Efficacy of intra-arterial carboplatin and bevacizumab in the C6 rat glioma model of glioblastoma multiforme.

BACKGROUND: Utilizing an endovascular rat glioma model, this study aimed to analyze the efficacy of intra-arterial (IA) carboplatin and bevacizumab delivery with blood-brain barrier breakdown (BBBB) for glioblastoma treatment. METHODS: C6-glioma cells were stereotactically injected into the left frontal lobe of Wistar rats. Tumor growth was confirmed on day 8 via MRI. On day 9, a microcatheter was navigated under fluoroscopy from the left femoral artery to the left internal carotid artery. A volume of 2.25 mL of 25% mannitol was administered, followed by either 10 mg/kg of bevacizumab or 2.4 mg/kg of carboplatin. Serial MRI was obtained post-treatment to assess tumor response via analysis of tumor size and radiomics. Histology was analyzed after termination. RESULTS: Control tumor rats and IA mannitol treated tumor rats had fatal tumor growths, with survival until 19.75±2.21 and 36.3±15.1 days, respectively. Carboplatin and bevacizumab treated rats lived >40 days, after which they were euthanized. From serial MRI and histology, IA carboplatin treated rats exhibited tumor regression and resolution by day 35. In IA bevacizumab treated rats, there was tumor regression near the basal ganglia of the brain, closer to the IA chemotherapy injection site, which had reorganized growth patterns. From MRI, 29 unique radiomic features were significantly different between control and treated tumors (notably for total energy and skewness), and treatment responders had a distinct, early manifesting radiomic profile. CONCLUSION: IA carboplatin and bevacizumab treatment resulted in varying degrees of tumor suppression, validating the first endovascular C6 glioma model as a reliable method to assess new IA therapies.

Hybrid Clot Histomic-Transcriptomic Models Predict Functional Outcome After Mechanical Thrombectomy in Acute Ischemic Stroke.

BACKGROUND AND OBJECTIVES: Histologic and transcriptomic analyses of retrieved stroke clots have identified features associated with patient outcomes. Previous studies have demonstrated the predictive capacity of histology or expression features in isolation. Few studies, however, have investigated how paired histologic image features and expression patterns from the retrieved clots can improve understanding of clot pathobiology and our ability to predict long-term prognosis. We hypothesized that computational models trained using clot histomics and mRNA expression can predict early neurological improvement (ENI) and 90-day functional outcome (modified Rankin Scale Score, mRS) better than models developed using histological composition or expression data alone. METHODS: We performed paired histological and transcriptomic analysis of 32 stroke clots. ENI was defined as a delta-National Institutes of Health Stroke Score/Scale > 4, and a good long-term outcome was defined as mRS ≤2 at 90 days after procedure. Clots were H&E-stained and whole-slide imaged at 40×. An established digital pathology pipeline was used to extract 237 histomic features and to compute clot percent composition (%Comp). When dichotomized by either the ENI or mRS thresholds, differentially expressed genes were identified as those with absolute fold-change >1.5 and q < 0.05. Machine learning with recursive feature elimination (RFE) was used to select clot features and evaluate computational models for outcome prognostication. RESULTS: For ENI, RFE identified 9 optimal histologic and transcriptomic features for the hybrid model, which achieved an accuracy of 90.8% (area under the curve [AUC] = 0.98 ± 0.08) in testing and outperformed models based on histomics (AUC = 0.94 ± 0.09), transcriptomics (AUC = 0.86 ± 0.16), or %Comp (AUC = 0.70 ± 0.15) alone. For mRS, RFE identified 7 optimal histomic and transcriptomic features for the hybrid model. This model achieved an accuracy of 93.7% (AUC = 0.94 ± 0.09) in testing, also outperforming models based on histomics (AUC = 0.90 ± 0.11), transcriptomics (AUC = 0.55 ± 0.27), or %Comp (AUC = 0.58 ± 0.16) alone. CONCLUSION: Hybrid models offer improved outcome prognostication for patients with stroke. Identified digital histology and mRNA signatures warrant further investigation as biomarkers of patient functional outcome after thrombectomy.

MicroCT and Histological Analysis of Clot Composition in Acute Ischemic Stroke : A Comparative Study of MT-Retrieved Clots and Clot Analogs.

PURPOSE: Assessing clot composition on prethrombectomy computed tomography (CT) imaging may help in stroke treatment planning. In this study we seek to use microCT imaging of fabricated blood clots to understand the relationship between CT radiographic signals and the biological makeup. METHODS: Clots (n = 10) retrieved by mechanical thrombectomy (MT) were collected, and 6 clot analogs of varying RBC composition were made. We performed paired microCT and histological image analysis of all 16 clots using a ScanCo microCT 100 (4.9 µm resolution) and standard H&E staining (imaged at 40×). From these data types, first order statistic (FOS) radiomics were computed from microCT, and percent composition of RBCs (%RBC) was computed from histology. Polynomial and linear regression (LR) were used to build statistical models based on retrieved thrombus microCT and %RBC that were evaluated for their ability to predict the %RBC of clot analogs from mean HU. Correlation analyses of microCT FOS with composition were completed for both retrieved clots and analogs. RESULTS: The LR model fits relating MT-retrieved clot %RBC with mean (R2 = 0.625, p = 0.006) and standard deviation (R2 = 0.564, p < 0.05) in HUs on microCT were significant. Similarly, LR models relating analog histological %RBC to analog protocol %RBC (R2 = 0.915, p = 0.003) and mean HUs on microCT (R2 = 0.872, p = 0.007) were also significant. When the LR model built using MT-retrieved clots was used to predict analog %RBC from mean HUs, significant correlation was observed between predictions and actual histological %RBC (R2 = 0.852, p = 0.009). For retrieved clots, significant correlations were observed for energy and total energy with %RBC and %FP (|R| > 0.7, q < 0.01). Analogs further demonstrated significant correlation between FOS energy, total energy, variance and %WBC (|R| > 0.9, q < 0.01). CONCLUSION: MicroCT can be used to build models that predict AIS clot composition from routine CT parameters and help us to better understand radiomic signatures associated with clot composition and first pass outcomes. In future work, such observations can be used to better infer clot composition and inform thrombectomy prognostics from pretreatment CTs.

Tectonic infarct analysis: A computational tool for automated whole-brain infarct analysis from TTC-stained tissue.

Background: Infarct volume measured from 2,3,5-triphenyltetrazolium chloride (TTC)-stained brain slices is critical to in vivo stroke models. In this study, we developed an interactive, tunable, software that automatically computes whole-brain infarct metrics from serial TTC-stained brain sections. Methods: Three rat ischemic stroke cohorts were used in this study (Total n = 91 rats; Cohort 1 n = 21, Cohort 2 n = 40, Cohort 3 n = 30). For each, brains were serially-sliced, stained with TTC and scanned on both anterior and posterior sides. Ground truth annotation and infarct morphometric analysis (e.g., brain-Vbrain, infarct-Vinfarct, and non-infarct-Vnon-infarct volumes) were completed by domain experts. We used Cohort 1 for brain and infarct segmentation model development (n = 3 training cases with 36 slices [18 anterior and posterior faces], n = 18 testing cases with 218 slices [109 anterior and posterior faces]), as well as infarct morphometrics automation. The infarct quantification pipeline and pre-trained model were packaged as a standalone software and applied to Cohort 2, an internal validation dataset. Finally, software and model trainability were tested as a use-case with Cohort 3, a dataset from a separate institute. Results: Both high segmentation and statistically significant quantification performance (correlation between manual and software) were observed across all datasets. Segmentation performance: Cohort 1 brain accuracy = 0.95/f1-score = 0.90, infarct accuracy = 0.96/f1-score = 0.89; Cohort 2 brain accuracy = 0.97/f1-score = 0.90, infarct accuracy = 0.97/f1-score = 0.80; Cohort 3 brain accuracy = 0.96/f1-score = 0.92, infarct accuracy = 0.95/f1-score = 0.82. Infarct quantification (cohort average): Vbrain (ρ = 0.87, p < 0.001), Vinfarct (0.92, p < 0.001), Vnon-infarct (0.80, p < 0.001), %infarct (0.87, p = 0.001), and infarct:non-infact ratio (ρ = 0.92, p < 0.001). Conclusion: Tectonic Infarct Analysis software offers a robust and adaptable approach for rapid TTC-based stroke assessment.