A sex-informed approach to improve the personalised decision making process in myelodysplastic syndromes: a multicentre, observational cohort study

Background Sex is a major source of diversity among patients and a sex-informed approach is becoming a new paradigm in precision medicine. We aimed to describe sex diversity in myelodysplastic syndromes in terms of disease genotype, phenotype, and clinical outcome. Moreover, we sought to incorporate sex information into the clinical decision-making process as a fundamental component of patient individuality. Methods In this multicentre, observational cohort study, we retrospectively analysed 13 284 patients aged 18 years or older with a diagnosis of myelodysplastic syndrome according to 2016 WHO criteria included in the EuroMDS network (n=2025), International Working Group for Prognosis in MDS (IWG-PM; n=2387), the Spanish Group of Myelodysplastic Syndromes registry (GESMD; n=7687), or the Dusseldorf MDS registry (n=1185). Recruitment periods for these cohorts were between 1990 and 2016. The correlation between sex and genomic features was analysed in the EuroMDS cohort and validated in the IWG-PM cohort. The effect of sex on clinical outcome, with overall survival as the main endpoint, was analysed in the EuroMDS population and validated in the other three cohorts. Finally, novel prognostic models incorporating sex and genomic information were built and validated, and compared to the widely used revised International Prognostic Scoring System (IPSS-R). This study is registered with ClinicalTrials.gov, NCT04889729. Findings The study included 7792 (58middot7%) men and 5492 (41middot3%) women. 10 906 (82middot1%) patients were White, and race was not reported for 2378 (17middot9%) patients. Sex biases were observed at the single-gene level with mutations in seven genes enriched in men (ASXL1, SRSF2, and ZRSR2 p<0middot0001 in both cohorts; DDX41 not available in the EuroMDS cohort vs p=0middot0062 in the IWG-PM cohort; IDH2 p<0middot0001 in EuroMDS vs p=0middot042 in IWG-PM; TET2 p=0middot031 vs p=0middot035; U2AF1 p=0middot033 vs p<0middot0001) and mutations in two genes were enriched in women (DNMT3A p<0middot0001 in EuroMDS vs p=0middot011 in IWG-PM; TP53 p=0middot030 vs p=0middot037). Additionally, sex biases were observed in co-mutational pathways of founding genomic lesions (splicing-related genes, predominantly in men, p<0middot0001 in both the EuroMDS and IWG-PM cohorts), in DNA methylation (predominantly in men, p=0middot046 in EuroMDS vs p<0middot0001 in IWG-PM), and TP53 mutational pathways (predominantly in women, p=0middot0073 in EuroMDS vs p<0middot0001 in IWG-PM). In the retrospective EuroMDS cohort, men had worse median overall survival (81middot3 months, 95% CI 70middot4-95middot0 in men vs 123middot5 months, 104middot5-127middot5 in women; hazard ratio [HR] 1middot40, 95% CI 1middot26-1middot52; p<0middot0001). This result was confirmed in the prospective validation cohorts (median overall survival was 54middot7 months, 95% CI 52middot4-59middot1 in men vs 74middot4 months, 69middot3-81middot2 in women; HR 1middot30, 95% CI 1middot23-1middot35; p<0middot0001 in the GEMSD MDS registry; 40middot0 months, 95% CI 33middot4-43middot7 in men vs 54middot2 months, 38middot6-63middot8 in women; HR 1middot23, 95% CI 1middot08-1middot36; p<0middot0001 in the Dusseldorf MDS registry). We developed new personalised prognostic tools that included sex information (the sex-informed prognostic scoring system and the sex-informed genomic scoring system). Sex maintained independent prognostic power in all prognostic systems; the highest performance was observed in the model that included both sex and genomic information. A five-to-five mapping between the IPSS-R and new score categories resulted in the re-stratification of 871 (43middot0%) of 2025 patients from the EuroMDS cohort and 1003 (42middot0%) of 2387 patients from the IWG-PM cohort by using the sex-informed prognostic scoring system, and of 1134 (56middot0%) patients from the EuroMDS cohort and 1265 (53middot0%) patients from the IWG-PM cohort by using the sex-informed genomic scoring system. We created a web portal that enables outcome predictions based on a sex-informed personalised approach. Interpretation Our results suggest that a sex-informed approach can improve the personalised decision making process in patients with myelodysplastic syndromes and should be considered in the design of clinical trials including low-risk patients. Copyright (c) 2022 Published by Elsevier Ltd. All rights reserved

Generative Adversarial Networks in Brain Imaging: A Narrative Review

Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated remarkable progress in many clinical tasks, mostly regarding the detection, segmentation, classification, monitoring, and prediction of diseases. Generative Adversarial Networks have been proposed as one of the most exciting applications of deep learning in radiology. GANs are a new approach to deep learning that leverages adversarial learning to tackle a wide array of computer vision challenges. Brain radiology was one of the first fields where GANs found their application. In neuroradiology, indeed, GANs open unexplored scenarios, allowing new processes such as image-to-image and cross-modality synthesis, image reconstruction, image segmentation, image synthesis, data augmentation, disease progression models, and brain decoding. In this narrative review, we will provide an introduction to GANs in brain imaging, discussing the clinical potential of GANs, future clinical applications, as well as pitfalls that radiologists should be aware of

The Use of Antiviral Agents against SARS-CoV-2: Ineffective or Time and Age Dependent Result? A Retrospective, Observational Study among COVID-19 Older Adults

Background: Our aim was to investigate the impact of therapeutics with antiviral activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on mortality of older adults affected by coronavirus disease 2019 (COVID-19), taking into consideration the time interval from symptoms onset to drugs administration. Methods: Data from 143 COVID-19 patients over 65 years of age admitted to the Humanitas Clinical and Research Center Emergency Department (Milan, Italy) and treated with Lopinavir/ritonavir (LPV/r) or Darunavir/cobicistat (DVR/c) associated to Hydroxychloroquine (HCQ) were retrospectively analyzed. Statistical analysis was performed by using a logistic regression model and survival analysis to assess the role of different predictors of in-hospital mortality, including an early (&lt;6 days from symptoms onset) vs. late treatment onset, signs and symptoms at COVID-19 presentation, type of antiviral treatment (LPV/r or DVR/c) and patients’ age (65–80 vs. &gt;80 years old). Results: Multivariate analysis showed that an older age (OR: 2.54) and dyspnea as presenting symptom (OR: 2.01) were associated with higher mortality rate, whereas cough as presenting symptom (OR: 0.53) and a timely drug administration (OR: 0.44) were associated with lower mortality. Survival analysis demonstrated that the timing of drug administration had an impact on mortality in 65–80 years-old patients (p = 0.02), whereas no difference was seen in those &gt;80 years-old. This impact was more evident in patients with dyspnea as primary symptom of COVID-19, in whom mortality decreased from 57.1% to 38.3% due to timely drug administration (OR: 0.5; p = 0.04). Conclusions: There was a significant association between the use of a combined antiviral regimen and HCQ and lower mortality, when timely-administered, in COVID-19 patients aged 65–80 years. Our findings support timely treatment onset as a key component in the treatment of COVID-19

Effectiveness of Streptococcus Pneumoniae Urinary Antigen Testing in Decreasing Mortality of COVID-19 Co-Infected Patients: A Clinical Investigation

Background and objectives: Streptococcus pneumoniae urinary antigen (u-Ag) testing has recently gained attention in the early diagnosis of severe and critical acute respiratory syndrome coronavirus-2/pneumococcal co-infection. The aim of this study is to assess the effectiveness of Streptococcus pneumoniae u-Ag testing in coronavirus disease 2019 (COVID-19) patients, in order to assess whether pneumococcal co-infection is associated with different mortality rate and hospital stay in these patients. Materials and Methods: Charts, protocols, mortality, and hospitalization data of a consecutive series of COVID-19 patients admitted to a tertiary hospital in northern Italy during COVID-19 outbreak were retrospectively reviewed. All patients underwent Streptococcus pneumoniae u-Ag testing to detect an underlying pneumococcal co-infection. Covid19+/u-Ag+ and Covid19+/u-Ag- patients were compared in terms of overall survival and length of hospital stay using chi-square test and survival analysis. Results: Out of 575 patients with documented pneumonia, 13% screened positive for the u-Ag test. All u-Ag+ patients underwent treatment with Ceftriaxone and Azithromycin or Levofloxacin. Lopinavir/Ritonavir or Darunavir/Cobicistat were added in 44 patients, and hydroxychloroquine and low-molecular-weight heparin (LMWH) in 47 and 33 patients, respectively. All u-Ag+ patients were hospitalized. Mortality was 15.4% and 25.9% in u-Ag+ and u-Ag- patients, respectively (p = 0.09). Survival analysis showed a better prognosis, albeit not significant, in u-Ag+ patients. Median hospital stay did not differ among groups (10 vs. 9 days, p = 0.71). Conclusions: The routine use of Streptococcus pneumoniae u-Ag testing helped to better target antibiotic therapy with a final trend of reduction in mortality of u-Ag+ COVID-19 patients having a concomitant pneumococcal infection. Randomized trials on larger cohorts are necessary in order to draw definitive conclusion

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study

Abstract Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to those of models implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular fastai and XGBoost models, with AUC 0.87 ± 0.02, F1 score 0.62 ± 0.10 and an MCC 0.52 ± 0.04 (p < 0.32). As for ICU admission, the combined model MCC was superior (p < 0.024) to the tabular models. Our results suggest that a combined textual and tabular model can effectively predict COVID-19 prognosis which may assist ED physicians in their decision-making process

Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis

Quantitative analysis of Tumor Microenvironment (TME) provides prognostic and predictive information in several human cancers but, with few exceptions, it is not performed in daily clinical practice since it is extremely time-consuming. We recently showed that the morphology of Tumor Associated Macrophages (TAMs) correlates with outcome in patients with Colo-Rectal Liver Metastases (CLM). However, as for other TME components, recognizing and characterizing hundreds of TAMs in a single histopathological slide is unfeasible. To fasten this process, we explored a deep-learning based solution. We tested three Convolutional Neural Networks (CNNs), namely UNet, SegNet and DeepLab-v3, with three different segmentation strategies, semantic segmentation, pixel penalties and instance segmentation. The different experiments are compared according to the Intersection over Union (IoU), a metric describing the similarity between what CNN predicts as TAM and the ground truth, and the Symmetric Best Dice (SBD), which indicates the ability of CNN to separate different TAMs. UNet and SegNet showed intrinsic limitations in discriminating single TAMs (highest SBD 61.34±2.21), whereas DeepLab-v3 accurately recognized TAMs from the background (IoU 89.13±3.85) and separated different TAMs (SBD 79.00±3.72). This deep-learning pipeline to recognize TAMs in digital slides will allow the characterization of TAM-related metrics in the daily clinical practice, allowing the implementation of prognostic tools

Clinical Outcomes in the Second versus First Pandemic Wave in Italy: Impact of Hospital Changes and Reorganization

The region of Lombardy was the epicenter of the COVID-19 outbreak in Italy. Emergency Hospital 19 (EH19) was built in the Milan metropolitan area during the pandemic’s second wave as a facility of Humanitas Clinical and Research Center (HCRC). The present study aimed to assess whether the implementation of EH19 was effective in improving the quality of care of COVID-19 patients during the second wave compared with the first one. The demographics, mortality rate, and in-hospital length of stay (LOS) of two groups of patients were compared: the study group involved patients admitted at HCRC and managed in EH19 during the second pandemic wave, while the control group included patients managed exclusively at HCRC throughout the first wave. The study and control group included 903 (56.7%) and 690 (43.3%) patients, respectively. The study group was six years older on average and had more pre-existing comorbidities. EH19 was associated with a decrease in the intensive care unit admission rate (16.9% vs. 8.75%, p &lt; 0.001), and an equal decrease in invasive oxygen therapy (3.8% vs. 0.23%, p &lt; 0.001). Crude mortality was similar but overlap propensity score weighting revealed a trend toward a potential small decrease. The adjusted difference in LOS was not significant. The implementation of an additional COVID-19 hospital facility was effective in improving the overall quality of care of COVID-19 patients during the first wave of the pandemic when compared with the second. Further studies are necessary to validate the suggested approach

Artificial intelligence and colonoscopy experience: lessons from two randomised trials

BACKGROUND AND AIMS: Artificial intelligence has been shown to increase adenoma detection rate (ADR) as the main surrogate outcome parameter of colonoscopy quality. To which extent this effect may be related to physician experience is not known. We performed a randomised trial with colonoscopists in their qualification period (AID-2) and compared these data with a previously published randomised trial in expert endoscopists (AID-1). METHODS: In this prospective, randomised controlled non-inferiority trial (AID-2), 10 non-expert endoscopists (<2000 colonoscopies) performed screening/surveillance/diagnostic colonoscopies in consecutive 40-80 year-old subjects using high-definition colonoscopy with or without a real-time deep-learning computer-aided detection (CADe) (GI Genius, Medtronic). The primary outcome was ADR in both groups with histology of resected lesions as reference. In a post-hoc analysis, data from this randomised controlled trial (RCT) were compared with data from the previous AID-1 RCT involving six experienced endoscopists in an otherwise similar setting. RESULTS: In 660 patients (62.3±10 years; men/women: 330/330) with equal distribution of study parameters, overall ADR was higher in the CADe than in the control group (53.3% vs 44.5%; relative risk (RR): 1.22; 95% CI: 1.04 to 1.40; p<0.01 for non-inferiority and p=0.02 for superiority). Similar increases were seen in adenoma numbers per colonoscopy and in small and distal lesions. No differences were observed with regards to detection of non-neoplastic lesions. When pooling these data with those from the AID-1 study, use of CADe (RR 1.29; 95% CI: 1.16 to 1.42) and colonoscopy indication, but not the level of examiner experience (RR 1.02; 95% CI: 0.89 to 1.16) were associated with ADR differences in a multivariate analysis. CONCLUSIONS: In less experienced examiners, CADe assistance during colonoscopy increased ADR and a number of related polyp parameters as compared with the control group. Experience appears to play a minor role as determining factor for ADR. TRIAL REGISTRATION NUMBER: NCT:04260321