PA- and NP-led Ommaya clinics to manage leptomeningeal carcinomatosis.

OBJECTIVES: Physician assistants (PAs) and NPs are essential to quality care delivery. The need to demonstrate value and optimize PA and NP roles in neurology subspecialty clinics is unmet. We outline the development of a PA- and NP-led neuro-oncology procedural clinic and provide metrics to support the institutional and clinician value added. METHODS: We designed a PA- and NP-led Geisinger Ommaya Clinic (GOC) to manage leptomeningeal carcinomatosis (LMC) with defined clinician roles and the GOC treatment protocol. A retrospective review of 135 patients (2012-2019) compared survival outcomes for patients treated on the protocol compared with those treated off the protocol. RESULTS: Centralized care in the GOCs minimized shared physician encounters and improved PA and NP autonomy and utility. LMC therapy as part of the GOC protocol improved care continuity and survival outcomes. CONCLUSIONS: PA- and NP-led procedural clinics optimize use of these clinicians and open physician availability for nonprocedural duties. This research highlights the institutional patient and financial benefit while demonstrating the operational and leadership growth potential for PAs and NPs.

Case series review of neuroradiologic changes associated with immune checkpoint inhibitor therapy.

While immuno-oncotherapy (IO) has significantly improved outcomes in the treatment of systemic cancers, various neurological complications have accompanied these therapies. Treatment with immune checkpoint inhibitors (ICIs) risks multi-organ autoimmune inflammatory responses with gastrointestinal, dermatologic, and endocrine complications being the most common types of complications. Despite some evidence that these therapies are effective to treat central nervous system (CNS) tumors, there are a significant range of related neurological side effects due to ICIs. Neuroradiologic changes associated with ICIs are commonly misdiagnosed as progression and might limit treatment or otherwise impact patient care. Here, we provide a radiologic case series review restricted to neurological complications attributed to ICIs, anti-CTLA-4, and PD-L-1/PD-1 inhibitors. We report the first case series dedicated to the review of CNS/PNS radiologic changes secondary to ICI therapy in cancer patients. We provide a brief case synopsis with neuroimaging followed by an annotated review of the literature relevant to each case. We present a series of neuroradiological findings including nonspecific parenchymal and encephalitic, hypophyseal, neural (cranial and peripheral), meningeal, cavity-associated, and cranial osseous changes seen in association with the use of ICIs. Misdiagnosis of radiologic abnormalities secondary to neurological immune-related adverse events can impact patient treatment regimens and clinical outcomes. Rapid recognition of various neuroradiologic changes associated with ICI therapy can improve patient tolerance and adherence to cancer therapies.

Early imaging marker of progressing glioblastoma: a window of opportunity.

PURPOSE: Therapeutic intervention at glioblastoma (GBM) progression, as defined by current assessment criteria, is arguably too late as second-line therapies fail to extend survival. Still, most GBM trials target recurrent disease. We propose integration of a novel imaging biomarker to more confidently and promptly define progression and propose a critical timepoint for earlier intervention to extend therapeutic exposure. METHODS: A retrospective review of 609 GBM patients between 2006 and 2019 yielded 135 meeting resection, clinical, and imaging inclusion criteria. We qualitatively and quantitatively analyzed 2000+ sequential brain MRIs (initial diagnosis to first progression) for development of T2 FLAIR signal intensity (SI) within the resection cavity (RC) compared to the ventricles (V) for quantitative inter-image normalization. PFS and OS were evaluated using Kaplan-Meier curves stratified by SI. Specificity and sensitivity were determined using a 2 × 2 table and pathology confirmation at progression. Multivariate analysis evaluated SI effect on the hazard rate for death after adjusting for established prognostic covariates. Recursive partitioning determined successive quantifiers and cutoffs associated with outcomes. Neurological deficits correlated with SI. RESULTS: Seventy-five percent of patients developed SI on average 3.4 months before RANO-assessed progression with 84% sensitivity. SI-positivity portended neurological decline and significantly poorer outcomes for PFS (median, 10 vs. 15 months) and OS (median, 20 vs. 29 months) compared to SI-negative. RC/V ratio ≥ 4 was the most significant prognostic indicator of death. CONCLUSION: Implications of these data are far-reaching, potentially shifting paradigms for glioma treatment response assessment, altering timepoints for salvage therapeutic intervention, and reshaping glioma clinical trial design.

Case Report of Immuno-Oncotherapy (IO) Provoked Encephalitis Mimicking Brain Metastasis in a Patient with History of Traumatic Brain Injury.

BACKGROUND: Immuno-oncotherapy (IO) has revolutionized systemic cancer care but remains experimental in brain tumors. IO treatment risks multiorgan autoimmune inflammatory responses that limit its use. The central nervous system (CNS) is an immune-specialized compartment with restricted cellular access, thus fewer cases are reported for immune-mediated encephalitis. Interestingly, patients with history of blood-brain barrier compromise are potentially at higher risk for immune cell trafficking to the CNS. CASE DESCRIPTION: We report the first case, to our knowledge, of a 70-year-old man with clear cell renal cell carcinoma with pulmonary metastases treated with lung irradiation, nephrectomy, and chemotherapy prior to switching to single-agent nivolumab IO. The patient presented with new-onset generalized tonic-clonic seizure and left visual field-cut. Review of patient history revealed remote traumatic brain injury (TBI). Brain imaging noted a solid-enhancing right occipital mass that was presumed metastasis versus lymphoma. Cerebrospinal fluid cytology was negative for malignancy but concerning for lymphoproliferative process not determined to be malignant. The patient started steroids and anti-epileptic therapy. After negative systemic cancer re-staging, IO was discontinued and steroids were initiated with demonstrated patient clinical improvement. CONCLUSIONS: We concluded the diagnosis of immune-mediated encephalitis secondary to IO with collection of reactive T-cells within the area of encephalomalacia. The area of encephalomalacia from prior TBI served to compartmentalize the reactive lymphocytes, giving the appearance of a mass. Taken together, new onset seizure in patients on IO might signal encephalitis and CNS metastatic mimicry should be considered in patients with a prior history of TBI and encephalomalacia.

Automated Estimation of Acute Infarct Volume from Noncontrast Head CT Using Image Intensity Inhomogeneity Correction.

Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density "infarcts," Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.

Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration.

Intracranial hemorrhage (ICH) requires prompt diagnosis to optimize patient outcomes. We hypothesized that machine learning algorithms could automatically analyze computed tomography (CT) of the head, prioritize radiology worklists and reduce time to diagnosis of ICH. 46,583 head CTs (~2 million images) acquired from 2007-2017 were collected from several facilities across Geisinger. A deep convolutional neural network was trained on 37,074 studies and subsequently evaluated on 9499 unseen studies. The predictive model was implemented prospectively for 3 months to re-prioritize "routine" head CT studies as "stat" on realtime radiology worklists if an ICH was detected. Time to diagnosis was compared between the re-prioritized "stat" and "routine" studies. A neuroradiologist blinded to the study reviewed false positive studies to determine whether the dictating radiologist overlooked ICH. The model achieved an area under the ROC curve of 0.846 (0.837-0.856). During implementation, 94 of 347 "routine" studies were re-prioritized to "stat", and 60/94 had ICH identified by the radiologist. Five new cases of ICH were identified, and median time to diagnosis was significantly reduced (p < 0.0001) from 512 to 19 min. In particular, one outpatient with vague symptoms on anti-coagulation was found to have an ICH which was treated promptly with reversal of anticoagulation, resulting in a good clinical outcome. Of the 34 false positives, the blinded over-reader identified four probable ICH cases overlooked in original interpretation. In conclusion, an artificial intelligence algorithm can prioritize radiology worklists to reduce time to diagnosis of new outpatient ICH by 96% and may also identify subtle ICH overlooked by radiologists. This demonstrates the positive impact of advanced machine learning in radiology workflow optimization.