AIMC Topic: Thyroid Neoplasms

Clear Filters Showing 161 to 170 of 304 articles

A deep learning-based algorithm for tall cell detection in papillary thyroid carcinoma.

PloS one
INTRODUCTION: According to the World Health Organization, the tall cell variant (TCV) is an aggressive subtype of papillary thyroid carcinoma (PTC) comprising at least 30% epithelial cells two to three times as tall as they are wide. In practice, app...

Simulating the restoration of normal gene expression from different thyroid cancer stages using deep learning.

BMC cancer
BACKGROUND: Thyroid cancer (THCA) is the most common endocrine malignancy and incidence is increasing. There is an urgent need to better understand the molecular differences between THCA tumors at different pathologic stages so appropriate diagnostic...

Classification of Thyroid Nodules by Using Deep Learning Radiomics Based on Ultrasound Dynamic Video.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: We aimed to design a radiomics model for differential diagnosis of thyroid carcinoma based on dynamic ultrasound video, and compare its diagnostic performance with that of radiomics model based on static ultrasound images.

Postsurgical complications after robot-assisted transaxillary thyroidectomy: critical analysis of a large cohort of European patients.

Updates in surgery
In the last decade, robot-assisted trans-axillary thyroidectomy has spread rapidly and has been proven to be a safe and effective procedure. However, several case series have reported new complications that have led to criticism regarding this approa...

Objective quantification of nerves in immunohistochemistry specimens of thyroid cancer utilising deep learning.

PLoS computational biology
Accurate quantification of nerves in cancer specimens is important to understand cancer behaviour. Typically, nerves are manually detected and counted in digitised images of thin tissue sections from excised tumours using immunohistochemistry. Howeve...

Multi-channel convolutional neural network architectures for thyroid cancer detection.

PloS one
Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations du...

A deep learning-based method for detecting and classifying the ultrasound images of suspicious thyroid nodules.

Medical physics
PURPOSE: The incidence of thyroid cancer has significantly increased in the last few decades. However, diagnosis of the thyroid nodules is labor and time intensive for radiologists and strongly depends on the personal experience of the radiologists. ...

Single-port transaxillary robotic thyroidectomy (START): 200-cases with two-step retraction method.

Surgical endoscopy
BACKGROUND: This study aims to report the results of a pioneering clinical study using the single-port transaxillary robotic thyroidectomy (START) for 200 patients with thyroid tumor and to introduce our novel two-step retraction method.

Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network.

Scientific reports
To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined s...

Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis.

PloS one
When approaching thyroid gland tumor classification, the differentiation between samples with and without "papillary thyroid carcinoma-like" nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing ...