Differentiation Between Invasive Adenocarcinoma and Focal Interstitial Fibrosis among Persistent Pulmonary Part-solid Nodules: With Emphasis on the CT Morphologic Analysis.

PURPOSE: Focal interstitial fibrosis (FIF) manifesting as a persistent part-solid nodule (PSN) has been mistakenly treated surgically due to similar imaging features to invasive adenocarcinoma (ADC). The purpose of this study was to observe predictive imaging features correlated with FIF through CT morphologic analysis. MATERIALS AND METHODS: From January 2009 to December 2020, 44 patients with surgically proven FIF in a single institution were enrolled and compared with 88 ADC patients through propensity score matching. Patient characteristics and CT morphologic analysis of persistent PSNs were used to identify predictive imaging features of FIF. Receiver operating characteristic (ROC) curve analysis was used to quantify the performance of imaging features. RESULTS: A total of 132 patients with 132 PSNs (44 FIF, 88 ADC; mean age, 67.7±7.58; 75 females) were involved in our analysis. Multivariable analysis demonstrated that preserved peritumoral vascular margin (preserved vascular margin), preserved secondary pulmonary lobule margin (preserved lobular margin), and lower coronal to axial ratio (C/A ratio; cutoff: 1.005) were significant independent predictors of FIF (P<0.05). ROC curve analysis to evaluate the predictive value of the logistic model based on the imaging features of FIF, and the AUC value was 0.881. CONCLUSION: CT imaging features of preserved vascular margin, preserved lobular margin, and lower C/A ratio (cutoff, <1.005) might be helpful imaging features in discriminating FIF over ADC among persistent PSN in clinical practice.

SMARCA4-deficient non-small cell lung carcinoma: clinicodemographic, computed tomography, and positron emission tomography-computed tomography features.

Background: SMARCA4-deficient non-small cell lung carcinoma (SD-NSCLC) is a relatively rare tumor, which occurs in 5-10% of NSCLC. Based on World Health Organization thoracic tumor classification system, SMARCA4-deficient undifferentiated tumor (SD-UT) is recognized as a separate entity from SD-NSCLC. Differentiation between SD-NSCLC and SD-UT is often difficult due to shared biological continuum, but often required for choosing appropriate treatment regimen. Therefore, the aim of our study was to identify the clinicopathologic, computed tomography (CT), and positron emission tomography (PET)-CT imaging features of SD-NSCLC. Methods: Nine patients of pathologically confirmed SD-NSCLC were included in our analysis. We reviewed electronic medical records for clinical information, demographic features, CT, and PET-CT imaging features were analyzed. Results: Smoking history and male predominance are observed in all patients with SD-NSCLC (n=9). On CT, SD-NSCLC appeared as relatively well-defined masses with lobulated contour (n=8) and peripheral location (n=7). Invasion of adjacent pleura or chest wall (n=7) were frequently observed, regardless of small tumor size. Four cases showed lymph node metastases. Among nine patients, three patients showed multiple bone metastases, and one patient showed lung-to-lung metastases. Conclusions: In patient with SD-NSCLC, there was tendency for male smokers, peripheral location and invasion of adjacent pleural or chest wall invasion regardless of small tumor size, when compared to SD-UT.

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

CT evaluation of systemic artery to pulmonary artery fistula: an underdiagnosed disease in patients with hemoptysis.

Background: Systemic artery to pulmonary artery fistula (SA-PAF) is an uncommon disease which is often incidentally diagnosed during evaluation of hemoptysis patients. The aim of our study was to describe the cases of SA-PAF in our institution and to report the correlating clinical and radiological findings. Methods: We reviewed 231 chest computed tomography (CT) scans performed in our institution due to hemoptysis from January 2020 to February 2023. In patients diagnosed with SA-PAF had their electronic medical records and CT images analyzed. Results: In 231 patients, 19 (8.2%) of them had SA-PAF findings which was characterized by a peripheral nodular soft tissue opacity in the subpleural lung and traceable vascular structure in continuity with one or more peripheral pulmonary artery branches in CT. Etiology of each patient was categorized as either congenital (7, 36.8%), and acquired (12, 63.2%). The origins of SA-PAFs were 16 intercostal, two anterior mediastinal, and one costocervical artery. Eight of 19 patients did not show any associated intralobar imaging abnormalities, while bronchiectasis, cellular bronchiolitis, centrilobular emphysema, and pleura effusion were observed in 11 patients. Conclusions: SA-PAF is a benign vascular anomaly which is frequently overlooked when evaluating hemoptysis by either clinician or radiologists but is an important factor in the differential diagnosis of patients with hemoptysis.

Placental transmogrification of the lung: CT findings with pathologic correlation in six patients.

Background: Placental transmogrification of the lung is a very rare benign lung disease with a characteristic finding being alveoli resembling chorionic villi of the placenta. The purpose of this study was to assess the computed tomography (CT) findings of placental transmogrification of the lung in six patients and their relation to the histopathologic findings. Methods: Six patients with histopathologically proven placental transmogrification of the lung from 2004 to 2021 were included. Their CT findings were analyzed and their imaging features were compared with pathology specimens. Results: In four of six cases, CT showed variable sized cystic lesions confined to a unilateral lung. One case presented nodule and cystic lesion together. The other case showed solitary pulmonary nodule without cystic lesion. Moreover, nodular interlobular septal thickening and clustered interstitial nodules were observed in all six cases. In four of the six cases, these nodules merged into dense nodular consolidation. Three cases showed dilated pulmonary vasculatures of the involved lung. Conclusions: On CT, placental transmogrification of the lung typically presents as cystic lesion confined to a unilateral lung. Pulmonary nodule with or without associated cystic lesion can also be seen. Nodular interlobular septal thickening and clustered interstitial nodules were observed in all cases. This might be attributable to the proliferation of chorionic villi-like structures in interstitium which are found in histopathologic specimens.

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT.

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.

Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study.

BACKGROUND: The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters. METHODS: In a retrospective single-institutional study, 72 patients, who obtained serial CXRs (n = 147) for pulmonary nodules or masses with corresponding chest CT images as the reference standards, were included. A pre-trained DLAD based on a convolutional neural network was developed to detect and localize nodules using 13,710 radiographs and to calculate a localization map and the derived parameters (e.g., the area and mean probability value of pulmonary nodules) for each CXR, including serial follow-ups. For validation, reference 3D CT volumes were measured semi-automatically. Volume prediction models for pulmonary nodules were established through univariable or multivariable, and linear or non-linear regression analyses with the parameters. A polynomial regression analysis was performed as a method of a non-linear regression model. RESULTS: Of the 147 CXRs and 208 nodules of 72 patients, the mean volume of nodules or masses was measured as 9.37 ± 11.69 cm3 (mean ± standard deviation). The area and CT volume demonstrated a linear correlation of moderate strength (i.e., R = 0.58, RMSE: 9449.9 mm3 m3 in a linear regression analysis). The area and mean probability values exhibited a strong linear correlation (R = 0.73). The volume prediction performance based on a multivariable regression model was best with a mean probability and unit-adjusted area (i.e. , RMSE: 7975.6 mm3, the smallest among the other variable parameters). CONCLUSIONS: The prediction model with the area and the mean probability based on the DLAD showed a rather accurate quantitative estimation of pulmonary nodule or mass volume and the change in serial CXRs.

Predictors of critical illness among young males with chest pain, abdominal pain, or headaches in the Republic of Korea Army.

Purpose: Chest pain, abdominal pain, and headache are common symptoms associated with critical illness. Here, we aimed to evaluate predictors associated with critical illness in young males of the Republic of Korea Army. Methods: We retrospectively reviewed previously healthy young males with chest pain, abdominal pain, or headaches who visited Armed Forces Seoul District Hospital between January 2019 and December 2020. Critical illness was defined as a condition that required hospitalization, a procedure or surgery, or referral to a tertiary hospital. The symptoms and signs of critical illness were evaluated. Results: Of the 762 enrolled patients, a critical illness was diagnosed in 45 patients (5.9%). Among chest pain signs, palpitation (odds ratio [OR], 22.8; 95% confidence interval [CI], 5.08-102.4; p < 0.001), exertional dyspnea (OR, 16.3; 95% CI, 3.38-78.8; p = 0.001), duration (> 5 minutes) (OR, 7.54; 95% CI, 1.93-29.49; p = 0.004), and squeezing type (OR, 5.28; 95% CI, 1.11-25.11; p = 0.037) were significantly associated with critical illness. Among abdominal pain signs, right-lower-quadrant tenderness (OR, 11.87; 95% CI, 4.671-31.87; p < 0.001) was an alarming sign. For headaches, criticality was low (1.5%), and half of patients with critical illness were diagnosed incidentally regardless of headache. Conclusion: We identified symptoms and signs significantly associated with critical illness in young male patients. This study might serve as a reference for deciding when to transfer soldiers in the field to a rear hospital, thereby contributing to the welfare and combat power of soldiers.

Comparison of 2 shear wave elastography systems in reproducibility and accuracy using an elasticity phantom.

ABSTRACT: This study aimed to compare the accuracy and inter- and intra-observer reproducibility of the measured elasticity between 2 shear wave elastography systems. Three breast radiologists examined 8 targets of 4 different levels of stiffness (size: 11 mm, 4 mm) in an elasticity phantom (Customized 049A Elasticity QA Phantom, CIRS, Norfolk, VA, USA) using 2 different shear wave ultrasound elastography systems: SuperSonic Imagine (SSI) (SSI, Aix en Provence, France) and ShearScan (RS-80A, Samsung Medison, Seoul, Korea). Three radiologists performed ultrasound (US) elastography examinations for the phantom lesions using 2 equipment over a 1-week interval. Intra- and inter-observer reproducibility and the accuracy of the measured elasticity were analyzed and compared between the 2 systems. The accuracy of shape was also analyzed by shape-matching between B-mode and elastography color image. Intra-class correlation coefficients (ICC) were used in statistical analysis. For measured elasticity, the intra-observer and inter-observer reproducibility were excellent in both SSI and ShearScan (0.994 and 0.998). The overall accuracy was excellent in both systems, but the accuracy in small lesions (4 mm target) was lower in SSI than ShearScan (0.780 vs 0.967). The accuracy of shape-matching on the elastography image was 59.0% and 81.4% in the SSI and ShearScan, respectively. In conclusion, the SSI and ShearScan showed excellent intra- and inter-observer reproducibility. The accuracy of the Young's modulus was high in both the SSI and ShearScan, but the SSI showed decreased accuracy in measurement of elasticity in small targets and poor shape-matching between the B-mode image and color-coded elastography image.

CT features and disease spread patterns in ROS1-rearranged lung adenocarcinomas: comparison with those of EGFR-mutant or ALK-rearranged lung adenocarcinomas.

The purpose of this study was to investigate the differences in CT characteristics and disease spread patterns between ROS1-rearranged adenocarcinomas and epidermal growth factor receptor (EGFR)-mutant or anaplastic lymphoma kinase (ALK)-rearranged adenocarcinomas. Patients with stage IIIb/IV adenocarcinoma with ROS1 rearrangement, EGFR mutations, or ALK rearrangement were retrospectively identified. Two radiologists evaluated CT features and disease spread patterns. A multivariable logistic regression model was applied to determine the clinical and CT characteristics that can discriminate between ROS1-rearranged and EGFR-mutant or ALK-rearranged adenocarcinomas. A cohort of 169 patients was identified (ROS1 = 23, EGFR = 120, and ALK = 26). Compared to EGFR-mutant adenocarcinomas, ROS1-rearranged adenocarcinomas were less likely to have air-bronchogram (p = 0.011) and pleural retraction (p = 0.048) and more likely to have pleural effusion (p = 0.025), pericardial metastases (p < 0.001), intrathoracic and extrathoracic nodal metastases (p = 0.047 and 0.023, respectively), and brain metastases (p = 0.017). Following multivariable analysis, age (OR = 1.06; 95% CI: 1.01, 1.12; p = 0.024), pericardial metastases (OR = 10.50; 95% CI: 2.10, 52.60; p = 0.005), and nodal metastases (OR = 8.55; 95% CI: 1.14, 62.52; p = 0.037) were found to be more common in ROS1-rearranged tumors than in non-ROS1-rearranged tumors. ROS1-rearranged adenocarcinomas appeared as solid tumors and were associated with young age, pericardial metastases and advanced nodal metastases relative to tumors with EGFR mutations or ALK rearrangement.

Resected Pure Small Cell Lung Carcinomas and Combined Small Cell Lung Carcinomas: Histopathology Features, Imaging Features, and Prognoses.

OBJECTIVE: The objective of our study was to investigate histopathology features, imaging features, and prognoses of surgically resected pure small cell lung carcinomas (SCLCs) and combined SCLCs. MATERIALS AND METHODS: Forty-one patients with a pure SCLC or a combined SCLC underwent preoperative chest CT and 18F-FDG PET/CT and subsequent surgical resection. The clinicopathologic findings were noted by reviewing the electronic medical records. The imaging features of individual tumors were analyzed on chest CT and PET/CT scans. Each tumor was classified as being located centrally (at or in the segmental bronchus or proximal to the segmental bronchus) or peripherally (distal to the segmental bronchus). The maximum standardized uptake value (SUVmax) of each tumor was measured at PET. The 7th edition of the TNM staging system was adopted for staging. RESULTS: The study group was composed of 34 men and seven women with a mean age of 62.0 ± 10.2 (SD) years. Sixteen of 41 (39%) patients had pure SCLC, and the remaining patients had combined SCLC. The most common combined SCLC histologic subgroup was combined SCLC and large cell neuroendocrine carcinoma in 17 (41%) patients. The mean SUVmax of pure SCLCs was 5.6 ± 2.2 and was significantly lower than that of combined SCLCs (p < 0.01). Thirty-one patients (76%) had a peripheral tumor, and 10 (24%) had a central tumor. The overall survival (OS) of the 10 patients with a central tumor was 44.6 months, significantly shorter than the OS of the 31 patients with a peripheral tumor (179.2 months) (p = 0.017). The OS of 21 patients with stage I disease was significantly longer than the OS of patients with higher-stage cancer (p = 0.004). CONCLUSION: In our study group of patients with surgically resected SCLC, patients with a peripheral tumor (including a purely endobronchial tumor) or stage I disease showed a better prognosis than those with a central tumor or higher-stage disease.

Subcentimeter hypervascular nodules with typical imaging findings of hepatocellular carcinoma on gadoxetic acid-enhanced MRI: Outcomes of early treatment and watchful waiting.

OBJECTIVES: To compare treatment outcomes of subcentimeter hypervascular nodules at high risk for developing into hepatocellular carcinomas (SHNHR) between early treatment and watchful waiting until progression to overt hepatocellular carcinoma (HCC) groups. METHODS: SHNHRs were defined as subcentimeter hypervascular nodules with the usual imaging features of HCC on gadoxetic acid-enhanced magnetic resonance imaging (MRI). Among 63 patients with 74 SHNHRs, 27 (37 SHNHRs) received early treatment (treatment of < 1 cm nodules), and 36 (37 SHNHRs) underwent watchful waiting until progression to overt HCC. Risk factor analysis was performed for recurrence-free and local recurrence-free survival. RESULTS: Among the 36 patients who adopted watchful waiting, 33 eventually underwent treatment because their SHNHRs progressed to overt HCC. For recurrence-free survival, significant risk factors included number of previous treatments (HR, 1.181; p < 0.001), tumour number (HR, 1.943; p = 0.009), and α-feto protein level (HR, 1.005; p = 0.037) in the multivariate analyses. Treatment strategy was not a significant risk factor for recurrence-free survival. For local recurrence-free survival from the date of treatment, only treatment modality (transarterial chemoembolization) (HR, 6.879; p = 0.002) was a significant risk factor. CONCLUSION: Recurrence-free survival was not significantly different between early treatment and watchful waiting for SHNHRs. KEY POINTS: • Recurrence-free survival did not vary between the two treatment strategies. • Treatment modality was a significant factor for local recurrence-free survival. • Number of treatments, tumour number, and α-FP were risk factors for recurrence.

Epithelioid hemangioendothelioma in the thorax: Clinicopathologic, CT, PET, and prognostic features.

Little is known about prognostic factors in epithelioid hemangioendothelioma (EHE). We aimed to identify prognostic factors among various clinicopathologic and imaging features of thoracic EHEs.Forty-two patients (male:female = 20:22; median age, 49 years) of EHEs with (n = 19) and without (n = 23) thoracic involvement were included. We reviewed electronic medical records for clinical information and computed tomography (CT) features for thoracic involvement. Differences in demographics and survival outcomes of patients with and without thoracic involvement were assessed. We also estimated overall survival.The most common pattern of thoracic involvement was multiple pulmonary nodules (n = 10), followed by parenchymal tumor with pleural invasion (n = 4), reticulonodular opacities (n = 3), and diffuse pleural thickening (n = 2). No significant difference in survival was found between the thoracic EHE group and nonthoracic EHE group (P = 0.68). Among 4 different thoracic involvement types, the lung multinodular pattern tended to demonstrate longer median survival (8.5 months) than other patterns, whereas the shortest median survival (1 month) was observed for the nodule/mass with pleural involvement pattern (P = 0.038).CT manifestations of thoracic EHEs are classified into 4 patterns, of which lung multinodular pattern is associated with longer survival. Survival is not different between patients with and without thoracic involvement.