Follicular neoplasms: The role for observation, fine needle aspiration biopsy, thyroid suppression, and surgery

Louis, James D. St.; Leight, George S.; Tyler, Douglas S.

doi:10.1002/(sici)1098-2388(199901/02)16:1<5::aid-ssu2>3.0.co;2-u

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…With its overwhelming success, thyroid FNA has completely supplanted all other previously used diagnostic techniques described above 8. Thyroid FNA is a safe1, 8, 21 and relatively simple8 test that is widely recognized as the critical, primary diagnostic procedure of first choice for the evaluation of patients presenting with thyroid nodules 1, 8, 17, 22, 23…”

mentioning

confidence: 99%

Fine-needle aspiration cytology of the thyroid

Cramer

2000

Cancer

100

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mentioning

confidence: 99%

Fine-needle aspiration cytology of the thyroid

Cramer

2000

Cancer

100

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“…7,8 Since follicular thyroid neoplasms cannot be distinguished on cytology, final diagnosis can only be made after surgical excision with microscopic examination for capsular or vascular invasion. 9,10 An estimated 80-90% of follicular thyroid neoplasms are benign, resulting in a significant number of surgeries performed for benign disease. 11…”

Section: Introductionmentioning

confidence: 99%

“…Since follicular thyroid neoplasms cannot be distinguished on cytology, final diagnosis can only be made after surgical excision with microscopic examination for capsular or vascular invasion [9,10].…”

Section: Introductionmentioning

confidence: 99%

A proof-of-concept investigation into predicting follicular carcinoma on ultrasound using topological data analysis and radiomics

Thomas,

Lin,

Deng

et al. 2023

Preprint

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Background: Multiple ultrasound (US) risk stratifications systems (RSS) have been developed to estimate malignancy risk in thyroid nodules and recommend the need for fine needle aspiration biopsy. However, sonographic risk patterns identified in established RSSs may not accurately stratify follicular carcinoma from adenoma, which share many similar US characteristics. Quantitative medical imaging analysis aims to extract high-dimensional textural features from tumor phenotypes imperceptible to the human eye. The purpose of this study is to investigate the performance of a multimodal machine learning model utilizing radiomics and topological data analysis (TDA) to predict malignancy in follicular thyroid neoplasms on ultrasonography. Methods: This is a retrospective study of patients who underwent thyroidectomy with pathology confirmed follicular adenoma or carcinoma at a single academic medical center between 2010-2022. The nodule of interest on pre-operative ultrasound was annotated and masked to only include pixels. Images were scaled to maintain aspect ratio and ensure similar image resolution. Features derived from radiomics and TDA were calculated. High-dimensional radiomics and TDA features were projected onto their first two principal components respectively. Logistic regression with L2 penalty was used to predict malignancy. Classifier performance was evaluated using leave-one-out cross-validation and area under the curve (AUC). Results: Patients with follicular adenomas (n=7) and follicular carcinomas (n=11) with available imaging were included. 910 radiomics features were extracted for each image. 180 topological features from the height filtration, mean and variance of pixel intensities, the aspect ratio of images and two additional persistence statistics were derived from each image. The models achieved an AUC of 0.68 (radiomics only) and 0.88 (radiomics and TDA). The subsampling bootstrap was used to assess the confidence of these estimates. Conclusions: We demonstrate that inclusion of topological features yields strong improvement over radiomics-based features alone in the prediction of follicular carcinoma on ultrasound. Despite low volume data, the TDA features of the height filtration with its connection to the persistent homology transform (PHT), explicitly capture shape information that likely augments performance of the multimodal machine learning model. This approach suggests that a quantitative based US RSS may contribute to the preoperative prediction of follicular carcinoma.

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“…The differential diagnosis between these two entities is made by identifying the presence of capsular, vascular or extrathyroidal tissue invasion, and nodal or distant metastasis [1]. Thus, in cases without overt extrathyroidal tissue invasion or nodal/distant metastasis on the preoperative examination, the differential diagnosis is made by a pathologic examination after surgical excision [2]. The prevalence of follicular adenoma in patients initially diagnosed with follicular neoplasm is roughly 80%, meaning that a majority of patients undergo diagnostic thyroid lobectomy despite having a benign condition [3,4].…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland

et al. 2020

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Purpose: This study was conducted to evaluate the diagnostic performance of machine learning in differentiating follicular adenoma from carcinoma using preoperative ultrasonography (US). Methods: In this retrospective study, preoperative US images of 348 nodules from 340 patients were collected from two tertiary referral hospitals. Two experienced radiologists independently reviewed each image and categorized the nodules according to the 2015 American Thyroid Association guideline. Categorization of a nodule as highly suspicious was considered a positive diagnosis for malignancy. The nodules were manually segmented, and 96 radiomic features were extracted from each region of interest. Ten significant features were selected and used as final input variables in our in-house developed classifier models based on an artificial neural network (ANN) and support vector machine (SVM). The diagnostic performance of radiologists and both classifier models was calculated and compared. Results: In total, 252 nodules from 245 patients were confirmed as follicular adenoma and 96 nodules from 95 patients were diagnosed as follicular carcinoma. As measures of diagnostic performance, the average sensitivity, specificity, and accuracy of the two experienced radiologists in discriminating follicular adenoma from carcinoma on preoperative US images were 24.0%, 84.0%, and 64.8%, respectively. The sensitivity, specificity, and accuracy of the ANN and SVMbased models were 32.3%, 90.1%, and 74.1% and 41.7%, 79.4%, and 69.0%, respectively. The kappa value of the two radiologists was 0.076, corresponding to slight agreement. Conclusion: Machine learning-based classifier models may aid in discriminating follicular adenoma from carcinoma using preoperative US.

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Follicular neoplasms: The role for observation, fine needle aspiration biopsy, thyroid suppression, and surgery

Cited by 9 publications

References 25 publications

Fine-needle aspiration cytology of the thyroid

Fine-needle aspiration cytology of the thyroid

A proof-of-concept investigation into predicting follicular carcinoma on ultrasound using topological data analysis and radiomics

Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland

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