"Will Artificial Intelligence Unveil Hepatocellular Carcinoma?"

Abstract

Dear Editor, due to continuous implementing of medical devices physicians are dealing with tremendous amount of data and clinical information. This is especially true within the oncological setting.Therefore, the management of oncological patients requires that clinical decisions be taken within multidisciplinary teams made up of clinicians, radiologists, geneticists, surgeons, pathologists, psychologists and oncologists. However, some lights may be at the end of the tunnel. Recent development of computer algorithms has reached excellent results and is now able to simulate human cognitive functions, such as learning or problem solving. This processing is called artificial intelligence (AI). AI utilized Machine Learning (ML) and deep learning (DL). The first one, ML is based on the ability of the computer to "learn" and improve from past examples without being programmed. DL is a subset of ML and is computer software that mimics the network of neurons in a brain.In DL, the learning phase occurs through a neural network. For the above reasons is clear that AI is potentially useful for making clinical diagnosis and taking clinical decisions especially in oncology.We believe that AI could become the new tool for the management of hepatocellular carcinoma (HCC) helping to predict the onset, recurrence and prognosis. Recently, Jiménez Pérez M and Grande RG and their colleagues published a review article showing how AI could help differentiate between normal liver, chronic liver disease, cirrhosis and HCC or benign and malignant nodules. AI is able to in the diagnostic accuracy, tumor staging, treatment planning by utilizing several types of radiological images (ultrasound, CTscan, MRI-scan, etc), WHO classifications, histopathological findings (malignant tumors non-HCC, indeterminate masses, dysplastic nodules etc.) [1]. Interestingly, the use of AI and ML techniques has also been applied on the predictivity of response both in terms of HCC recurrence after resection and after transarterial chemoembolization (TACE). In the first case, radiomics can improve predictive accuracy for HCC recurrence after curative resection [2]. Also, the effects of transarterial chemoembolization in patients with HCC can be predicted by combining clinical data and MR imaging.The images obtained from CT, MRI or PET exams are converted into numerical data through radiomics. These data are manipulated ARTICLE INFO