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. This study aims to assess the safety and effectiveness of UAE in this new environment.
Methods
From June 2019 to July 2022, a single-center, retrospective cohort study was conducted at Tanzania’s first IR service on all patients who underwent UAE for the management of symptomatic fibroids or adenomyosis. Patients were selected for the procedure based on symptom severity, imaging findings, and medical management failure. Procedural technical success and adverse events were recorded for all UAEs. Self-reported symptom severity and volumetric response on imaging were compared between baseline and six-months post-procedure using paired sample t-tests.
Results
During the study period, 92.1% (
n
= 35/38) of patients underwent UAE for the management of symptomatic fibroids and 7.9% (
n
= 3/38) for adenomyosis. All (
n
= 38/38) were considered technically successful and one minor adverse event occurred (2.7%). Self-reported symptom-severity scores at six-months post-procedure decreased in all categories: abnormal uterine bleeding from 8.8 to 3.1 (-5.7), pain from 6.7 to 3.2 (-3.5), and bulk symptoms from 2.8 to 1 (-1.8) (
p
< 0.01). 100% of patients reported satisfaction with outcomes. Among the nine patients with follow-up imaging, there was a mean volumetric decrease of 35.5% (
p
= 0.109).
Conclusions
UAE for fibroids and adenomyosis can be performed with high technical success and low complication rates in a low-resource setting like Tanzania, resulting in significant symptom relief for patients. Building capacity for UAE has major public health implications not only for fibroids and adenomyosis, but can help address the region’s leading cause of maternal mortality, postpartum hemorrhage.
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.
This study included 36 patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization. The cohort (age 62 ± 8.9 years; 31 men; 13 white; 24 Eastern Cooperative Oncology Group performance status 0, 10 status 1, 2 status 2; 31 Child-Pugh stage A, 4 stage B, 1 stage C; 1 Barcelona Clinic Liver Cancer stage 0, 12 stage A, 10 stage B, 13 stage C; tumor size 5.2 ± 3.0 cm; number of tumors 2.6 ± 1.1; and 30 conventional transarterial chemoembolization, 6 with drug-eluting embolic agents). MR imaging was obtained before and 1 month after transarterial chemoembolization. Image-based tumor response to transarterial chemoembolization was assessed with the use of the 3D quantitative European Association for the Study of the Liver (qEASL) criterion. Clinical information, baseline imaging, and therapeutic features were used to train logistic regression (LR) and random forest (RF) models to predict patients as treatment responders or nonresponders under the qEASL response criterion. The performance of each model was validated using leave-one-out cross-validation.
Both LR and RF models predicted transarterial chemoembolization treatment response with an overall accuracy of 78% (sensitivity 62.5%, specificity 82.1%, positive predictive value 50.0%, negative predictive value 88.5%). The strongest predictors of treatment response included a clinical variable (presence of cirrhosis) and an imaging variable (relative tumor signal intensity >27.0).
Transarterial chemoembolization outcomes in patients with HCC may be predicted before procedures by combining clinical patient data and baseline MR imaging with the use of AI and ML techniques.
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.
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. Multiparametric MRI and CT were performed to quantify tumor enhancement, diffusion, and lipiodol coverage of the tumor posttherapy. In addition, incomplete cTACE with reduced chemoembolic doses was applied to mimic undertreatment and exploit pH
mapping to detect viable tumor residuals. Imaging findings were correlated with histopathologic markers indicative of metabolic state (HIF-1α, GLUT-1, and LAMP-2) and viability (proliferating cell nuclear antigen and terminal deoxynucleotidyl-transferase dUTP nick-end labeling).
Untreated VX2 tumors demonstrated a significantly lower pH
(6.80 ± 0.09) than liver parenchyma (7.19 ± 0.03,
< 0.001). Upregulation of HIF-1α, GLUT-1, and LAMP-2 confirmed a hyperglycolytic tumor phenotype and acidosis. A gradual tumor pH
increase toward normalization similar to parenchyma was revealed within 2 weeks after complete cTACE, which correlated with decreasing detectability of metabolic markers. In contrast, pH
mapping after incomplete cTACE indicated both acidic viable residuals and increased tumor pH
of treated regions. Multimodal imaging revealed durable tumor devascularization immediately after complete cTACE, gradually increasing necrosis, and sustained lipiodol coverage of the tumor.
MRSI-based pH
mapping can serve as a longitudinal monitoring tool for viable tumors. As most liver tumors are hyperglycolytic creating microenvironmental acidosis, therapy-induced normalization of tumor pH
may be used as a functional biomarker for positive therapeutic outcome.
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. It involves pooling clinical, demographic and imaging data across a cohort of patients and using these data to train a machine learning model. The trained model is applied to new patients in order to predict their likelihood of response to intra-arterial therapy. The method entails the acquisition and parsing of clinical, demographic and imaging data from N patients who have already undergone trans-arterial therapies. These data are parsed into discrete features (age, sex, cirrhosis, degree of tumor enhancement, etc.) and binarized into true/false values (e.g., age over 60, male gender, tumor enhancement beyond a set threshold, etc.). Low-variance features and features with low univariate associations with the outcome are removed. Each treated patient is labeled according to whether they responded or did not respond to treatment. Each training patient is thus represented by a set of binary features and an outcome label. Machine learning models are trained using N - 1 patients with testing on the left-out patient. This process is repeated for each of the N patients. The N models are averaged to arrive at a final model. The technique is extensible and enables inclusion of additional features in the future. It is also a generalizable process that may be applied to clinical research questions outside of interventional radiology. The main limitation is the need to derive features manually from each patient. A popular modern form of machine learning called deep learning does not suffer from this limitation, but requires larger datasets.
The aim of this project is the sustainable implementation of a vascular anomalies (VA) program in Tanzania.
In 2021 the first interdisciplinary VA program was initiated at Muhimbili National Hospital ...(MNH), Dar Es Salaam, Tanzania in a stepwise approach. During the planning phase the clinical need for minimally-invasive therapies of VAs and the preexisting structures were assessed by the local Interventional Radiology (IR) team at MNH. During the initiation phase, an IR team from two German VA centers joined the interdisciplinary team at MNH for clinical workup, image-guided procedures and follow-up. VA patients were recruited from existing patient records or seen at clinics as
presentations following nationwide advertisement. In the post-processing phase joined online conferences for follow-up and support in management of new patients were established. Further follow-up was supported by attending providers from other established VA centers, traveling to bolster the primary operators of MNH.
The first interdisciplinary VA program was successfully launched in Tanzania. Minimally-invasive treatments were successfully trained, by performing ultrasound-guided sclerotherapy with polidocanol and bleomycin in twelve patients with slow-flow malformations, one endovascular embolization of a high-flow malformation, and medical treatment of an aggressive infantile hemangioma. Regular online follow-up presentations have been initiated. Follow-up evaluation and required treatment was sustained when appropriate.
The presented "hands-on" training set the ground for the first interdisciplinary VA program in Tanzania. This framework is expected to establish comprehensive and sustainable care of patients with VAs in East Africa and can serve as a blueprint for other sites.
PDF
