Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy.

Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and virtual biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.

18 F-FET PET/CT in Calcifying Pseudoneoplasm of the Neuraxis : First Case.

ABSTRACT: A 66-year-old man has been treated in a psychiatric department for 4-5 years for a depressive syndrome, which is associated with poor motor initiative, confusional state, and dysosmia. Dynamic 18 F-FET PET/CT showed only faint uptake of radiotracer just above the background on the left frontal calcific lesion. The time-activity curve of the neoplasms showed a descending pattern. After a left fronto-orbitary minicraniotomy surgery, histology examination concluded for a rare calcifying pseudoneoplasm of the neuraxis (CAPNON). To our knowledge, no data are available on the metabolic behavior of CAPNON in 18 F-FET PET/CT. This case highlighted that a faint uptake and descending pattern on dynamic 18 F-FET PET/CT may be helpful in suspected CAPNON before surgery.

Adoption of AI in Oncological Imaging: Ethical, Regulatory, and Medical-Legal Challenges.

Artificial Intelligence (AI) algorithms have shown great promise in oncological imaging, outperforming or matching radiologists in retrospective studies, signifying their potential for advanced screening capabilities. These AI tools offer valuable support to radiologists, assisting them in critical tasks such as prioritizing reporting, early cancer detection, and precise measurements, thereby bolstering clinical decision-making. With the healthcare landscape witnessing a surge in imaging requests and a decline in available radiologists, the integration of AI has become increasingly appealing. By streamlining workflow efficiency and enhancing patient care, AI presents a transformative solution to the challenges faced by oncological imaging practices. Nevertheless, successful AI integration necessitates navigating various ethical, regulatory, and medical-legal challenges. This review endeavors to provide a comprehensive overview of these obstacles, aiming to foster a responsible and effective implementation of AI in oncological imaging.

Radiomics and Artificial Intelligence in Renal Lesion Assessment.

Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.

Personalized Pressure Conditions and Calibration for a Predictive Computational Model of Coronary and Myocardial Blood Flow.

Predictive modeling of hyperemic coronary and myocardial blood flow (MBF) greatly supports diagnosis and prognostic stratification of patients suffering from coronary artery disease (CAD). In this work, we propose a novel strategy, using only readily available clinical data, to build personalized inlet conditions for coronary and MBF models and to achieve an effective calibration for their predictive application to real clinical cases. Experimental data are used to build personalized pressure waveforms at the aortic root, representative of the hyperemic state and adapted to surrogate the systolic contraction, to be used in computational fluid-dynamics analyses. Model calibration to simulate hyperemic flow is performed in a "blinded" way, not requiring any additional exam. Coronary and myocardial flow simulations are performed in eight patients with different clinical conditions to predict FFR and MBF. Realistic pressure waveforms are recovered for all the patients. Consistent pressure distribution, blood velocities in the large arteries, and distribution of MBF in the healthy myocardium are obtained. FFR results show great accuracy with a per-vessel sensitivity and specificity of 100% according to clinical threshold values. Mean MBF shows good agreement with values from stress-CTP, with lower values in patients with diagnosed perfusion defects. The proposed methodology allows us to quantitatively predict FFR and MBF, by the exclusive use of standard measures easily obtainable in a clinical context. This represents a fundamental step to avoid catheter-based exams and stress tests in CAD diagnosis.

How do patients perceive the AI-radiologists interaction? Results of a survey on 2119 responders.

PURPOSE: In this study we investigate how patients perceive the interaction between artificial intelligence (AI) and radiologists by designing a survey. METHOD: We created a survey focused on the application of Artificial Intelligence in radiology which consisted of 20 questions distributed in three sections:Only completed questionnaires were considered for analysis. RESULTS: 2119 subjects completed the survey. Among them, 1216 respondents were over 60 years old, showing interest in AI even though they were not digital natives. Although >45% of the respondents reported a high level of education, only 3% said they were AI experts. 87% of respondents favored using AI to support diagnosis but would like to be informed. Only 10% would consult another specialist if their doctor used AI support. Most respondents (76%) said they would not feel comfortable if the diagnosis was made by the AI alone, highlighting the importance of the physician's role in the emotional management of the patient. Finally, 36% of respondents were willing to discuss the topic further in a focus group. CONCLUSION: Patients' perception of the use of AI in radiology was positive, although still strictly linked to the supervision of the radiologist. Respondents showed interest and willingness to learn more about AI in the medical field, confirming how patients' confidence in AI technology and its acceptance is central to its widespread use in clinical practice.

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.

OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power and sensitivity. Deep learning-based AI relies on training data sets, which should be sufficiently large and diverse to effectively adjust network parameters, avoid biases, and enable generalization of the outcome. However, large sets of diagnostic images acquired at doses of CA outside the standard-of-care are not commonly available. Here, we propose a method to generate synthetic data sets to train an "AI agent" designed to amplify the effects of CAs in magnetic resonance (MR) images. The method was fine-tuned and validated in a preclinical study in a murine model of brain glioma, and extended to a large, retrospective clinical human data set. MATERIALS AND METHODS: A physical model was applied to simulate different levels of MR contrast from a gadolinium-based CA. The simulated data were used to train a neural network that predicts image contrast at higher doses. A preclinical MR study at multiple CA doses in a rat model of glioma was performed to tune model parameters and to assess fidelity of the virtual contrast images against ground-truth MR and histological data. Two different scanners (3 T and 7 T, respectively) were used to assess the effects of field strength. The approach was then applied to a retrospective clinical study comprising 1990 examinations in patients affected by a variety of brain diseases, including glioma, multiple sclerosis, and metastatic cancer. Images were evaluated in terms of contrast-to-noise ratio and lesion-to-brain ratio, and qualitative scores. RESULTS: In the preclinical study, virtual double-dose images showed high degrees of similarity to experimental double-dose images for both peak signal-to-noise ratio and structural similarity index (29.49 dB and 0.914 dB at 7 T, respectively, and 31.32 dB and 0.942 dB at 3 T) and significant improvement over standard contrast dose (ie, 0.1 mmol Gd/kg) images at both field strengths. In the clinical study, contrast-to-noise ratio and lesion-to-brain ratio increased by an average 155% and 34% in virtual contrast images compared with standard-dose images. Blind scoring of AI-enhanced images by 2 neuroradiologists showed significantly better sensitivity to small brain lesions compared with standard-dose images (4.46/5 vs 3.51/5). CONCLUSIONS: Synthetic data generated by a physical model of contrast enhancement provided effective training for a deep learning model for contrast amplification. Contrast above that attainable at standard doses of gadolinium-based CA can be generated through this approach, with significant advantages in the detection of small low-enhancing brain lesions.

Ultrasound elastography in chronic kidney disease: a systematic review and meta-analysis.

Ultrasound elastography (USE) is a noninvasive technique for assessing tissue elasticity, and its application in nephrology has aroused growing interest in recent years. The purpose of this article is to systematically review the clinical application of USE in patients with chronic kidney disease (CKD), including native and transplanted kidneys, and quantitatively investigate differences in elasticity values between healthy individuals and CKD patients. Furthermore, we provide a qualitative analysis of the studies included, discussing the potential interplay between renal stiffness, estimated glomerular filtration rate, and fibrosis. In January 2022, a systematic search was carried out on the MEDLINE (PubMed) database, concerning studies on the application of USE in patients with CKD, including patients with transplanted kidneys. The results of the included studies were extracted by two independent researchers and presented mainly through a formal narrative summary. A meta-analysis of nine study parts from six studies was performed. A total of 647 studies were screened for eligibility and, after applying the exclusion and inclusion criteria, 69 studies were included, for a total of 6728 patients. The studies proved very heterogeneous in terms of design and results. The shear wave velocity difference of - 0.82 m/s (95% CI: - 1.72-0.07) between CKD patients and controls was not significant. This result agrees with the qualitative evaluation of included studies that found controversial results for the relationship between renal stiffness and glomerular filtration rate. On the contrary, a clear relationship seems to emerge between USE values and the degree of fibrosis. At present, due to the heterogeneity of results and technical challenges, large-scale application in the monitoring of CKD patients remains controversial.

Development of Predictive Models for the Response of Vestibular Schwannoma Treated with Cyberknife®: A Feasibility Study Based on Radiomics and Machine Learning.

PURPOSE: to predict vestibular schwannoma (VS) response to radiosurgery by applying machine learning (ML) algorithms on radiomic features extracted from pre-treatment magnetic resonance (MR) images. METHODS: patients with VS treated with radiosurgery in two Centers from 2004 to 2016 were retrospectively evaluated. Brain T1-weighted contrast-enhanced MR images were acquired before and at 24 and 36 months after treatment. Clinical and treatment data were collected contextually. Treatment responses were assessed considering the VS volume variation based on pre- and post-radiosurgery MR images at both time points. Tumors were semi-automatically segmented and radiomic features were extracted. Four ML algorithms (Random Forest, Support Vector Machine, Neural Network, and extreme Gradient Boosting) were trained and tested for treatment response (i.e., increased or non-increased tumor volume) using nested cross-validation. For training, feature selection was performed using the Least Absolute Shrinkage and Selection Operator, and the selected features were used as input to separately build the four ML classification algorithms. To overcome class imbalance during training, Synthetic Minority Oversampling Technique was used. Finally, trained models were tested on the corresponding held out set of patients to evaluate balanced accuracy, sensitivity, and specificity. RESULTS: 108 patients treated with Cyberknife® were retrieved; an increased tumor volume was observed at 24 months in 12 patients, and at 36 months in another group of 12 patients. The Neural Network was the best predictive algorithm for response at 24 (balanced accuracy 73% ± 18%, specificity 85% ± 12%, sensitivity 60% ± 42%) and 36 months (balanced accuracy 65% ± 12%, specificity 83% ± 9%, sensitivity 47% ± 27%). CONCLUSIONS: radiomics may predict VS response to radiosurgery avoiding long-term follow-up as well as unnecessary treatment.

Lesion or Pseudolesion? A Comprehensive Description of Perfusion-Based Liver Alterations on Contrast-Enhanced Computed Tomography and Literature Review.

ABSTRACT: Pseudolesions on contrast-enhanced computed tomography represent a diagnostic challenge for radiologists because they could be difficult to distinguish from true space-occupying lesions. This article aims to provide a detailed overview of these entities based on radiological criteria (hyperattenuation or hypoattenuation, localization, morphology), as well as a brief review of the hepatic vascular anatomy and pathophysiological process. Relevant examples from hospital case series are reported as helpful hints to assist radiologists in recognizing and correctly diagnosing these abnormalities.

OCT Analysis of Retinal Pigment Epithelium in Myopic Choroidal Neovascularization: Correlation Analysis with Different Treatments.

Objective: The objective of this study was to analyze the status of the retinal pigment epithelium (RPE) by means of the spectral domain optical coherence tomography (SD-OCT) overlying the myopic neovascular lesions in the involutive phase, looking for any correlations between the status of the RPE and the size of the lesions and the type and duration of the treatment. Methods: SD-OCT examinations of 83 consecutive patients with myopic choroidal neovascularization (CNV) were reviewed and divided into two groups: group A, patients with CNV characterized by uniformity of the overlying RPE, and group B, patients with CNV characterized by non-uniformity of the overlying RPE. Results: The median lesion area, major diameter, and minimum diameter were, respectively, 0.42 mm2 (0.30−1.01 mm2), 0.76 mm2 (0.54−1.28 mm2), and 0.47 mm2 (0.63−0.77 mm2) in group A, and 1.60 mm2 (0.72−2.67 mm2), 1.76 mm2 (1.13−2.23 mm2), and 0.98 mm2 (0.65−1.33 mm2) in group B. These values were lower in group A than in group B (p < 0.001). The number of treatments with a period free of disease recurrence for at least 6 months was greater (p < 0.010) in group B (6.54 ± 2.82) than in group A (3.67 ± 2.08), and treatments include intravitreal anti-vascular endothelial growth factor injection, photodynamic therapy, or both. Conclusions: Our results showed that the size of myopic neovascular lesion influences the development of a uniform RPE above the lesion and therefore the disease prognosis. The presence of uniform RPE was found to be extremely important in the follow-up of patients with myopic CNV, as it influences the duration of the disease and the number of treatments required.

The Network That Unites a Qualitative Study on Clinical Psychological Intervention for Women with a History of Breast Cancer and Chronic Pain.

The aim of this qualitative research is to deepen the knowledge in the field of psycho-oncology and the consequences of chronic and persistent pain by listening to patients' experiences, their emotions and difficulties in facing this hard condition, and assessing their perception of the role of the psychologist in pain management. In this qualitative study, a semistructured interview was used, designed from three research questions: chronic pain and quality of life; chronic pain and psychological well-being; and the role and perception of the psychologist in pain management. The sample consists of 29 women who suffered or have recovered from breast carcinoma, and who currently report having chronic pain due either to the presence of the cancer or as a result of surgery or treatment. Three themes emerged from the thematic analysis: quality of life and psychological well-being, relational well-being, and perception and role of the psychologist. Two subthemes have been identified for each theme: common features of chronic pain and consequences and resilience for the first theme; not feeling understood and willingness to protect loved ones for the second theme; and improvements perceived by users and reasons for not making use of the service for the last theme. In conclusion, the results obtained from the literature and those from the analysis of the interviews are discussed and compared, and reflections are made on possible future implications.

Quantification of epicardial adipose tissue in obese patients using an open-bore MR scanner.

BACKGROUND: Our aim was to evaluate the reproducibility of epicardial adipose tissue (EAT) volume, measured on scans performed using an open-bore magnetic resonance scanner. METHODS: Consecutive patients referred for bariatric surgery, aged between 18 and 65 years who agreed to undergo cardiac imaging (MRI), were prospectively enrolled. All those with cardiac pathology or contraindications to MRI were excluded. MRI was performed on a 1.0-T open-bore scanner, and EAT was segmented on all scans at both systolic and diastolic phase by two independent readers (R1 with four years of experience and R2 with one year). Data were reported as median and interquartile range; agreement and differences were appraised with Bland-Altman analyses and Wilcoxon tests, respectively. RESULTS: Fourteen patients, 11 females (79%) aged 44 (41-50) years, underwent cardiac MRI. For the first and second readings, respectively, EAT volume was 86 (78-95) cm3 and 85 (79-91) cm3 at systole and 82 (74-95) cm3 and 81 (75-94) cm3 at diastole for R1, and 89 (79-99) cm3 and 93 (84-98) cm3 at systole and 92 (85-103) cm3 and 93 (82-94) cm3 at diastole for R2. R1 had the best reproducibility at diastole (bias 0.3 cm3, standard deviation of the differences (SD) 3.3 cm3). R2 had the worst reproducibility at diastole (bias 3.9 cm3, SD 12.1 cm3). The only significant difference between systole and diastole was at the first reading by R1 (p = 0.016). The greatest bias was that of inter-reader reproducibility at diastole (-9.4 cm3). CONCLUSIONS: Reproducibility was within clinically acceptable limits in most instances.

Quantitative Assessment of Late Gadolinium Enhancement and Edema at Cardiac Magnetic Resonance in Low-Risk Myocarditis Patients.

In this study, we aimed to quantify LGE and edema at short-tau inversion recovery sequences on cardiac magnetic resonance (CMR) in patients with myocarditis. We retrospectively evaluated CMR examinations performed during the acute phase and at follow-up. Forty-seven patients were eligible for retrospective LGE assessment, and, among them, twenty-five patients were eligible for edema evaluation. Both groups were paired with age- and sex-matched controls. The median left ventricle LGE was 6.4% (interquartile range 5.0−9.2%) at the acute phase, 4.4% (3.3−7.2%) at follow-up, and 4.3% (3.0−5.3%) in controls, the acute phase being higher than both follow-up and controls (p < 0.001 for both), while follow-up and controls did not differ (p = 0.139). An optimal threshold of 5.0% was obtained for LGE with 87% sensitivity and 48% specificity; the positive likelihood ratio (LR) was 1.67, and the negative LR was 0.27. Edema was 12.8% (9.4−18.1%) at the acute phase, 7.3% (5.5−8.8%) at follow-up, and 6.7% (5.6−8.6%) in controls, the acute phase being higher than both follow-up and controls (both p < 0.001), while follow-up and controls did not differ (p = 0.900). An optimal threshold of 9.5% was obtained for edema with a sensitivity of 76% and a specificity of 88%; the positive LR was 6.33, and the negative LR was 0.27. LGE and edema thresholds are useful in cases of suspected mild myocarditis.

Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus.

Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years ± 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years ± 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen κ) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification. Keywords: Mammography, Breast, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2022.

AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study.

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features.

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia.

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

Right and left ventricle native T1 mapping in systolic phase in patients with congenital heart disease.

BACKGROUND: T1 mapping is emerging as a powerful tool in cardiac magnetic resonance (CMR) to evaluate diffuse fibrosis. However, right ventricular (RV) T1 mapping proves difficult due to the limited wall thickness in diastolic phase. Several studies focused on systolic T1 mapping, albeit only on the left ventricle (LV). PURPOSE: To estimate intra- and inter-observer variability of native T1 (nT1) mapping of the RV, and its correlations with biventricular and pulmonary function in patients with congenital heart disease (CHD). MATERIAL AND METHODS: In this retrospective, observational, cross-sectional study we evaluated 36 patients with CHD, having undergone CMR on a 1.5-T scanner. LV and RV functional evaluations were performed. A native modified look-locker inversion recovery short-axis sequence was acquired in the systolic phase. Intra- and inter-reader reproducibility were reported as complement to 100% of the ratio between coefficient of reproducibility and mean. Spearman ρ and Mann-Whitney U-test were used to compare distributions. RESULTS: Intra- and inter-reader reproducibility was 84% and 82%, respectively. Median nT1 was 1022 ms (interquartile range [IQR] 1108-972) for the RV and 947 ms (IQR 986-914) for the LV. Median RV-nT1 was 1016 ms (IQR 1090-1016) in patients with EDVI ≤100 mL/m2 and 1100 ms (IQR 1113-1100) in patients with EDVI >100 mL/m2 (P = 0.049). A significant negative correlation was found between RV ejection fraction and RV-nT1 (ρ = -0.284, P = 0.046). CONCLUSION: Systolic RV-nT1 showed a high reproducibility and a negative correlation with RV ejection fraction, potentially reflecting an adaptation of the RV myocardium to pulmonary valve/conduit (dys)-function.

Pulmonary thromboembolism in hospitalised COVID-19 patients at moderate to high risk by Wells score: a report from Lombardy, Italy.

OBJECTIVES: To present a single-centre experience on CT pulmonary angiography (CTPA) for the assessment of hospitalised COVID-19 patients with moderate-to-high risk of pulmonary thromboembolism (PTE). METHODS: We analysed consecutive COVID-19 patients (RT-PCR confirmed) undergoing CTPA in March 2020 for PTE clinical suspicion. Clinical data were retrieved. Two experienced radiologists reviewed CTPAs to assess pulmonary parenchyma and vascular findings. RESULTS: Among 34 patients who underwent CTPA, 26 had PTE (76%, 20 males, median age 61 years, interquartile range 54-70), 20/26 (77%) with comorbidities (mainly hypertension, 44%), and 8 (31%) subsequently dying. Eight PTE patients were under thromboprophylaxis with low-molecular-weight heparin, four PTE patients had lower-limbs deep vein thrombosis at ultrasound examination (performed in 33/34 patients). Bilateral PTE characterised 19/26 cases, with main branches involved in 10/26 cases. Twelve patients had a parenchymal involvement >75%, the predominant pneumonia pattern being consolidation in 10/26 patients, ground glass opacities in 9/26, crazy paving in 5/26, and both ground glass opacities and consolidation in 2/26. CONCLUSION: COVID-19 patients are prone to PTE. ADVANCES IN KNOWLEDGE: PTE, potentially attributable to an underlying thrombophilic status, may be more frequent than expected in COVID-19 patients. Extension of prophylaxis and adaptation of diagnostic criteria should be considered.