Faculty Bibliography

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.

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.

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.

PURPOSE/OBJECTIVE:DNA methylation profiling stratifies isocitrate dehydrogenase (IDH)-mutant astrocytomas into methylation low-grade and high-grade groups. We investigated the utility of the T2-FLAIR mismatch sign for predicting DNA methylation grade and cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion, a molecular biomarker for grade 4 IDH-mutant astrocytomas, according to the 2021 World Health Organization (WHO) classification. EXPERIMENTAL DESIGN/METHODS:Preoperative MRI scans of IDH-mutant astrocytomas subclassified by DNA methylation profiling (n=71) were independently evaluated by two radiologists for the T2-FLAIR mismatch sign. The diagnostic utility of T2-FLAIR mismatch in predicting methylation grade, CDKN2A/B status, copy number variation, and survival was analyzed. RESULTS:The T2-FLAIR mismatch sign was present in 21 of 45 (46.7%) methylation low-grade and 1 of 26 (3.9%) methylation high-grade cases (p<0.001), resulting in 96.2% specificity, 95.5% positive predictive value, and 51.0% negative predictive value for predicting low methylation grade. The T2-FLAIR mismatch sign was also significantly associated with intact CDKN2A/B status (p=0.028) with 87.5% specificity, 86.4% positive predictive value, and 42.9% negative predictive value. Overall multivariable Cox analysis showed that retained CDKN2A/B status remained significant for PFS (p=0.01). Multivariable Cox analysis of the histologic grade 3 subset, which was nearly evenly divided by CDKN2A/B status, CNV, and methylation grade, showed trends toward significance for DNA methylation grade with OS (p=0.045) and CDKN2A/B status with PFS (p=0.052). CONCLUSIONS:The T2-FLAIR mismatch sign is highly specific for low methylation grade and intact CDKN2A/B in IDH-mutant astrocytomas.

BACKGROUND:Isocitrate dehydrogenase (IDH) mutant astrocytoma grading, until recently, has been entirely based on morphology. The 5th edition of the Central Nervous System World Health Organization (WHO) introduces CDKN2A/B homozygous deletion as a biomarker of grade 4. We sought to investigate the prognostic impact of DNA methylation-derived molecular biomarkers for IDH mutant astrocytoma. METHODS:We analyzed 98 IDH mutant astrocytomas diagnosed at NYU Langone Health between 2014 and 2022. We reviewed DNA methylation subclass, CDKN2A/B homozygous deletion, and ploidy and correlated molecular biomarkers with histological grade, progression free (PFS), and overall (OS) survival. Findings were confirmed using 2 independent validation cohorts. RESULTS:There was no significant difference in OS or PFS when stratified by histologic WHO grade alone, copy number complexity, or extent of resection. OS was significantly different when patients were stratified either by CDKN2A/B homozygous deletion or by DNA methylation subclass (P value = .0286 and .0016, respectively). None of the molecular biomarkers were associated with significantly better PFS, although DNA methylation classification showed a trend (P value = .0534). CONCLUSIONS:The current WHO recognized grading criteria for IDH mutant astrocytomas show limited prognostic value. Stratification based on DNA methylation shows superior prognostic value for OS.

The most widely used fluorophore in glioma-resection surgery, 5-aminolevulinic acid (5-ALA), is thought to cause the selective accumulation of fluorescent protoporphyrin IX (PpIX) in tumour cells. Here we show that the clinical detection of PpIX can be improved via a microscope that performs paired stimulated Raman histology and two-photon excitation fluorescence microscopy (TPEF). We validated the technique in fresh tumour specimens from 115 patients with high-grade gliomas across four medical institutions. We found a weak negative correlation between tissue cellularity and the fluorescence intensity of PpIX across all imaged specimens. Semi-supervised clustering of the TPEF images revealed five distinct patterns of PpIX fluorescence, and spatial transcriptomic analyses of the imaged tissue showed that myeloid cells predominate in areas where PpIX accumulates in the intracellular space. Further analysis of external spatially resolved metabolomics, transcriptomics and RNA-sequencing datasets from glioblastoma specimens confirmed that myeloid cells preferentially accumulate and metabolize PpIX. Our findings question 5-ALA-induced fluorescence in glioma cells and show how 5-ALA and TPEF imaging can provide a window into the immune microenvironment of gliomas.