A CT-Based Lung Radiomics Nomogram for Classifying the Severity of Chronic Obstructive Pulmonary Disease.

Background: Chronic obstructive pulmonary disease (COPD) is a major global health concern, and while traditional pulmonary function tests are effective, recent radiomics advancements offer enhanced evaluation by providing detailed insights into the heterogeneous lung changes.

Purpose: To develop and validate a radiomics nomogram based on clinical and whole-lung computed tomography (CT) radiomics features to stratify COPD severity.

Patients And Methods: One thousand ninety-nine patients with COPD (including 308, 132, and 659 in the training, internal and external validation sets, respectively), confirmed by pulmonary function test, were enrolled from two institutions.

CT-based whole lung radiomics nomogram to identify middle-aged and elderly COVID-19 patients at high risk of progressing to critical disease.

Background: COVID-19 remains widespread and poses a threat to people's physical and mental health, especially middle-aged and elderly individuals. Early identification of COVID-19 patients at high risk of progressing to critical disease helps improve overall patient outcomes and healthcare efficiency.

Purpose: To develop a radiomics nomogram to predict the risk of newly admitted middle-aged and elderly COVID-19 patients progressing to critical disease.

Preoperative prediction of early recurrence in patients with BRAF mutant colorectal cancer using a intergrated nomogram.

Objective To explore the predictive value of radiomics nomogram combining with CT radiomics features and clinical features for postoperative early recurrence in patients with BRAF-mutant colorectal cancer. Methods A total of 220 patients with surgically and pathologically confirmed BRAF-mutant colorectal cancer from 2 institutions were retrospectively included. All patients from institution 1 were randomized at a 7:3 ratio into a training cohort (n = 108) and an internal validation cohort (n = 45), and patients from institution 2 were used as an external validation cohort (n = 67).

Lung field-based severity score (LFSS): a feasible tool to identify COVID-19 patients at high risk of progressing to critical disease.

Background: Coronavirus disease 2019 (COVID-19) still poses a threat to people's physical and mental health. We proposed a new semi-quantitative visual classification method for COVID-19, and this study aimed to evaluate the clinical usefulness and feasibility of lung field-based severity score (LFSS).

Methods: This retrospective study included 794 COVID-19 patients from two hospitals in China between December 2022 and January 2023.

Preoperative nomogram for predicting spread through air spaces in clinical-stage IA non-small cell lung cancer using F-fluorodeoxyglucose positron emission tomography/computed tomography.

Purpose: This study aims to assess the predictive value of F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) radiological features and the maximum standardized uptake value (SUV) in determining the presence of spread through air spaces (STAS) in clinical-stage IA non-small cell lung cancer (NSCLC).

Methods: A retrospective analysis was conducted on 180 cases of NSCLC with postoperative pathological assessment of STAS status, spanning from September 2019 to September 2023. Of these, 116 cases from hospital one comprised the training set, while 64 cases from hospital two formed the testing set.

CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer.

The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model.

Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules.

Objective: To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics.

Methods: This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively.

Multivariate analysis based on the maximum standard unit value of F-fluorodeoxyglucose positron emission tomography/computed tomography and computed tomography features for preoperative predicting of visceral pleural invasion in patients with subpleural clinical stage IA peripheral lung adenocarcinoma.

Purpose: Preoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUV) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery.

Methods: A total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio.

YAP via interacting with STAT3 regulates VEGF-induced angiogenesis in human retinal microvascular endothelial cells.

Endothelial dysfunction is a main feature of retinal neovascular diseases which are the leading cause of blindness in developed countries. Yes-associated protein (YAP) and signal transducer and activator of transcription factor 3 (STAT3) participate in angiogenesis via vascular endothelial growth factor (VEGF) signaling. Additionally, YAP can bind STAT3 in endothelial cells.

Inhibition of TRAF6 alleviates choroidal neovascularization in vivo.

Choroidal neovascularization (CNV) is a type of wet age-related macular degeneration (AMD) which is a major cause of blindness in elder patients. Tumor necrosis factor receptor-associated factor 6 (TRAF6) promotes tumor angiogenesis via upregulating the expression of hypoxia-inducible factor 1α (HIF-1α) and vascular endothelial growth factor (VEGF). Additionally, TRAF6 facilitates the inflammatory response in macrophages and microglia.