[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"prognostic-cancer-model\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:prognostic-cancer-model":31},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,5,0,[8,52,74,97,123],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":19,"enrollmentInfo":20,"targetDuration":23,"studyType":24,"phases":4,"briefSummary":25,"conditions":26,"keywords":34,"overallStatus":39,"whyStopped":4,"lastUpdateSubmitDate":40,"lastUpdatePostDateStruct":41,"startDateStruct":44,"completionDateStruct":46,"leadSponsor":48,"locationsCount":51},"100601955","validation-of-rcc-predicting-model-with-emulated-target-trial-100601955",false,"NCT07117227","Validation of RCC Predicting Model With Emulated-target Trial","Emulated-target Trial for Guiding Stratified Treatment for Renal Cell Carcinoma With Venous Tumor Thrombus","RCCETT","Inclusion Criteria:\n\n* Adults ≥18 years of age;\n* Diagnosis of primary renal cell carcinoma before and during the surgery;\n* Received radical nephrectomy\u002Fnephron-sparing surgery.\n\nExclusion Criteria:\n\n* Subjects with severely missing clinical information;\n* History of other malignant tumors.\n* Recurrence observed before first postoperative follow-up.\n* Discontinue the adjuvant treatment within in the first two course because of severe adverse react.","ALL","18 Years","80 Years",{"count":21,"type":22},4700,"ESTIMATED","2 Years","OBSERVATIONAL","This single-center study utilizes real-world data (2012-2024) from 4700 renal cell carcinoma (RCC) patients at Peking University Third Hospital to: (1) Develop and validate a prognostic prediction model specifically for RCC patients, including those with venous tumor thrombus (VTT); (2) Compare the performance of this new model against existing RCC prediction models in both the overall RCC cohort and the VTT subgroup; (3) Employ an emulated target trial (ETT) methodology to evaluate whether risk-stratified treatment based on the prediction model (grouping patients as high\u002Fmedium\u002Flow risk) improves survival outcomes .",[27,28,29,30,31,32,33],"Renal Cell Carcinoma (Kidney Cancer)","Renal Cell Carcinoma (RCC)","Tumor Thrombus","Prognosis","Prognostic Cancer Model","Real World Study","Observational Study",[35,36,37,38],"renal cell carcinoma","tumor thrombus","prognostic model","emulated-target trial","RECRUITING","2026-04-08",{"date":42,"type":43},"2026-04-14","ACTUAL",{"date":45,"type":43},"2025-09-01",{"date":47,"type":22},"2027-08-01",{"name":49,"class":50},"Peking University Third Hospital","OTHER",1,{"id":53,"slug":54,"hasResults":11,"nctId":55,"briefTitle":56,"officialTitle":57,"acronym":4,"eligibilityCriteria":58,"healthyVolunteers":11,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":59,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":61,"conditions":62,"keywords":4,"overallStatus":39,"whyStopped":4,"lastUpdateSubmitDate":65,"lastUpdatePostDateStruct":66,"startDateStruct":68,"completionDateStruct":70,"leadSponsor":72,"locationsCount":51},"100522862","contrast-enhanced-ct-based-deep-learning-model-for-preoperative-prediction-of-disease-free-survival-dfs-in-localized-clear-cell-renal-cell-carcinoma-ccrcc-100522862","NCT06088134","Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)","Urology Department of the First Affiliated Hospital of Chongqing Medical University","Inclusion Criteria:\n\n* underwent partial\u002Fradical nephrectomies\n* histologically diagnosed as ccRCC\n* with complete clinical data and preoperative CT image data\n\nExclusion Criteria:\n\n* with incomplete clinic-pathological data\n* lack of preoperative contrast-enhanced CT images or the image quality was unsuitable for analysis\n* who received pre-surgery neoadjuvant or adjuvant therapies\n* with multiple renal tumors or\u002Fand had synchronous metastasis",{"count":60,"type":22},800,"This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's predictive ability with traditional models.",[63,31,64],"Clear Cell Renal Cell Carcinoma","Recurrent Renal Cell Cancer","2025-05-27",{"date":67,"type":43},"2025-05-31",{"date":69,"type":43},"2022-09-01",{"date":71,"type":22},"2025-08-01",{"name":73,"class":50},"Mingzhao Xiao",{"id":75,"slug":76,"hasResults":11,"nctId":77,"briefTitle":78,"officialTitle":78,"acronym":4,"eligibilityCriteria":79,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":80,"enrollmentInfo":81,"targetDuration":83,"studyType":24,"phases":4,"briefSummary":84,"conditions":85,"keywords":4,"overallStatus":39,"whyStopped":4,"lastUpdateSubmitDate":88,"lastUpdatePostDateStruct":89,"startDateStruct":91,"completionDateStruct":93,"leadSponsor":95,"locationsCount":51},"100539785","colorectal-cancer-dataset-in-xijing-hospital-from-2011-100539785","NCT06308354","Colorectal Cancer Dataset in Xijing Hospital From 2011","Inclusion Criteria:\n\n* 1\\. Pathologically confirmed colorectal adenocarcinoma, including hereditary colorectal cancer syndrome, such as Lynch syndrome and familial adenocarcinoma tumor polyposis, Peutz Jeghers syndrome, juvenile polyposis syndrome, and serrated polyposis syndrome;\n* 2\\. Visit our center with complete medical records and pathological information;\n* 3\\. Colorectal cancer patients diagnosed between 2013 and 2023.\n\nExclusion Criteria:\n\n* 1\\. The patient has other colorectal malignancies other than adenocarcinoma;\n* 2\\. Malignant tumors adjacent to organs invading the colon and rectum;\n* 3\\. Malignant tumors from distant organs metastasize to the colon and rectum;\n* 4\\. Benign colorectal lesions; 5. Patients with recurrent colorectal cancer.","100 Years",{"count":82,"type":22},40000,"10 Years","To compare the differences of clinical pathological, treatment and prognosis in the guided subgroups in colorectal cancer, the investigator enrolled all the colorectal cancer patients who underwent surgery and were hospitalized in the Xijing hospital.",[31,86,87],"Chemotherapy","Nutrition Related Cancer","2024-03-11",{"date":90,"type":43},"2024-03-13",{"date":92,"type":43},"2011-01-01",{"date":94,"type":22},"2048-01-01",{"name":96,"class":50},"Xijing Hospital",{"id":98,"slug":99,"hasResults":11,"nctId":100,"briefTitle":101,"officialTitle":102,"acronym":4,"eligibilityCriteria":103,"healthyVolunteers":11,"sex":104,"minAge":4,"maxAge":4,"enrollmentInfo":105,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":107,"conditions":108,"keywords":4,"overallStatus":39,"whyStopped":4,"lastUpdateSubmitDate":113,"lastUpdatePostDateStruct":114,"startDateStruct":116,"completionDateStruct":118,"leadSponsor":120,"locationsCount":122},"100538087","ai-assisted-system-for-accurate-diagnosis-and-prognosis-of-breast-phyllodes-tumors-100538087","NCT06286267","AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors","Development of an Artificial Intelligence-Based System for Precise Diagnosis and Prognosis of Breast Phyllodes Tumors","Inclusion Criteria:\n\n* Patients diagnosed with a phyllodes tumor of the breast\n\nExclusion Criteria:\n\n* Blurred images, imaging artifacts","FEMALE",{"count":106,"type":22},4000,"Breast phyllodes tumor (PT) is a rare fibroepithelial tumor, accounting for 1% to 3% of all breast tumors, categorized by the WHO into benign, borderline, and malignant, based on histopathology features such as tumor border, stromal cellularity, stromal atypia, mitotic activity and stromal overgrowth. Malignant PTs account for 18%-25%, with high local recurrence (up to 65%) and distant metastasis rates (16%-25%). Benign PT could progress to malignancy after multiple recurrences. Therefore, Early, accurate diagnosis and identification of therapeutic targets are crucial for improving outcomes and survival rates.\n\nIn recent years, there has been growing interest in the application of artificial intelligence (AI) in medical diagnostics. AI can integrate clinical information, histopathological images, and multi-omics data to assist in pathological and clinical diagnosis, prognosis prediction, and molecular profiling.AI has shown promising results in various areas, including the diagnosis of different cancers such as colorectal cancer, breast cancer, and prostate cancer. However, PT differs from breast cancer in diagnosis and treatment approach. Therefore, establishing an AI-based system for the precise diagnosis and prognosis assessment of PT is crucial for personalized medicine.\n\nThe research team, led by Dr. Nie Yan, is one of the few in Guangdong Province and even nationally, specializing in PT research. Their team has been conducting research on the malignant progression, metastasis mechanisms, and molecular markers for PT. The team has identified key mechanisms, such as fibroblast-to-myofibroblast differentiation, and the role of tumor-associated macrophages in promoting this differentiation. They have also identified molecular markers, including miR-21, α-SMA, CCL18, and CCL5, which are more accurate in predicting tumor recurrence risk compared to traditional histopathological grading.\n\nThe project has collected high-quality data from nearly a thousand breast PT patients, including imaging, histopathology, and survival data, and has performed transcriptome gene sequencing on tissue samples. They aim to build a comprehensive multi-omics database for breast PT and create an AI-based model for early diagnosis and prognosis prediction. This research has the potential to improve the diagnosis and treatment of breast PT, address the disparities in breast PT care across different regions in China, and contribute to the development of new therapeutic targets.",[109,110,111,31,112],"Phyllodes Breast Tumor","Artificial Intelligence","Multiomics","Diagnosis","2024-02-22",{"date":115,"type":43},"2024-02-29",{"date":117,"type":43},"2023-03-01",{"date":119,"type":22},"2027-12-31",{"name":121,"class":50},"Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University",4,{"id":124,"slug":125,"hasResults":11,"nctId":126,"briefTitle":127,"officialTitle":128,"acronym":4,"eligibilityCriteria":129,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":130,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":132,"conditions":133,"keywords":134,"overallStatus":39,"whyStopped":4,"lastUpdateSubmitDate":138,"lastUpdatePostDateStruct":139,"startDateStruct":141,"completionDateStruct":143,"leadSponsor":144,"locationsCount":5},"100536338","prognostic-significance-of-ctdna-in-hl-100536338","NCT06263530","Prognostic Significance of ctDNA in HL","Prognostic Significance of Circulating Tumor DNA in Hodgkin Lymphoma","Inclusion Criteria:\n\n* Patients ≥ 18 years with newly histologically confirmed classical Hodgkin lymphoma (cHL) will be enrolled\n* signing the informed consent\n\nExclusion Criteria:\n\n* Pacients without signing the informed consent",{"count":131,"type":22},500,"Specific somatic mutations using ctDNA will be analyzed in predefined subgroups of cHL (e.g., age \\\u003C60 and ≥ 60 years, EBV). These mutations will be correlated with response to the treatment in the first line, in the relapse, during brentuximab vedotin and\u002For nivolumab treatment. Circulating tumor DNA will be correlated with the extent of tumor mass and chemo\u002Fradiotherapy.",[31],[135,136,137],"Hodgkin lymphoma","circulating tumor DNA","next generation sequencing","2024-02-08",{"date":140,"type":43},"2024-02-16",{"date":142,"type":43},"2022-01-02",{"date":119,"type":22},{"name":145,"class":146},"Interni hematologicka klinika FNKV","NETWORK"]