Background
Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults. Current treatment options at diagnosis are multimodal and include surgical resection, radiation, and chemotherapy. Significant advances in the understanding of the molecular pathology of GBM and associated cell signaling pathways have opened opportunities for new therapies for recurrent and newly diagnosed disease. Innovative treatments, such as tumor-treating fields (TTFields) and immunotherapy, give hope for enhanced survival.

Glioblastoma: Overview of Disease and Treatment.

Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.

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A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Glioblastoma (GBM) is the most common and lethal tumor of the central nervous system. The natural history of treated GBM remains very poor with 5-year survival rates of 5 %. Survival has not significantly improved over the last decades. Currently, the best that can be offered is a modest 14-month overall median survival in patients undergoing maximum safe resection plus adjuvant chemoradiotherapy. Prognostic factors involved in survival include age, performance status, grade, specific markers (MGMT methylation, mutation of IDH1, IDH2 or TERT, 1p19q codeletion, overexpression of EGFR, etc.) and, likely, the extent of resection. Certain adjuncts to surgery, especially cortical mapping and 5-ALA fluorescence, favor higher rates of gross total resection with apparent positive impact on survival. Recurrent tumors can be offered re-intervention, participation in clinical trials, anti-angiogenic agent or local electric field therapy, without an evident impact on survival. Molecular-targeted therapies, immunotherapy and gene therapy are promising tools currently under research.

Survival in glioblastoma: a review on the impact of treatment modalities.

Glioblastoma (GBM) is the most common primary intracranial neoplasia, and is characterized by its extremely poor prognosis. Despite maximum surgery, chemotherapy, and radiation, the histological heterogeneity of GBM makes total eradication impossible, due to residual cancer cells invading the parenchyma, which is not otherwise seen in radiographic images. Even with gross total resection, the heterogeneity and the dormant nature of brain tumor initiating cells allow for therapeutic evasion, contributing to its recurrence and malignant progression, and severely impacting survival. Visual delimitation of the tumor’s margins with common surgical techniques is a challenge faced by many surgeons. In an attempt to achieve optimal safe resection, advances in approaches allowing intraoperative analysis of cancer and non-cancer tissue have been developed and applied in humans resulting in improved outcomes. In addition, functional paradigms based on stimulation techniques to map the brain’s electrical activity have optimized glioma resection in eloquent areas such as the Broca’s, Wernike’s and perirolandic areas. In this review, we will elaborate on the current standard therapy for newly diagnosed and recurrent glioblastoma with a focus on surgical approaches. We will describe current technologies used for glioma resection, such as awake craniotomy, fluorescence guided surgery, laser interstitial thermal therapy and intraoperative mass spectrometry. Additionally, we will describe a newly developed tool that has shown promising results in preclinical experiments for brain cancer: optical coherence tomography.

Advances in Brain Tumor Surgery for Glioblastoma in Adults

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. Regardless of ideal multidisciplinary treatment, including maximal surgical resection, followed by radiotherapy plus concomitant and maintenance temozolomide (TMZ), almost all patients experience tumor progression with nearly universal mortality and a median survival of less than 15 months. The addition of bevacizumab to standard treatment with TMZ revealed no increase in overall survival (OS) but improved progression-free survival (PFS). In newly diagnosed GBM, methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter has been shown to predict response to alkylating agents, as well as prognosis. Therefore, MGMT promoter status may have a crucial role in the choice of single modality treatment in fragile elderly population. No standard of care is established in recurrent or progressive GBM. Treatment alternatives may include supportive care, surgery, re-irradiation, systemic therapies, and combined modality therapy. Despite numerous clinical trials, the identification of effective therapies is complex because of the lack of appropriate control arms, selection bias, small sample sizes, and disease heterogeneity. Tumor-treating fields plus TMZ represent a major advance in the field of GBM therapy, and should be considered for patients with newly diagnosed GBM with no contraindications. As a disease with such a poor prognosis, treatment of GBM should go beyond improving survival and aim at preserving and even improving the quality of life of both the patient and the caregiver.

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Current Standards of Care in Glioblastoma Therapy

This manuscript discusses the current surgical management of glioblastoma. This paper highlights the common pathophysiology attributes of glioblastoma, surgical options for diagnosis/treatment, current thoughts of extent of resection (EOR) of tumor, and post-operative (neo)adjuvant treatment. Glioblastoma is not a disease that can be cured with surgery alone, however safely performed maximal surgical resection is shown to significantly increase progression free and overall survival while maximizing quality of life. Upon invariable tumor recurrence, re-resection also is shown to impact survival in a select group of patients. As adjuvant therapy continues to improve survival, the role of surgical resection in the treatment of glioblastoma looks to be further defined.

Current trends in the surgical management and treatment of adult glioblastoma.

Introduction: As one of the most common medical conditions for which patients seek medical care, chronic pain can be debilitating. The relationship between chronic pain and sleep is thought to be bidirectional, suggesting that treatment of one can be beneficial to the other. There is mounting evidence that spinal cord stimulation (SCS) improves aspects of sleep. How meaningful that is to patients’ lives has not been ascertained. Objective: The aim of the current study was to further elucidate the effect of SCS on sleep by examining the relationship between pain outcome measures with the insomnia severity index (ISI) and to establish the minimally clinical important difference (MCID), which is defined as the smallest noticeable change that an individual perceives as clinically significant. Materials and Methods: We prospectively collected ISI, Epworth sleepiness scale (ESS), Numerical Rating Scale, McGill Pain Questionnaire-Short Form, Oswestry Disability Index, Beck Depression Inventory, and Pain Catastrophizing Scale data both pre- and postoperatively for chronic pain patients who underwent SCS placement and had long-term outcomes. The ISI is a well-studied questionnaire used to assess an individual’s level of insomnia. Results: We correlated the ESS and ISI with pain outcome measures in sixty-four patients at a mean follow-up of 9.8 ± 2.9 months. The ISI showed correlations with disability as measured through the Oswestry Disability Index (p = 0.014) and depression as measured through the Beck Depression Inventory (p = 0.024). MCID values for the ISI were calculated using both anchor- and distribution-based methods. The minimal detectable change method resulted in an MCID of 2.4 points, standard error of measurement resulted in an MCID of 2.6 points, and the change difference resulted in an MCID of 2.45. The receiver operating characteristic method yielded an MCID of 0.5-point change with an area under the curve of 0.61. Conclusion: This study successfully established MCID ranges for the ISI outcome measure to help gauge improvement in insomnia after SCS. The ISI has ample evidence of its validity in assessment of insomnia, and MCID values of 2.4–2.6 correlate with improvement in disability and depression in our patients.

Establishing Minimal Clinically Important Difference in Sleep Outcomes after Spinal Cord Stimulation in Patients with Chronic Pain Disorders

As one of the most common medical conditions for which patients seek medical care, chronic pain can be debilitating impair sleep. The relationship between chronic pain and sleep is thought to be bi-directional, suggesting treatment of one can be beneficial to the other [1,2]. There is mounting evidence that spinal cord stimulation (SCS) improves aspects of sleep. How meaningful that is to patients’ lives has not been ascertained. The aim of the current study is to further elucidate the effect of SCS on sleep by examining the relationship between pain outcome measures with the insomnia severity index (ISI) and to establish the minimally clinical important difference (MCID). 

Gregory Davis

Papers

Current trends in the surgical management and treatment of adult glioblastoma.

Establishing Minimal Clinically Important Difference in Sleep Outcomes after Spinal Cord Stimulation in Patients with Chronic Pain Disorders

ID: 195625 Establishing MCID in Sleep Outcomes After Spinal Cord Stimulation in Patients With Chronic Pain Disorders