Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors.

Purpose: This study aims to identify radiomic features extracted from contrast-enhanced CT scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in patients with head and neck cancer (HNC) treated with radiotherapy (RT). Materials and Methods: Contrast-enhanced CT (CECT) images were collected for 150 patients (80% train, 20% test) with confirmed ORN diagnosis at The University of Texas MD Anderson Cancer Center between 2008 and 2018. Using PyRadiomics, radiomic features were extracted from manually segmented ORN regions and the corresponding automated control regions, the later defined as the contralateral healthy mandible region. A subset of pre-selected features was obtained based on correlation analysis (r > 0.95) and used to train a Random Forest (RF) classifier with Recursive Feature Elimination. Model explainability SHapley Additive exPlanations (SHAP) analysis was performed on the 20 most important features identified by the trained RF classifier. Results: From a total of 1316 radiomic features extracted, 810 features were excluded due to high collinearity. From a set of 506 pre-selected radiomic features, the optimal subset resulting on the best discriminative accuracy of the RF classifier consisted of 67 features. The RF classifier was well calibrated (Log Loss 0.296, ECE 0.125) and achieved an accuracy of 88% and a ROC AUC of 0.96. The SHAP analysis revealed that higher values of Wavelet-LLH First-order Mean and Median were associated with ORN of the jaw (ORNJ). Conversely, higher Exponential GLDM Dependence Entropy and lower Square First-order Kurtosis were more characteristic of normal mandibular tissue. Conclusion: This study successfully developed a CECT-based radiomics model for differentiating ORNJ from healthy mandibular tissue in HNC patients after RT. Future work will focus on the detection of subclinical ORNJ regions to guide earlier interventions.

Distribution and Disparities of Industry Payments to Neuroradiologists.

BACKGROUND AND PURPOSE: Physician-industry relationships can be useful for driving innovation and technologic progress, though little is known about the scale or impact of industry involvement in neuroradiology. The purpose of this study was to assess the trends and distributions of industry payments to neuroradiologists. MATERIALS AND METHODS: Neuroradiologists were identified using a previously-validated method based on Work Relative Value Units and Neiman Imaging Types of Service classification. Data on payments from industry were obtained from the Open Payments database from the Centers for Medicare & Medicaid Services, from 2016 to 2021. Payments were grouped into 7 categories, including consulting fees, education, gifts, medical supplies, research, royalties/ownership, and speaker fees. Descriptive statistics were calculated. RESULTS: A total of 3019 neuroradiologists were identified in this study. Between 2016 and 2021, 48% (1440/3019) received at least 1 payment from industry, amounting to a total number of 21,967 payments. Each year, among those receiving payments from industry, each unique neuroradiologist received between a mean of 5.49-7.42 payments and a median of 2 payments, indicating a strong rightward skew to the distribution of payments. Gifts were the most frequent payment type made (60%, 13,285/21,967) but accounted for only 4.1% ($689,859/$17,010,546) of payment value. The greatest aggregate payment value came from speaker fees, which made up 36% ($6,127,484/$17,010,546) of the total payment value. The top 5% highest paid neuroradiologists received 42% (9133/21,967) of payments, which accounted for 84% ($14,284,120/$17,010,546) of the total dollar value. Since the start of the coronavirus 2019 (COVID-19) pandemic, the number of neuroradiologists receiving industry payments decreased from a mean of 671 neuroradiologists per year prepandemic (2016-2019) to 411 in the postpandemic (2020-2021) era (P = .030). The total number of payments to neuroradiologists decreased from 4177 per year prepandemic versus 2631 per year postpandemic (P = .011). CONCLUSIONS: Industry payments to neuroradiologists are highly concentrated among top earners, particularly among the top 5% of payment recipients. The number of payments decreased during the COVID-19 pandemic, though the dollar value of payments was offset by coincidental increases in royalty payments. Further investigation is needed in subsequent years to determine if the postpandemic changes in industry payment trends continue.

The Impact of MRI-Based Advanced Neuroimaging on Neurooncologists' Clinical Decision-Making in Patients With Posttreatment High-Grade Glioma: A Prospective Survey-Based Study.

Background: Advanced MRI-based neuroimaging techniques, such as perfusion and spectroscopy, have been increasingly incorporated into routine follow-up protocols in patients treated for high-grade glioma (HGG), to help differentiate tumor progression from treatment effect. However, these techniques' influence on clinical management remains poorly understood. Objective: To evaluate the impact of MRI-based advanced neuroimaging on clinical decision-making in patients with HGG in the posttreatment setting. Methods: This prospective study, performed at a comprehensive cancer center from March 1, 2017, to October 31, 2020, included adult patients treated by chemoradiation for WHO grade 4 diffuse glioma who underwent MRIbased advanced neuroimaging (comprising multiple perfusion imaging sequences and spectroscopy) to further evaluate findings on conventional MRI equivocal for tumor progression versus treatment effect. The ordering neuro-oncologists completed surveys before and after each advanced neuroimaging session. The percent of care episodes with a change between the intended and actual management plan on the surveys conducted before and after advanced neuroimaging, respectively, was computed and compared with a previously published percent using the Wald test for independent samples proportions. Results: The study included 63 patients (mean age, 55±13 years; 36 women, 27 men) who underwent 70 advanced neuroimaging sessions. Ordering neuro-oncologists' intended and actual management plans on the surveys completed before and after advanced neuroimaging, respectively, differed in 44% (31/70, [95% CI: 33-56%]) of episodes, which differed from the previously published frequency of 8.5% (5/59) (p<.001). These management plan changes included selection of a different plan for 6/8 episodes with an intended plan to enroll patients in a clinical trial, 12/19 episodes with an intended plan to change chemotherapeutic agents, 4/8 episodes with an intended plan of surgical intervention, and 1/2 episodes with an intended plan of re-irradiation. The ordering neuro-oncologists found advanced neuroimaging to be helpful in 93% (95% CI: 87%-99%) (65/70) of episodes. Conclusion: Neuro-oncologists' management plans changed in a substantial fraction of adult patients with HGG who underwent advanced neuroimaging to further evaluate conventional MRI findings equivocal for tumor progression versus treatment effect. Clinical Impact: The findings support incorporation of advanced neuroimaging into HGG posttreatment monitoring protocols.

Additive Value of Magnetic Resonance Simulation Before Chemoradiation in Evaluating Treatment Response and Pseudoprogression in High-Grade Gliomas.

PURPOSE: A dedicated magnetic resonance imaging simulation (MRsim) for radiation treatment (RT) planning in patients with high-grade glioma (HGG) can detect early radiologic changes, including tumor progression after surgery and before standard of care chemoradiation. This study aimed to determine the effect of using postoperative magnetic resonance imaging (MRI) versus MRsim as the baseline for response assessment and reporting pseudoprogression on follow-up imaging at 1 month (FU1) after chemoradiation. METHODS AND MATERIALS: Histologically confirmed patients with HGG were planned for 6 weeks of RT in a prospective study for adaptive RT planning. All patients underwent postoperative MRI, MRsim, and follow-up MRI scans every 2 to 3 months. Tumor response was assessed by 3 independent blinded reviewers using Response Assessment in Neuro-Oncology criteria when baseline was either postoperative MRI or MRsim. Interobserver agreement was calculated using Light's kappa. RESULTS: Thirty patients (median age, 60.5 years; IQR, 54.5-66.3) were included. Median interval between surgery and RT was 34 days (IQR, 27-41). Response assessment at FU1 differed in 17 patients (57%) when the baseline was postoperative MRI versus MRsim, including true progression versus partial response or stable disease in 11 (37%) and stable disease versus partial response in 6 (20%) patients. True progression was reported in 19 patients (63.3%) on FU1 when the baseline was postoperative MRI versus 8 patients (26.7%) when the baseline was MRsim (P = .004). Pseudoprogression was observed at FU1 in 12 (40%) versus 4 (13%) patients, when the baseline was postoperative MRI versus MRsim (P = .019). Interobserver agreement between observers was moderate (κ = 0.579; P < .001). CONCLUSIONS: Our study demonstrates the value of acquiring an updated MR closer to RT in patients with HGG to improve response assessment, and accuracy in evaluation of pseudoprogression even at the early time point of first follow-up after RT. Earlier identification of patients with true progression would enable more timely salvage treatments including potential clinical trial enrollment to improve patient outcomes.

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.

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.

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.