Search

• New methods are needed for accurate and rapid tumor margin assessment in Breast Conservation Surgery. • Raman Spectroscopy coupled with deep learning was used to discriminate normal and cancerous basal and luminal breast tissues. • This method discriminated normal and cancerous breast tissue with 90 % accuracy, 88.8 % sensitivity and 90.8 % specificity. • Pseudo Raman maps generated from identified biomarkers helped accurate identification of tumor margins. Better knowledge of the breast tumor microenvironment is required for surgical resection and understanding the processes of tumor development. Raman spectroscopy is a promising tool that can assist in uncovering the molecular basis of disease and provide quantifiable molecular information for diagnosis and treatment evaluation. In this work, eighty-eight frozen breast tissue sections, including forty-four normal and forty-four tumor sections, were mapped in their entirety using a 250-μm-square measurement grid. Two or more smaller regions of interest within each tissue were additionally mapped using a 25 μm-square step size. A deep learning algorithm, convolutional neural network (CNN), was developed to distinguish histopathologic features with-in individual and across multiple tissue sections. Cancerous breast tissue were discriminated from normal breast tissue with 90 % accuracy, 88.8 % sensitivity and 90.8 % specificity with an excellent Area Under the Receiver Operator Curve (AUROC) of 0.96. Features that contributed significantly to the model were identified and used to generate RGB images of the tissue sections. For each grid point (pixel) on a Raman map, color was assigned to intensities at frequencies of 1002 cm−1 (Phenylalanine), 869 cm−1 (Proline, C C stretching of hydroxyproline-collagen assignment, single bond stretching vibrations for the amino acids proline, valine and polysaccharides) and 1309 cm−1 (CH 3 /CH 2 twisting or bending mode of lipids). The Raman images clearly associate with hematoxylin and eosin stained tissue sections and allow clear visualization of boundaries between normal adipose, connective tissue and tumor. We demonstrated that this simple imaging technique allows high-resolution, straightforward molecular interpretation of Raman images. Raman spectroscopy provides rapid, label-free imaging of microscopic features with high accuracy. This method has application as laboratory tool and can assist with intraoperative tissue assessment during Breast Conserving surgery. [ABSTRACT FROM AUTHOR]

Background: This study profiles ceramides extracted from visceral and subcutaneous adipose tissue of human subjects by liquid chromatography‐mass spectrometry to determine a correlation with status of diabetes and gender. Methods: Samples of visceral and abdominal wall subcutaneous adipose tissue (n = 36 and n = 31, respectively) were taken during laparoscopic surgery from 36 patients (14 nondiabetic, 22 diabetic and prediabetic) undergoing bariatric surgery with a body mass index (BMI) >35 kg/m2 with ≥1 existing comorbidity or BMI ≥40 kg/m2. Sphingolipids were extracted and analyzed using liquid chromatography‐mass spectrometry. Results: After logarithm 2 conversion, paired analysis of visceral to subcutaneous tissue showed differential accumulation of Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) in visceral tissue of prediabetic/diabetic female subjects, but not in males. Within‐tissue analysis showed higher mean levels of ceramide species linked to insulin resistance, such as Cer(d18:1/18:0) and Cer(d18:1/16:0), in visceral tissue of prediabetic/diabetic patients compared with nondiabetic subjects and higher content of Cer(d18:1/14:0) in subcutaneous tissue of insulin‐resistant female patients compared with prediabetic/diabetic males. Statistically significant differences in mean levels of ceramide species between insulin‐resistant African American and insulin‐resistant Caucasian patients were not evident in visceral or subcutaneous tissue. Conclusions: Analysis of ceramides is important for developing a better understanding of biological processes underlying type 2 diabetes, metabolic syndrome, and obesity. Knowledge of the accumulated ceramides/dihydroceramides may reflect on the prelipolytic state that leads the lipotoxic phase of insulin resistance and may shed light on the predisposition to insulin resistance by gender. [ABSTRACT FROM AUTHOR]