Artificial intelligence in ultrasound of thyroid nodules, prognosis of I-131 uptake

封面


如何引用文章

全文:

详细

BACKGROUND: Thyroid nodules are a prevalent issue, with an estimated incidence of 19% to 35% based on ultrasound examination and 8% to 65% based on autopsy findings [1]. In some cases, Plummer’s disease is observed, and nodular masses may be observed in 10% to 35% of Graves’ disease cases, with iodine accumulation of a different nature [2, 3]. One of the principal treatments for Graves’ and Plummer’s diseases is radioiodine therapy, which serves to exclude the possibility of malignancy in nodules. Furthermore, the pharmacokinetics of iodine is investigated, which represents the most time-consuming and labor-intensive stage of preparation for radioiodine therapy. In clinical practice, ultrasound is performed in accordance with the TI-RADS system, followed (if necessary) by fine-needle aspiration puncture biopsy, stratified according to the Bethesda system. However, the interpretation of ultrasound examinations is inherently subjective, whereas the use of decision support systems can reduce the number of fine-needle aspiration puncture biopsies by 27% and the number of missed malignant neoplasms by 1.9%. Furthermore, the quantitative characterization of nodal ultrasound may enhance the investigation of the pharmacokinetics of I-131 [4, 5].

AIM: The study aimed to develop a method for quantitatively characterizing ultrasound images of thyroid nodular masses for predicting malignancy and I-131 accumulation by nodular masses.

MATERIALS AND METHODS: The study included 125 nodules with pathomorphologic findings (65 benign, 60 malignant) and 25 benign nodules (established by cytologic examination) of patients who underwent radioiodotherapy as part of the Russian Science Foundation grant project No. 22-15-00135. Longitudinal and transverse projections of thyroid nodules were obtained using GE Voluson E8 (36% of all benign nodules and 27% of malignant nodules) and GE Logiq E (64% of benign and 73% of malignant nodules). A pharmacokinetics study was conducted on 25 nodes obtained on a GE Logiq V2 device. The accumulation index of I-131 was determined after 24 hours. A spatial adjacency matrix, gray level line length matrix, gray level zone size matrix, and histogram were employed to investigate features based on ultrasound images.

RESULTS: The malignancy prediction model, developed on the basis of the most significant features and after KNN correlation analysis, exhibited a diagnostic accuracy value of 72±3%, a sensitivity of 73±5%, and a specificity of 73±5%. An investigation of I-131 pharmacokinetics revealed that the maximum histogram intensity gradient (r=–0.48, p=0.08) and intensity entropy (r=–0.51, p=0.06) exhibited the highest Spearman correlation coefficient modulus with I-131 accumulation after 24 hours.

CONCLUSIONS: The present study demonstrates the feasibility of using quantitative characterization of ultrasound images of nodal masses as a tool to monitor nodules before radioiodotherapy. This is with a view to subsequent adjunctive fine-needle aspiration puncture biopsy and prediction of I-131 accumulation after 24 hours.

全文:

BACKGROUND: Thyroid nodules are a prevalent issue, with an estimated incidence of 19% to 35% based on ultrasound examination and 8% to 65% based on autopsy findings [1]. In some cases, Plummer’s disease is observed, and nodular masses may be observed in 10% to 35% of Graves’ disease cases, with iodine accumulation of a different nature [2, 3]. One of the principal treatments for Graves’ and Plummer’s diseases is radioiodine therapy, which serves to exclude the possibility of malignancy in nodules. Furthermore, the pharmacokinetics of iodine is investigated, which represents the most time-consuming and labor-intensive stage of preparation for radioiodine therapy. In clinical practice, ultrasound is performed in accordance with the TI-RADS system, followed (if necessary) by fine-needle aspiration puncture biopsy, stratified according to the Bethesda system. However, the interpretation of ultrasound examinations is inherently subjective, whereas the use of decision support systems can reduce the number of fine-needle aspiration puncture biopsies by 27% and the number of missed malignant neoplasms by 1.9%. Furthermore, the quantitative characterization of nodal ultrasound may enhance the investigation of the pharmacokinetics of I-131 [4, 5].

AIM: The study aimed to develop a method for quantitatively characterizing ultrasound images of thyroid nodular masses for predicting malignancy and I-131 accumulation by nodular masses.

MATERIALS AND METHODS: The study included 125 nodules with pathomorphologic findings (65 benign, 60 malignant) and 25 benign nodules (established by cytologic examination) of patients who underwent radioiodotherapy as part of the Russian Science Foundation grant project No. 22-15-00135. Longitudinal and transverse projections of thyroid nodules were obtained using GE Voluson E8 (36% of all benign nodules and 27% of malignant nodules) and GE Logiq E (64% of benign and 73% of malignant nodules). A pharmacokinetics study was conducted on 25 nodes obtained on a GE Logiq V2 device. The accumulation index of I-131 was determined after 24 hours. A spatial adjacency matrix, gray level line length matrix, gray level zone size matrix, and histogram were employed to investigate features based on ultrasound images.

RESULTS: The malignancy prediction model, developed on the basis of the most significant features and after KNN correlation analysis, exhibited a diagnostic accuracy value of 72±3%, a sensitivity of 73±5%, and a specificity of 73±5%. An investigation of I-131 pharmacokinetics revealed that the maximum histogram intensity gradient (r=–0.48, p=0.08) and intensity entropy (r=–0.51, p=0.06) exhibited the highest Spearman correlation coefficient modulus with I-131 accumulation after 24 hours.

CONCLUSIONS: The present study demonstrates the feasibility of using quantitative characterization of ultrasound images of nodal masses as a tool to monitor nodules before radioiodotherapy. This is with a view to subsequent adjunctive fine-needle aspiration puncture biopsy and prediction of I-131 accumulation after 24 hours.

×

作者简介

Almaz Manaev

Endocrinology Research Centre; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)

Email: a.manaew2016@yandex.ru
ORCID iD: 0009-0003-8035-676X
SPIN 代码: 2902-9767
俄罗斯联邦, Moscow; Moscow

Alexey A. Trukhin

Endocrinology Research Centre; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)

Email: alexey.trukhin12@gmail.com
ORCID iD: 0000-0001-5592-4727
SPIN 代码: 4398-9536

Cand. Sci. (Engin.)

俄罗斯联邦, Moscow; Moscow

Svetlana Zakharova

Endocrinology Research Centre; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)

Email: smzakharova@mail.ru
ORCID iD: 0000-0001-6059-2827
SPIN 代码: 9441-4035

MD, Cand. Sci. (Med.)

俄罗斯联邦, Moscow; Moscow

Marina Sheremeta

Endocrinology Research Centre

Email: marina888@yandex.ru
ORCID iD: 0000-0003-3785-0335
SPIN 代码: 7845-2194

MD, Cand. Sci. (Med.)

俄罗斯联邦, Moscow

Ekaterina Troshina

Endocrinology Research Centre; National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)

编辑信件的主要联系方式.
Email: troshina@inbox.ru
ORCID iD: 0000-0002-8520-8702
SPIN 代码: 8821-8990
俄罗斯联邦, Moscow; Moscow

参考

  1. Dean DS, Gharib H. Epidemiology of thyroid nodules. Best Pract Res Clin Endocrinol Metab. 2008;22(6):901–911. doi: 10.1016/j.beem.2008.09.019
  2. Carnell NE, Valente WA. Thyroid nodules in graves’ disease: Classification, characterization, and response to treatment. Thyroid. 1998;8(7):571–576. doi: 10.1089/thy.1998.8.571
  3. Kim WB, Han SM, Kim TY, et al. Ultrasonographic screening for detection of thyroid cancer in patients with Graves’ disease.Clinical endocrinology. 2004;60(6):719–725. doi: 10.1111/j.1365-2265.2004.02043.x
  4. Kim HG, Kwak JY, Kim EK, et al. Man to man training: Can it help improve the diagnostic performances and interobserver variabilities of thyroid ultrasonography in residents? European Journal of Radiology. 2012;81(3):e352–e356. doi: 10.1016/j.ejrad.2011.11.011
  5. Peng S, Liu Y, Lv W, et al. Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: A multicentre diagnostic study. The Lancet Digital Health. 2021;3(4):e250–e259. doi: 10.1016/S2589-7500(21)00041-8

补充文件

附件文件
动作
1. JATS XML
下载

版权所有 © Eco-Vector, 2024

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。
СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77 - 79539 от 09 ноября 2020 г.


##common.cookie##