Anatomic Approach to Common and Uncommon Manifestations of Thoracic Leukemias with Radiologic-Pathologic Correlation.

Leukemias are hematopoietic malignancies characterized by the production of abnormal leukocytes in the bone marrow. Clinical manifestations arise from either bone marrow suppression or leukemic organ infiltration. Lymphadenopathy is the most common direct manifestation of intrathoracic leukemia. However, leukemic cells may also infiltrate the lungs, pleura, heart, bones, and soft tissues. Pulmonary complications in patients with leukemia typically include pneumonia, hemorrhage, pulmonary edema, and sequelae of leukemia treatment. However, pulmonary abnormalities can also be related directly to leukemia, including leukemic pulmonary infiltration. The direct, non-treatment-related effects of leukemia on intrathoracic structures will be the focus of this imaging essay. Given the typical anatomic approach for image interpretation, an organ-based depiction of common and less common intrathoracic findings directly caused by leukemic involvement is presented, emphasizing imaging findings with pathologic correlations. Keywords: Leukemia, Pulmonary, Thorax, Soft Tissues/Skin, Hematologic, Bone Marrow © RSNA, 2023.

Quantitative CT and machine learning classification of fibrotic interstitial lung diseases.

OBJECTIVES: To evaluate quantitative computed tomography (QCT) features and QCT feature-based machine learning (ML) models in classifying interstitial lung diseases (ILDs). To compare QCT-ML and deep learning (DL) models' performance. METHODS: We retrospectively identified 1085 patients with pathologically proven usual interstitial pneumonitis (UIP), nonspecific interstitial pneumonitis (NSIP), and chronic hypersensitivity pneumonitis (CHP) who underwent peri-biopsy chest CT. Kruskal-Wallis test evaluated QCT feature associations with each ILD. QCT features, patient demographics, and pulmonary function test (PFT) results trained eXtreme Gradient Boosting (training/validation set n = 911) yielding 3 models: M1 = QCT features only; M2 = M1 plus age and sex; M3 = M2 plus PFT results. A DL model was also developed. ML and DL model areas under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs) were compared for multiclass (UIP vs. NSIP vs. CHP) and binary (UIP vs. non-UIP) classification performances. RESULTS: The majority (69/78 [88%]) of QCT features successfully differentiated the 3 ILDs (adjusted p ≤ 0.05). All QCT-ML models achieved higher AUC than the DL model (multiclass AUC micro-averages 0.910, 0.910, 0.925, and 0.798 and macro-averages 0.895, 0.893, 0.925, and 0.779 for M1, M2, M3, and DL respectively; binary AUC 0.880, 0.899, 0.898, and 0.869 for M1, M2, M3, and DL respectively). M3 demonstrated statistically significant better performance compared to M2 (∆AUC: 0.015, CI: [0.002, 0.029]) for multiclass prediction. CONCLUSIONS: QCT features successfully differentiated pathologically proven UIP, NSIP, and CHP. While QCT-based ML models outperformed a DL model for classifying ILDs, further investigations are warranted to determine if QCT-ML, DL, or a combination will be superior in ILD classification. KEY POINTS: • Quantitative CT features successfully differentiated pathologically proven UIP, NSIP, and CHP. • Our quantitative CT-based machine learning models demonstrated high performance in classifying UIP, NSIP, and CHP histopathology, outperforming a deep learning model. • While our quantitative CT-based machine learning models performed better than a DL model, additional investigations are needed to determine whether either or a combination of both approaches delivers superior diagnostic performance.

Epithelioid hemangioendothelioma: evaluation by 18F-FDG PET/CT.

PURPOSE: The purpose of this study was to evaluate the imaging characteristics of epithelioid hemangioendothelioma (EHE) on staging 18F-FDG PET/CT. MATERIALS AND METHODS: An IRB-approved retrospective review was conducted for patients with biopsy-proven EHE who underwent FDG PET/CT at our institution between 2005 and 2019. Patients with a history of surgery, chemotherapy, or radiotherapy prior to PET/CT were excluded. PET/CT exams were analyzed, noting metabolic activity, distribution of involvement, and CT morphologic features. PET/CT findings were correlated with comparative CT and MRI performed within three months. RESULTS: There were 35 patients [21 females, 14 males; average age 55.1±16.9 years (range 15-82 years)]. 18/35 patients (52%) had more than one organ affected on PET/CT. The most common sites were liver [21/35 (60%)], lung [(19/35 (54%)], bone [5/35 (14%)], lymph nodes [4/35 (11%)], and vasculature [4/35 (11%)]. Most patients [30/35, (86%)] presented with multiple lesions. The average largest lesion dimension was 4.0±3.6 cm (range 0.6-15.0 cm). The average SUVmax of the most metabolically active lesion at any site was 5.3±3.3 (range 1.2-17.1), and for bone was 7.9±5.4 (range 3.5-17.1), liver was 5.1±2.1 (range 2.6-10.5), and lung was 3.0±1.9 (range 1.2-8.5). Of patients with pulmonary lesions, 9/19 (47%) showed calcification, and 4/19 (21%) had nodules that were either non FDG-avid or too small for accurate SUV assessment. Of patients with hepatic lesions, 11/21 (52%) demonstrated capsular retraction, and 12/21 (57%) were found to have additional hepatic lesions on contrast-enhanced CT or MRI that were occult on PET/CT. CONCLUSION: EHE demonstrates variable, but most commonly moderate FDG activity on PET/CT. The most common sites of disease are the liver, lungs, and bones, and most patients present with multiple lesions and more than one organ involved. Given the intrinsic metabolic activity and multi-organ involvement, FDG PET/CT represents an attractive modality for EHE evaluation. However, it may be best used in combination with CT or MRI given that EHE pulmonary or hepatic lesions may be missed by PET/CT.