Citations

Abstract BACKGROUND: Endpoints in clinical trials using novel treatments are evaluated by RANO criteria, which provide an estimate of tumor size from two-dimensional measurements along the most prominent axial slice. However, pediatric low-grade gliomas (pLGG) commonly have variegated shapes with solid and cystic components, potentially resulting in misevaluation of true tumor volume and thus, ultimately, trial outcome. OBJECTIVES: We aim to characterize treatment response through volumetric assessment of progressive/recurrent pLGGs treated with single-agent everolimus on PNOC001 clinical trial. We seek to identify clinically relevant criteria that provide added value to response assessment beyond 2D measurements. METHODS: In a cohort of 44 patients we performed 3D-segmentation of solid, cystic and whole tumor within our PACS-framework and compared results to previously carried-out central imaging review by RANO criteria which had yielded 15 PD, 27 SD, 2 PR and 0 CR. RESULTS: 8 tumors were solid only and 36 had a mixed solid-cystic appearance. When evaluating the entire tumor (i.e. solid and cystic components combined) and using the same RANO cutoff criteria, one case changed from PR to SD, one changed from SD to PR, 3 changed from SD to PD, and 7 changed from PD to SD, resulting in an overall discordance of 27% of cases. CONCLUSION: We propose that incorporation of volumetrics into response assessment provides additional and potentially more accurate information beyond RANO-based measurements. It is crucial to note that the above-reported changes represent numerical discrepancies as opposed to true-to-reality changes in clinical outcome. Determining representative thresholds for the deployment of volumetric measures in clinical trials will be critical. Future work will include data from the PNOC002 clinical trial and evaluate inter-reader agreement and reader discordance. With the availability of PACS-based 3D-tools in neuroradiology practice, well-defined volumetric criteria could be incorporated prospectively into treatment response analysis in clinical trials.

The published literature on cervicovaginal cytology includes fewer than ten reported cases of Cokeromyces recurvatus identified in Pap test samples. We report a unique case of an asymptomatic 27-year-old female with persistent gynecologic tract colonization by C. recurvatus in which distinctive fungal microorganisms were identified in three samples collected over three consecutive years.

Background: Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD. Methods: Consecutive patients (4/8/2015 – 12/31/2020) undergoing rest and postexercise ankle–brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 – 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance. Results: Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92–0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91–0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison). Conclusion: An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.

Oncolytic viruses (OVs) encoding a variety of transgenes have been evaluated as therapeutic tools to increase the efficacy of chimeric antigen receptor (CAR)–modified T cells in the solid tumor microenvironment (TME). Here, using systemically delivered OVs and CAR T cells in immunocompetent mouse models, we have defined a mechanism by which OVs can potentiate CAR T cell efficacy against solid tumor models of melanoma and glioma. We show that stimulation of the native T cell receptor (TCR) with viral or virally encoded epitopes gives rise to enhanced proliferation, CAR-directed antitumor function, and distinct memory phenotypes. In vivo expansion of dual-specific (DS) CAR T cells was leveraged by in vitro preloading with oncolytic vesicular stomatitis virus (VSV) or reovirus, allowing for a further in vivo expansion and reactivation of T cells by homologous boosting. This treatment led to prolonged survival of mice with subcutaneous melanoma and intracranial glioma tumors. Human CD19 CAR T cells could also be expanded in vitro with TCR reactivity against viral or virally encoded antigens and was associated with greater CAR-directed cytokine production. Our data highlight the utility of combining OV and CAR T cell therapy and show that stimulation of the native TCR can be exploited to enhance CAR T cell activity and efficacy in mice.

Abstract Background Magnetic Resonance Imaging (MRI) is the mainstay for neurosurgical oncology but not for informing us about glioma biology. An obstacle to developing MR-based glioma biomarkers is the absence of rigorous correlation between MRI features and glioma biology as assessed in multi-regional biopsies, within and across patients. Methods We directly addressed this obstacle by collating a unique cohort of 202 MRI-localized biopsies from 58 patients. We define a low-dimensional transcriptional pseudotime continuum along which heterogeneous high-grade glioma (HGG) samples organize both within and across patients. Results We observe three polarized transcriptional tissue states: infiltrated brain, immune/inflammatory, and proliferative associated with patterns of cohabitation of cellular subpopulations. The states and deconvolved populations show correlation with enhancement status on T1Gd MRI. Moreover, discrete MRI habitats, regions sharing common imaging features, defined as combinations of high or low signal intensity across multiparametric MRI revealed 14 MRI habitats. We order the MRI habitats according to the average pseudotime on the transcriptional continuum. We find that MRI habitats with low pseudotime (associated with early tumor development and diffusely invaded brain tissue) localized at the periphery of the tumor whilst high pseudotime either proliferative or immune/inflammatory states were towards the core of the lesion. We find that composition of MRI habitats are impacted by MGMT status and patient sex. Conclusion This suggests that ongoing aggregation of MRI-localized biopsies may augment our projection of biology onto MRI habitats to support tracking of the evolution of cellular ecologies within and across each patient’s tumor over time. Key Points Trajectory inference of HGG samples reveals a continuum connecting three polarized tissue states reflecting transitions in cellular population ecologies and gene set enrichment Combining multiparametric MRI features into regional MRI habitats reveal clinically-distinct patterns of cell co-habitation and tissue state MGMT methylation status shifts the cellular patterns of cohabitation within MRI habitats Importance of the Study Regional heterogeneity is a defining characteristic of high-grade glioma (HGG). Mapping the transcriptomic biology of multiregionally sampled MRI-localized biopsies from high-grade gliomas onto a low-dimensional space reveals a continuum between three primary tissue states: infiltrated brain, immune/inflammatory, and proliferative. Each state is associated with distinct cell cohabitation ecologies. Transitions between the tissue states coordinate with changes in tumor composition, revealing potential direct connections between tumor biology shifts and their manifestation on clinical imaging.

AbstractIntroductionPatients with small‐cell lung cancer (SCLC) have a very poor prognosis. However, a subset of SCLC achieves long‐term survival. The objective of this study was to investigate factors and pattern of long‐term survival in patients with limited‐stage small cell lung cancer (LS‐SCLC) who achieved a complete response (CR) after chemoradiotherapy.Patient and MethodsThis was a single‐center retrospective study. The analysis of hazard ratio (HR) and 95% confidence interval (CI) was performed using Cox proportional hazards model. For pattern analysis, the date of recurrence was used as the endpoint. The nominal categorical variables were analyzed by the χ2 test. Survival was estimated using the Kaplan–Meier model, and the results were reported as the median and interquartile range.ResultsWe identified 162 patients, median age was 64.7 (56.2–70.2) years, and 94 (58%) were females. Eighty‐one patients (50%) had recurrence during follow‐up. Gastroesophageal reflux disease (GERD) (HR, 0.65; 95% CI, 0.45–0.93; p = 0.016) and neurological paraneoplastic syndrome (PNS) (HR, 0.46; 95% CI, 0.29–0.72; p < 0.001) were independent factors associated with improved overall survival (OS). Patients with GERD had prolonged recurrence free survival (RFS) compared to patients without GERD (median, 29.1 months vs. 13.9 months, p < 0.001), whereas patients with neurological PNS had a reduced recurrence rate compared to those patients without neurological PNS (No. [%], 8 [20.5] vs. 73 [59.3], p < 0.001).ConclusionsPatients with LS‐SCLC achieving a CR after chemoradiotherapy, GERD, and neurological PNS were associated with improved OS. GERD and neurological PNS were associated with longer RFS and lower recurrence rate, respectively.

Metastatic neuroendocrine tumour (NET) to brain has been reported in 1.5–5% of patients with NETs. Differentiation between intracranial NET metastasis and meningiomas can cause a diagnostic dilemma. We present a symptomatic case of a 66-year-old male with a history of left-sided skull base mass. The diagnosis of a meningioma was made based on the MRI findings and clinical presentation. The patient received radiation and the mass remained stable on serial MRI images at follow-up visits. Five years after his initial presentation, the patient’s mass showed further growth. He also complained of worsening of his recent diagnosis of irritable bowel syndrome and fluctuations in his blood pressure. Surgical resection was performed, and histopathological features were consistent with moderately differentiated neuroendocrine tumour. Further evaluation with 68 Gallium-DOTATATE positron emission-computed tomography (Ga-68 PET/CT) showed metastatic disease involving the bones, lymph nodes, and liver without convincing evidence of the location of primary malignancy within the bowel loops or the pancreas. The patient was started on combination of capecitabine and temozolomide with partial response and significant improvement of his symptoms. This case highlights the clinical and radiological behaviour of intracranial NET that can mimic the diagnosis of meningioma.

Abstract INTRODUCTION Dendritic cells (DCs) are potent antigen presenting cells that can be exploited to initiate an adaptive anti-tumoral immune response. DC vaccine clinical trials for primary glioblastoma (GBM) have reported prolonged progression-free survival without any impact on overall survival (OS). We report a radiomics approach that identifies a subpopulation of patients with prolonged OS in clinical trial MC1272. METHODS Twenty adults with primary GBM undergoing standard-of-care therapy were enrolled in MC1272. Autologous DCs were pulsed with allogenic GBM cell lines to generate vaccines that were administered for up to twelve cycles. Standard brain imaging was obtained at the initiation of treatment and two months afterwards. An independent cohort of image-localized biopsies underwent RNA sequencing followed by cellular deconvolution to estimate T cell abundance. A machine learning model was trained to predict intratumoral T cell abundance from imaging features, and the model was applied to MC1272 patient imaging. RESULTS Voxelwise predictions of T cell abundance were generated for each patient’s pre- and post-treatment images. The difference in total intratumoral T cell abundance between imaging timepoints classified patients into increasing or decreasing T cell groups. Patients whose T cells increased had longer OS (median, 21 months) than those whose T cells decreased (median, 10 months; p=0.0035). The significance remained in a Cox proportional hazards analysis that adjusted for patient age and sex (p=0.011). CONCLUSIONS A spatially-resolved radiomics model detected that an intratumoral T cell influx after DC vaccine therapy was associated with prolonged OS. The “post-treatment” imaging in this study was obtained a mere two months after DC vaccine initiation, suggesting that our radiomics model can provide an early indication of treatment responsiveness and prognosis in these patients.