BACKGROUND: Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to devel...
OBJECTIVE: Radiomic and deep learning studies based on magnetic resonance imaging (MRI) of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and tumor exhibits limitations.
BACKGROUND: Laparoscopic gastrectomy (LG) is considered a standard treatment for clinical stage I gastric cancer. Nevertheless, LG has some drawbacks, such as motion restriction and difficulties in spatial perception. Robot-assisted gastrectomy (RG) ...
BACKGROUND AND OBJECTIVE: In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artifi...
BACKGROUND: To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa).
BACKGROUND: Robotic nipple-sparing mastectomy (RNSM) has emerged as a new treatment option for breast cancer and risk-reducing mastectomy (RRM) for women who have a high risk of pathogenic variants. Even though several studies have reported that RNSM...
BACKGROUND: Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Active health screening for CRC yielded detection of an increasingly younger adults. However, current machine learning algorithms that are trained using older ...
BACKGROUND: Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy develo...
BACKGROUND: Evaluation of treated tumors according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria is an important but time-consuming task in medical imaging. Deep learning methods are expected to automate the evaluation process and...
BACKGROUND: Radiotherapy has been widely used to treat various cancers, but its efficacy depends on the individual involved. Traditional gene-based machine-learning models have been widely used to predict radiosensitivity. However, there is still a l...