A Novel Model for Predicting Microsatellite Instability in Endometrial Cancer: Integrating Deep Learning-Pathomics and MRI-Based Radiomics.

Rationale And Objectives: To develop and validate a novel model based on multiparametric MRI (mpMRI) and whole slide images (WSIs) for predicting microsatellite instability (MSI) status in endometrial cancer (EC) patients.

Materials And Methods: A total of 136 surgically confirmed EC patients were included in this retrospective study. Patients were randomly divided into a training set (96 patients) and a validation set (40 patients) in a 7:3 ratio.

Predicting symptomatic carotid artery plaques with radiomics-based carotid perivascular adipose tissue characteristics: a multicenter, multiclassifier study.

Objective: This study aims to differentiate between symptomatic and asymptomatic plaques using a computed tomography angiography (CTA)-based radiomics model of perivascular adipose tissue (PVAT).

Methods: Patients were categorized into symptomatic and asymptomatic groups based on the presence or absence of acute ischemic stroke or transient ischemic attack in the anterior cerebral circulation within two weeks prior to the CTA examination. The clinical information of all patients was collected and analyzed, and the PVAT features of CTA images were further analyzed to clarify their correlation with plaque classification.

Multi-DECT image-based radiomics with interpretable machine learning for preoperative prediction of tumor budding grade and prognosis in colorectal cancer: a dual-center study.

Purpose: This study evaluates the predictive ability of multiparametric dual-energy computed tomography (multi-DECT) radiomics for tumor budding (TB) grade and prognosis in patients with colorectal cancer (CRC).

Methods: This study comprised 510 CRC patients at two institutions. The radiomics features of multi-DECT images (including polyenergetic, virtual monoenergetic, iodine concentration [IC], and effective atomic number images) were screened to build radiomics models utilizing nine machine learning (ML) algorithms.

Comparison of clinical features and outcomes of Chinese patients with Takotsubo syndrome and acute myocardial infarction-results from the first Chinese Takotsubo syndrome registry.

Background: Takotsubo syndrome (TTS) differs significantly from acute myocardial infarction (AMI) in clinical features and pathological mechanisms, but evidence in Asian populations remains limited. The aim of this study is to compare clinical features and outcomes between patients with TTS and AMI in the first Chinese registry of TTS (ChiTTS Registry, ChiCTR1900026725).

Methods: In this multicenter 6-year retrospective cohort study (February 2016-June 2022), a total of 116 consecutive TTS patients diagnosed according to the international Takotsubo diagnostic criteria and 232 age- and sex-matched AMI patients (1:2 ratio) meeting the 2023 European Society of Cardiology guidelines criteria were enrolled from 10 tertiary medical centers across China.

Diagnosing pathologic myopia by identifying morphologic patterns using ultra widefield images with deep learning.

Pathologic myopia is a leading cause of visual impairment and blindness. While deep learning-based approaches aid in recognizing pathologic myopia using color fundus photography, they often rely on implicit patterns that lack clinical interpretability. This study aims to diagnose pathologic myopia by identifying clinically significant morphologic patterns, specifically posterior staphyloma and myopic maculopathy, by leveraging ultra-widefield (UWF) images that provide a broad retinal field of view.

Microenvironment responsive nanoplatform for targeted removal of cholesterol and reshaping inflammatory microenvironment in atherosclerotic plaques.

The microenvironment of atherosclerotic plaques features abundant cholesterol crystals (CCs), reactive oxygen species (ROS), and pro-inflammatory M1 macrophages, leading to the progression and mortality of atherosclerotic cardiovascular disease (ASCVD). Targeted removal of CCs and scavenging of ROS are crucial for treatment of ASCVD. In this study, an intelligent nanoformulation consisting of epigallocatechin gallate (EGCG), cysteine (Cys), ursodeoxycholic acid (UDCA) and VHPK (Valine-Histidine-Proline-Lysine)-Lipo, was designed to precisely target and remodel the inflammatory microenvironment of atherosclerotic plaques.

iRGD-NaGd(MoO): A Tumor-Targeted Magnetic Resonance Contrast Agent Activating Immunotherapy via the cGAS-STING Pathway.

Because of the high incidence and mortality of cancer, there is an urgent need for more effective tumor diagnostic imaging and treatment strategies. Single-molecule therapeutics have emerged as a promising strategy to address the limitations of independent imaging and therapeutic modalities, thereby significantly reducing adverse side effects for patients. For the first time, we successfully synthesized NaGd(MoO) nanoparticles via a homogeneous precipitation method in polyol media, which serve as a single-molecule agent for MRI imaging and metallic immunotherapy.

Right Ventricular Strain as a Key Feature in Interpretable Machine Learning for Identification of Takotsubo Syndrome: A Multicenter CMR-based Study.

Rationale And Objectives: To develop an interpretable machine learning (ML) model based on cardiac magnetic resonance (CMR) multimodal parameters and clinical data to discriminate Takotsubo syndrome (TTS), acute myocardial infarction (AMI), and acute myocarditis (AM), and to further assess the diagnostic value of right ventricular (RV) strain in TTS.

Materials And Methods: This study analyzed CMR and clinical data of 130 patients from three centers. Key features were selected using least absolute shrinkage and selection operator regression and random forest.

A multimodal MRI-based machine learning framework for classifying cognitive impairment in cerebral small vessel disease.

The heterogeneity of cerebral small vessel disease (CSVD) with mild cognitive impairment (MCI) presents a challenge for diagnosis and classification. This study aims to propose a multimodal magnetic resonance imaging (MRI)-based machine learning framework to effectively classify MCI and NCI in CSVD patients. We enrolled 165 CSVD patients, categorized into NCI (n = 81) and MCI (n = 84) groups based on neurocognitive assessments.

Multiparametric MRI-based Interpretable Machine Learning Radiomics Model for Distinguishing Between Luminal and Non-luminal Tumors in Breast Cancer: A Multicenter Study.

Rationale And Objectives: To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic resonance imaging (MRI) images to differentiate between luminal and non-luminal breast cancer (BC) subtypes.

Methods: This study enrolled 1098 BC participants from four medical centers, categorized into a training cohort (n = 580) and validation cohorts 1-3 (n = 252, 89, and 177, respectively). Multiparametric MRI-based radiomics features, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) imaging, were extracted.

Radiomics based on dual-energy CT virtual monoenergetic images to identify symptomatic carotid plaques: a multicenter study.

This study aims to create a radiomics nomogram using dual-energy computed tomography (DECT) virtual monoenergetic images (VMI) to accurately identify symptomatic carotid plaques. Between January 2018 and May 2023, data from 416 patients were collected from two centers for retrospective analysis. Center 1 provided data for the training (n = 213) and internal validation (n = 93) sets, and center 2 supplied the external validation set (n = 110).

Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study.

Background: To explore the value of intratumoral and peritumoral radiomics in preoperative prediction of anaplastic lymphoma kinase (ALK) mutation status and survival in patients with lung adenocarcinoma.

Methods: We retrospectively collected data from 505 eligible patients with lung adenocarcinoma from four hospitals (training and external validation sets 1-3). The CT-based radiomics features were extracted separately from the gross tumor volume (GTV) and GTV incorporating peritumoral 3-, 6-, 9-, 12-, and 15-mm regions (GPTV, GPTV, GPTV, GPTV, and GPTV), and screened the most relevant features to construct radiomics models to predict ALK (+).

Using Computed Tomography Coronary Angiography to Differentiate Atypical Cardiac Myxoma From Thrombus.

Objective: Atypical cardiac myxoma usually presents as an isolated mass attached to the atrial septum on imaging, with no movement and a wider attachment base. It is difficult to distinguish it from cardiac thrombus through conventional echocardiography or computed tomography (CT). The purpose of this study is to evaluate the value of CT coronary angiography imaging features in distinguishing atypical cardiac myxoma from cardiac thrombus.

Lactate-Fueled Theranostic Nanoplatforms for Enhanced MRI-Guided Ferroptosis Synergistic with Immunotherapy of Hepatocellular Carcinoma.

Treatment for hepatocellular carcinoma (HCC) may be improved with ferroptosis, a regulated form of cell death. However, the sensitivity of HCC to ferroptosis was strongly limited by lactic acid. In this study, a platelet membrane (PM)-engineered nanoparticle loaded with erastin, superparamagnetic iron oxide nanoparticles (SPIO) and lactate oxidase (LOX) (termed PM@ESL NPs) was designed for magnetic resonance imaging (MRI)-guided enhanced ferroptosis-immunotherapy of HCC.

Predicting early recurrence in locally advanced gastric cancer after gastrectomy using CT-based deep learning model: a multicenter study.

Background: Early recurrence in patients with locally advanced gastric cancer (LAGC) portends aggressive biological characteristics and a dismal prognosis. Predicting early recurrence may help determine treatment strategies for LAGC. The goal is to develop a deep learning model for early recurrence prediction (DLER) based on preoperative multiphase computed tomography (CT) images and to further explore the underlying biological basis of the proposed model.

Cholesterol Targeted Catalytic Hydrogel Fueled by Tumor Debris can Enhance Microwave Ablation Therapy and Anti-Tumor Immune Response.

The immunosuppressive residual tumor microenvironment (IRTM) is a key factor in the high recurrence and metastasis rates of hepatocellular carcinoma (HCC) after microwave ablation (MWA). Cholesterol-rich tumor fragments significantly contribute to IRTM deterioration. This study developed a cholesterol-targeted catalytic hydrogel, DA-COD-OD-HCS, to enhance the synergy between MWA and immune checkpoint inhibitors (ICIs) for HCC treatment.

Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study.

Rationale And Objectives: This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (MRI).

Materials And Methods: The study retrospectively enrolled 739 patients with pathologically confirmed meningioma from three medical centers, dividing them into four cohorts: training (n = 294), internal test (n = 126), external test 1 (n = 217), and external test 2 (n = 102). Radiomics characteristics were derived from T2-weighted and contrast-enhanced T1-weighted MRI images, followed by feature selection.

Dual-energy computed tomography for predicting histological grading and survival in patients with pancreatic ductal adenocarcinoma.

Objectives: We evaluated the value of dual-energy computed tomography (DECT) parameters derived from pancreatic ductal adenocarcinoma (PDAC) to discriminate between high- and low-grade tumors and predict overall survival (OS) in patients.

Methods: Data were retrospectively collected from 169 consecutive patients with pathologically confirmed PDAC who underwent third-generation dual-source DECT enhanced dual-phase scanning before surgery between January 2017 and March 2023. Patients with prior treatments, other malignancies, small tumors, or poor-quality scans were excluded.

Dual-energy computed tomography for predicting cervical lymph node metastasis in laryngeal squamous cell carcinoma.

Rationale And Objectives: We constructed a dual-energy computed tomography (DECT)-based model to assess cervical lymph node metastasis (LNM) in patients with laryngeal squamous cell carcinoma (LSCC).

Materials And Methods: We retrospectively analysed 164 patients with LSCC who underwent preoperative DECT from May 2019 to May 2023. The patients were randomly divided into training (n = 115) and validation (n = 49) cohorts.

MRI Radiomics Combined with Clinicopathological Factors for Predicting 3-Year Overall Survival of Hepatocellular Carcinoma After Hepatectomy.

Background: A limited number of studies have examined the use of radiomics to predict 3-year overall survival (OS) after hepatectomy in patients with hepatocellular carcinoma (HCC). This study develops 3-year OS prediction models for HCC patients after liver resection using MRI radiomics and clinicopathological factors.

Materials And Methods: A retrospective analysis of 141 patients who underwent surgical resection of HCC was performed.

Acidic/hypoxia dual-alleviated nanoregulators for enhanced treatment of tumor chemo-immunotherapy.

Chemotherapy plays a crucial role in triple-negative breast cancer (TNBC) treatment as it not only directly kills cancer cells but also induces immunogenic cell death. However, the chemotherapeutic efficacy was strongly restricted by the acidic and hypoxic tumor environment. Herein, we have successfully formulated PLGA-based nanoparticles concurrently loaded with doxorubicin (DOX), hemoglobin (Hb) and CaCO by a CaCO-assisted emulsion method, aiming at the effective treatment of TNBC.