Objective To explore the effect of individualized chemotherapy plans which was designed depend on secific genetic characters in patients with advanced cancer.Methods The surgery or biopsy specimen samples from 25 patients with advanced recurrent tumors (study group) were analyzed.Different gene mRNA expressions were detected by PCR and sequencing.According to detection results,the most appropriate chemotherapy would be applied on 25 cases patients of study group.The chemotherapy from traditional experience and evidence-based medical evidence were applied for 20 cases patients of control group.The difference of RR and disease control rate (DCR) between two groups were compared.Results The DCR and RR were 84 ％ (21/25) and 44 ％ (11/25) in study group,35 ％ (7/20) and 15 ％ (3/20) in control group.The DCR and RR in study group were significantly higher than those in control group (P ＜ 0.01).Conclusion Individualized chemotherapy could improve the efficient and prolong the survival period of the patients with advanced recurrent tumors.

Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the current literature, however, impedes the progress of this domain. Existing research perspectives, as well as the architectures, tasks, datasets, and performance measures examined in the present literature, are reviewed in this work, and we also provide a brief description of possible future directions in the field, aiming to provide researchers and healthcare professionals with a detailed summary of existing academic research and to provide rational insights to facilitate future research.
Humans ; Natural Language Processing ; Surveys and Questionnaires