Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer

Simple Summary Colorectal cancer is the second most malignant tumor per number of deaths after lung cancer and the third per number of new cases after breast and lung cancer. The correct and rapid identification (i.e., segmentation of the cancer regions) is a fundamental task for correct patient diagnosis. In this study, we propose a novel automated pipeline for the segmentation of MRI scans of patients with LARC in order to predict the response to nCRT using radiomic features. This study involved the retrospective analysis of T-2-weighted MRI scans of 43 patients affected by LARC. The segmentation of tumor areas was on par or better than the state-of-the-art results, but required smaller sample sizes. The analysis of radiomic features allowed us to predict the TRG score, which agreed with the state-of-the-art results. Background: Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using a fully automated artificial intelligence-based model for the segmentation and consequent characterization of the tumor areas using radiomic features was evaluated. The TRG score was used to evaluate the clinical outcome. Methods: Forty-three patients under treatment in the IRCCS Sant'Orsola-Malpighi Polyclinic were retrospectively selected for the study; a U-Net model was trained for the automated segmentation of the tumor areas; the radiomic features were collected and used to predict the tumor regression grade (TRG) score. Results: The segmentation of tumor areas outperformed the state-of-the-art results in terms of the Dice score coefficient or was comparable to them but with the advantage of considering mucinous cases. Analysis of the radiomic features extracted from the lesion areas allowed us to predict the TRG score, with the results agreeing with the state-of-the-art results. Conclusions: The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice

Reproducibility of HPV DNA Testing by Hybrid Capture 2 in a Screening Setting

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Health Technology Assessment of Belimumab: A New Monoclonal Antibody for the Treatment of Systemic Lupus Erythematosus

Objective. Systemic lupus erythematosus (SLE) is treated with anti-inflammatory and immunosuppressive drugs and off-label biologics. Belimumab is the first biologic approved after 50 years as an add-on therapy for active disease. This paper summarizes a health technology assessment performed in Italy. Methods. SLE epidemiology and burden were assessed using the best published international and national evidences and efficacy and safety of belimumab were synthesized using clinical data. A cost-effectiveness analysis was performed by a lifetime microsimulation model comparing belimumab to standard of care (SoC). Organizational and ethical implications were discussed. Results. Literature review showed that SLE affects 47 per 100,000 people for a total of 28,500 patients in Italy, 50% of whom are affected by active form of the disease despite SoC. These patients, if autoantibodies and anti-dsDNA positive with low complement, are eligible for belimumab. SLE determines work disability and a 2-5-fold increase in mortality. Belimumab with SoC may prevent 4,742 flares in three years being cost-effective with an incremental costeffectiveness ratio of C32,859 per quality adjusted life year gained. From the organizational perspective, the development of clear and comprehensive clinical pathways is crucial. Conclusions. The assessment supports the use of belimumab into the SLE treatment paradigm in Italy

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

Applicazione di un sistema di mappatura dei rischi nella Diagnostica per Immagini

[Applying a system of risk mapping in Diagnostic Imaging]Six hospitals in Emilia-Romagna have developed a system of detection and classification of adverse events in Diagnostic Imaging, according to a mapping of hazards, in order to assess and ensure greater patient’s safety. A seventeen-month trial was carried out using this system and involving 19 operational units in several Diagnostic Imaging-related branches (Diagnostic and Interventional Radiology, Nuclear Medicine, Neuroradiology, Ultrasound). The project is a multi-center spin-off of the broader program of the Agenzia Sanitaria e Sociale Regionale – Regione Emilia-Romagna (ASSR-RER) of clinical risk management in healthcare organizations. This experience has demonstrated the importance of the classification of adverse events and near misses in radiology and the preparation of a specific incident reporting form that will facilitate the voluntary reporting of events by medical personnel. The data analysis has provided reassuring data concerning the overall safety of the Radiology Departments examined