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Purpose: To evaluate the staging performance of positron emission tomography/magnetic resonance imaging (PET/MRI) for confirmed esophageal cancer based on the TNM classification system as well as compare it to other alternative modalities (e.g., endoscopic ultrasonography (EUS), computed tomography (CT), MRI, and PET/CT) in a full head-to-head manner.

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A deep learning based model for diabetic retinopathy grading.

Sci Rep

January 2025

Department of Networks and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy.

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Background: This study proposes a modified lymph node (LN) staging category (BALN) on the basis of the number of positive LNs before (prepN) and after (ypN) neoadjuvant chemoradiotherapy (nCRT) to improve prognostic stratification in esophageal squamous cell carcinoma (ESCC).

Patients And Methods: A total of 381 patients with ESCC who underwent nCRT at three medical centers were retrospectively enrolled. The ypN categories were scored according to the eighth edition of the American Joint Committee of Cancer (AJCC) staging manual.

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Background: The intercondylar space is defined as the topographic area walled by the intercondylar notch (IN) and encasing the central ligaments of the knee joint. As the anterior cruciate ligament (ACL) is not only impinged against the roof but also against the lateral wall of the IN, information concerning changes to the IN during the progression of osteoarthritis could be potentially relevant in predicting the future risk for ACL-rupture and -degeneration and ensure preventive measures as early as possible. Therefore, the purpose of this study was to evaluate the influence of osteoarthritis on osseous notch morphology.

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The 2021 World Health Organisation classification of lung adenocarcinoma is based on the predominance and percentage of high-grade histological patterns, e.g. solid and micropapillary patterns, determined by semiquantitative estimation.

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