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Abstract Background The clinical potential of Raman spectroscopy is well established but has yet to become established in routine oncology workflows. One barrier slowing clinical adoption is a lack of evidence demonstrating that data taken on one spectrometer transfers across to data taken on another spectrometer to provide consistent diagnoses. Methods We investigated multi-centre transferability using human oesophageal tissue. Raman spectra were taken across three different centres with different spectrometers of the same make and model. By using a common protocol, we aimed to minimise the difference in machine learning performance between centres. Results 61 oesophageal samples from 51 patients were interrogated by Raman spectroscopy at each centre and classified into one of five pathologies. The overall accuracy and log-loss did not significantly vary when a model trained upon data from any one centre was applied to data taken at the other centres. Computational methods to correct for the data during pre-processing were not needed. Conclusion We have found that when using the same make and model of spectrometer, together with a common protocol, across different centres it is possible to achieve system transferability without the need for additional computational instrument correction.

We aimed to develop deep learning (DL) models to detect protein expression in immunohistochemically (IHC) stained tissue-sections, and to compare their accuracy and performance with manually scored clinically relevant proteins in common cancer types.Five cancer patient cohorts (colon, two prostate, breast, and endometrial) were included. We developed separate DL models for scoring IHC-stained tissue-sections with nuclear, cytoplasmic, and membranous staining patterns. For training, we used images with annotations of cells with positive and negative staining from the colon cohort stained for Ki-67 and PMS2 (nuclear model), the prostate cohort 1 stained for PTEN (cytoplasmic model) and β-catenin (membranous model). The nuclear DL model was validated for MSH6 in the colon, MSH6 and PMS2 in the endometrium, Ki-67 and CyclinB1 in prostate, and oestrogen and progesterone receptors in the breast cancer cohorts. The cytoplasmic DL model was validated for PTEN and Mapre2, and the membranous DL model for CD44 and Flotillin1, all in prostate cohorts. When comparing the results of manual and DL scores in the validation sets, using manual scores as the ground truth, we observed an average correct classification rate of 91.5 % (76.9–98.5 %) for the nuclear model, 85.6 % (73.3–96.6 %) for the cytoplasmic model, and 78.4 % (75.5–84.3 %) for the membranous model. In survival analyses, manual and DL scores showed similar prognostic impact, with similar hazard ratios and p-values for all DL models. Our findings demonstrate that DL models offer a promising alternative to manual IHC scoring, providing efficiency and reproducibility across various data sources and markers.

In 2020, a girl aged 5 years presented to the coronavirus assessment centre on a remote Scottish island with symptoms consistent with novel coronavirus 2019 (COVID-19). Her mother was concerned as she had noted the patient to have an irregular pulse. COVID-19 has been shown to cause cardiac arrhythmia, and so after discussion with tertiary paediatric cardiology services an ECG was recommended. In order to minimise potential spread of coronavirus in the healthcare setting a portable ECG device was immediately delivered to the patient, with the ECG tracing being sent electronically to a cardiologist. A formal diagnosis was then communicated to the parents within 2 hours of the initial contact.

Serrated adenomas form a distinct subtype of colorectal pre-malignant lesions that may progress to malignancy along a different molecular pathway than the conventional adenoma-carcinoma pathway. Previous studies have hypothesised that BRAF mutation and promoter hypermethylation plays a role, but the evidence for this is not robust. We aimed to carry out a whole-genome loss of heterozygosity analysis, followed by targeted promoter methylation and expression analysis to identify potential pathways in serrated adenomas. An initial panel of 9 sessile serrated adenomas (SSA) and one TSA were analysed using Illumina Goldengate HumanLinkage panel arrays to ascertain regions of loss of heterozygosity. This was verified via molecular inversion probe analysis and microsatellite analysis of a further 32 samples. Methylation analysis of genes of interest was carried out using methylation specific PCR (verified by pyrosequencing) and immunohistochemistry used to correlate loss of expression of genes of interest. All experiments used adenoma samples and normal tissue samples as control. SSA samples were found on whole-genome analysis to have consistent loss of heterozygosity at 4p15.1-4p15.31, which was not found in the sole TSA, adenomas, or normal tissues. Genes of interest in this region were PDCH7 and SLIT2, and combined MSP/IHC analysis of these genes revealed significant loss of SLIT2 expression associated with promoter methylation of SLIT2. Loss of expression of SLIT2 by promoter hypermethylation and loss of heterozygosity events is significantly associated with serrated adenoma development, and SLIT2 may represent a epimutated tumour suppressor gene according to the Knudson "two hit" hypothesis.

Early detection of (pre-)cancerous changes improves prognosis, therefore in the UK patients at high risk of developing gastrointestinal cancers are enrolled on endoscopic surveillance programmes or the Bowel Cancer Screening Programme. The current gold standard technique for the detection of pre-cancerous changes in the gastrointestinal tract is histopathological analysis of biopsy tissue collected at endoscopy. This relies upon subjective assessment of morphological changes within the excised tissue samples and poor targeting of pre-malignant lesions. Raman spectroscopy offers a number of potential advantages for in vivoassessment of tissue at endoscopy. The performance of a custom built Raman probe as a biopsy targeting tool has been evaluated using excised biopsy material. Multivariate classification models have been used to demonstrate the likely ability of a miniature, confocal, fibre optic Raman probe to be used as an optical biopsy tool at endoscopy to provide spectral information in clinically practicable timescales. This technique could facilitate improved targeting of excisional biopsy with associated clinical benefits. [ABSTRACT FROM AUTHOR]

Histopathology remains the gold standard technique for the diagnosis of intraepithelial neoplasia (dysplasia) in Barrett's oesophagus, but it is highly subjective and relies on blind biopsy targeting. The aim of this study was to evaluate Raman spectroscopy, a rapid, non-invasive, molecular, specific analytical technique, for the objective identification and classification of Barrett's neoplasia in vitro. A secondary objective was to demonstrate the need for a rigorous gold standard in the development of new diagnostic techniques. Forty-four patients with a mean age of 69 years (range 34-89 years) undergoing surveillance for Barrett's oesophagus were included in the study. Three consultant pathologists independently assessed snap-frozen oesophageal biopsy specimens. Raman spectra were measured on 87 histopathologically homogeneous samples. Spectral classification models were developed using multivariate analysis for the prediction of pathology. Histopathology and Raman classification results were compared. Raman spectral prediction with a consensus pathology classification model gave sensitivities between 73% and 100% and specificities of 90-100%. A high level of agreement (κ = 0.89) was demonstrated between the three-subset biopsy targeting model and consensus pathology opinion. This compares favourably with the agreement measured between an independent pathologist and the consensus pathology opinion for the same spectra (κ = 0.76). Raman spectroscopy appears to provide a highly sensitive and specific technique for the identification and classification of neoplasia in Barrett's oesophagus. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Rapid Raman mapping has the potential to be used for automated histopathology diagnosis, providing an adjunct technique to histology diagnosis. The aim of this work is to evaluate the feasibility of automated and objective pathology classification of Raman maps using linear discriminant analysis. Raman maps of esophageal tissue sections are acquired. Principal component (PC)-fed linear discriminant analysis (LDA) is carried out using subsets of the Raman map data (6483 spectra). An overall (validated) training classification model performance of 97.7 (sensitivity 95.0 to 100 and specificity 98.6 to 100) is obtained. The remainder of the map spectra (131,672 spectra) are projected onto the classification model resulting in Raman images, demonstrating good correlation with contiguous hematoxylin and eosin (HE) sections. Initial results suggest that LDA has the potential to automate pathology diagnosis of esophageal Raman images, but since the classification of test spectra is forced into existing training groups, further work is required to optimize the training model. A small pixel size is advantageous for developing the training datasets using mapping data, despite lengthy mapping times, due to additional morphological information gained, and could facilitate differentiation of further tissue groups, such as the basal cellslamina propria, in the future, but larger pixels sizes (and faster mapping) may be more feasible for clinical application. [ABSTRACT FROM AUTHOR]