Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma.

BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. RESULTS: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. CONCLUSIONS: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM.

Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign.

PURPOSE: The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+. METHODS: In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets. RESULTS: A total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (p = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (p = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign. CONCLUSION: The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.

U-Net Based Segmentation and Characterization of Gliomas.

(1) Background: Gliomas are the most common primary brain neoplasms accounting for roughly 40−50% of all malignant primary central nervous system tumors. We aim to develop a deep learning-based framework for automated segmentation and prediction of biomarkers and prognosis in patients with gliomas. (2) Methods: In this retrospective two center study, patients were included if they (1) had a diagnosis of glioma with known surgical histopathology and (2) had preoperative MRI with FLAIR sequence. The entire tumor volume including FLAIR hyperintense infiltrative component and necrotic and cystic components was segmented. Deep learning-based U-Net framework was developed based on symmetric architecture from the 512 × 512 segmented maps from FLAIR as the ground truth mask. (3) Results: The final cohort consisted of 208 patients with mean ± standard deviation of age (years) of 56 ± 15 with M/F of 130/78. DSC of the generated mask was 0.93. Prediction for IDH-1 and MGMT status had a performance of AUC 0.88 and 0.62, respectively. Survival prediction of <18 months demonstrated AUC of 0.75. (4) Conclusions: Our deep learning-based framework can detect and segment gliomas with excellent performance for the prediction of IDH-1 biomarker status and survival.

Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion.

BACKGROUND: Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma. METHODS: In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed. RESULTS: From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR). CONCLUSION: Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.

Impact of COVID-19 social distancing regulations on outpatient diagnostic imaging volumes and no-show rates.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted outpatient radiology practices, necessitating change in practice infrastructure and workflow. OBJECTIVE: The purpose of this study was to assess the consequences of social distancing regulations on 1) outpatient imaging volume and 2) no-show rates per imaging modality. METHODS: Volume and no-show rates of a large, multicenter metropolitan healthcare system outpatient practice were retrospectively stratified by modality including radiography, CT, MRI, ultrasonography, PET, DEXA, and mammography from January 2 to July 21, 2020. Trends were assessed relative to timepoints of significant state and local social distancing regulatory changes. RESULTS: The decline in imaging volume and rise in no-show rates was first noted on March 10, 2020 following the declaration of a state of emergency in New York State (NYS). Total outpatient imaging volume declined 85% from baseline over the following 5 days. Decreases varied by modality: 88% for radiography, 75% for CT, 73% for MR, 61% for PET, 80% for ultrasonography, 90% for DEXA, and 85% for mammography. Imaging volume and no-show rate recovery preceded the mask mandate of April 15, 2020, and further trended along with New York City's reopening phases. No-show rates recovered within 2 months of the height of the pandemic, however, outpatient imaging volume has yet to recover to baseline after 3 months. CONCLUSION: The total outpatient imaging volume declined alongside an increase in the no-show rate following the declaration of a state of emergency in NYS. No-show rates recovered within 2 months of the height of the pandemic with imaging volume yet to recover after 3 months. CLINICAL IMPACT: Understanding the impact of social distancing regulations on outpatient imaging volume and no-show rates can potentially aid other outpatient radiology practices and healthcare systems in anticipating upcoming changes as the COVID-19 pandemic evolves.

Association of Coronavirus Disease (COVID-19) With Large Vessel Occlusion Strokes: A Case-Control Study.

BACKGROUND. An increase in frequency of acute ischemic strokes has been observed among patients presenting with acute neurologic symptoms during the coronavirus disease (COVID-19) pandemic. OBJECTIVE. The purpose of this study was to investigate the association between COVID-19 and stroke subtypes in patients presenting with acute neurologic symptoms. METHODS. This retrospective case-control study included patients for whom a code for stroke was activated from March 16 to April 30, 2020, at any of six New York City hospitals that are part of a single health system. Demographic data (age, sex, and race or ethnicity), COVID-19 status, stroke-related risk factors, and clinical and imaging findings pertaining to stroke were collected. Univariate and multivariate analyses were conducted to evaluate the association between COVID-19 and stroke subtypes. RESULTS. The study sample consisted of 329 patients for whom a code for stroke was activated (175 [53.2%] men, 154 [46.8%] women; mean age, 66.9 ± 14.9 [SD] years). Among the 329 patients, 35.3% (116) had acute ischemic stroke confirmed with imaging; 21.6% (71) had large vessel occlusion (LVO) stroke; and 14.6% (48) had small vessel occlusion (SVO) stroke. Among LVO strokes, the most common location was middle cerebral artery segments M1 and M2 (62.0% [44/71]). Multifocal LVOs were present in 9.9% (7/71) of LVO strokes. COVID-19 was present in 38.3% (126/329) of the patients. The 61.7% (203/329) of patients without COVID-19 formed the negative control group. Among individual stroke-related risk factors, only Hispanic ethnicity was significantly associated with COVID-19 (38.1% of patients with COVID-19 vs 20.7% of patients without COVID-19; p = 0.001). LVO was present in 31.7% of patients with COVID-19 compared with 15.3% of patients without COVID-19 (p = 0.001). SVO was present in 15.9% of patients with COVID-19 and 13.8% of patients without COVID-19 (p = 0.632). In multivariate analysis controlled for race and ethnicity, presence of COVID-19 had a significant independent association with LVO stroke (odds ratio, 2.4) compared with absence of COVID-19 (p = 0.011). CONCLUSION. COVID-19 is associated with LVO strokes but not with SVO strokes. CLINICAL IMPACT. Patients with COVID-19 presenting with acute neurologic symptoms warrant a lower threshold for suspicion of large vessel stroke, and prompt workup for large vessel stroke is recommended.

Body Mass Index Correlates with Skin to Spinal Canal Distance: A Large Retrospective Single-Center Study.

BACKGROUND AND PURPOSE: Despite increasing demand for fluoroscopy-guided lumbar puncture (FG-LP), there is limited quantitative and epidemiological data on patients undergoing this procedure. Additionally, data are scarce on the correlation of iliac crest landmarks to the actual anatomical lumbar level (intercristal line). The aim of this study is to determine if (1) body mass index (BMI) correlates with skin to spinal canal distance (SCD) and (2) the iliac crest landmark correlates with the presumed anatomical landmark on cross-sectional imaging. METHODS: In this retrospective, single-center IRB-approved study, we assessed 495 patients who underwent FG-LP and had lumbar computed tomography/magnetic resonance imaging within 6 months of presentation. SCD was measured on the sagittal view at the L3-L4, L4-L5, and L5-S1 intervertebral levels. RESULTS: In our cohort of 495 adults (mean age ± standard deviation [SD], 53.2 ± 16.4 years), there was a statistically significant linear correlation between BMI and SCD at each intervertebral level. Mean ± SD (R2 ) SCD at L3-4, L4-5, and L5-S1 was 6.7 ± 1.6 cm (.5486), 7.4 ± 1.9 cm (.5894), and 7.8 ± 1.9 cm (.5968), respectively. The intercristal line aligned with L3-L4, L4-L5, and L5-S1 in 2.1%, 72.4%, and 6.2% of patients, respectively. CONCLUSIONS: There was direct, positive linear correlation between BMI and SCD at clinically relevant lumbar disc levels. Furthermore, there is considerable anatomical variance in the intervertebral space that aligns with the superior aspect of the iliac crest.

Incidental COVID-19 related lung apical findings on stroke CTA during the COVID-19 pandemic.

BACKGROUND: Authors have noticed an increase in lung apex abnormalities on CT angiography (CTA) of the head and neck performed for stroke workup during the coronavirus disease 2019 (COVID-19) pandemic. OBJECTIVE: To evaluate the incidence of these CTA findings and their relation to COVID-19 infection. METHODS: In this retrospective multicenter institutional review board-approved study, assessment was made of CTA findings of code patients who had a stroke between March 16 and April 5, 2020 at six hospitals across New York City. Demographic data, comorbidities, COVID-19 status, and neurological findings were collected. Assessment of COVID-19 related lung findings on CTA was made blinded to COVID-19 status. Incidence rates of COVID-19 related apical findings were assessed in all code patients who had a stroke and in patients with a stroke confirmed by imaging. RESULTS: The cohort consisted of a total of 118 patients with mean±SD age of 64.9±15.7 years and 57.6% (68/118) were male. Among all code patients who had a stroke, 28% (33/118) had COVID-19 related lung findings. RT-PCR was positive for COVID-19 in 93.9% (31/33) of these patients with apical CTA findings.Among patients who had a stroke confirmed by imaging, 37.5% (18/48) had COVID-19 related apical findings. RT-PCR was positive for COVID-19 in all (18/18) of these patients with apical findings. CONCLUSION: The incidence of COVID-19 related lung findings in stroke CTA scans was 28% in all code patients who had a stroke and 37.5% in patients with a stroke confirmed by imaging. Stroke teams should closely assess the lung apices during this COVID-19 pandemic as CTA findings may be the first indicator of COVID-19 infection.

Magnetic hyperthermia therapy for the treatment of glioblastoma: a review of the therapy's history, efficacy and application in humans.

Hyperthermia therapy (HT) is the exposure of a region of the body to elevated temperatures to achieve a therapeutic effect. HT anticancer properties and its potential as a cancer treatment have been studied for decades. Techniques used to achieve a localised hyperthermic effect include radiofrequency, ultrasound, microwave, laser and magnetic nanoparticles (MNPs). The use of MNPs for therapeutic hyperthermia generation is known as magnetic hyperthermia therapy (MHT) and was first attempted as a cancer therapy in 1957. However, despite more recent advancements, MHT has still not become part of the standard of care for cancer treatment. Certain challenges, such as accurate thermometry within the tumour mass and precise tumour heating, preclude its widespread application as a treatment modality for cancer. MHT is especially attractive for the treatment of glioblastoma (GBM), the most common and aggressive primary brain cancer in adults, which has no cure. In this review, the application of MHT as a therapeutic modality for GBM will be discussed. Its therapeutic efficacy, technical details, and major experimental and clinical findings will be reviewed and analysed. Finally, current limitations, areas of improvement, and future directions will be discussed in depth.

Small extracellular vesicles as tumor biomarkers for glioblastoma.

Small extracellular organelles such as exosomes and microvesicles are currently being studied as a novel way to track tumor progression, pseudoprogression, and treatment monitoring. Their role in intercellular communication shows potential in the treatment of even the most formidable cancers. Glioblastoma (GBM) is the most common malignancy of the brain and has no known cure. A large emphasis has been placed on trying to improve the prognosis of this aggressive primary brain tumor. It has recently been discovered that small extracellular vesicles, mainly exosomes and microvesicles, play a role in the cell signaling process that leads to uncontrollable cell growth indicative of a tumor state. Here we describe the role of exosomes and microvesicles as a tumor biomarker for tracking the progression of different types of cancer, with an emphasis on GBM.