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: Measurement of mediastinal lymph nodes (LNs) is an integral part of patient assessment, and is performed by manually measuring the short axis length (SAL) of the LNs on axial slices. LNs with SAL ≥10 mm are considered pathologically enlarged. We aimed to quantify the interobserver agreement and variability of SAL measurements, compare them to automatically computed SALs from manual LN delineations, and establish the mean SAL measurement error.

Materials and methods: Two radiologists independently measured the SALs of 451 LNs in 40 contrast-enhanced chest CT (CECT) scans. One of them also manually delineated the LN contours in each CECT slice, and this served to automatically classify LN as normal/enlarged based on their SALs. Differences between SAL measurements and Bland-Altman statistics were computed.

Results: The normal/enlarged LN overall agreement (371 normal, 52 enlarged) between both radiologists was 93.8% (423/451). For agreement/disagreement, the SAL differences were 1.1 (1.0) mm (17%) and 3.5 (3.2) mm (40%). The disagreement differences were nearly twice as large as the agreement differences. The agreement between the manual and the computed SALs for both radiologists was 92.7% (418/451), similar to the interobserver variability.

Conclusion: Classification of mediastinal lymph nodes based on SAL measurements demonstrates high agreement. It indicates that SAL measurements automatically computed from manual LN delineations could be a reliable and time-saving tool. In cases of disagreement, the ±2 mm error supports the use of 3 size categories: normal (<8 mm), possibly enlarged (8 to 12 mm), and definitely enlarged (>12 mm).

Purpose: To investigate imaging phenotypes in posthospitalized COVID-19 patients by integrating quantitative CT (QCT) and machine learning (ML), with a focus on small airway disease (SAD) and its correlation with plethysmography.

Materials and methods: In this single-center cross-sectional retrospective study, a subanalysis of a larger prospective cohort, 257 adult survivors from the initial COVID-19 peak (mean age, 56±13 y; 49% male) were evaluated. Patients were admitted to a quaternary hospital between March 30 and August 31, 2020 (median length of stay: 16 [8-26] d) and underwent plethysmography along with volumetric inspiratory and expiratory chest CT 6 to 12 months after hospitalization. QCT parameters were derived using AI-Rad Companion Chest CT (Siemens Healthineers).

Results: Hierarchical clustering of QCT parameters identified 4 phenotypes among survivors, named "SAD," "intermediate," "younger fibrotic," and "older fibrotic," based on clinical and imaging characteristics. The SAD cluster (n=37, 14%) showed higher residual volume (RV) and RV/total lung capacity (TLC) ratios as well as lower FEF25-75/forced vital capacity (FVC) on plethysmography. The older fibrotic cluster (n=42, 16%) had the lowest TLC and FVC values. The younger fibrotic cluster (n=79, 31%) demonstrated lower RV and RV/TLC ratios and higher FEF25-75 than the other phenotypes. The intermediate cluster (n=99, 39%) exhibited characteristics that were intermediate between those of SAD and fibrotic phenotypes.

Conclusion: The integration of inspiratory and expiratory chest CT with quantitative analysis and ML enables the identification of distinct imaging phenotypes in long COVID patients, including a unique SAD cluster strongly associated with specific pulmonary function abnormalities.

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: To evaluate the role of chest CT radiomics in classifying mediastinal lymphadenopathy caused by hematologic malignancies and abdominopelvic solid cancers.

Materials and methods: A total of 231 patients with mediastinal lymphadenopathy were selected from the Mediastinal-Lymph-Node-SEG collection in The Cancer Imaging Archive, including 145 patients with hematologic malignancies (74 with chronic lymphocytic leukemia and 71 with lymphoma) and 86 with abdominopelvic solid cancers. Patients were randomly stratified into train and test sets in a 7:3 ratio. Radiomics features were extracted from enhanced CT images of mediastinal lymph nodes, followed by feature selection using univariate analysis and least absolute shrinkage and selection operator regression. A support vector machine algorithm was used to develop classification models, with performance evaluated using the area under the receiver operating characteristic curve (AUC-ROC), accuracy, and 95% CI.

Results: For differentiating mediastinal lymphadenopathy between hematologic malignancies and abdominopelvic solid cancers, the model incorporated 23 features and achieved an AUC-ROC of 0.931 (95% CI: 0.891-0.971) and an accuracy of 0.866 in the train set, and an AUC-ROC of 0.830 (95% CI: 0.730-0.929) and an accuracy of 0.759 in the test set. For distinguishing chronic lymphocytic leukemia from lymphoma, the model utilized 4 features, achieving an AUC-ROC of 0.880 (95% CI: 0.813-0.947) and an accuracy of 0.752 in the train set, and an AUC-ROC of 0.872 (95% CI: 0.763-0.982) and an accuracy of 0.836 in the test set.

Conclusions: Chest CT radiomics shows promise for classifying mediastinal lymphadenopathy in patients with hematologic malignancies and abdominopelvic solid cancers.

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.