Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images

(1) Background: Chest Computed Tomography (CT) has been proposed as a non-invasive method for confirming the diagnosis of SARS-CoV-2 patients using radiomic features (RFs) and baseline clinical data. The performance of Machine Learning (ML) methods using RFs derived from semi-automatically segmented lungs in chest CT images was investigated regarding the ability to predict the mortality of SARS-CoV-2 patients. (2) Methods: A total of 179 RFs extracted from 436 chest CT images of SARS-CoV-2 patients, and 8 clinical and 6 radiological variables, were used to train and evaluate three ML methods (Least Absolute Shrinkage and Selection Operator [LASSO] regularized regression, Random Forest Classifier [RFC], and the Fully connected Neural Network [FcNN]) for their ability to predict mortality using the Area Under the Curve (AUC) of Receiver Operator characteristic (ROC) Curves. These three groups of variables were used separately and together as input for constructing and comparing the final performance of ML models. (3) Results: All the ML models using only RFs achieved an informative level regarding predictive ability, outperforming radiological assessment, without however reaching the performance obtained with ML based on clinical variables. The LASSO regularized regression and the FcNN performed equally, both being superior to the RFC. (4) Conclusions: Radiomic features based on semi-automatically segmented CT images and ML approaches can aid in identifying patients with a high risk of mortality, allowing a fast, objective, and generalizable method for improving prognostic assessment by providing a second expert opinion that outperforms human evaluation.

Trichodynia and telogen effluvium in COVID-19 patients: Results of an international expert opinion survey on diagnosis and management.

BACKGROUND: The cutaneous manifestations of COVID-19 may be useful disease markers and prognostic indicators. Recently, postinfectious telogen effluvium and trichodynia have also been reported. OBJECTIVE: To evaluate the presence of trichodynia and telogen effluvium in patients with COVID-19 and describe their characteristics in relation to the other signs and symptoms of the disease. METHODS: Patients with a history of COVID-19 presenting to the clinics of a group of hair experts because of telogen effluvium and/or scalp symptoms were questioned about their hair signs and symptoms in relation to the severity of COVID-19 and associated symptoms. RESULTS: Data from 128 patients were collected. Telogen effluvium was observed in 66.3% of the patients and trichodynia in 58.4%. Trichodynia was associated with telogen effluvium in 42.4% of the cases and anosmia and ageusia in 66.1% and 44.1% of the cases, respectively. In majority (62.5%) of the patients, the hair signs and symptoms started within the first month after COVID-19 diagnosis, and in 47.8% of the patients, these started after 12 weeks or more. LIMITATIONS: The recruitment of patients in specialized hair clinics, lack of a control group, and lack of recording of patient comorbidities. CONCLUSION: The severity of postviral telogen effluvium observed in patients with a history of COVID-19 infection may be influenced by COVID-19 severity. We identified early-onset (<4 weeks) and late-onset (>12 weeks) telogen effluvium.

Classification Performance for COVID Patient Prognosis from Automatic AI Segmentation—A Single-Center Study

Background: COVID assessment can be performed using the recently developed individual risk score (prediction of severe respiratory failure in hospitalized patients with SARS-COV2 infection, PREDI-CO score) based on High Resolution Computed Tomography. In this study, we evaluated the possibility of automatizing this estimation using semi-supervised AI-based Radiomics, leveraging the possibility of performing non-supervised segmentation of ground-glass areas. Methods: We collected 92 from patients treated in the IRCCS Sant’Orsola-Malpighi Policlinic and public databases;each lung was segmented using a pre-trained AI method;ground-glass opacity was identified using a novel, non-supervised approach;radiomic measurements were collected and used to predict clinically relevant scores, with particular focus on mortality and the PREDI-CO score. We compared the prediction obtained through different machine learning approaches. Results: All the methods obtained a well-balanced accuracy (70%) on the PREDI-CO score but did not obtain satisfying results on other clinical characteristics due to unbalance between the classes. Conclusions: Semi-supervised segmentation, implemented using a combination of non-supervised segmentation and feature extraction, seems to be a viable approach for patient stratification and could be leveraged to train more complex models. This would be useful in a high-demand situation similar to the current pandemic to support gold-standard segmentation for AI training.

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia.

Segmentation is a complex task, faced by radiologists and researchers as radiomics and machine learning grow in potentiality. The process can either be automatic, semi-automatic, or manual, the first often not being sufficiently precise or easily reproducible, and the last being excessively time consuming when involving large districts with high-resolution acquisitions. A high-resolution CT of the chest is composed of hundreds of images, and this makes the manual approach excessively time consuming. Furthermore, the parenchymal alterations require an expert evaluation to be discerned from the normal appearance; thus, a semi-automatic approach to the segmentation process is, to the best of our knowledge, the most suitable when segmenting pneumonias, especially when their features are still unknown. For the studies conducted in our institute on the imaging of COVID-19, we adopted 3D Slicer, a freeware software produced by the Harvard University, and combined the threshold with the paint brush instruments to achieve fast and precise segmentation of aerated lung, ground glass opacities, and consolidations. When facing complex cases, this method still requires a considerable amount of time for proper manual adjustments, but provides an extremely efficient mean to define segments to use for further analysis, such as the calculation of the percentage of the affected lung parenchyma or texture analysis of the ground glass areas.