Current Applications of Diffusion Tensor Imaging and Tractography in Intracranial Tumor Resection

In the treatment of brain tumors, surgical intervention remains a common and effective therapeutic option. Recent advances in neuroimaging have provided neurosurgeons with new tools to overcome the challenge of differentiating healthy tissue from tumor-infiltrated tissue, with the aim of increasing the likelihood of maximizing the extent of resection volume while minimizing injury to functionally important regions. Novel applications of diffusion tensor imaging (DTI), and DTI-derived tractography (DDT) have demonstrated that preoperative, non-invasive mapping of eloquent cortical regions and functionally relevant white matter tracts (WMT) is critical during surgical planning to reduce postoperative deficits, which can decrease quality of life and overall survival. In this review, we summarize the latest developments of applying DTI and tractography in the context of resective surgery and highlight its utility within each stage of the neurosurgical workflow: preoperative planning and intraoperative management to improve postoperative outcomes

Glioma Through the Looking GLASS: Molecular Evolution of Diffuse Gliomas and the Glioma Longitudinal AnalySiS Consortium

Adult diffuse gliomas are a diverse group of brain neoplasms that inflict a high emotional toll on patients and their families. The Cancer Genome Atlas (TCGA) and similar projects have provided a comprehensive understanding of the somatic alterations and molecular subtypes of glioma at diagnosis. However, gliomas undergo significant cellular and molecular evolution during disease progression. We review the current knowledge on the genomic and epigenetic abnormalities in primary tumors and after disease recurrence, highlight the gaps in the literature, and elaborate on the need for a new multi-institutional effort to bridge these knowledge gaps and how the Glioma Longitudinal AnalySiS Consortium (GLASS) aims to systemically catalog the longitudinal changes in gliomas. The GLASS initiative will provide essential insights into the evolution of glioma toward a lethal phenotype, with the potential to reveal targetable vulnerabilities, and ultimately, improved outcomes for a patient population in need

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

The Supraorbital Keyhole Craniotomy through an Eyebrow Incision: Its Origins and Evolution

In the modern era of neurosurgery, the use of the operative microscope, rigid rod-lens endoscope, and neuronavigation has helped to overcome some of the previous limitations of surgery due to poor lighting and anatomic localization available to the surgeon. Over the last thirty years, the supraorbital craniotomy and subfrontal approach through an eyebrow incision have been developed and refined to play a legitimate role in the armamentarium of the modern skull base neurosurgeon. With careful patient selection, the supraorbital “keyhole” approach offers a less invasive but still efficacious approach to a number of lesions along the subfrontal corridor. Well over 1000 cases have been reported in the literature utilizing this approach establishing its safety and efficacy. This paper discusses the nuances of this approach, including the benefits and limitations of its use described through our technique, review of the literature, and case illustration

MRI Findings of Causalgia of the Lower Extremity Following Transsphenoidal Resection of Pituitary Tumor

Background. Causalgia is continuing pain, allodynia, or hyperalgesia after nerve injury with edema, changes in skin blood flow, or abnormal sudomotor activity. Here we report a case of lower extremity causalgia following elective transsphenoidal resection of a pituitary tumor in a young man. Clinical Presentation. A 33-year-old man with acromegaly underwent elective sublabial transsphenoidal resection of his pituitary tumor. During the three-hour surgery, the lower limbs were kept in a supine, neutral position with a pillow under the knees. The right thigh was slightly internally rotated with a tape to expose fascia lata, which was harvested to repair the sella. Postoperatively, he developed causalgia in a distal sciatic and common peroneal nerve distribution. Pain was refractory to several interventions. Finally, phenoxybenzamine improved his pain significantly. Conclusions. Malpositioning in the operating room resulted in causalgia in this young man. Phenoxybenzamine improved, and ultimately resolved, his symptoms. Improvement in his pain symptoms correlated with resolution of imaging changes in the distal sciatic and peroneal nerves on the side of injury

Biopsy Confirmed Glioma Recurrence Predicted by Multi-Modal Neuroimaging Metrics

Histopathological verification is currently required to differentiate tumor recurrence from treatment effects related to adjuvant therapy in patients with glioma. To bypass the complications associated with collecting neural tissue samples, non-invasive classification methods are needed to alleviate the burden on patients while providing vital information to clinicians. However, uncertainty remains as to which tissue features on magnetic resonance imaging (MRI) are useful. The primary objective of this study was to quantitatively assess the reliability of combining MRI and diffusion tensor imaging metrics to discriminate between tumor recurrence and treatment effects in histopathologically identified biopsy samples. Additionally, this study investigates the noise adjuvant radiation therapy introduces when discriminating between tissue types. In a sample of 41 biopsy specimens, from a total of 10 patients, we derived region-of-interest samples from MRI data in the ipsilateral hemisphere that encompassed biopsies obtained during resective surgery. This study compares normalized intensity values across histopathology classifications and contralesional volumes reflected across the midline. Radiation makes noninvasive differentiation of abnormal-nontumor tissue to tumor recurrence much more difficult. This is because radiation exhibits opposing behavior on key MRI modalities: specifically, on post-contrast T1, FLAIR, and GFA. While radiation makes noninvasive differentiation of tumor recurrence more difficult, using a novel analysis of combined MRI metrics combined with clinical annotation and histopathological correlation, we observed that it is possible to successfully differentiate tumor tissue from other tissue types. Additional work will be required to expand upon these findings

Fiber-tract localized diffusion coefficients highlight patterns of white matter disruption induced by proximity to glioma.

Gliomas account for 26.5% of all primary central nervous system tumors. Recent studies have used diffusion tensor imaging (DTI) to extract white matter fibers and the diffusion coefficients derived from MR processing to provide useful, non-invasive insights into the extent of tumor invasion, axonal integrity, and gross differentiation of glioma from metastasis. Here, we extend this work by examining whether a tract-based analysis can improve non-invasive localization of tumor impact on white matter integrity. This study retrospectively analyzed preoperative magnetic resonance sequences highlighting contrast enhancement and DTI scans of 13 subjects that were biopsy-confirmed to have either high or low-grade glioma. We reconstructed the corticospinal tract and superior longitudinal fasciculus by applying atlas-based regions of interest to fibers derived from whole-brain deterministic streamline tractography. Within-subject comparison of hemispheric diffusion coefficients (e.g., fractional anisotropy and mean diffusivity) indicated higher levels of white matter degradation in the ipsilesional hemisphere. Novel application of along-tract analyses revealed that tracts traversing the tumor region showed significant white matter degradation compared to the contralesional hemisphere and ipsilesional tracts displaced by the tumor