Middle meningeal artery embolization using cone-beam computed tomography augmented guidance in patients with cancer.

PURPOSE: The purpose of this study was to evaluate the safety and efficacy of middle meningeal artery embolization (MMAE) performed under cone-beam computed tomography (CBCT) augmented guidance in patients with cancer. MATERIALS AND METHODS: Eleven patients with cancer (seven women, four men; median age, 75 years; age range: 42-87 years) who underwent 17 MMAEs under CBCT with a combination of particles and coils for chronic subdural hematoma (SDH) (n = 6), postoperative SDH (n = 3), or preoperative embolization of meningeal tumor (n = 2) from 2022 to 2023 were included. Technical success, fluoroscopy time (FT), reference dose (RD), kerma area product (KAP) were analyzed. Adverse events and outcomes were recorded. RESULTS: The technical success rate was 100% (17/17). Median MMAE procedure duration was 82 min (interquartile range [IQR]: 70, 95; range: 63-108 min). The median FT was 24 min (IQR: 15, 48; range: 21.5-37.5 min); the median RD was 364 mGy (IQR: 37, 684; range: 131.5-444.5 mGy); and the median KAP was 46.4 Gy.cm2 (9.6, 104.5; range: 30.2-56.6 Gy.cm2). No further interventions were needed. The adverse event rate was 9% (1/11), with one pseudoaneurysm at the puncture site in a patient with thrombocytopenia, which was treated by stenting. The median follow-up was 48 days (IQR; 14, 251; range: 18.5-91 days]. SDH reduced in 11 of 15 SDHs (73%) as evidenced by follow-up imaging, with a size reduction greater than 50% in 10/15 SDHs (67%) . CONCLUSION: MMAE under CBCT is a highly effective treatment option, but appropriate patient selection and careful consideration of potential risks and benefits is important for optimal patient outcomes.

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. 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.

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 ± 8.9 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. RESULTS: 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). CONCLUSIONS: 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.

Informatics in radiology: improving clinical work flow through an AIM database: a sample web-based lesion tracking application.

Quantitative assessments on images are crucial to clinical decision making, especially in cancer patients, in whom measurements of lesions are tracked over time. However, the potential value of quantitative approaches to imaging is impeded by the difficulty and time-intensive nature of compiling this information from prior studies and reporting corresponding information on current studies. The authors believe that the quantitative imaging work flow can be automated by making temporal data computationally accessible. In this article, they demonstrate the utility of the Annotation and Image Markup standard in a World Wide Web-based application that was developed to automatically summarize prior and current quantitative imaging measurements. The system calculates the Response Evaluation Criteria in Solid Tumors metric, along with several alternative indicators of cancer treatment response, by using the data stored in the annotation files. The application also allows the user to overlay the recorded metrics on the original images for visual inspection. Clinical evaluation of the system demonstrates its potential utility in accelerating the standard radiology work flow and in providing a means to evaluate alternative response metrics that are difficult to compute by hand. The system, which illustrates the utility of capturing quantitative information in a standard format and linking it to the image from which it was derived, could enhance quantitative imaging in clinical practice without adversely affecting the current work flow.