An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer.

Objective: To establish a combined radiomics-clinical model for the early prediction of a prostate-specific antigen(PSA) response in patients with metastatic castration-resistant prostate cancer(mCRPC) after treatment with abiraterone acetate(AA).

Methods: The data of a total of 60 mCRPC patients from two hospitals were retrospectively analyzed and randomized into a training group(n=48) or a validation group(n=12). By extracting features from biparametric MRI, including T2-weighted imaging(T2WI), diffusion-weighted imaging(DWI), and apparent diffusion coefficient(ADC) maps, radiomics features from the training dataset were selected using least absolute shrinkage and selection operator(LASSO) regression.

Evaluation of a Novel Gd-FAPI Dimer Molecular Probe Targeting Fibroblast Activation Protein for Imaging of Solid Tumors.

Cancer-associated fibroblasts (CAFs) are essential components of the tumor microenvironment. Fibroblast activation protein (FAP) is overexpressed in CAFs. FAP-targeted molecular imaging agents, including the FAP inhibitors (FAPIs), have shown promising results in tumor diagnosis.

AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection.

Background & Aims: Tumor-infiltrating lymphocytes (TILs), particularly CD8+ TILs, are key prognostic markers in many cancers. However, their prognostic value in hepatocellular carcinoma (HCC) remains controversial, with different evidence. Given the heterogeneous outcomes in patients with HCC undergoing liver resection, this study aims to develop an AI-based system to quantify CD8+ TILs and assess their prognostic value for patients with HCC.

An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks.

Mammography is the recommended imaging modality for breast cancer screening. Expressions of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) are critical to the development of therapeutic strategies for breast cancer. In this study, a deep learning model (CBAM ResNet-18) was developed to predict the expression of these three receptors on mammography without manual segmentation of masses.

Neural Activity Alterations and Their Association With Neurotransmitter and Genetic Profiles in Schizophrenia: Evidence From Clinical Patients and Unaffected Relatives.

Background: The pattern of abnormal resting-state brain function has been documented in schizophrenia (SCZ). However, as of yet, it remains unclear whether this pattern is of genetic predisposition or related to the illness itself.

Methods: A systematical meta-analysis was performed to identify resting-state functional differences in probands and their high-risk first-degree relatives of schizophrenia (FDRs-SCZ) using Seed-based d Mapping software.

Longitudinal MRI-Driven Multi-Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer.

Accurately predicting pathological complete response (pCR) to neoadjuvant treatment (NAT) in breast cancer remains challenging due to tumor heterogeneity. This study enrolled 2279 patients across 12 centers and develops a novel multi-modality model integrating longitudinal magnetic resonance imaging (MRI) spatial habitat radiomics, transcriptomics, and single-cell RNA sequencing for predicting pCR. By analyzing tumor subregions on multi-timepoint MRI, the model captures dynamic intra-tumoral heterogeneity during NAT.

Effect of potent nucleos(t)ide analog on alpha fetoprotein changes and occurrence of hepatocellular carcinoma in patients with chronic hepatitis B.

Background: Successful antiviral therapy significantly decreases the incidence of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). Alpha-fetoprotein (AFP) in the serum is a valuable early indicator of HCC. However, it is unclear whether different antiviral medications have varying effects on AFP levels.

MT2A promotes angiogenesis in chronically ischemic brains through a copper-mitochondria regulatory mechanism.

Background: Approximately half of patients with chronic ischemic cerebrovascular disease (CICD) exhibit poor revascularization. Metallothionein 2 A (MT2A) has a high affinity for metal ions and is potentially capable of chelating toxic copper ions to alleviate the impairment of angiogenesis. Therefore, we hypothesized that MT2A could promote angiogenesis in chronically ischemic brains by neutralizing excessive copper ions during copper overload (CPO).

Multi-parametric diffusion spectrum imaging in tuberous sclerosis complex: Identifying cortical tubers and predicting genotypes.

Objectives: This study employed advanced MRI diffusion imaging techniques to identify cortical tubers in Tuberous Sclerosis Complex (TSC) patients and compared the diagnostic efficacy of various diffusion model parameters in predicting TSC genotypes.

Methods: From July 2019 to April 2024, a prospective study was conducted at our Hospital. Participants meeting specific criteria underwent genetic testing and Diffusion Spectrum Imaging (DSI) data collection.

Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer.

Objectives: This study investigates the association between baseline CT body composition parameters and clinical outcomes in patients with resectable non-small cell lung cancer (NSCLC).

Methods: Patients who underwent surgical resection for NSCLC between January 2006 and December 2017 were retrospectively enrolled in this multicenter study. Body composition metrics, including the area of skeletal muscle, intermuscular adipose tissue, subcutaneous adipose tissue, visceral adipose tissue, muscle radiodensity, and derivative parameters from five basic metrics mentioned before, were calculated based on preoperative non-contrast-enhanced chest CT images at L1 level.

The impact of the novel CovBat harmonization method on enhancing radiomics feature stability and machine learning model performance: A multi-center, multi-device study.

Purpose: This study aims to assess whether the novel CovBat harmonization method can further reduce radiomics feature variability from different imaging devices in multi-center studies and improve machine learning model performance compared to the ComBat method.

Materials: Non-contrast abdominal CT scans of 1,000 healthy subjects from three medical institutions (from four manufacturers and eight different models) were retrospectively included: Hospital A (n = 513), Hospital B (n = 338), and Hospital C (n = 149). 93 radiomics features were extracted from liver and spleen tissues using PyRadiomics.

Predicting symptomatic intracranial hemorrhage after endovascular treatment of vertebrobasilar artery occlusion: PEACE score.

Background: Current clinical decision tools for assessing the risk of symptomatic intracranial hemorrhage (sICH) in patients with vertebrobasilar artery occlusion (VBAO) who received endovascular treatment (EVT) have limited performance. This study develops and validates a clinical risk score to precisely estimate the risk of sICH in VBAO patients.

Methods: The derivation cohort recruited patients with VBAO who received EVT from the Posterior Circulation IschemIc Stroke Registry in China.

Transarterial Chemoembolization with Radiofrequency Ablation versus Surgical Resection for Small Late-Recurrence Hepatocellular Carcinoma.

Background Radiofrequency ablation (RFA) has comparable clinical outcomes to surgical resection (SR) for treating small recurrent hepatocellular carcinoma (HCC). However, whether combined transarterial chemoembolization (TACE) with RFA (hereafter, TACE-RFA) outperforms SR for treating small late-recurrence HCCs remains unknown. Purpose To compare the clinical outcome of TACE-RFA with that of SR in patients with small late-recurrence HCCs.

A Multidimensional Regression Model for Predicting Recurrence in Chronic Low Back Pain.

Background: Recurrence is common in chronic low back pain (CLBP). However, predicting the recurrence risk remains a challenge. The aim is to develop and validate a machine learning tool to predict the recurrence risk in patients with CLBP by using multidimensional medical information.

Accuracy of a Cascade Network for Semi-Supervised Maxillary Sinus Detection and Sinus Cyst Classification.

Objective: Maxillary sinus mucosal cysts represent prevalent oral and maxillofacial diseases, and their precise diagnosis is essential for surgical planning in maxillary sinus floor elevation. This study aimed to develop a deep learning-based pipeline for the classification of maxillary sinus lesions in cone beam computed tomography (CBCT) images to provide auxiliary support for clinical diagnosis.

Methods: This study utilized 45 136 maxillary sinus images from CBCT scans of 541 patients.

Outcomes of Worsened Left Ventricular Ejection Fraction After Transcatheter Aortic Valve Replacement.

Background: Left ventricular ejection fraction (LVEF) worsening after transcatheter aortic valve replacement (TAVR) was common in clinical practice. However, the effect of acute worsening LVEF is unclear.

Methods: All consecutive patients who underwent TAVR between January 2016 and May 2022 were analyzed.

Brain Iron Alteration in Pediatric Tourette Syndrome: A Quantitative Susceptibility Mapping Study.

Background: The cortico-striato-thalamo-cortical circuits play a crucial role in the pathogenesis of Tourette syndrome (TS). While iron deficiency has been reported in adult TS, the iron content in pediatric TS remains poorly understood. This study aims to quantitatively assess whole-brain iron deposition in pediatric TS compared to typically developing (TD) children using quantitative susceptibility mapping (QSM).

AI prediction model for endovascular treatment of vertebrobasilar occlusion with atrial fibrillation.

Endovascular treatment (EVT) for vertebrobasilar artery occlusion (VBAO) with atrial fibrillation presents complex clinical challenges. This comprehensive multicenter study of 525 patients across 15 Chinese provinces investigated nuanced predictors beyond conventional metrics. While 45.

Lenvatinib and tislelizumab versus atezolizumab and bevacizumab in combination with TAE-HAIC for unresectable hepatocellular carcinoma with high tumor burden: a multicenter retrospective cohort study.

Background: Systemic and locoregional combination therapy has demonstrated promising outcomes for unresectable hepatocellular carcinoma (HCC); However, the best combination option remains unknown. This study compared the efficacy and safety of lenvatinib and tislelizumab versus atezolizumab and bevacizumab in combination with transarterial embolization (TAE) plus hepatic artery infusion chemotherapy (HAIC) for unresectable HCC with high tumor burden.

Methods: This multicenter retrospective cohort study enrolled treatment-naive patients with unresectable HCC treated with TAE-HAIC plus lenvatinib and tislelizumab (THLP group) or TAE-HAIC plus atezolizumab and bevacizumab (THTA group).

Nomogram to screen older adult patients attending the radiology department for sarcopenia.

Background: Sarcopenia is an age-related syndrome that can impact the physical and mental health of older adults. However, it is often overlooked in clinical practice. Therefore, we aim to construct a nomogram based on simplified discriminant parameters for screening older adult patients for sarcopenia risk.