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Objective: To investigate the clinical characteristics and treatment methods of rhabdomyosarcoma in children and the efficacy of the methods. Methods: The clinical data of 30 children with rhabdomyosarcoma who were admitted to our hospital from August 2013 to August 2017 were retrospectively analyzed. The clinical characteristics were summarized, and the curative effect and prognosis were evaluated. Results: Among all the children (N=30), there were 20 males and 10 females, with a median age of 3.5 years. As to the primary site, there were 13 cases of head and neck, 11 cases of trunk, three cases of urogenital system and three cases of limbs. There were 25 cases of embryonic type, 4 cases of alveolar type and one case of polymorphic type. As to the clinical stage, there were one case of stage I, 9 cases of stage II, 13 cases of stage III and 7 cases of stage IV. There were one case of low risk, 19 cases of medium risk and 10 cases of high risk. Eight cases received surgery alone, 22 cases received combined treatment of surgery and chemotherapy (the chemotherapeutics followed three schemes, low-risk group (VAC+VA), moderate risk group (VAC) and high risk group (alternating use of VDC and IE). Among all the cases (N=30), there were 14 cases of complete remission (CR), five cases of partial remission (PR), four cases of stable disease (SD), and 7 cases of progressive disease (PD). The CR rate was (N=14, 46.7%). The three-year overall survival (OS) rate was (N=19, 63.3%). The clinical efficacy and prognosis of children receiving surgery and chemotherapy were better than those of children receiving surgery alone, and the difference was statistically significant (P<0.05). Conclusion: Rhabdomyosarcoma in children frequently happens in the head, neck and trunk. Embryonic type is the main pathological type of rhabdomyosarcoma. Comprehensive and standardized treatment based on surgery and chemotherapy is an important way to improve the curative effect in the treatment of rhabdomyosarcoma in children. doi: https://doi.org/10.12669/pjms.36.5.1829 How to cite this:Ning Z, Liu X, Qin G, Wei L, Li X, Shen J. Evaluation of clinical efficacy of Chemotherapy for Rhabdomyosarcoma in children. Pak J Med Sci. 2020;36(5):1069-1074. doi: https://doi.org/10.12669/pjms.36.5.1829 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article is oriented to the research of the vehicle-mounted intelligent traditional Chinese medicine (TCM) syndrome differentiation system to provide a quick and accurate diagnosis suggestion for smart and mobile medical vehicles. Nowadays, TCM modernization has attached more and more attention due to its remarkable clinical effect. The typical TCM diagnosis process is to induces the syndrome types of a patient from the four-diagnosis symptoms based on syndrome differentiation theory, that is, given a set of symptoms to treat, an overall syndrome representation is learned by effectively fusing all the symptoms in the set to mimic how a doctor induce the syndromes. Therefore, we believe that an overall description of symptoms of a patient is very important for the follow-up treatment and should be handled carefully and properly. However, due to the complexity and diversity of syndromes, most recommended prescription of a patient lacks the explicit ground-truth of syndrome. Therefore, in this article, a new TCM syndrome differentiation method based on multi-label classification method and deep learning method was proposed to learn the implicit symptoms induction process in the real diagnosis, A Deep Belief Network (DBN) was used to reconstruct the TCM diagnosis model based on the Rrestricted Boltzman Machine (RBM) mechanism. Towards symptoms-syndrome groups learning, a symptom vector representing the symptom collection of a patient is constructed as the input of DBN, which was used to train the symptom data and labeled samples to build an unsupervised model at first. And then the parameters of DBN was adjusted gradually based on back propagation method. Secondly, binary classification algorithm was used to convert the multi-label classification problem into several corresponding labels. Each binary classifier model corresponds to a binary classifier to complete the classification from symptoms to corresponding syndrome types. Finally, Experiments were conducted on a self-built TCM clinical records database, demonstrating obvious improvements on the classification accuracy and convergence rate. Further studies should focus on the promotion of effectiveness of the proposed model.

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