AIMC Topic: Thyroid Neoplasms
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Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer.
Scientific reports
Jul 16, 2024
Fluorescence polarization (Fpol) imaging of methylene blue (MB) is a promising quantitative approach to thyroid cancer detection. Clinical translation of MB Fpol technology requires reduction of the data analysis time that can be achieved via deep le...
Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning.
BMC cancer
Jul 7, 2024
BACKGROUND AND AIMS: The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasound radiomics signatur...
Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis through bioinformatics analysis and machine learning.
Scientific reports
Jul 6, 2024
There is a growing body of evidence suggesting that Hashimoto's thyroiditis (HT) may contribute to an increased risk of papillary thyroid carcinoma (PTC). However, the exact relationship between HT and PTC is still not fully understood. The objective...
Dual-source dual-energy CT and deep learning for equivocal lymph nodes on CT images for thyroid cancer.
European radiology
Jun 21, 2024
OBJECTIVES: This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes (LNs) on CT images in thyroid cancer patients.
Smart scanning: automatic detection of superficially located lymph nodes using ultrasound - initial results.
RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
Jun 17, 2024
Over the last few years, there has been an increasing focus on integrating artificial intelligence (AI) into existing imaging systems. This also applies to ultrasound. There are already applications for thyroid and breast lesions that enable AI-assis...
Deep learning models for thyroid nodules diagnosis of fine-needle aspiration biopsy: a retrospective, prospective, multicentre study in China.
The Lancet. Digital health
Jun 6, 2024
BACKGROUND: Accurately distinguishing between malignant and benign thyroid nodules through fine-needle aspiration cytopathology is crucial for appropriate therapeutic intervention. However, cytopathologic diagnosis is time consuming and hindered by t...
Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.
Sensors (Basel, Switzerland)
May 17, 2024
In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on h...
Prediction of TNFRSF9 expression and molecular pathological features in thyroid cancer using machine learning to construct Pathomics models.
Endocrine
May 16, 2024
BACKGROUND: The TNFRSF9 molecule is pivotal in thyroid carcinoma (THCA) development. This study utilizes Pathomics techniques to predict TNFRSF9 expression in THCA tissue and explore its molecular mechanisms.
A fully autonomous robotic ultrasound system for thyroid scanning.
Nature communications
May 11, 2024
The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasou...
Machine learning algorithms for identifying contralateral central lymph node metastasis in unilateral cN0 papillary thyroid cancer.
Frontiers in endocrinology
May 10, 2024
PURPOSE: The incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papillary thyroid cancer whether to dissect contralateral central lymph node is still under debating. Here,...
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