Differentiating between Lymphoma and Metastasis Presenting as Solid Cerebellar Mass Lacking Necrosis.

This study aimed to identify radiologic features that differentiate lymphoma from metastasis manifesting as a solid enhancing mass lacking necrosis in the cerebellum. Pathologically confirmed 24 primary central nervous system lymphoma (PCNSL) and 32 metastasis patients with solid enhancing cerebellar masses without necrotic or hemorrhagic components were retrospectively analyzed. We evaluated the imaging characteristics using contrast-enhanced magnetic resonance imaging (MRI).

Investigation and quantification of composition variability in urinary stone analysis.

Purpose: To investigate the variability in urinary stone composition analysis due to sampling and suggest potential solutions.

Materials And Methods: We collected 1,135 stone fragments from 149 instances that had undergone a stone removal at Hanoi Medical University Hospital from January 2022 to August 2022. Each fragment was ground into fine powder and divided into separate specimens if the amount was abundant.

Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.

Objectives: Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF.

Materials And Methods: Patients with renal cell carcinoma who underwent surgery between April 2019 and February 2022 at Chonnam National University Hwasun Hospital were retrospectively screened, and 119 patients included.

Accurate prediction of pure uric acid urinary stones in clinical context via a combination of radiomics and machine learning.

Purpose: Oral chemolysis is an effective and non-invasive treatment for uric acid urinary stones. This study aimed to classify urinary stones into either pure uric acid (pUA) or other composition (Others) using non-contrast-enhanced computed tomography scans (NCCTs).

Methods: Instances managed at our institution from 2019 to 2021 were screened.

Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI.

Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively reviewed patients with intracranial meningiomas from two hospitals.

Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography.

Objective: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer.

Materials And Methods: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021.

Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms.

The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strategies are different between pure DCIS without invasive component and upgraded DCIS. We demonstrated the potential of using deep learning models for differentiating between upgraded versus pure DCIS in DCIS diagnosed by core-needle biopsy. Preoperative axial dynamic contrast-enhanced magnetic resonance imaging (MRI) data from 352 lesions were used to train, validate, and test three different types of deep learning models.

A Radiomics Approach on Chest CT Distinguishes Primary Lung Cancer from Solitary Lung Metastasis in Colorectal Cancer Patients.

Purpose: This study utilized a radiomics approach combined with a machine learning algorithm to distinguish primary lung cancer (LC) from solitary lung metastasis (LM) in colorectal cancer (CRC) patients with a solitary pulmonary nodule (SPN).

Materials And Methods: In a retrospective study, 239 patients who underwent chest computerized tomography (CT) at three different institutions between 2011 and 2019 and were diagnosed as primary LC or solitary LM were included. The data from the first institution were divided into training and internal testing datasets.

Prediction of the histologic upgrade of ductal carcinoma using a combined radiomics and machine learning approach based on breast dynamic contrast-enhanced magnetic resonance imaging.

Objective: To investigate whether support vector machine (SVM) trained with radiomics features based on breast magnetic resonance imaging (MRI) could predict the upgrade of ductal carcinoma (DCIS) diagnosed by core needle biopsy (CNB) after surgical excision.

Materials And Methods: This retrospective study included a total of 349 lesions from 346 female patients (mean age, 54 years) diagnosed with DCIS by CNB between January 2011 and December 2017. Based on histological confirmation after surgery, the patients were divided into pure (n = 198, 56.

Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.

Objective: This study was conducted in order to investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer.

Materials And Methods: One hundred seventy-four MR-detected additional lesions (benign,  = 86; malignancy,  = 88) from 158 patients with ipsilateral primary breast cancer from a tertiary medical center were included in this retrospective study. The entire data were randomly split to training (80%) and independent test sets (20%).

Investigating the Feasibility of In Vivo Perfusion Imaging Methods for Spinal Cord Using Hyperpolarized [C]t-Butanol and [C,N]Urea.

Purpose: This study examined the feasibility of using two novel agents, hyperpolarized [C]t-butanol and [C,N]urea, for assessing in vivo perfusion of the intact spinal cord in rodents. Due to their distinct permeabilities to blood brain barrier (BBB), we hypothesized that [C]t-butanol and [C,N]urea exhibit unique C signal characteristics in the spinal cord.

Procedures: Dynamic C t-butanol MRI data were acquired from healthy Long-Evans rats using a symmetric, ramp-sampled, partial-Fourier C echo-planar imaging sequence after the injection of hyperpolarized [C]t-butanol solution.

In Vivo Assessment of Metabolic Abnormality in Alport Syndrome Using Hyperpolarized [1-C] Pyruvate MR Spectroscopic Imaging.

Alport Syndrome (AS) is a genetic disorder characterized by impaired kidney function. The development of a noninvasive tool for early diagnosis and monitoring of renal function during disease progression is of clinical importance. Hyperpolarized C MRI is an emerging technique that enables non-invasive, real-time measurement of in vivo metabolism.

Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study.

Background: The diagnostic performance of convolutional neural networks (CNNs) for diagnosing several types of skin neoplasms has been demonstrated as comparable with that of dermatologists using clinical photography. However, the generalizability should be demonstrated using a large-scale external dataset that includes most types of skin neoplasms. In this study, the performance of a neural network algorithm was compared with that of dermatologists in both real-world practice and experimental settings.

Automatic Assessment of ASPECTS Using Diffusion-Weighted Imaging in Acute Ischemic Stroke Using Recurrent Residual Convolutional Neural Network.

The early detection and rapid quantification of acute ischemic lesions play pivotal roles in stroke management. We developed a deep learning algorithm for the automatic binary classification of the Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) using diffusion-weighted imaging (DWI) in acute stroke patients. Three hundred and ninety DWI datasets with acute anterior circulation stroke were included.

Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders.

Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,300 images; 10 disorders) and SNU datasets (2,201 images; 134 disorders). The algorithm could accurately predict malignancy, suggest primary treatment options, render multi-class classification among 134 disorders, and improve the performance of medical professionals.

Effects of sitting posture and bolus volume on activation of swallowing-related muscles.

Background: The pharyngeal phase is a particularly important clinical factor related to swallowing dysfunctions. Head and neck posture, as well as bolus volume, are important factors affecting the pharyngeal stages of normal swallowing.

Objective: The aim of our study was to identify the effects of sitting posture and bolus volume on the activation of swallowing-related muscles.

Comparison between 8- and 32-channel phased-array receive coils for in vivo hyperpolarized C imaging of the human brain.

Purpose: To compare the performance of an 8-channel surface coil/clamshell transmitter and 32-channel head array coil/birdcage transmitter for hyperpolarized C brain metabolic imaging.

Methods: To determine the field homogeneity of the radiofrequency transmitters, B + mapping was performed on an ethylene glycol head phantom and evaluated by means of the double angle method. Using a 3D echo-planar imaging sequence, coil sensitivity and noise-only phantom data were acquired with the 8- and 32-channel receiver arrays, and compared against data from the birdcage in transceiver mode.

Metabolic Changes in Different Stages of Liver Fibrosis: In vivo Hyperpolarized C MR Spectroscopy and Metabolic Imaging.

Purpose: The objective was to assess metabolic changes in different stages of liver fibrosis using hyperpolarized C-13 magnetic resonance spectroscopy (MRS) and metabolic imaging.

Procedures: Mild and severe liver fibrosis were induced in C3H/HeN mice (n = 14) by injecting thioacetamide (TAA). Other C3H/HeN mice (n = 7) were injected with phosphate buffer saline (PBS) (7.

Impact of isolated basal ganglia infarction at pretreatment DWI on outcomes after endovascular thrombectomy in acute anterior circulation stroke.

Purpose: Acute infarction confined to the basal ganglia (BG) is occasionally observed on baseline imaging before endovascular thrombectomy. This study aimed to investigate the impact of isolated BG infarction revealed on pretreatment DWI in a large cohort of patients with acute anterior circulation stroke who underwent thrombectomy.

Methods: We retrospectively analyzed clinical and DWI data from 328 patients who underwent thrombectomy for emergent occlusions of the intracranial internal carotid artery or the middle cerebral artery.

Translation of Carbon-13 EPI for hyperpolarized MR molecular imaging of prostate and brain cancer patients.

Purpose: To develop and translate a metabolite-specific imaging sequence using a symmetric echo planar readout for clinical hyperpolarized (HP) Carbon-13 ( C) applications.

Methods: Initial data were acquired from patients with prostate cancer (N = 3) and high-grade brain tumors (N = 3) on a 3T scanner. Samples of [1- C]pyruvate were polarized for at least 2 h using a 5T SPINlab system operating at 0.

Correlation of Tumor Perfusion Between Carbon-13 Imaging with Hyperpolarized Pyruvate and Dynamic Susceptibility Contrast MRI in Pre-Clinical Model of Glioblastoma.

Purpose: The purpose of this study was to compare C-13 imaging parameters with hyperpolarized [1-C]pyruvate with conventional gadolinium (Gd)-based perfusion weighted imaging using an orthotopic xenograft model of human glioblastoma multiforme (GBM).

Procedures: C-13 3D magnetic resonance spectroscopic imaging (MRSI) data were obtained from 14 tumor-bearing rats after the injection of hyperpolarized [1-C]pyruvate at a 3T scanner. Dynamic susceptibility contrast (DSC) perfusion-weighted MR images were obtained following intravenous administration of Gd-DTPA.

Measuring Tumor Metabolism in Pediatric Diffuse Intrinsic Pontine Glioma Using Hyperpolarized Carbon-13 MR Metabolic Imaging.

Objective: The purpose of this study was to demonstrate the feasibility of using hyperpolarized carbon-13 (C) metabolic imaging with [1-C]-labeled pyruvate for evaluating real-time metabolism of orthotopic diffuse intrinsic pontine glioma (DIPG) xenografts.

Materials And Methods: 3D C magnetic resonance spectroscopic imaging (MRSI) data were acquired on a 3T scanner from 8 rats that had been implanted with human-derived DIPG cells in the brainstem and 5 healthy controls, following injection of 2.5 mL (100 mM) hyperpolarized [1-C]-pyruvate.