Development of a rabbit human glioblastoma model for testing of endovascular selective intra-arterial infusion (ESIA) of novel stem cell-based therapeutics.

BACKGROUND: Endovascular selective intra-arterial (ESIA) infusion of cellular oncotherapeutics is a rapidly evolving strategy for treating glioblastoma. Evaluation of ESIA infusion requires a unique animal model. Our goal was to create a rabbit human GBM model to test IA infusions of cellular therapies and to test its usefulness by employing clinical-grade microcatheters and infusion methods to deliver mesenchymal stem cells loaded with an oncolytic adenovirus, Delta-24-RGD (MSC-D24). METHODS: Rabbits were immunosuppressed with mycophenolate mofetil, dexamethasone, and tacrolimus. They underwent stereotactic xenoimplantation of human GBM cell lines (U87, MDA-GSC-17, and MDA-GSC-8-11) into the right frontal lobe. Tumor formation was confirmed on magnetic resonance imaging, histologic, and immunohistochemistry analysis. Selective microcatheter infusion of MSC-D24 was performed via the ipsilateral internal carotid artery to assess model utility and the efficacy and safety of this approach. RESULTS: Twenty-five rabbits were implanted (18 with U87, 2 MDA-GSC-17, and 5 MDA-GSC-8-11). Tumors formed in 68% of rabbits (77.8% for U87, 50.0% for MDA-GSC-17, and 40.0% for MDA-GSC-8-11). On MRI, the tumors were hyperintense on T2-weighted image with variable enhancement (evidence of blood brain barrier breakdown). Histologically, tumors showed phenotypic traits of human GBM including varying levels of vascularity. ESIA infusion into the distal internal carotid artery of 2 ml of MSCs-D24 (107 cells) was safe in the model. Examination of post infusion specimens documented that MSCs-D24 homed to the implanted tumor at 24 hours. CONCLUSIONS: The intracranial immunosuppressed rabbit human GBM model allows testing of ESIA infusion of novel therapeutics (eg, MSC-D24) in a clinically relevant fashion.

Current Reimbursement Landscape of Artificial Intelligence.

One of the biggest hurdles to widespread adoption of new procedures and technology such as artificial intelligence (AI) algorithms is payment and coverage policy. Noninvasive assessment of coronary fractional flow reserve is one AI imaging algorithm that will successfully achieve reimbursement through multiple pathways of CMS payment mechanisms in 2024. CMS is the largest provider of health care in the United States. Understanding how this AI algorithm is paid through the different fee schedules will help to understand the challenges CMS has in paying for new services and innovation in the United States.

Correlating volumetric and linear measurements of brain metastases on MRI scans using intelligent automation software: a preliminary study.

PURPOSE: The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) working group proposed a guide for treatment responses for BMs by utilizing the longest diameter; however, despite recognizing that many patients with BMs have sub-centimeter lesions, the group referred to these lesions as unmeasurable due to issues with repeatability and interpretation. In light of RANO-BM recommendations, we aimed to correlate linear and volumetric measurements in sub-centimeter BMs on contrast-enhanced MRI using intelligent automation software. METHODS: In this retrospective study, patients with BMs scanned with MRI between January 1, 2018, and December 31, 2021, were screened. Inclusion criteria were: (1) at least one sub-centimeter BM with an integer millimeter-longest diameter was noted in the MRI report; (2) patients were a minimum of 18 years of age; (3) patients with available pre-treatment three-dimensional T1-weighted spoiled gradient-echo MRI scan. The screening was terminated when there were 20 lesions in each group. Lesion volumes were measured with the help of intelligent automation software Jazz (AI Medical, Zollikon, Switzerland) by two readers. The Kruskal-Wallis test was used to compare volumetric differences. RESULTS: Our study included 180 patients. The agreement for volumetric measurements was excellent between the two readers. The volumes of the following groups were not significantly different: 1-2 mm, 1-3 mm, 1-4 mm, 2-3 mm, 2-4 mm, 3-4 mm, 3-5 mm, 4-5 mm, 5-6 mm, 5-7 mm, 6-7 mm, 6-8 mm, 6-9 mm, 7-8 mm, 7-9 mm, 8-9 mm. CONCLUSION: Our findings indicate that the largest diameter of a lesion may not accurately represent its volume. Additional research is required to determine which method is superior for measuring radiologic response to therapy and which parameter correlates best with clinical improvement or deterioration.

Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models.

At present, clinicians are expected to manage a large volume of complex clinical, laboratory, and imaging data, necessitating sophisticated analytic approaches. Machine learning-based models can use this vast amount of data to create forecasting models. We aimed to predict short- and medium-term functional outcomes in acute ischemic stroke (AIS) patients with proximal middle cerebral artery (MCA) occlusions using machine learning models with clinical, laboratory, and quantitative imaging data as inputs. Included were consecutive AIS patients with MCA M1 and proximal M2 occlusions. The XGBoost, LightGBM, CatBoost, and Random Forest were used to predict the outcome. Minimum redundancy maximum relevancy was used for selecting features. The primary outcomes were the National Institutes of Health Stroke Scale (NIHSS) shift and the modified Rankin Score (mRS) at 90 days. The algorithm with the highest area under the receiver operating characteristic curve (AUROC) for predicting the favorable and unfavorable outcome groups at 90 days was LightGBM. Random Forest had the highest AUROC when predicting the favorable and unfavorable groups based on the NIHSS shift. Using clinical, laboratory, and imaging parameters in conjunction with machine learning, we accurately predicted the functional outcome of AIS patients with proximal MCA occlusions.

Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis.

Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022. Inclusion criteria were: patients with BMs; deep learning using MRI images was applied to detect the BMs; sufficient data were present in terms of detective performance; original research articles. Exclusion criteria were: reviews, letters, guidelines, editorials, or errata; case reports or series with less than 20 patients; studies with overlapping cohorts; insufficient data in terms of detective performance; machine learning was used to detect BMs; articles not written in English. Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Finally, 24 eligible studies were identified for the quantitative analysis. The pooled proportion of patient-wise and lesion-wise detectability was 89%. Articles should adhere to the checklists more strictly. Deep learning algorithms effectively detect BMs. Pooled analysis of false positive rates could not be estimated due to reporting differences.

Contrast Enhancement Patterns in Pediatric Glioblastomas.

BACKGROUND AND PURPOSE: Brain tumors are the most common cause of cancer-related deaths among the pediatric population. Among these, pediatric glioblastomas (GBMs) comprise 2.9% of all central nervous system tumors and have a poor prognosis. The purpose of this study is to determine whether the imaging findings can be a prognostic factor for survival in children with GBMs. MATERIALS AND METHODS: The imaging studies and clinical data from 64 pediatric patients with pathology-proven GBMs were evaluated. Contrast enhancement patterns were classified into focal, ring-like, and diffuse, based on preoperative postcontrast T1-weighted magnetic resonance images. We used the Kaplan-Meier method and Cox proportional hazard regression to evaluate the prognostic value of imaging findings. RESULTS: Patients with ring-enhanced GBMs who underwent gross total resection or subtotal resection were found to have a significantly shorter progression-free survival ( P = 0.03) comparing with other enhancing and nonenhancing glioblastomas. CONCLUSIONS: In this study, we analyzed survival factors in children with pediatric glioblastomas. In the group of patients who underwent gross total resection or subtotal resection, those patients with focal-enhanced GBMs had significantly longer progression-free survival ( P = 0.03) than did those with other types of enhancing GBMs (diffuse and ring-like).

Artificial intelligence in oncologic imaging.

Radiology is integral to cancer care. Compared to molecular assays, imaging has its advantages. Imaging as a noninvasive tool can assess the entirety of tumor unbiased by sampling error and is routinely acquired at multiple time points in oncological practice. Imaging data can be digitally post-processed for quantitative assessment. The ever-increasing application of Artificial intelligence (AI) to clinical imaging is challenging radiology to become a discipline with competence in data science, which plays an important role in modern oncology. Beyond streamlining certain clinical tasks, the power of AI lies in its ability to reveal previously undetected or even imperceptible radiographic patterns that may be difficult to ascertain by the human sensory system. Here, we provide a narrative review of the emerging AI applications relevant to the oncological imaging spectrum and elaborate on emerging paradigms and opportunities. We envision that these technical advances will change radiology in the coming years, leading to the optimization of imaging acquisition and discovery of clinically relevant biomarkers for cancer diagnosis, staging, and treatment monitoring. Together, they pave the road for future clinical translation in precision oncology.

Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19.

OBJECTIVES: Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. METHODS: We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. RESULTS: These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. CONCLUSIONS: Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.

Perfusion-guided endovascular super-selective intra-arterial infusion for treatment of malignant brain tumors.

BACKGROUND: Survival for glioblastoma remains very poor despite decades of research, with a 5-year survival of only 5%. The technological improvements that have revolutionized treatment of ischemic stroke and brain aneurysms have great potential in providing more precise and selective delivery of cancer therapeutic agents to brain tumors. METHODS: We describe for the first time the use of perfusion guidance to enhance the precision of endovascular super-selective intra-arterial (ESIA) infusions of mesenchymal stem cells loaded with Delta-24 (MSC-D24) in the treatment of glioblastoma (NCT03896568). RESULTS: MRI imaging, which best defines the location of the tumor, is co-registered and fused with the patient's position using cone beam CT, resulting in optimal vessel selection and confirmation of targeted delivery through volumetric perfusion imaging. CONCLUSIONS: This technique of perfusion guided-ESIA injections (PG-ESIA) enhances our ability to perform targeted super-selective delivery of therapeutic agents for brain tumors.

Delayed FDG PET Provides Superior Glioblastoma Conspicuity Compared to Conventional Image Timing.

Background: Glioblastomas are malignant, often incurable brain tumors. Reliable discrimination between recurrent disease and treatment changes is a significant challenge. Prior work has suggested glioblastoma FDG PET conspicuity is improved at delayed time points vs. conventional imaging times. This study aimed to determine the ideal FDG imaging time point in a population of untreated glioblastomas in preparation for future trials involving the non-invasive assessment of true progression vs. pseudoprogression in glioblastoma. Methods: Sixteen pre-treatment adults with suspected glioblastoma received FDG PET at 1, 5, and 8 h post-FDG injection within the 3 days prior to surgery. Maximum standard uptake values were measured at each timepoint for the central enhancing component of the lesion and the contralateral normal-appearing brain. Results: Sixteen patients (nine male) had pathology confirmed IDH-wildtype, glioblastoma. Our results revealed statistically significant improvements in the maximum standardized uptake values and subjective conspicuity of glioblastomas at later time points compared to the conventional (1 h time point). The tumor to background ratio at 1, 5, and 8 h was 1.4 ± 0.4, 1.8 ± 0.5, and 2.1 ± 0.6, respectively. This was statistically significant for the 5 h time point over the 1 h time point (p > 0.001), the 8 h time point over the 1 h time point (p = 0.026), and the 8 h time point over the 5 h time point (p = 0.036). Conclusions: Our findings demonstrate that delayed imaging time point provides superior conspicuity of glioblastoma compared to conventional imaging. Further research based on these results may translate into improvements in the determination of true progression from pseudoprogression.

Who Will Pay for AI?

In 2020, the largest U.S. health care payer, the Centers for Medicare & Medicaid Services (CMS), established payment for artificial intelligence (AI) through two different systems in the Medicare Physician Fee Schedule (MPFS) and the Inpatient Prospective Payment System (IPPS). Within the MPFS, a new Current Procedural Terminology code was valued for an AI tool for diagnosis of diabetic retinopathy, IDx-RX. In the IPPS, Medicare established a New Technology Add-on Payment for Viz.ai software, an AI algorithm that facilitates diagnosis and treatment of large-vessel occlusion strokes. This article describes reimbursement in these two payment systems and proposes future payment pathways for AI. Keywords: Computer Applications-General (Informatics), Technology Assessment © RSNA, 2021.

Multidisciplinary Recommendations Regarding Post-Vaccine Adenopathy and Radiologic Imaging: Radiology Scientific Expert Panel.

Vaccination-associated adenopathy is a frequent imaging finding after administration of COVID-19 vaccines that may lead to a diagnostic conundrum in patients with manifest or suspected cancer, in whom it may be indistinguishable from malignant nodal involvement. To help the medical community address this concern in the absence of studies and evidence-based guidelines, this special report offers recommendations developed by a multidisciplinary panel of experts from three of the leading tertiary care cancer centers in the United States. According to these recommendations, some routine imaging examinations, such as those for screening, should be scheduled before or at least 6 weeks after the final vaccination dose to allow for any reactive adenopathy to resolve. However, there should be no delay of other clinically indicated imaging (eg, for acute symptoms, short-interval treatment monitoring, urgent treatment planning or complications) due to prior vaccination. The vaccine should be administered on the side contralateral to the primary or suspected cancer, and both doses should be administered in the same arm. Vaccination information-date(s) administered, injection site(s), laterality, and type of vaccine-should be included in every preimaging patient questionnaire, and this information should be made readily available to interpreting radiologists. Clear and effective communication between patients, radiologists, referring physician teams, and the general public should be considered of the highest priority when managing adenopathy in the setting of COVID-19 vaccination.

Effect of brain normalization methods on the construction of functional connectomes from resting-state functional MRI in patients with gliomas.

PURPOSE: Spatial normalization is an essential step in resting-state functional MRI connectomic analysis with atlas-based parcellation, but brain lesions can confound it. Cost-function masking (CFM) is a popular compensation approach, but may not benefit modern normalization methods. This study compared three normalization methods with and without CFM and determined their impact on connectomic measures in patients with glioma. METHODS: Fifty patients with glioma were included. T1 -weighted images were normalized using three different methods in SPM12, with and without CFM, which were then overlaid on the ICBM152 template and scored by two neuroradiologists. The Dice coefficient of gray-matter correspondence was also calculated. Normalized resting-state functional MRI data were parcellated using the AAL90 atlas to construct an individual connectivity matrix and calculate connectomic measures. The R2 among the different normalization methods was calculated for the connectivity matrices and connectomic measures. RESULTS: The older method (Original) performed significantly worse than the modern methods (Default and DARTEL; P < .005 in observer ranking). The use of CFM did not significantly improve the normalization results. The Original method had lower correlation with the Default and DARTEL methods (R2 = 0.71-0.74) than Default with DARTEL (R2 = 0.96) in the connectivity matrix. The clustering coefficient appears to be the most, and modularity the least, sensitive connectomic measures to normalization performance. CONCLUSION: The spatial normalization method can have an impact on resting-state functional MRI connectome and connectomic measures derived using atlas-based brain parcellation. In patients with glioma, this study demonstrated that Default and DARTEL performed better than the Original method, and that CFM made no significant difference.

Adult Primary Brain Neoplasm, Including 2016 World Health Organization Classification.

In 2016, the World Health Organization (WHO) central nervous system (CNS) classification scheme incorporated molecular parameters in addition to traditional microscopic features for the first time. Molecular markers add a level of objectivity that was previously missing for tumor categories heavily dependent on microscopic observation for pathologic diagnosis. This article provides a brief discussion of the major 2016 updates to the WHO CNS classification scheme and reviews typical MR imaging findings of adult primary CNS neoplasms, including diffuse infiltrating gliomas, ependymal tumors, neuronal/glioneuronal tumors, pineal gland tumors, meningiomas, nerve sheath tumors, solitary fibrous tumors, and lymphoma.

Endovascular Selective Intra-Arterial Infusion of Mesenchymal Stem Cells Loaded With Delta-24 in a Canine Model.

BACKGROUND: Delta-24-RGD, an oncolytic adenovirus, shows promise against glioblastoma. To enhance virus delivery, we recently demonstrated that human bone marrow-derived mesenchymal stem cells loaded with Delta-24-RGD (hMSC-D24) can eradicate glioblastomas in mouse models. There are no studies examining the safety of endovascular selective intra-arterial (ESIA) infusions of MSC-D24 in large animals simulating human clinical situations. OBJECTIVE: To perform canine preclinical studies testing the feasibility and safety of delivering increasing doses of hMSCs-D24 via ESIA infusions. METHODS: ESIA infusions of hMSC-D24 were performed in the cerebral circulation of 10 normal canines in the target vessels (internal carotid artery [ICA]/P1) via transfemoral approach using commercially available microcatheters. Increasing concentrations of hMSC-D24 or particles (as a positive control) were injected into 1 hemisphere; saline (negative control) was infused contralaterally. Toxicity (particularly embolic stroke) was assessed on postinfusion angiography, diffusion-weighted magnetic resonance imaging, clinical exam, and necropsy. RESULTS: ESIA injections were performed in the ICA (n = 7) or P1 (n = 3). In 2 animals injected with particles (positive control), strokes were detected by all assays. Of 6 canines injected with hMSC-D24 through the anterior circulation, escalating dose from 2 × 106 cells/20 mL to 1 × 108 cells/10 mL resulted in no strokes. Two animals had ischemic and hemorrhagic strokes after posterior cerebral artery catheterization. A survival experiment of 2 subjects resulted in no complications detected for 24-h before euthanization. CONCLUSION: This novel study simulating ESIA infusion demonstrates that MSCs-D24 can be infused safely at least up to doses of 1 × 108 cells/10 mL (107 cells/ml) in the canine anterior circulation using commercially available microcatheters. These findings support a clinical trial of ESIA infusion of hMSCs-D24.

The role of resting-state functional MRI for clinical preoperative language mapping.

BACKGROUND: Task-based functional MRI (tb-fMRI) is a well-established technique used to identify eloquent cortex, but has limitations, particularly in cognitively impaired patients who cannot perform language paradigms. Resting-state functional MRI (rs-fMRI) is a potential alternative modality for presurgical mapping of language networks that does not require task performance. The purpose of our study is to determine the utility of rs-fMRI for clinical preoperative language mapping when tb-fMRI is limited. METHODS: We retrospectively reviewed 134 brain tumor patients who underwent preoperative fMRI language mapping. rs-fMRI was post-processed with seed-based correlation (SBC) analysis, when language tb-fMRI was limited. Two neuroradiologists reviewed both the tb-fMRI and rs-fMRI results. Six neurosurgeons retrospectively rated the usefulness of rs-fMRI for language mapping in their patients. RESULTS: Of the 134 patients, 49 cases had limited tb-fMRI and rs-fMRI was post-processed. Two neuroradiologists found rs-fMRI beneficial for functional language mapping in 41(84%) and 43 (88%) cases respectively; Cohen's kappa is 0.83, with a 95% confidence interval (0.61, 1.00). The neurosurgeons found rs-fMRI "definitely" useful in 26 cases (60%) and "somewhat" useful in 13 cases (30%) in locating potential eloquent language centers of clinical interest. Six unsuccessful rs-fMRI cases were due to: head motion (2 cases), nonspecific functionality connectivity outside the posterior language network (1 case), and an unknown system instability (3 cases). CONCLUSIONS: This study is a proof of concept that shows SBC rs-fMRI may be a viable alternative for clinical language mapping when tb-fMRI is limited.