Preoperative clinical and contrasted-enhanced CT features to predict perineural invasion in gallbladder carcinoma: focus on clinical T3-4 stage.

Purpose: To investigate the utility of combining clinical and contrasted-enhanced tomography (CECT) parameters for the preoperative evaluation of perineural invasion (PNI) in gallbladder carcinoma (GBC).

Methods: A total of 134 patients with GBC (male/female, 52/82; age, 64.4 ± 9.

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

Purpose: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T-weighted MR imaging (TWI) for gastric cancer (GC).

Methods: 112 patients with GCs undergoing gastric MRI were prospectively enrolled between Aug 2022 and Dec 2022. Axial DLSB-TWI and BLADE-TWI of stomach were scanned with same spatial resolution.

Utility of Combined Use of Imaging Features From Abdominopelvic CT and CA 125 to Identify Presence of CT Occult Peritoneal Metastases in Advanced Gastric Cancer.

Objective: The purpose of this study is to identify the presence of occult peritoneal metastasis (OPM) in patients with advanced gastric cancer (AGC) by using clinical characteristics and abdominopelvic computed tomography (CT) features.

Methods: This retrospective study included 66 patients with OPM and 111 patients without peritoneal metastasis (non-PM [NPM]) who underwent preoperative contrast-enhanced CT between January 2020 and December 2021. Occult PMs means PMs that are missed by CT but later diagnosed by laparoscopy or laparotomy.

SurvivalCNN: A deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables.

Radiological images have shown promising effects in patient prognostication. Deep learning provides a powerful approach for in-depth analysis of imaging data and integration of multi-modal data for modeling. In this work, we propose SurvivalCNN, a deep learning structure for cancer patient survival prediction using CT imaging data and non-imaging clinical data.

LiSNet: An artificial intelligence -based tool for liver imaging staging of hepatocellular carcinoma aggressiveness.

Background: Presurgical assessment of hepatocellular carcinoma (HCC) aggressiveness can benefit patients' treatment options and prognosis.

Purpose: To develop an artificial intelligence (AI) tool, namely, LiSNet, in the task of scoring and interpreting HCC aggressiveness with computed tomography (CT) imaging.

Methods: A total of 358 patients with HCC undergoing curative liver resection were retrospectively included.

Comparison of Computed Tomography Features of Gastric and Small Bowel Gastrointestinal Stromal Tumors With Different Risk Grades.

Objective: This study aimed to compare the computed tomography (CT) features of gastric and small bowel gastrointestinal stromal tumors (GISTs) and further identify the predictors for risk stratification of them, respectively.

Methods: According to the modified National Institutes of Health criteria, patients were classified into low-malignant potential group and high-malignant potential group. Two experienced radiologists reviewed the CT features including the difference of CT values between arterial phase and portal venous phase (PVPMAP) by consensus.

Identifying Prognostic Markers From Clinical, Radiomics, and Deep Learning Imaging Features for Gastric Cancer Survival Prediction.

Background: Gastric cancer is one of the leading causes of cancer death in the world. Improving gastric cancer survival prediction can enhance patient prognostication and treatment planning.

Methods: In this study, we performed gastric cancer survival prediction using machine learning and multi-modal data of 1061 patients, including 743 for model learning and 318 independent patients for evaluation.

Radiomics analysis of pretreatment MRI in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study.

Background And Objective: To develop a machine-learning model by integrating clinical and imaging modalities for predicting tumor response and survival of hepatocellular carcinoma (HCC) with transarterial chemoembolization (TACE).

Methods: 140 HCC patients with TACE were retrospectively included from two centers. Tumor response were evaluated using modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria.

Prognostic aspects of lymphovascular invasion in localized gastric cancer: new insights into the radiomics and deep transfer learning from contrast-enhanced CT imaging.

Objectives: Lymphovascular invasion (LVI) is a factor significantly impacting treatment and outcome of patients with gastric cancer (GC). We aimed to investigate prognostic aspects of a preoperative LVI prediction in GC using radiomics and deep transfer learning (DTL) from contrast-enhanced CT (CECT) imaging.

Methods: A total of 1062 GC patients (728 training and 334 testing) between Jan 2014 and Dec 2018 undergoing gastrectomy were retrospectively included.

Prediction of prognostic risk factors in hepatocellular carcinoma with transarterial chemoembolization using multi-modal multi-task deep learning.

Background: Due to heterogeneity of hepatocellular carcinoma (HCC), outcome assessment of HCC with transarterial chemoembolization (TACE) is challenging.

Methods: We built histologic-related scores to determine microvascular invasion (MVI) and Edmondson-Steiner grade by training CT radiomics features using machine learning classifiers in a cohort of 494 HCCs with hepatic resection. Meanwhile, we developed a deep learning (DL)-score for disease-specific survival by training CT imaging using DL networks in a cohort of 243 HCCs with TACE.

Quantitative assessment of diffusion kurtosis imaging depicting deep myometrial invasion: a comparative analysis with diffusion-weighted imaging.

Purpose: We aimed to investigate histogram analysis of diffusion kurtosis imaging (DKI) and conventional diffusion-weighted imaging (DWI) to distinguish between deep myometrial invasion and superficial myometrial invasion in endometrial carcinoma (EC).

Methods: A total of 118 pathologically confirmed EC patients with preoperative DWI were included. The data were postprocessed with a DKI (b value of 0, 700, 1400, and 2000 s/mm2) model for quantitation of apparent diffusion values (D) and apparent kurtosis coefficient values (K) for non-Gaussian distribution.

Direct Comparison of Four Presurgical Stratifying Schemes for Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Gadoxetic Acid-Enhanced MRI.

Background: Microvascular invasion (MVI) is implicated in the poor prognosis of hepatocellular carcinoma (HCC). Presurgical stratifying schemes have been proposed for HCC-MVI but lack external validation.

Purpose: To perform external validation and comparison of four presurgical stratifying schemes for the prediction of MVI using gadoxetic acid-based MRI in a cohort of HCC patients.

RNA-binding protein Musashi2 stabilizing androgen receptor drives prostate cancer progression.

The androgen receptor (AR) pathway is critical for prostate cancer carcinogenesis and development; however, after 18-24 months of AR blocking therapy, patients invariably progress to castration-resistant prostate cancer (CRPC), which remains an urgent problem to be solved. Therefore, finding key molecules that interact with AR as novel strategies to treat prostate cancer and even CRPC is desperately needed. In the current study, we focused on the regulation of RNA-binding proteins (RBPs) associated with AR and determined that the mRNA and protein levels of AR were highly correlated with Musashi2 (MSI2) levels.

Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma.

Aims: The aim of the study was to predict and assess treatment response by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to patients with locally advanced esophageal squamous cell carcinoma receiving chemoradiotherapy (CRT).

Materials And Methods: Seventy-two patients with locally advanced esophageal squamous cell carcinoma who underwent DCE-MRI before and after chemoradiotherapy were enrolled and divided into the complete response (CR) group and the non-CR group based on RECIST. The histogram parameters (10th percentile, 90th percentile, median, mean, standard deviation, skewness, and kurtosis) of pre-CRT and post-CRT were compared using a paired Student's t test in the CR and non-CR groups, respectively.

Machine Learning-Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer.

Introduction: Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale imaging factors, especially radiomic features at contrast-enhanced computed tomography, to predict AHS and clinical outcomes of patients with GC.

Methods: Five hundred fifty-four patients with GC (370 training and 184 test) undergoing gastrectomy were retrospectively included.

Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection.

Background: Controversy still exists on the optimal surgical resection for potentially curable gastric cancer (GC). Use of radiologic evaluation and machine learning algorithms might predict extent of lymphadenectomy to limit unnecessary surgical treatment. We purposed to design a machine learning-based clinical decision-support model for predicting extent of lymphadenectomy (D1 vs.

A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma.

Objectives: This study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its prognostic value.

Methods: For this retrospective study, a radiomics model was developed in a primary cohort of 103 IHC patients who underwent curative-intent resection and lymphadenectomy. Radiomics features were extracted from arterial phase computed tomography (CT) scans.

Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma.

Background & Aims: Microvascular invasion (MVI) impairs surgical outcomes in patients with hepatocellular carcinoma (HCC). As there is no single highly reliable factor to preoperatively predict MVI, we developed a computational approach integrating large-scale clinical and imaging modalities, especially radiomic features from contrast-enhanced CT, to predict MVI and clinical outcomes in patients with HCC.

Methods: In total, 495 surgically resected patients were retrospectively included.

An Intelligent Clinical Decision Support System for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer.

Purpose: The aim of this study was to develop and validate a computational clinical decision support system (DSS) on the basis of CT radiomics features for the prediction of lymph node (LN) metastasis in gastric cancer (GC) using machine learning-based analysis.

Methods: Clinicopathologic and CT imaging data were retrospectively collected from 490 patients who were diagnosed with GC between January 2002 and December 2016. Radiomics features were extracted from venous-phase CT images.

USP10 regulates Musashi-2 stability via deubiquitination and promotes tumour proliferation in colon cancer.

Recent studies have demonstrated that ubiquitin-specific protease 10 (USP10) plays a catalytic role in tumour suppression mainly by deubiquitinating its target proteins to enhance their stabilities. However, we found that USP10 could interact with and regulate the expression of oncogenic factor Musashi-2 (MSI2). We investigated whether USP10 positively regulates the expression of MSI2 by deubiquitination and confirmed the type of polyubiquitin chain that is linked to MSI2.

Cartilage oligomeric matrix protein is a prognostic factor and biomarker of colon cancer and promotes cell proliferation by activating the Akt pathway.

Purpose: Recent studies have determined that cartilage oligomeric matrix protein (COMP) plays a vital role in carcinogenesis. We sought to clarify the role of COMP in colon cancer.

Methods: We investigated gene expression data from The Cancer Genome Atlas (TCGA) dataset.

Diffusion Kurtosis Imaging Helps to Predict Upgrading in Biopsy-Proven Prostate Cancer With a Gleason Score of 6.

Objective: The purpose of this study was to investigate whether diffusion kurtosis imaging (DKI) is useful for predicting upgrades in Gleason score (GS) in biopsy-proven prostate cancer with a GS of 6.

Materials And Methods: A total of 46 patients with biopsy-proven GS 6 prostate cancer, 3-T DWI results, and surgical pathologic results were retrospectively included in the study. DWI data were postprocessed with monoexponential and DK models to quantify the apparent diffusion coefficient (ADC), apparent diffusion for gaussian distribution (D), and apparent kurtosis coefficient (K).