Coronary artery disease (CAD) extension-derived risk stratification for asymptomatic diabetic patients: usefulness of low-dose coronary computed tomography angiography (CCTA) in detecting high-risk profile patients

Palumbo, Pierpaolo; Cannizzaro, Ester; Bruno, Federico; Schicchi, Nicolò; Fogante, Marco; Agostini, Andrea; Donato, Maria Carmela De; Cataldo, C; Giovagnoni, Andrea; Barile, Antonio; Splendiani, Alessandra; Masciocchi, Carlo; Cesare, Ernesto Di

doi:10.1007/s11547-020-01204-z

Cited by 18 publications

(11 citation statements)

References 38 publications

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“…In the context of the COVID-19 outbreak, there is a painstaking need for ready-touse resources for data acquisition and artificial intelligence (AI) algorithms to accelerate the search for effective and safe treatments. The progressive integration of radiomics approaches and AI-based solutions in healthcare is already changing established paradigms in the entire healthcare ecosystem, leveraging the progressive digitalization of medical data [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53]. Specifically, diagnostic and decision support systems developed for medical imaging are the first successful examples of innovation for health.…”

Section: Introductionmentioning

confidence: 99%

“…AI-based methods have led to diagnostic applications that accelerate image acquisition, preprocessing, annotation and interpretation, offering an "augmentation" of the radiologists, rather than their unrealistic substitution. In particular, the application of AI in medical imaging has improved the assessment, diagnosis and early detection of neurodegenerative diseases, heart diseases, with a specifically high impact on breast and lung cancer [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Fusco

Grassi

Granata

et al. 2021

JPM

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Objective: To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. Methods: Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). Results: Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4–99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0–100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0–99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0–100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). Conclusions: Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Fusco

Grassi

Granata

et al. 2021

JPM

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“…In the emergency setting, an effective communication of imaging data to referring physicians is crucial for patient care [ 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 ]. Despite the technical developments in the radiological setting, the radiology report, representing the most important feature of communication with clinical partners, has not progressed significantly [ 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 ]. It remains poorly structured and its quality depends on the radiologist and their experience with a particular field.…”

Section: Radiologists: How We Should Reportmentioning

confidence: 99%

Complications Risk Assessment and Imaging Findings of Thermal Ablation Treatment in Liver Cancers: What the Radiologist Should Expect

Granata

Fusco

Muzio

et al. 2022

JCM

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One of the major fields of application of ablation treatment is liver tumors. With respect to HCC, ablation treatments are considered as upfront treatments in patients with early-stage disease, while in colorectal liver metastases (CLM), they can be employed as an upfront treatment or in association with surgical resection. The main prognostic feature of ablation is the tumor size, since the goal of the treatment is the necrosis of all viable tumor tissue with an adequate tumor-free margin. Radiofrequency ablation (RFA) and microwave ablation (MWA) are the most employed ablation techniques. Ablation therapies in HCC and liver metastases have presented a challenge to radiologists, who need to assess response to determine complication-related treatment. Complications, defined as any unexpected variation from a procedural course, and adverse events, defined as any actual or potential injury related to the treatment, could occur either during the procedure or afterwards. To date, RFA and MWA have shown no statistically significant differences in mortality rates or major or minor complications. To reduce the rate of major complications, patient selection and risk assessment are essential. To determine the right cost-benefit ratio for the ablation method to be used, it is necessary to identify patients at high risk of infections, coagulation disorders and previous abdominal surgery interventions. Based on risk assessment, during the procedure as part of surveillance, the radiologists should pay attention to several complications, such as vascular, biliary, mechanical and infectious. Multiphase CT is an imaging tool chosen in emergency settings. The radiologist should report technical success, treatment efficacy, and complications. The complications should be assessed according to well-defined classification systems, and these complications should be categorized consistently according to severity and time of occurrence.

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“…However, it is often difficult to distinguish AMI from other disorders that result in elevated troponin levels, such as myocarditis, pulmonary embolism (PE), or Takotsubo cardiomyopathy (i.e., non-coronary myocardial disease) [ 50 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

“…Over the years, the role of CCTA has grown exponentially as it allows for plaque characterization and the visualization of the anatomy of the coronary and extra-cardiac structures [ 54 , 57 , 58 , 59 , 60 ]. Furthermore, CCTA is able to detect other life-threatening non-cardiac causes of chest pain such as acute aortic dissection (AAS) and PE [ 6 , 15 , 38 , 61 ].…”

Section: Introductionmentioning

confidence: 99%

The Radiologist as a Gatekeeper in Chest Pain

Pradella

Zantonelli

Grazzini

et al. 2021

IJERPH

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Chest pain is a symptom that can be found in life-threatening conditions such as acute coronary syndrome (ACS). Those patients requiring invasive coronary angiography treatment or surgery should be identified. Often the clinical setting and laboratory tests are not sufficient to rule out a coronary or aortic syndrome. Cardiac radiological imaging has evolved in recent years both in magnetic resonance (MR) and in computed tomography (CT). CT, in particular, due to its temporal and spatial resolution, the quickness of the examination, and the availability of scanners, is suitable for the evaluation of these patients. In particular, the latest-generation CT scanners allow the exclusion of diagnoses such as coronary artery disease and aortic pathology, thereby reducing the patient’s stay in hospital and safely selecting patients by distinguishing those who do not need further treatment from those who will need more- or less-invasive therapies. CT additionally reduces costs by improving long-term patient outcome. The limitations related to patient characteristics and those related to radiation exposure are weakening with the improvement of CT technology.

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Coronary artery disease (CAD) extension-derived risk stratification for asymptomatic diabetic patients: usefulness of low-dose coronary computed tomography angiography (CCTA) in detecting high-risk profile patients

Cited by 18 publications

References 38 publications

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Complications Risk Assessment and Imaging Findings of Thermal Ablation Treatment in Liver Cancers: What the Radiologist Should Expect

The Radiologist as a Gatekeeper in Chest Pain

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