AIMC Topic: Thyroid Neoplasms
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Optimizing thyroid AUS nodules malignancy prediction: a comprehensive study of logistic regression and machine learning models.
Frontiers in endocrinology
Nov 6, 2024
BACKGROUND: The accurate diagnosis of thyroid nodules with indeterminate cytology, particularly in the atypia of undetermined significance (AUS) category, remains challenging. This study aims to predict the risk of malignancy in AUS nodules by compar...
Classifying driver mutations of papillary thyroid carcinoma on whole slide image: an automated workflow applying deep convolutional neural network.
Frontiers in endocrinology
Nov 6, 2024
BACKGROUND: Informative biomarkers play a vital role in guiding clinical decisions regarding management of cancers. We have previously demonstrated the potential of a deep convolutional neural network (CNN) for predicting cancer driver gene mutations...
Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.
Thyroid : official journal of the American Thyroid Association
Oct 15, 2024
Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently artificial intelligence (AI) algorithms has improved nodule classification. The purpose of this stud...
Avoidable biopsies? Validating artificial intelligence-based decision support software in indeterminate thyroid nodules.
Surgery
Oct 12, 2024
BACKGROUND: Multiple artificial intelligence (AI) systems have been approved to risk-stratify thyroid nodules through sonographic characterization. We sought to validate the ability of one such AI system, Koios DS (Koios Medical, Chicago, IL), to aid...
Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.
Endocrine
Oct 7, 2024
PURPOSE: Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and malignant cases. This study aimed to investigate the diagnostic performance of different strategies, ...
Artificial intelligence-based pathological application to predict regional lymph node metastasis in Papillary Thyroid Cancer.
Current problems in cancer
Sep 28, 2024
In this study, a model for predicting lymph node metastasis in papillary thyroid cancer was trained using pathology images from the TCGA(The Cancer Genome Atlas) public dataset of papillary thyroid cancer, and a front-end inference model was trained ...
Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer.
Scientific reports
Sep 27, 2024
Prophylactic dissection of paratracheal lymph nodes in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC) remains controversial. This study aims to integrate preoperative and intraoperative variables to compare traditional nomogra...
Predicting overall survival in anaplastic thyroid cancer using machine learning approaches.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Sep 21, 2024
PURPOSE: Anaplastic thyroid carcinoma (ATC) is a highly aggressive and lethal thyroid cancer subtype with a poor prognosis. Recent advancements in machine learning (ML) have the potential to improve survival predictions. This study aimed to develop a...
Development of machine learning models to predict papillary carcinoma in thyroid nodules: The role of immunological, radiologic, cytologic and radiomic features.
Auris, nasus, larynx
Sep 20, 2024
OBJECTIVE: Approximately 30 % of thyroid nodules yield an indeterminate diagnosis through conventional diagnostic strategies. The aim of this study was to develop machine learning (ML) models capable of identifying papillary thyroid carcinomas using ...
The impact of deep learning on diagnostic performance in the differentiation of benign and malignant thyroid nodules.
Medical ultrasonography
Sep 4, 2024
AIMS: This study aims to use deep learning (DL) to classify thyroid nodules as benign and malignant with ultrasonography (US). In addition, this study investigates the impact of DL on the diagnostic success of radiologists with different experiences....
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