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De Vincentis S, Brigante G, Ansaloni A, Madeo B, Zirilli L, Diazzi C, Belli S, Vezzani S, Simoni M, Rochira V. Value of repeated US-guided fine-needle aspiration (US-FNAB) in the follow-up of benign thyroid nodules: a real-life study based on the MoCyThy (Modena's Cytology of the Thyroid) DATABASE with a revision of the literature. Endocrine 2024; 84:193-202. [PMID: 38123877 DOI: 10.1007/s12020-023-03641-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023]
Abstract
PURPOSE The utility of repeating ultrasound-guided fine-needle aspiration (US-FNAB) in the follow-up of benign (THY2) thyroid nodules is still debated. The aim of this study was to retrospectively investigate the diagnostic value of re-biopsy of thyroid nodules following an initially benign result. METHODS We retrospectively analyzed US-FNABs performed at the Unit of Endocrinology of Modena from 2006 to 2009. The firstly benign cytological result was compared with the cytological results of subsequent US-FNABs (2nd and/or 3rd) executed on the same nodule. RESULTS Among 10449 US-FNABs, 6270 (60%) received a THY2 cytological categorization. Of them, 278 (4.43%) underwent a subsequent US-FNAB: 86.7% maintained the same cytology, 32 (11.5%) changed to THY3 (indeterminate) and 5 (1.8%) to THY4 (suspicious of malignancy). Among the 24 nodules addressed to surgery, 9 (37%) were histologically malignant, with an overall miss rate of 3.2%. Male patients had higher risk of discordant results at subsequent US-FNAB (p = 0.005, OR:3.59, 95%CI:1.453-7.769) while dimensional increase above 5 mm was predictive of concordant benign cytology (p = 0.036, OR:0.249, 95%CI:0.068-0.915). Age, suspicious US characteristics, and distance between US-FNABs resulted not predictive. CONCLUSIONS Re-biopsy of benign nodules confirmed the benign nature in most cases. In case of discordant cytology, relocation in indeterminate category was the most common. The histological diagnosis of cancer occurred in one quarter of nodules surgically removed, with a low overall clinically significant miss rate. Thus, a small percentage of false negatives exists; males and subjects with US suspicious nodules should be carefully followed-up, considering case by case re-biopsy possibility.
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Affiliation(s)
- Sara De Vincentis
- Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena & Reggio Emilia, Modena, Italy
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Ospedale Civile of Baggiovara, Modena, Italy
| | - Giulia Brigante
- Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena & Reggio Emilia, Modena, Italy
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Ospedale Civile of Baggiovara, Modena, Italy
| | - Anna Ansaloni
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Ospedale Civile of Baggiovara, Modena, Italy
| | - Bruno Madeo
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Ospedale Civile of Baggiovara, Modena, Italy
| | - Lucia Zirilli
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Ospedale Civile of Baggiovara, Modena, Italy
| | - Chiara Diazzi
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Ospedale Civile of Baggiovara, Modena, Italy
| | - Serena Belli
- Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena & Reggio Emilia, Modena, Italy
| | - Silvia Vezzani
- Endocrinology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Manuela Simoni
- Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena & Reggio Emilia, Modena, Italy
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Ospedale Civile of Baggiovara, Modena, Italy
| | - Vincenzo Rochira
- Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena & Reggio Emilia, Modena, Italy.
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Ospedale Civile of Baggiovara, Modena, Italy.
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Chen Y, Gao Z, He Y, Mai W, Li J, Zhou M, Li S, Yi W, Wu S, Bai T, Zhang N, Zeng W, Lu Y, Liu H. An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules. Radiology 2022; 303:613-619. [PMID: 35315719 DOI: 10.1148/radiol.211455] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background US-based diagnosis of thyroid nodules is subjective and influenced by radiologists' experience levels. Purpose To develop an artificial intelligence model based on American College of Radiology Thyroid Imaging Reporting and Data System characteristics for diagnosing thyroid nodules and identifying nodule characteristics (hereafter, MTI-RADS) and to compare the performance of MTI-RADS, radiologists, and a model trained on benign and malignant status based on surgical histopathologic analysis (hereafter, MDiag). Materials and Methods In this retrospective study, 1588 surgically proven nodules from 636 consecutive patients (mean age, 49 years ± 14 [SD]; 485 women) were included. MTI-RADS and MDiag were trained on US images of 1345 nodules (January 2018 to December 2019). The performance of MTI-RADS was compared with that of MDiag and radiologists with different experience levels on the test data set (243 nodules, January 2019 to December 2019) with the DeLong method and McNemar test. Results The area under the receiver operating characteristic curve (AUC) and sensitivity of MTI-RADS were 0.91 and 83% (55 of 66 nodules), respectively, which were not significantly different from those of experienced radiologists (0.93 [P = .45] and 92% [61 of 66 nodules; P = .07]) and exceeded those of junior radiologists (0.78 [P < .001] and 70% [46 of 66 nodules; P = .04]). The specificity of MTI-RADS (87% [154 of 177 nodules]) was higher than that of both experienced and junior radiologists (80% [141 of 177 nodules; P = .02] and 75% [133 of 177 nodules; P = .001], respectively). The AUC of MTI-RADS was higher than that of MDiag (0.91 vs 0.84, respectively; P = .001). In the test set of 243 nodules, the consistency rates between MTI-RADS and the experienced group were higher than those between MTI-RADS and the junior group for composition (79% [n = 193] vs 73% [n = 178], respectively; P = .02), echogenicity (75% [n = 183] vs 68% [n = 166]; P = .04), shape (93% [n = 227] vs 88% [n = 215]; P = .04), and smooth or ill-defined margin (72% [n = 174] vs 63% [n = 152]; P = .002). Conclusion The area under the receiver operating characteristic curve (AUC) of an artificial intelligence model based on the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) was higher than that of a model trained on benign and malignant status based on surgical histopathologic analysis. The AUC and sensitivity of the model based on TI-RADS exceeded those of junior radiologists; the specificity of the model was higher than that of both experienced and junior radiologists. © RSNA, 2022.
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Affiliation(s)
- Yufan Chen
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Zixiong Gao
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Yanni He
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Wuping Mai
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Jinhua Li
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Meijun Zhou
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Sushu Li
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Wenhong Yi
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Shuyu Wu
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Tong Bai
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Ning Zhang
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Weibo Zeng
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Yao Lu
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
| | - Hongmei Liu
- From the Department of Ultrasound, Institute of Ultrasound in Musculoskeletal Sports Medicine, Guangdong Second Provincial General Hospital, Guangzhou 510317, PR China (Y.C., Y.H., W.M., J.L., M.Z., S.L., W.Y., T.B., N.Z., W.Z., H.L.); the Second School of Clinical Medicine, Southern Medical University, Guangzhou, PR China (Y.C., W.M., H.L.); and School of Computer Science and Engineering (Z.G., S.W., Y.L.) and Guangdong Province Key Laboratory of Computational Science (Z.G., S.W., Y.L.), Sun Yat-Sen University, Guangzhou, China
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7
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Brigante G, Craparo A, Pignatti E, Marino M, Monzani ML, De Vincentis S, Casarini L, Sperduti S, Boselli G, Margiotta G, Ippolito M, Rochira V, Simoni M. Real-life use of BRAF-V600E mutation analysis in thyroid nodule fine needle aspiration: consequences on clinical decision-making. Endocrine 2021; 73:625-632. [PMID: 33759074 DOI: 10.1007/s12020-021-02693-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 03/10/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE This study aimed to evaluate the real-life use of BRAF-V600E mutation analysis in washout liquid from thyroid nodule fine needle aspiration (FNA), and the consequences of genetic result on clinical decision-making. METHODS We retrospectively considered subjects tested for BRAF-V600E among those attending the Endocrinology Unit of Modena for FNA between 2014 and 2018. Washing fluid was collected together with cytological sample and stored at -20 °C. If the clinician deemed it necessary, the sample was thawed, DNA extracted, and genetic test performed by high-resolution melting technique. We collected data on cytology according to the Italian Consensus for the cytological classification of thyroid nodules, type of surgery (when performed), histology, and adverse events. RESULTS Out of 7112 subjects submitted to FNA, BRAF analysis was requested for 683 (9.6%). Overall, 896 nodules were analyzed: 74% were indeterminate at cytology, mainly TIR3A (low risk). Twenty-two nodules were mutant (BRAF+). Only 2% of indeterminate, mainly TIR3B, were BRAF+. Based on final histological diagnosis, BRAF test had high specificity (100%) but poor sensitivity (21%), also in indeterminate nodules. Mutant subjects underwent more extensive surgery compared to wild type (p = 0.000), with frequent prophylactic central lymph node dissection. One third had local metastases. Higher prevalence of hypoparathyroidism was found in BRAF+ compared to wild type (p = 0.018). CONCLUSIONS The analysis of BRAF-V600E outside of gene panels has low sensitivity, especially in indeterminate nodules, and a positive result could lead to more extensive surgery with greater risk of hypoparathyroidism and questionable clinical utility.
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Affiliation(s)
- Giulia Brigante
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy.
| | - Andrea Craparo
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - Elisa Pignatti
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Marco Marino
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Maria Laura Monzani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - Sara De Vincentis
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - Livio Casarini
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Center for Genomic Research, University of Modena and Reggio Emilia, Modena, Italy
| | - Samantha Sperduti
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Center for Genomic Research, University of Modena and Reggio Emilia, Modena, Italy
| | - Gisella Boselli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - Gianluca Margiotta
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - Margherita Ippolito
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Vincenzo Rochira
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - Manuela Simoni
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
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