Journal of Thoracic Imaging最新文献

Purpose: To develop and validate a nomogram to predict hemoptysis after percutaneous transthoracic needle biopsy (PTNB) by integrating clinical and radiologic data, facilitating pre-biopsy decision-making.

Materials and methods: This single-center, retrospective cohort study included 1383 patients who underwent 1389 PTNB procedures between 2020 and 2022. The participants were randomly allocated to the training and validation cohorts. Logistic regression was performed to discern the independent predictors of hemoptysis within the clinical and radiologic variables. A nomogram was developed based on pre-biopsy variables obtained before the biopsy, and its performance was subsequently evaluated. The goodness of fit of the nomogram was compared with that of another model, which integrated pre-biopsy and post-biopsy variables.

Results: Among the 1389 procedures, hemoptysis was observed in 128 (9.2%) cases. Current smoking status, lesion size of <25 mm, consolidation-type lesions, presence of the computed tomography bronchus sign, and perilesional vascularity were independent predictors of PTNB-related hemoptysis. The nomogram based on the pre-biopsy variables showed fair discrimination abilities (area under the receiver operating characteristic curve = 0.79 and 0.76 in the training and validation cohorts, respectively) and strong calibration agreement in the training and validation cohorts. The model fit was good in both cohorts (P = 0.41 and 0.55 in the training and validation cohorts, respectively). No significant difference was observed in the model fit between the pre-biopsy nomogram and the model incorporating pre-biopsy and post-biopsy variables (P = 0.88).

Conclusion: The proposed nomogram utilizing pre-biopsy variables could predict hemoptysis before PTNB.

Purpose: The aim of this study was to evaluate T1 and T2 values and to investigate their association with left ventricular (LV) hypertrophy and strains in hypertrophic cardiomyopathy (HCM) without late gadolinium enhancement (LGE).

Materials and methods: Forty-eight HCM patients without LGE and 20 age-matched and sex-matched healthy subjects who underwent 3.0 T cardiovascular magnetic resonance imaging (CMR) were enrolled. Cine, T1, and T2 mapping and LGE sequencing were conducted. Unpaired t test, Mann-Whitney U test, χ2 test, Spearman correlation analysis, and univariable and multivariable linear regression were performed in this study.

Results: Patients with HCM without LGE had a relatively higher global circumferential strain (GCS) than the control group (-19.82% [-21.81%, -17.52%] vs -17.48% ± 3.42; P = 0.020). In contrast, the global longitudinal strain (GLS) in HCM patients without LGE was lower than that in the control group (-12.07% ± 2.89 vs -13.93% ± 3.03; P = 0.021). In addition, native T1 values, extracellular volume (ECV), and T2 values were elevated in HCM patients without LGE compared with those in the control group (all P < 0.05). Moreover, higher native T1 values were associated with elevated T2 values (r = 0.301, P = 0.038). LV mass index (β = 0.375 [95% CI: 8.107 to 35.151], P = 0.002) and GCS (β = 0.623 [95% CI: 0.974 to 2.883], P < 0.001) were independently associated with elevated LV ejection fraction when max LV wall thickness, T2 value, global radial strain (GRS), and GLS were added to the multivariate regression model.

Conclusions: In HCM without LGE, elevated T1, T2, and ECV values and reduced GLS occurred despite preserved LV ejection fraction. These findings demonstrate that myocardial interstitial fibrosis and cellular edema may precede the early stages of HCM.

Purpose: Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using computed tomography pulmonary angiography (CTPA), clinical data, and PE Severity Index (PESI) scores to predict PE survival.

Materials and methods: In total, 918 patients (median age 64 y, range 13 to 99 y, 48% male) with 3978 CTPAs were identified via retrospective review across 3 institutions. To predict survival, an AI model was used to extract disease-related imaging features from CTPAs. Imaging features and clinical variables were then incorporated into independent DL models to predict survival outcomes. Cross-modal fusion CoxPH models were used to develop multimodal models from combinations of DL models and calculated PESI scores. Five multimodal models were developed as follows: (1) using CTPA imaging features only, (2) using clinical variables only, (3) using both CTPA and clinical variables, (4) using CTPA and PESI score, and (5) using CTPA, clinical variables, and PESI score. Performance was evaluated using the concordance index (c-index). Kaplan-Meier analysis was performed to stratify patients into high-risk and low-risk groups. Additional factor-risk analysis was conducted to account for right ventricular (RV) dysfunction.

Results: For both data sets, the multimodal models incorporating CTPA features, clinical variables, and PESI score achieved higher c-indices than PESI alone. Following the stratification of patients into high-risk and low-risk groups by models, survival outcomes differed significantly (both P <0.001). A strong correlation was found between high-risk grouping and RV dysfunction.

Conclusions: Multiomic DL models incorporating CTPA features, clinical data, and PESI achieved higher c-indices than PESI alone for PE survival prediction.

Purpose: Evaluate the false positive rate (FPR) of nodule detection software in real-world use.

Materials and methods: A total of 250 nonenhanced chest computed tomography (CT) examinations were randomly selected from an academic institution and submitted to the ClearRead nodule detection system (Riverain Technologies). Detected findings were reviewed by a thoracic imaging fellow. Nodules were classified as true nodules, lymph nodes, or other findings (branching opacity, vessel, mucus plug, etc.), and FPR was recorded. FPR was compared with the initial published FPR in the literature. True diagnosis was based on pathology or follow-up stability. For cases with malignant nodules, we recorded whether malignancy was detected by clinical radiology report (which was performed without software assistance) and/or ClearRead.

Results: Twenty-one CTs were excluded due to a lack of thin-slice images, and 229 CTs were included. A total of 594 findings were reported by ClearRead, of which 362 (61%) were true nodules and 232 (39%) were other findings. Of the true nodules, 297 were solid nodules, of which 79 (27%) were intrapulmonary lymph nodes. The mean findings identified by ClearRead per scan was 2.59. ClearRead mean FPR was 1.36, greater than the published rate of 0.58 ( P <0.0001). If we consider true lung nodules <6 mm as false positive, FPR is 2.19. A malignant nodule was present in 30 scans; ClearRead identified it in 26 (87%), and the clinical report identified it in 28 (93%) ( P =0.32).

Conclusion: In real-world use, ClearRead had a much higher FPR than initially reported but a similar sensitivity for malignant nodule detection compared with unassisted radiologists.

The Society of Thoracic Radiology (STR) membership enthusiastically embraced the launch of its mentorship program, with peaks in participation and engagement after annual meetings and during the COVID pandemic. The program provides a valuable resource for early to mid-career thoracic radiologists, especially those lacking local resources. This report describes the program's inception and design, and summarizes the program's successes and challenges at 5 years, based on a 2023 mentorship survey. STR mentees, spanning early to mid-career stages, most frequently sought mentorship in career development, graduate medical education, research portfolio development, publishing, cardiac imaging, grant funding, and artificial intelligence. Mentors offered expertise in these areas, plus lung cancer screening, career development, and workplace navigation. The committee prioritized creating dyads based on mutual interest and expertise, achieving mutual top-choice match rates of 70% to 97%. Enduring dyads flourished as the program matured. At 5 years, a survey of participants was fielded. Mentees reported moderate to high program impact on scholarly activities, leadership, networking, clinical service, education, and career satisfaction. Mentors described satisfaction in their roles, highlighting networking, career satisfaction, and the opportunity to influence upcoming generations of cardiothoracic radiologists, thereby impacting the field's future. Most participants expressed high career satisfaction. Descriptive comments further enriched findings. Survey results confirmed that strengthening dyad formation and enhancing mentoring outcomes remain pivotal. Remote mentorship, while valuable, presents challenges-personal connections and contextual familiarity, considered essential to successful mentorship relationships, are typically absent in these settings. Activities to potentially enhance the STR mentorship program are offered.

Background: Bronchial arterial chemoembolization (BACE), as a safe and effective minimally invasive treatment method, is increasingly being accepted by more and more patients with advanced nonsmall-cell lung cancer (NSCLC). In recent years, drug-eluting beads (DEB)-BACE has also been applied in the field of lung cancer. It is still unclear which is more recommended due to the limited number of comparative studies between conventional BACE (C-BACE) and DEB-BACE.

Purpose: To compare the safety and efficacy of C-BACE (BACE with gelfoam particles) and DEB-BACE for advanced NSCLC.

Materials and methods: From January 2021 to April 2023, 48 consecutive patients (37 males and 11 females) with advanced NSCLC treated with DEB-BACE (group A) or C-BACE (group B) at our center were collected retrospectively in this study. There were 18 patients in group A and 30 patients in group B. The technical success rate, adverse events, objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were compared between the 2 groups.

Results: The technical success rate in both groups was 100%. The median OS times were 19.5 months and 12.5 months in group A and group B, respectively ( P =0.0062). The median PFS times were 13 months and 7 months in group A and group B, respectively ( P =0.0072). The ORRs at 6 months were 72.2% and 46.7% in group A and group B, respectively ( P =0.084). The DCRs at 6 months were 88.9% and 63.3% in group A and group B, respectively ( P =0.043). Grade 1 adverse events like chest pain, and cough were common, while serious adverse events did not occur.

Conclusions: BACE with DEB or gelfoam particles were equally safe. The DEB-BACE showed better survival and tumor response than C-BACE for advanced NSCLC.

Purpose: Computed tomography angiography (CTA) of the head and neck includes the pulmonary apices, a common location for pulmonary nodules. Computer-aided detection (CAD) is an adjunctive tool for the detection of lung nodules and is widely used in standard chest CT scans. We evaluated whether the available software can be applied to CTA head and neck examinations, which include the lung apices, resulting in improved accuracy for lung nodule detection.

Materials and methods: In this retrospective single-center study, 191 previously reported head and neck CTA scans were re-evaluated for apical pulmonary nodules by 2 radiologists. Subsequently, CAD software ( Syngo .via, Siemens Healthiness AG) was applied to the lung apices and the results were compared between CAD and research radiologists (first reading) or clinical radiologist (null reading). In addition, the CAD performance in limited lung fields was compared with the accepted CAD assessment applied to whole lungs.

Results: Of the 191 patients, 110 (57.6%) were men, with a mean age of 68 years. In the 24 CT scans, the research radiologists detected 40 nodules. In the 180 scans evaluated by CAD, the software detected 39 nodules in 22 examinations, with a sensitivity of 60.8% and a PPV of 63.6%. In the remaining 158 examinations in which CAD did not detect nodules, the radiologists concurred in 149 scans, with a specificity of 94.9%, NPV of 94.3%, and accuracy of 90.6%.

Conclusion: The study results indicate that CAD is an unexpected quick supportive tool for nodule detection, particularly for excluding clinically significant nodules in lung apices on CTA head and neck, showing similar results for partial and full lung fields.

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.