View clinical trials related to Predictive Cancer Model.
Filter by:This study evaluates the clinical prognostic impact (on DFS and OS) of liquid biopsy guided treatment vs. standard of care (physicians choice) in localized pancreatic cancer (despite because of CA 19-9 levels and computed tomography, upfront surgery is recommended by tumor board). ctDNA positive patients will receive neoadjvuant chemotherapy at current gold standard physicians choice instead of upfront surgery, because of assumed high biological risk for early recurrence.
The goal of this observational study is to establish a dynamic multi-omics integration model for predicting pathological complete response (pCR) after neoadjuvant treatment in locally advanced (T3-4NxM0) rectal cancer, providing support for subsequent patient selection for the watch-and-wait strategy. The main question it aims to answer is: What is the predictive value of this model to assess individual achievement of pathological complete response (pCR) after neoadjuvant treatment? Eligible patients will be prospectively enrolled, and the clinical features of their pre-neoadjuvant treatment, during-treatment, and post-treatment preoperative will be collected and annotated.
In our prior research, a risk scoring model for the occurrence of lymph node metastasis in patients who underwent radical gastrectomy for gastric cancer was established. To further validate this scoring model, a prospective study has been designed with the aim of prospectively assessing the model's clinical applicability.
A pre-metastatic target organ/primary lesion fusion radiomics model was developed based on the "soil-seed" theory to predict comman tumor metastasis in retrospective settings. To prospectively verify the performance of the target organ/primary lesion fusion radiomics model in predicting tumor metastasis patterns (brain metastasis in lung cancer, liver metastasis in colorectal cancer, lung metastasis in breast cancer), we designed this prospective observational trial.
The goal of this prospective observational cohort study is to validate previously developed Hepatocellular Carcinoma (HCC) risk prediction algorithms, the Liver Risk Computation (LIRIC) models, which are based on electronic health records. The main questions it aims to answer are: - Will our retrospectively developed general population LIRIC models, developed on routine EHR data, perform similarly when prospectively validated, and reliably and accurately predict HCC in real-time? - What is the average time from model deployment and risk prediction, to the date of HCC development and what is the stage of HCC at diagnosis? The risk model will be deployed on data from individuals eligible for the study. Each individual will be assigned a risk score and tracked over time to assess the model's discriminatory performance and calibration.
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.
1. Establish a predictive model for the efficacy of immune checkpoint inhibitors (ICI) in Chinese patients with biliary tract cancers. By analyzing the dynamic changes of circulating tumor DNA (ctDNA) and other clinical and pathological features before and after ICI treatment in a cohort of patients with biliary tract tumors, a predictive model can be established to evaluate the efficacy of ICI treatment in the early stages or even before treatment, serving as a reliable tool for selecting patients who are likely to benefit from ICI treatment. 2. Investigate the clinical features of populations that benefit from different immune combination therapies. By comparing the differences and enrichment of mutations between patients receiving different treatment regimens, and if patients have sufficient pre-treatment tissue, further comparisons of differentially expressed genes and pathways may be made.
A previous study of investigators established a risk scoring model for the occurrence of postoperative hepatic metastases in patients who underwent curative gastrectomy directly without neoadjuvant therapy. In order to further validate the clinical applicability of abovementioned model, investigators designed this prospective study, which also included patients who received neoadjuvant therapy before surgery, with the aim of exploring the applicability of the risk scoring model to this group of patients.
Importance: Lymphovascular invasion (LVI) is a poor prognosis pathologic feature in pancreatic ductal adenocarcinoma (PDAC) patients. Neoadjuvant therapy may bring survival benefits to these patients. Objective: To construct a preoperative model which could predict LVI in PDAC patients and further validate it in other cohorts. Design, Setting, and Participants: Patients from 3 three tertiary hospitals were included in this study. Univariate and multivariate Logistic regression analyses were conducted to define independent prediction factors of LVI. A nomogram was constructed based on the result of multivariate analysis.The predictive value of the model was assessed using receiver operating characteristic (ROC) curves and the maximum Youden index of the ROC curve was defined as the cut-off point. The calibration plot was utilized to assess the concordance of the model. The decision curve analyses (DCA) were applied to estimate the clinical benefit of using this model to predict LVI.
The goal of this prospective observational cohort study is to validate a previously developed pancreatic cancer risk prediction algorith (the PRISM model) using electronic health records from the general population. The main questions it aims to answer are: - Will a pancreatic cancer risk model, developed on routine EHR data, reliably and accurately predict pancreatic cancer in real-time? - What is the average time from model deployment and risk prediction, to the date of pancreatic cancer development and what is the stage of pancreatic cancer at diagnosis? The risk model will be deployed on data from individuals eligible for the study. Each individual will be assigned a risk score and tracked over time to assess the model's discriminatory performance and calibration.