Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.

RATIONALE AND OBJECTIVES: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac segmentation; however, its effectiveness is limited by the slice range selection from base to apex. MATERIALS AND METHODS: In this study, we integrated an automated slice range classification step to identify basal to apical short-axis slices before DL-based segmentation. We employed publicly available Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI data set with short-axis cine data from 160 training, 40 validation, and 160 testing cases. Three classification and seven segmentation DL models were studied. The top-performing segmentation model was assessed with and without the classification model. Model validation to compare automated and manual segmentation was performed using Dice score and Hausdorff distance and clinical indices (correlation score and Bland-Altman plots). RESULTS: The combined classification (CBAM-integrated 2D-CNN) and segmentation model (2D-UNet with dilated convolution block) demonstrated superior performance, achieving Dice scores of 0.952 for left ventricle (LV), 0.933 for right ventricle (RV), and 0.875 for myocardium, compared to the stand-alone segmentation model (0.949 for LV, 0.925 for RV, and 0.867 for myocardium). Combined classification and segmentation model showed high correlation (0.92-0.99) with manual segmentation for biventricular volumes, ejection fraction, and myocardial mass. The mean absolute difference (2.8-8.3 mL) for clinical parameters between automated and manual segmentation was within the interobserver variability range, indicating comparable performance to manual annotation. CONCLUSION: Integrating an initial automated slice range classification step into the segmentation process improves the performance of DL-based cardiac chamber segmentation.

Role of FDG PET/CT for detection of primary tumor in patients with extracervical metastases from carcinoma of unknown primary.

AIM: To explore the diagnostic performance of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) to detect the primary tumor site in patients with extracervical metastases from carcinoma of unknown primary (CUP). We evaluated patient outcomes as overall survival (OS). MATERIALS AND METHODS: In a single-center, retrospective study (2005-2019), patients with extracervical metastases from CUP underwent FDG PET/CT to detect primary tumor sites. The final diagnosis was based on histopathology/or clinical follow-up of at least 12 months. RESULTS: A total of 83 patients [Male 41 (49%), mean age 59 ± 14 years, range: 32-83 years] fulfilled the inclusion/exclusion criteria and were enrolled for analysis. The primary tumor was detected in 36 out of 83 (43%) patients based on histopathology/or clinical follow-up. PET/CT suggested the primary tumor site in 39 (47%) patients with diagnostic accuracy of 87%, sensitivity 89%, specificity 85%, PPV 82%, NPV 91% and detection rate 39%. Patients with oligometastases (<3) (2.16 years, 1.04-2.54) and primary unidentified (1 year, 0.34-2.14) had longer median survival time compared to the patients with multiple metastases (0.67 years, 0.17-1.58, p = 0.009) and primary identified (0.67 years,0.16-1.33, p = 0.002). The SUVmax of the primary or metastatic lesions with maximum uptake was not significantly related to survival. CONCLUSIONS: PET/CT could reveal the primary tumor site in 39% of the patients. It demonstrated the metastatic disease burden and distribution in patients with 'primary obscured', which directs management. Patients with multiple metastases and primary identified had a poorer prognosis. In patients with primary unidentified after PET/CT, a further search was futile.