A Prediction Model for Detecting Dysthyroid Optic Neuropathy Based on Clinical Factors and Imaging Markers of the Optic Nerve and Cerebrospinal Fluid in the Optic Nerve Sheath.

Objective: This study aimed to develop and test a model for predicting dysthyroid optic neuropathy (DON) based on clinical factors and imaging markers of the optic nerve and cerebrospinal fluid (CSF) in the optic nerve sheath.

Methods: This retrospective study included patients with thyroid-associated ophthalmopathy (TAO) without DON and patients with TAO accompanied by DON at our hospital. The imaging markers of the optic nerve and CSF in the optic nerve sheath were measured on the water-fat images of each patient and, together with clinical factors, were screened by Least absolute shrinkage and selection operator.

A Radiomics-Based Nomogram Using Ultrasound Carotid Plaque Evaluation For Predicting Cerebro-Cardiovascular Events In Asymptomatic Patients.

Rationale And Objectives: This study aims to assess whether a radiomics-based nomogram correlates with a higher risk of future cerebro-cardiovascular events in patients with asymptomatic carotid plaques. Additionally, it investigates the nomogram's contribution to the revised Framingham Stroke Risk Profile (rFSRP) for predicting cerebro-cardiovascular risk.

Materials And Methods: Predictive models aimed at identifying an increased risk of future cerebro-cardiovascular events were developed and internally validated at one center, then externally validated at two other centers.

A Comparative Study of Two Radiomics-Based Blood Flow Modes with Thyroid Imaging Reporting and Data System in Predicting Malignancy of Thyroid Nodules and Reducing Unnecessary Fine-Needle Aspiration Rate.

Rationale And Objectives: We aimed to compare superb microvascular imaging (SMI)-based radiomics methods, and contrast-enhanced ultrasound (CEUS)-based radiomics methods to the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for classifying thyroid nodules (TNs) and reducing unnecessary fine-needle aspiration biopsy (FNAB) rate.

Materials And Methods: This retrospective study enrolled a dataset of 472 pathologically confirmed TNs. Radiomics characteristics were extracted from B-mode ultrasound (BMUS), SMI, and CEUS images, respectively.

Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature.

Background: Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning.

Purpose: To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy.

Material And Methods: A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs).

Dual-modal radiomics nomogram based on contrast-enhanced ultrasound to improve differential diagnostic accuracy and reduce unnecessary biopsy rate in ACR TI-RADS 4-5 thyroid nodules.

Background: American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS, TR) 4 and 5 thyroid nodules (TNs) demonstrate much more complicated and overlapping risk characteristics than TR1-3 and have a rather wide range of malignancy possibilities (> 5%), which may cause overdiagnosis or misdiagnosis. This study was designed to establish and validate a dual-modal ultrasound (US) radiomics nomogram integrating B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) imaging to improve differential diagnostic accuracy and reduce unnecessary fine needle aspiration biopsy (FNAB) rates in TR 4-5 TNs.

Methods: A retrospective dataset of 312 pathologically confirmed TR4-5 TNs from 269 patients was collected for our study.

Deep learning predicts cervical lymph node metastasis in clinically node-negative papillary thyroid carcinoma.

Objectives: Precise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes.

Methods: Preoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set.

Ultrasound radiomics based on axillary lymph nodes images for predicting lymph node metastasis in breast cancer.

Objectives: To determine whether ultrasound radiomics can be used to distinguish axillary lymph nodes (ALN) metastases in breast cancer based on ALN imaging.

Methods: A total of 147 breast cancer patients with 41 non-metastatic lymph nodes and 109 metastatic lymph nodes were divided into a training set (105 ALN) and a validation set (45 ALN). Radiomics features were extracted from ultrasound images and a radiomics signature (RS) was built.

Predicting Malignancy of Thyroid Micronodules: Radiomics Analysis Based on Two Types of Ultrasound Elastography Images.

Rationale And Objectives: To develop a multimodal ultrasound radiomics nomogram for accurate classification of thyroid micronodules.

Materials And Methods: A retrospective study including 181 thyroid micronodules within 179 patients was conducted. Radiomics features were extracted from strain elastography (SE), shear wave elastography (SWE) and B-mode ultrasound (BMUS) images.

Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAF Mutations in Papillary Thyroid Carcinoma.

Unlabelled: BRAF is the most common mutated gene in thyroid cancer and is most closely related to papillary thyroid carcinoma(PTC). We investigated the value of elasticity and grayscale ultrasonography for predicting BRAF mutations in PTC.

Methods: 138 patients with PTC who underwent preoperative ultrasound between January 2014 and 2021 were retrospectively examined.

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer.

Objectives: To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer.

Methods: Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images.

Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound.

Purpose: This study aimed to develop a radiomics nomogram based on contrast-enhanced ultrasound (CEUS) for preoperatively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.

Methods: A retrospective dataset of 313 HCC patients who underwent CEUS between September 20, 2016 and March 20, 2020 was enrolled in our study. The study population was randomly grouped as a primary dataset of 192 patients and a validation dataset of 121 patients.

The Added Value of a Computer-Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience.

Objectives: To evaluate the value of the computer-aided diagnosis system, S-Detect (based on deep learning algorithm), in distinguishing benign and malignant breast masses and reducing unnecessary biopsy based on the experience of radiologists.

Methods: From February 2018 to March 2019, 266 breast masses in 192 women were included in our study. Ultrasound (US) examination, including S-Detect technique, was performed by the radiologist with about 10 years of clinical experience in breast US imaging.

Artificial Intelligence in Medical Imaging of the Breast.

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women's physical and mental health. Early screening for breast cancer mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients.

Management of breast lesions seen on US images: dual-model radiomics including shear-wave elastography may match performance of expert radiologists.

Purpose: To develop a nomogram incorporating B-mode ultrasound (BMUS) and shear-wave elastography (SWE) radiomics to predict malignant status of breast lesions seen on US non-invasively.

Methods: Data on 278 consecutive patients from Hospital #1 (training cohort) and 123 cases from Hospital #2 (external validation cohort) referred for breast US with subsequent histopathologic analysis between May 2017 and October 2019 were retrospectively collected. Using their BMUS and SWE images, we built a radiomics nomogram to improve radiology workflow for management of breast lesions.

Deep Learning Based on ACR TI-RADS Can Improve the Differential Diagnosis of Thyroid Nodules.

Objective: The purpose of this study was to improve the differentiation between malignant and benign thyroid nodules using deep learning (DL) in category 4 and 5 based on the Thyroid Imaging Reporting and Data System (TI-RADS, TR) from the American College of Radiology (ACR).

Design And Methods: From June 2, 2017 to April 23, 2019, 2082 thyroid ultrasound images from 1396 consecutive patients with confirmed pathology were retrospectively collected, of which 1289 nodules were category 4 (TR4) and 793 nodules were category 5 (TR5). Ninety percent of the B-mode ultrasound images were applied for training and validation, and the residual 10% and an independent external dataset for testing purpose by three different deep learning algorithms.

Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer.

Purpose: The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) based on the pre- and post-treatment ultrasound.

Methods: Patients with locally advanced breast cancer (LABC) proved by biopsy who proceeded to undergo preoperative NAC were enrolled from hospital #1 (training cohort, 356 cases) and hospital #2 (independent external validation cohort, 236 cases). Deep learning and handcrafted radiomic features reflecting the phenotypes of the pre-treatment (radiomic signature [RS] 1) and post-treatment tumour (RS2) were extracted.

Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study.

Objectives: To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes.

Methods: A dataset of 4828 US images from 1275 patients with primary breast cancer were used as the training samples. DCNN models were constructed primarily to predict the four St.

Study Processes and Applications of Ultrasomics in Precision Medicine.

Ultrasomics is the science of transforming digitally encrypted medical ultrasound images that hold information related to tumor pathophysiology into mineable high-dimensional data. Ultrasomics data have the potential to uncover disease characteristics that are not found with the naked eye. The task of ultrasomics is to quantify the state of diseases using distinctive imaging algorithms and thereby provide valuable information for personalized medicine.

The value of S-Detect in improving the diagnostic performance of radiologists for the differential diagnosis of thyroid nodules.

Aims: To compare the diagnostic value of S-Detect (a computer aided diagnosis system using deep learning) in differentiating thyroid nodules in radiologists with different experience and to assess if S-Detect can improve the diagnostic performance of radiologists.

Materials And Methods: Between February 2018 and October 2019, 204 thyroid nodules in 181 patients were included. An experienced radiologist performed ultrasound for thyroid nodules and obtained the result of S-Detect.

Nomogram for predicting transmural bowel infarction in patients with acute superior mesenteric venous thrombosis.

Background: The prognosis of acute mesenteric ischemia (AMI) caused by superior mesenteric venous thrombosis (SMVT) remains undetermined and early detection of transmural bowel infarction (TBI) is crucial. The predisposition to develop TBI is of clinical concern, which can lead to fatal sepsis with hemodynamic instability and multi-organ failure. Early resection of necrotic bowel could improve the prognosis of AMI, however, accurate prediction of TBI remains a challenge for clinicians.