High-resolution Computed Tomography Research Articles

Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis

BackgroundCoal workers’ pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future.MethodsAll chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers. We segmented regions of interest according to the diagnostic results, which were evaluated by radiologists. These regions were then classified regions into four categories. We employed an efficient deep learning model and various image augmentation techniques (DenseNet-ECA). The classification performance of the different deep learning models was assessed, and receiver operating characteristic (ROC) curves and accuracy (ACC) were used to determine the optimal algorithm for classifying CWP clinical imaging features obtained from HRCT images.ResultsFour primary clinical imaging features in HRCT images, with a total of more than 1700 regions of interest (ROIs), were annotated, augmented, and used as a training set for tenfold cross-validation to generate the model. We selected DenseNet-Attention Net as the optimal model through assessing the performance of different classification algorithms, which yielded an average area under the ROC curve (AUC) of 0.98, and all clinical imaging features were classified with an AUC greater than 0.92. For the individual classifications, the AUCs were as follows: small miliary opacities, 0.99; nodular opacities, 1.0; interstitial changes, 0.92; and emphysema, 1.0.ConclusionWe successfully applied a data augmentation strategy to develop a deep learning model by combining DenseNet with ECA-Net. We used our novel model to automatically classify CWP clinical imaging features from 2D HRCT images. This successful application of a deep learning-data augmentation algorithm can help clinical radiologists by providing reliable diagnostic information for classification.Trial registration: Chinese Clinical Trial Registry, ChiCTR2100050379. Registered on 27 August 2021, https://www.chictr.org.cn/bin/project/edit?pid=132619.

Primary Ewing's sarcoma of the temporal bone: a rare oncological enigma

Ewing's sarcoma is an uncommon neuroectodermal malignancy that rarely develops in the temporal bone. The early clinical recognition and definitive management of this condition pose unique challenges. This report presents a case of a 4-year-old girl with right ear pain, right post-aural swelling and progressively increasing right-sided facial nerve palsy for the past 6 months. High-resolution computed tomography (HRCT) temporal bone revealed an ill-defined soft tissue lytic lesion epi centred in the right mastoid extending to the external auditory canal and intracranially. Microscopic sections revealed round blue cells which were strongly positive for CD-99. The diagnosis of primary Ewing's sarcoma of the temporal bone was made. Considering the intracranial extension of the tumour, chemotherapy as the first line of treatment was initiated and 9 cycles have been administered resulting in marked regression of the tumour. A range of nonspecific symptoms, such as otalgia, otorrhea, sensorineural hearing loss, post aural mass, and facial nerve palsy, are indicative of the clinical manifestation of primary Ewing's sarcoma in the temporal bone. Because of how subtle these symptoms are, more clinical knowledge is required, especially in light of the small number of documented cases. When children and adolescents present with similar symptoms, clinicians should rule out Ewing's sarcoma even though it is a rare entity.

Warrick score in rheumatoid-arthritis interstitial lung disease: a promising tool for assessing the extent and progression of lung involvement

BackgroundThe clinical manifestations and course of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) exhibits considerable heterogeneity. In this study, we aimed to explore radiographic progression over a defined period, employing the Warrick score as a semi-quantitative measure in early RA-ILD, and to assess the associated risk factors for progression.MethodsRA-ILD patients underwent consecutive Warrick scoring based on initial high-resolution computed tomography (HRCT) at diagnosis and the first follow-up. Associations between Warrick scores, pulmonary function tests, and patient characteristics were analyzed. The ROC curve assessed the predictive performance of the Warrick score change rate for ILD progression, while multivariable logistic regression analysis identified risk factors for progression.ResultsSignificant correlations were found between Warrick scores and age at RA-ILD diagnosis, age at ILD diagnosis, and baseline DAS28-ESR. For the severity score, correlations were r = 0.359, r = 0.372, and r = 0.298 (p = 0.001, p < 0.001, p = 0.014, respectively); for the extent score, r = 0.364, r = 0.318, and r = 0.255 (p = 0.001, p = 0.005, p = 0.038, respectively); and for the total score, r = 0.376, r = 0.367, and r = 0.280 (p < 0.001, p = 0.001, p = 0.022, respectively). Annual changes in severity, extent, and total Warrick scores showed sensitivities of 91–97% and specificities of 98% for predicting progression over a 5-year follow-up. Cut-off values were 0.0278 for the severity score (AUC 0.954), 0.0227 for extent score (AUC 0.976), and 0.0694 for total score (AUC 0.946). Warrick severity, extent, and total scores increased significantly during follow-up. Age > 50 years (OR 7.7; p = 0.028) and baseline usual interstitial pneumonia (UIP) pattern (OR 3.1, p = 0.041) were identified as risk factors for progression.ConclusionsAdvanced age and UIP pattern were significant risk factors for progression. Warrick scoring may may help predict progression in RA-ILD, particularly through changes in severity, extent, and total scores. Due to the retrospective design and small sample size, further prospective studies with larger cohorts are needed to confirm these findings and validate Warrick scoring as a reliable marker for RA-ILD progression.

Integrating Radiomics Signature into Clinical Pathway for Patients with Progressive Pulmonary Fibrosis

Interstitial lung diseases (ILDs) are a heterogeneous group of pulmonary disorders characterised by variable degrees of inflammation, interstitial thickening, and fibrosis leading to distortion of the pulmonary architecture and gas exchange impairment. There are approximately 200 different entities in this category. ILDs are commonly classified based on several criteria, including causes, clinical features, and radiological patterns. Chest HRCT is the gold standard for the recognition of lung alteration patterns underlying interstitial lung diseases (ILDs), diagnosing specific patterns, and evaluating radiologic progression. Methods based on artificial intelligence (AI) may be used in computational medicine, especially in image-based specialties such as radiology. The evolving field of radiomics offers a unique and non-invasive approach to extracting quantitative information from medical images, particularly high-resolution computed tomography (HRCT) scans. This comprehensive review explores the burgeoning role of radiomics in unravelling the intricacies of interstitial lung disease. It focuses on its potential applications in diagnosis, prognostication, and treatment response evaluation.

Anti-Scattering Perovskite Scintillator Arrays for High-Resolution Computed Tomography Imaging.

Computed tomography (CT) imaging has emerge as an effective medical diagnostic technique due to its rapid and 3D imaging capabilities, often employing indirect imaging methods through scintillator materials. Arraying scintillators that can confine light scattering to enable high-resolution CT imaging remains an area of ongoing exploration for emerging perovskite scintillators. Here an anti-scattering cesium lead bromide (CsPbBr3) scintillator array embedded within a polyurethane acrylate matrix for CT imaging using a cost-effective solution-processed method is reported. Due to the large refractive index contrast between the scintillator and matrix, photon propagation can be well confined within the CsPbBr3 scintillator array to significantly suppress the light scattering and enhance the light collection efficiency by nearly two times compared to the monolithic film. Furthermore, the scintillator array exhibits low-dosage and high-resolution CT imaging capability by reconstructing a 3D tooth image with a good spatial resolution of 20.1 lp cm-1 at a low effective dose of 0.22 mSv. This work highlights that the CsPbBr3 scintillator array is a highly promising candidate for CT imaging.

Efficacy of a novel interstitial lung disease monitoring program in breast cancer patients undergoing treatment with trastuzumab-deruxtecan.

Interstitial lung disease (ILD) is a well described and potentially fatal complication of trastuzumab-deruxtecan (T-DXd). It is currently unknown if specific monitoring is beneficial in the early detection of ILD in these patients. We describe the efficacy and feasibility of a novel ILD monitoring protocol in breast cancer patients treated with T-DXd at our institution. An ILD monitoring protocol developed at our institution included baseline and ongoing monitoring with pulmonary function testing (PFTs) and high-resolution chest computed tomography (HRCT) at pre-specified intervals. Patients with metastatic HER2+ or HER2-low breast cancer treated at Huntsman Cancer Institute who received ≥ 1 cycle of T-DXd between 2020 and 2023 were included (n = 68). Patient outcomes and provider adherence to the protocol were retrospectively evaluated. Providers were classified as "no adherence" if they did not elect to participate in any elements of the recommended protocol or as "some adherence" if they had at least some monitoring per protocol. 10 cases of ILD were identified with an incidence of 12% (3/25) in the no adherence group and 16% (7/43) in the some adherence group. ILD cases in the no adherence group included one grade 2 and two grade 5 cases. The some adherence group included three grade 1 and four grade 2 cases. An ILD monitoring protocol consisting of baseline PFTs and ongoing monitoring with PFTs and HRCT is a feasible approach as evidenced by a majority provider adherence rate. This type of protocol may be effective in preventing severe cases of ILD and identifying grade 1 events that may permit treatment re-challenge.

Clinical Profile of Suspected and Confirmed COVID-19 Pneumonia Patients Admitted in a Tertiary Care Hospital, Sylhet, Bangladesh

Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-COV-2) has been declared a pandemic by the World Health Organization (WHO) with increasing morbidity and mortality. This cross-sectional study on 408 patients with suspected and confirmed COVID-19 pneumonia was conducted at the isolation ward for COVID-19 patients at Jalalabad Ragib-Rabeya Medical College Hospital, Sylhet, from October 2020 to December 2020. Their RT-PCR test was done from the designated laboratory and 48 of them were confirmed to be COVID-19 patients. The detailed demographics and clinical outcomes of the suspected and confirmed patients were analyzed. The mean age was 59±25 in the confirmed group and 56±15 in the negative group. The majority of the confirmed and suspected COVID-19 patients were between the ages of 61 and 70 (27.1% and 25.8%, respectively). Most of the patients were male, both in the confirmed (58.3%) and in the suspected group (53.6%). Fever, cough, dyspnoea, fatigue, headache, anorexia, and sore throat were common in both the suspected and confirmed groups, but dyspnoea (66.7%, p=0.0001), headache (33.3%, p=0.018), vomiting (33.3%, p=0.001), and sore throat (16.7%, p=0.002) were more frequently reported in the confirmed patients group than in the suspected patients. Fewer patients had COPD in the COVID-19 positive group than in the negative group (8.3% vs 26.7%, p=0.009), but diabetes mellitus was more common in the confirmed group than the suspected group (66.7% vs 48.9%, p=0.030). There were no significant differences in a range of other comorbidities, including hypertension (66.7% vs 56.8%, p=0.308), cerebrovascular disease (10.42% vs 13.3%, P=0.73), and asthma (16.7% vs 14.4%, p=0.848) in the confirmed and suspected groups. On the patient’s blood test, reduced WBC count was more common in the confirmed group than in the suspected group (16.7% vs 2.2%, p=0.000). C-reactive protein (CRP) levels were above normal in 48 (100%) of confirmed patients and 333 (92.2%) of suspected patients, which was not statistically significant (p=0.089). However, D-Dimer (100% vs 78.9%, p=0.000), Serum Ferritin (100% vs 78.9%, p=0.000), and Serum ALT (100% vs 51.1%, p=0.000) levels were significantly higher in the COVID-19 positive group. On CXR, bilateral consolidation was more common in the COVID-19 positive group than the COVID-19 negative group (83.3% vs 42.2%, p=0.000), whereas unilateral consolidation was more common in the suspected group than the confirmed group (38.9% vs 0%, p=0.000). On high resolution computed tomography (HRCT), ground glass opacities (GGO) were more common in confirmed patients than suspected patients (95% vs 55%, p=0.04). A higher proportion of patients with COVID-19 positive were referred to COVID dedicated hospitals than COVID negative patients (66.7% vs 5.6%, p=0.000). Sixteen (33.3%) of confirmed patients were discharged with advice. No death was observed in the confirmed group and 1.9% patients died in the suspected group. The maximum COVID-19 patients were aged over 60 and male. Typical presentations were fever, cough, dyspnoea, fatigue, anorexia, headache, vomiting, and sore throat, common in both confirmed and suspected groups. Hypertension and diabetes mellitus were the most common comorbidities. In confirmed COVID 19 patients, reduced WBC count and high CRP, Serum Ferritine, D-dimer, and Serum ALT were common laboratory findings, and bilateral consolidation on chest X-ray and ground glass opacities in HRCT were common radiological findings. Jalalabad Med J 2022; 19 (1): 11-20

Utilization of Staging Laparoscopy for Patients With Gastric Cancer in Ukraine.

The use of advanced diagnostic methods, such as high-resolution CT or PET-CT scans, are limited in low and middle-income countries (LMICs). In gastric cancer, occult peritoneal disease may be present in up to 20% of cases, limiting the benefits of surgical resection. Accurate staging of gastric cancer is essential to ensure optimal patient management and prevent undue morbidity from unnecessary surgery. Our study evaluated the diagnostic accuracy and safety of staging laparoscopy (SL) versus computed tomography (CT) in detecting peritoneal metastases for gastric cancer in Ukraine. Patients who underwent SL between October 2017 and October 2022 were retrospectively reviewed. SL was performed for each newly diagnosed patient with gastric cancer without evidence of distant metastases, except when peritoneal carcinomatosis was in question. Pathological findings at SL were compared with initial CT reports, and analysis of the sensitivity and specificity for SL was performed. Of 516 patients undergoing SL, 410 had no radiological considerations for distant metastases. Among them, radiographically occult peritoneal metastatic disease was found in 134 (32.7%). Overall SL sensitivity for peritoneal metastases was 90.6% (95% confidence interval [CI]: 85.8%-94.1%) and specificity 100.0% (95% CI: 98.8%-100.0%). Complications after SL occurred in 5 (1%) of patients. No mortality was associated with SL. SL is a safe and effective method to evaluate the presence of peritoneal disease in patients with gastric cancer. This method may be appropriate for LMICs, where availability of advanced imaging technologies is limited.

Role of IPF genetic risk loci in post-COVID-19 lung abnormalities: a cohort study

IntroductionPersistent lung abnormalities following COVID-19 infection are common. Similar parenchymal changes are observed in idiopathic pulmonary fibrosis (IPF). We investigated whether common genetic risk factors in IPF are associated with...

High-resolution X-ray phase-contrast tomography of human placenta with different wavefront markers

Phase-contrast micro-tomography (CT) with synchrotron radiation can aid in the differentiation of subtle density variations in weakly absorbing soft tissue specimens. Modulation-based imaging (MBI) extracts phase information from the distortion of reference patterns, generated by periodic or randomly structured wavefront markers (e.g., gratings or sandpaper). The two approaches have already found application for the virtual inspection of biological samples. Here, we perform high-resolution CT scans of an unstained human placenta specimen, using MBI with both a 2D grating and sandpaper as modulators, as well as conventional propagation-based imaging (PBI). The 3D virtual representation of placenta offers a valuable tool for analysing its intricate branching villous network and vascular structure, providing new insights into its complex architecture. Within this study, we assess reconstruction quality achieved with all three evaluated phase-contrast methods. Both MBI datasets are processed with the Unified Modulated Pattern Analysis (UMPA) model, a pattern-matching algorithm. In order to evaluate the benefits and suitability of MBI for virtual histology, we discuss how the complexities of the technique influence image quality and correlate the obtained volumes to 2D techniques, such as conventional histology and X-ray fluorescence (XRF) elemental maps.

Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT

BackgroundBronchiolitis Obliterans Syndrome (BOS), a fibrotic airway disease that may develop after lung transplantation, conventionally relies on pulmonary function tests (PFTs) for diagnosis due to limitations of CT imaging. Deep neural networks (DNNs) have not previously been used for BOS detection. This study aims to train a DNN to detect BOS in CT scans using an approach tailored for low-data scenarios.MethodsWe trained a DNN to detect BOS in CT scans using a co-training method designed to enhance performance in low-data environments. Our method employs an auxiliary task that makes the DNN more sensitive to disease manifestations and less sensitive to the patient’s anatomical features. The DNN was tasked with predicting the sequence of two CT scans taken from the same BOS patient at least six months apart. We evaluated this approach on CT scans from 75 post-transplant patients, including 26 with BOS, and used a ROC-AUC metric to assess performance.ResultsWe show that our DNN method achieves a ROC-AUC of 0.90 (95% CI: 0.840–0.953) in distinguishing BOS from non-BOS in CT scans. Performance correlates with BOS progression, with ROC-AUC values of 0.88 for stage I, 0.91 for stage II, and 0.94 for stage III BOS. Notably, the DNN shows comparable performance on standard- and high-resolution CT scans. It also demonstrates the ability to predict BOS in at-risk patients (FEV1 between 80% and 90% of best FEV1) with a ROC-AUC of 0.87 (95% CI: 0.735–0.974). Using visual interpretation techniques for DNNs, we reveal sensitivity to hyperlucent/hypoattenuated areas indicative of air-trapping or bronchiectasis.ConclusionsOur approach shows potential for improving BOS diagnosis by enabling early detection and management. The ability to detect BOS from standard-resolution scans at any stage of respiration makes this method more accessible than previous approaches. Additionally, our findings highlight that techniques to limit overfitting are crucial for unlocking the potential of DNNs in low-data settings, which could assist clinicians in BOS studies with limited patient data.

Case Report: First use of dental bisphenol A-glycidyl methacrylate composite without UV light polymerization for repair of iatrogenic CSF leak following a frontal craniotomy plus tumor resection

ObjectiveThis study aimed to evaluate the efficacy and safety of bisphenol A-glycidyl methacrylate (bis-GMA) without UV light polymerization for the repair of refractory iatrogenic cerebrospinal fluid (CSF) leaks with large skull base defects.BackgroundCSF leakage remains a common complication after neurosurgical interventions with a substantial resultant impact on morbidity and increased healthcare costs. The management of refractory CSF leaks with large skull base defects remains challenging. Radiological investigations are highly contributive as they visualize the defect and assess the herniated content. Optimal treatment depends on the breach parameters and the consequent hernia. Surgery, when indicated, consists of exposure of the defect and its reconstruction using different grafts. The dental composite bis-GMA has been investigated and has shown effectiveness for the repair of anterior skull base defects. This is due to its compactible mechanical properties, long-term stability, and good osteo-integration. Hence, it presents a promising solution for refractory CSF leaks not responding to extradural endoscopic techniques.Case reportWe describe the case of a 40-year-old female with persistent CSF rhinorrhea following a left frontal craniotomy performed 4 years before. A high-resolution cerebral CT scan and MRI revealed a bilateral fronto-ethmoidal osteo-meningeal breach and a hyperintense T2 signal in the ethmoidal sinus interrupting the hypo-intensity of the bone, respectively. In our patient, surgical treatment involved a bifrontal craniotomy and osteo-meningeal reconstruction with the use of bis-GMA without UV light polymerization. This reconstruction gave rigid structural support and watertight closure of the defect. Postoperatively, the CSF rhinorrhea ceased and there were no perceivable associated complications.ConclusionGiven the favorable outcome, the composite bis-GMA without UV light polymerization can be used as a reliable material for the repair of iatrogenic CSF leaks.

Determinants of Success in Revision Cochlear Implant Surgeries: A Comprehensive Evaluation of Patient, Surgical, and Radiological Factors.

Background/Objectives: This study aims to evaluate factors influencing revision cochlear implant surgeries, including patient demographics, surgical techniques, and radiological findings. The main aim was to identify factors influencing surgical success to inform clinical treatment and enhance patient outcomes. Methods: This cross-sectional study included adult patients over the age of 18 who underwent revision cochlear implant surgery due to implant-related complications. Data were collected from systematic chart reviews of electronic medical records, including demographics, surgical techniques, radiological output from standard high-resolution CT and MRI, and intraoperative data. The primary outcome was surgical success, defined by improved auditory function, stable device positioning, and absence of postoperative complications. Statistical analyses, including multivariate logistic regression, were conducted to identify independent predictors of surgical success. Results: A total of 188 patients were included, with a mean age of 45.67 years. Younger age, shorter duration of implant use, modified surgical techniques, and absence of co-morbidities are key factors contributing to surgical success. Modified surgical techniques were associated with better outcomes (39.29% vs. 68.75%, OR: 2.78, 95% CI: 1.25-6.14, p = 0.012). Radiological patterns significantly affected outcomes, with normal findings associated with higher success rates. In contrast, abnormalities such as electrode migration (OR: 2.14, 95% CI: 1.12-4.09, p = 0.021) and ossification (r = -0.51, β = -0.44, adjusted R2 = 0.25, p < 0.01) were correlated with poorer results. Smoking status, when comparing smokers to non-smokers, was associated with unfavorable outcomes (20.00% vs. 45.83%, OR: 2.01, 95% CI: 1.01-4.01, p = 0.048), and a history of previous surgeries (21.43% vs. 37.50%, OR: 1.95, 95% CI: 1.03-3.71, p = 0.033) was significantly associated with unfavorable outcomes. Conclusions: Statistically, significantly better outcomes are positively correlated with the duration of the first cochlear implant experience and negatively with prior otologic pathology and nonauditory environmental influences on surgical success. Younger age, modified surgical techniques, and normal radiological findings were related to better outcomes; complications such as electrode migration and ectopic ossification were strong predictive factors for surgical failure. These findings underscore the need for tailored surgical approaches and early intervention to enhance outcomes in revision cochlear implant surgeries.

Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases

BackgroundThis retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT).MethodsTwenty-eight patients with verified pattern-based ILD diagnoses were split into two equal datasets (1 and 2). The images were assessed by two radiology residents (3rd and 5th year) and one expert radiologist in four sessions. Dataset 1 was used for sessions A and C, assessing diagnostic accuracy and confidence with mandatory and without CBIR software. Dataset 2 was used for sessions B and D with optional CBIR use, assessing time spending and frequency of CBIR usage. Accuracy was assessed on the CT pattern level, comparing readers’ diagnoses with reference diagnoses and CBIR results with region-of-interest (ROI) patterns.ResultsDiagnostic accuracy and confidence of readers showed an increasing trend with CBIR use compared to no CBIR use (53.6% versus 35.7% and 50.0% versus 32.2%, respectively). Time for reading significantly decreased in both datasets (A versus C: 104 s versus 54 s, p < 0.001; B versus D: 88.5 s versus 70 s, p = 0.009), whereas time for research increased with CBIR software use (A versus C: 31 s versus 81 s, p = 0.040). CBIR results showed a high pattern-based accuracy of overall 73.4%. Comparison between readers indicates a slightly higher accuracy of CBIR results when more than one ROI was used as input (77.7% versus 70.1%).ConclusionCBIR software improves in-training radiologist diagnostic accuracy and confidence while reducing interpretation time in ILD assessment.Relevance statementContent-based image retrieval software improves the assessment of interstitial lung diseases (ILD) in high-resolution CT, especially for radiology residents, by increasing diagnostic accuracy and confidence while reducing interpretation time. This can provide educational benefits and more time-efficient management of complex cases.Key PointsA content-based image retrieval (CBIR) software improves diagnostic accuracy and confidence for in-training radiologists for interstitial lung disease (ILD) assessment on computed tomography (CT).A CBIR application provides condensed information about similar HRCT cases reducing time for ILD assessment.CBIR algorithms benefit from the input of multiple regions of interest per ILD case.Graphical

Diagnostic Challenges and Innovations in Thoracic Endometriosis (TE)

Endometriosis is a condition characterized by the presence of endometrial tissue in ectopic locations outside the gynecological organs. Thoracic endometriosis (TE) is a rare and underdiagnosed condition characterized by the presence of endometrial-like tissue in the thoracic cavity, leading to cyclical respiratory symptoms. TE is often misdiagnosed or overlooked, primarily due to its nonspecific symptoms. Advancements in imaging techniques and biomarkers research are promising in improving the diagnosis of TE. High-resolution imaging modalities, such as high-resolution computed tomography (HRCT) and magnetic resonance imaging (MRI), provide detailed visualization of pleural and pulmonary lesions and leverage hormonal responsiveness for dynamic imaging during the menstrual cycle. The identification of novel biomarkers, such as cystatin C and circulating microRNAs, highlights the potential for non-invasive diagnostic tools that can stratify patients based on disease severity and guide personalized treatment strategies. Early diagnosis and treatment of thoracic endometriosis are crucial to prevent severe complications such as recurrent pneumothorax, pleural adhesions, and progressive lung damage. Timely intervention also reduces the burden of chronic pain and improves quality of life, enabling more effective management of this underdiagnosed condition. The aim of this paper is to explore current diagnostic possibilities for TE, particularly through the lens of advanced diagnostic innovations, and assess their potential to improve outcomes for patients with this complex condition.

Current approaches in the diagnosis and treatment of organizing pneumonia

Organizing pneumonia (OP), is a rare yet well-defined form of interstitial lung disease. The condition can be idiopathic, termed cryptogenic organizing pneumonia (COP), or secondary to various causes, including infections, connective tissue diseases, medications, and malignancies. OP typically presents with nonspecific symptoms such as cough, dyspnea, fever, and weight loss, often mimicking other respiratory conditions like pneumonia or interstitial lung diseases. Radiological findings are varied but commonly include bilateral, patchy consolidations or ground-glass opacities with preserved lung volumes. Diagnosis often requires high-resolution computed tomography (HRCT) and may be confirmed by lung biopsy when clinical and imaging findings are inconclusive. Laboratory studies and bronchoalveolar lavage can aid in excluding alternative diagnoses. Glucocorticoids remain the mainstay of treatment, with most patients responding well to therapy. This review explores current advances in the diagnosis, differential diagnosis, and management of OP, emphasizing the importance of a multidisciplinary approach to optimize patient outcomes.

Baseline characteristics of patients in the Chinese Bronchiectasis Registry (BE-China): a multicentre prospective cohort study.

Bronchiectasis is a disease with a global impact, but most published data come from high-income countries. We aimed to describe the clinical characteristics of patients with bronchiectasis in China. The Chinese Bronchiectasis Registry (BE-China) is a prospective, observational cohort enrolling patients from 111 hospitals in China. Data on demographics, comorbidities, and aetiological testing results were collected from adult patients with bronchiectasis at baseline and annual follow-up. Patients who met the inclusion criteria (age ≥18 years; received chest high-resolution CT in the past year showing bronchiectasis affecting one or more lung lobes; and clinical history consistent with bronchiectasis, including chronic cough, daily sputum production, and history of exacerbations) were included. Patients with known cystic fibrosis were excluded. To investigate variations according to different economic regions, two groups were compared based on whether per capita disposable income of residents was greater than US$5553. Clinical characteristics were compared with the European (EMBARC) registry and other national registries. Between Jan 10, 2020, and March 31, 2024, 10 324 patients from 97 centres were included in the study. Among 9501 participants with available data, the most common cause of bronchiectasis was post-infective disease (4101 [43·2%] patients), followed by idiopathic (2809 [29·6%] patients). 6676 (70·0%) of 9541 patients with available data had at least one exacerbation in the year before enrolment and 5427 (57·2%) of 9489 patients with available data were hospitalised at least once due to exacerbations. Treatments commonly used in high-income countries, such as inhaled antibiotics and macrolides, were infrequently used in China. Implementation of airway clearance in China was scarce, with only 1177 (12·2%) of 9647 patients having used at least one method of airway clearance. Compared with upper-middle-income regions, patients from lower-middle-income regions were younger (61·0 years [SD 14·0] vs 63·9 years [14·2]) with a higher proportion of pulmonary comorbidities (521 [17·8%] of 2922 patients vs 639 [8·6%] of 7402 with chronic obstructive pulmonary disease and 194 [6·6%] of 2922 patients vs 364 [4·9%] of 7402 patients with asthma), a higher tuberculosis burden (442 [16·0%] of 2768 patients vs 715 [10·6%] of 6733 patients), more severe radiological involvement (1160 [42·4%] of 2736 patients vs 2415 [35·4%] of 6816 patients with cystic bronchiectasis), more exacerbations (median 1·4 [IQR 0-2] in both groups; mean 1·4 [SD 1·6] vs 1·2 [1·4] in the previous year) and hospitalisations (1662 [60·6%] of 2743 patients vs 3765 [55·8%] of 6746 patients hospitalised at least once in the previous year), and poorer quality of life (median 57·4 [IQR 53·5-63·1] vs 58·7 [54·8-64·8] assessed by the Bronchiectasis Health Questionnaire). The clinical characteristics of patients with bronchiectasis in China show differences compared with cohorts in Europe and India. Bronchiectasis is more severe with a higher burden of exacerbations in lower-income regions. The management of patients with bronchiectasis in China urgently needs standardisation and improvement. National Natural Science Foundation of China, Innovation Program of the Shanghai Municipal Education Commission, Program of the Shanghai Municipal Science and Technology Commission, and Program of the Shanghai Shenkang Development Center. For the Chinese translation of the abstract see Supplementary Materials section.

Lung ultrasound for assessing disease progression in UIP and NSIP: a comparative study with HRCT and PFT/DLCO

BackgroundThis study aims to compare Lung Ultrasound (LUS) findings with High-Resolution Computerized Tomography (HRCT) and Pulmonary Function Tests (PFTs) to detect the severity of lung involvement in patients with Usual Interstitial Pneumonia (UIP) and Non-Specific Interstitial Pneumonia (NSIP).MethodsA cross-sectional study was conducted on 35 UIP and 30 NSIP patients at a referral hospital. All patients underwent LUS, HRCT, and PFT. LUS findings such as B-lines, pleural fragmentation, and pleural thickening were compared with HRCT-based lung involvement and PFT parameters.ResultsIn UIP patients, B-lines > 18 and pleural fragmentation significantly differentiated between < 50% and > 50% HRCT involvement. A logistic regression model showed that B-lines > 18 (OR = 39, p = 0.04) and pleural fragmentation (OR = 22, p = 0.037) independently predicted > 50% HRCT involvement. ROC analysis of the model revealed 84.2% sensitivity and 84.5% specificity. Furthermore, the crude number of B-lines (OR = 1.2, p = 0.038) and > 50% HRCT involvement (OR = 9.5, p = 0.045) independently predicted severe DLCO impairment, with a sensitivity of 94.7% and specificity of 84.5%. Linear regression showed that each additional B-line was associated with a 0.4% decrease in DLCO (Beta = -0.377, p = 0.043), independent of patient diagnosis. In NSIP patients, no significant correlation was observed between LUS findings and > 50% HRCT involvement (p > 0.05), though B-line numbers and pleural thickening increased in cases with severe DLCO impairment (p < 0.05).ConclusionsLUS shows promise as a sensitive, radiation-free alternative to HRCT in monitoring the severity of UIP. It is particularly valuable in predicting the extent of lung involvement and severe DLCO impairment in UIP patients but has limited application in NSIP.

High resolution chest computed tomography responses for a cohort of adult with pulmonary Langerhans cell histiocytosis.

Langerhans cell histiocytosis (LCH) is a heterogeneous histiocytosis with various pulmonary manifestations and imaging. We aim to evaluate the pulmonary response of LCH by high resolution chest computed tomography (HRCT) through continuous follow-up.We conducted a retrospective analysis of 73 adult LCH patients with pulmonary involvement. HRCT response was assessed by the change of HRCT global score (nodule score plus cyst score) between the baseline and after therapy. Among them, 69 patients (94.5%) had multi-system LCH with pulmonary involvement. 42 patients received methotrexate and cytarabine regimen, 15 received cytarabine monotherapy, 7 underwent target therapy. 14 (19.2%) achieved complete response (CR) and 45 (61.6%) achieved partial response (PR). The mean global lung-lesion score decreased from 12.2 to 10.6. The mean nodule score decreased from 4.7 to 4.1 and the mean cyst score decreased from 7.4 to 6.5. Overall, 25 (34.2%) exhibited an HRCT response and 3 (4.1%) had HRCT progression, while 45 (61.6%) maintained the same. Among patients with CR or PR, 57.1% and 40.5%, respectively, experienced an HRCT response, whereas no patient with SD or PD had an HRCT response. Multivariable analyses revealed that patients who received low dose cytarabine regimen and those with HRCT score ≥ 10 predicted a shorter PFS. Long Chang, Luo Wang, and Zheng-zheng Liu contributed equally to this work.

Evaluating the Application of Machine Learning in Predicting the Mortality of Hospitalized COVID-19 Patients Using the Confusion Matrix and the Matthews Correlation Coefficient

Background: The COVID-19 pandemic, which occurred between 2019 and 2023, posed a significant threat to global health. Its high transmissibility, the emergence of new variants, and the novel nature of the disease made treatment and control highly challenging. Objectives: This study aimed to develop an algorithm for predicting the mortality of hospitalized COVID-19 patients using machine learning methods. Methods: This cross-sectional study was conducted on 581 hospitalized COVID-19 patients. The approach integrated multi-model features derived from computed tomography (CT) scans and electronic health record (EHR) data. High-resolution computed tomography (HRCT) images were initially processed using the Pulmonary Toolkit package in MATLAB software. Subsequently, the extracted variables were entered into the model as predictive factors, alongside demographic characteristics, underlying conditions, and laboratory results of the patients. The machine learning model was developed using the AdaBoost method by incorporating demographic and laboratory data with HRCT features. Results: In this study, 581 hospitalized COVID-19 patients were included. Among them, 199 (34.25%) patients died, while 382 (65.75%) recovered. According to the machine learning algorithm, the most effective variables for predicting COVID-19 mortality were lymphocyte variables, CRP, age, mean lung density, lung tissue percentage, RBC count, D-dimer levels, and emphysema. The MCC Index in this study was 0.73, and the area under the ROC curve was 0.96. Conclusions: According to our results, the three variables with the greatest impact on predicting mortality in COVID-19 patients were related to HRCT findings, laboratory results, and patient age. Therefore, it is recommended that, given the high cost of HRCT, this diagnostic test should only be performed if other risk factors are identified in laboratory results. If necessary, HRCT should be conducted promptly.