Reshaping free-text radiology notes into structured reports with generative question answering transformers.

BACKGROUND: Radiology reports are typically written in a free-text format, making clinical information difficult to extract and use. Recently, the adoption of structured reporting (SR) has been recommended by various medical societies thanks to the advantages it offers, e.g. standardization, completeness, and information retrieval. We propose a pipeline to extract information from Italian free-text radiology reports that fits with the items of the reference SR registry proposed by a national society of interventional and medical radiology, focusing on CT staging of patients with lymphoma. METHODS: Our work aims to leverage the potential of Natural Language Processing and Transformer-based models to deal with automatic SR registry filling. With the availability of 174 Italian radiology reports, we investigate a rule-free generative Question Answering approach based on the Italian-specific version of T5: IT5. To address information content discrepancies, we focus on the six most frequently filled items in the annotations made on the reports: three categorical (multichoice), one free-text (free-text), and two continuous numerical (factual). In the preprocessing phase, we encode also information that is not supposed to be entered. Two strategies (batch-truncation and ex-post combination) are implemented to comply with the IT5 context length limitations. Performance is evaluated in terms of strict accuracy, f1, and format accuracy, and compared with the widely used GPT-3.5 Large Language Model. Unlike multichoice and factual, free-text answers do not have 1-to-1 correspondence with their reference annotations. For this reason, we collect human-expert feedback on the similarity between medical annotations and generated free-text answers, using a 5-point Likert scale questionnaire (evaluating the criteria of correctness and completeness). RESULTS: The combination of fine-tuning and batch splitting allows IT5 ex-post combination to achieve notable results in terms of information extraction of different types of structured data, performing on par with GPT-3.5. Human-based assessment scores of free-text answers show a high correlation with the AI performance metrics f1 (Spearman's correlation coefficients>0.5, p-values<0.001) for both IT5 ex-post combination and GPT-3.5. The latter is better at generating plausible human-like statements, even if it systematically provides answers even when they are not supposed to be given. CONCLUSIONS: In our experimental setting, a fine-tuned Transformer-based model with a modest number of parameters (i.e., IT5, 220 M) performs well as a clinical information extraction system for automatic SR registry filling task. It can extract information from more than one place in the report, elaborating it in a manner that complies with the response specifications provided by the SR registry (for multichoice and factual items), or that closely approximates the work of a human-expert (free-text items); with the ability to discern when an answer is supposed to be given or not to a user query.

CT and MRI radiomic features of lung cancer (NSCLC): comparison and software consistency.

BACKGROUND: Radiomics is a quantitative approach that allows the extraction of mineable data from medical images. Despite the growing clinical interest, radiomics studies are affected by variability stemming from analysis choices. We aimed to investigate the agreement between two open-source radiomics software for both contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) of lung cancers and to preliminarily evaluate the existence of radiomic features stable for both techniques. METHODS: Contrast-enhanced CT and MRI images of 35 patients affected with non-small cell lung cancer (NSCLC) were manually segmented and preprocessed using three different methods. Sixty-six Image Biomarker Standardisation Initiative-compliant features common to the considered platforms, PyRadiomics and LIFEx, were extracted. The correlation among features with the same mathematical definition was analyzed by comparing PyRadiomics and LIFEx (at fixed imaging technique), and MRI with CT results (for the same software). RESULTS: When assessing the agreement between LIFEx and PyRadiomics across the considered resampling, the maximum statistically significant correlations were observed to be 94% for CT features and 95% for MRI ones. When examining the correlation between features extracted from contrast-enhanced CT and MRI using the same software, higher significant correspondences were identified in 11% of features for both software. CONCLUSIONS: Considering NSCLC, (i) for both imaging techniques, LIFEx and PyRadiomics agreed on average for 90% of features, with MRI being more affected by resampling and (ii) CT and MRI contained mostly non-redundant information, but there are shape features and, more importantly, texture features that can be singled out by both techniques. RELEVANCE STATEMENT: Identifying and selecting features that are stable cross-modalities may be one of the strategies to pave the way for radiomics clinical translation. KEY POINTS: • More than 90% of LIFEx and PyRadiomics features contain the same information. • Ten percent of features (shape, texture) are stable among contrast-enhanced CT and MRI. • Software compliance and cross-modalities stability features are impacted by the resampling method.

Bayesian Networks in the Management of Hospital Admissions: A Comparison between Explainable AI and Black Box AI during the Pandemic.

Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are "black box" to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffers from poor generalization ability in the presence of dataset shifts. Here, we present a comparison between an explainable-by-design ("white box") model (Bayesian Network (BN)) versus a black box model (Random Forest), both studied with the aim of supporting clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.

Correlation between imaging and histology in benign solitary retroperitoneal nerve sheath tumors: a pictorial review.

BACKGROUND: Benign nerve sheath tumors presenting as solitary retroperitoneal masses (RBNSTs) pose a complex diagnostic challenge for multidisciplinary teams regarding differential diagnosis, staging, and treatment planning. This article reviews the role played by different imaging techniques in assessing RBNSTs and elucidates their typical pathological features with a particular emphasis on the correlation between imaging and histological findings. Furthermore, some examples of retroperitoneal tumors that merit consideration in the process of differential diagnosis based on cross-sectional investigations (CSIs) are reported. The correlation between tissue architecture and appearance on imaging can help increase the accuracy of differential diagnosis with other retroperitoneal neoplasms at CSIs. CRITICAL RELEVANCE STATEMENT: This educational review critically examines the correlation between imaging and histological features in solitary retroperitoneal benign nerve sheath tumors, offering valuable insights for improving the accuracy of differential diagnosis in clinical radiology. KEY POINTS: RBNSTs are challenging to diagnose because they lack specific radiological features. Differential diagnosis of RBNSTs from other retroperitoneal neoplasms on imaging is complex. Surgical removal of RBNSTs is recommended for an accurate diagnosis.

The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey.

Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.

Study rationale and design of the PEOPLHE trial.

PURPOSE: Lung cancer screening (LCS) by low-dose computed tomography (LDCT) demonstrated a 20-40% reduction in lung cancer mortality. National stakeholders and international scientific societies are increasingly endorsing LCS programs, but translating their benefits into practice is rather challenging. The "Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation Of Preventive Lung HEalth" (PEOPLHE) is an Italian multicentric LCS program aiming at testing LCS feasibility and implementation within the national healthcare system. PEOPLHE is intended to assess (i) strategies to optimize LCS workflow, (ii) radiological quality assurance, and (iii) the need for dedicated resources, including smoking cessation facilities. METHODS: PEOPLHE aims to recruit 1.500 high-risk individuals across three tertiary general hospitals in three different Italian regions that provide comprehensive services to large populations to explore geographic, demographic, and socioeconomic diversities. Screening by LDCT will target current or former (quitting < 10 years) smokers (> 15 cigarettes/day for > 25 years, or > 10 cigarettes/day for > 30 years) aged 50-75 years. Lung nodules will be volumetric measured and classified by a modified PEOPLHE Lung-RADS 1.1 system. Current smokers will be offered smoking cessation support. CONCLUSION: The PEOPLHE program will provide information on strategies for screening enrollment and smoking cessation interventions; administrative, organizational, and radiological needs for performing a state-of-the-art LCS; collateral and incidental findings (both pulmonary and extrapulmonary), contributing to the LCS implementation within national healthcare systems.

Fate of melatonin orally administered in preterm newborns: Antioxidant performance and basis for neuroprotection.

Preterm infants cannot counteract excessive reactive oxygen species (ROS) production due to preterm birth, leading to an excess of lipid peroxidation with malondialdehyde (MDA) production, capable of contributing to brain damage. Melatonin (ME), an endogenous brain hormone, and its metabolites, act as a free radical scavenger against ROS. Unfortunately, preterms have an impaired antioxidant system, resulting in the inability to produce and release ME. This prospective, multicenter, parallel groups, randomized, double-blind, placebo-controlled trial aimed to assess: (i) the endogenous production of ME in very preterm infants (gestational age ≤ 29 + 6 WE, 28 infants in the ME and 26 in the placebo group); (ii) the exogenous hormone availability and its metabolization to the main metabolite, 6-OH-ME after 15 days of ME oral treatment; (iii) difference of MDA plasma concentration, as peroxidation marker, after treatment. Blood was collected before the first administration (T1) and after 15 days of administration (T2). ME and 6-OH-ME were detected by liquid chromatography tandem mass spectrometry, MDA was measured by liquid chromatograph with fluorescence detection. ME and 6-OH-ME were not detectable in the placebo group at any study time-point. ME was absent in the active group at T1. In contrast, after oral administration, ME and 6-OH-ME resulted highly detectable and the difference between concentrations T2 versus T1 was statistically significant, as well as the difference between treated and placebo groups at T2. MDA levels seemed stable during the 15 days of treatment in both groups. Nevertheless, a trend in the percentage of neonates with reduced MDA concentration at T2/T1 was 48.1% in the ME group versus 38.5% in the placebo group. We demonstrated that very preterm infants are not able to produce endogenous detectable plasma levels of ME during their first days of life. Still, following ME oral administration, appreciable amounts of ME and 6-OH-ME were available. The trend of MDA reduction in the active group requires further clinical trials to fix the dosage, the length of ME therapy and to identify more appropriate indexes to demonstrate, at biological and clinical levels, the antioxidant activity and consequent neuroprotectant potential of ME in very preterm newborns.

Current management and future challenges in salivary glands cancer.

Salivary gland cancers (SGCs) are rare, accounting for less than 5% of all malignancies of the head and neck region, and are morphologically heterogeneous. The diagnosis is mainly based on histology, with the complementary aid of molecular profiling, which is helpful in recognizing some poorly differentiated, borderline, or atypical lesions. Instrumental imaging defines the diagnosis, representing a remarkable tool in the treatment plan. Ultrasound and magnetic resonance are the most common procedures used to describe the primary tumour. The treatment of SGCs is multimodal and consists of surgery, radiotherapy, and systemic therapy; each treatment plan is, however, featured on the patient and disease's characteristics. On 24 June 2022, in the meeting "Current management and future challenges in salivary gland cancers" many experts in this field discussed the state of the art of SGCs research, the future challenges and developments. After the meeting, the same pool of experts maintained close contact to keep these data further updated in the conference proceedings presented here. This review collects the insights and suggestions that emerged from the discussion during and after the meeting per se.

The Role of Native T1 and T2 Mapping Times in Identifying PD-L1 Expression and the Histological Subtype of NSCLCs.

We investigated the association of T1/T2 mapping values with programmed death-ligand 1 protein (PD-L1) expression in lung cancer and their potential in distinguishing between different histological subtypes of non-small cell lung cancers (NSCLCs). Thirty-five patients diagnosed with stage III NSCLC from April 2021 to December 2022 were included. Conventional MRI sequences were acquired with a 1.5 T system. Mean T1 and T2 mapping values were computed for six manually traced ROIs on different areas of the tumor. Data were analyzed through RStudio. Correlation between T1/T2 mapping values and PD-L1 expression was studied with a Wilcoxon-Mann-Whitney test. A Kruskal-Wallis test with a post-hoc Dunn test was used to study the correlation between T1/T2 mapping values and the histological subtypes: squamocellular carcinoma (SCC), adenocarcinoma (ADK), and poorly differentiated NSCLC (PD). There was no statistically significant correlation between T1/T2 mapping values and PD-L1 expression in NSCLC. We found statistically significant differences in T1 mapping values between ADK and SCC for the periphery ROI (p-value 0.004), the core ROI (p-value 0.01), and the whole tumor ROI (p-value 0.02). No differences were found concerning the PD NSCLCs.

Performance of an AI algorithm during the different phases of the COVID pandemics: what can we learn from the AI and vice versa.

Background: Artificial intelligence (AI) has proved to be of great value in diagnosing and managing Sars-Cov-2 infection. ALFABETO (ALL-FAster-BEtter-TOgether) is a tool created to support healthcare professionals in the triage, mainly in optimizing hospital admissions. Methods: The AI was trained during the pandemic's "first wave" (February-April 2020). Our aim was to assess the performance during the "third wave" of the pandemics (February-April 2021) and evaluate its evolution. The neural network proposed behavior (hospitalization vs home care) was compared with what was actually done. If there were discrepancies between ALFABETO's predictions and clinicians' decisions, the disease's progression was monitored. Clinical course was defined as "favorable/mild" if patients could be managed at home or in spoke centers and "unfavorable/severe" if patients need to be managed in a hub center. Results: ALFABETO showed accuracy of 76%, AUROC of 83%; specificity was 78% and recall 74%. ALFABETO also showed high precision (88%). 81 hospitalized patients were incorrectly predicted to be in "home care" class. Among those "home-cared" by the AI and "hospitalized" by the clinicians, 3 out of 4 misclassified patients (76.5%) showed a favorable/mild clinical course. ALFABETO's performance matched the reports in literature. Conclusions: The discrepancies mostly occurred when the AI predicted patients could stay at home but clinicians hospitalized them; these cases could be handled in spoke centers rather than hubs, and the discrepancies may aid clinicians in patient selection. The interaction between AI and human experience has the potential to improve both AI performance and our comprehension of pandemic management.

Pulmonary Hypertension in Chronic Lung Diseases: What Role Do Radiologists Play?

Pulmonary hypertension (PH) is a pathophysiological disorder, defined by a mean pulmonary arterial pressure (mPAP) > 20 mmHg at rest, as assessed by right heart catheterization (RHC). PH is not a specific disease, as it may be observed in multiple clinical conditions and may complicate a variety of thoracic diseases. Conditions associated with the risk of developing PH are categorized into five different groups, according to similar clinical presentations, pathological findings, hemodynamic characteristics, and treatment strategy. Most chronic lung diseases that may be complicated by PH belong to group 3 (interstitial lung diseases, chronic obstructive pulmonary disease, combined pulmonary fibrosis, and emphysema) and are associated with the lowest overall survival among all groups. However, some of the chronic pulmonary diseases may develop PH with unclear/multifactorial mechanisms and are included in group 5 PH (sarcoidosis, pulmonary Langerhans' cell histiocytosis, and neurofibromatosis type 1). This paper focuses on PH associated with chronic lung diseases, in which radiological imaging-particularly computed tomography (CT)-plays a crucial role in diagnosis and classification. Radiologists should become familiar with the hemodynamical, physiological, and radiological aspects of PH and chronic lung diseases in patients at risk of developing PH, whose prognosis and treatment depend on the underlying disease.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.

BACKGROUND: The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. METHODS: LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. RESULTS: Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. CONCLUSIONS: Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. KEY POINTS: We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Prevalence and Complications of Aberrant Subclavian Artery in Patients With Heritable and Nonheritable Arteriopathies.

BACKGROUND: An aberrant subclavian artery (ASA) (or lusoria) is the most common congenital anomaly of the aortic arch (0.5%-2.2%; female-to-male ratio 2:1 to 3:1). ASA can become aneurysmal and result in dissection, involving Kommerell's diverticulum when present and the aorta. Data of its significance in genetic arteriopathies are not available. OBJECTIVES: The purpose of this study was to assess the prevalence and complications of ASA in gene-positive and -negative nonatherosclerotic arteriopathies. MATERIALS: The series includes 1,418 consecutive patients with gene-positive (n = 854) and gene-negative arteriopathies (n = 564) diagnosed as part of institutional work-up for nonatherosclerotic syndromic and nonsyndromic arteriopathies. Comprehensive evaluation includes genetic counseling, next-generation sequencing multigene testing, cardiovascular and multidisciplinary assessment, and whole-body computed tomography angiography. RESULTS: ASA was found in 34 of 1,418 cases (2.4%), with a similar prevalence in gene-positive (n = 21 of 854, 2.5%) and gene-negative (n = 13 of 564, 2.3%) arteriopathies. Of the former 21 patients, 14 had Marfan syndrome, 5 had Loeys-Dietz syndrome, 1 had type-IV Ehlers-Danlos syndrome, and 1 had periventricular heterotopia type 1. ASA did not segregate with genetic defects. Dissection occurred in 5 of 21 patients with genetic arteriopathies (23.8%; 2 Marfan syndrome and 3 Loeys-Dietz syndrome), all with associated Kommerell's diverticulum. No dissections occurred in gene-negative patients. At baseline, none of the 5 patients with ASA dissection fulfilled criteria for elective repair according to guidelines. CONCLUSIONS: The risk of complications of ASA is higher in patients with genetic arteriopathies and is difficult to predict. In these diseases, imaging of the supra-aortic trunks should enter baseline investigations. Determination of precise indications for repair can prevent unexpected acute events such as those described.

The role of radiomics in tongue cancer: A new tool for prognosis prediction.

BACKGROUND: Radiomics represents an emerging field of precision-medicine. Its application in head and neck is still at the beginning. METHODS: Retrospective study about magnetic resonance imaging (MRI) based radiomics in oral tongue squamous cell carcinoma (OTSCC) surgically treated (2010-2019; 79 patients). All preoperative MRIs include different sequences (T1, T2, DWI, ADC). Tumor volume was manually segmented and exported to radiomic-software, to perform feature extraction. Statistically significant variables were included in multivariable analysis and related to survival endpoints. Predictive models were elaborated (clinical, radiomic, clinical-radiomic models) and compared using C-index. RESULTS: In almost all clinical-radiomic models radiomic-score maintained statistical significance. In all cases C-index was higher in clinical-radiomic models than in clinical ones. ADC provided the best fit to the models (C-index 0.98, 0.86, 0.84 in loco-regional recurrence, cause-specific mortality, overall survival, respectively). CONCLUSION: MRI-based radiomics in OTSCC represents a promising noninvasive method of precision medicine, improving prognosis prediction before surgery.

Effect of Magnetic Resonance Imaging at 1.5 T and 3 T on Temperature and Bond Strength of Orthodontic Bands with Welded Tubes: An In Vitro Study.

Magnetic resonance imaging (MRI) is a widely used diagnostic technique. Patients wearing orthodontic devices are often requested to remove their appliances before an MRI exam, even when the exam involves anatomical areas far from the head, in order to prevent the heating and detachment of the appliances. The present report aims to evaluate changes in temperature and adhesive forces of molar bands after MRI at two different strength outputs. Sixty stainless steel molar bands were bonded on permanent human upper molars using two different cements: Unitek Multi-Cure Glass Ionomer Band Cement (3M Unitek, Monrovia, CA, USA) and Transbond Plus Light Cure Band Adhesive (3M Unitek). Appliances were subjected to MRI with two different strengths (1.5 Tesla and 3 Tesla). Tubes and band temperature was measured before and after MRI. Subsequently, the shear bond strength (SBS) test was calculated. Data underwent statistical analysis (p < 0.05). After MRI, molar bands exhibited significant heating, even though not clinically relevant, with a temperature increase ranging between 0.48 °C and 1.25 °C (p < 0.05). SBS did not show significant differences (p > 0.05). The present study suggests that, under MRI, the molar bands tested are safe; therefore, their removal could be not recommended for non-head and neck MRI exams. Removal would be necessary just in artifact risk areas.

The T-N tract involvement as a new prognostic factor for PORT in locally advanced oral cavity tumors.

OBJECTIVE: The space comprised between tumor and neck lymph nodes (T-N tract) is one of the main routes of tumor spread in oral cavity tumors. Aim of the study was to investigate the impact of T-N tract involvement on the postoperative radiotherapy (PORT) outcomes. MATERIALS AND METHODS: Patients (pts) treated between 2000 and 2016 with indication to PORT were retrospectively retrieved. Inclusion criteria were: (a) locally advanced tumors of the oral cavity, (b) who received with indication to PORT (c) with a minimum follow-up of six months. RESULTS: One hundred and fifty-seven pts met the inclusion criteria (136 pts treated with PORT and 21 pts not treated with PORT). In the PORT cohort, the T-N tract involvement had no impact on both OS (p = .09) and LRFS (p = .2). Among the non-PORT cohort, both OS (p = .007) and LRFS (p = .017) were worse for pts with positive T-N tract compared to those with negative T-N tract. PORT improved both OS (p = .008) and LRFS (p = .003) in pts with positive T-N tract but not in those with negative T-N tract (p = .36 and p = .37, respectively). CONCLUSIONS: Our results suggest that involvement of T-N tract should be considered as prognostic factors informing the indication to PORT.

Role of cardiac magnetic resonance in the differential diagnosis between arrhythmogenic cardiomyopathy with left ventricular involvement and previous infectious myocarditis.

AIMS: Arrhythmogenic cardiomyopathy with left ventricular involvement (ACM-LV), particularly in case of isolated left ventricular involvement (i.e. left dominant arrhythmogenic cardiomyopathy, LDAC) and previous infectious myocarditis (pIM) may have overlapping clinical and cardiac magnetic resonance (CMR) features. To date, there are no validated CMR criteria for the differential diagnosis between these conditions. The present study aimed to identify CMR characteristics to distinguish ACM-LV from pIM. METHODS AND RESULTS: This observational, retrospective, single-centre study included 30 pIM patients and 30 ACM-LV patients. In ACM-LV patients CMR was performed at diagnosis; in patients with pIM, CMR was performed six months after acute infection. CMR analysis included quantitative assessment of left ventricle (LV) volumes, systolic function and wall thicknesses, qualitative and quantitative assessment of late gadolinium enhancement (LGE) sequences. Compared with pIM, ACM-LV patients showed slightly larger LV volumes, more frequent regional wall motion anomalies and reduced wall thicknesses. ACM-LV patients had higher amounts of LV LGE and extension. Notably, the LDAC subgroup had the highest amount of LV LGE. LV LGE amount > 15 g and a LV LGE percentage > 30% of LV mass discriminated ACM-LV from pIM with a 100% specificity. LGE segmental distribution was superimposable among the groups, except for septal segments that were more frequently involved in ACM-LV and LDAC patients. CONCLUSIONS: A great extension of LV LGE (a cut-off of LGE >15 g and a percentage above 30% of LV LGE in relation to total myocardial mass) discriminates ACM-LV from pIM with extremely high specificity.

Mid-term follow-up chest CT findings in recovered COVID-19 patients with residual symptoms.

OBJECTIVES: More than a year has passed since the initial outbreak of SARS-CoV-2, which caused many hospitalizations worldwide due to COVID-19 pneumonia and its complications. However, there is still a lack of information detailing short- and long-term outcomes of previously hospitalized patients. The purpose of this study is to analyze the most frequent lung CT findings in recovered COVID-19 patients at mid-term follow-ups. METHODS: A total of 407 consecutive COVID-19 patients who were admitted to the Fondazione IRCCS Policlinico San Matteo, Pavia and discharged between February 27, 2020, and June 26, 2020 were recruited into this study. Out of these patients, a subset of 108 patients who presented with residual asthenia and dyspnea at discharge, altered spirometric data, positive lung ultrasound and positive chest X-ray was subsequently selected, and was scheduled to undergo a mid-term chest CT study, which was evaluated for specific lung alterations and morphological patterns. RESULTS: The most frequently observed lung CT alterations, in order of frequency, were ground-glass opacities (81%), linear opacities (74%), bronchiolectases (64.81%), and reticular opacities (63.88%). The most common morphological pattern was the non-specific interstitial pneumonia pattern (63.88%). Features consistent with pulmonary fibrosis were observed in 32 patients (29.62%). CONCLUSIONS: Our work showed that recovered COVID-19 patients who were hospitalized and who exhibited residual symptoms after discharge had a slow radiological recovery with persistent residual lung alterations. ADVANCES IN KNOWLEDGE: This slow recovery process should be kept in mind when determining the follow-up phases in order to improve the long-term management of patients affected by COVID-19.

Correlation between PD-L1 Expression of Non-Small Cell Lung Cancer and Data from IVIM-DWI Acquired during Magnetic Resonance of the Thorax: Preliminary Results.

This study aims to investigate the correlation between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in magnetic resonance imaging (MRI) and programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC). Twenty-one patients diagnosed with stage III NSCLC from April 2021 to April 2022 were included. The tumors were distinguished into two groups: no PD-L1 expression (<1%), and positive PD-L1 expression (≥1%). Conventional MRI and IVIM-DWI sequences were acquired with a 1.5-T system. Both fixed-size ROIs and freehand segmentations of the tumors were evaluated, and the data were analyzed through a software using four different algorithms. The diffusion (D), pseudodiffusion (D*), and perfusion fraction (pf) were obtained. The correlation between IVIM parameters and PD-L1 expression was studied with Pearson correlation coefficient. The Wilcoxon−Mann−Whitney test was used to study IVIM parameter distributions in the two groups. Twelve patients (57%) had PD-L1 ≥1%, and 9 (43%) <1%. There was a statistically significant correlation between D* values and PD-L1 expression in images analyzed with algorithm 0, for fixed-size ROIs (189.2 ± 65.709 µm²/s × 104 in no PD-L1 expression vs. 122.0 ± 31.306 µm²/s × 104 in positive PD-L1 expression, p = 0.008). The values obtained with algorithms 1, 2, and 3 were not significantly different between the groups. The IVIM-DWI MRI parameter D* can reflect PD-L1 expression in NSCLC.