[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"malignant-tumours\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:malignant-tumours":65},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":4,"hasResults":10,"nctId":11,"briefTitle":12,"officialTitle":13,"acronym":4,"eligibilityCriteria":14,"healthyVolunteers":15,"sex":16,"minAge":4,"maxAge":4,"enrollmentInfo":17,"targetDuration":20,"studyType":21,"phases":4,"briefSummary":22,"conditions":23,"keywords":41,"overallStatus":52,"whyStopped":4,"lastUpdateSubmitDate":53,"lastUpdatePostDateStruct":54,"startDateStruct":57,"completionDateStruct":59,"leadSponsor":61,"locationsCount":64},"100579293",false,"NCT06822413","Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis","A Novel Raman Spectroscopy-Based Method for Pan-Cancers Early Diagnosis Supported by Deep Learning: A Prospective, Single-Arm, Multicentre Study","Inclusion Criteria:\n\n* Histopathological diagnosis of malignant tumors, including colorectal cancer, gastric cancer, hepatic cancer, pancreatic cancer, and esophageal cancer.\n* Patients in normal physiological conditions without any malignant tumors or precancerous lesions.\n* Patients with malignant tumor without recieving any interventions, including chemotherapy, surgery, radiotherapy, immunotherapy or other anti-tumor treatments.\n* Patients with a histopathological diagnosis of any precancerous lesions or non-malignant disease.\n\nExclusion Criteria:\n\n* Patients with metastatic tumors or in the condition with two or more kinds of malignant tumors at the same time\n* Post-cancer treatment patients.",true,"ALL",{"count":18,"type":19},600,"ESTIMATED","1 Year","OBSERVATIONAL","The goal of this observational study is to explore whether a Raman-based, deep learning-assisted approach can be used to develop an effective method for early pan-cancer screening. The study includes healthy individuals, patients at risk of cancer, and patients with diagnosed cancers. The main questions it aims to answer are:\n\n* Evaluating the deep-learning model's accuracy and specificity in identifying cancer-specific features in Raman spectral data and determining whether this method can accurately classify patients based on risk.\n* Identifying which model is more adaptable to the Raman spectrum\n* Providing an interpretable analysis of the model-generated diagnosis Participants are already being diagnosed and follow-up to determine the type of cancer.",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"Cancer Diagnosis","Liver Cancer, Adult","Cancer Screening","Colorectal Cancer (CRC)","Gastric Cancers","Normal Physiology","Pancreatic Cancer, Adult","Raman Spectroscopy","Deep Learning Model","Esophageal Cancer","Malignant Tumours","Precancerous Conditions","Pancreatitis","Adenoma Colon Polyp","Gastric Ulcer","Oesophagitis","Cirrhoses, Liver",[42,43,26,31,44,45,46,33,47,48,36,49,50,39,51],"Pan-cancer","Deep Learning Models","Colorectal Cancer","Pancreatic Cancer","Gastric Cancer","malignant tumour","Precancerous Condtions","Colorectal Adenoma","Gastirc Ulcer","Cirrhoses","RECRUITING","2025-04-19",{"date":55,"type":56},"2025-04-24","ACTUAL",{"date":58,"type":56},"2022-09-01",{"date":60,"type":19},"2025-07-28",{"name":62,"class":63},"Second Affiliated Hospital, School of Medicine, Zhejiang University","OTHER",4,""]