Citations

Oncolytic viral therapy is quickly emerging as a promising subset of immunotherapy, which theoretically can target tumor cells while sparing surrounding healthy cells by harnessing the replication machinery of viruses with tropism for tumor cells, resulting in direct oncolysis, and by transforming immunologically "cold" tumor into areas that elicit the host’s immune response. This review provides an overview of oncolytic viral therapy until the present day, starting with the original concept in 1912. The general mechanism of oncolytic viruses (OVs) depends on selectively integrating them into tumor cells based on genetic engineering of viral genomic material, inducing oncolysis and eliciting the host’s innate immune response. Moreover, a major component of oncolytic viral therapy has been herpes simplex virus, with talimogene laherparepvec being the only FDA-approved oncolytic viral therapy for the treatment of melanomas. This review explores the characteristics, advantages, disadvantages, and therapeutic uses of several DNA and RNA viral families. A snapshot of the oncolytic viral treatments used in the most recent and advanced clinical trials is also provided. Lastly, the challenges of implementing oncolytic viral therapy are explored, both at a molecular and clinical level, with a highlight of promising future directions. In particular, the lack of an optimal delivery method based on tumor type for oncolytic viral therapy poses a significant obstacle, even in clinical studies. Intrathecal continuous delivery of OVs is a promising prospect, potentially by adapting the novel continuous irrigation and drainage IRRAflow catheter. Further exploration and testing of the IRRAflow catheter should be undertaken.

OBJECTIVE Chordomas are slow-growing tumors derived from notochord remnants. Despite margin-negative excision and postoperative radiation therapy, spinal chordomas (SCs) often progress. The potential of immunohistochemical (IHC) markers, such as epithelial membrane antigen (EMA), combined with machine learning algorithms to predict long-term (≥ 12 months) postoperative tumor progression, has been understudied. The authors aimed to identify IHC markers using trained tree-based algorithms to predict long-term (≥ 12 months) postoperative tumor progression. METHODS The authors reviewed the records of patients who underwent resection of SCs between January 2017 and June 2021 across the Mayo Clinic enterprise. Demographics, type of treatment, histopathology, and other relevant clinical factors were abstracted from each patient’s record. Low tumor progression was defined as more than a 94.3-mm3 decrease in the tumor size at the latest radiographic follow-up. Decision trees and random forest classifiers were trained and tested to predict the long-term volumetric progression after an 80/20 data split. RESULTS Sixty-two patients diagnosed with and surgically treated for SC were identified, of whom 31 were found to have a more advanced tumor progression based on the tumor volume change cutoff of 94.3 mm3. The mean age was 54.3 ± 13.8 years, and most patients were male (62.9%) and White (98.4%). The most common treatment modality was subtotal resection with radiation therapy (35.5%), with proton beam therapy being the most common (71%). Most SCs were sacrococcygeal (41.9%), followed by cervical (32.3%). EMA-positive SCs had a postoperative progression risk of 67%. Pancytokeratin-positive SCs had a progression rate of 67%; however, patients with S100 protein–positive SCs had a 54% risk of progression. The accuracy of this model in predicting the progression of unseen test data was 66%. Pancytokeratin (mean minimal depth = 1.57), EMA (mean minimal depth = 1.58), cytokeratin A1/A3 (mean minimal depth = 1.59), and S100 protein (mean minimal depth = 1.6) predicted the long-term volumetric progression. Multiway variable importance plots show the relative importance of the top 10 variables based on three measures of varying significance and their predictive role. CONCLUSIONS These IHC variables with tree-based machine learning tools successfully demonstrate a high capacity to identify a patient’s tumor progression pattern with an accuracy of 66%. Pancytokeratin, EMA, cytokeratin A1/A3, and S100 protein were the IHC drivers of a low tumor progression. This shows the power of machine learning algorithms in analyzing and predicting outcomes of rare conditions in a small sample size.

Introduction Poor‐grade aneurysmal subarachnoid hemorrhage (aSAH) is associated with high mortality. Overall, 20–30% of aSAH are described as poor grade. Despite recent advances in management strategies, prognosis of poor‐grade aSAH remains dismal. As such, we review the most current literature regarding management strategies that can be used to decrease the risk of the four most common causes of secondary brain injury: 1) aneurysm re‐rupture, 2) hydrocephalus, 3) cerebral salt wasting, and 4) delayed cerebral ischemia/vasospasm. Methods We present a 46‐year‐old female patient with poor‐grade aSAH after a loss of consciousness following thunderclap headache. Examination showed a dilated left pupil and Glasgow Coma Scale (GCS) of 4. Imaging revealed a ruptured anterior communicating artery aneurysm, and she was subsequently taken to the neuro‐interventional radiology suite for EVD placement. On POD 7 Transcranial Doppler (TCD) suggested moderate vasospam of left MCA. Two successful balloon angioplasties of the basilar, ACA‐A1, and MCA‐M1 were successfully performed on separate consecutive days. By POD 10, intermittent severe ICP spikes required targeted temperature management (TTM) with hypothermia. On POD 24, her ICP normalized, she was no longer comatose, and her EVD was clamped and removed. She was rapidly weaned off ventilator and transitioned to rehabilitation. 3‐months postoperatively, Modified Rankin Scale score was 0. Results Based upon the result of our literature review, we propose maintaining blood pressure below 160 mmHg prior to intervention, after which it can be increased to 160–240 mmHg to prevent vasospasm. Transcranial Doppler is essential to detect vasospasm as the aSAH symptoms subtle; once identified, vasospasm can be successfully treated with balloon angioplasty. Finally, targeted temperature management, mannitol, hypertonic saline, and neuromuscular paralysis are essential for postoperative management of ICP levels. Conclusions The multimodal approach should be considered when managing poor grade aSAH. Further research in aSAH management would benefit the patients who present with poor prognosis, thereby improving patient care and leading to a standard, effective approach for aSAH management.

Background: The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality. Methods: We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms “artificial intelligence”, “machine learning”, and “pituitary”. Results: 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60–72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80–0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability. Conclusion: Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI’s real-world utility and improve clinical practice.

Stroke is a leading cause of morbidity and mortality. The advent of mechanical thrombectomy has largely improved patient outcomes. This article reviews the features and outcomes associated with aspiration, stent retrievers, and combination catheters used in current practice. There is also a discussion on clinical considerations based on anatomical features and clot composition. The reperfusion grading scale and outcome metrics commonly used following thrombectomy when a patient is still in the hospital are reviewed. Lastly, there are proposed discharge and outpatient follow-up goals in caring for patients hospitalized for a stroke.

Abstract 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.