Tissue Classification Research Articles

A remarkable and aggressive aneurysmal bone cyst ravaging the femoral shaft, advancing to lung metastases: An extremely rare case

Aneurysmal bone cysts (ABCs) are relatively uncommon tumor-like lesions that require careful management. Reporting such cases is crucial to highlight the importance of understanding the physiopathology and treatment options associated with ABCs. Literature-based writing emphasizes the significance of reporting cases like the one presented here. The aneurysmal bone cyst, classified as a Tumor Rich in Giant Cells according to the latest World Health Organization (WHO) classification of soft tissue and bone tumors, has posed several hypotheses and challenge. This aggressive form and rare localization of this lesion necessitates thorough discussions regarding its management.We present a compelling case of a 37-year-old patient, without any specific pathological history, with a giant diaphyseal aneurysmal bone cyst located in the right femur. Remarkably, the lesion had been progressing for a decade, leading to extensive destruction of the entire femoral diaphysis despite undergoing previous surgical interventions. The patient's unique circumstances highlight the unpredictable behavior and destructive potential of aneurysmal bone cysts in rare anatomical locations.This case underscores the need for a comprehensive understanding of aneurysmal bone cysts and their management. The utilization of denosumab, an inhibitor of the RANK/RANKL system, as a medical treatment in conjunction with surgery resulted in a favorable therapeutic response, including a reduction in tumor mass. The current WHO classification, recognizing ABCs as Tumors Rich in Giant Cells, consolidates previous hypotheses and enables the development of new therapeutic protocols. The integration of surgery and medical treatment holds promise for improving outcomes in patients with aneurysmal bone cysts. In conclusion, advancements in understanding the physiopathology and treatment options are crucial for developing effective therapeutic protocols to these aggressive forms of ABCs.

Superficial mesenchymal tumours expressing epithelial markers on immunohistochemistry: Diagnostic clues and pitfalls.

The diagnosis of mesenchymal neoplasms arising in the superficial soft tissue can be challenging as some entities are rare and show overlapping features. Moreover, the spectrum of mesenchymal tumours has expanded recently to include potential new entities, some of which have been described after the 5th edition of the World Health Organisation (WHO) classification of soft tissue and bone tumours published in 2020. In the skin and superficial soft tissue, tumours of epidermal, melanocytic and appendageal origin are more commonly encountered than mesenchymal neoplasms. However, specific entities from the latter category can occasionally express epithelial markers on immunohistochemistry, some of them in a strong and diffuse manner. It is therefore crucial to be aware of diagnostic pitfalls when encountering cytokeratin positivity in superficial soft tissue neoplasms. This article provides an overview on the differential diagnosis of these mesenchymal tumours that can sporadically occur also in the skin, including myoepithelial neoplasms, epithelioid sarcoma, keratin positive giant cell tumour of soft tissue / xanthogranulomatous epithelial tumour, superficial CD34-positive fibroblastic tumour / PRDM10-rearranged soft tissue tumour, and perineurioma.

GAN-based data augmentation for transcriptomics: survey and comparative assessment.

Transcriptomics data are becoming more accessible due to high-throughput and less costly sequencing methods. However, data scarcity prevents exploiting deep learning models' full predictive power for phenotypes prediction. Artificially enhancing the training sets, namely data augmentation, is suggested as a regularization strategy. Data augmentation corresponds to label-invariant transformations of the training set (e.g. geometric transformations on images and syntax parsing on text data). Such transformations are, unfortunately, unknown in the transcriptomic field. Therefore, deep generative models such as generative adversarial networks (GANs) have been proposed to generate additional samples. In this article, we analyze GAN-based data augmentation strategies with respect to performance indicators and the classification of cancer phenotypes. This work highlights a significant boost in binary and multiclass classification performances due to augmentation strategies. Without augmentation, training a classifier on only 50 RNA-seq samples yields an accuracy of, respectively, 94% and 70% for binary and tissue classification. In comparison, we achieved 98% and 94% of accuracy when adding 1000 augmented samples. Richer architectures and more expensive training of the GAN return better augmentation performances and generated data quality overall. Further analysis of the generated data shows that several performance indicators are needed to assess its quality correctly. All data used for this research are publicly available and comes from The Cancer Genome Atlas. Reproducible code is available on the GitLab repository: https://forge.ibisc.univ-evry.fr/alacan/GANs-for-transcriptomics.

Transfer function approaches for SEEG brain electrode interface applied to tissue classification

This paper is about transfer function approaches for brain-electrode interface modelling in the context of StereoElectroEncephaloGraphy, and their possible use in tissue classification (between grey and white matter). Monopolar and bipolar configurations are first reviewed, giving rise to possible nonparametric and parametric identification methods, as well as related possible classification results (for identical tissues and distinct tissues at measurement points, respectively). A method combining both approaches is then proposed, so as to end up with a classification at each measurement point in any case. The proposed methodology is implemented with clinical data collected from a set of epileptic patients, confirming its interest by providing more than 70% of accuracy in the obtained results.

Effect of Different Parameter Values for Pre-processing of Using Mammography Images

Breast cancer is one of the most common types of cancer in women. To make a fast diagnosis, mammography images should have high contrast. Computer-assisted diagnosis (CAD) models are computer systems that help diagnose lesioned areas on medical images. The aim of this study is to examine the contribu-tion of the changes in parameter values of various pre-processing methods used to increase the visibility of mammography images and reduce the noise in the images, to the classification performance. In this study, the mini-MIAS database were used. Gaussian filter, Contrast Limited Adaptive Histogram Equalization and Fast local Laplacian filtering methods were applied as pre-processing method. In this study, two different parameter values were applied for two different image processing methods (Ⅰ. Parameter values are Gauss filter 𝜎=3, Laplacian filter 𝜎=0.6 and 𝛼=0.6; Ⅱ. Parameter values are Gauss filter 𝜎=1, Laplacian filter 𝜎=2 and 𝛼=2). In the normal-abnormal tissue classification, higher accuracy and area under the curve were obtained in the 2nd parameter values in all classification methods. As a result, it has been acquired that different parameter values of the pre-processing methods used to improve mammography images can change the success of the classification methods.

Alcohol, White Adipose Tissue, and Brown Adipose Tissue: Mechanistic Links to Lipogenesis and Lipolysis.

According to data from the World Health Organization, there were about 3 million deaths caused by alcohol consumption worldwide in 2016, of which about 50% were related to liver disease. Alcohol consumption interfering with the normal function of adipocytes has an important impact on the pathogenesis of alcoholic liver disease. There has been increasing recognition of the crucial role of adipose tissue in regulating systemic metabolism, far beyond that of an inert energy storage organ in recent years. The endocrine function of adipose tissue is widely recognized, and the significance of the proteins it produces and releases is still being investigated. Alcohol consumption may affect white adipose tissue (WAT) and brown adipose tissue (BAT), which interact with surrounding tissues such as the liver and intestines. This review briefly introduces the basic concept and classification of adipose tissue and summarizes the mechanism of alcohol affecting lipolysis and lipogenesis in WAT and BAT. The adipose tissue-liver axis is crucial in maintaining lipid homeostasis within the body. Therefore, this review also demonstrates the effects of alcohol consumption on the adipose tissue-liver axis to explore the role of alcohol consumption in the crosstalk between adipose tissue and the liver.

Sarcoma Research with Cancer Registry Data: Data and Peculiarities of Germany in the Light of Other Countries

Introduction: Sarcomas are documented in population-based and in clinic-associated databases. This study evaluated the status quo regarding the potential and obstacles of cancer registry-based research on sarcomas exemplified by Germany in comparison to similar databases in the US and Europe. Completeness and quality of data are discussed based on statistical analyses of a pooled data set established for the German Cancer Congress 2020. Methods: We analyzed data derived from 16 German institutions (federal state cancer registries and some facility-based registries). Malignant sarcomas in adults diagnosed between 2000 and 2018 with information on histology were grouped according to the WHO classification of soft tissue and bone tumors. Descriptive analyses of the study population regarding the distribution of age, sex, histology, localization of primary tumors, and metastases were performed. Survival for the ten most frequent histological groups and UICC stages was evaluated according to Kaplan-Meier and Cox regression. Time interval between surgery and subsequent radiation was calculated. Results: The initial data set contained 35,091 sarcomas. After several steps of data cleaning, 28,311 patients with known sex and unambiguous assignment to a histological subgroup remained (13,682 women and 14,629 men). Between 40 and 54 years, women were more likely to develop sarcomas, whereas in the older age groups more men were affected. Gastrointestinal stromal tumors, fibroblastic, and myofibroblastic tumors, smooth muscle tumors (mostly non-uterine leiomyosarcomas), and adipocytic tumors represented 48% of all sarcomas. Preferential sites for fibrosarcomas were the limbs, the trunk, and the head and neck region. The liposarcoma occurred most frequently on the trunk and limbs. Distant primary metastases were mostly located in the lung (43%), followed by the liver (14%), and bones (13%). Vascular and smooth muscle tumors showed the worst survival prognosis (5-year survival: approx. 15%, median survival approx. 8–16 months), whereas in low stages, the probability of survival of many sarcoma patients was beyond 5 years. Adjuvant radiotherapy was applied within 90 days in 71% of patients (n = 2,534). Conclusion: Our results correspond to the data from the literature. However, a lack of data quality and completeness hampers further meaningful analyses, especially nonspecific or missing information about morphology and stage. Compared to some other countries, a comprehensive database is presently missing in Germany. However, currently, there are important efforts and legislative initiatives to create a comprehensive database on a national level within the near future.

A new look to the classification of inflammatory diseases of the periapital tissues

The authors conducted a comparative analytical description of the classification (ICD-10) of periodontitis and periapical tooth tissues. The terms “apical periodontitis”, “destructive periodontitis”, traditionally used in the formulation of the diagnosis, do not reflect the pathology of the affected tissue. Mistakes made in the diagnosis using the above terms lead to an incorrect formulation of the diagnosis and the choice of treatment tactics.
 The obtained results led to the conclusion that the correct diagnosis of inflammatory and destructive diseases of the periapical tissues will significantly reduce the incidence of complications and increase the effectiveness of the treatment of periodontitis and diseases of the periapical tissues of the tooth.
 The results of a comprehensive study showed that a high percentage of diagnostic errors is associated with confusion in terminology. The terms “apical periodontitis”, “destructive periodontitis”, traditionally used in the formulation of the diagnosis, do not reflect the pathology of the affected tissue.

A novel approach for brain tissue segmentation and classification in infants' MRI images based on seeded region growing, foster corner detection theory, and sparse autoencoder

Brain tissue segmentation and classification in infant MRI images play a crucial role in early diagnosis of neurological disorders. In this paper, we propose a novel approach based on Seeded Region Growing, Foster Corner Detection Theory, and Sparse Autoencoder. Proposed method incorporates advantages of these techniques to improve segmentation and classification accuracy of infant brain MRI. An unsupervised segmentation method proposed in this study has four stages. First stage involves pre-processing input image by removing noise using a non-local means method and rectifying intensity inhomogeneity field employing Contrast-limited adaptive histogram equalization. In second stage, modified and optimized approach of region growth is utilized, where Forstner Corner Identification principle is used to choose and position seeds to a producer. Third stage involves applying the region growth technique to image, where similarity criterion is optimized using Predator Prey algorithm. In fourth stage, tissues are classified using weighted KNN and Sparse Auto Encoder classifiers. Result shows high Dice factor of 93%, outperforming existing works. This method improves accuracy of brain tissue segmentation and classification, leading to better clinical decision-making. Applications of proposed method include early detection and diagnosis of neurological disorders, monitoring of disease progression, and assessment of treatment effectiveness.

Hyperspectral Imaging Combined with Deep Learning to Detect Ischemic Necrosis in Small Intestinal Tissue

Obtaining adequate resection margins in small intestinal necrotic tissue remains challenging due to the lack of intraoperative feedback. Here, we used hyperspectral imaging (HSI), an imaging technique for objective identification, combined with deep learning methods for automated small intestine tissue classification. As part of a prospective experimental study, we recorded hyperspectral datasets of small intestine biopsies from seven white rabbits. Based on the differences in the spectral characteristics of normal and ischemic necrotic small intestinal tissues in the wavelength range of 400–1000 nm, we applied deep learning techniques to objectively distinguish between these two types of tissues. The results showed that three-dimensional convolutional neural networks were more effective in extracting both spectral and spatial features of small intestine tissue hyperspectral data for classification. The combination of a deep learning model and HSI provided a new idea for the objective identification of ischemic necrotic tissue in the small intestine.

"PRRX1-rearranged mesenchymal tumors": expanding the immunohistochemical profile and molecular spectrum of a recently described entity with the proposed revision of nomenclature.

Since the publication of the 2020 World Health Organization classification of soft tissue and bone tumors, the classification of "fibroblastic" tumors has expanded to include a novel subset of tumors characterized by PRRX1::NCOA1/2 gene fusions. These tumors defy conventional classification and are morphologically distinct, characterized by a multi-nodular growth of bland spindle cells suspended in a myxo-collagenous stroma with mild cytologic atypia, "staghorn-like" vessels, and variable perivascular hyalinization. Mitotic activity is rare, and necrosis is not identified. Herein, we present six additional cases of PRRX1-rearranged mesenchymal tumors, including five cases with PRRX1::NCOA1 fusion and one case with PRRX1::KMT2D fusion. Three cases (3/6, 50%) demonstrated focal co-expression of S100 protein and SOX10, thereby expanding the immunohistochemical profile of this emerging entity. Like prior reported cases, there was no evidence of malignant behavior on short-term follow-up. The novel fusion, PRRX1::KMT2D, further expands the molecular spectrum of this entity and leads to a proposed revision of the provisional nomenclature to "PRRX1-rearranged mesenchymal tumor" to both accommodate non-NCOA1/2 fusion partners and allow for the possibility of partial neural or neuroectodermal differentiation.

Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction.

Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic 18F-Fluorothymidine (FLT) PET scans from BC patients, extending conventional static radiomics methods to the time domain-termed as 'Dynomics'. Radiomic features were extracted from both static and dynamic PET images on lesion and reference tissue masks. The extracted features were used to train an XGBoost model for classifying tumor versus reference tissue and complete versus partial responders to neoadjuvant chemotherapy. The results underscored the superiority of dynamic and static radiomics over standard PET imaging, achieving accuracy of 94% in tumor tissue classification. Notably, in predicting BC prognosis, dynomics delivered the highest performance, achieving accuracy of 86%, thereby outperforming both static radiomics and standard PET data. This study illustrates the enhanced clinical utility of dynomics in yielding more precise and reliable information for BC diagnosis and prognosis, paving the way for improved treatment strategies.

Accurate OCT-based diffuse adult-type glioma WHO grade 4 tissue classification using comprehensible texture feature analysis

Maximum safe resection of diffuse adult-type glioma WHO grade 4 (glioma °4) is a well-established procedure in order to prolong a patient’s lifetime. However, differentiation between glioma and healthy brain tissue remains a challenge in an intraoperative setting. As shown previously, the attenuation coefficient extracted from OCT images does not provide sufficient discrimination between healthy gray matter and tumor infiltrated tissue. On the other hand, the majority of published approaches based on structural information in OCT images uses sophisticated machine learning approaches as e.g. deep convolutional neural networks which bring the need for large data sets for reasonable modeling. In this work, we propose a method, which is based on comprehensible texture features and provides reasonable classification performance for healthy gray and white matter against glioma °4 samples, while requiring a relatively low number of samples for training. Our sample collective consists of 36 tissue samples from 27 different patients. Texture features based on the average contrast were optimized with respect to best discrimination of the tissue types of interest. Best discrimination could be obtained for the contrast of structure sizes of approximately 100μm. By using a linear discriminant analysis (LDA), we could achieve a sensitivity and specificity of 97.7% and 91.7%, respectively, when classifying white matter and samples with a mixture of white and gray matter against glioma °4 samples. For an extended data set, also including pure gray matter samples, we determined a sensitivity of 86.7% and a specificity of 86.3%.

Deep learning-derived spatial organization features on histopathology images to predict prognosis in patients with colorectal liver metastasis, after hepatectomy.

3581 Background: Histopathological images of colorectal liver metastasis (CRLM) contain rich morphometric information that may predict patient outcomes, but current indicators depend on labor-intensive and subjective visual estimation. Herein, we aimed to develop an automated framework for tissue classification of routine H&E stained whole-slide images and establish a risk-scoring model for better prognosis prediction in patients with CRLM. Methods: Using 161,371 hand-delineated image patches, we trained a robust deep convolutional neural network- CRLM-SPA for accurate classification of CRLM into seven tissue types. With the tissue classification results on two independent in-house cohorts (SYSUCC: n = 433, BJCH: n = 404), we systematically quantified spatial organization features (SOFs), involving whole-slide, tumor-infiltrating and tumor-distal SOFs for different tissue types as well as the interactions between tumor and non-tumor tissues. Subsequently, univariate Cox proportional hazards regression analysis was performed to investigate the association between various SOFs and patient outcomes. Nonredundant SOFs that are clinically relevant were selected to build a risk-scoring model for prognosis using multivariate Cox regression analysis in the SYSUCC cohort, followed by validation in the BJCH cohort. Results: CRLM-SPA achieved an overall classification accuracy of > 93% in an independent set of 17,653 image patches. With the classification result, we calculated various SOFs and built a four-SOF risk-scoring model that significantly predicted overall survival (OS) in the discovery cohort, SYSUCC ( P = 1.40 10-5; HR = 2.26; 95%CI: 1.55 - 3.29) and the independent validation cohort, BJCH ( P = 4.59 × 10-4; HR = 2.00; 95%CI: 1.35 - 2.97). The prognostic performance of our SOF risk-scoring model is independent of the clinical risk score (CRS) system. Further stratification analyses in patients without preoperative chemotherapy (CTx) indicated that adjuvant CTx consistently improved OS in the SOF high-risk subgroups in both cohorts ( P = 0.018 and 0.049), but not in the SOF low-risk subgroups. Furthermore, a combined scoring system incorporating SOF and CRS considerably improved the prognostic performance in both cohorts over the individual SOF and CRS systems. Conclusions: CRLM-SPA showed a high accuracy in tissue classification and robustness in extracting prognostic information from H&E images. The SOF risk-scoring system demonstrated a strong and robust prognostic value that is independent of CRS, and could therefore provide a time- and cost-efficient tool to assist clinical decision making for patients with CRLM.

Intra-operative desorption-electrospray ionization mass-spectrometry for real-time diagnosis, margin assessment, and maximization of brain cancer resection.

2071 Background: Despite advances in our understanding of brain cancer, the ability to discern between infiltrated and non-infiltrated tissue with current adjuncts, remains elusive. Isocitrate dehydrogenase (IDH) mutation is unique to IDH-mut glioma cells and can be used as a surrogate for tumor infiltration. Further, emerging evidence has shown unique signatures in brain cancer lipidomic profiling, with lipid metabolism playing a key role in brain cancer biology. Here, we aimed to evaluate the impact of desorption electrospray ionization-mass spectrometry (DESI-MS) as a near real-time method to determine infiltration status and the use of DESI-MS to perform intra-operative histological diagnosis. Methods: This is a prospective multi-institution cohort study using intra-operative DESI-MS on freshly obtained tumor samples to evaluate 2-hydroxyglutarate (2HG), with post-hoc validation by review by a board-certified neuropathologist. For tumor cell estimation and tissue diagnosis, high-throughput DESI-MS was used for rapid analysis of high-density arrays using lipidomic profiling. Analysis of the data was performed using a pre-written algorithm employing a combination of Python- and MATLAB-based custom software allowing results in less than a second per sample. Results: A total of 496 tumor biopsies from 72 patients have been analyzed. Earlier iterations focused on absolute quantitation of 2HG in 247 biopsies from 49 patients to determine IDH genotype yielding sensitivity, specificity, and accuracy values of 89, 100, and 94% for core biopsies (71). Improvement in accuracy was obtained with relative measurement of 2HG in comparison to glutamate allowing for detection of IDH-mut infiltration at the margin, resulting in 93% sensitivity, 100% specificity, and 98% accuracy with turnover times averaging 3 minutes in 183 biopsies from 23 patients. Margin assessment beyond the limits of neuro-navigation and surgeon assessment showed tumor infiltration in 88% of margin biopsies from IDH mutant tumors (30 of 34 biopsies) with 100% accuracy. Intra-operative tissue diagnosis was pursued in 66 unique tumor samples using DESI-MS lipidomic profiling, including samples of normal brain parenchyma, gliomas, meningiomas and pituitary adenomas, split into two microarray sets (36 and 40 samples). Using estimated principal components as input features, high accuracy ( > 90%; ROC AUCs ≥ 0.89) was obtained for supervised tissue classification using simple machine learning algorithms. Conclusions: We present a novel system for intra-operative evaluation of tumor infiltration at the margin in gliomas and for intra-operative tissue diagnosis of brain cancer. Our method identified tumor infiltration beyond the surgical margins and serves as a foundation for future uses employing DESI-MS to allow for molecularly guided surgery in IDH-mut gliomas.

Whole slide imaging-based deep learning to predict the treatment response of patients with non-small cell lung cancer.

This study developed and validated a deep learning (DL) model based on whole slide imaging (WSI) for predicting the treatment response to chemotherapy and radiotherapy (CRT) among patients with non-small cell lung cancer (NSCLC). We collected the WSI of 120 nonsurgical patients with NSCLC treated with CRT from three hospitals in China. Based on the processed WSI, two DL models were established: a tissue classification model which was used to select tumor-tiles, and another model which predicted the treatment response of the patients based on the tumor-tiles (predicting the treatment response of each tile). A voting method was employed, by which the label of tiles with the greatest quantity from 1 patient would be used as the label of the patient. The tissue classification model had a great performance (accuracy in the training set/internal validation set =0.966/0.956). Based on 181,875 tumor-tiles selected by the tissue classification model, the model for predicting the treatment response demonstrated strong predictive ability (accuracy of patient-level prediction in the internal validation set/external validation set 1/external validation set 2 =0.786/0.742/0.737). A DL model was constructed based on WSI to predict the treatment response of patients with NSCLC. This model can help doctors to formulate personalized CRT plans and improve treatment outcomes.

Molecular characterization of human peripheral nerves using desorption electrospray ionization mass spectrometry imaging.

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is a molecular imaging method that can be used to elucidate the small-molecule composition of tissues and map their spatial information using two-dimensional ion images. This technique has been used to investigate the molecular profiles of variety of tissues, including within the central nervous system, specifically the brain and spinal cord. To our knowledge, this technique has yet to be applied to tissues of the peripheral nervous system (PNS). Data generated from such analyses are expected to advance the characterization of these structures. The study aimed to: (i) establish whether DESI-MSI can discriminate the molecular characteristics of peripheral nerves and distinguish them from surrounding tissues and (ii) assess whether different peripheral nerve subtypes are characterized by unique molecular profiles. Four different nerves for which are known to carry various nerve fiber types were harvested from a fresh cadaveric donor: mixed, motor and sensory (sciatic and femoral); cutaneous, sensory (sural); and autonomic (vagus). Tissue samples were harvested to include the nerve bundles in addition to surrounding connective tissue. Samples were flash-frozen, embedded in optimal cutting temperature compound in cross-section, and sectioned at 14 μm. Following DESI-MSI analysis, identical tissue sections were stained with hematoxylin and eosin. In this proof-of-concept study, a combination of multivariate and univariate statistical methods was used to evaluate molecular differences between the nerve and adjacent tissue and between nerve subtypes. The acquired mass spectral profiles of the peripheral nerve samples presented trends in ion abundances that seemed to be characteristic of nerve tissue and spatially corresponded to the associated histology of the tissue sections. Principal component analysis (PCA) supported the separation of the samples into distinct nerve and adjacent tissue classes. This classification was further supported by the K-means clustering analysis, which showed separation of the nerve and background ions. Differences in ion expression were confirmed using ANOVA which identified statistically significant differences in ion expression between the nerve subtypes. The PCA plot suggested some separation of the nerve subtypes into four classes which corresponded with the nerve types. This was supported by the K-means clustering. Some overlap in classes was noted in these two clustering analyses. This study provides emerging evidence that DESI-MSI is an effective tool for metabolomic profiling of peripheral nerves. Our results suggest that peripheral nerves have molecular profiles that are distinct from the surrounding connective tissues and that DESI-MSI may be able to discriminate between nerve subtypes. DESI-MSI of peripheral nerves may be a valuable technique that could be used to improve our understanding of peripheral nerve anatomy and physiology. The ability to utilize ambient mass spectrometry techniques in real time could also provide an unprecedented advantage for surgical decision making, including in nerve-sparing procedures in the future.

Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model.

Orthopedic surgery is frequently performed but currently lacks consensus and availability of ideal guidance methods, resulting in high variability of outcomes. Misdirected insertion of surgical instruments can lead to weak anchorage and unreliable fixation along with risk to critical structures including the spinal cord. Current methods for surgical guidance using conventional medical imaging are indirect and time-consuming with unclear advantages. The purpose of this study was to investigate the potential of intraoperative in situ near-infrared Raman spectroscopy (RS) combined with machine learning in guiding pedicular screw insertion in the spine. A portable system equipped with a hand-held RS probe was used to make fingerprint measurements on freshly excised porcine vertebrae, identifying six tissue types: bone, spinal cord, fat, cartilage, ligament, and muscle. Supervised machine learning techniques were used to train-and test on independent hold-out data subsets-a six-class model as well as two-class models engineered to distinguish bone from soft tissue. The two-class models were further tested using in vivo spectral fingerprint measurements made during intra-pedicular drilling in a porcine spine model. The five-class model achieved accuracy in distinguish all six tissue classes when applied onto a hold-out testing data subset. The binary classifier detecting bone versus soft tissue (all soft tissue or spinal cord only) yielded 100% accuracy. When applied onto in vivo measurements performed during interpedicular drilling, the soft tissue detection models correctly detected all spinal canal breaches. We provide a foundation for RS in the orthopedic surgical guidance field. It shows that RS combined with machine learning is a rapid and accurate modality capable of discriminating tissues that are typically encountered in orthopedic procedures, including pedicle screw placement. Future development of integrated RS probes and surgical instruments promises better guidance options for the orthopedic surgeon and better patient outcomes.

Computed Tomographic and Histopathologic Studies of Lung Function Immediately Post Natum in Canine Neonates.

The lung tissue in newborn canine neonates is still in a morphologically and functionally immature, canalicular-saccular stage. True alveoli are only formed postnatally. The aim of this study was to analyze the spatial and temporal development of the ventilation of the lung tissue in vital canine neonates during the first 24 h post natum (p.n.). Forty pups (birth weight Ø 424 g ± 80.1 g) from three litters of large dog breeds (>20 kg live weight) were included in the studies. Thirty-three pups (29 vital, 2 vitally depressed, 2 stillborn neonates) originated from controlled, uncomplicated births (n = 3); moreover, six stillborn pups as well as one prematurely deceased pup were birthed by other dams with delivery complications. Computed tomography (CT) was used in 39 neonates, and histopathologic tissue classification techniques (HALO) were used in 11 neonates (eight stillborn and three neonates died early post natum, respectively) to quantify the degree of aerated neonatal lung tissue. It was shown that, in vital born pups, within the first 10 min p.n., the degree of ventilation reached mean values of -530 (±114) Hounsfield units (HU) in the dorsal and -453.3 (±133) HU in the ventral lung area. This is about 75-80% of the final values obtained after 24 h p.n. for dorsal -648.0 (±89.9) HU and ventral quadrants -624.7 (±76.8) HU. The dorsal lung areas were always significantly better ventilated than the ventral regions (p = 0.0013). CT as well as histopathology are suitable to clearly distinguish the nonventilated lungs of stillborns from neonates that were initially alive after surviving neonatal respiratory distress syndrome but who died prematurely (p = 0.0398). The results of this study are clinically relevant since the lung tissue of canine neonates presents an aeration profile as early as 10 min after birth and continues progressively, with a special regard to the dorsal lung areas. This is the basis for resuscitation measures that should be performed, preferably with the pup in the abdomen-chest position.

ASO Visual Abstract: Menstrual Phase and Menopausal Status Classification of Benign Breast Tissue Using Hormone-Regulated Gene Expression and Histomorphology: A Validation Study.

ASO Visual Abstract: Menstrual Phase and Menopausal Status Classification of Benign Breast Tissue Using Hormone-Regulated Gene Expression and Histomorphology: A Validation Study.