Pre-operative prediction of histopathological growth patterns of colorectal cancer liver metastasis using MRI-based radiomic models.

Purpose: Histopathological growth patterns (HGPs) of colorectal liver metastases (CRLMs) have prognostic value. However, the differentiation of HGPs relies on postoperative pathology. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomic model to predict HGP pre-operatively, following the latest guidelines.

MRI-based tumor shrinkage patterns after early neoadjuvant therapy in breast cancer: correlation with molecular subtypes and pathological response after therapy.

Background: MRI-based tumor shrinkage patterns (TSP) after neoadjuvant therapy (NAT) have been associated with pathological response. However, the understanding of TSP after early NAT remains limited. We aimed to analyze the relationship between TSP after early NAT and pathological response after therapy in different molecular subtypes.

Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging.

Background: The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging.

Methods: The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022.

Differentiation of histopathological growth patterns of colorectal liver metastases by MRI features.

Background: It is necessary to develop an accurate non-invasive method to determine the histopathological growth pattern (HGP) of colorectal liver metastasis (CRLM) before surgery. The present study aimed to identify various HGPs of CRLM by magnetic resonance imaging (MRI) features.

Methods: This retrospective study included 53 chemo-naïve patients with CRLM between December 2013 and September 2019.

Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features.

Purpose: The purpose of this study was to distinguish axillary lymph node (ALN) status using preoperative breast DCE-MRI radiomics and compare the effects of two-dimensional (2D) and three-dimensional (3D) analysis.

Methods: A retrospective study including 154 breast cancer patients all confirmed by pathology; 80 with ALN metastasis and 74 without. All MRI scans were achieved at a 3.

Diffusion-weighted imaging or dynamic contrast-enhanced curve: a retrospective analysis of contrast-enhanced magnetic resonance imaging-based differential diagnoses of benign and malignant breast lesions.

Objective: To compare the diagnostic performance of models based on a combination of contrast-enhanced (CE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) or time-intensity curves (TIC) in diagnosing malignancies of breast lesions.

Methods: A double-blind retrospective study was conducted in 328 patients (254 for training and the following 74 for validation) who underwent dynamic contrast-enhanced MRI (DCE-MRI) of the breast with pathological results. Two score models, the DWI model (apparent diffusion coefficient (ADC) + morphology + enhanced information) and the TIC model (TIC + morphology + enhanced information), were established with binary logistic regression for mass and non-mass enhancements (NMEs) in the training set.

Utility of radiomics based on contrast-enhanced CT and clinical data in the differentiation of benign and malignant gallbladder polypoid lesions.

Purpose: To develop and validate a novel method based on radiomics for the preoperative differentiation of benign and malignant gallbladder polypoid lesions (PLG).

Patients And Methods: A total of 145 patients with pathological proven gallbladder polypoid lesions ≥ 1 cm were included in this retrospective study. All the patients underwent abdominal contrast-enhanced computed tomography (CT) examinations 3 weeks before cholecystectomy from January 2013 to January 2019.

Automated deep learning method for whole-breast segmentation in diffusion-weighted breast MRI.

Background: Diffusion-weighted imaging (DWI) in MRI plays an increasingly important role in diagnostic applications and developing imaging biomarkers. Automated whole-breast segmentation is an important yet challenging step for quantitative breast imaging analysis. While methods have been developed on dynamic contrast-enhanced (DCE) MRI, automatic whole-breast segmentation in breast DWI MRI is still underdeveloped.

Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences.

Background: The axillary lymph node status is critical for breast cancer staging and individualized treatment planning.

Purpose: To assess the effect of determining axillary lymph node (ALN) metastasis by breast MRI-derived radiomic signatures, and compare the discriminating abilities of different MR sequences.

Study Type: Retrospective.

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI.

The accurate and noninvasive preoperative prediction of the state of the axillary lymph nodes is significant for breast cancer staging, therapy and the prognosis of patients. In this study, we analyzed the possibility of axillary lymph node metastasis directly based on Magnetic Resonance Imaging (MRI) of the breast in cancer patients. After mass segmentation and feature analysis, the SVM, KNN, and LDA three classifiers were used to distinguish the axillary lymph node state in 5-fold cross-validation.

T and N Staging of Gastric Cancer Using Dual-Source Computed Tomography.

Aim: This study is aimed at comparing gastric cancer T and N staging between virtual monochromatic energy images and fusion images generated by dual-source computed tomography (DSCT) dual-energy mode data acquisition prospectively while measuring the iodine concentration of gastric cancer and lymph nodes at different T and N stages from iodine map retrospectively.

Methods: A total of 71 patients (50 males and 21 females; mean age: 59 ± 11 years) confirmed with gastric cancer by endoscopic biopsy with no neoadjuvant chemotherapy were enrolled for the CT examination before surgeries. The preoperative T and N staging results were compared between groups with pathological results as the gold standard.

A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings.

Objective: To develop and validate a radiomic nomogram based on an apparent diffusion coefficient (ADC) map for differentiating benign and malignant lesions in suspicious breast findings classified as Breast Imaging Reporting and Data System (BI-RADS) category 4 on breast magnetic resonance imaging (MRI).

Methods: Eighty-eight patients diagnosed with BI-RADS 4 findings on breast MRI in the First Affiliated Hospital of China Medical University from December 2014 to December 2015 were retrospectively analyzed in this study. Sixty-three were randomized electronically to establish forecasting models, and the other 25 were used for validation.

The primary study of low-dose pancreas perfusion by 640- slice helical CT: a whole-organ perfusion.

To discuss the feasibility of low-dose whole-pancreas imaging utilizing 640-slice dynamic volume CT.80 patients (40 cases of normal pancreas and 40 patients supposed of having pancreatic carcinoma or focal pancreatic space-occupying lesions were mainly refered) referred for CT pancreas perfusion were enrolled in the study. 80 patients randomly assigned to 3 groups: Group ① (whole sequence).