Characterization and automatic screening of reactive and abnormal neoplastic B lymphoid cells from peripheral blood

The objective was to advance in the automatic, image-based, characterization and recognition of a heterogeneous set of lymphoid cells from peripheral blood, including normal, reactive, and five groups of abnormal lymphocytes: hairy cells, mantle cells, follicular lymphoma, chronic lymphocytic leukemia, and prolymphocytes. Methods: A number of 4389 images from 105 patients were selected by pathologists, based on morphologic visual appearance, from patients whose diagnosis was confirmed by all the remaining complementary tests. Besides geometry, new color and texture features were extracted using six alternative color spaces to obtain rich information to characterize the cell groups. The recognition system was designed using support vector machines trained with the whole image set. Results: In the experimental tests, individual sets of images from 21 new patients were analyzed by the trained recognition system and compared with the true diagnosis. An overall recognition accuracy of 97.67% was achieved when the cell screening was performed into three groups: normal lymphocytes, abnormal lymphoid cells, and reactive lymphocytes. The accuracy of the whole experimental study was 91.23% when considering the further discrimination of the abnormal lymphoid cells into the specific five groups. Conclusion: The excellent automatic screening of the three groups of normal, reactive, and abnormal lymphocytes is useful as it discriminates between malignancy and not malignancy. The discrimination of the five groups of abnormal lymphoid cells is encouraging toward the idea that the system could be an automated image-based screening method to identify blood involvement by a variety of B lymphomas.Preprin

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks

Background and Objectives: Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells circulating in blood to add objectivity, accuracy, and speed in diagnosing hematological and non-hematological diseases. However, the variability in staining protocols across different laboratories can affect the color of images and performance of automatic recognition models. The objective of this work is to develop, train and evaluate a new system for the normalization of color staining of peripheral blood cell images, so that it transforms images from different centers to map the color staining of a reference center (RC) while preserving the structural morphological features. Methods: The system has two modules, GAN1 and GAN2. GAN1 uses the PIX2PIX technique to fade original color images to an adaptive gray, while GAN2 transforms them into RGB normalized images. Both GANs have a similar structure, where the generator is a U-NET convolutional neural network with ResNet and the discriminator is a classifier with ResNet34 structure. Digitally stained images were evaluated using GAN metrics and histograms to assess the ability to modify color without altering cell morphology. The system was also evaluated as a pre-processing tool before cells undergo a classification process. For this purpose, a CNN classifier was designed for three classes: abnormal lymphocytes, blasts and reactive lymphocytes. Results: Training of all GANs and the classifier was performed using RC images, while evaluations were conducted using images from four other centers. Classification tests were performed before and after applying the stain normalization system. The overall accuracy reached a similar value around 96% in both cases for the RC images, indicating the neutrality of the normalization model for the reference images. On the contrary, it was a significant improvement in the classification performance when applying the stain normalization to the other centers. Reactive lymphocytes were the most sensitive to stain normalization, with true positive rates (TPR) increasing from 46.3% - 66% for the original images to 81.2% - 97.2% after digital staining. Abnormal lymphocytes TPR ranged from 31.9% - 95.7% with original images to 83% - 100% with digitally stained images. Blast class showed TPR ranges of 90.3% - 94.4% and 94.4% - 100%, for original and stained images, respectively. Conclusions: The proposed GAN-based normalization staining approach improves the performance of classifiers with multicenter data sets by generating digitally stained images with a quality similar to the original images and adaptability to a reference staining standard. The system requires low computation cost and can help improve the performance of automatic recognition models in clinical settings.This work is part of a research project funded by the Ministry of Science and Innovation of Spain, with reference PID2019-104087RB-I00.Peer ReviewedPostprint (published version

A dataset of microscopic peripheral blood cell images for development of automatic recognition systems

This article makes available a dataset that was used for the development of an automatic recognition system of peripheral blood cell images using convolutional neural networks [1]. The dataset contains a total of 17,092 images of individual normal cells, which were acquired using the analyzer CellaVision DM96 in the Core Laboratory at the Hospital Clinic of Barcelona. The dataset is organized in the following eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts and platelets or thrombocytes. The size of the images is 360¿×¿363 pixels, in format jpg, and they were annotated by expert clinical pathologists. The images were captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection. This high-quality labelled dataset may be used to train and test machine learning and deep learning models to recognize different types of normal peripheral blood cells. To our knowledge, this is the first publicly available set with large numbers of normal peripheral blood cells, so that it is expected to be a canonical dataset for model benchmarking.Peer ReviewedPostprint (published version

A deep learning pproach for the morphological recognition of reactive lymphocytes in patients with COVID-19 infection

Laboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive lymphocytes (COVID-19 RL) in some of the infected patients and demonstrated that it was an indicator of a better prognosis of the disease. Visual morphological analysis is time consuming, requires smear review by expert clinical pathologists, and is prone to subjectivity. This paper presents a convolutional neural network system designed for automatic recognition of COVID-19 RL. It is based on the Xception71 structure and is trained using images of blood cells from real infected patients. An experimental study is carried out with a group of 92 individuals. The input for the system is a set of images selected by the clinical pathologist from the blood smear of a patient. The output is the prediction whether the patient belongs to the group associated with better prognosis of the disease. A threshold is obtained for the classification system to predict that the smear belongs to this group. With this threshold, the experimental test shows excellent performance metrics: 98.3% sensitivity and precision, 97.1% specificity, and 97.8% accuracy. The system does not require costly calculations and can potentially be integrated into clinical practice to assist clinical pathologists in a more objective smear review for early prognosis.Peer ReviewedPostprint (published version

Profibrotic role of inducible heat shock protein 90α isoform in systemic sclerosis

Systemic sclerosis (SSc) is an autoimmune disease that affects skin and multiple internal organs. TGF-β, a central trigger of cutaneous fibrosis, activates fibroblasts with the involvement of the stress-inducible chaperone heat shock protein 90 isoform α (Hsp90α). Available evidence supports overexpression and secretion of Hsp90α as a feature in profibrotic pathological conditions. The aim of this work is to investigate the expression and function of Hsp90α in experimental models of skin fibrosis such as human fibroblasts, C57BL/6 mice, and in human SSc. For this purpose, we generated a new experimental model based on doxorubicin administration with improved characteristics with respect to the bleomycin model. We visualized disease progression in vivo by fluorescence imaging. In this work, we obtained Hsp90α mRNA overexpression in human skin fibroblasts, in bleomycin- and doxorubicin-induced mouse fibrotic skin, and in lungs of bleomycin- and doxorubicin-treated mice. Hsp90α-deficient mice showed significantly decreased skin thickness compared with wild-type mice in both animal models. In SSc patients, serum Hsp90α levels were increased in patients with lung involvement and in patients with the diffuse form of SSc (dSSc) compared with patients with the limited form of SSc. The serum Hsp90α levels of patients dSSc were correlated with the Rodnan score and the forced vital capacity variable. These results provide new supportive evidence of the contribution of the Hsp90α isoform in the development of skin fibrosis. In SSc, these results indicated that higher serum levels were associated with dSSc and lung fibrosis.This work was supported by Spanish Ministerio de Economía, Industria y Competitividad, Gobierno de España Grant RTI2018-095214-B-I00, as well as by the Instituto de Formación e Investigación Marqués de Valdecilla IDIVAL (InnVal 17/22; InnVal 20/34), 2020UCI22-PUB-0003 Gobierno de Cantabria (to A.V.V.), SAF2016-75195-R (to J.M.), SAF2017-82905-R (to R.M.), and (NextVal 18/14) to A.P

Long-Term Real-World Effectiveness and Safety of Ustekinumab in Crohn’s Disease Patients: The SUSTAIN Study

Background Large real-world-evidence studies are required to confirm the durability of response, effectiveness, and safety of ustekinumab in Crohn’s disease (CD) patients in real-world clinical practice. Methods A retrospective, multicentre study was conducted in Spain in patients with active CD who had received ≥1 intravenous dose of ustekinumab for ≥6 months. Primary outcome was ustekinumab retention rate; secondary outcomes were to identify predictive factors for drug retention, short-term remission (week 16), loss of response and predictive factors for short-term efficacy and loss of response, and ustekinumab safety. Results A total of 463 patients were included. Mean baseline Harvey-Bradshaw Index was 8.4. A total of 447 (96.5%) patients had received prior biologic therapy, 141 (30.5%) of whom had received ≥3 agents. In addition, 35.2% received concomitant immunosuppressants, and 47.1% had ≥1 abdominal surgery. At week 16, 56% had remission, 70% had response, and 26.1% required dose escalation or intensification; of these, 24.8% did not subsequently reduce dose. After a median follow-up of 15 months, 356 (77%) patients continued treatment. The incidence rate of ustekinumab discontinuation was 18% per patient-year of follow-up. Previous intestinal surgery and concomitant steroid treatment were associated with higher risk of ustekinumab discontinuation, while a maintenance schedule every 12 weeks had a lower risk; neither concomitant immunosuppressants nor the number of previous biologics were associated with ustekinumab discontinuation risk. Fifty adverse events were reported in 39 (8.4%) patients; 4 of them were severe (2 infections, 1 malignancy, and 1 fever). Conclusions Ustekinumab is effective and safe as short- and long-term treatment in a refractory cohort of CD patients in real-world clinical practice

Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab

Ustekinumab has shown efficacy in Crohn's Disease (CD) patients. To identify patient profiles of those who benefit the most from this treatment would help to position this drug in the therapeutic paradigm of CD and generate hypotheses for future trials. The objective of this analysis was to determine whether baseline patient characteristics are predictive of remission and the drug durability of ustekinumab, and whether its positioning with respect to prior use of biologics has a significant effect after correcting for disease severity and phenotype at baseline using interpretable machine learning. Patients' data from SUSTAIN, a retrospective multicenter single-arm cohort study, were used. Disease phenotype, baseline laboratory data, and prior treatment characteristics were documented. Clinical remission was defined as the Harvey Bradshaw Index <= 4 and was tracked longitudinally. Drug durability was defined as the time until a patient discontinued treatment. A total of 439 participants from 60 centers were included and a total of 20 baseline covariates considered. Less exposure to previous biologics had a positive effect on remission, even after controlling for baseline disease severity using a non-linear, additive, multivariable model. Additionally, age, body mass index, and fecal calprotectin at baseline were found to be statistically significant as independent negative risk factors for both remission and drug survival, with further risk factors identified for remission

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Effectiveness and safety of integrase strand transfer inhibitors in Spain: a prospective real-world study

IntroductionSecond-generation integrase strand transfer inhibitors (INSTIs) are preferred treatment options worldwide, and dolutegravir (DTG) is the treatment of choice in resource-limited settings. Nevertheless, in some resource-limited settings, these drugs are not always available. An analysis of the experience with the use of INSTIs in unselected adults living with HIV may be of help to make therapeutic decisions when second-generation INSTIs are not available. This study aimed to evaluate the real-life effectiveness and safety of dolutegravir (DTG), elvitegravir/cobicistat (EVG/c), and raltegravir (RAL) in a large Spanish cohort of HIV-1-infected patients.MethodsReal-world study of adults living with HIV who initiated integrase INSTIs DTG, EVG/c, and RAL-based regimens in three settings (ART-naïve patients, ART-switching, and ART-salvage patients). The primary endpoint was the median time to treatment discontinuation after INSTI-based regimen initiation. Proportion of patients experiencing virological failure (VF) (defined as two consecutive viral loads (VL) ≥200 copies/mL at 24 weeks or as a single determination of VL ≥1,000 copies/mL while receiving DTG, EVG/c or RAL, and at least 3 months after INSTI initiation) and time to VF were also evaluated.ResultsVirological effectiveness of EVG/c- and RAL-based regimens was similar to that of DTG when given as first-line and salvage therapy. Treatment switching for reasons other than virological failure was more frequent in subjects receiving EVG/c and, in particular, RAL. Naïve patients with CD4+ nadir &lt;100 cells/μL were more likely to develop VF, particularly if they initiated RAL or EVG/c. In the ART switching population, initiation of RAL and EVG/c was associated with both VF and INSTI discontinuation. There were no differences in the time to VF and INSTI discontinuation between DTG, EVG/c and RAL. Immunological parameters improved in the three groups and for the three drugs assessed. Safety and tolerability were consistent with expected safety profiles.DiscussionWhereas second-generation INSTIs are preferred treatment options worldwide, and DTG is one of the treatment of choices in resource-limited settings, first-generation INSTIs may still provide high virological and immunological effectiveness when DTG is not available