Virtual MR Elastography and Multi-b-value DWI Models for Predicting Microvascular Invasion in Solitary BCLC Stage A Hepatocellular Carcinoma.

Rationale And Objectives: To evaluate the performance of virtual MR elastography (vMRE) for predicting microvascular invasion (MVI) in Barcelona Clinic Liver Cancer (BCLC) stage A (≤ 5.0 cm) hepatocellular carcinoma (HCC) and to construct a combined nomogram based on vMRE, multi-b-value DWI models, and clinical-radiological (CR) features.

Methods: Consecutive patients with suspected HCC who underwent multi-b-value DWI examinations were prospectively collected.

Predicting the compressive strength of high-performance concrete using an interpretable machine learning model.

Reliable predictions of concrete strength can reduce construction time and labor costs, providing strong support for building construction quality inspection. To enhance the accuracy of concrete strength prediction, this paper introduces an interpretable framework for machine learning (ML) models to predict the compressive strength of high-performance concrete (HPC). By leveraging information from a concrete dataset, an additional six features were added as inputs in the training process of the random forest (RF), AdaBoost, XGBoost and LightGBM models, and the optimal hyperparameters of the models were determined using 5-fold cross-validation and random search methods.

Bi-specific T1 positive-contrast-enhanced magnetic resonance imaging molecular probe for hepatocellular carcinoma in an orthotopic mouse model.

Background: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality. HCC-targeted magnetic resonance imaging (MRI) is an effective noninvasive diagnostic method that involves targeting clinically-related HCC biomarkers, such as alpha-fetoprotein (AFP) or glypican-3 (GPC3), with iron oxide nanoparticles. However, studies of HCC-targeted MRI utilize single-target iron oxide nanoprobes as negative (T2) contrast agents, which might weaken their future clinical applications due to tumor heterogeneity and negative MRI contrast.

Development and study of a bi-specific magnetic resonance imaging molecular probe for hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) ranks second in terms of cancer mortality worldwide. Molecular magnetic resonance imaging (MRI) targeting HCC biomarkers such as alpha-fetoprotein (AFP) or glypican-3 (GPC3) offers new strategies to enhance specificity and help early diagnosis of HCC. However, the existing iron oxide nanoparticle-based MR molecular probes singly target AFP or GPC3, which may hinder their efficiency to detect heterogeneous micro malignant HCC tumors < 1 cm (MHCC).