ABSTRACT
Retinoblastoma is a kind of malignant eye tumor commonly seen in children, which is one of the main causes threatening children's vision and life. The diagnosis and evaluation of retinoblastoma has always been a hot topic in clinic. In the past few years, the application of artificial intelligence(AI)technology has made significant progress in the medical field, providing new opportunities and challenges for the diagnosis and treatment of retinoblastoma, for example, the use of AI algorithms to analyze massive clinical data, which can help doctors diagnose the disease more accurately and provide personalized treatment plans. In addition, AI technology also plays an important role in medical image analysis, genomics research and other aspects, which can help the development of new drugs and improve patient prognosis. This article reviews the application progress of AI in retinoblastoma.
ABSTRACT
Objective To evaluate the feasibility of using medical image analysis for adaptive radiotherapy of lung cancer by drawing the individual contours automatically or interactively and adjusting the planning target volume weekly for reducing the volume of the excessive irradiated normal lung tissue.Methods Many CT images of the GTV and dynamic X-ray fluoroscope data of 23 patients were acquired and all the data were used for working out the tumor radiotherapy plan(TPS) and positioning the patient and to measure reduction in GTV volume.A planning study was conducted to determine the amount of lung-sparing that could have been achieved if adaptive therapy had been used.Treatment plans were created in which the target volumes were reduced after tumor reduction was measured.Results A total of 6313 CT imaging sessions and 78 groups of X-ray dynamic fluoroscope data were performed on 23 lung patients.The GTV was reduced by 70%～80% during the course of treatment.Based on these treatment planning studies,the dose of ipsilateral lung can be reduced significantly by adapting the treatment delivery.Conclusion Medical image analysis can be used to position the GTV and CTV of the tumors.This technology can help to work out the individual TPS and adjust the radiotherapy treatment plan adaptively during the course of treatment and reduce the dose of normal tissues as well as insure the effect of tumor treatment.