Publications by authors named "Henan Lou"
Article Synopsis
- The study investigates the effectiveness of a new automatic segmentation model, AttSEResUNet, for identifying rectal cancer in MRI scans, aiming to improve efficiency for doctors.
- A total of 65 rectal cancer patients were included, with their MRI images divided into training and validation sets, and multiple models compared based on segmentation accuracy and inter-observer consistency.
- Results showed AttSEResUNet achieved a perfect lesion recognition rate and outperformed other models, with performance matching that of experienced radiologists, indicating it could be a reliable tool for tumor contouring.
Background: The invasive pattern called spread through air spaces (STAS) is linked to an unfavorable prognosis in patients with lung adenocarcinoma (LUAD). Using computed tomography (CT) signs alone to assess STAS is subjective and lacks quantitative evaluation, whereas spectral CT can provide quantitative analysis of tumors. The aim of this study was to investigate the association between spectral CT quantitative parameters and STAS in LUAD.
View Article and Find Full Text PDFObjective: This study aimed to observe the value of computed tomography (CT) spectral imaging parameters in the diagnosis of solitary pulmonary nodules, during the contrast-enhanced early phase and late phase.
Materials And Methods: This study was approved by the institutional review board and written informed consent was obtained from all patients. One hundred thirty-nine patients with solitary pulmonary nodules proved by pathology underwent double-phase enhanced CT scan using gemstone spectral imaging mode on a Discovery CT750 HD, and were divided into an active inflammatory group (43 cases), a malignant group (65 cases), and a tuberculosis group (31 cases).