Search

Tumor heterogeneity in glioblastoma complicates delineation of active tumor using standard MR imaging. Pseudo-progression following treatment with chemotherapy and radiation (chemoRT) further complicates how tumors appear. T1-weighted subtraction maps (T1S) have been used to better identify subtly enhancing regions containing infiltrative tumor. This study examines the differences in tumor appearance between patients treated with chemoRT compared to a cohort opting out at autopsy, to understand how chemoRT changes contrast enhancement on MRI. Ten patients diagnosed with glioblastoma were recruited for whole brain donation for this study. Three patients received no treatment (chemoRT-), and seven received a combination of chemoRT and additional treatments (chemoRT+), including but not limited to bevacizumab (Bev) and tumor treating fields (TTF). Large tissue samples were taken at autopsy from whole brain samples sliced axially to align with the last clinical MRI using patient-specific 3D-printed slicing jigs. All tissue samples were hematoxylin and eosin (HE) stained and digitized at 40X resolution (27 total samples). The whole slide images (WSI) were annotated to outline regions containing necrosis without pseudopalisading cells, tumor with pseudopalisading necrosis, and infiltrative tumor. T1S were created for each patient by subtracting intensity normalized T1-weighted images from T1 post-contrast images (T1C). The annotated WSIs were aligned and resampled into MRI space using a custom software. A mixed effect model was used to compare the T1S intensity between chemoRT +/- cohorts within each pathological annotation, incorporating a random effect for subject. Mean T1S intensity was greater in untreated subjects when compared to chemoRT-subjects within each pathological annotation, including necrosis without pseudopalisading cells, tumor with pseudopalisading necrosis, and infiltrative tumor (p< 0.001). We show that chemoRT reduces the contrast enhancement in all aspects of pathologically validated tumor compartments, including infiltrative tumor. Future research is needed to examine if other patterns are evident in additional MR sequences.

Current MRI signatures of brain cancer often fail to identify regions of hypercellularity beyond the contrast enhancing region. Therefore, this study used autopsy tissue samples aligned to clinical MRIs in order to quantify the relationship between intensity values and cellularity, as well as to develop a radio-pathomic model to predict cellularity using MRI data. This study used 93 samples collected at autopsy from 44 brain cancer patients. Tissue samples were processed, stained for hematoxylin and eosin (HE) and digitized for nuclei segmentation and cell density calculation. Pre- and post-gadolinium contrast T1-weighted images (T1, T1C), T2 fluid-attenuated inversion recovery (FLAIR) images, and apparent diffusion coefficient (ADC) images calculated from diffusion imaging were collected from each patients’ final acquisition prior to death. In-house software was used to align tissue samples to the FLAIR image via manually defined control points. Mixed effect models were used to assess the relationship between single image intensity and cellularity for each image. An ensemble learner was trained to predict cellularity using 5 by 5 voxel tiles from each image, employing a 2/3-1/3 train-test split for validation. Single image analyses found subtle associations between image intensity and cellularity, with a less pronounced relationship within GBM patients. The radio-pathomic model was able to accurately predict cellularity in the test set (RMSE = 1015 cells/mm2) and identified regions of hypercellularity beyond the contrast enhancing region. We concluded that a radio-pathomic model for cellularity is able to identify regions of hypercellular tumor beyond traditional imaging signatures.

Tumor treating fields (TTFields) are thought to disrupt the cell division process in glioma. This study uses autopsy tissue samples from patients recruited for brain donation to determine whether TTFields treatment duration and usage affects cellularity distributions beyond the gadolinium contrast enhancing region on T1-weighted (T1w) magnetic resonance imaging (MRI). At autopsy, 43 tissue samples from 18 patients who had undergone TTFields treatment (inclusion criteria: >50% compliance and 25-day duration) were collected from brain slices sectioned in-line with slices from the patients’ final MRI prior to death. Nuclei were segmented on the digitized hematoxylin and eosin (HE) stained tissue samples, which were then used to compute cell count across the slides. Tissue was then aligned to the patient’s final MRI scan prior to death using a custom in-house software, where manually defined control points were used to compute a nonlinear transform to warp the tissue to match the MRI. Histogram features including mean, median, 90th percentile, variance, and skewness, were computed from the cellularity distributions within regions outside the traditional tumor margin defined by T1w contrast enhancement for each subject. General linear models were fit to assess the effects of TTFields usage (percent of day) and duration (in days) on each histogram feature, controlling for age, overall survival, and time between TTFields treatment and death, as well as time between MRI acquisition and death. Longer TTFields treatment duration and overall survival was associated with significantly decreased skewness in the non-enhancing cellularity distributions (p< 0.05) while associations with other metrics remained statistically insignificant. These preliminary results in a small patient cohort suggest that TTFields duration may reduce the presence of high cellularity tails in the non-contrast enhancing distributions. Additional research in larger patient populations is warranted to better understand these findings given the number of confounding factors.

Infiltrative glioma beyond contrast enhancement on MRI is often difficult to identify with conventional imaging. In this study, we use large-format autopsy samples aligned to multi-parametric MRI to test the hypothesis that radio-pathomic machine learning models are able to accurately identify areas of infiltrative tumor beyond the contrast enhancing region. At autopsy, 140 tissue samples from 62 brain cancer patients were collected from brain slices sectioned to align with the patients’ last clinical MRI prior to death. Cell, extra-cellular fluid (ECF), and cytoplasm densities were computed from digitized, hematoxylin and eosin-stained samples, and a subset of 20 slides from 9 patients were annotated for tumor presence by a pathologist-trained technician. In-house custom software was used to align the tissue samples to the patients’ last clinical imaging, which included pre- and post-contrast T1, FLAIR, and ADC images. Bagging random forest models were then trained to predict cellularity, ECF, and cytoplasm density using 5-by-5 voxel tiles from each MRI as input. A 2/3-1/3 train-test split was used to validate model generalizability. A naïve Bayes classifier was trained to predict tumor class using cellularity, ECF, and cytoplasm segmentations within the annotation data set, again using a 2/3-1/3 train-test split to validate performance. The random forest models each accurately predicted cellularity, ECF, and cytoplasm density within the test data set, with root-mean-squared error values for each falling within one standard deviation of the ground truth. The histology-based tumor prediction model accurately predicted tumor, with a test set ROC AUC of 0.86. When using whole brain cellularity, ECF, and cytoplasm predictions from the random forest models as inputs for the naïve Bayes classifier, tumor probability maps identified regions of infiltrative tumor beyond contrast enhancement. Our results suggest that radio-pathomic maps of tumor probability accurately identify regions of infiltrative tumor beyond currently accepted MRI signatures.

e14035 Background: With an average overall survival of 12-18 months, glioblastoma has a particularly grim diagnosis. Standard treatment of glioblastoma, following detection on MRI, is surgical resection followed by radiation therapy and chemotherapy and is monitored through MR imaging. Glioblastoma has a unique heterogenous nature that complicates visualization of subtly enhancing tumor. This study used autopsy tissue samples taken from glioblastoma patients with varying treatment, to examine the effects of treatment on cell density within regions of contrast enhancement, using T1-weighted subtraction maps (T1S) from the last MR images to death. Methods: Eight patients diagnosed with glioblastoma at autopsy were recruited for this study. Two patients had no treatment and six received a combination of chemo-radiation and other treatments, including but not limited to bevacizumab (Bev) and TTFields therapy. At autopsy, whole brain samples were sliced axially aligned to the patient’s final MRI to death. Time between last MRI and death ranged from 4-27 days (mean 16 days). Overall survival (OS) ranged from 4-538 days (mean 307 days). Large tissue samples were taken from regions of suspected tumor or treatment effect, for a total of 18 tissue samples. Tissue samples were processed, H&E stained, and digitized at 40X resolution (Huron Slide Scanner). Cell density (cells/mm2) was calculated using digital histology. T1S were created for each patient by subtracting intensity normalized T1 weighted images from T1 post contrast images (T1C). Digital histology was aligned and resampled into MRI space using manual control point registration. Mixed effect models were used to compare differences in cell density across contrast enhancement (T1SE vs. NE) as well as across treatment groups (treatment vs. no treatment). Results: Cellularity was compared across regions of T1S enhancement (T1SE) and non-enhancement (NE) within manually selected regions of interest. Cell density compared between regions of T1SE and NE was not different (p=0.219). Total cell density was increased in patients who had received treatment compared to no treatment in both regions of T1SE and NE (p=0.014). Conclusions: Overall, cell density was increased in patients who had received treatment after diagnosis of glioblastoma. Additional research is needed to examine the extent of treatment’s effect on cellularity of glioblastoma. This work begins to characterize the use of T1S in evaluating glioblastoma tumor burden in patients with varying treatment histories.[Table: see text]