Publications by authors named "Lilong Lan"
This study aimed to compare contrast-enhanced ultrasound (CEUS) features of hepatic angiomyolipoma (HAML) and challenging cases of HCC, mainly those with no hepatitis infection but also with a low level of AFP (non-viral AFP- HCC). The study included pathologically confirmed HAMLs and non-viral AFP- HCCs undergoing CEUS from 2012 to 2023. Sonovue (SV) CEUS and Sonazoid (SZ) CEUS characteristics of the two groups were compared.
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- Accurate identification of primary central nervous system lymphoma (PCNSL) during surgery is crucial for effective neurosurgical decisions, but pathologists struggle to differentiate it from other brain lesions like glioma using frozen sections.
- Researchers developed a deep learning model named LGNet to improve this differentiation, utilizing H&E-stained images, and benchmarked its accuracy against pathologists with varying experience.
- LGNet demonstrated exceptional performance, achieving high accuracy metrics (AUROCs) in distinguishing PCNSL from glioma and non-PCNSL lesions, significantly surpassing many pathologists, highlighting its potential as a vital tool in surgical diagnoses.
Purpose: Head and neck squamous cell carcinoma (HNSCC) ranks sixth among all cancers globally regarding morbidity, and it has a poor prognosis, high mortality, and highly aggressive properties. In this study, we established a model for predicting prognosis based on immunohistochemical (IHC) scores.
Methods: Data on 402 HNSCC cases were collected, the glmnet Cox proportional hazards model was used, risk factors were analyzed for predicting the prognosis of survival, and the IHC score was established.
Epstein-Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce a deep convolutional neural network called EBVNet and its fusion with pathologists for predicting EBVaGC from histopathology.
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