The diagnostic utility of CT attenuation values in detecting calcification within costal cartilage.

Background: To establish and verify diagnostic criteria for the identification of costal cartilage calcification based on computed tomography (CT) attenuation value.

Methods: 360 chest CT slices of 120 patients were reviewed and annotated retrospectively and receiver operating characteristic curve was used to evaluate the diagnostic ability of CT attenuation value. Another 20 slices containing calcification were randomly selected and annotated by 4 doctors for further validation.

Prediction of microvascular invasion in hepatocellular carcinoma based on preoperative Gd-EOB-DTPA-enhanced MRI: Comparison of predictive performance among 2D, 2D-expansion and 3D deep learning models.

Purpose: To evaluate and compare the predictive performance of different deep learning models using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma.

Methods: The data of 233 patients with pathologically confirmed hepatocellular carcinoma (HCC) treated at our hospital from June 2016 to June 2021 were retrospectively analyzed. Three deep learning models were constructed based on three different delineate methods of the region of interest (ROI) using the Darwin Scientific Research Platform (Beijing Yizhun Intelligent Technology Co.

A Deep Learning System to Predict the Histopathological Results From Urine Cytopathological Images.

Background: Although deep learning systems (DLSs) have been developed to diagnose urine cytology, more evidence is required to prove if such systems can predict histopathology results as well.

Methods: We retrospectively retrieved urine cytology slides and matched histological results. High-power field panel images were annotated by a certified urological pathologist.

An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions.

Objectives: To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images.

Methods: This retrospective study collected 965 pure NME lesions (539 benign and 426 malignant) confirmed by histopathology or follow-up in 903 women. The 754 NME lesions acquired by one MR scanner were randomly split into the training set, validation set, and test set A (482/121/151 lesions).

Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists.

Background: Differentiating benign from malignant renal tumors is important for selection of the most effective treatment.

Purpose: To develop magnetic resonance imaging (MRI)-based deep learning (DL) models for differentiation of benign and malignant renal tumors and to compare their discrimination performance with the performance of radiomics models and assessment by radiologists.

Study Type: Retrospective.

A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images.

Introduction: We designed 5 convolutional neural network (CNN) models and ensemble models to differentiate malignant and benign thyroid nodules on CT, and compared the diagnostic performance of CNN models with that of radiologists.

Material And Methods: We retrospectively included CT images of 880 patients with 986 thyroid nodules confirmed by surgical pathology between July 2017 and December 2019. Two radiologists retrospectively diagnosed benign and malignant thyroid nodules on CT images in a test set.