Search

INTRODUCTION Glioblastomas (GBMs) are highly aggressive tumors. Despite multimodal treatment, its median overall survival ranges between 16 and 20 months. The standard treatment regimen consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. Despite temozolomide’s effectiveness, it may cause the clinical challenge of treatment-related progression also known as pseudoprogression(PsP). Usually, PSP resolves or stabilizes without further treatment, whereas a true progression (TP) requires more aggressive management. Identifying PSP from TP will affect the patient’s treatment plan. Conventional magnetic resonance imaging (MRI) reading techniques cannot distinguish these entities. This study investigated the feasibility of using deep learning to distinguish PsP from TP. METHOD We included GBM patients who met our inclusion criteria. We evaluated all cases to see if they had a new enhancing lesion within the original radiation field or an increase in the size of an existing lesion. The challenging MRIs were collected. Clinical notes regarding tumor and recurrence location, clinical history, and medication were collected. We labeled the ones who stayed stable or improved in the imaging and clinical situation as PSP and those with further imaging and clinical deterioration as TP. We coregistered Contrast-enhanced-T1 MRIs with T2-weighted images for each patient. We performed five-fold cross-validation to generalize the performance. We trained A 3-D Densenet121 model to establish the prediction. We selected the best models with the highest accuracy. RESULT After reviewing 1000 patients, we included 124 patients whose imaging showed suspicious progression and their medicational histories were completely retrievable; 63 PsP, and 61 TP. We developed a deep learning model based on the whole dataset. The 5-fold cross-validation revealed that the mean area under the curve (AUC) was 0.81. CONCLUSION We report the development of a deep learning model that diagnoses PsP from TP in patients who received temozolomide. Further refinement and external validation are required prior to widespread adoption in clinical practice.

GBM is the most common primary malignant brain tumor in adults. O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status is an important prognostic biomarker that predicts the response to temozolomide and guides the treatment decisions. At present, the only reliable way to determine MGMT promoter methylation status is through the analysis of tumor tissues. Given the limitations and complications of histology-based methods, an imaging-based approach for non-invasively MGMT promoter methylation status prediction is beneficial. This study aimed to develop and compare three different deep-learning-based approaches for predicting MGMT promoter methylation status non-invasively. We obtained 576 T2 weighted images (T2WIs) with their corresponding tumor masks and MGMT promoter methylation status from, The Brain Tumor Segmentation (BraTS) 2021 datasets. Dataset was split into five folds at the patient's level stratified by MGMT promoter methylation status to perform a 5-fold cross-validation. We developed three different models: voxel-wise, slice-wise, and whole-brain. For voxel-wise classification, methylated and unmethylated MGMT tumor masks were made into 1 and 2 with 0 background, respectively. We converted each T2WI into 32x32x32 patches. We trained a 3D-Vnet model for tumor segmentation. After inference, we constructed the whole brain volume based on the patch's coordinates. The final prediction of MGMT methylation status was made by majority voting between the predicted voxel values of the biggest connected component. For slice-wise classification, we trained an object detection model for tumor detection and MGMT methylation status prediction; then, we used majority voting for the final prediction. For the whole-brain approach, we trained a 3D Densenet121 for prediction. Whole-brain, slice-wise, voxel-wise, accuracy was 65.42%(SD 3.97%), 61.37%(SD 1.48%), and 56.84%(SD 4.38%) respectively. We found that across the whole-brain, slice-wise, and voxel-wise deep learning approaches, the whole-brain approach is the most effective approach for MGMT methylation status prediction on the BraTS 2021 dataset.

GBMs are highly aggressive tumors, evidenced by their rapid growth. Despite maximal treatment, median overall survival ranges between 16 and 20 months. Several studies use patients' diagnosis time(pre-surgical) imaging to predict their survival, but having a model that can predict the days till death for the patients who received the first standard step treatments (Stupp protocol) and now showing secondary progression signs is highly needed. In this study, we aimed to predict the overall survival of Glioblastoma(GBM) patients by developing a multimodal deep learning model using post-operative magnetic resonance imaging(MRI) and patients' age. We included GBM patients who met the following inclusion criteria: having maximum surgical resection followed by concurrent chemoradiation and showing secondary progression signs in follow-up imaging while being on adjuvant temozolomide treatment. We obtained contrast-enhanced T1(CET1) and T2-weighted(T2W) MRIs at progression time and patients' age and death dates. We calculated the days of survival from the day of the MRI. Dataset was split into train, validation, and test set at the patient level. CET1 and T2W MRIs were skull-stripped and coregistered for each patient. To mitigate data imbalance, we used the deep imbalance regression method for training a 3-D Densenet121 model with the concatenated CET1 and T2W as inputs. One hundred features were extracted from the optimized model and concatenated with the normalized age of each patient; the resulting output vectors were passed through the second dense neural network for final prediction. We included 110 patients who met the inclusion criteria. With the overall survival ranging from 16 to 4500 days, a mean of 582.4 and a standard deviation of 694.3, we achieved the test set's root mean square error of 261 days. Further refinement with more data, including clinical data such as comorbidities and external validation, is required prior to adoption in clinical practice.

Missing MRI sequences are common in radiology due to differences in protocols, time constraints, and image artifacts. Missing sequences are a nuisance for subsequent algorithms, such as automated segmentation algorithms requiring multiple MRI sequences (e.g., HD-GLIO, which requires T2, T1, post-contrast T1 (T1c), and FLAIR). Accurate tumor segmentation is a fundamental step in extracting radiomics analysis to use, for example, to predict the IDH status of glioma or their overall survival. We validated a conditional (pix2pix) and super-resolution GAN (SOUP-GAN) for imputing MRI sequences on 285 adult glioma with complete MRI sequences available. We simulated missing T1 sequences and subsequently imputed them from T1c using pix2pix and from sagittal T1 using SOUP-GAN. Using HD-GLIO, we obtained the true segmentations from the complete MRI sequences. HD-GLIO was also used after replacing the original T1 sequence with the one imputed with pix2pix and SOUP-GAN. Thus, there were three sets of segmentations for each subject. We compared T2 and T1c radiomics features extracted with Pyradiomics using segmentations obtained with complete data with features extracted from segmentation obtained with the imputed T1 sequences using intraclass correlation coefficient (ICC) and Bland-Altman (BA) analysis. Median (interquartile range) ICC for shape features extracted from T2 and T1c were 0.82(0.76-0.92) using pix2pix and 0.89(0.86-0.94) using SOUP-GAN. Median ICC for first-order-statistics features were 0.90(0.83-0.93) for T2 and 0.90(0.88-0.92) for T1c using pix2pix, and 0.61(0.22-0.82) for T2 and 0.42(0.34-0.57) for T1c using SOUP-GAN. Median ICC for texture features were 0.86(0.82-0.87) for T2 and 0.87(0.79-0.90) for T1c using pix2pix, and 0.69(0.47-0.81) for T2 and 0.76(0.65-0.83) for T1c using SOUP-GAN. We show that T2 and T1c shape features (e.g., volumetric analysis) can be adequately extracted from segmentations obtained with an algorithm requiring multiple MRI sequences using T1 sequences imputed with GANs; however, the effect of imputation differed for first-order-statistics and texture features.

INTRODUCTION Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP resolves or stabilizes without further treatment or a course of steroids, whereas true progression (TP) requires more aggressive management. Differentiating PsP from TP will affect the patient's outcome. This study investigated using deep learning to distinguish PsP MRI features from progressive disease. METHOD We included newly diagnosed GBM patients who met the inclusion criteria, including a new or increasing enhancing lesion size within the original radiation field and who had clinical, medication, and any histopathology available. The interpretation of the MR images at the image inclusion time point had to be indeterminate. We labeled those who subsequently were stable or improved on imaging and clinically as PSP and those with clinical and imaging deterioration as TP. A subset of subjects underwent a second resection. We labeled these subjects as PSP or TP based on the histological diagnosis. We performed skull stripping, coregistered contrast-enhanced T1 MRIs with T2-weighted images for each patient and used them as input to a 3-D Densenet121 model. We used several augmentation techniques and five-fold cross-validation to achieve more robust predictions. RESULT We included 124 patients who met the criteria, and of those, 63 were PsP and 61 were TP. We trained a deep learning model that achieved 76.4% (ranged 70%-84%, SD 5.122) mean accuracy over the 5 folds, 0.7560(ranged 0.6553-0.8535, SD 0.069) mean AUROCC, 88.72% (SD 6.86) mean sensitivity, and 62.05%(SD 9.11) mean specificity. CONCLUSION We report the development of a deep learning model that distinguishes PsP from TP in GBM patients treated per the Stupp protocol. Further refinement and external validation are required prior to widespread adoption in clinical practice.

PURPOSE Though patients with glioblastoma generally have poor overall survival (OS), OS estimates range significantly. We sought to develop a radiomics-based model predictive of OS in glioblastoma using preoperative MRI scans to identify these differences and guide clinical care. METHODS The primary endpoint was OS, determined from the date of surgery to death or last follow-up. T1, FLAIR, T2, and post-contrast T1 (T1c) MRI sequences were obtained for all patients. Using HD-GLIO-AUTO, lesion segmentation yielded three masks corresponding to the regions of contrast enhancement, non-enhancement, and total volume. Radiomics features were extracted from each region from the T2 and T1c sequences independently (i.e. either from T2 or T1c) and in combination. Age at surgery and gender were also considered. The data was split into training (75%) and test (25%) sets, stratifying for age at surgery. Kaplan-Meier analysis and the Coxnet model were used. For Coxnet, Elastic Net was used as the penalty, with optimization of the L1 coefficient using 5-fold cross-validation and evaluation via the concordance index (C-index). RESULTS From 3 institutions, 383 patients were included. Median OS was 395 days: 346 patients died, and 37 were right-censored. Age > 65 years was predictive of decreased OS (p < 0.005), but gender was not (p=0.16). For the Coxnet model, the C-index was 0.67 (standard deviation: 0.01) considering T2 and T1c sequences for the total lesion volume, with a C-index of 0.65 for the test set. For the enhancing volume, the model had a C-index of 0.71 for the test set using T1c sequences. Overall, C-indices ranged from 0.62-0.71. CONCLUSIONS We present a novel radiomics-based model predictive of OS in glioblastoma across a multi-institutional dataset. Ongoing efforts will incorporate immediate postoperative imaging and IDH and MGMT status into the proposed model to further improve prognostic ability and help guide clinical decision-making.

Abnormalities on brain MRI may be identified in ~15% of individuals aged 50-66. It can be difficult to discriminate glioma, CNS lymphoma, Inflammatory Demyelinating Disease (CNSIDD) and solitary metastasis, and misdiagnosis may expose patients to unnecessary anxiety, surgery, or radiotherapy. CNS lymphoma requires only biopsy, solitary metastasis may be resected and radiated or radiated empirically, and high-grade glioma requires maximal safe resection followed by chemoradiation. CNSIDD should only be biopsied when diagnostic uncertainty requires it, and resection and radiotherapy are unnecessary, introducing unwarranted morbidity. Polygenic risk models can identify patients at the highest risk of developing glioma; we hypothesized that these polygenic models could help with differential diagnosis of indeterminate brain lesions. We also hypothesized that race would be an important contributing factor in the models. In the initial discovery and validation European (EUR) cohorts the mean probability of glioma for IDHmut non-codeleted glioma was 0.55 and 0.52, respectively, and in healthy controls was 0.19 and 0.21, respectively. To further evaluate sensitivity, we analyzed additional genotype data from 867 gliomas (764 EUR, 54 AFR, 24 AMR, 15 EAS, 10 SAS) from The Cancer Genome Atlas (TCGA). Across 764 EUR IDHmut non-codeleted glioma, the mean probability 0.53, whereas across 54 AFR and 24 AMR the mean was 0.22 and 0.32, respectively. To evaluate specificity, we analyzed 3200 TCGA patients with primary tumor types that commonly metastasize to the brain (2676 EUR, 365 AFR, 46 AMR, 95 EAS, 18 SAS), and 236 AFR healthy controls. For patients with non-CNS primary tumors, the mean probability ranged from 0.09-0.18 for EUR and 0.04-0.07 for AFR. For AFR healthy controls, the mean was 0.05. Overall, race is a significant factor for polygenic risk models. Further work entails evaluating polygenic risk models in IDHwt glioma, CNS lymphoma and CNSIDD cohorts as well as in additional races.

BACKGROUND Appropriate patient stratification is of paramount importance in patients with primary central nervous system lymphomas (PCNSL) in order to improve therapeutic choices and to reduce treatment-related neurotoxicity. Age and performance status, two widely used prognostic indicators, may not be the most appropriate parameters for an optimal patient stratification, as age seldom reveals the true biological frailty of a patient and performance status scores are subject to inter-rater variability. Quantitative muscle biomarkers such as the skeletal-muscle-index at the third lumbar vertebra (L3-SMI) and temporal muscle thickness (TMT) are associated with worse prognosis in several oncological diseases. We aim to evaluate the role of these biomarkers in predicting survival in patients with PCNSL undergoing high-dose methotrexate-based chemotherapy. MATERIAL AND METHODS L3-SMI and TMT were calculated on abdominal CT and brain high-resolution 3D-T1-weighted-MR images, respectively, using predefined validated methods. Standardized sex-specific cut-offs were used to divide patients in different risk categories. Kaplan-Meier plots were calculated, and survival analysis was performed using log-rank tests, univariate, and multivariable Cox-regression models, calculating hazard ratios (HR) and 95% confidence intervals (CI), also adjusting for potential confounders (age, sex, and performance status). RESULTS Forty-three patients were included in this study. Median follow-up was 23 months (interquartile range 12–40); at median follow-up, rates of progression-free and overall survival for the cohort were 46% and 57%, respectively. Thirteen (30%) and 11 (26%) patients showed L3-SMI or TMT values below the predefined cut-offs. Subgroup analyses showed a significant association between quantitative muscle mass biomarkers and progression-free and overall survival. One-year progression free and overall survival rates were 8% and 21% for the 13 patients with L3-SMI below the standard cut-off value, respectively, compared to 66% and 68% for the 30 patients with L3-SMI above the cut-off values. Likewise, one-year progression free and overall survival rates were 10% and 15% for the 11 patients with low TMT, respectively, compared to 61% and 70% for the 32 patients with high TMT. In Cox-regression multivariable analysis patients with low L3-SMI or TMT showed significantly worse progression-free (HR 4.40, 95%CI 1.66–11.61, p = 0.003; HR 4.40, 95%CI 1.68–11.49, p=0.003, respectively) and overall survival (HR 3.16, 95%CI 1.09–9.11, p = 0.034; HR 4.93, 95%CI 1.78–13.65, p=0.002, respectively) compared to patients with high L3-SMI or TMT. CONCLUSION Quantitative muscle mass evaluation assessed by both L3-SMI and TMT is a promising tool to identify PCNSL patients at high risk of negative outcome. Confirmatory studies on larger independent series are warranted.