoldati Rinaldo Guglielmi Jeffrey Garber Enrico Papini |
| author_sort | Vincenzo Triggiani |
| collection | DOAJ |
| description | BackgroundThe detection of thyroid nodules has been increasing over time, resulting in an extensive use of fine-needle aspiration (FNA) and cytology. Tailored methods are required to improve the management of thyroid nodules, including algorithms and web-based tools.Study aimsTo assess the performance of the Thyroid Nodule App (TNAPP), a web-based, readily modifiable, interactive algorithmic tool, in improving the management of thyroid nodules.MethodsOne hundred twelve consecutive patients with 188 thyroid nodules who underwent FNA from January to December 2016 and thyroid surgery were retrospectively evaluated. Neck ultrasound images were collected from a thyroid nodule registry and re-examined to extract data to run TNAPP. Each nodule was evaluated for ultrasonographic risk and suitability for FNA. The sensitivity, specificity, positive and negative predictive values, and overall accuracy of TNAPP were calculated and compared to the diagnostic performance of the other two algorithms by the American Association of Clinical Endocrinology/American College of Endocrinology/Associazione Medici Endocrinologi (AACE/ACE/AME), which it was derived from the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS).ResultsTNAPP performed better in terms of sensitivity (>80%) and negative predictive value (68%) with an overall accuracy of 50.5%, which was similar to that found with the AACE/ACE/AME algorithm. TNAPP displayed a slightly better performance than AACE/ACE/AME and ACR TI-RADS algorithms in selectively discriminating unnecessary FNA for nodules with benign cytology (TIR 2 - Bethesda class II: TNAPP 32% vs. AACE/ACE/AME 31% vs. ACR TI-RADS 29%). The TNAPP reduced the number of missed diagnoses of thyroid nodules with suspicious and highly suspicious cytology (TIR 4 + TIR 5 - Bethesda classes V + VI: TNAPP 18% vs. AACE/ACE/AME 26% vs. ACR TI-RADS 20.5%). A total of 14 nodules that would not have been aspirated were malignant, 13 of which were microcarcinomas (92.8%).DiscussionThe TNAPP algorithm is a reliable, easy-to-learn tool that can be readily employed to improve the selection of thyroid nodules requiring cytological characterization. The rate of malignant nodules missed because of inaccurate characterization at baseline by TNAPP was lower compared to the other two algorithms and, in almost all the cases, the tumors were microcarcinomas. TNAPP’s use of size >20 mm as an independent determinant for considering or recommending FNA reduced its specificity.ConclusionTNAPP performs well compared to AACE/ACE/AME and ACR-TIRADS algorithms. Additional retrospective and, ultimately, prospective studies are needed to confirm and guide the development of future iterations that incorporate different risk stratification systems and targets for diagnosing malignancy while reducing unnecessary FNA procedures. |
| first_indexed | 2024-04-10T20:00:12Z |
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| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-10T20:00:12Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-3c4a4eb049c4457d9897f66985ac63582023-01-27T07:19:01ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-01-011310.3389/fendo.2022.10801591080159The TNAPP web-based algorithm improves thyroid nodule management in clinical practice: A retrospective validation studyVincenzo Triggiani0Giuseppe Lisco1Giuseppina Renzulli2Andrea Frasoldati3Rinaldo Guglielmi4Jeffrey Garber5Enrico Papini6Interdisciplinary Department of Medicine, Section of Internal Medicine, Geriatrics, Endocrinology and Rare Diseases, School of Medicine, University of Bari “Aldo Moro”, Bari, ItalyInterdisciplinary Department of Medicine, Section of Internal Medicine, Geriatrics, Endocrinology and Rare Diseases, School of Medicine, University of Bari “Aldo Moro”, Bari, ItalyDepartment of Emergency and Organ Transplantation, Section of Pathological Anatomy, University of Bari “Aldo Moro”, Bari, ItalyEndocrinology and Metabolism Department, Arcispedale Santa Maria Nuova Istituto di Ricovero e Cura a Carattere Scientifico-Azienda Sanitaria Locale, Reggio Emilia, ItalyEndocrinology and Metabolism Department, Regina Apostolorum Hospital, Rome, ItalyEndocrine Division, Harvard Vanguard Medical Associates Harvard Medical School, Boston, MA, United StatesEndocrinology and Metabolism Department, Regina Apostolorum Hospital, Rome, ItalyBackgroundThe detection of thyroid nodules has been increasing over time, resulting in an extensive use of fine-needle aspiration (FNA) and cytology. Tailored methods are required to improve the management of thyroid nodules, including algorithms and web-based tools.Study aimsTo assess the performance of the Thyroid Nodule App (TNAPP), a web-based, readily modifiable, interactive algorithmic tool, in improving the management of thyroid nodules.MethodsOne hundred twelve consecutive patients with 188 thyroid nodules who underwent FNA from January to December 2016 and thyroid surgery were retrospectively evaluated. Neck ultrasound images were collected from a thyroid nodule registry and re-examined to extract data to run TNAPP. Each nodule was evaluated for ultrasonographic risk and suitability for FNA. The sensitivity, specificity, positive and negative predictive values, and overall accuracy of TNAPP were calculated and compared to the diagnostic performance of the other two algorithms by the American Association of Clinical Endocrinology/American College of Endocrinology/Associazione Medici Endocrinologi (AACE/ACE/AME), which it was derived from the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS).ResultsTNAPP performed better in terms of sensitivity (>80%) and negative predictive value (68%) with an overall accuracy of 50.5%, which was similar to that found with the AACE/ACE/AME algorithm. TNAPP displayed a slightly better performance than AACE/ACE/AME and ACR TI-RADS algorithms in selectively discriminating unnecessary FNA for nodules with benign cytology (TIR 2 - Bethesda class II: TNAPP 32% vs. AACE/ACE/AME 31% vs. ACR TI-RADS 29%). The TNAPP reduced the number of missed diagnoses of thyroid nodules with suspicious and highly suspicious cytology (TIR 4 + TIR 5 - Bethesda classes V + VI: TNAPP 18% vs. AACE/ACE/AME 26% vs. ACR TI-RADS 20.5%). A total of 14 nodules that would not have been aspirated were malignant, 13 of which were microcarcinomas (92.8%).DiscussionThe TNAPP algorithm is a reliable, easy-to-learn tool that can be readily employed to improve the selection of thyroid nodules requiring cytological characterization. The rate of malignant nodules missed because of inaccurate characterization at baseline by TNAPP was lower compared to the other two algorithms and, in almost all the cases, the tumors were microcarcinomas. TNAPP’s use of size >20 mm as an independent determinant for considering or recommending FNA reduced its specificity.ConclusionTNAPP performs well compared to AACE/ACE/AME and ACR-TIRADS algorithms. Additional retrospective and, ultimately, prospective studies are needed to confirm and guide the development of future iterations that incorporate different risk stratification systems and targets for diagnosing malignancy while reducing unnecessary FNA procedures.https://www.frontiersin.org/articles/10.3389/fendo.2022.1080159/fullthyroid nodulethyroid carcinomaweb-based algorithmTNAPPfine-needle aspiration (FNA)retrospective study |
| spellingShingle | Vincenzo Triggiani Giuseppe Lisco Giuseppina Renzulli Andrea Frasoldati Rinaldo Guglielmi Jeffrey Garber Enrico Papini The TNAPP web-based algorithm improves thyroid nodule management in clinical practice: A retrospective validation study Frontiers in Endocrinology thyroid nodule thyroid carcinoma web-based algorithm TNAPP fine-needle aspiration (FNA) retrospective study |
| title | The TNAPP web-based algorithm improves thyroid nodule management in clinical practice: A retrospective validation study |
| title_full | The TNAPP web-based algorithm improves thyroid nodule management in clinical practice: A retrospective validation study |
| title_fullStr | The TNAPP web-based algorithm improves thyroid nodule management in clinical practice: A retrospective validation study |
| title_full_unstemmed | The TNAPP web-based algorithm improves thyroid nodule management in clinical practice: A retrospective validation study |
| title_short | The TNAPP web-based algorithm improves thyroid nodule management in clinical practice: A retrospective validation study |
| title_sort | tnapp web based algorithm improves thyroid nodule management in clinical practice a retrospective validation study |
| topic | thyroid nodule thyroid carcinoma web-based algorithm TNAPP fine-needle aspiration (FNA) retrospective study |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.1080159/full |
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