Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.

No abstract

Club wheat (Triticum aestivum ssp. compactum) is an important component of the export grain market for the Pacific Northwest. Our objective was to develop a club wheat cultivar with resistance to stripe rust (caused by Puccinia striiformis f. sp. tritici Westend.) and strawbreaker foot rot [caused by Oculimacula yallundae Crous & W. Gams (Wallwork & Spooner) and O. acuformis (Boerema, R. Pieters & Hamers) Crous. & W.Gams] and with the end-use qualities that make club wheat a specialty product. The bulk pedigree breeding method was used to select 'Cara' (Reg. No. CV-1078, PI 643435) from the cross WA7752//WA6581/WA7217 made in 1992. Cara is a semidwarf wheat with the Rht-D1b dwarfing allele. Cara has winter hardiness equal to that of other club wheat cultivars-Bruehl, Chukar, and Coda-but less than the soft white wheat cultivars Eltan (PI 536994) or Masami (PI 634715). Cara was evaluated in multi-environment replicated plot trials in the Washington since 2003. Grain yields were equal to or better than other club and soft white cultivars in the 30-to 40-cm and the 40-to 50-cm annual precipitation zones. The milling and baking quality of Cara was equal to those of the best club wheat check cultivars. Cara is resistant to all races of stripe rust prevalent since 2001, possessing Yr17 and unknown genes for stripe rust resistance. It is also resistant to strawbreaker foot rot, possessing Pch1. Cara provides growers with an agronomically competitive cultivar with resistance to stripe rust and strawbreaker foot rot plus excellent club wheat end-use quality.

Defective DNA mismatch repair is one pathogenic pathway to colorectal cancer. It is characterised by microsatellite instability which provides a molecular biomarker for its detection. Clinical guidelines for universal testing of this biomarker are not met due to resource limitations; thus, there is interest in developing novel methods for its detection. Raman spectroscopy (RS) is an analytical tool able to interrogate the molecular vibrations of a sample to provide a unique biochemical fingerprint. The resulting datasets are complex and high-dimensional, making them an ideal candidate for deep learning, though this may be limited by small sample sizes. This study investigates the potential of using RS to distinguish between normal, microsatellite stable (MSS) and microsatellite unstable (MSI-H) adenocarcinoma in human colorectal samples and whether deep learning provides any benefit to this end over traditional machine learning models. A 1D convolutional neural network (CNN) was developed to discriminate between healthy, MSI-H and MSS in human tissue and compared to a principal component analysis–linear discriminant analysis (PCA–LDA) and a support vector machine (SVM) model. A nested cross-validation strategy was used to train 30 samples, 10 from each group, with a total of 1490 Raman spectra. The CNN achieved a sensitivity and specificity of 83% and 45% compared to PCA–LDA, which achieved a sensitivity and specificity of 82% and 51%, respectively. These are competitive with existing guidelines, despite the low sample size, speaking to the molecular discriminative power of RS combined with deep learning. A number of biochemical antecedents responsible for this discrimination are also explored, with Raman peaks associated with nucleic acids and collagen being implicated.

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited its routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance: primarily, small sample sizes which compound upon sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.