0.05),but the total expenses and antibiotic cost of the medical treatment in group B were obviously lower than those in group A(P

Ascites is the most frequent decompensating event of cirrhosis.At present,ascites recurs at a high rate due to lack of effective management strategy and is frequently complicated with spontaneous bacterial peritonitis,hepatorenal syndrome,and liver failure,which increase the fatality rate.Albumin treatment for hepatocirrhosic ascites has a long history,but it is limited as an acute or short-term treatment.In contrast,long-term albumin administration represents a completely different treatment paradigm.Results from several recent clinical studies indicate that long-term albumin treatment can be able to modify the disease courses of some decompensated cirrhosis when albumin is given at a sufficient dose for a sufficient time.In this review,we analyze the available data acquired from long-term albumin treatments,trying to establish a secure and effective management scheme involving maximal target popula-tion,albumin dose,administration time,and standards for albumin withdrawal,and thus provide references for the clinical practice.

In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.
Deep Learning ; Humans ; Neoplasms ; Precision Medicine ; Prognosis