BACKGROUND/AIM: In pathology, the digitization of tissue slide images and the development of image analysis by deep learning have dramatically increased the amount of information obtainable from tissue slides. This advancement is anticipated to not o...
BACKGROUND/AIM: Ewing sarcoma is a highly malignant tumour predominantly found in children. The radiological signs of this malignancy can be mistaken for acute osteomyelitis. These entities require profoundly different treatments and result in comple...
BACKGROUND/AIM: To analyze the effects of laparoscopic partial nephrectomy (LPN) and robot-assisted partial nephrectomy (RAPN) for the treatment of renal cell carcinoma (RCC) on subsequent split renal function using renal scintigraphy.
AIM: Anastomotic leakage (AL) in left-sided colorectal cancer is a serious complication, with an incidence rate of 6-18%. We developed a novel predictive model for AL in colorectal surgery with double-stapling technique (DST) anastomosis using auto-a...
BACKGROUND/AIM: We aimed to investigate the role of radiogenomic and deep learning approaches in predicting the KRAS mutation status of a tumor using radiotherapy planning computed tomography (CT) images in patients with locally advanced rectal cance...
BACKGROUND/AIM: Many cancer patients face multiple primary cancers. It is challenging to find an anticancer therapy that covers both cancer types in such patients. In personalized medicine, drug response is predicted using genomic information, which ...
BACKGROUND/AIM: The aim of this study was to analyze the survival predictions obtained from a web platform allowing for computation of the so-called Bone Metastases Ensemble Trees for Survival (BMETS). This prediction model is based on a machine lear...
BACKGROUND/AIM: Accumulating evidence has shown therapeutic effects of herbals on breast cancer, a commonly diagnosed malignancy in women worldwide. However, their underlying mechanisms remain unclear. We aimed to explore the mode of action of a rece...
BACKGROUND/AIM: The potential advantages of robot-assisted thoracoscopic esophagectomy (RATE) have yet to be verified. This study focused on the degree of lymph node dissection around the left recurrent laryngeal nerve (RLN).
Artificial intelligence was recognised many years ago as a potential and powerful tool to predict disease outcome in many clinical situations. The conventional approaches using statistical methods have provided much information, but are subject to li...