Journal of Thoracic Imaging最新文献

Purpose: To describe the spectrum of computed tomography (CT) findings in various chronic pulmonary aspergillosis (CPA) subtypes.

Material and methods: This retrospective study analyzed the CT scans of consecutively diagnosed CPA cases. Two radiologists independently evaluated the CT findings (both qualitatively and quantitatively) to characterize the lung cavities, intracavitary contents, pericavitary opacities and fibrosis, mediastinal shift, pleural thickening, and underlying structural lung disease. Patients were then classified into CPA subtypes, and between-group differences were assessed using the sample t test, Wilcoxon test, χ 2 test, and Fisher exact test.

Results: Among 103 patients with CPA (mean age: 47.26 ± 1.98 y; 69 men), 77.7%, 15.5%, and 6.8% were categorized as chronic cavitary pulmonary aspergillosis, chronic fibrosing pulmonary aspergillosis (CFPA), and single/simple aspergilloma, respectively. The mean symptom duration was 2.7 ± 3.96 years, with cough being the most common symptom (86.4%). Underlying post-tubercular lung abnormalities were observed in 97.1% of the patients. Cavities were observed in all patients (100%), most commonly in the left upper lobe (68.0%). The difference in cavity number among CPA subtypes was statistically significant ( P = 0.003), with 87.5% CFPA and 41.5% chronic cavitary pulmonary aspergillosis cases showing multiple cavities. The overall median cavity wall thickness was 6 mm (interquartile range: 2.8), with the highest value in the CFPA. Pericavitary fibrosis was observed in 70.9% of overall cases and in 100% of CFPA cases ( P < 0.001). Intracavitary contents were identified in 89.3% of patients. The median pleural thickness was 8 mm (interquartile range: 4), which was significantly different among CPA subtypes ( P = 0.001). There was excellent interobserver agreement (k = 0.94) between the two readers. Posterior intercostal lymph nodes were identified in 66%, a novel CPA observation.

Conclusion: Comprehensive qualitative and quantitative assessment of CT findings improves the characterization of the CPA subtypes. The number and size of lung cavities, mediastinal shift, and pleural thickness, among other quantitative parameters, vary significantly across CPA subtypes, facilitating more accurate differentiation between them.

Purpose: To investigate the clinical and computed tomography (CT) features of nodular pulmonary amyloidosis (NPA) to enhance our understanding of the disease and improve the ability to differentiate it from other similar conditions.

Materials and methods: A retrospective analysis was conducted on the clinical data, chest CT imaging findings, and pathologic characteristics of 13 patients with NPA in our hospital from April 2014 to April 2024. All 13 patients underwent chest CT plain scan examination. The basic data, medical history, clinical manifestations, and lung lesion features on chest CT imaging were analyzed and summarized.

Results: Among the 13 patients, there were 3 males (23.08%) and 10 females (76.92%). Their ages ranged from 37 to 68 years, with a mean age of (57.85±8.40) years and a median age of 59 years. Three (23.08%) patients had cough and sputum, while the others (76.92%) had no clinical symptoms. Before surgery, 6 patients underwent chest CT scans, and NPA changes in size, shape, and density were observed. Six cases (46.15%) were located in the left lung (4 in the upper lobe and 2 in the lower lobe), and 7 cases (53.85%) in the right lung (3 in the upper lobe, 2 in the middle lobe, and 2 in the lower lobe). Seven cases (53.85%) of NPA were round or oval, while 6 cases (46.15%) were irregularly shaped. Out of the NPA cases, 11 (84.62%) were solid nodules with well-defined boundaries, including 2 cases of solid nodules with surrounding calcification. In addition, 2 cases presented as solid nodules with cavities. Ten cases (76.92%) had multiple cystic lesions in the bilateral lungs, in which 7 cases had more than 10 cysts with obvious cyst walls, and 1 case showed a solid nodule on the cyst wall. During the postoperative follow-up, 1 patient experienced an increase in the size of the original nodule and the appearance of new solid nodules. Subsequent surgery revealed mucosal-associated lymphoid tissue lymphoma (MALT). The remaining patients were followed up regularly, and their conditions remained stable.

Conclusions: NPA is more common in middle-aged and elderly people and is more likely to occur in women. Most cases are asymptomatic, and bilateral lungs can be involved. For nodules with multiple pulmonary cysts found by chest CT, the possibility of NPA should be considered, and further histopathologic examination is needed to confirm the diagnosis. Most patients with NPA have a good long-term prognosis after surgical resection, but some patients require further investigation and close follow-up due to underlying causes.

Purpose: Trauma-induced rib fractures are common injuries. The gold standard for diagnosing rib fractures is computed tomography (CT), but the sensitivity in the acute setting is low, and interpreting CT slices is labor-intensive. This has led to the development of new diagnostic approaches leveraging deep learning (DL) models. This systematic review and pooled analysis aimed to compare the performance of DL models in the detection, segmentation, and classification of rib fractures based on CT scans.

Materials and methods: A literature search was performed using various databases for studies describing DL models detecting, segmenting, or classifying rib fractures from CT data. Reported performance metrics included sensitivity, false-positive rate, F1-score, precision, accuracy, and mean average precision. A meta-analysis was performed on the sensitivity scores to compare the DL models with clinicians.

Results: Of the 323 identified records, 25 were included. Twenty-one studies reported on detection, four on segmentation, and 10 on classification. Twenty studies had adequate data for meta-analysis. The gold standard labels were provided by clinicians who were radiologists and orthopedic surgeons. For detecting rib fractures, DL models had a higher sensitivity (86.7%; 95% CI: 82.6%-90.2%) than clinicians (75.4%; 95% CI: 68.1%-82.1%). In classification, the sensitivity of DL models for displaced rib fractures (97.3%; 95% CI: 95.6%-98.5%) was significantly better than that of clinicians (88.2%; 95% CI: 84.8%-91.3%).

Conclusions: DL models for rib fracture detection and classification achieved promising results. With better sensitivities than clinicians for detecting and classifying displaced rib fractures, the future should focus on implementing DL models in daily clinics.

Level of evidence: Level III-systematic review and pooled analysis.

Purpose: To explore the CT features in prognostic evaluations for pulmonary mucormycosis in patients with hematological diseases.

Materials and methods: A retrospective analysis of clinical data and chest CT features of 53 HD patients with PM was conducted. Univariate and multivariate logistic regression analyses were used to determine the risk factors for death. The Cox regression model was used to analyze the factors affecting the survival rate.

Results: A total of 30 patients with proven PM and 23 with probable PM were included. All 30 patients with proven PM underwent bronchoscopy-guided biopsy, among which 9 cases underwent surgical resection. Of the 23 patients with probable PM, 5 cases had positive results in sputum smear microscopy, 4 cases in sputum culture, 13 cases in bronchoalveolar lavage fluid (BALF) microscopy, and 1 case in BALF culture. All identification of pathogen genera and partial species was conducted by metagenomic next-generation sequencing (mNGS) testing. In the multivariate regression analysis, the CT feature of multiple lesions (≥2) on the initial CT scan was an independent risk factor for mortality ( P =0.019). Cox survival analysis demonstrated a significantly lower survival rate ( P =0.043) in patients exhibiting the CT feature of multiple lesions on the initial CT scan.

Conclusions: The CT feature of multiple lesions (≥2) on the initial CT may serve as an independent risk factor for mortality in patients with hematologic disorders with pulmonary mucormycosis.

Acute respiratory distress syndrome (ARDS) is a life-threatening condition characterized by widespread inflammation in the lungs. It is associated with high mortality and morbidity in critically ill patients. ARDS are conditions that cause acute respiratory failure due to noncardiogenic pulmonary edema, leading to severe hypoxemia and diffuse, bilateral lung injury. These conditions represent a spectrum of lung injury with varying severity and complexity. ARDS is a more severe form of ALI. ALI can also describe a range of clinical and paraclinical findings that include one or both pathologic patterns of organizing pneumonia (OP) or diffuse alveolar damage (DAD). The pathologic correlate of ARDS is DAD. This damage can be triggered by various risk factors, including pneumonia, sepsis, trauma, and the inhalation of harmful substances. The alveolar capillary damage that accompanies DAD leads to a loss in barrier function and is associated with the accumulation of fluid into the alveolar space. This fluid accumulation (pulmonary edema), along with subsequent organization and scarring, impairs gas exchange, which leads to hypoxemia and respiratory failure. Despite advances in understanding the pathophysiology of ARDS and improvements in supportive care, the mortality rates from ARDS still range from 25% to 45%. It is crucial to recognize that radiographic and histologic findings in a patient with ARDS can vary significantly depending on the phase of the disease. This is because the pathophysiological processes underlying these conditions evolve over time, leading to changes in both clinical presentation and imaging findings. Misinterpretation of these findings could lead to incorrect diagnoses and inappropriate treatment strategies. Therefore, understanding the temporal evolution of this condition is essential for accurate diagnosis and effective management. Our paper seeks to examine the existing literature focusing on radiology and pathology at different phases of injury and resolution to enhance management of ARDS.

Purpose: To investigate and compare the diagnostic performance of 10 different large language models (LLMs) and 2 board-certified general radiologists in thoracic radiology cases published by The Society of Thoracic Radiology.

Materials and methods: We collected publicly available 124 "Case of the Month" from the Society of Thoracic Radiology website between March 2012 and December 2023. Medical history and imaging findings were input into LLMs for diagnosis and differential diagnosis, while radiologists independently visually provided their assessments. Cases were categorized anatomically (parenchyma, airways, mediastinum-pleura-chest wall, and vascular) and further classified as specific or nonspecific for radiologic diagnosis. Diagnostic accuracy and differential diagnosis scores (DDxScore) were analyzed using the χ 2 , Kruskal-Wallis, Wilcoxon, McNemar, and Mann-Whitney U tests.

Results: Among the 124 cases, Claude 3 Opus showed the highest diagnostic accuracy (70.29%), followed by ChatGPT 4/Google Gemini 1.5 Pro (59.75%), Meta Llama 3 70b (57.3%), ChatGPT 3.5 (53.2%), outperforming radiologists (52.4% and 41.1%) and other LLMs ( P <0.05). Claude 3 Opus DDxScore was significantly better than other LLMs and radiologists, except ChatGPT 3.5 ( P <0.05). All LLMs and radiologists showed greater accuracy in specific cases ( P <0.05), with no DDxScore difference for Perplexity and Google Bard based on specificity ( P >0.05). There were no significant differences between LLMs and radiologists in the diagnostic accuracy of anatomic subgroups ( P >0.05), except for Meta Llama 3 70b in the vascular cases ( P =0.040).

Conclusions: Claude 3 Opus outperformed other LLMs and radiologists in text-based thoracic radiology cases. LLMs hold great promise for clinical decision systems under proper medical supervision.

Purpose: Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model.

Materials and methods: A QML model analyzed 52 CT exams from patients who underwent same-day conventional and PCD-CT for suspected ILD. Lin's concordance correlation coefficient (CCC) assessed agreement between conventional and PCD-CT QML results. A CCC >0.90 was regarded as excellent, 0.9 to 0.8 as good, and <0.80 as a poor concordance. Spearman rank correlation evaluated the association between pulmonary function test results (PFT) and QML features (reticulation [R], honeycombing [HC], ground glass [GG], interstitial lung disease [ILD], and vessel-related structures [VRS]). Correlations were statistically significant if the 95% CI did not include 0.00 and P value <0.05.

Results: Conventional and PCD-CT QML results had good to excellent concordance (CCC ≥0.8) except for total HC (CCC <0.8), likely related to better PCD-CT honeycombing delineation. Overall, compared with conventional CT, PCD-CT had consistently more statistically significant correlation with PFT for HC (9 PCD vs. 2 conventional of 28 total and regional associations), similar correlation for R (20 PCD vs. 18 conventional of 28 associations) and VRS (19 PCD vs. 23 conventional of 28 associations), and less correlation for GG extent (12 PCD vs. 20 conventional associations).

Conclusions: There is strong agreement between conventional and PCD-CT QML ILD features except for HC. PCD-CT improved HC but decreased GG extent correlation with PFT. Therefore, even though most quantitative features were not impacted by the newer PCD-CT technology, model adjustment is necessary.

Acute pulmonary injury can occur in response to any number of inciting factors. The body's response to these insults is much less diverse and usually categorizable as one of several patterns of disease defined by histopathology, with corresponding patterns on chest CT. Common patterns of acute injury include diffuse alveolar damage, organizing pneumonia, acute eosinophilic pneumonia, and hypersensitivity pneumonitis. The ultimate clinical diagnosis is multidisciplinary, requiring a detailed history and relevant laboratory investigations from referring clinicians, identification of injury patterns on imaging by radiologists, and sometimes tissue evaluation by pathologists. In this review, several clinical diagnoses will be explored, grouped by imaging pattern, with a representative clinical presentation, a review of the current literature, and a discussion of typical imaging findings. Additional information on terminology and disambiguation will be provided to assist with comprehension and standardization of descriptions. The focus will be on the acute phase of illness from presentation to diagnosis; treatment methods and chronic sequela of acute disease are beyond the scope of this review.

Purpose: To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).

Material and methods: This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location.

Results: The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6 mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively).

Conclusions: At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.