Evaluation of pseudoprogression in patients with glioblastoma

Liquid biomarkers in glioma

An ideal biomarker must meet several parameters to enable its successful adoption; however, the nature of glioma makes it challenging to discover valuable biomarkers. While biomarkers require simplicity for clinical implementation, anatomical features and the complexity of the brain make it challenging to perform histological examination. Therefore, compared to biomarkers from general histological examination, liquid biomarkers for brain disease offer many more advantages in these minimally invasive methods. Ideal biomarkers should have high sensitivity and specificity, especially in malignant tumors. The heterogeneous nature of glioma makes it challenging to determine useful common biomarkers, and no liquid biomarker has yet been adopted clinically. The low incidence of brain tumors also hinders research progress. To overcome these problems, clinical applications of new types of specimens, such as extracellular vesicles and comprehensive omics analysis, have been developed, and some candidate liquid biomarkers have been identified. As against previous reviews, we focused on and reviewed the sensitivity and specificity of each liquid biomarker for its clinical application. Perusing an ideal glioma biomarker would help uncover the common underlying mechanism of glioma and develop new therapeutic targets. Further multicenter studies based on these findings will help establish new treatment strategies in the future.

Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review

Background: Glioblastoma is the most frequent malignant primitive brain tumor in adults. The treatment includes surgery, radiotherapy, and chemotherapy. During follow-up, combined chemoradiotherapy can induce treatment-related changes mimicking tumor progression on medical imaging, such as pseudoprogression (PsP). Differentiating PsP from true progression (TP) remains a challenge for radiologists and oncologists, who need to promptly start a second-line treatment in the case of TP. Advanced magnetic resonance imaging (MRI) techniques such as diffusion-weighted imaging, perfusion MRI, and proton magnetic resonance spectroscopic imaging are more efficient than conventional MRI in differentiating PsP from TP. None of these techniques are fully effective, but current advances in computer science and the advent of artificial intelligence are opening up new possibilities in the imaging field with radiomics (i.e., extraction of a large number of quantitative MRI features describing tumor density, texture, and geometry). These features are used to build predictive models for diagnosis, prognosis, and therapeutic response. Method: Out of 7350 records for MR spectroscopy, GBM, glioma, recurrence, diffusion, perfusion, pseudoprogression, radiomics, and advanced imaging, we screened 574 papers. A total of 228 were eligible, and we analyzed 72 of them, in order to establish the role of each imaging modality and the usefulness and limitations of radiomics analysis.

Leveraging the CSF proteome toward minimally-invasive diagnostics surveillance of brain malignancies

Background

Diagnosis and prognostication of intra-axial brain tumors hinges on invasive brain sampling, which carries risk of morbidity. Minimally-invasive sampling of proximal fluids, also known as liquid biopsy, can mitigate this risk. Our objective was to identify diagnostic and prognostic cerebrospinal fluid (CSF) proteomic signatures in glioblastoma (GBM), brain metastases (BM), and primary central nervous system lymphoma (CNSL).

Methods

CSF samples were retrospectively retrieved from the Penn State Neuroscience Biorepository and profiled using shotgun proteomics. Proteomic signatures were identified using machine learning classifiers and survival analyses.

Results

Using 30 µL CSF volumes, we recovered 755 unique proteins across 73 samples. Proteomic-based classifiers identified malignancy with area under the receiver operating characteristic (AUROC) of 0.94 and distinguished between tumor entities with AUROC ≥0.95. More clinically relevant triplex classifiers, comprised of just three proteins, distinguished between tumor entities with AUROC of 0.75-0.89. Novel biomarkers were identified, including GAP43, TFF3 and CACNA2D2, and characterized using single cell RNA sequencing. Survival analyses validated previously implicated prognostic signatures, including blood-brain barrier disruption.

Conclusions

Reliable classification of intra-axial malignancies using low CSF volumes is feasible, allowing for longitudinal tumor surveillance.

Case Report: Single-Cell Transcriptomic Analysis of an Anaplastic Oligodendroglioma Post Immunotherapy

Glioma is the most common primary malignant brain tumor with a poor prognosis. Immune checkpoint inhibitors have been of great interest in investigation of glioma treatments. Here, we report single-cell transcriptomic analyses of two tumor areas from an oligodendroglioma taken from a patient who had multiple tumor recurrences, following several chemotherapies and radiation treatments. The patient subsequently received nivolumab and was considered have disease progression based on conventional diagnostic imaging after two cycles of treatment. He underwent a debulking surgical resection and pathological diagnosis was recurrent disease. During the surgery, tumor tissues were also collected from the enhancing and non-enhancing areas for a scRNAseq analysis to investigate the tumor microenvironment of these radiographically divergent areas. The scRNAseq analysis reveals a plethora of immune cells, suggesting that the increased mass observed on MRI may be partially a result of immune cell infiltration. The patient continued to receive immunotherapy after a short course of palliative radiation and remained free of disease progression for at least 12 months after the last surgery, suggesting a sustained response to immunotherapy. The scRNAseq analysis indicated that the radiological progression was in large part due to immune cell infiltrate and continued immunotherapy led to a positive clinical outcome in a patient who would have otherwise been admitted to hospice care with halting of immunotherapy. Our study demonstrates the potential of scRNAseq analyses in understanding the tumor microenvironment, which may assist the clinical decision-making process for challenging glioma cases following immunotherapy.

Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential

Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58-0.98), accuracy (0.69-0.98), and C-index (0.66-0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion.

Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients

This study aimed to investigate the performance of a deep learning-based survival-prediction model, which predicts the overall survival (OS) time of glioblastoma patients who have received surgery followed by concurrent chemoradiotherapy (CCRT). The medical records of glioblastoma patients who had received surgery and CCRT between January 2011 and December 2017 were retrospectively reviewed. Based on our inclusion criteria, 118 patients were selected and semi-randomly allocated to training and test datasets (3:1 ratio, respectively). A convolutional neural network–based deep learning model was trained with magnetic resonance imaging (MRI) data and clinical profiles to predict OS. The MRI was reconstructed by using four pulse sequences (22 slices) and nine images were selected based on the longest slice of glioblastoma by a physician for each pulse sequence. The clinical profiles consist of personal, genetic, and treatment factors. The concordance index (C-index) and integrated area under the curve (iAUC) of the time-dependent area-under-the-curve curves of each model were calculated to evaluate the performance of the survival-prediction models. The model that incorporated clinical and radiomic features showed a higher C-index (0.768 (95% confidence interval (CI): 0.759, 0.776)) and iAUC (0.790 (95% CI: 0.783, 0.797)) than the model using clinical features alone (C-index = 0.693 (95% CI: 0.685, 0.701); iAUC = 0.723 (95% CI: 0.716, 0.731)) and the model using radiomic features alone (C-index = 0.590 (95% CI: 0.579, 0.600); iAUC = 0.614 (95% CI: 0.607, 0.621)). These improvements to the C-indexes and iAUCs were validated using the 1000-times bootstrapping method; all were statistically significant (p < 0.001). This study suggests the synergistic benefits of using both clinical and radiomic parameters. Furthermore, it indicates the potential of multi-parametric deep learning models for the survival prediction of glioblastoma patients.

Glioblastoma Recurrence Versus Treatment Effect in a Pathology-Documented Series

OBJECTIVE

Patients diagnosed with glioblastoma (GBM) are treated with surgery followed by fractionated radiotherapy with concurrent and adjuvant temozolomide. Patients are monitored with serial magnetic resonance imaging (MRI). However, treatment-related changes frequently mimic disease progression. We reviewed a series of patients undergoing surgery for presumed first-recurrence GBM, where pathology reports were available for tissue diagnosis, in order to better understand factors associated with a diagnosis of treatment-related changes on final pathology.

METHODS

Patient records at a single institution between 2005 and 2015 were retrospectively reviewed. Pathology reports were reviewed to determine diagnosis of recurrent GBM or treatment effect. Survival analysis was performed interrogating overall survival (OS) and progression-free survival (PFS). Correlation with radiation treatment plans was also examined.

RESULTS

One-hundred-twenty-three patients were identified. One-hundred-sixteen patients (94%) underwent resection and seven underwent biopsy. Treatment-related changes were reported in 20 cases (16%). These patients had longer median OS and PFS from the time of recurrence than patients with true disease progression. However, there was no significant difference in OS from the time of initial diagnosis. Treatment effect was associated with surgery within 90 days of completing radiation. In patients receiving radiation at our institution (n = 53), larger radiation target volume and a higher maximum dose were associated with treatment effect.

CONCLUSION

Treatment effect was associated with surgery nearer to completion of radiation, a larger radiation target volume, and a higher maximum point dose. Treatment effect was associated with longer PFS and OS from the time of recurrence, but not from the time of initial diagnosis.

Defining Treatment-Related Adverse Effects in Patients with Glioma: Distinctive Features of Pseudoprogression and Treatment-Induced Necrosis

Background

Pseudoprogression (PP) and treatment‐induced brain tissue necrosis (TN) are challenging cancer treatment–related effects. Both phenomena remain insufficiently defined; differentiation from recurrent disease frequently necessitates tissue biopsy. We here characterize distinctive features of PP and TN to facilitate noninvasive diagnosis and clinical management.

Materials and Methods

Patients with glioma and confirmed PP (defined as appearance <5 months after radiotherapy [RT] completion) or TN (>5 months after RT) were retrospectively compared using clinical, radiographic, and histopathological data. Each imaging event/lesion (region of interest [ROI]) diagnosed as PP or TN was longitudinally evaluated by serial imaging.

Results

We identified 64 cases of mostly (80%) biopsy‐confirmed PP (n = 27) and TN (n = 37), comprising 137 ROIs in total. Median time of onset for PP and TN was 1 and 11 months after RT, respectively. Clinically, PP occurred more frequently during active antineoplastic treatment, necessitated more steroid‐based interventions, and was associated with glioblastoma (81 vs. 40%), fewer IDH1 mutations, and shorter median overall survival. Radiographically, TN lesions often initially manifested periventricularly (n = 22/37; 60%), were more numerous (median, 2 vs. 1 ROIs), and contained fewer malignant elements upon biopsy. By contrast, PP predominantly developed around the tumor resection cavity as a non‐nodular, ring‐like enhancing structure. Both PP and TN lesions almost exclusively developed in the main prior radiation field. Presence of either condition appeared to be associated with above‐average overall survival.

Conclusion

PP and TN occur in clinically distinct patient populations and exhibit differences in spatial radiographic pattern. Increased familiarity with both conditions and their unique features will improve patient management and may avoid unnecessary surgical procedures.

Implications for Practice

Pseudoprogression (PP) and treatment‐induced brain tissue necrosis (TN) are challenging treatment‐related effects mimicking tumor progression in patients with brain cancer. Affected patients frequently require surgery to guide management. PP and TN remain arbitrarily defined and insufficiently characterized. Lack of clear diagnostic criteria compromises treatment and may adversely affect outcome interpretation in clinical trials. The present findings in a cohort of patients with glioma with PP/TN suggest that both phenomena exhibit unique clinical and imaging characteristics, manifest in different patient populations, and should be classified as distinct clinical conditions. Increased familiarity with PP and TN key features may guide clinicians toward timely noninvasive diagnosis, circumvent potentially unnecessary surgical procedures, and improve response assessment in neuro‐oncology.

Cancer treatment–related adverse effects on the brain are a major diagnostic and therapeutic challenge in neuro‐oncology. This article describes the key clinical and imaging features of pseudoprogression and treatment‐induced brain tissue necrosis in patients with malignant glioma in an attempt to improve the current understanding of these conditions, facilitate the noninvasive diagnosis of treatment‐related adverse effects, and improve response assessment in neuro‐oncology.

Unexpected but transient tumour enlargement preceded complete regression and long-term control after irradiation of squamous cell carcinoma in a red-eared slider (Trachemys scripta elegans)

A red-eared slider with a chronic non-healing ulcerative shell lesion was diagnosed with cutaneous squamous cell carcinoma (SCC). The animal underwent surgical debulking, and adjuvant hypofractionated radiation therapy. The lesion initially responded, with near complete tumour regression, but then began growing again just a few months after finishing radiotherapy. Then, after several months with no additional tumour-directed therapy, the lesion again regressed. Five years post-irradiation and with no further treatment, the turtle now remains tumour-free. This unusual pattern of disease regression followed by transient growth and then long-term local tumour control suggests either spontaneous remission, or a pseudoprogression-like phenomenon. Careful clinical follow-up and reporting of future cases will aid in determining whether this pseudoprogression-like event was random, versus being a common component of the chelonian response to irradiation of cutaneous SCC.