Longitudinal molecular trajectories of diffuse glioma in adults.

The evolutionary processes that drive universal therapeutic resistance in adult patients with diffuse glioma remain unclear1,2. Here we analysed temporally separated DNA-sequencing data and matched clinical annotation from 222 adult patients with glioma. By analysing mutations and copy numbers across the three major subtypes of diffuse glioma, we found that driver genes detected at the initial stage of disease were retained at recurrence, whereas there was little evidence of recurrence-specific gene alterations. Treatment with alkylating agents resulted in a hypermutator phenotype at different rates across the glioma subtypes, and hypermutation was not associated with differences in overall survival. Acquired aneuploidy was frequently detected in recurrent gliomas and was characterized by IDH mutation but without co-deletion of chromosome arms 1p/19q, and further converged with acquired alterations in the cell cycle and poor outcomes. The clonal architecture of each tumour remained similar over time, but the presence of subclonal selection was associated with decreased survival. Finally, there were no differences in the levels of immunoediting between initial and recurrent gliomas. Collectively, our results suggest that the strongest selective pressures occur during early glioma development and that current therapies shape this evolution in a largely stochastic manner.

The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Radio-pathomic maps of glioblastoma identify phenotypes of non-enhancing tumor infiltration associated with bevacizumab treatment response.

BACKGROUND: Autopsy-based radio-pathomic maps of glioma pathology have shown substantial promise inidentifying areas of non-enhancing tumor presence, which may be able to differentiate subsets of patients that respond favorably to treatments such as bevacizumab that have shown mixed efficacy evidence. We tested the hypthesis that phenotypes of non-enhancing tumor fronts can distinguish between glioblastoma patients that will respond favorably to bevacizumab and will visually capture treatment response. METHODS: T1, T1C, FLAIR, and ADC images were used to generate radio-pathomic maps of tumor characteristics for 79 pre-treatment patients with a primary GBM or high-grade IDH1-mutant astrocytoma for this study. Novel phenotyping (hypercellular, hypocellular, hybrid, or well-circumscribed front) of the non-enhancing tumor front was performed on each case. Kaplan Meier analyses were then used to assess differences in survival and bevacizumab efficacy between phenotypes. Phenotype compartment segmentations generated longitudinally for a subset of 26 patients over the course of bevacizumab treatment, where a mixed effect model was used to detect longitudinal changes. RESULTS: Well-Circumscribed patients showed significant/trending increases in survival compared to Hypercellular Front (HR = 2.0, p = 0.05), Hypocellular Front (HR = 2.02, p = 0.03), and Hybrid Front tumors (HR = 1.75, p = 0.09). Only patients with hypocellular or hybrid fronts showed significant survival benefits from bevacizumab treatment (HR = 2.35, p = 0.02; and HR = 2.45, p = 0.03, respectively). Hypocellular volumes decreased by an average 50.52 mm3 per day of bevacizumab treatment (p = 0.002). CONCLUSION: Patients with a hypocellular tumor front identified by radio-pathomic maps showed improved treatment efficacy when treated with bevacizumab, and reducing hypocellular volumes over the course of treatment may indicate treatment response.

Quantitative Histomorphometric Features of Prostate Cancer Predict Patients Who Biochemically Recur Following Prostatectomy.

Prostate cancer is the most commonly diagnosed cancer in men, accounting for 27% of the new male cancer diagnoses in 2022. If organ-confined, removal of the prostate through radical prostatectomy is considered curative; however, distant metastases may occur, resulting in a poor patient prognosis. This study sought to determine whether quantitative pathomic features of prostate cancer differ in patients who biochemically experience biological recurrence after surgery. Whole-mount prostate histology from 78 patients was analyzed for this study. In total, 614 slides were hematoxylin and eosin stained and digitized to produce whole slide images (WSI). Regions of differing Gleason patterns were digitally annotated by a genitourinary fellowship-trained pathologist, and high-resolution tiles were extracted from each annotated region of interest for further analysis. Individual glands within the prostate were identified using automated image processing algorithms, and histomorphometric features were calculated on a per-tile basis and across WSI and averaged by patients. Tiles were organized into cancer and benign tissues. Logistic regression models were fit to assess the predictive value of the calculated pathomic features across tile groups and WSI; additionally, models using clinical information were used for comparisons. Logistic regression classified each pathomic feature model at accuracies >80% with areas under the curve of 0.82, 0.76, 0.75, and 0.72 for all tiles, cancer only, noncancer only, and across WSI. This was comparable with standard clinical information, Gleason Grade Groups, and CAPRA score, which achieved similar accuracies but areas under the curve of 0.80, 0.77, and 0.70, respectively. This study demonstrates that the use of quantitative pathomic features calculated from digital histology of prostate cancer may provide clinicians with additional information beyond the traditional qualitative pathologist assessment. Further research is warranted to determine possible inclusion in treatment guidance.

The impeded diffusion fraction quantitative imaging assay demonstrated in multi-exponential diffusion phantom and prostate cancer.

PURPOSE: To demonstrate a method for quantification of impeded diffusion fraction (IDF) using conventional clinical DWI protocols. METHODS: The IDF formalism is introduced to quantify contribution from water coordinated by macromolecules to DWI voxel signal based on fundamentally different diffusion constants in vascular capillary, bulk free, and coordinated water compartments. IDF accuracy was studied as a function of b-value set. The IDF scaling with restricted compartment size and polyvinylpirrolidone (PVP) macromolecule concentration was compared to conventional apparent diffusion coefficient (ADC) and isotropic kurtosis model parameters for a diffusion phantom. An in vivo application was demonstrated for six prostate cancer (PCa) cases with low and high grade lesions annotated from whole mount histopathology. RESULTS: IDF linearly scaled with known restricted (vesicular) compartment size and PVP concentration in phantoms and increased with histopathologic score in PCa (from median 9% for atrophy up to 60% for Gleason 7). IDF via non-linear fit was independent of b-value subset selected between b = 0.1 and 2 ms/µm2 , including standard-of-care (SOC) PCa protocol. With maximum sensitivity for high grade PCa, the IDF threshold below 51% reduced false positive rate (FPR = 0/6) for low-grade PCa compared to apparent diffusion coefficient (ADC > 0.81 µm2 /ms) of PIRADS PCa scoring (FPR = 3/6). CONCLUSION: The proposed method may provide quantitative imaging assays of cancer grading using common SOC DWI protocols.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

BACKGROUND: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. PURPOSE: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. STUDY TYPE: Prospective. POPULATION: Thirty-three patients prospectively imaged prior to prostatectomy. FIELD STRENGTH/SEQUENCE: 3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence. ASSESSMENT: Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). STATISTICAL TEST: Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant. RESULTS: The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size. DATA CONCLUSION: We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

Image Quality Evaluation in Dual-Energy CT of the Chest, Abdomen, and Pelvis in Obese Patients With Deep Learning Image Reconstruction.

OBJECTIVE: The aim of this study was to evaluate image quality in vascular and oncologic dual-energy computed tomography (CT) imaging studies performed with a deep learning (DL)-based image reconstruction algorithm in patients with body mass index of ≥30. METHODS: Vascular and multiphase oncologic staging dual-energy CT examinations were evaluated. Two image reconstruction algorithms were applied to the dual-energy CT data sets: standard of care Adaptive Statistical Iterative Reconstruction (ASiR-V) and TrueFidelity DL image reconstruction at 2 levels (medium and high). Subjective quality criteria were independently evaluated by 4 abdominal radiologists, and interreader agreement was assessed. Signal-to-noise ratio (SNR) and contrast-to-noise ratio were compared between image reconstruction methods. RESULTS: Forty-eight patients were included in this study, and the mean patient body mass index was 39.5 (SD, 7.36). TrueFidelity-High (DL-High) and TrueFidelity-Medium (DL-Med) image reconstructions showed statistically significant higher Likert scores compared with ASiR-V across all subjective image quality criteria ( P < 0.001 for DL-High vs ASiR-V; P < 0.05 for DL-Med vs ASiR-V), and SNRs for aorta and liver were significantly higher for DL-High versus ASiR-V ( P < 0.001). Contrast-to-noise ratio for aorta and SNR for aorta and liver were significantly higher for DL-Med versus ASiR-V ( P < 0.05). CONCLUSIONS: TrueFidelity DL image reconstruction provides improved image quality compared with ASiR-V in dual-energy CTs obtained in obese patients.

Corpora amylacea in benign prostatic acini are associated with concurrent, predominantly low-grade cancer.

BACKGROUND: Corpora amylacea (CAM), in benign prostatic acini, contain acute-phase proteins. Do CAM coincide with carcinoma? METHODS: Within 270 biopsies, 83 prostatectomies, and 33 transurethral resections (TURs), CAM absence was designated CAM 0; corpora in less than 5% of benign acini: CAM 1; in 5% to 25%: CAM 2; in more than 25%: CAM 3. CAM were compared against carcinoma presence, clinicopathologic findings, and grade groups (GG) 1 to 2 vs 3 to 5. The frequency of CAM according to anatomic zone was counted. A pilot study was conducted using paired initial benign and repeat biopsies (33 benign, 24 carcinoma). RESULTS: A total of 68.9% of biopsies, 96.4% of prostatectomies, and 66.7% of TURs disclosed CAM. CAM ≥1 was common at an older age (P = .019). In biopsies, 204 cases (75%) had carcinoma; and CAM of 2 to 3 (compared to 0-1) were recorded in 25.0% of carcinomas but only 7.4% of benign biopsies (P = .005; odds ratio [OR] = 5.1). CAM correlated with high percent Gleason pattern 3, low GG (P = .035), and chronic inflammation (CI). CI correlated inversely with carcinoma (P = .003). CAM disclosed no association with race, body mass index, serum prostate specific antigen (PSA), acute inflammation (in biopsies), atrophy, or carcinoma volume. With CAM 1, the odds of GG 3 to 5 carcinoma, by comparison to CAM 0, decreased more than 2× (OR = 0.48; P = .032), with CAM 2, more than 3× (OR = 0.33; P = .005), and with CAM 3, almost 3× (OR = 0.39, P = .086). For men aged less than 65, carcinoma predictive model was: Score = (2 × age) + (5 × PSA) - (20 × degree of CAM); using our data, area under the ROC curve was 78.17%. When the transition zone was involved by cancer, it showed more CAM than in cases where it was uninvolved (P = .012); otherwise zonal distributions were similar. In the pilot study, CAM ≥1 predicted carcinoma on repeat biopsy (P < .05; OR = 8), as did CAM 2 to 3 (P < .0001; OR = 30). CI was not significant, and CAM retained significance after adjusting for CI. CONCLUSION: CAM correlate with carcinoma. Whether abundant CAM in benign biopsies adds value amidst high clinical suspicion, warrants further study.

Host genetic modifiers of nonproductive angiogenesis inhibit breast cancer.

PURPOSE: Multiple aspects of the tumor microenvironment (TME) impact breast cancer, yet the genetic modifiers of the TME are largely unknown, including those that modify tumor vascular formation and function. METHODS: To discover host TME modifiers, we developed a system called the Consomic/Congenic Xenograft Model (CXM). In CXM, human breast cancer cells are orthotopically implanted into genetically engineered consomic xenograft host strains that are derived from two parental strains with different susceptibilities to breast cancer. Because the genetic backgrounds of the xenograft host strains differ, whereas the inoculated tumor cells are the same, any phenotypic variation is due to TME-specific modifier(s) on the substituted chromosome (consomic) or subchromosomal region (congenic). Here, we assessed TME modifiers of growth, angiogenesis, and vascular function of tumors implanted in the SSIL2Rγ and SS.BN3IL2Rγ CXM strains. RESULTS: Breast cancer xenografts implanted in SS.BN3IL2Rγ (consomic) had significant tumor growth inhibition compared with SSIL2Rγ (parental control), despite a paradoxical increase in the density of blood vessels in the SS.BN3IL2Rγ tumors. We hypothesized that decreased growth of SS.BN3IL2Rγ tumors might be due to nonproductive angiogenesis. To test this possibility, SSIL2Rγ and SS.BN3IL2Rγ tumor vascular function was examined by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), micro-computed tomography (micro-CT), and ex vivo analysis of primary blood endothelial cells, all of which revealed altered vascular function in SS.BN3IL2Rγ tumors compared with SSIL2Rγ. Gene expression analysis also showed a dysregulated vascular signaling network in SS.BN3IL2Rγ tumors, among which DLL4 was differentially expressed and co-localized to a host TME modifier locus (Chr3: 95-131 Mb) that was identified by congenic mapping. CONCLUSIONS: Collectively, these data suggest that host genetic modifier(s) on RNO3 induce nonproductive angiogenesis that inhibits tumor growth through the DLL4 pathway.

Evaluation of pre-radiotherapy apparent diffusion coefficient (ADC): patterns of recurrence and survival outcomes analysis in patients treated for glioblastoma multiforme.

PURPOSE: To investigate the association of pre-radiotherapy apparent diffusion coefficient (ADC) abnormalities with patterns of recurrence and outcomes in patients with glioblastoma multiforme (GBM). MATERIALS AND METHODS: Fifty-two patients with recurrent GBM were retrospectively evaluated. Diffusion MRI images were acquired for all patients postoperatively prior to radiotherapy. ADC images were evaluated for geographic regions of diffusion restriction (hypointensity) within the FLAIR volume. If identified, the ADC map and the T1+C MRI at the time of recurrence were registered to the original plan to determine the pattern of recurrence and the coverage of the ADC abnormality by the 60 Gy isodose line (IDL). Progression-free and overall survival was determined for patients with and without an ADC hypointensity. RESULTS: An ADC hypointensity was identified in 32 (62%) of cases. The recurrence pattern in these cases was central in 27/32 (84%), marginal in 4/32 (13%) and distant in 1/32 (3%). The recurrence overlapped with the ADC hypointensity in 28 (88%) patients. The ADC hypointensity was covered by 95% of the 60 Gy IDL in all cases. Kaplan-Meier analysis revealed inferior progression free survival and overall survival in patients with an ADC hypointensity compared to those without, despite similarities between the groups in terms of age, RT dose, performance status, and extent of resection. CONCLUSIONS: The presence of an ADC hypointensity on pre-radiotherapy diffusion-weighted imaging is associated with the location of tumor recurrence as demonstrated by frequent overlap in this series, and is associated with a trend toward inferior outcomes. This abnormality may reflect a high risk region of hypercellularity and warrants consideration with respect to radiotherapy planning.

Location of brain tumor intersecting white matter tracts predicts patient prognosis.

Brain tumor cells invade adjacent normal brain along white matter (WM) bundles of axons. We therefore hypothesized that the location of tumor intersecting WM tracts would be associated with differing survival. This study introduces a method, voxel-wise survival analysis (VSA), to determine the relationship between the location of brain tumor intersecting WM tracts and patient prognosis. 113 primary glioblastoma (GBM) patients were retrospectively analyzed for this study. Patient specific tumor location, defined by contrast-enhancement, was combined with diffusion tensor imaging derived tractography to determine the location of axons intersecting tumor enhancement (AXITEs). VSA was then used to determine the relationship between the AXITE location and patient survival. Tumors intersecting the right anterior thalamic radiation (ATR), right inferior fronto-occipital fasciculus (IFOF), right and left cortico-spinal tract (CST), and corpus callosum (CC) were associated with decreased overall survival. Tumors intersecting the CST, body of the CC, right ATR, posterior IFOF, and inferior longitudinal fasciculus are associated with decreased progression-free survival (PFS), while tumors intersecting the right genu of the CC and anterior IFOF are associated with increased PFS. Patients with tumors intersecting the ATR, IFOF, CST, or CC had significantly improved survival prognosis if they were additionally treated with bevacizumab. This study demonstrates the usefulness of VSA by locating AXITEs associated with poor prognosis in GBM patients. This information should be included in patient-physician conversations, therapeutic strategy, and clinical trial design.

Comprehensive characterization of glioblastoma tumor tissues for biomarker identification using mass spectrometry-based label-free quantitative proteomics.

Cancer is a complex disease; glioblastoma (GBM) is no exception. Short survival, poor prognosis, and very limited treatment options make it imperative to unravel the disease pathophysiology. The critically important identification of proteins that mediate various cellular events during disease is made possible with advancements in mass spectrometry (MS)-based proteomics. The objective of our study is to identify and characterize proteins that are differentially expressed in GBM to better understand their interactions and functions that lead to the disease condition. Further identification of upstream regulators will provide new potential therapeutic targets. We analyzed GBM tumors by SDS-PAGE fractionation with internal DNA markers followed by liquid chromatography-tandem mass spectrometry (MS). Brain tissue specimens obtained for clinical purposes during epilepsy surgeries were used as controls, and the quantification of MS data was performed by label-free spectral counting. The differentially expressed proteins were further characterized by Ingenuity Pathway Analysis (IPA) to identify protein interactions, functions, and upstream regulators. Our study identified several important proteins that are involved in GBM progression. The IPA revealed glioma activation with z score 2.236 during unbiased core analysis. Upstream regulators STAT3 and SP1 were activated and CTNNα was inhibited. We verified overexpression of several proteins by immunoblot to complement the MS data. This work represents an important step towards the identification of GBM biomarkers, which could open avenues to identify therapeutic targets for better treatment of GBM patients. The workflow developed represents a powerful and efficient method to identify biomarkers in GBM.

Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma.

Abnormal brain tumor vasculature has recently been highlighted by a dynamic susceptibility contrast (DSC) MRI processing technique. The technique uses independent component analysis (ICA) to separate arterial and venous perfusion. The overlap of the two, i.e. arterio-venous overlap or AVOL, preferentially occurs in brain tumors and predicts response to anti-angiogenic therapy. The effects of contrast agent leakage on the AVOL biomarker have yet to be established. DSC was acquired during two separate contrast boluses in ten patients undergoing clinical imaging for brain tumor diagnosis. Three components were modeled with ICA, which included the arterial and venous components. The percentage of each component as well as a third component were determined within contrast enhancing tumor and compared. AVOL within enhancing tumor was also compared between doses. The percentage of enhancing tumor classified as not arterial or venous and instead into a third component with contrast agent leakage apparent in the time-series was significantly greater for the first contrast dose compared to the second. The amount of AVOL detected within enhancing tumor was also significantly greater with the second dose compared to the first. Contrast leakage results in large signal variance classified as a separate component by the ICA algorithm. The use of a second dose mitigates the effect and allows measurement of AVOL within enhancement.

Morphologic features of prostate cancer-encased native vessels: An image analysis study.

BACKGROUND: Vasculature plays a crucial role in the progression of prostate cancer (PC). Changes to the prostatic native vessels have not been studied since 2000 when Garcia et al. demonstrated marked media hypercellularity and increased artery thickness in prostatic native arteries within PC. We aim to further evaluate and characterize prostatic native vessels with a more accurate method with the use of virtual slides and digital analysis. DESIGN: Pathologist-annotated whole-mount digital slides from 96 entirely submitted prostatectomies were annotated for PC (color-coded by Gleason) using Omero platform. A subset of 44 cases met criteria for further analysis of media thickness, cellularity, and wall thickness to lumen ratio. Cases were included based on containing ≥5 native arteries (≥100 µm diameter) encased on at least 3 sides by PC, with vessels (≥100 µm diameter) designated as controls if they were ≥ 1000 µm away from PC. Annotated vessels were segmented and processed using Matlab 2023b. Mean media thickness (corrected for oblique sections), media: lumen ratio (based on numbers of pixels), and media cellularity (nuclei count) were studied by analysis with SPSS by linear mixed model with nested random effects for subject and slide to account for repeated measures. RESULTS: Vessels encased by PC showed greater media thickness (p=0.02), cellularity (p=0.02) and wall thickness/lumen ratio (p= <0.001) compared to vessels away from PC. These values showed an increasing trend according to stage in cellularity (p=0.14), media thickness (p=0.12) and wall thickness/ lumen ratio (p= 0.33) with higher stage (pT3). A Gleason group comparison showed a borderline-significant gradewise trend when analyzing wall thickness/lumen ratio (p=0.06). Grade 5 emerged as significantly different (p=0.02) from grades 3 or 4 non-cribriform. CONCLUSIONS: Similar to the 2000 study, increased media thickness and hypercellularity of vessels encased by PC were evident compared to controls. Borderline grade-dependent increased vessel cellularity changes were seen, suggesting a possible role in PC progression; the predictive value of these changes for outcome is uncertain. Whether the etiology of changes reflects locally increased intravascular pressure of vessels within tumor should be investigated.

Radio-pathomic maps of glioblastoma identify phenotypes of non-enhancing tumor infiltration associated with bevacizumab treatment response.

Background: Autopsy-based radio-pathomic maps of glioma pathology have shown substantial promise inidentifying areas of non-enhancing tumor presence, which may be able to differentiate subsets of patients that respond favorably to treatments such as bevacizumab that have shown mixed efficacy evidence. We tested the hypthesis that phenotypes of non-enhancing tumor fronts can distinguish between glioblastoma patients that will respond favorably to bevacizumab and will visually capture treatment response. Methods: T1, T1C, FLAIR, and ADC images were used to generate radio-pathomic maps of tumor characteristics for 79 pre-treatment patients with a primary GBM or high-grade IDH1-mutant astrocytoma for this study. Novel phenotyping (hypercellular, hypocellular, hybrid, or well-circumscribed front) of the non-enhancing tumor front was performed on each case. Kaplan Meier analyses were then used to assess differences in survival and bevacizumab efficacy between phenotypes. Phenotype compartment segmentations generated longitudinally for a subset of 26 patients over the course of bevacizumab treatment, where a mixed effect model was used to detect longitudinal changes. Results: Well-Circumscribed patients showed significant/trending increases in survival compared to Hypercellular Front (HR = 2.0, p = 0.05), Hypocellular Front (HR = 2.02, p = 0.03), and Hybrid Front tumors (HR = 1.75, p = 0.09). Only patients with hypocellular or hybrid fronts showed significant survival benefits from bevacizumab treatment (HR = 2.35, p = 0.02; and HR = 2.45, p = 0.03, respectively). Hypocellular volumes decreased by an average 50.52 mm3 per day of bevacizumab treatment (p = 0.002). Conclusion: Patients with a hypocellular tumor front identified by radio-pathomic maps showed improved treatment efficacy when treated with bevacizumab, and reducing hypocellular volumes over the course of treatment may indicate treatment response.

Noninvasive Autopsy-Validated Tumor Probability Maps Identify Glioma Invasion Beyond Contrast Enhancement.

BACKGROUND AND OBJECTIVES: This study identified a clinically significant subset of patients with glioma with tumor outside of contrast enhancement present at autopsy and subsequently developed a method for detecting nonenhancing tumor using radio-pathomic mapping. We tested the hypothesis that autopsy-based radio-pathomic tumor probability maps would be able to noninvasively identify areas of infiltrative tumor beyond traditional imaging signatures. METHODS: A total of 159 tissue samples from 65 subjects were aligned to MRI acquired nearest to death for this retrospective study. Demographic and survival characteristics for patients with and without tumor beyond the contrast-enhancing margin were computed. An ensemble algorithm was used to predict pixelwise tumor presence from pathological annotations using segmented cellularity (Cell), extracellular fluid, and cytoplasm density as input (6 train/3 test subjects). A second level of ensemble algorithms was used to predict voxelwise Cell, extracellular fluid, and cytoplasm on the full data set (43 train/22 test subjects) using 5-by-5 voxel tiles from T1, T1 + C, fluid-attenuated inversion recovery, and apparent diffusion coefficient as input. The models were then combined to generate noninvasive whole brain maps of tumor probability. RESULTS: Tumor outside of contrast was identified in 41.5% of patients, who showed worse survival outcomes (hazard ratio = 3.90, P < .001). Tumor probability maps reliably tracked nonenhancing tumor on a range of local and external unseen data, identifying tumor outside of contrast in 69% of presurgical cases that also showed reduced survival outcomes (hazard ratio = 1.67, P = .027). CONCLUSION: This study developed a multistage model for mapping gliomas using autopsy tissue samples as ground truth, which was able to identify regions of tumor beyond traditional imaging signatures.

Effects of perfusion on diffusion changes in human brain tumors.

PURPOSE: To characterize the influence of perfusion on the measurement of diffusion changes over time when ADC is computed using standard two-point methods. MATERIALS AND METHODS: Functional diffusion maps (FDMs), which depict changes in diffusion over time, were compared with rCBV changes in patients with brain tumors. The FDMs were created by coregistering and subtracting ADC maps from two time points and categorizing voxels where ADC significantly increased (iADC), decreased (dADC), or did not change (ncADC). Traditional FDMs (tFDMs) were computed using b = 0,1000 s/mm(2). Flow-compensated FDMs (fcFDMs) were calculated using b = 500,1000 s/mm(2). Perfusion's influence on FDMs was determined by evaluating changes in rCBV in areas where the ADC change significantly differed between the two FDMs. RESULTS: The mean ΔrCBV in voxels that changed from iADC (dADC) on the tFDM to ncADC on the fcFDM was significantly greater (less) than zero. In addition, mean ΔrCBV in iADC (dADC) voxels on the tFDM was significantly higher (lower) than in iADC (dADC) voxels on the fcFDM. CONCLUSION: The ability to accurately identify changes in diffusion on traditional FDMs is confounded in areas where perfusion and diffusion changes are colocalized. Flow-compensated FDMs, which use only non-zero b-values, should therefore be the standard approach.

The role of pre-treatment white matter abnormalities in developing white matter changes following whole brain radiation: a volumetric study.

White matter injury is a known complication of whole brain radiation (WBRT). Little is known about the factors that predispose a patient to such injury. The current study used MR volumetrics to examine risk factors, in particular the influence of pre-treatment white matter health, in developing white matter change (WMC) following WBRT. Thirty-four patients with unilateral metastatic disease underwent FLAIR MRI pre-treatment and at several time points following treatment. The volume of abnormal FLAIR signal in the white matter was measured in the hemisphere contralateral to the diseased hemisphere at each time point. Analyses were restricted to the uninvolved hemisphere to allow for the measurement of WBRT effects without the potential confounding effects of the disease on imaging findings. The relationship between select pre-treatment clinical variables and the degree of WMC following treatment was examined using correlational and regression based analyses. Age when treated and volume of abnormal FLAIR prior to treatment were significantly associated with WMC following WBRT; however, pre-treatment FLAIR volume was the strongest predictor of post-treatment WMCs. Age did not add any predictive value once white matter status was considered. No significant relationships were found between biological equivalent dose and select cerebrovascular risk factors (total glucose, blood pressure, BMI) and development of WMCs. The findings from this study identify pre-treatment white matter health as an important risk factor in developing WMC following WBRT. This information can be used to make more informed decisions and counsel patients on their risk for treatment effects.

Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology.

One in eight men will be affected by prostate cancer (PCa) in their lives. While the current clinical standard prognostic marker for PCa is the Gleason score, it is subject to inter-reviewer variability. This study compares two machine learning methods for discriminating between cancerous regions on digitized histology from 47 PCa patients. Whole-slide images were annotated by a GU fellowship-trained pathologist for each Gleason pattern. High-resolution tiles were extracted from annotated and unlabeled tissue. Patients were separated into a training set of 31 patients (Cohort A, n = 9345 tiles) and a testing cohort of 16 patients (Cohort B, n = 4375 tiles). Tiles from Cohort A were used to train a ResNet model, and glands from these tiles were segmented to calculate pathomic features to train a bagged ensemble model to discriminate tumors as (1) cancer and noncancer, (2) high- and low-grade cancer from noncancer, and (3) all Gleason patterns. The outputs of these models were compared to ground-truth pathologist annotations. The ensemble and ResNet models had overall accuracies of 89% and 88%, respectively, at predicting cancer from noncancer. The ResNet model was additionally able to differentiate Gleason patterns on data from Cohort B while the ensemble model was not. Our results suggest that quantitative pathomic features calculated from PCa histology can distinguish regions of cancer; however, texture features captured by deep learning frameworks better differentiate unique Gleason patterns.