Chest X-ray versus chest computed tomography for outcome prediction in hospitalized patients with COVID-19

Borghesi, Andrea; Golemi, Salvatore; Scrimieri, Alessandra; Nicosia, Costanza Maria Carlotta; Zigliani, Angelo; Farina, Davide; Maroldi, Roberto

doi:10.1007/s11547-022-01456-x

Cited by 18 publications

(17 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding imaging, some studies have focused on the prognostic role of chest radiography (CXR) and computed tomography (CT) [ 84 , 85 , 86 , 87 , 88 ].…”

Section: Sex Differences In Respiratory Pathologiesmentioning

confidence: 99%

Gender Medicine in Clinical Radiology Practice

Giacobbe

Granata

Trovato

et al. 2023

JPM

View full text Add to dashboard Cite

Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.

show abstract

“…Regarding imaging, some studies have focused on the prognostic role of chest radiography (CXR) and computed tomography (CT) [ 84 , 85 , 86 , 87 , 88 ].…”

Section: Sex Differences In Respiratory Pathologiesmentioning

confidence: 99%

Gender Medicine in Clinical Radiology Practice

Giacobbe

Granata

Trovato

et al. 2023

JPM

View full text Add to dashboard Cite

show abstract

“…On chest CT, the typical findings of COVID-19 pneumonia are the multifocal and bilateral distribution of ground-glass opacities ( Figure 7 ) and consolidations and the thickening of the peripherally interlobular septa [ 125 , 126 , 127 , 128 , 129 ]. These findings go into differential diagnosis with the other patterns of pneumonia mentioned above for which they should be integrated with an RT-PCR test (that represents the gold standard for patients with ongoing COVID-19 pneumonia) or with an accurate history to exclude a previous infection [ 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 ].…”

Section: Diagnostic Managementmentioning

confidence: 99%

Radiation Recall Pneumonitis: The Open Challenge in Differential Diagnosis of Pneumonia Induced by Oncological Treatments

Grassi

Granata

Fusco³

et al. 2023

JCM

View full text Add to dashboard Cite

The treatment of primary and secondary lung neoplasms now sees the fundamental role of radiotherapy, associated with surgery and systemic therapies. The improvement in survival outcomes has also increased attention to the quality of life, treatment compliance and the management of side effects. The role of imaging is not only limited to recognizing the efficacy of treatment but also to identifying, as soon as possible, the uncommon effects, especially when more treatments, such as chemotherapy, immunotherapy and radiotherapy, are associated. Radiation recall pneumonitis is an uncommon treatment complication that should be correctly characterized, and it is essential to recognize the mechanisms of radiation recall pneumonitis pathogenesis and diagnostic features in order to promptly identify them and adopt the best therapeutic strategy, with the shortest possible withdrawal of the current oncological drug. In this setting, artificial intelligence could have a critical role, although a larger patient data set is required.

show abstract

“…Radiological images, such as chest X-rays (CXR) and computed tomography (CT) images, have been extensively used in the stratification of COVID-19 patients [ 19 , 20 , 21 , 22 , 23 , 24 ]. Poly et al [ 25 ] have performed a systematic review with a meta-analysis of relevant studies to quantify the performance of deep learning algorithms in the automatic stratification of COVID-19 patients using CXR images.…”

Section: Related Workmentioning

confidence: 99%

Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning

et al. 2022

View full text Add to dashboard Cite

With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients.

show abstract

Chest X-ray versus chest computed tomography for outcome prediction in hospitalized patients with COVID-19

Cited by 18 publications

References 19 publications

Gender Medicine in Clinical Radiology Practice

Gender Medicine in Clinical Radiology Practice

Radiation Recall Pneumonitis: The Open Challenge in Differential Diagnosis of Pneumonia Induced by Oncological Treatments

Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning

Contact Info

Product

Resources

About