Fast intraoperative detection of primary CNS lymphoma and differentiation from common CNS tumors using stimulated Raman histology and deep learning.

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. 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 tumor/non-tumor lesions. Training and test data were collected from four tertiary international medical centers. The final histopathological diagnosis served as ground-truth. 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. 78.94%). 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. RapidLymphoma is valid and reliable in detecting PCNSL and differentiating from other CNS entities within three minutes, as well as visual feedback in an intraoperative setting. This leads to fast clinical decision-making and further treatment strategy planning.

Streamlined Intraoperative Brain Tumor Classification and Molecular Subtyping in Stereotactic Biopsies Using Stimulated Raman Histology and Deep Learning.

PURPOSE: Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep learning algorithms for rapid intraoperative diagnosis from SRH images in small stereotactic-guided brain biopsies. It defines a minimum tissue sample size threshold to ensure diagnostic accuracy. EXPERIMENTAL DESIGN: A prospective single-center study examined 121 SRH images from 84 patients with unclear intracranial lesions undergoing stereotactic brain biopsy. Unprocessed, label-free samples were imaged using a portable fiber laser Raman scattering microscope. Three deep learning models were tested to (i) identify tumorous/nontumorous tissue as qualitative biopsy control; (ii) subclassify into high-grade glioma (central nervous system World Health Organization grade 4), diffuse low-grade glioma (central nervous system World Health Organization grades 2-3), metastases, lymphoma, or gliosis; and (iii) molecularly subtype IDH and 1p/19q statuses of adult-type diffuse gliomas. Model predictions were evaluated against frozen section analysis and final neuropathologic diagnoses. RESULTS: The first model identified tumorous/nontumorous tissue with 91.7% accuracy. Sample size on slides impacted accuracy in brain tumor subclassification (81.6%, κ = 0.72 frozen section; 73.9%, κ = 0.61 second model), with SRH images being smaller than hematoxylin and eosin images (4.1 ± 2.5 mm2 vs. 16.7 ± 8.2 mm2, P < 0.001). SRH images with more than 140 high-quality patches and a mean squeezed sample of 5.26 mm2 yielded 89.5% accuracy in subclassification and 93.9% in molecular subtyping of adult-type diffuse gliomas. CONCLUSIONS: Artificial intelligence-based SRH image analysis is non-inferior to frozen section analysis in detecting and subclassifying brain tumors during small stereotactic-guided biopsies once a critical squeezed sample size is reached. Beyond frozen section analysis, it enables valid molecular glioma subtyping, allowing faster treatment decisions in the future; however, refinement is needed for long-term application.

Ultra-Rapid Droplet Digital PCR Enables Intraoperative Tumor Quantification.

The diagnosis and treatment of tumors often depends 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. To address this gap, we developed Ultra-Rapid droplet digital PCR (UR-ddPCR), which can be completed in 15 minutes from tissue to result with an accuracy comparable to standard ddPCR. We demonstrate UR-ddPCR assays for the IDH1 R132H and BRAF V600E clonal mutations that are present in many low-grade gliomas and melanomas, respectively. We illustrate the clinical feasibility of UR-ddPCR by performing it intraoperatively for 13 glioma cases. We further combine UR-ddPCR measurements with UR-stimulated Raman histology intraoperatively to estimate tumor cell densities in addition to tumor cell percentages. We anticipate that UR-ddPCR, along with future refinements in assay instrumentation, will enable novel point-of-care diagnostics and the development of molecularly-guided surgeries that improve clinical outcomes.

Rapid, label-free classification of glioblastoma differentiation status combining confocal Raman spectroscopy and machine learning.

Label-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91.7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra- and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.

Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence.

Introduction: The intrinsic autofluorescence of biological tissues interferes with the detection of fluorophores administered for fluorescence guidance, an emerging auxiliary technique in oncological surgery. Yet, autofluorescence of the human brain and its neoplasia is sparsely examined. This study aims to assess autofluorescence of the brain and its neoplasia on a microscopic level by stimulated Raman histology (SRH) combined with two-photon fluorescence. Methods: With this experimentally established label-free microscopy technique unprocessed tissue can be imaged and analyzed within minutes and the process is easily incorporated in the surgical workflow. In a prospective observational study, we analyzed 397 SRH and corresponding autofluorescence images of 162 samples from 81 consecutive patients that underwent brain tumor surgery. Small tissue samples were squashed on a slide for imaging. SRH and fluorescence images were acquired with a dual wavelength laser (790 nm and 1020 nm) for excitation. In these images tumor and non-tumor regions were identified by a convolutional neural network that reliably differentiates between tumor, healthy brain tissue and low quality SRH images. The identified areas were used to define regions.of- interests (ROIs) and the mean fluorescence intensity was measured. Results: In healthy brain tissue, we found an increased mean autofluorescence signal in the gray (11.86, SD 2.61, n=29) compared to the white matter (5.99, SD 5.14, n=11, p<0.01) and in the cerebrum (11.83, SD 3.29, n=33) versus the cerebellum (2.82, SD 0.93, n=7, p<0.001), respectively. The signal of carcinoma metastases, meningiomas, gliomas and pituitary adenomas was significantly lower (each p<0.05) compared to the autofluorescence in the cerebrum and dura, and significantly higher (each p<0.05) compared to the cerebellum. Melanoma metastases were found to have a higher fluorescent signal (p<0.01) compared to cerebrum and cerebellum. Discussion: In conclusion we found that autofluorescence in the brain varies depending on the tissue type and localization and differs significantly among various brain tumors. This needs to be considered for interpreting photon signal during fluorescence-guided brain tumor surgery.

102 AI-Based Molecular Classification of Diffuse Gliomas using Rapid, Label-Free Optical Imaging.

INTRODUCTION: Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. Access to timely molecular diagnostic testing for brain tumor patients is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. METHODS: By combining stimulated Raman histology (SRH), a rapid, label-free, non-consumptive, optical imaging method, and deep learning-based image classification, we are able to predict the molecular genetic features used by the World Health Organization (WHO) to define the adult-type diffuse glioma taxonomy, including IDH-1/2, 1p19q-codeletion, and ATRX loss. We developed a multimodal deep neural network training strategy that uses both SRH images and large-scale, public diffuse glioma genomic data (i.e. TCGA, CGGA, etc.) in order to achieve optimal molecular classification performance. RESULTS: One institution was used for model training (University of Michigan) and four institutions (NYU, UCSF, Medical University of Vienna, and University Hospital Cologne) were included for patient enrollment in the prospective testing cohort. Using our system, called DeepGlioma, we achieved an average molecular genetic classification accuracy of 93.2% and identified the correct diffuse glioma molecular subgroup with 91.5% accuracy within 2 minutes in the operating room. DeepGlioma outperformed conventional IDH1-R132H immunohistochemistry (94.2% versus 91.4% accuracy) as a first-line molecular diagnostic screening method for diffuse gliomas and can detect canonical and non-canonical IDH mutations. CONCLUSIONS: Our results demonstrate how artificial intelligence and optical histology can be used to provide a rapid and scalable alternative to wet lab methods for the molecular diagnosis of brain tumor patients during surgery.

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging.

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n = 153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3 ± 1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

Novel rapid intraoperative qualitative tumor detection by a residual convolutional neural network using label-free stimulated Raman scattering microscopy.

Determining the presence of tumor in biopsies and the decision-making during resections is often dependent on intraoperative rapid frozen-section histopathology. Recently, stimulated Raman scattering microscopy has been introduced to rapidly generate digital hematoxylin-and-eosin-stained-like images (stimulated Raman histology) for intraoperative analysis. To enable intraoperative prediction of tumor presence, we aimed to develop a new deep residual convolutional neural network in an automated pipeline and tested its validity. In a monocentric prospective clinical study with 94 patients undergoing biopsy, brain or spinal tumor resection, Stimulated Raman histology images of intraoperative tissue samples were obtained using a fiber-laser-based stimulated Raman scattering microscope. A residual network was established and trained in ResNetV50 to predict three classes for each image: (1) tumor, (2) non-tumor, and (3) low-quality. The residual network was validated on images obtained in three small random areas within the tissue samples and were blindly independently reviewed by a neuropathologist as ground truth. 402 images derived from 132 tissue samples were analyzed representing the entire spectrum of neurooncological surgery. The automated workflow took in a mean of 240 s per case, and the residual network correctly classified tumor (305/326), non-tumorous tissue (49/67), and low-quality (6/9) images with an inter-rater agreement of 89.6% (κ = 0.671). An excellent internal consistency was found among the random areas with 90.2% (Cα = 0.942) accuracy. In conclusion, the novel stimulated Raman histology-based residual network can reliably detect the microscopic presence of tumor and differentiate from non-tumorous brain tissue in resection and biopsy samples within 4 min and may pave a promising way for an alternative rapid intraoperative histopathological decision-making tool.

The maintenance of ambiguity in Martian exobiology.

How do scientists maintain their research programs in the face of not finding anything? Continual failure to produce results can result in declining support, scientific controversy and credibility challenges. We elaborate on a crucial mechanism for sustaining the credibility of research programs through periods of non-detection: the maintenance of ambiguity. By this, we refer to scientific strategies that resist closure or an experiment's premature end by creating doubt in negative findings and fostering hope for future positive results. To illustrate this concept, we draw upon the recent history of Martian exobiology. Since the 1960s, planetary scientists have continually tried and failed to find evidence of life on Mars. And yet, interest in extraterrestrial life detection remains high, with more missions to Mars underway. Through three destabilizing events of non-detection, we show how exobiologists sustained the search for Martian life by casting doubt on negative findings, pointing to other possible unexplored routes to success, and finally reconfiguring operations around new methods or goals. New approaches may take the form of shifts in scale, method and object of interest. By pivoting to a different scale, method or object, exobiologists have continued to study a subject continually lacking proof of existence and made important discoveries about life on Earth.

Resection of symptomatic non-small cell lung cancer brain metastasis in the setting of multiple brain metastases.

OBJECTIVE: Current guidelines primarily suggest resection of brain metastases (BMs) in patients with limited lesions. With a growing number of highly effective local and systemic treatment options, this view may be challenged. The purpose of this study was to evaluate the role of metastasectomy, disregarding BM count, in a comprehensive treatment setting. METHODS: In this monocentric retrospective analysis, the authors included patients who underwent resection for at least 1 BM and collected demographic, clinical, and tumor-associated parameters. Prognostic factors for local control and overall survival (OS) were analyzed with the log-rank test and Cox proportional hazards analysis. RESULTS: The authors analyzed 216 patients. One hundred twenty-nine (59.7%) patients were diagnosed with a single/solitary BM, whereas 64 (29.6%) patients had 2-3 BMs and the remaining 23 (10.6%) had more than 3 BMs. With resection of symptomatic BMs, a significant improvement in Karnofsky Performance Scale (KPS) was achieved (p < 0.001), thereby enabling adjuvant radiotherapy for 199 (92.1%) patients and systemic treatment for 119 (55.1%) patients. During follow-up, 83 (38.4%) patients experienced local recurrence. BM count did not significantly influence local control rates. By the time of analysis, 120 (55.6%) patients had died; the leading cause of death was systemic tumor progression. The mean (range) OS after surgery was 12.7 (0-88) months. In univariate analysis, the BM count did not influence OS (p = 0.844), but age < 65 years (p = 0.007), preoperative and postoperative KPS ≥ 70 (p = 0.002 and p = 0.005, respectively), systemic metastases other than BM (p = 0.004), adjuvant radiation therapy (p < 0.001), and adjuvant systemic treatment (p < 0.001) were prognostic factors. In regression analysis, the presence of extracranial metastases (HR 2.30, 95% CI 1.53-3.48, p < 0.001), adjuvant radiation therapy (HR 0.97, 95% CI 0.23-0.86, p = 0.016), and adjuvant systemic treatment (HR 0.37, 95% CI 0.25-0.55, p < 0.001) remained as independent factors for survival. CONCLUSIONS: Surgery for symptomatic BM from non-small cell lung cancer may be indicated even for patients with multiple lesions in order to alleviate their neurological symptoms and to consequently facilitate further treatment.

Clinical Characteristics and Magnetic Resonance Imaging-Based Prediction of the KLF4K409Q Mutation in Meningioma.

BACKGROUND: Meningioma is the most common primary brain tumor in adults. In recent years, several non-neurofibromin 2 mutations, i.e., AKT1, SMO, TRAF7, and KLF4 mutations, specific for meningioma have been identified. This study aims to analyze the clinical impact and imaging characteristics of the KLF4K409Q mutation in meningioma. METHODS: Clinical, neuropathologic, and imaging data of 170 patients who underwent meningioma resection between 2013 and 2018 were retrospectively collected and tumors were analyzed for the presence of the KLF4K409Q mutation. We collected imaging characteristics, performed volumetric analysis of tumor size and peritumoral edema (PTBE), and calculated the edema index (EI, i.e., ratio of PTBE to tumor volume). Receiver operating characteristic curve analysis was performed to identify cut-off EI values to predict the mutational status of KLF4. RESULTS: Eighteen (10.6%) of the meningiomas carried the KLF4K409Q mutation; these were significantly associated with a secretory subtype (P < 0.001) and sphenoid wing location (P = 0.029). Smaller tumor size (P = 0.007), an increased PTBE (P = 0.012), and an increased EI (P = 0.001) proved to be significantly associated with the KLF4K409Q mutation. In receiver operating characteristic curve analysis, EI predicted the KLF4K409Q mutation with an area under the curve of 0.728 (P = 0.0016). CONCLUSIONS: The KLF4K409Q mutation is associated with a distinct small tumor subtype, prone to substantial PTBE. EI is a reliable parameter to predict the KLF4K409Q mutation in meningioma, thus providing a tool for improvement of pre- and perioperative medical management.

When funding fails: Planetary exploration at NASA in an era of austerity, 1967-1976.

We can learn most about how science funding works when it stops working. Like moments of breakdown surfacing the inner workings of infrastructure, periodic fiscal crises reveal the social life of science funds at the level of everyday practice. Through a case study of NASA-funded planetary science in an era of austerity, the article explores how scientists navigate uncertain funding environments and articulate financially defensible projects. Examining the development of the Mariner 10 mission to Venus and Mercury in the aftermath of a significant downturn in science support, the article offers a middle path between the macro-politics of government funding and the micro-politics of doing science. In shaping how the mission was conceived and later operated, Mariner 10's cost-driven paradigm translated the austerity of the period into the projectized work of robotic spaceflight missions.

Facet Joint Replacement: Intermediate- and Long-Term Outcome in a Large Case Series.

BACKGROUND: The long-term outcome of facet joint replacement (FJR) still is to be proven. METHODS: We present a prospective case series of 26 (male-to-female ratio of 1:1; mean age: 61 years) patients undergoing FJR with a follow-up of at least 1 year (range: 12-112; mean: 67 months). Visual analog scale (VAS) for back and leg pain, Oswestry Disability Index (ODI), and the 12-Item Short Form Health Survey (SF-12) were applied pre- and postoperatively (after 3, 6, and 12 months) as well as at the last follow-up (N = 24). Using X-rays of the lumbar spine (N = 20), the range of motion (ROM) and disk height in the indicator and adjacent levels were assessed. RESULTS: FJR was performed at L3/L4 (N = 7), L4/L5 (N = 17), and L5/S1 (N = 2). Mean VAS (mm) for back pain decreased from 71 to 18, mean VAS for right leg pain from 61 to 7, and from 51 to 3 for the left leg. Mean ODI dropped from 51 to 22% (for all p < 0.01). Eighty seven percent of patients were satisfied and pretreatment activities were completely regained in 78.3% of patients. Disk height at the indicator and adjacent levels and ROM at the indicator segment and the entire lumbar spine were preserved. No loosening of implants was observed. Explantation of FJR and subsequent fusion had to be performed in four cases (15.4%). CONCLUSIONS: In selected cases, long-term results of FJR show good outcome concerning pain, quality of life, preservation of lumbar spine motion, and protection of adjacent level.