Uterine artery embolization in Tanzania: a procedure with major public health implications.

Background: The burden of uterine fibroids is substantial in sub-Saharan Africa (SSA), with up to 80% of black women harboring them in their lifetime. While uterine artery embolization (UAE) has emerged as an effective alternative to surgery to manage this condition, the procedure is not available to the vast majority of women living in SSA due to limited access to interventional radiology (IR) in the region. One of the few countries in SSA now offering UAE in a public hospital setting is Tanzania.

Molecular Imaging of Extracellular Tumor pH to Reveal Effects of Locoregional Therapy on Liver Cancer Microenvironment.

Purpose: To establish magnetic resonance (MR)-based molecular imaging paradigms for the noninvasive monitoring of extracellular pH (pH) as a functional surrogate biomarker for metabolic changes induced by locoregional therapy of liver cancer.

Experimental Design: Thirty-two VX2 tumor-bearing New Zealand white rabbits underwent longitudinal imaging on clinical 3T-MRI and CT scanners before and up to 2 weeks after complete conventional transarterial chemoembolization (cTACE) using ethiodized oil (lipiodol) and doxorubicin. MR-spectroscopic imaging (MRSI) was employed for pH mapping.

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma.

Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. The objective of this study was to develop a method to predict response to intra-arterial treatment prior to intervention. The method provides a general framework for predicting outcomes prior to intra-arterial therapy.

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

Purpose: To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques.

Materials And Methods: This study included 36 patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization. The cohort (age 62 ± 8.

Science to Practice: Decrypting the Enigma of Ablation-induced Off-Target Effects-Is Network Pathway Analysis the Final Piece of the Puzzle?

As part of the ongoing effort to better understand and mitigate pro-oncogenic off-target effects of imaging-guided radiofrequency ablation (RFA), Kumar et al ( 1 ) used gene expression and network pathway analysis to examine the gene activation profiles in the peri-ablational zone after RFA in a breast adenocarcinoma liver metastasis animal model. Their analysis identified STAT3 (signal transducer and activator of transcription 3) as a key transcription factor upregulated in many signaling pathways in the peri-ablational zone after RFA. Consequently, the authors successfully used two STAT3 inhibitors to reduce distant tumor growth after treatment with RFA.