Predicting axillary response to neoadjuvant chemotherapy using peritumoral and intratumoral ultrasound radiomics in breast cancer subtypes.

To explore machine learning (ML)-based breast tumor peritumoral (P) and intratumoral ultrasound radiomics signatures (IURS) for predicting axillary response to neoadjuvant chemotherapy (NAC) in patients with breast cancer (BC) with node-positive. A total of 435 patients were divided into hormone receptor (HR)+/human epidermal growth factor receptor (HER)2-, HER2+, and triple-negative (TN) subtypes. ML classifiers including random forest (RF), support vector machine (SVM), and linear discriminant analysis (LDA) were applied to construct PURS, IURS, and the combined P-IURS radiomics models.

Predicting central lymph node metastasis in papillary thyroid cancer: A nomogram based on clinical, ultrasound and contrast‑enhanced computed tomography characteristics.

Central lymph node (CLN) status is considered to be an important risk factor in patients with papillary thyroid carcinoma (PTC). The aim of the present study was to identify risk factors associated with CLN metastasis (CLNM) for patients with PTC based on preoperative clinical, ultrasound (US) and contrast-enhanced computed tomography (CT) characteristics, and establish a prediction model for treatment plans. A total of 786 patients with a confirmed pathological diagnosis of PTC between January 2021 to December 2022 were included in the present retrospective study, with 550 patients included in the training group and 236 patients enrolled in the validation group (ratio of 7:3).

Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early‑stage invasive breast cancer.

Axillary lymph node (ALN) status is a key prognostic factor in patients with early-stage invasive breast cancer (IBC). The present study aimed to develop and validate a nomogram based on multimodal ultrasonographic (MMUS) features for early prediction of axillary lymph node metastasis (ALNM). A total of 342 patients with early-stage IBC (240 in the training cohort and 102 in the validation cohort) who underwent preoperative conventional ultrasound (US), strain elastography, shear wave elastography and contrast-enhanced US examination were included between August 2021 and March 2022.

Machine Learning-Based Breast Tumor Ultrasound Radiomics for Pre-operative Prediction of Axillary Sentinel Lymph Node Metastasis Burden in Early-Stage Invasive Breast Cancer.

Objective: The aim of the work described here was to assess the application of ultrasound (US) radiomics with machine learning (ML) classifiers to the prediction of axillary sentinel lymph node metastasis (SLNM) burden in early-stage invasive breast cancer (IBC).

Methods: In this study, 278 early-stage IBC patients with at least one SLNM (195 in the training set and 83 in the test set) were studied at our institution. Pathologic SLNM burden was used as the reference standard.

Association of sonographic features and molecular subtypes in predicting breast cancer disease outcomes.

Background: Features in preoperative ultrasound could predict the prognosis of triple-negative breast cancer (TNBC), while its prognostic value in other molecular subtypes of breast cancer (BC) was unknown. The study aimed to assess the prognostic value of preoperative sonographic features, including orientations, on long-term outcomes in BC and its association with different molecular subtypes.

Methods: Women diagnosed with invasive BC > 5 mm who underwent surgery were retrospectively reviewed.

A computer-aided diagnosing system in the evaluation of thyroid nodules-experience in a specialized thyroid center.

Background: The evaluation of thyroid nodules with ultrasonography has created a large burden for radiologists. Artificial intelligence technology has been rapidly developed in recent years to reduce the cost of labor and improve the differentiation of thyroid malignancies. This study aimed to investigate the diagnostic performance of a novel computer-aided diagnosing system (CADs: S-detect) for the ultrasound (US) interpretation of thyroid nodule subtypes in a specialized thyroid center.

Clinical characteristics and ultrasonographic features for predicting central lymph node metastasis in clinically node-negative papillary thyroid carcinoma without capsule invasion.

Background: To evaluate the association of preoperative clinical and sonographic features with central lymph node metastasis (CLNM) in patients with clinically node-negative (cN0) papillary thyroid carcinoma (PTC) without capsule invasion.

Methods: Clinical and sonographic features of 635 cN0 PTC nodules without capsule invasion were retrospectively reviewed. CLNM was confirmed by pathology.

Size and Ultrasound Features Affecting Results of Ultrasound-Guided Fine-Needle Aspiration of Thyroid Nodules.

Objectives: Our goal was to assess the diagnostic efficacy of ultrasound (US)-guided fine-needle aspiration (FNA) of thyroid nodules according to size and US features.

Methods: A retrospective correlation was made with 1745 whole thyroidectomy and hemithyroidectomy specimens with preoperative US-guided FNA results. All cases were divided into 5 groups according to nodule size (≤5, 5.

Sonographic Features of Ductal Carcinoma In Situ of the Breast With Microinvasion: Correlation With Clinicopathologic Findings and Biomarkers.

Objectives: To compare the sonographic results, clinicopathologic characteristics, and biomarkers in pure ductal carcinoma in situ (DCIS) of the breast and DCIS with microinvasion.

Methods: A total of 218 patients with pathologically proven DCIS based on sonography in our hospital (2009-2013) were retrospectively enrolled. Clinicopathologic characteristics and biomarkers were examined.

Sonographic findings of localized Castleman disease of the abdomen and pelvis.

Purpose: The purpose of this study was to sonographically evaluate the diagnosis of localized Castleman disease in the abdomen and pelvis.

Methods: This was a retrospective analysis of 18 cases of Castleman disease localized in the abdomen and pelvis. The following features of the lesions were assessed on sonography (US): location, size, margin, echogenicity, echotexture, intralesional cystic necrosis, intralesional calcification, posterior acoustic enhancement, and blood supply.

Comparison of fine-needle aspiration and fine-needle capillary sampling of thyroid nodules: a prospective study with emphasis on the influence of nodule size.

Background: The objective of this study was to compare the sampling efficiency of ultrasound-guided fine-needle aspiration (FNA) and fine-needle capillary (FNC) sampling in thyroid nodules, in which the authors specifically analyzed the influence of nodule size.

Methods: This study included 280 thyroid nodules in 275 consecutive patients. The nodules were divided into 4 size subgroups: ≤5.