A Transfer Learning Radiomics Nomogram to Predict the Postoperative Recurrence of Advanced Gastric Cancer.

Background And Aim: In this study, a transfer learning (TL) algorithm was used to predict postoperative recurrence of advanced gastric cancer (AGC) and to evaluate its value in a small-sample clinical study.

Methods: A total of 431 cases of AGC from three centers were included in this retrospective study. First, TL signatures (TLSs) were constructed based on different source domains, including whole slide images (TLS-WSIs) and natural images (TLS-ImageNet).

Role of high-resolution magnetic resonance imaging in preoperative tumor-node-metastasis staging evaluation of esophageal cancer: a narrative review.

Background And Objective: Esophageal cancer (EC) is an aggressive disease characterized by high mortality rates and a propensity for locoregional or distant recurrence. The treatment strategies and prognostic estimation for EC depend on accurate pre-treatment tumor-node-metastasis (TNM) staging. The objective of this review was to illustrate the role of various imaging modalities in achieving accurate preoperative TNM staging of EC, with a particular focus on the utilization of advanced high-resolution magnetic resonance imaging (MRI) sequences for T classification, which have shown promise in enhancing the delineation of tumor depth and extent.

Development and validation of a dynamic contrast-enhanced magnetic resonance imaging-based habitat and peritumoral radiomic model to predict axillary lymph node metastasis in patients with breast cancer: a retrospective study.

Background: The heterogeneity within breast cancer and its microenvironment are associated with metastasis. Analyzing distinct tumor subregions using habitat analysis and characterizing the tumor microenvironment through radiomics may be valuable for predicting axillary lymph node metastasis (ALNM) in breast cancer. This study aimed to develop and validate a nomogram for predicting ALNM in breast cancer patients by integrating clinicopathological, intra- or peri-tumoral radiomic, and habitat signatures based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and determine the optimal peritumoral region size for accurate prediction.

Genetic syndromes associated with pancreatic neuroendocrine neoplasms and imaging diagnostic strategies.

Pancreatic neuroendocrine neoplasms (pNENs) are the second most common pancreatic malignancy. While most cases are sporadic, a small proportion is associated with genetic syndromes, such as Multiple Endocrine Neoplasia (MEN), Von Hippel-Lindau Syndrome (VHL), Neurofibromatosis Type 1 (NF1), and Tuberous Sclerosis Complex (TSC). This review aims to use pNENs as a clue to reveal the full spectrum of disease, providing a comprehensive understanding of diagnosis.

Tissue-matched analysis of MRI evaluating the tumor infiltrating lymphocytes in hepatocellular carcinoma.

Tumor-infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi-parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi-parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28).

AI-based fingerprint index of visceral adipose tissue for the prediction of bowel damage in patients with Crohn's disease.

The fingerprint features of visceral adipose tissue (VAT) are intricately linked to bowel damage (BD) in patients with Crohn's disease (CD). We aimed to develop a VAT fingerprint index (VAT-FI) using radiomics and deep learning features extracted from computed tomography (CT) images of 1,135 CD patients across six hospitals (training cohort,  = 600; testing cohort,  = 535) for predicting BD, and to compare it with a subcutaneous adipose tissue (SAT)-FI. VAT-FI exhibited greater predictive accuracy than SAT-FI in both training (area under the receiver operating characteristic curve [AUC] = 0.

Rapid and simultaneous determination of mixed pesticide residues in apple using SERS coupled with multivariate analysis.

This study aims to apply multivariate analysis algorithms for modeling the same spectra, for simultaneous determination of pymetrozine and carbendazim residues in apple. To mitigate the impact of competitive adsorption, SERS spectra are obtained from mixed solutions of pymetrozine and carbendazim at varying concentration ratios, which are then utilized for modeling. Results suggest that the PLSR model based on full-band SNV processed spectra shows the best performance for predicting pymetrozine and carbendazim contents, with R2 of 0.

Prediction of the early hepatocellular carcinoma development in patients with chronic hepatitis B virus infection using gadoxetic acid-enhanced magnetic resonance imaging.

Background: Non-hypervascular hypointense nodules (NHHNs) can transform into hypervascular hepatocellular carcinoma (HCC) during the long-term follow-up. However, the risk factors for NHHN hypervascular transformation in chronic hepatitis B virus (HBV)-infected populations are unknown. This study assessed the predictive value of gadoxetic acid-enhanced magnetic resonance imaging (MRI) for HCC development in patients with chronic HBV infection.

Multiomics reveals microbial metabolites as key actors in intestinal fibrosis in Crohn's disease.

Intestinal fibrosis is the primary cause of disability in patients with Crohn's disease (CD), yet effective therapeutic strategies are currently lacking. Here, we report a multiomics analysis of gut microbiota and fecal/blood metabolites of 278 CD patients and 28 healthy controls, identifying characteristic alterations in gut microbiota (e.g.

Development and validation of a CT-based radiomics model to predict survival-graded fibrosis in pancreatic ductal adenocarcinoma.

Background: Tumor fibrosis plays an important role in chemotherapy resistance in pancreatic ductal adenocarcinoma (PDAC), however there remains a contradiction in the prognostic value of fibrosis. We aimed to investigate the relationship between tumor fibrosis and survival in patients with PDAC, classify patients into high- and low-fibrosis groups, and develop and validate a CT-based radiomics model to non-invasively predict fibrosis before treatment.

Materials And Methods: This retrospective, bicentric study included 295 patients with PDAC without any treatments before surgery.

A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn's disease.

Objectives: Differentiating intestinal tuberculosis (ITB) from Crohn's disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach for distinguishing ITB from CD.

Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.

Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonance imaging. The diagnostic systems achieve areas under the curve of 0.

Quantitative assessment of brown adipose tissue whitening in a high-fat-diet murine model using synthetic magnetic resonance imaging.

Purpose: This study aimed to quantitatively evaluate the whitening process of brown adipose tissue (BAT) in mice using synthetic magnetic resonance imaging (SyMRI) and analyzed the correlation between SyMRI quantitative measurements of BAT and serum lipid profiles.

Methods: Fifteen C57BL/6 mice were divided into three groups and fed different diets as follows: normal chow diet for 12 weeks, NCD group; high-fat diet (HFD) for 12 weeks, HFD-12w group; and HFD for 36 weeks, HFD-36w group. Mice were scanned using 3.

Predicting One-year Recurrence of HCC based on Gadoxetic Acid-enhanced MRI by Machine Learning Models.

Objective: Accurate prediction of recurrence risk after resction in patients with Hepatocellular Carcinoma (HCC) may help to individualize therapy strategies. This study aimed to develop machine learning models based on preoperative clinical factors and multiparameter Magnetic Resonance Imaging (MRI) characteristics to predict the 1-year recurrence after HCC resection.

Methods: Eighty-two patients with single HCC who underwent surgery were retrospectively analyzed.

Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma.

Background: Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. The authors aimed to develop and validate a computed tomography (CT)-based deep learning-based radiomics (DLR) model to identify OPM in PDAC before treatment.

Methods: This retrospective, bicentric study included 302 patients with PDAC (training: n =167, OPM-positive, n =22; internal test: n =72, OPM-positive, n =9: external test, n =63, OPM-positive, n =9) who had undergone baseline CT examinations between January 2012 and October 2022.

CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn's disease resistant to infliximab.

Purpose: To develop a CT-based radiomics model combining with VAT and bowel features to improve the predictive efficacy of IFX therapy on the basis of bowel model.

Methods: This retrospective study included 231 CD patients (training cohort, n = 112; internal validation cohort, n = 48; external validation cohort, n = 71) from two tertiary centers. Machine-learning VAT model and bowel model were developed separately to identify CD patients with primary nonresponse to IFX.

Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence.

The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support. In practice, the collection of medical data faces obstacles related to privacy protection.

Quantitative assessment of inflammation and evaluation of spatial heterogeneity for non-alcoholic fatty liver disease in mice based on iron-adjustive T1.

Background: A sensitive and non-invasive method is necessary to diagnose non-alcoholic fatty liver disease (NAFLD). We explored the iron-adjustive T1 (aT1) ability to quantify the degree of liver inflammation and evaluate the spatial heterogeneity.

Methods: Male C57BL/6J mice were randomly categorized as the NAFLD model (n=40), NAFLD-related liver cirrhosis model (n=20), and normal mice (n=10).

Preoperative assessment of peripheral vascular invasion of pancreatic ductal adenocarcinoma based on high-resolution MRI.

Objectives: Preoperative imaging of vascular invasion is important for surgical resection of pancreatic ductal adenocarcinoma (PDAC). However, whether MRI and CT share the same evaluation criteria remains unclear. This study aimed to compare the diagnostic accuracy of high-resolution MRI (HR-MRI), conventional MRI (non-HR-MRI) and CT for PDAC vascular invasion.

Dual-energy CT improves differentiation of non-hypervascular pancreatic neuroendocrine neoplasms from CA 19-9-negative pancreatic ductal adenocarcinomas.

Purpose: To evaluate the utility of dual-energy CT (DECT) in differentiating non-hypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs) with negative carbohydrate antigen 19-9 (CA 19-9).

Methods: This retrospective study included 26 and 39 patients with pathologically confirmed non-hypervascular PNENs and CA 19-9-negative PDACs, respectively, who underwent contrast-enhanced DECT before treatment between June 2019 and December 2021. The clinical, conventional CT qualitative, conventional CT quantitative, and DECT quantitative parameters of the two groups were compared using univariate analysis and selected by least absolute shrinkage and selection operator regression (LASSO) analysis.

Harnessing dual-energy CT for glycogen quantification: a phantom analysis.

Background: Non-invasive glycogen quantification could provide crucial information on biological processes for glycogen storage disorder. Using dual-energy computed tomography (DECT), this study aimed to assess the viability of quantifying glycogen content .

Methods: A fast kilovolt-peak switching DECT was used to scan a phantom containing 33 cylinders with different proportions of glycogen and iodine mixture at varying doses.

Clinical outcome is distinct between radiological stricture and endoscopic stricture in ileal Crohn's disease.

Objectives: Differences in clinical adverse outcomes (CAO) based on different intestinal stricturing definitions in Crohn's disease (CD) are poorly documented. This study aims to compare CAO between radiological strictures (RS) and endoscopic strictures (ES) in ileal CD and explore the significance of upstream dilatation in RS.

Methods: This retrospective double-center study included 199 patients (derivation cohort, n = 157; validation cohort, n = 42) with bowel strictures who simultaneously underwent endoscopic and radiologic examinations.