Faculty Bibliography

TARGET POPULATION/METHODS:Adult patients with suspected or histologically proven WHO Grade II diffuse glioma. QUESTION 1: In adult patients with suspected or histologically proven WHO Grade II diffuse glioma, do advanced MRI techniques using magnetic resonance spectroscopy, perfusion weighted imaging or diffusion weighted imaging provide superior assessment of tumor grade, margins, progression, treatment-related effects, and prognosis compared to standard neuroimaging? RECOMMENDATION/CONCLUSIONS:Level II: The use of diffusion imaging and dynamic susceptibility contrast (DSC), dynamic contrast enhancement (DCE) and arterial spin labeling (ASL) sequences are suggested to differentiate WHO Grade II diffuse glioma from higher grade gliomas when this is not accomplished by T2 weighted and pre- and post-gadolinium contrast enhanced T1 weighted imaging. LEVEL III/METHODS:The use of diffusion and perfusion is suggested for obtaining information in genomics, prognosis, and post treatment monitoring when this information would be of value to the clinician and is not obtained through other methods. LEVEL III/METHODS:The use of MR Spectroscopy is suggested to differentiate WHO Grade II diffuse glioma from higher grade gliomas when this is not accomplished by standard MRI, perfusion and diffusion techniques and when such information would be of value to the clinician. QUESTION 2: In adult patients with suspected or histologically proven WHO Grade II diffuse glioma, does molecular imaging using amino acid PET tracers provide superior assessment of tumor grade, margins, progression, treatment-related effects, and prognosis compared to standard neuroimaging? RECOMMENDATION/CONCLUSIONS:Level III: If not already evident by MRI studies, the addition of amino acid PET with FET and FDOPA as a tracer is suggested to help determine if a brain lesion is a low grade glioma or high grade glioma. LEVEL III/METHODS:If the standard clinical prognostic parameters are unclear and novel PET tracers are available, the clinician may consider FET to assist in determination of prognosis in an individual with grade II diffuse glioma. LEVEL III/METHODS:Clinicians may use FDOPA PET in addition to MRI if additional information is required for detection of tumor progression.

BACKGROUND:Pituitary neuroendocrine tumors (PitNETs) are the most common intracranial neuroendocrine tumors. PitNETs can be challenging to classify, and current recommendations include a large immunohistochemical panel to differentiate among 14 WHO-recognized categories. METHODS:In this study, we analyzed clinical, immunohistochemical and DNA methylation data of 118 PitNETs to develop a clinico-molecular approach to classifying PitNETs and identify epigenetic classes. RESULTS:CNS DNA methylation classifier has an excellent performance in recognizing PitNETs and distinguishing the three lineages when the calibrated score is ≥0.3. Unsupervised DNA methylation analysis separated PitNETs into two major clusters. The first was composed of silent gonadotrophs, which form a biologically distinct group of PitNETs characterized by clinical silencing, weak hormonal expression on immunohistochemistry, and simple copy number profile. The second major cluster was composed of corticotrophs and Pit1 lineage PitNETs, which could be further classified using DNA methylation into distinct subclusters that corresponded to clinically functioning and silent tumors and are consistent with transcription factor expression. Analysis of promoter methylation patterns correlated with lineage for corticotrophs and Pit1 lineage subtypes. However, the gonadotrophic genes did not show a distinct promoter methylation pattern in gonadotroph tumors compared to other lineages. Promoter of the NR5A1 gene, which encodes SF1, was hypermethylated across all PitNETs clinical and molecular subtypes including gonadotrophs with strong SF1 protein expression indicating alternative epigenetic regulation. CONCLUSION/CONCLUSIONS:Our findings suggest that classification of PitNETs may benefit from DNA methylation for clinicopathological stratification.

UNLABELLED:QUESTIONS AND RECOMMENDATIONS FROM THE PRIOR VERSION OF THESE GUIDELINES WITHOUT CHANGE: TARGET POPULATION: Adult patients (age ≥ 18 years) who have suspected low-grade diffuse glioma. QUESTION/OBJECTIVE:What are the optimal neuropathological techniques to diagnose low-grade diffuse glioma in the adult? RECOMMENDATION/CONCLUSIONS:Level I Histopathological analysis of a representative surgical sample of the lesion should be used to provide the diagnosis of low-grade diffuse glioma. Level III Both frozen section and cytopathologic/smear evaluation should be used to aid the intra-operative assessment of low-grade diffuse glioma diagnosis. A resection specimen is preferred over a biopsy specimen, to minimize the potential for sampling error issues. TARGET POPULATION/METHODS:Patients with histologically-proven WHO grade II diffuse glioma. QUESTION/OBJECTIVE:In adult patients (age ≥ 18 years) with histologically-proven WHO grade II diffuse glioma, is testing for IDH1 mutation (R132H and/or others) warranted? If so, is there a preferred method? RECOMMENDATION/CONCLUSIONS:Level II IDH gene mutation assessment, via IDH1 R132H antibody and/or IDH1/2 mutation hotspot sequencing, is highly-specific for low-grade diffuse glioma, and is recommended as an additional test for classification and prognosis. TARGET POPULATION/METHODS:Patients with histologically-proven WHO grade II diffuse glioma. QUESTION/OBJECTIVE:In adult patients (age ≥ 18 years) with histologically-proven WHO grade II diffuse glioma, is testing for 1p/19q loss warranted? If so, is there a preferred method? RECOMMENDATION/CONCLUSIONS:Level III 1p/19q loss-of-heterozygosity testing, by FISH, array-CGH or PCR, is recommended as an additional test in oligodendroglial cases for prognosis and potential treatment planning. TARGET POPULATION/METHODS:Patients with histologically proven WHO grade II diffuse glioma. QUESTION/OBJECTIVE:In adult patients (age > 18 years) with histologically-proven WHO grade II diffuse glioma, is methyl-guanine methyl-transferase (MGMT) promoter methylation testing warranted? If so, is there a preferred method? RECOMMENDATION/CONCLUSIONS:There is insufficient evidence to recommend MGMT promoter methylation testing as a routine for low-grade diffuse gliomas. It is recommended that patients be enrolled in properly designed clinical trials to assess the value of this and related markers for this target population. TARGET POPULATION/METHODS:Patients with histologically-proven WHO grade II diffuse glioma. QUESTION/OBJECTIVE:In adult patients (age ≥ 18 years) with histologically proven WHO grade II diffuse glioma, is Ki-67/MIB1 immunohistochemistry warranted? If so, is there a preferred method to quantitate results? RECOMMENDATION/CONCLUSIONS:Level III Ki67/MIB1 immunohistochemistry is recommended as an option for prognostic assessment. NEW RECOMMENDATION/CONCLUSIONS:TARGET POPULATION: Adult patients (age ≥ 18 years) who have suspected WHO grade II diffuse glioma. QUESTION/OBJECTIVE:Is testing for ATRX mutations helpful for predicting survival and making treatment recommendations? RECOMMENDATION/CONCLUSIONS:There is insufficient evidence to recommend ATRX mutation testing as a means of predicting survival or making treatment recommendations. TARGET POPULATION/METHODS:Adult patients (age ≥ 18 years) who have suspected WHO grade II diffuse glioma. QUESTION/OBJECTIVE:Does the addition of intraoperative optical histologic methods provide accuracy beyond the use of conventional histologic methods in diagnosis and management? RECOMMENDATION/CONCLUSIONS:There is insufficient evidence at this time to suggest that intraoperative optical histologic methods offer increased diagnostic accuracy when compared to conventional techniques.

BACKGROUND:The diagnosis and treatment of tumors often depend on molecular-genetic data. However, rapid and iterative access to molecular data is not currently feasible during surgery, complicating intraoperative diagnosis and precluding measurement of tumor cell burdens at surgical margins to guide resections. METHODS:Here, we introduce Ultra-Rapid droplet digital PCR (UR-ddPCR), a technology that achieves the fastest measurement, to date, of mutation burdens in tissue samples, from tissue to result in 15 min. Our workflow substantially reduces the time from tissue biopsy to molecular diagnosis and provides a highly accurate means of quantifying residual tumor infiltration at surgical margins. FINDINGS/RESULTS: = 0.995). CONCLUSIONS:The technology and workflow developed here enable intraoperative molecular-genetic assays with unprecedented speed and sensitivity. We anticipate that our method will facilitate novel point-of-care diagnostics and molecularly guided surgeries that improve clinical outcomes. FUNDING/BACKGROUND:This study was funded by the National Institutes of Health and NYU Grossman School of Medicine institutional funds. Reagents and instruments were provided in kind by Bio-Rad.

The adoption of large language models (LLMs) in healthcare demands a careful analysis of their potential to spread false medical knowledge. Because LLMs ingest massive volumes of data from the open Internet during training, they are potentially exposed to unverified medical knowledge that may include deliberately planted misinformation. Here, we perform a threat assessment that simulates a data-poisoning attack against The Pile, a popular dataset used for LLM development. We find that replacement of just 0.001% of training tokens with medical misinformation results in harmful models more likely to propagate medical errors. Furthermore, we discover that corrupted models match the performance of their corruption-free counterparts on open-source benchmarks routinely used to evaluate medical LLMs. Using biomedical knowledge graphs to screen medical LLM outputs, we propose a harm mitigation strategy that captures 91.9% of harmful content (F1 = 85.7%). Our algorithm provides a unique method to validate stochastically generated LLM outputs against hard-coded relationships in knowledge graphs. In view of current calls for improved data provenance and transparent LLM development, we hope to raise awareness of emergent risks from LLMs trained indiscriminately on web-scraped data, particularly in healthcare where misinformation can potentially compromise patient safety.

BACKGROUND:In-field or in-margin recurrence after partial gland cryosurgical ablation (PGCA) of prostate cancer (PCa) remains a limitation of the paradigm. Stimulated Raman histology (SRH) is a novel microscopic technique allowing real time, label-free, high-resolution microscopic images of unprocessed, un-sectioned tissue which can be interpreted by humans or artificial intelligence (AI). We evaluated surgical team and AI interpretation of SRH for real-time pathologic feedback in the planning and treatment of PCa with PGCA. METHODS:About 12 participants underwent prostate mapping biopsies during PGCA of their PCa between January and June 2022. Prostate biopsies were immediately scanned in a SRH microscope at 20 microns depth using 2 Raman shifts to create SRH images which were interpreted by the surgical team intraoperatively to guide PGCA, and retrospectively assessed by AI. The cores were then processed, hematoxylin and eosin stained as per normal pathologic protocols and used for ground truth pathologic assessment. RESULTS:Surgical team interpretation of SRH intraoperatively revealed 98.1% accuracy, 100% sensitivity, 97.3% specificity for identification of PCa, while AI showed a 97.9% accuracy, 100% sensitivity and 97.5% specificity for identification of clinically significant PCa. 3 participants' PGCA treatments were modified after SRH visualized PCa adjacent to an expected MRI predicted tumor margin or at an untreated cryosurgical margin. CONCLUSION/CONCLUSIONS:SRH allows for accurate rapid identification of PCa in PB by a surgical team interpretation or AI. PCa tumor mapping and margin assessment during PGCA appears to be feasible and accurate. Further studies evaluating impact on clinical outcomes are warranted.

INTRODUCTION/UNASSIGNED:Balancing surgical margins and functional outcomes is crucial during radical prostatectomy for prostate cancer. Stimulated Raman Histology (SRH) is a novel, real-time imaging technique that provides histologic images of fresh, unprocessed, and unstained tissue within minutes, which can be interpreted by either humans or artificial intelligence. METHODS/UNASSIGNED:Twenty-two participants underwent robotic-assisted laparoscopic radical prostatectomy (RALP) with intraoperative SRH surgical bed assessment. Surgeons resected and imaged surgical bed tissue using SRH and adjusted treatment accordingly. An SRH convolutional neural network (CNN) was developed and tested on 10 consecutive participants. The accuracy, sensitivity, and specificity of the surgical team's interpretation were compared to final histopathological assessment. RESULTS/UNASSIGNED:A total of 121 SRH periprostatic surgical bed tissue (PSBT) assessments were conducted, an average of 5.5 per participant. The accuracy of the surgical team's SRH interpretation of resected PSBT samples was 98%, with 83% sensitivity, and 99% specificity. Intraoperative SRH assessment identified 43% of participants with a pathologic positive surgical margin intraoperatively. PSBT assessment using the CNN demonstrated no overlap in tumor probability prediction between benign and tumor infiltrated samples, mean 0.30% (IQR 0.10-0.43%) and 26% (IQR 18-34%, p<0.005), respectively. CONCLUSION/UNASSIGNED:SRH demonstrates potential as a valuable tool for real-time intraoperative assessment of surgical margins during RALP. This technique may improve nerve-sparing surgery and facilitate decision-making for further resection, reducing the risk of positive surgical margins and minimizing the risk of recurrence. Further studies with larger cohorts and longer follow-up periods are warranted to confirm the benefits of SRH in RALP.

BACKGROUND:Accurate intraoperative diagnosis is crucial for differentiating between primary CNS lymphoma (PCNSL) and other CNS entities, guiding surgical decision-making, but represents significant challenges due to overlapping histomorphological features, time constraints, and differing treatment strategies. We combined stimulated Raman histology (SRH) with deep learning to address this challenge. METHODS:We imaged unprocessed, label-free tissue samples intraoperatively using a portable Raman scattering microscope, generating virtual H&E-like images within less than three minutes. We developed a deep learning pipeline called RapidLymphoma based on a self-supervised learning strategy to (1) detect PCNSL, (2) differentiate from other CNS entities, and (3) test the diagnostic performance in a prospective international multicenter cohort and two additional independent test cohorts. We trained on 54,000 SRH patch images sourced from surgical resections and stereotactic-guided biopsies, including various CNS neoplastic/non-neoplastic lesions. Training and test data were collected from four tertiary international medical centers. The final histopathological diagnosis served as ground-truth. RESULTS:In the prospective test cohort of PCNSL and non-PCNSL entities (n=160), RapidLymphoma achieved an overall balanced accuracy of 97.81% ±0.91, non-inferior to frozen section analysis in detecting PCNSL (100% vs. 77.77%). The additional test cohorts (n=420, n=59) reached balanced accuracy rates of 95.44% ±0.74 and 95.57% ±2.47 in differentiating IDH-wildtype diffuse gliomas and various brain metastasis from PCNSL. Visual heatmaps revealed RapidLymphoma's capabilities to detect class-specific histomorphological key features. CONCLUSIONS:RapidLymphoma proves reliable and valid for intraoperative PCNSL detection and differentiation from other CNS entities. It provides visual feedback within three minutes, enabling fast clinical decision-making and subsequent treatment strategy planning.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Intraoperative red blood cell (RBC) salvage is frequently used in contemporary spine surgery, despite clinical concern in its efficacy as a surrogate for blood-banked allogeneic packed RBCs (pRBCs). During spine surgery, salvaged RBCs (sRBCs) are exposed to injurious high-heat electrocautery, prolonged stasis, and abrasive pharmaceuticals, potentially making sRBCs a poor blood substitute. We therefore sought to scientifically and objectively define the quality of sRBCs in the context of complex spine surgery. METHODS:This is a single-center, prospective, nonrandomized controlled trial of patients undergoing posterior-based multilevel thoracolumbar instrumented fusion for spinal deformity with planned use of intraoperative RBC salvage between June 2022 and July 2023. Surgeries were performed by fellowship-trained spinal neurosurgeons and orthopedic surgeons. The participants were split based on transfusion of sRBCs (given sufficient yield) vs no sRBC transfusion. Primary outcomes were RBC electrolyte composition, indices, deformability, and integrity, which were evaluated in comparison blood samples: Baseline, pRBC, and sRBC. Secondary outcomes were related to clinical effects of sRBC transfusion. Morphological assessment used Stimulated Raman Histology and machine learning. Deformability was assessed using ektacytometry. RESULTS:A total of 174 patients were included. The mean age was 50.2years ±25.4, 58.6% was female, the mean level fused was 10.0 ± 3.9, and 58.0% received sRBCs (median 207.0 mL). sRBCs differed significantly on standard laboratory measures, had a high proportion (30.7%) of shrunken and irregularly spiculated morphologies, and demonstrated abnormal deformability and relaxation kinetics. The hemolysis index was significantly elevated in sRBCs (2.9 ± 1.8) compared with Baseline samples and pRBCs (P < .01). Transfusion of sRBCs was associated with suboptimal resuscitation and provided no practical clinical benefit. CONCLUSION/CONCLUSIONS:RBCs salvaged during posterior thoracolumbar spine surgery are irreversibly injured, with hemolysis index exceeding Food and Drug Administration and Council of Europe transfusion standards in all samples, questioning their efficacy and safety as a blood substitute.

The detection and tracking of metastatic cancer over the lifetime of a patient remains a major challenge in clinical trials and real-world care. Advances in deep learning combined with massive datasets may enable the development of tools that can address this challenge. We present NYUMets-Brain, the world's largest, longitudinal, real-world dataset of cancer consisting of the imaging, clinical follow-up, and medical management of 1,429 patients. Using this dataset we developed Segmentation-Through-Time, a deep neural network which explicitly utilizes the longitudinal structure of the data and obtained state-of-the-art results at small (<10 mm³) metastases detection and segmentation. We also demonstrate that the monthly rate of change of brain metastases over time are strongly predictive of overall survival (HR 1.27, 95%CI 1.18-1.38). We are releasing the dataset, codebase, and model weights for other cancer researchers to build upon these results and to serve as a public benchmark.