Lung cancer is the leading cause of cancerrelated mortality worldwide. In addition to
localizing and segmenting lung nodules, a non-invasive risk assessment system can also …

This study utilized deep learning to classify osteoporosis and predict bone density using
opportunistic CT scans and independently tested the models on data from different hospitals …

Objectives To establish deep learning models for malignancy risk estimation of sub-
centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical …

Objective To investigate the value of predicting axillary lymph node (ALN) metastasis based
on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and …

Rationale and objectives To explore the characteristics of brain structure in Chinese children
with autism spectrum disorder (ASD) using artificial intelligence automatic brain …

Background Xerostomia is one of the most common side effects in nasopharyngeal
carcinoma (NPC) patients after chemoradiotherapy. To establish a Delta radiomics model for …

Background This study aims to utilize the deep learning method of VB-Net to locate and
segment the trigeminal nerve, and employ radiomics methods to distinguish between CTN …

Rationale and Objectives Clear cell renal cell carcinoma (ccRCC) is the most common
malignant neoplasm affecting the kidney, exhibiting a dismal prognosis in metastatic …

Real-time diagnosis of intracerebral hemorrhage after thrombectomy is crucial for follow-up
treatment. However, this is difficult to achieve with standard single-energy CT (SECT) due to …

Objective To explore the effect of cognitive reserve (CR) on brain volume and cerebrospinal
fluid (CSF) in patients with mild cognitive impairment (MCI) and healthy elders (HE) …