Fluctuating obliterative bronchiolitis in RET-mutant medullary thyroid cancer patient treated with selpercatinib.

Highly selective RET inhibitor selpercatinib has demonstrated notable efficacy in advanced/progressive RET-mutant medullary thyroid cancer (MTC) patients. However, despite a more tolerable toxicity profile than multikinase inhibitors, peculiar adverse events (AEs) have been described. Obliterative bronchiolitis (OB) is a respiratory disease characterized by inflammation and fibrosis in small conducting airways. We evaluated a 70-year-old man with advanced RET-mutant MTC who developed OB during treatment with selpercatinib. Radiological features of OB occurred early and persisted during selpercatinib treatment, with a waxing and waning pattern. Notably, a partial response of MTC was achieved during the treatment, and selpercatinib was never reduced or interrupted. The almost complete absence of symptoms and the fluctuating trend, without specific treatment for OB, suggested that it is necessary to carefully evaluate the risks mediated by this AE with the risks of modifying or discontinuing the anti-cancer therapy.

Interstitial Lung Disease Phenotypes and Predictive Risk Factors in Primary Sjögren's Syndrome.

Background/Objectives: The prevalence of Interstitial Lung Disease (ILD) and risk factors for its development in patients with primary Sjögren's syndrome (pSS) are still debated, possibly due to the existence of heterogeneous pSS-related ILD phenotypes. The aims of this study were: 1. To investigate the prevalence and predictive factors for ILD development in a single-center pSS cohort; 2. To characterize different pSS-ILD phenotypes. Methods: Clinical, laboratory and imaging data of pSS patients attending our center from January 2019 to September 2023 were retrospectively analyzed. ILD presence was confirmed on HRCT. Results: Forty-three out of 474 enrolled pSS patients presented ILD (M:F = 6:37), accounting for an overall ILD prevalence of 9.1%. In 19 cases, ILD was the first manifestation of pSS (ILD-onset), while in 24 ILD was diagnosed after pSS (ILD-incident). Compared to ILD-onset, ILD-incident patients more often presented pSS-related hematologic abnormalities (p = 0.012), cutaneous involvement (p = 0.027), inflammatory arthralgias (p = 0.026), C4 hypocomplementemia (p = 0.012) and positive RF (p = 0.031). On the other hand, ILD-onset patients were significantly older at pSS diagnosis (p = 0.008) and presented more severe fibrosis on HRCT (p = 0.008). On the univariate analysis, higher ESSDAI (p = 0.011), Raynaud's phenomenon (p = 0.009), anti-Ro52 (p = 0.031), hypergammaglobulinemia (p = 0.011), Rheumatoid Factor (RF) (p = 0.038) and C4 hypocomplementemia (p = 0.044) at baseline were associated to ILD development during follow-up. On the multivariate analysis, the ESSDAI at baseline (p = 0.05) and Raynaud's phenomenon (p = 0.013) at baseline were the only independent predictors of ILD development. Conclusions: ILD is a relatively common and clinically heterogenous pSS manifestation. Elevated disease activity at pSS onset is a risk factor for ILD development, prompting careful follow-up and intriguingly suggesting that immunomodulatory therapies may prevent ILD.

Baseline Cell-Free DNA Can Predict Malignancy of Nodules Observed in the ITALUNG Screening Trial.

The role of total plasma cell-free DNA (cfDNA) in lung cancer (LC) screening with low-dose computed tomography (LDCT) is uncertain. We hypothesized that cfDNA could support differentiation between malignant and benign nodules observed in LDCT. The baseline cfDNA was measured in 137 subjects of the ITALUNG trial, including 29 subjects with screen-detected LC (17 prevalent and 12 incident) and 108 subjects with benign nodules. The predictive capability of baseline cfDNA to differentiate malignant and benign nodules was compared to that of Lung-RADS classification and Brock score at initial LDCT (iLDCT). Subjects with prevalent LC showed both well-discriminating radiological characteristics of the malignant nodule (16 of 17 were classified as Lung-RADS 4) and markedly increased cfDNA (mean 18.8 ng/mL). The mean diameters and Brock scores of malignant nodules at iLDCT in subjects who were diagnosed with incident LC were not different from those of benign nodules. However, 75% (9/12) of subjects with incident LC showed a baseline cfDNA ≥ 3.15 ng/mL, compared to 34% (37/108) of subjects with benign nodules (p = 0.006). Moreover, baseline cfDNA was correlated (p = 0.001) with tumor growth, measured with volume doubling time. In conclusion, increased baseline cfDNA may help to differentiate subjects with malignant and benign nodules at LDCT.

Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis.

PURPOSE: To evaluate the diagnostic accuracy of computed tomography (CT)-based radiomic algorithms and deep learning models to preoperatively identify lymph node metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: PubMed, CENTRAL, Scopus, Web of Science and IEEE databases were searched to identify relevant studies published up until February 11, 2024. Two reviewers screened all papers independently for eligibility. Studies reporting the accuracy of CT-based radiomics or deep learning models for detecting LNM in PDAC, using histopathology as the reference standard, were included. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2, the Radiomics Quality Score (RQS) and the the METhodological RadiomICs Score (METRICS). Overall sensitivity (SE), specificity (SP), diagnostic odds ratio (DOR), and the area under the curve (AUC) were calculated. RESULTS: Four radiomics studies comprising 213 patients and four deep learning studies with 272 patients were included. The average RQS total score was 12.00 ± 3.89, corresponding to an RQS percentage of 33.33 ± 10.80, while the average METRICS score was 63.60 ± 10.88. A significant and strong positive correlation was found between RQS and METRICS (p = 0.016; r = 0.810). The pooled SE, SP, DOR, and AUC of all the studies were 0.83 (95 %CI = 0.77-0.88), 0.76 (95 %CI = 0.62-0.86), 15.70 (95 %CI = 8.12-27.50) and 0.85 (95 %CI = 0.77-0.88). Meta-regression analysis results indicated that neither the study type (radiomics vs deep learning) nor the dataset size of the studies had a significant effect on the DOR (p = 0.09 and p = 0.26, respectively). CONCLUSION: Based on our meta-analysis findings, preoperative CT-based radiomics algorithms and deep learning models demonstrate favorable performance in predicting LNM in patients with PDAC, with a strong correlation between RQS and METRICS of the included studies.

A case report of unusual recurrent bronchopneumonia infections in Mounier-Kuhn syndrome.

Mounier-Kuhn syndrome is a rare airway disease characterized by tracheal and bronchial dilatation, primarily affecting middle-aged men. We present a case of Mounier-Kuhn syndrome in a 40-year-old man with a history of recurrent respiratory infections since adolescence. The diagnostic journey involved a multidisciplinary approach incorporating clinical evaluation, radiological imaging, and bronchoscopy. Computed tomography findings, including maximum intensity projection reconstructions and 3D rendering, facilitated the diagnosis by revealing significant airway dilation and associated abnormalities. Treatment primarily focused on supportive measures, including antibiotic therapy and respiratory physiotherapy. This case underscores the importance of considering Mounier-Kuhn syndrome in patients with recurrent respiratory infections and highlights the role of advanced imaging techniques in diagnosis.

Blood oxygenation state in COVID-19 patients: Unexplored role of 2,3-bisphosphoglycerate.

BACKGROUND: COVID-19 reduces lung functionality causing a decrease of blood oxygen levels (hypoxemia) often related to a decreased cellular oxygenation (hypoxia). Besides lung injury, other factors are implicated in the regulation of oxygen availability such as pH, partial arterial carbon dioxide tension (PaCO2), temperature, and erythrocytic 2,3-bisphosphoglycerate (2,3-BPG) levels, all factors affecting hemoglobin saturation curve. However, few data are currently available regarding the 2,3-BPG modulation in SARS-CoV-2 affected patients at the hospital admission. MATERIAL AND METHODS: Sixty-eight COVID-19 patients were enrolled at hospital admission. The lung involvement was quantified using chest-Computer Tomography (CT) analysed with automatic software (CALIPER). Haemoglobin concentrations, glycemia, and routine analysis were evaluated in the whole blood, while partial arterial oxygen tension (PaO2), PaCO2, pH, and HCO3- were assessed by arterial blood gas analysis. 2,3-BPG levels were assessed by specific immunoenzymatic assays in RBCs. RESULTS: A higher percentage of interstitial lung disease (ILD) and vascular pulmonary-related structure (VRS) volume on chest-CT quantified with CALIPER had been found in COVID-19 patients with a worse disease outcome (R = 0.4342; and R = 0.3641, respectively). Furthermore, patients with lower PaO2 showed an imbalanced acid-base equilibrium (pH, p = 0.0208; PaCO2, p = 0.0496) and a higher 2,3-BPG levels (p = 0.0221). The 2,3-BPG levels were also lower in patients with metabolic alkalosis (p = 0.0012 vs. no alkalosis; and p = 0.0383 vs. respiratory alkalosis). CONCLUSIONS: Overall, the data reveal a different pattern of activation of blood oxygenation compensatory mechanisms reflecting a different course of the COVID-19 disease specifically focusing on 2,3-BPG modulation.

Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters.

BACKGROUND: To quantitatively evaluate CT lung abnormalities in COVID-19 survivors from the acute phase to 24-month follow-up. Quantitative CT features as predictors of abnormalities' persistence were investigated. METHODS: Patients who survived COVID-19 were retrospectively enrolled and underwent a chest CT at baseline (T0) and 3 months (T3) after discharge, with pulmonary function tests (PFTs). Patients with residual CT abnormalities repeated the CT at 12 (T12) and 24 (T24) months after discharge. A machine-learning-based software, CALIPER, calculated the CT percentage of the whole lung of normal parenchyma, ground glass (GG), reticulation (Ret), and vascular-related structures (VRSs). Differences (Δ) were calculated between time points. Receiver operating characteristic (ROC) curve analyses were performed to test the baseline parameters as predictors of functional impairment at T3 and of the persistence of CT abnormalities at T12. RESULTS: The cohort included 128 patients at T0, 133 at T3, 61 at T12, and 34 at T24. The GG medians were 8.44%, 0.14%, 0.13% and 0.12% at T0, T3, T12 and T24. The Ret medians were 2.79% at T0 and 0.14% at the following time points. All Δ significantly differed from 0, except between T12 and T24. The GG and VRSs at T0 achieved AUCs of 0.73 as predictors of functional impairment, and area under the curves (AUCs) of 0.71 and 0.72 for the persistence of CT abnormalities at T12. CONCLUSIONS: CALIPER accurately quantified the CT changes up to the 24-month follow-up. Resolution mostly occurred at T3, and Ret persisting at T12 was almost unchanged at T24. The baseline parameters were good predictors of functional impairment at T3 and of abnormalities' persistence at T12.

Mortality surrogates in combined pulmonary fibrosis and emphysema.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) with coexistent emphysema, termed combined pulmonary fibrosis and emphysema (CPFE) may associate with reduced forced vital capacity (FVC) declines compared to non-CPFE IPF patients. We examined associations between mortality and functional measures of disease progression in two IPF cohorts. METHODS: Visual emphysema presence (>0% emphysema) scored on computed tomography identified CPFE patients (CPFE/non-CPFE: derivation cohort n=317/n=183, replication cohort n=358/n=152), who were subgrouped using 10% or 15% visual emphysema thresholds, and an unsupervised machine-learning model considering emphysema and interstitial lung disease extents. Baseline characteristics, 1-year relative FVC and diffusing capacity of the lung for carbon monoxide (D LCO) decline (linear mixed-effects models), and their associations with mortality (multivariable Cox regression models) were compared across non-CPFE and CPFE subgroups. RESULTS: In both IPF cohorts, CPFE patients with ≥10% emphysema had a greater smoking history and lower baseline D LCO compared to CPFE patients with <10% emphysema. Using multivariable Cox regression analyses in patients with ≥10% emphysema, 1-year D LCO decline showed stronger mortality associations than 1-year FVC decline. Results were maintained in patients suitable for therapeutic IPF trials and in subjects subgrouped by ≥15% emphysema and using unsupervised machine learning. Importantly, the unsupervised machine-learning approach identified CPFE patients in whom FVC decline did not associate strongly with mortality. In non-CPFE IPF patients, 1-year FVC declines ≥5% and ≥10% showed strong mortality associations. CONCLUSION: When assessing disease progression in IPF, D LCO decline should be considered in patients with ≥10% emphysema and a ≥5% 1-year relative FVC decline threshold considered in non-CPFE IPF patients.

Lung ultrasound and high-resolution computed tomography quantitative variations during nintedanib treatment for systemic sclerosis-associated interstitial lung disease.

OBJECTIVES: Lung ultrasound (LUS) and high-resolution computed tomography (HRCT) are commonly used for the evaluation of interstitial lung disease (ILD). Nintedanib (NIN) is an antifibrotic therapy approved for systemic sclerosis-associated ILD (SSc-ILD). We assessed LUS and quantitative HRCT changes in SSc-ILD patients treated with NIN during a one-year follow-up, evaluating relationships between imaging variations and functional or quality-of-life outcomes. METHODS: SSc-ILD patients who started NIN were enrolled and followed for twelve months. Pulmonary function tests and patient-reported outcome measures (PROMs) were assessed half-yearly and quarterly, respectively. LUS was performed quarterly evaluating the presence of B-lines (BL) and pleural line irregularities (PLI). HRCT was repeated after one year and quantitatively analysed with CALIPER software. RESULTS: Ten patients (70% female, mean age 62 years) were enrolled. The mean total number of both BL and PLI was constantly decreased during NIN treatment, being significantly reduced after twelve months (from 175.1 ± 66.7-120.8 ± 70.3 for BL, p= 0.005 and from 50.6 ± 32.5-37.2 ± 22.4 for PLI, p= 0.05). Male gender, smoking habit and baseline forced vital capacity <70% predicted were associated with worse LUS outcomes. A greater reduction of both BL and PLI was observed in those who improved in PROMs, especially modified Medical Research Council dyspnoea scale (p= 0.016 and p= 0.04, respectively) and Saint George's Respiratory Questionnaire (p= 0.006 and p= 0.026, respectively). No significant changes in the CALIPER percentages of normal parenchyma or ILD elements were observed after twelve months of NIN, thus paralleling the stabilization obtained at pulmonary function tests. CONCLUSIONS: We present preliminary results on NIN effects on SSc-ILD as assessed by LUS, a useful method for frequently repeated monitoring, and CALIPER, a valid implementation whenever a HRCT is performed.

Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.

Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of COVID-19, which are crucial to be detected and quantified in order to obtain an accurate disease stratification and prognosis. However, visual assessment and quantification represent a time-consuming task for radiologists. In this regard, tools for semi-automatic segmentation, such as those based on Convolutional Neural Networks, can facilitate the detection of pathological lesions by delineating their contour. In this work, we compared four state-of-the-art Convolutional Neural Networks based on the encoder-decoder paradigm for the binary segmentation of COVID-19 infections after training and testing them on 90 HRCT volumetric scans of patients diagnosed with COVID-19 collected from the database of the Pisa University Hospital. More precisely, we started from a basic model, the well-known UNet, then we added an attention mechanism to obtain an Attention-UNet, and finally we employed a recurrence paradigm to create a Recurrent-Residual UNet (R2-UNet). In the latter case, we also added attention gates to the decoding path of an R2-UNet, thus designing an R2-Attention UNet so as to make the feature representation and accumulation more effective. We compared them to gain understanding of both the cognitive mechanism that can lead a neural model to the best performance for this task and the good compromise between the amount of data, time, and computational resources required. We set up a five-fold cross-validation and assessed the strengths and limitations of these models by evaluating the performances in terms of Dice score, Precision, and Recall defined both on 2D images and on the entire 3D volume. From the results of the analysis, it can be concluded that Attention-UNet outperforms the other models by achieving the best performance of 81.93%, in terms of 2D Dice score, on the test set. Additionally, we conducted statistical analysis to assess the performance differences among the models. Our findings suggest that integrating the recurrence mechanism within the UNet architecture leads to a decline in the model's effectiveness for our particular application.