The Lumbar Stenosis Prognostic Subgroups for Personalizing Care and Treatment (PROSPECTS) study: protocol for an inception cohort study.

BACKGROUND: Lumbar spinal stenosis (LSS) is a common degenerative condition that contributes to back and back-related leg pain in older adults. Most patients with symptomatic LSS initially receive non-operative care before surgical consultation. However, there is a scarcity of data regarding prognosis for patients seeking non-surgical care. The overall goal of this project is to develop and evaluate a clinically useful model to predict long-term physical function of patients initiating non-surgical care for symptomatic LSS. METHODS: This is a protocol for an inception cohort study of adults 50 years and older who are initiating non-surgical care for symptomatic LSS in a secondary care setting. We plan to recruit up to 625 patients at two study sites. We exclude patients with prior lumbar spine surgeries or those who are planning on lumbar spine surgery. We also exclude patients with serious medical conditions that have back pain as a symptom or limit walking. We are using weekly, automated data pulls from the electronic health records to identify potential participants. We then contact patients by email and telephone within 21 days of a new visit to determine eligibility, obtain consent, and enroll participants. We collect data using telephone interviews, web-based surveys, and queries of electronic health records. Participants are followed for 12 months, with surveys completed at baseline, 3, 6, and 12 months. The primary outcome measure is the 8-item PROMIS Physical Function (PF) Short Form. We will identify distinct phenotypes using PROMIS PF scores at baseline and 3, 6, and 12 months using group-based trajectory modeling. We will develop and evaluate the performance of a multivariable prognostic model to predict 12-month physical function using the least absolute shrinkage and selection operator and will compare performance to other machine learning methods. Internal validation will be conducted using k-folds cross-validation. DISCUSSION: This study will be one of the largest cohorts of individuals with symptomatic LSS initiating new episodes of non-surgical care. The successful completion of this project will produce a cross-validated prognostic model for LSS that can be used to tailor treatment approaches for patient care and clinical trials.

From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response.

Glioblastomas are aggressive primary brain tumors known for their inter- and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from intrinsic or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from different sources. To better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model. When fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. When fitting our model using imaging and cell scale data, we determined that environmental heterogeneity alone is insufficient to match the single cell data, and intrinsic heterogeneity is required to fully capture the migration behavior. The wide spectrum of in silico tumors also had a wide variety of responses to an application of an anti-proliferative treatment. Recurrent tumors were generally less proliferative than pre-treatment tumors as measured via the model simulations and validated from human GBM patient histology. Further, we found that all tumors continued to grow with an anti-migratory treatment alone, but the anti-proliferative/anti-migratory combination generally showed improvement over an anti-proliferative treatment alone. Together our results emphasize the need to better understand the underlying phenotypes and tumor heterogeneity present in a tumor when designing therapeutic regimens.

Providing Epidemiological Data in Lumbar Spine Imaging Reports Did Not Affect Subsequent Utilization of Spine Procedures: Secondary Outcomes from a Stepped-Wedge Randomized Controlled Trial.

OBJECTIVE: To evaluate the effect of inserting epidemiological information into lumbar spine imaging reports on subsequent nonsurgical and surgical procedures involving the thoracolumbosacral spine and sacroiliac joints. DESIGN: Analysis of secondary outcomes from the Lumbar Imaging with Reporting of Epidemiology (LIRE) pragmatic stepped-wedge randomized trial. SETTING: Primary care clinics within four integrated health care systems in the United States. SUBJECTS: 238,886 patients ≥18 years of age who received lumbar diagnostic imaging between 2013 and 2016. METHODS: Clinics were randomized to receive text containing age- and modality-specific epidemiological benchmarks indicating the prevalence of common spine imaging findings in people without low back pain, inserted into lumbar spine imaging reports (the "LIRE intervention"). The study outcomes were receiving 1) any nonsurgical lumbosacral or sacroiliac spine procedure (lumbosacral epidural steroid injection, facet joint injection, or facet joint radiofrequency ablation; or sacroiliac joint injection) or 2) any surgical procedure involving the lumbar, sacral, or thoracic spine (decompression surgery or spinal fusion or other spine surgery). RESULTS: The LIRE intervention was not significantly associated with subsequent utilization of nonsurgical lumbosacral or sacroiliac spine procedures (odds ratio [OR] = 1.01, 95% confidence interval [CI] 0.93-1.09; P = 0.79) or any surgical procedure (OR = 0.99, 95 CI 0.91-1.07; P = 0.74) involving the lumbar, sacral, or thoracic spine. The intervention was also not significantly associated with any individual spine procedure. CONCLUSIONS: Inserting epidemiological text into spine imaging reports had no effect on nonsurgical or surgical procedure utilization among patients receiving lumbar diagnostic imaging.

Sex-specific impact of patterns of imageable tumor growth on survival of primary glioblastoma patients.

BACKGROUND: Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. METHODS: Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). RESULTS: Among males, tumor (T1Gd) radius was a predictor of overall survival (HR = 1.027, p = 0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR = 1.011, p < 0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p = 0.010 t-test), but tumor size was not correlated with female overall survival (p = 0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p = 0.004, F p = 0.001, t-test). CONCLUSION: Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.

A phase II trial of PTK787/ZK 222584 in recurrent or progressive radiation and surgery refractory meningiomas.

When surgery and radiation are no longer treatment options, salvage systemic therapy has been used for recurrent meningiomas with little compelling evidence to suggest effectiveness. Patients with surgery and radiation refractory recurrent meningiomas were treated with the oral multifunctional tyrosine kinase inhibitor PTK787/ZK 222584 (PTK787) at a dose of 500 mg twice a day. Each treatment cycle was 4 weeks with MRI done every 8 weeks. Twenty-five patients (14 men; 11 women) with a median age of 59 years and KPS of 80 were treated. Meningioma WHO Grade was I in 2 patients, II in 14 patients and III in 8 patients; 1 patient had a hemangiopericytoma. All patients had prior surgery, external beam radiation therapy or radiosurgery and 11 patients prior systemic chemotherapy. Median number of cycles of PTK 787 administered was 4 (range <1-22). Best response in the 22 evaluable patients was stable disease in 15 (68.2 %). Predominant PTK787 related toxicities included fatigue (60 %), hypertension (24 %) and elevated transaminases (24 %). Grade II patients had a progression free survival (PFS)-6 of 64.3 %, a median PFS of 6.5 months and an overall survival (OS) of 26.0 months; grade III patients had a PFS-6 of 37.5 %, median PFS of 3.6 months and OS 23 months. PTK787 was modestly toxic at the dose of 500 mg administered twice per day. Activity as determined by PFS-6 suggests that targeting PDGF/VEGF pathway warrants further investigation.

Multi-Omics Profiles of Chronic Low Back Pain and Fibromyalgia - Study Protocol.

Background: Chronic low back pain (CLBP) and fibromyalgia (FM) are leading causes of suffering, disability, and social costs. Current pharmacological treatments do not target molecular mechanisms driving CLBP and FM, and no validated biomarkers are available, hampering the development of effective therapeutics. Omics research has the potential to substantially advance our ability to develop mechanism-specific therapeutics by identifying pathways involved in the pathophysiology of CLBP and FM, and facilitate the development of diagnostic, predictive, and prognostic biomarkers. We will conduct a blood and urine multi-omics study in comprehensively phenotyped and clinically characterized patients with CLBP and FM. Our aims are to identify molecular pathways potentially involved in the pathophysiology of CLBP and FM that would shift the focus of research to the development of target-specific therapeutics, and identify candidate diagnostic, predictive, and prognostic biomarkers. Methods: We are conducting a prospective cohort study of adults ≥18 years of age with CLBP (n=100) and FM (n=100), and pain-free controls (n=200). Phenotyping measures include demographics, medication use, pain-related clinical characteristics, physical function, neuropathiccomponents (quantitative sensory tests and DN4 questionnaire), pain facilitation (temporal summation), and psychosocial function as moderator. Blood and urine samples are collected to analyze metabolomics, lipidomics and proteomics. We will integrate the overall omics data to identify common mechanisms and pathways, and associate multi-omics profiles to pain-related clinical characteristics, physical function, indicators of neuropathic pain, and pain facilitation, with psychosocial variables as moderators. Discussion: Our study addresses the need for a better understanding of the molecular mechanisms underlying chronic low back pain and fibromyalgia. Using a multi-omics approach, we hope to identify converging evidence for potential targets of future therapeutic developments, as well as promising candidate biomarkers for further investigation by biomarker validation studies. We believe that accurate patient phenotyping will be essential for the discovery process, as both conditions are characterized by high heterogeneity and complexity, likely rendering molecular mechanisms phenotype specific.

Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.

Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convolutional neural network. Maximal fracture scores resulted in a performant model for subject-level fracture prediction. Combining individual deep learning vertebral body fracture scores and demographic covariates for subject-level classification of osteoporotic fracture achieved excellent performance (AUC-ROC of 0.968) on a large dataset of radiographs with basic demographic data. PURPOSE: Osteoporotic vertebral fractures are common and morbid. Automated opportunistic screening for incidental vertebral fractures from radiographs, the highest volume imaging modality, could improve osteoporosis detection and management. We consider how to form patient-level fracture predictions and summarization to guide management, using our previously developed vertebral fracture classifier on segmented radiographs from a prospective cohort study of US men (MrOS). We compare the performance of logistic regression (LR) and generalized additive models (GAM) with combinations of individual vertebral scores and basic demographic covariates. METHODS: Subject-level LR and GAM models were created retrospectively using all fracture predictions or summary variables such as order statistics, adjacent vertebral interactions, and demographic covariates (age, race/ethnicity). The classifier outputs for 8663 vertebrae from 1176 thoracic and lumbar radiographs in 669 subjects were divided by subject to perform stratified fivefold cross-validation. Models were assessed using multiple metrics, including receiver operating characteristic (ROC) and precision-recall (PR) curves. RESULTS: The best model (AUC-ROC = 0.968) was a GAM using the top three maximum vertebral fracture scores and age. Using top-ranked scores only, rather than all vertebral scores, improved performance for both model classes. Adding age, but not ethnicity, to the GAMs improved performance slightly. CONCLUSION: Maximal vertebral fracture scores resulted in the highest-performing models. While combining multiple vertebral body predictions risks decreasing specificity, our results demonstrate that subject-level models maintain good predictive performance. Thresholding strategies can be used to control sensitivity and specificity as clinically appropriate.

Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs.

BACKGROUND AND PURPOSE: Vertebral compression fractures may indicate osteoporosis but are underdiagnosed and underreported by radiologists. We have developed an ensemble of vertebral body (VB) segmentation models for lateral radiographs as a critical component of an automated, opportunistic screening tool. Our goal is to detect the approximate location of thoracic and lumbar VBs, including fractured vertebra, on lateral radiographs. MATERIALS AND METHODS: The Osteoporotic Fractures in Men Study (MrOS) data set includes spine radiographs of 5994 men aged ≥65 years from 6 clinical centers. Two segmentation models, U-Net and Mask-RCNN (Region-based Convolutional Neural Network), were independently trained on the MrOS data set retrospectively, and an ensemble was created by combining them. Primary performance metrics for VB detection success included precision, recall, and F1 score for object detection on a held-out test set. Intersection over union (IoU) and Dice coefficient were also calculated as secondary metrics of performance for the test set. A separate external data set from a quaternary health care enterprise was acquired to test generalizability, comprising diagnostic clinical radiographs from men and women aged ≥65 years. RESULTS: The trained models achieved F1 score of U-Net = 83.42%, Mask-RCNN = 86.30%, and ensemble = 88.34% in detecting all VBs, and F1 score of U-Net = 87.88%, Mask-RCNN = 92.31%, and ensemble = 97.14% in detecting severely fractured vertebrae. The trained models achieved an average IoU per VB of 0.759 for U-Net and 0.709 for Mask-RCNN. The trained models achieved F1 score of U-Net = 81.11%, Mask-RCNN = 79.24%, and ensemble = 87.72% in detecting all VBs in the external data set. CONCLUSIONS: An ensemble model combining predictions from U-Net and Mask-RCNN resulted in the best performance in detecting VBs on lateral radiographs and generalized well to an external data set. This model could be a key component of a pipeline to detect fractures on all vertebrae in a radiograph in an automated, opportunistic screening tool under development.

Mortality with concurrent treatment with gabapentin and opioids among people with spine diagnoses in the U.S. Medicare population: a propensity-matched cohort study.

IMPORTANCE: Given the negative impact of opioid use on population health, prescriptions for alternative pain-relieving medications, including gabapentin, have increased. Concurrent gabapentin and opioid prescriptions are commonly reported in retrospective studies of opioid-related overdose deaths. OBJECTIVE: To determine whether people who filled gabapentin and opioid prescriptions concurrently ('gabapentin + opioids') had greater mortality than those who filled an active control medication (tricyclic antidepressants [TCAs] or duloxetine) and opioids concurrently ('TCAs/duloxetine + opioids'). We hypothesized that people treated with gabapentin + opioids would have higher mortality rates compared to people treated with TCAs/duloxetine + opioids. DESIGN: Propensity score-matched cohort study with an incident user, active control design. The median (maximum) follow-up was 45 (1093) days. SETTING: Population-based. PARTICIPANTS: Medicare beneficiaries with spine-related diagnoses 2017-2019. The primary analysis included those who concurrently (within 30 days) filled at least 1 incident gabapentin + at least 1 opioid or at least 1 incident TCA/duloxetine + at least 1 opioid. EXPOSURES: People treated with gabapentin + opioids (n=67,133) were matched on demographic and clinical factors in a 1:1 ratio to people treated with TCAs/duloxetine + opioids (n=67,133). MAIN OUTCOMES AND MEASURES: The primary outcome was mortality at any time. A secondary outcome was occurrence of a major medical complication at any time. RESULTS: Among 134,266 participants (median age 73.4 years; 66.7% female), 2360 died before the end of follow-up. No difference in mortality was observed between groups (adjusted hazard ratio (HR) and 95% confidence interval (CI) for gabapentin + opioids was 0.98 (0.90, 1.06); p=0.63). However, people treated with gabapentin + opioids were at slightly increased risk of a major medical complication (1.02 (1.00, 1.04); p=0.03) compared to those treated with TCAs/duloxetine + opioids. Results were similar in analyses (a) restricted to less than or = 30-day follow-up and (b) that required at least 2 fills of each prescription. CONCLUSIONS AND RELEVANCE: When treating pain in older adults taking opioids, the addition of gabapentin did not increase mortality risk relative to addition of TCAs or duloxetine. However, providers should be cognizant of a small increased risk of major medical complications among opioid users initiating gabapentin compared to those initiating TCAs or duloxetine.

Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria.

RATIONALE AND OBJECTIVES: Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool. MATERIALS AND METHODS: We retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar). Each vertebral body was annotated using an adaption of the modified-2 algorithm-based qualitative criteria. The Osteoporotic Fractures in Men (MrOS) Study dataset provided thoracic and lumbar spine radiographs of 5994 men from six clinical centers. Using both datasets, five deep learning algorithms were trained to classify each individual vertebral body of the spine radiographs. Classification performance was compared for these models using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and positive predictive value (PPV). RESULTS: Our best model, built with ensemble averaging, achieved an AUC-ROC of 0.948 and 0.936 on the local dataset's test set and the MrOS dataset's test set, respectively. After setting the cutoff threshold to prioritize PPV, this model achieved a sensitivity of 54.5% and 47.8%, a specificity of 99.7% and 99.6%, and a PPV of 89.8% and 94.8%. CONCLUSION: Our model achieved an AUC-ROC>0.90 on both datasets. This testing shows some generalizability to real-world clinical datasets and a suitable performance for a future opportunistic osteoporosis screening tool.

Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria.

RATIONALE AND OBJECTIVES: Osteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic compression fractures (OCFs), an osteoporosis biomarker, are often incidental and under-reported. Accurate automated opportunistic OCF screening can increase the diagnosis rate and ensure adequate treatment. We aimed to develop a deep learning classifier for OCFs, a critical component of our future automated opportunistic screening tool. MATERIALS AND METHODS: The dataset from the Osteoporotic Fractures in Men Study comprised 4461 subjects and 15,524 spine radiographs. This dataset was split by subject: 76.5% training, 8.5% validation, and 15% testing. From the radiographs, 100,409 vertebral bodies were extracted, each assigned one of two labels adapted from the Genant semiquantitative system: moderate to severe fracture vs. normal/trace/mild fracture. GoogLeNet, a deep learning model, was trained to classify the vertebral bodies. The classification threshold on the predicted probability of OCF outputted by GoogLeNet was set to prioritize the positive predictive value (PPV) while balancing it with the sensitivity. Vertebral bodies with the top 0.75% predicted probabilities were classified as moderate to severe fracture. RESULTS: Our model yielded a sensitivity of 59.8%, a PPV of 91.2%, and an F1 score of 0.72. The areas under the receiver operating characteristic curve (AUC-ROC) and the precision-recall curve were 0.99 and 0.82, respectively. CONCLUSION: Our model classified vertebral bodies with an AUC-ROC of 0.99, providing a critical component for our future automated opportunistic screening tool. This could lead to earlier detection and treatment of OCFs.

Hydroxyurea for recurrent surgery and radiation refractory meningioma: a retrospective case series.

Hydroxyurea (HU), an orally administered chemotherapy, has become the de facto standard therapeutic agent in patients with surgically and radiation refractory meningiomas based on a limited literature. A retrospective case series of 60 patients with recurrent WHO grade 1 meningioma treated with HU following progression after surgery and radiotherapy was conducted with primary study objective progression free survival (PFS) at 6- and 12-months. Sixty patients (45 women; 15 men: median age 61.5 years, range 26-88) with recurrent meningioma were treated with HU (1000 mg/m(2)/day orally divided twice per day; one cycle operationally defined as 4-weeks of daily HU). All patients had progressed radiographically after prior therapy with surgery (60/60) and radiotherapy (external beam radiotherapy 60/60; stereotactic radiotherapy 53/60). No patient received prior chemotherapy or targeted therapy before instituting HU. Patients received 1-12 cycles (median 2.0) of HU with modest toxicity (10% grade 3 + anemia or fatigue). There were no radiographic responses, 35% of patients had stable disease and 65% manifested progressive disease. Duration of stable disease ranged from 3 to 12 months (median 4.0 months). The overall PFS was 10% (median PFS 2.0 months). The majority of patients (80%) following progression on HU were subsequently treated on an investigational trial. In this retrospective case series, HU though generally well tolerated and convenient, appeared to have very limited activity which raises questions of what constitutes effective salvage therapy and indicates an unmet need for alternative treatments for recurrent meningiomas.

Salvage therapy with single agent bendamustine for recurrent glioblastoma.

The treatment of recurrent glioblastoma (GBM) remains challenging notwithstanding the recent approval of bevacizumab for this indication. Bendamustine has a bifunctional mechanism of action including alkylation, penetrates the CNS and does not show cross resistance to other alkylator chemotherapies. In a single institution phase 2 trial, patients with recurrent GBM were treated with bendamustine (100 mg/m(2)/day administered intravenously for two consecutive days every 4 weeks). The primary study endpoint was 6-month progression free survival (PFS-6). An interim analysis for futility was conducted according to a Simon two-stage minimax design. Complete blood counts were obtained bimonthly, clinical evaluations and brain imaging every month for the first cycle and bimonthly thereafter. Treatment responses were based upon MacDonald criteria. Sixteen patients were enrolled (nine men; seven women), with a median age of 53 years (range 36-68) and a median Karnofsky performance status of 90 (range 70-100). Nine patients were treated at first relapse and seven at second relapse (five patients were bevacizumab failures). A total of 25 cycles of bendamustine were administered with a median of 1 (range 1-6). Bendamustine-related toxicity was seen in eight patients; lymphopenia in seven (5 grade 3; 2 Grade 4), thrombocytopenia in two (1 Grade 3; 1 Grade 4), and neutropenia in one (1 Grade 3). Fourteen patients have died due to disease progression, two patients are alive and on alternative therapies. Only one patient was progression-free at 6 months, triggering the stopping rule for futility. Bendamustine was reasonably well tolerated but failed to meet the study criteria for activity in adults with recurrent GBM.

Recurrent spinal cord glioblastoma: salvage therapy with bevacizumab.

Primary spinal cord tumors constitute 2-4% of all primary central nervous system malignancies in adults of which less than 5% are glioblastoma. A retrospective evaluation to determine toxicity and response to bevacizumab in patients with recurrent spinal cord glioblastoma. Six patients (4 males; 2 females: median age 34 years) with recurrent spinal cord glioblastoma were treated with bevacizumab (10 mg/kg given once every 2 weeks wherein 2 treatments constituted a cycle of therapy). All patients had failed surgery and temozolomide-based chemoradiotherapy and post-radiotherapy temozolomide. Blood counts, chemistry panel, urine protein to creatinine ratio and neurologic examination were obtained bi-weekly. Contrast-enhanced spine MRI was performed after one cycle of therapy and thereafter following every two cycles of bevacizumab. Treatment-related complications included fatigue in six patients, constipation in 4, hypertension in 2, venous thrombosis in 2, and infection without neutropenia in 2. There were three grade 3 toxicities (1 each fatigue, leukopenia and venous thrombosis). There were no treatment-related deaths. After one cycle of bevacizumab, one patient (17%) demonstrated progressive disease, 2 (34%) partial responses and three (51%) stable disease. Overall median response or stable disease duration (disease free progression) was 7 months (range 3-11 months). Overall median survival was 9 months (range of 5-13 months). Bevacizumab is well tolerated, has tolerable toxicity and apparent activity in this small cohort of adults with recurrent spinal cord glioblastoma.

Patient, Provider, and Clinic Characteristics Associated with Opioid and Non-Opioid Pain Prescriptions for Patients Receiving Low Back Imaging in Primary Care.

BACKGROUND: To describe characteristics of patients, providers, and clinics associated with opioid or non-opioid pain medication prescribing patterns for patients who received lower spine imaging in primary care clinics. METHODS: In these secondary analyses of the Lumbar Imaging with Reporting of Epidemiology (LIRE) study, a randomized controlled trial conducted in 4 health systems in the United States, we evaluated characteristics associated with receipt of pain medication prescriptions. The outcomes were receipt of prescriptions for opioid or, separately, non-opioid pain medications within 90 days after imaging. Among patients who received opioid or non-opioid prescriptions, we evaluated receipt of multiple prescriptions in the year following imaging. Mixed models were used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Compared with whites, patients identified as Asian (OR, 0.53; 95% CI, 0.51-0.56), Native Hawaiian/Pacific Islander (OR, 0.73; 95% CI, 0.64-0.83), multiracial (OR, 0.84; 95% CI, 0.71-0.98) or Black (OR, 0.92; 95% CI, 0.89-0.96) had significantly reduced odds for receiving prescriptions for opioids within 90 days. Patients identified as Native American/Alaska Native had greater odds for receiving prescriptions for non-opioid pain medications within 90 days (OR, 1.12; 95% CI, 1.01-1.24). Receipt of pain prescriptions 120 days before imaging was strongly predictive of subsequent receipt of pain prescriptions across all categories. CONCLUSIONS: After adjusting for factors that could affect prescribing, the strongest differences observed in pain-medication prescribing were across racial categories and for patients with previous pain prescriptions. Further research is needed to understand these differences and to optimize prescribing.

Salvage therapy with single agent bevacizumab for recurrent glioblastoma.

A retrospective evaluation of single agent bevacizumab in adults with recurrent glioblastoma (GBM) with an objective of determining progression free survival (PFS). There is no standard therapy for recurrent GBM after failure of alkylator-based chemotherapy. A total of 50 adults, ages 36-70 years (median 64), with recurrent GBM were treated. All patients had previously been treated with surgery, concurrent radiotherapy and temozolomide, post-radiotherapy temozolomide and in 34 patients, one salvage regimen (PCV: 21, cyclophosphamide: 13). A total of 13 patients underwent repeat surgery. Patients were treated at first or second recurrence with bevacizumab, once every 2 weeks, defined as a single cycle. Neurological evaluation was performed every 2 weeks and neuroradiographic assessment following the initial 2 cycles of bevacizumab and subsequently after every 4 cycles of bevacizumab. A total of 468 cycles of bevacizumab (median 2 cycles; range 1-30) was administered. Bevacizumab-related toxicity included fatigue (16 patients; 4 grade 3), leukopenia (9; 1 grade 3), anemia (5; 0 grade 3), hypertension (7; 1 grade 3), deep vein thrombosis (4; 1 grade 3) and wound dehiscence (2; 1 grade 3). 21 patients (42%) demonstrated a partial radiographic response and 29 (58%) progressive disease following 1-2 cycles of bevacizumab. Time to tumor progression ranged from 0.5 to 15 months (median: 1.0 months). Survival ranged from 2 to 17 months (median: 8.5 months). 6-month and 12-month PFS were 42% and 22% respectively. Single agent bevacizumab demonstrated efficacy and acceptable toxicity in this cohort of adults with recurrent alkylator-refractory GBM.

Assessment of Prognostic Value of Cystic Features in Glioblastoma Relative to Sex and Treatment With Standard-of-Care.

Glioblastoma (GBM) is the most aggressive primary brain tumor and can have cystic components, identifiable through magnetic resonance imaging (MRI). Previous studies suggest that cysts occur in 7-23% of GBMs and report mixed results regarding their prognostic impact. Using our retrospective cohort of 493 patients with first-diagnosis GBM, we carried out an exploratory analysis on this potential link between cystic GBM and survival. Using pretreatment MRIs, we manually identified 88 patients with GBM that had a significant cystic component at presentation and 405 patients that did not. Patients with cystic GBM had significantly longer overall survival and were significantly younger at presentation. Within patients who received the current standard of care (SOC) (N = 184, 40 cystic), we did not observe a survival benefit of cystic GBM. Unexpectedly, we did not observe a significant survival benefit between this SOC cystic cohort and patients with cystic GBM diagnosed before the standard was established (N = 40 with SOC, N = 19 without SOC); this significant SOC benefit was clearly observed in patients with noncystic GBM (N = 144 with SOC, N = 111 without SOC). When stratified by sex, the survival benefit of cystic GBM was only preserved in male patients (N = 303, 47 cystic). We report differences in the absolute and relative sizes of imaging abnormalities on MRI and the prognostic implication of cysts based on sex. We discuss hypotheses for these differences, including the possibility that the presence of a cyst could indicate a less aggressive tumor.

Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma.

BACKGROUND: Temozolomide (TMZ) has been the standard-of-care chemotherapy for glioblastoma (GBM) patients for more than a decade. Despite this long time in use, significant questions remain regarding how best to optimize TMZ therapy for individual patients. Understanding the relationship between TMZ response and factors such as number of adjuvant TMZ cycles, patient age, patient sex, and image-based tumor features, might help predict which GBM patients would benefit most from TMZ, particularly for those whose tumors lack O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation. METHODS AND FINDINGS: Using a cohort of 90 newly-diagnosed GBM patients treated according to the standard of care, we examined the relationships between several patient and tumor characteristics and volumetric and survival outcomes during adjuvant chemotherapy. Volumetric changes in MR imaging abnormalities during adjuvant therapy were used to assess TMZ response. T1Gd volumetric response is associated with younger patient age, increased number of TMZ cycles, longer time to nadir volume, and decreased tumor invasiveness. Moreover, increased adjuvant TMZ cycles corresponded with improved volumetric response only among more nodular tumors, and this volumetric response was associated with improved survival outcomes. Finally, in a subcohort of patients with known MGMT methylation status, methylated tumors were more diffusely invasive than unmethylated tumors, suggesting the improved response in nodular tumors is not driven by a preponderance of MGMT methylated tumors. CONCLUSIONS: Our finding that less diffusely invasive tumors are associated with greater volumetric response to TMZ suggests patients with these tumors may benefit from additional adjuvant TMZ cycles, even for those without MGMT methylation.

Days gained response discriminates treatment response in patients with recurrent glioblastoma receiving bevacizumab-based therapies.

BACKGROUND: Accurate assessments of patient response to therapy are a critical component of personalized medicine. In glioblastoma (GBM), the most aggressive form of brain cancer, tumor growth dynamics are heterogenous across patients, complicating assessment of treatment response. This study aimed to analyze days gained (DG), a burgeoning model-based dynamic metric, for response assessment in patients with recurrent GBM who received bevacizumab-based therapies. METHODS: DG response scores were calculated using volumetric tumor segmentations for patients receiving bevacizumab with and without concurrent cytotoxic therapy (N = 62). Kaplan-Meier and Cox proportional hazards analyses were implemented to examine DG prognostic relationship to overall (OS) and progression-free survival (PFS) from the onset of treatment for recurrent GBM. RESULTS: In patients receiving concurrent bevacizumab and cytotoxic therapy, Kaplan-Meier analysis showed significant differences in OS and PFS at DG cutoffs consistent with previously identified values from newly diagnosed GBM using T1-weighted gadolinium-enhanced magnetic resonance imaging (T1Gd). DG scores for bevacizumab monotherapy patients only approached significance for PFS. Cox regression showed that increases of 25 DG on T1Gd imaging were significantly associated with a 12.5% reduction in OS hazard for concurrent therapy patients and a 4.4% reduction in PFS hazard for bevacizumab monotherapy patients. CONCLUSION: DG has significant meaning in recurrent therapy as a metric of treatment response, even in the context of anti-angiogenic therapies. This provides further evidence supporting the use of DG as an adjunct response metric that quantitatively connects treatment response and clinical outcomes.

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data.

Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively ( p < 0.00007 ). The DL method achieved a mean Dice coefficient of 0.87 on the test cases. Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its "human teachers" and produced output that was better, on average, than its training data.