Diagnosis Of The Chronic Lymphocytic Leukemia (CLL) Using A Raman-Based Scanner Optimized For Blood Smear Analysis (M3s Project)

Introduction/ Background In hematology, actual diagnosis of B chronic lymphocyte-leukemia (CLL) is based on the microscopic analysis of cell morphology from patient blood smear. However, new photonic technologies appear promising to facilitate and improve the early diagnosis, prognostic and monitoring of personalized therapy. The development of automated diagnostic approaches could assist clinicians in improving the efficiency and quality of health services, but also reduce medical costs. Aims The M3S project aims at improving the diagnosis and prognosis of the CLL pathology by developing a multimodal microscopy platform, including Raman spectrometry, dedicated to the automatic analysis of lymphocytes. Methods Blood smears were prepared on glass slides commonly used in pathology laboratories for microscopy. Two types of sample per patient were prepared: a conventional blood smear and a deposit of “pure” lymphocyte subtypes (i.e. normal B, CLL B, T and NK), sorted out in flow cytometry by using the negative double labeling technique. The second sample is used for the construction of a database of spectral markers specific of these different cell types. The preparations were analyzed with the multimodal machine which combines i) a Raman micro-spectrometer, equipped with a 532nm diode laser excitation source; ii) a microscope equipped with 40x and 150x lenses and a high precision xyz motorized stage for scanning the blood smear, and localizing x-y coordinates of representative series (~100 for each patient) of lymphocyte cells before registering three Raman spectra; these cells of interest being previously localized by an original method based on the morphology analysis. After the Raman acquisitions, the conventional blood smears were submitted to immunolabelling using specific antibodies. For the establishment of the Raman classifiers, this post-acquisition treatment was used as reference to distinguish the different lymphocyte sub-populations. Raman data were then analyzed using chemometric processing and supervised statistical classifiers in order to construct a spectral library of markers highly specific of the lymphocyte type and status (normal or pathological). Results Currently, a total of 60 patients (CLL and healthy) were included in the study. Various classification methods such as LDA (Linear Discriminant Analysis), PLS-DA (Partial Least Square Discriminant Analysis), RF (Random Forest) and SVM (Support Vector Machine), were tested in the purpose to distinguish tumoral B lymphocytes from other cell types. These classification algorithms were combined with feature selection approaches. The best performances were around 70% of correct identification when a three-class model (B-CLL vs B-normal vs T and NK lymphocytes) was considered, and 80% in case of a two-class model (B-CLL vs B-normal lymphocytes). These encouraging results demonstrate the potential of Raman micro-spectroscopy coupled to supervised classification algorithms for leukemic cell classification. The approach can find interest more generally in the field of cyto-hematology. Further developments will concern the integration of additional modality such as Quantitative Phase Imaging on one hand to speed the exploration process of cells of interest to be probed, and on the other hand to extract additional characteristics likely to be informative for CLL diagnosis. In addition, the identification of prognostic markers will be investigated by confronting the photonic data to clinical patient information.

Variation in minimum desired cardiovascular disease-free longevity benefit from statin and antihypertensive medications : A cross-sectional study of patient and primary care physician perspectives

Objective Expressing therapy benefit from a lifetime perspective, instead of only a 10-year perspective, is both more intuitive and of growing importance in doctor-patient communication. In cardiovascular disease (CVD) prevention, lifetime estimates are increasingly accessible via online decision tools. However, it is unclear what gain in life expectancy is considered meaningful by those who would use the estimates in clinical practice. We therefore quantified lifetime and 10-year benefit thresholds at which physicians and patients perceive statin and antihypertensive therapy as meaningful, and compared the thresholds with clinically attainable benefit. Design Cross-sectional study. Settings (1) continuing medical education conference in December 2016 for primary care physicians;(2) information session in April 2017 for patients. Participants 400 primary care physicians and 523 patients in the Netherlands. Outcome Months gain of CVD-free life expectancy at which lifelong statin therapy is perceived as meaningful, and months gain at which 10 years of statin and antihypertensive therapy is perceived as meaningful. Physicians were framed as users for lifelong and prescribers for 10-year therapy. Results Meaningful benefit was reported as median (IQR). Meaningful lifetime statin benefit was 24 months (IQR 23-36) in physicians (as users) and 42 months (IQR 12-42) in patients willing to consider therapy. Meaningful 10-year statin benefit was 12 months (IQR 10-12) for prescribing (physicians) and 14 months (IQR 10-14) for using (patients). Meaningful 10-year antihypertensive benefit was 12 months (IQR 8-12) for prescribing (physicians) and 14 months (IQR 10-14) for using (patients). Women desired greater benefit than men. Age, CVD status and co-medication had minimal effects on outcomes. Conclusion Both physicians and patients report a large variation in meaningful longevity benefit. Desired benefit differs between physicians and patients and exceeds what is clinically attainable. Clinicians should recognise these discrepancies when prescribing therapy and implement individualised medicine and shared decision-making. Decision tools could provide information on realistic therapy benefit

Emotion and attention: predicting electrodermal activity through video visual descriptors

This paper contributes to the field of affective video content analysis through the novel employment of electrodermal activity (EDA) measurements as ground truth for machine learning algorithms. The variation of the electrical properties of the skin, known as EDA, is a psychophysiological indicator widely used in medicine, psychology and neuroscience which can be considered a somatic marker of the emotional and attentional reaction of subjects towards stimuli. One of its main advantages is that the recorded information is not biased by the cognitive process of giving an opinion or a score to characterize the subjective perception. In this work, we predict the levels of emotion and attention, derived from EDA records, by means of a small set of low-level visual descriptors computed from the video stimuli. Linear regression experiments show that our descriptors predict significantly well the sum of emotion and attention levels, reaching a coefficient of determination R 2 = 0.25. This result sets a promising path for further research on the prediction of emotion and attention from videos using EDA