View clinical trials related to Predictive Cancer Model.
Filter by:Patients with symptomatic spinal metatstasis will be prosepectively included in a database after theu signes informed consent. Minimally six months after inclusion the survival status is analyzed. These are correlated with factors that are used in an earlier develloped prediction model
The generation of predictive models in radiotherapy has seen a significant increase. In 2017, Raymond published the largest systematic review of predictive prognostic models for biochemical relapse (BR), metastasis-free survival, and overall survival in patients with localized prostate cancer treated with radiotherapy (14), attempting to identify whether they were adequately developed and validated. He found 72 unique predictive models for external radiotherapy: 22 corresponding to BR risk, 20 corresponding to Cancer-Specific Survival, 10 corresponding to Overall Survival, and 20 for Disease/Metastasis-Free Survival detection. In his analysis, he highlighted a significant variation in the quality of these predictive models, understanding that they were developed prior to the existence of TRIPOD guidelines. In this regard, he pointed out that 54% of these models did not report their accuracy, and 61% of the models lacked validation (either internal or external). He also noted that they had limited follow-up (only 65% had follow-up beyond 5 years), that the treatment doses in these models were lower than current standards, and that the radiation techniques were different from current practices. Although in his final assessment, Raymond maintains that predictive models provide more certainty in predicting oncological outcomes than professional assessments, he considers it vital to validate these models for each population that wants to use them (the vast majority of these models are based on U.S. populations) or, even better, to generate predictive models specific to the local population while adhering to the TRIPOD guidelines. Probably due to the lack of validation in our patients for existing predictive models and/or the absence of predictive models originating from our population, in our routine clinical practice (Multidisciplinary Oncology Committees), phisycians do not apply any predictive models to patients diagnosed with localized prostate cancer.
This study aims to investigate the feasibility and efficiency of CT radiomic analysis which serves as a high through-put analytical strategy applied to image big-data resource in evaluating and predicting the response of immunotherapeutics. A multi-center retrospective diagnostic test has been designed for this aim to compare the predictive performance of clinical model, qualitative model incorporating semantic CT features and image-based quantitative radiomic model. The reference standard of therapeutic effect is determined by the latest evaluation result utilizing iRECIST within 365 days after recruited. This study intends to enroll 400 participates who had been diagnosed with advanced somatic solid tumor confirmed by histo- or cyto-pathological examination and were planning to receive immunotherapy.