Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery 1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin-staining of processed tissue is time-, resource-, and labor-intensive 2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed pathology workforce 4. Here, we report a parallel workflow that combines stimulated Raman histology (SRH) 5-7 , a label-free optical imaging method, and deep convolutional neural networks (CNN) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNN, trained on over 2.5 million SRH images, predicts brain tumor diagnosis in the operating room in under 150 seconds, an order of magnitude faster than conventional techniques (e.g., 20-30 minutes) 2. In a multicenter, prospective clinical trial (n = 278) we demonstrated that CNN-based diagnosis of SRH images was non-inferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% vs. 93.9%). Our CNN learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. Additionally, we implemented a semantic segmentation method to identify tumor infiltrated, diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complimentary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

BackgroundInnovations in mobile and electronic healthcare are revolutionizing the involvement of both doctors and patients in the modern healthcare system by extending the capabilities of physiological monitoring devices. Despite significant progress within the monitoring device industry, the widespread integration of this technology into medical practice remains limited. The purpose of this review is to summarize the developments and clinical utility of smart wearable body sensors.MethodsWe reviewed the literature for connected device, sensor, trackers, telemonitoring, wireless technology and real time home tracking devices and their application for clinicians.ResultsSmart wearable sensors are effective and reliable for preventative methods in many different facets of medicine such as, cardiopulmonary, vascular, endocrine, neurological function and rehabilitation medicine. These sensors have also been shown to be accurate and useful for perioperative monitoring and rehabilitation medicine.ConclusionAlthough these devices have been shown to be accurate and have clinical utility, they continue to be underutilized in the healthcare industry. Incorporating smart wearable sensors into routine care of patients could augment physician-patient relationships, increase the autonomy and involvement of patients in regards to their healthcare and will provide for novel remote monitoring techniques which will revolutionize healthcare management and spending.

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ABBREVIATIONS 5-ALA = 5-aminolevulinic acid; BBB = blood-brain barrier; CE = contrast enhancing; CUMC = Columbia University Medical Center; EOR = extent of resection; GBM = glioblastoma; GTR = gross-total resection; HGG = high-grade glioma; n-ANOVA = multiway ANOVA; NCE = non-contrast enhancing; OS = overall survival; PFS = progression-free survival; PPV = positive predictive value; ROI = region of interest; STR = subtotal resection; UD = undetermined; WHO = World Health Organization; Y560 = YELLOW 560. OBJECTIVE Extent of resection is an important prognostic factor in patients undergoing surgery for glioblastoma (GBM). Recent evidence suggests that intravenously administered fluorescein sodium associates with tumor tissue, facilitating safe maximal resection of GBM. In this study, the authors evaluate the safety and utility of intraoperative fluorescein guidance for the prediction of histopathological alteration both in the contrast-enhancing (CE) regions, where this relationship has been established, and into the non-CE (NCE), diffusely infiltrated margins. METHODS Thirty-two patients received fluorescein sodium (3 mg/kg) intravenously prior to resection. Fluorescence was intraoperatively visualized using a Zeiss Pentero surgical microscope equipped with a YELLOW 560 filter. Stereotactically localized biopsy specimens were acquired from CE and NCE regions based on preoperative MRI in conjunction with neuronavigation. The fluorescence intensity of these specimens was subjectively classified in real time with subsequent quantitative image analysis, histopathological evaluation of localized biopsy specimens, and radiological volumetric assessment of the extent of resection. RESULTS Bright fluorescence was observed in all GBMs and localized to the CE regions and portions of the NCE margins of the tumors, thus serving as a visual guide during resection. Gross-total resection (GTR) was achieved in 84% of the patients with an average resected volume of 95%, and this rate was higher among patients for whom GTR was the surgical goal (GTR achieved in 93.1% of patients, average resected volume of 99.7%). Intraoperative fluorescein staining correlated with histopathological alteration in both CE and NCE regions, with positive predictive values by subjective fluorescence evaluation greater than 96% in NCE regions. CONCLUSIONS Intraoperative administration of fluorescein provides an easily visualized marker for glioma pathology in both CE and NCE regions of GBM. These findings support the use of fluorescein as a microsurgical adjunct for guiding GBM resection to facilitate safe maximal removal. SUBMITTEDhttps://thejns.org/doi/abs/10.3171/2016.7.JNS16232

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INTRODUCTION Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery. The existing workflow for intraoperative diagnosis based on H&E staining of processed tissue is time-, resource-, and labor-intensive. Moreover, interpretation of intraoperative histologic images is dependent on a pathology workforce that is contracting and unevenly distributed across the centers where cancer surgery is performed worldwide. METHODS We developed an automated workflow, independent of traditional H&E histology, that combines stimulated Raman histology (SRH), a rapid label-free optical imaging method, and deep convolutional neural networks (CNN) to predict diagnosis at the bedside in near real time. Specifically, our CNN, trained on over 2.5 million SRH images, predicts brain tumor diagnosis in the operating room in under 150 s, which is an order of magnitude faster than conventional techniques (eg, 20-30 min). RESULTS To validate our workflow in the clinical setting, we designed a multicenter, prospective, noninferiority clinical trial (N = 204) that compares SRH plus CNN vs traditional H&E histology. Primary endpoint was overall diagnostic accuracy. We show that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% vs 95.5%). Additionally, our CNN learned a hierarchy of interpretable histologic feature representations to classify the major histopathologic classes of brain tumors. We then developed and implemented a semantic segmentation method that can identify tumor infiltrated and diagnostic regions within SRH images. Mean intersection over union values was 61 ± 28.6 for ground truth diagnostic class and 86.0 ± 28.6 for tumor-infiltrated regions. CONCLUSION We have demonstrated how combining bedside optical histology with deep learning can result in near real-time intraoperative brain tumor diagnosis. Our workflow provides a means of delivering expert-level intraoperative diagnosis where neuropathology resources are scarce and improve diagnostic accuracy in resource-rich centers.

BACKGROUND: The coronavirus 2019 (COVD-19) pandemic has drastically disrupted the delivery of neurosurgical care, especially for the already at-risk neuro-oncology population. The sudden change to clinic visits has rapidly spurned the implementation of telemedicine. A recommendation care paradigm of neuro-oncologic patients limited by telemedicine has not been reported.-METHODS: A summary of a multi-institution experience detailing the potential benefits, pitfalls, and the necessary considerations to outpatient care of neurosurgical oncology patients.-RESULTS: There are limitations and advantages to incorporating telemedicine into the outpatient care of neuro-oncology patients. Telemedicine-specific considerations for each step and stakeholder of the appointment (physician, patient, scheduling, previsit, imaging, and physical examination) are examined.-CONCLUSIONS: Telemedicine, pushed to prominence during this COVID-19 pandemic, is a powerful and possibly preferential tool for the future of outpatient neurooncologic care.