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Significance: Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons. Aim: We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices. Approach: We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting. Results: The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing. Conclusions: The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.

Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55–0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.

Significance: Ensuring spectral quality is prerequisite to Raman spectroscopy applied to surgery. This is because the inclusion of poor-quality spectra in the training phase of Raman-based pathology detection models can compromise prediction robustness and generalizability to new data. Currently, there exists no quantitative spectral quality assessment technique that can be used to either reject low-quality data points in existing Raman datasets based on spectral morphology or, perhaps more importantly, to optimize the in vivo data acquisition process to ensure minimal spectral quality standards are met. Aim: To develop a quantitative method evaluating Raman signal quality based on the variance associated with stochastic noise in important tissue bands, including C─C stretch, CH2  /  CH3 deformation, and the amide bands. Approach: A single-point hand-held Raman spectroscopy probe system was used to acquire 315 spectra from 44 brain cancer patients. All measurements were classified as either high or low quality based on visual assessment (qualitative) and using a quantitative quality factor (QF) metric. Receiver-operator-characteristic (ROC) analyses were performed to evaluate the performance of the quantitative metric to assess spectral quality and improve cancer detection accuracy. Results: The method can separate high- and low-quality spectra with a sensitivity of 89% and a specificity of 90% which is shown to increase cancer detection sensitivity and specificity by up to 20% and 12%, respectively. Conclusions: The QF threshold is effective in stratifying spectra in terms of spectral quality and the observed false negatives and false positives can be linked to limitations of qualitative spectral quality assessment.

IntroductionThe diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30 min routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyse the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy.Methods and analysisThe protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyse the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area under the curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modelling, and comment on the reproducibility of the different studies.Ethics and disseminationEthical approval was not required. Findings will be disseminated through peer-reviewed publication and presented at conferences related to this field.PROSPERO registration numberCRD42022292261.

Purpose Ancillary tests are frequently used in death determination by neurologic criteria (DNC), particularly when the clinical neurologic examination is unreliable. Nevertheless, their diagnostic accuracy has not been extensively studied. Our objective was to synthesize the sensitivity and specificity of commonly used ancillary tests for DNC. Source We performed a systematic review and meta-analysis by searching MEDLINE, EMBASE, Cochrane databases, and CINAHL Ebsco from their inception to 4 February 2022. We selected cohort and case–control studies including patients with 1) clinically diagnosed death by neurologic criteria or 2) clinically suspected death by neurologic criteria who underwent ancillary testing for DNC. We excluded studies without a priori diagnostic criteria and studies conducted solely on pediatric patients. Accepted reference standards were clinical examination, four-vessel conventional angiography, and radionuclide imaging. Data were directly extracted from published reports. We assessed the methodological quality of studies with the QUADAS-2 tool and estimated ancillary test sensitivities and specificities using hierarchical Bayesian models with diffuse priors. Principal findings Overall, 137 records met the selection criteria. One study (0.7%) had a low risk of bias in all QUADAS-2 domains. Among clinically diagnosed death by neurologic criteria patients ( n = 8,891), ancillary tests had similar pooled sensitivities (range, 0.82–0.93). Sensitivity heterogeneity was greater within (σ = 0.10–0.15) than between (σ = 0.04) ancillary test types. Among clinically suspected death by neurologic criteria patients ( n = 2,732), pooled ancillary test sensitivities ranged between 0.81 and 1.00 and specificities between 0.87 and 1.00. Most estimates had high statistical uncertainty. Conclusion Studies assessing ancillary test diagnostic accuracy have an unclear or high risk of bias. High-quality studies are required to thoroughly validate ancillary tests for DNC. Study registration PROSPERO (CRD42013005907); registered 7 October 2013.

Because of its infiltrative nature and concealment behind the blood-brain barrier, primary brain cancer remains one of the most challenging oncological condition to diagnose and treat. The mainstay of treatment is maximal surgical resection. Raman spectroscopy has shown great promise to guide surgeons intraoperatively by identifying, in real-time, dense cancer regions that appear normal to the naked eye. The Raman signal of living tissue is, however, very challenging to interpret, and while most advances in Raman systems targeted the hardware, appropriate statistical modeling techniques are lacking. As a result, there is conflicting evidence as to which molecular processes are captured by Raman probes. This limitation hinders clinical translation and usage of the technology by the cancer-research community. This work focuses on the analytical aspect of Raman-based surgical systems. Its objective is to develop a robust data processing pipeline to confidently identify which molecular phenomena allow Raman systems to differentiate healthy brain and cancer during neurosurgeries. We first selected high-yield Raman regions based on previous literature on the subject, resulting in a list of reproducible Raman bands with high likelihood of brain-specific Raman signal. We then developed a peak-fitting algorithm to extract the shape (height and width) of the Raman signal at those specific bands. We described a mathematical model that accounted for all possible interactions between the selected Raman peaks, and the interaction between the peaks’ shape and the patient’s age. To validate the model, we compared its capacity to compress the signal while maintaining high information content against a Principal Component Analysis (PCA) of the Raman spectra, the fields’ standard. As a final step, we applied the feature engineering model to a dataset of intraoperative human Raman spectra to identify which molecular processes were indicative of brain cancer. Our method showed better information retention than PCA. Our analysis of in vivo Raman measurement showed that areas with high-density of malignant cells had increased expression of nucleic acids and protein compounds, notably collagen, tryptophan and phenylalanine. Patient age seemed to affect the impact of nucleic acids, proteins and lipids on the Raman spectra. Our work demonstrates the importance of appropriate statistical modeling in the implementation of Raman-based surgical devices.

Extrait: \"La Société des Places-aux-Jeunes, dont cette étude est destinée à éterniser le souvenir, – au moins pendant une heure, – florissait dans les dernières années du Second Empire. Elle en a partagé le sort à la guerre de 1870. Malgré son nom un peu batailleur, – et qui je le crois, s'explique de lui-même à des Parisiens, – cette société n'avait point pour but, avoué ou secret, de renverser les gouvernements et même de changer les cultes reconnus\". À PROPOS DES ÉDITIONS LIGARAN: Les éditions LIGARAN proposent des versions numériques de grands classiques de la littérature ainsi que des livres rares, dans les domaines suivants: • Fiction: roman, poésie, théâtre, jeunesse, policier, libertin. • Non fiction: histoire, essais, biographies, pratiques.