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Machine Learning can be used to Predict need to see a Dietitian in Patients with Advanced Lung Cancer
No full textThe DAIL (Dietetic Assessment and Intervention in Lung Cancer) study investigated the need for dietetic input in patients with Non-Small Cell Lung Cancer (NSCLC). It based need to see a dietician on the PG-SGA (Patient Generated Subjective Global Assessment), as the gold standard test. This abstract reports on a sub-study aimed at identifying if machine learning could be used to predict the need to see a dietitian using alternative data points collected during the study, when compared to the PG-SGA.Methods96 patients with stage 3b and 4 lung cancer were recruited between April 2017 and June 2019. Of these 20 had incomplete data, leaving 76 patients; 56 from Royal Surrey County Hospital (RSH) and 20 from Frimley Park Hospital (FPH). The PG-SGA was completed in all cases. This was compared to data points collected from the study, which included: the G8 frailty assessment, EORTC QLQ C30 and LC13 quality of life assessments, hand grip strength, psoas muscle surface area, spirometry, routine blood tests, Body Mass Index (BMI) and weight change, leading to 137 data points for each patient. Univariate analysis was used to find the strongest single correlates with “need to see a dietitian” (NTSD) and “critical need to see a dietitian” (CNTSD). The correlates with a Spearman correlation above +/-0.4 were selected to train a Support Vector Machine (SVM) to predict NTSD and CNTSD (SVM1) and the misclassification error calculated.ResultsThe number of measures with Spearman correlation coefficients above +/-0.4 was 18 and 13 out of a total of 137 for NTSD and CNTSD respectively. SVMs trained with these measures produced 3% and 7% misclassification error. For the SVM trained on the RSH data and tested on the FPH data the results were weaker with errors of 20% or more. This is likely to be due to the fact that only 20 patients were included in the FPH data set.ConclusionThis work suggests that machine learning can be used to predict the need to see a dietician for lung cancer patients. The results are promising, producing low misclassification rates. It could potentially automate screening for need to see a dietitian. However the results for FPH data using a model trained on RSH data suggest more work is needed to transfer the model between datasets from different hospitals.</p
Modern Raman spectromicroscopy: Some early investigations with deuterated compounds
Full text linkRaman spectroscopy is used less commonly than other spectroscopic techniques by isotopic chemists. The University of Surrey has recently become a regional centre for the technique following our acquisition of a powerful high-resolution multi-laser Raman spectromicroscope (Renishaw inVia, model RE04, via EPSRC grant EP/M022749/1). This poster reports some early experience with the technique as applied to the analysis of deuterated compounds. The spectrometer is based around Raman scattering from any of five lasers ranging from the ultraviolet to blue, green, red and near infrared (244–785 nm). The sampling beam has an area of just 2-10µm and hence the spectrometer has the ability to automatically scan tiny sample areas, offering the possibility of obtaining Raman imaging for 2-D and (via confocality) even 3-D samples. Investigations into isotopic applications of these multidimensional abilities are in progress. The high resolution of the system also enables excellent spectra to be recorded from very tiny samples, e.g. from a small part of this 200µm crystal of [2H8]naphthalene. The poster provides examples of the following advantages when deuterated compounds are analysed by Raman:Improved sensitivity is available via powerful lasers and digital spectral accumulationRapid generation of high quality one-dimensional Raman spectra from various sample typesLow matrix effects for glass enables direct analysis within ordinary sealed glass lab vialsHigh spectral and spacial resolution provides the ability to work with tiny samples/areasHighly specific results, as vibrational modes can be very sensitive to isotopic substitutionDirect quantitative analysis of isotopic mixtures is possible by selecting the appropriate peaksLow background from silicagel means TLC & HPTLC applications are possibleSurface enhanced Raman spectra (SERS) can be simply obtained via stable silver colloids</p
Radiogenemoics: A ‘Virtual Biopsy’in Nonsmall Cell Lung Cancer?
No full textNone of the patient-and/or tumor-related variables were significantly correlated with non-response. Without harmonization, none of the CE-CT radiomic features identified in the training/validation set had predictive power in the testing set. After ComBat harmonization, Zone Size Percentage GLZSM was significantly correlated with non-response to chemotherapy in the training set (AUC= 0.67, Se= 70%, Sp= 64%, p= 0.04) and obtained a satisfactory performance in the validation set (Se= 80%, Sp= 67%, p= 0.03).</p
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