[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Chinese Academy of Sciences\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":261},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,10,0,[8,49,77,92,122,144,171,196,218,239],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":20,"targetDuration":23,"studyType":24,"phases":4,"briefSummary":25,"conditions":26,"keywords":30,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":37,"lastUpdatePostDateStruct":38,"startDateStruct":41,"completionDateStruct":43,"leadSponsor":45,"locationsCount":48},"100637610","prospective-multicenter-screening-for-thoracic-disease-via-radiography-100637610",false,"NCT07585214","Prospective Multicenter Screening for Thoracic Disease Via Radiography","Thoracic Disease Screening Via Radiography: a Prospective Multicenter Study","PROMPT","Inclusion Criteria:\n\n* Age over 18.\n* Ability to understand and willingness to sign a written informed consent form.\n* Individuals presenting for routine health examinations or community-based screening.\n\nExclusion Criteria:\n\n* Women who are pregnant, lactating, or planning to become pregnant during the study period.\n* Physical limitations preventing stable positioning or breath-holding, or presence of metallic implants that may significantly degrade image quality.\n* Known history of advanced thoracic diseases currently under treatment (e.g., active lung cancer, end-stage pulmonary fibrosis, or congestive heart failure).\n* Individuals who have undergone chest CT or X-ray examinations within the past 3 months.",true,"ALL","18 Years",{"count":21,"type":22},300000,"ESTIMATED","5 Years","OBSERVATIONAL","This multicenter, prospective study aims to evaluate the real-world clinical utility of chest X-ray (CXR) for large-scale thoracic disease screening. Adult participants presenting for health examinations will undergo digital CXR screening, and any suspected abnormalities will be confirmed via a gold standard reference, such as a chest CT scan or clinical follow-up. The primary outcome measure is the detection rate (screening yield) of confirmed thoracic conditions. Secondary measures will assess diagnostic accuracy (sensitivity and specificity), false-positive rates, and multi-center reading consistency. By providing prospective, large-cohort evidence, this research seeks to validate the cost-effectiveness and feasibility of CXR in identifying early-stage lung, pleural, and cardiac abnormalities, ultimately guiding public health strategies and optimizing early medical intervention.",[27,28,29],"Pulmonary Diseases","Respiratory Disease","Esophageal Diseases",[15,31,32,33,34,35],"Thoracic","Screen","Radiography","Multicenter","Prospective","RECRUITING","2026-05-09",{"date":39,"type":40},"2026-05-13","ACTUAL",{"date":42,"type":22},"2026-05-01",{"date":44,"type":22},"2029-12-31",{"name":46,"class":47},"Chinese Academy of Sciences","OTHER_GOV",1,{"id":50,"slug":51,"hasResults":11,"nctId":52,"briefTitle":53,"officialTitle":54,"acronym":4,"eligibilityCriteria":55,"healthyVolunteers":17,"sex":18,"minAge":56,"maxAge":57,"enrollmentInfo":58,"targetDuration":4,"studyType":60,"phases":61,"briefSummary":63,"conditions":64,"keywords":4,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":69,"lastUpdatePostDateStruct":70,"startDateStruct":72,"completionDateStruct":74,"leadSponsor":76,"locationsCount":48},"100592111","probiotic-intervention-on-body-weight-100592111","NCT06989177","Probiotic Intervention on Body Weight","An Interventional Study Investigating the Effects of Probiotics on Body Weight and Metabolic Homeostasis","Inclusion Criteria:\n\n* Aged 20 to 50 years.\n* Normal-weight participants: 18.5 kg\u002Fm² ≤ Body Mass Index (BMI) \\\u003C 24 kg\u002Fm².\n* Overweight or obese participants:\n\nBMI ≥ 28 kg\u002Fm², or 24 kg\u002Fm² ≤ BMI \\\u003C 28 kg\u002Fm² and a clinical diagnosis meeting semaglutide treatment indications (e.g., hyperglycemia, hypertension, dyslipidemia, fatty liver, obstructive sleep apnea, cardiovascular disease, etc.).\n\n* Willingness to participate in this study and provide signed informed consent.\n\nExclusion Criteria:\n\n* Abnormal metabolic indicators (meeting any one of the following criteria is grounds for exclusion):\n\n  1\\. Normal weight (18.5 kg\u002Fm² ≤ BMI \\\u003C 24 kg\u002Fm²):\n  1. Waist circumference ≥ 90 cm for men or ≥ 85 cm for women.\n  2. Fasting glucose ≥ 6.1 mmol\u002FL or 2-hour postprandial glucose ≥ 7.8 mmol\u002FL, or a confirmed diagnosis of diabetes.\n  3. Systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg, or currently under antihypertensive treatment.\n  4. Fasting triglycerides (TG) ≥ 1.7 mmol\u002FL; high-density lipoprotein cholesterol (HDL-C) \\\u003C 0.9 mmol\u002FL in men or \\\u003C 1.0 mmol\u002FL in women.\n\n  2\\. Overweight or obese (BMI ≥ 24 kg\u002Fm²):\n  1. Fasting plasma glucose \\> 11.1 mmol\u002FL or HbA1c \\> 9%, or previously diagnosed diabetes, or currently using insulin or any antidiabetic medication.\n  2. Blood pressure ≥ 160\u002F100 mmHg, or clinically diagnosed stage 2 or stage 3 (moderate or severe) hypertension, or currently under antihypertensive treatment.\n  3. Use of lipid-lowering drugs (e.g., fibrates, bile acid sequestrants, statins, PCSK9 inhibitors) within the past 3 months, or TG ≥ 5.7 mmol\u002FL, or LDL ≥ 4.9 mmol\u002FL.\n* Pregnancy or lactation.\n* Self-reported weight change of more than 5 kg within the 90 days prior to screening.\n* Use of antibiotics, antimicrobials, or anti-inflammatory\u002Fanalgesic salicylates (e.g., aspirin) within the 3 months prior to screening for 3 days or more.\n* Use of estrogen therapy or other hormonal medications within the past 6 months.\n* Use of GLP-1 receptor agonists or probiotics within the past 3 months.\n* Heavy alcohol consumption (females \\> 40 g\u002Fday, approximately 250 mL of huangjiu \\[yellow rice wine\\], or 1000 mL of beer, or 100 mL of liquor per day; males \\> 80 g\u002Fday).\n* Severe liver or kidney dysfunction (ALT, AST, or serum creatinine exceeding 3 times the upper limit of normal, UACR ≥ 30 mg\u002Fg, or eGFR \\\u003C 60 mL\u002Fmin).\n* Gastrointestinal diseases affecting digestion and absorption (e.g., severe diarrhea, severe constipation, severe inflammatory bowel disease, peptic ulcer, gallstones, cholecystitis).\n* Underwent surgery within the past year (excluding appendectomy or hernia repair).\n* Severe cardiovascular or cerebrovascular diseases (e.g., heart failure, myocardial infarction, stroke, acute myocarditis, severe arrhythmia, or receiving interventional therapy).\n* Presence of metallic implants such as a cardiac stent or pacemaker.\n* Cancer or having received radiation or chemotherapy within the past 5 years.\n* Personal or family history of medullary thyroid carcinoma or multiple endocrine neoplasia, or a personal history of hyperthyroidism or hypothyroidism.\n* Chronic or acute pancreatitis.\n* Positive hepatitis B surface antigen (HBsAg), active tuberculosis, HIV, or other infectious diseases.\n* Currently participating in another clinical study or having done so within the past 3 months.\n* Claustrophobia.\n* Any psychiatric disorder such as attention-deficit\u002Fhyperactivity disorder (ADHD), bipolar disorder, or epilepsy (including current use of antiepileptic medications), or use of antidepressant medications.\n* Inability to read, write, operate a smartphone, or perform daily activities independently.","20 Years","50 Years",{"count":59,"type":22},140,"INTERVENTIONAL",[62],"NA","This study is a randomized controlled trial with 120 overweight or obese (body-mass index, BMI, ≥ 24 kg\u002Fm²) participants and 20 normal-weight participants. Twelve weeks of energy-restricted nutritional and lifestyle intervention with placebo, Lactobacillus paracasei LC-19 (LC-19), or semaglutide (a glucagon-like peptide-1 receptor agonist, GLP-1RA) will be randomly conducted in the overweight or obese participants. The primary goal is to clarify the roles of LC-19 and semaglutide in weight reduction and in improving energy, glucose, and lipid metabolism, while also comparing side effects, adverse events, and long-term outcomes such as weight regain between these two interventions. In addition, the study will explore key factors affecting intervention response to provide evidence for optimizing individualized intervention strategies.",[65,66,67,68],"Obesity","Homeostasis","Weight Loss","Probiotic Intervention","2026-03-16",{"date":71,"type":40},"2026-03-17",{"date":73,"type":40},"2025-06-10",{"date":75,"type":22},"2026-12-31",{"name":46,"class":47},{"id":78,"slug":79,"hasResults":11,"nctId":80,"briefTitle":81,"officialTitle":82,"acronym":4,"eligibilityCriteria":55,"healthyVolunteers":17,"sex":18,"minAge":56,"maxAge":57,"enrollmentInfo":83,"targetDuration":4,"studyType":60,"phases":84,"briefSummary":85,"conditions":86,"keywords":4,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":69,"lastUpdatePostDateStruct":88,"startDateStruct":89,"completionDateStruct":90,"leadSponsor":91,"locationsCount":48},"100592113","protein-supplementation-intervention-on-body-weight-100592113","NCT06989203","Protein Supplementation Intervention on Body Weight","An Intervention Study Investigating Effect of Dietary Protein Supplementation on Body Weight and Metabolic Homeostasis",{"count":59,"type":22},[62],"This study is a randomized clinical trail with a parallel design, involving 120 overweight\u002Fobese (body-mass index, BMI ≥ 24 kg\u002Fm²) participants and 20 normal-weight participants. Overweight\u002Fobese participants will be randomly allocated to one of three groups: 1) calorie restricted balanced diet (CRD)group; 2) CRD + semaglutide group; or 3) CRD + segaglutide with protein supplementation. Through a 3-month weight loss intervention and 6-month follow-up, this study aims to investigate the effects of dietary protein supplementation combined with semaglutide on weight loss, energy and glucose and lipid metabolism, muscle loss, and weight regain. Additionally, the study will explore key factors affecting intervention efficacy, including obesity phenotypes, gut microbiota profiles, genetic backgrounds, and lifestyle factors, to provide evidence for optimizing individualized intervention strategies.",[65,67,87],"Protein Supplementation",{"date":71,"type":40},{"date":73,"type":40},{"date":75,"type":22},{"name":46,"class":47},{"id":93,"slug":94,"hasResults":11,"nctId":95,"briefTitle":96,"officialTitle":97,"acronym":98,"eligibilityCriteria":99,"healthyVolunteers":11,"sex":18,"minAge":19,"maxAge":19,"enrollmentInfo":100,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":102,"conditions":103,"keywords":106,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":113,"lastUpdatePostDateStruct":114,"startDateStruct":116,"completionDateStruct":118,"leadSponsor":120,"locationsCount":121},"100606038","computer-aided-diagnosis-for-hepatocellular-carcinoma-microvascular-invasion-100606038","NCT07170345","Computer-Aided Diagnosis for Hepatocellular Carcinoma Microvascular Invasion","Development of a Computer-Aided Diagnosis System for Hepatocellular Carcinoma Microvascular Invasion Based on Preoperative Image Analysis","HCC-MVI-CAD","Inclusion Criteria:\n\n* Age ≥ 18 years.\n* Confirmed diagnosis of hepatocellular carcinoma (HCC) according to the Chinese Clinical Practice Guidelines for Primary Liver Cancer.\n* Eligible for surgical intervention (hepatic resection or liver transplantation) according to the Chinese Clinical Practice Guidelines for Cancer, including stages Ia, Ib, and IIa.\n* Preoperative imaging examination performed within 1 month before surgery.\n* Availability of histopathological evaluation with documented microvascular invasion (MVI) status.\n\nExclusion Criteria:\n\n* History of prior antitumor treatment, including preoperative surgical intervention, transarterial chemoembolization (TACE), radiofrequency ablation (RFA), systemic therapy, or any other preoperative intervention.\n* Presence of major vascular invasion, bile duct invasion\u002Fthrombosis, extrahepatic metastasis, or lymph node involvement.\n* Diffuse hepatocellular carcinoma or tumor rupture with hemorrhage.\n* Lack of key data required for primary analysis.\n* Poor image quality that prevents reliable qualitative or radiomics analysis.",{"count":101,"type":22},400,"Hepatocellular carcinoma (HCC) is a common malignancy in China with a high mortality rate. Its early recurrence and long-term prognosis are closely associated with tumor aggressiveness. Microvascular invasion (MVI), defined as the presence of tumor cells within small branches of the portal or hepatic veins, is a key indicator of malignant biological behavior in HCC. Clinically, MVI is strongly correlated with postoperative early recurrence and serves as an important factor in determining surgical margin extension, adjuvant therapy, and postoperative management strategies.\n\nAt present, definitive diagnosis of MVI still relies on postoperative pathological examination, and stable, effective preoperative assessment methods are lacking. Although some studies have attempted to predict MVI using preoperative imaging features, their clinical translation remains limited by poor generalizability, weak interpretability, and insufficient cross-center adaptability.\n\nThis study aims to leverage multiphase preoperative CT imaging, artificial intelligence techniques, and clinical prior knowledge to develop a high-performance, generalizable, and interpretable computer-aided diagnostic system for preoperative prediction of HCC-MVI. An observational, prospective evaluation will be conducted to assess system performance and to facilitate the clinical translation of intelligent diagnostic technologies in real-world practice.",[104,105],"Hepatocellular Carcinoma (HCC)","Microvascular Invasion (MVI)",[107,108,109,110,111,112],"Hepatocellular Carcinoma","Microvascular Invasion","Computer-Aided Diagnosis System","Artificial Intelligence","Medical Image","Multimodal Model","2025-09-05",{"date":115,"type":40},"2025-09-12",{"date":117,"type":40},"2025-09-01",{"date":119,"type":22},"2027-09-01",{"name":46,"class":47},19,{"id":123,"slug":124,"hasResults":11,"nctId":125,"briefTitle":126,"officialTitle":126,"acronym":4,"eligibilityCriteria":127,"healthyVolunteers":11,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":128,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":130,"conditions":131,"keywords":134,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":136,"lastUpdatePostDateStruct":137,"startDateStruct":138,"completionDateStruct":140,"leadSponsor":142,"locationsCount":143},"100605119","research-on-identifying-critical-surgical-anatomy-in-cholecystectomy-videos-based-on-deep-learning-100605119","NCT07158372","Research on Identifying Critical Surgical Anatomy in Cholecystectomy Videos Based on Deep Learning","Inclusion Criteria:\n\n* Patients aged 18 or above who are diagnosed by a doctor as needing laparoscopic cholecystectomy\n\nExclusion Criteria:\n\n* Patients who did not undergo surgery at the original hospital and those whose videos were blurry were excluded.",{"count":129,"type":22},200,"Laparoscopic cholecystectomy is a common surgical procedure, but it carries the potential for bile duct injury and other surgical risks. To provide visual assistance to surgeons during surgery and mitigate these risks, this research project aims to develop a real-time object recognition algorithm based on deep learning technology. This algorithm will label key anatomical structures in laparoscopic cholecystectomy videos, providing surgeons with immediate information on dangerous and safe areas.",[132,133],"Cholecystectomy","Surgical Video Identification",[133,135,132,110],"Deep Learning","2025-08-28",{"date":113,"type":40},{"date":139,"type":40},"2025-08-15",{"date":141,"type":22},"2028-08-15",{"name":46,"class":47},5,{"id":145,"slug":146,"hasResults":11,"nctId":147,"briefTitle":148,"officialTitle":149,"acronym":4,"eligibilityCriteria":150,"healthyVolunteers":11,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":151,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":152,"conditions":153,"keywords":157,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":163,"lastUpdatePostDateStruct":164,"startDateStruct":166,"completionDateStruct":168,"leadSponsor":169,"locationsCount":170},"100518800","ai-prediction-of-gastric-cancer-response-to-neoadjuvant-chemotherapy-100518800","NCT06035250","AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy","Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy","Inclusion Criteria:\n\n* Age 18 years or older;\n* Pathologically diagnosed with advanced gastric cancer in accordance with the American AJCC's TNM staging standards;\n* Have not undergone any systematic anti-cancer treatments before neoadjuvant chemotherapy and have not had surgery for local progression or distant metastasis;\n* Received standard neoadjuvant chemotherapy as recommended by the clinical guidelines, and have documented treatment details;\n* CT imaging and biopsy pathology images strictly taken within one month prior to starting neoadjuvant treatment;\n* Patients possess comprehensive preoperative clinical information and post-operative TRG grading.\n\nExclusion Criteria:\n\n* Patients whose CT or pathology images are unclear, making lesion assessment infeasible;\n* Patients diagnosed with other concurrent tumors.",{"count":129,"type":22},"This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.",[154,155,156],"Gastric Cancer","Image","Pathology",[154,158,159,160,161,162],"Neoadjuvant Chemotherapy","Radiomics","Treatment Outcome Prediction","Pathomics","Radiopathomics","2023-09-26",{"date":165,"type":40},"2023-09-28",{"date":167,"type":40},"2023-09-10",{"date":44,"type":22},{"name":46,"class":47},22,{"id":172,"slug":173,"hasResults":11,"nctId":174,"briefTitle":175,"officialTitle":176,"acronym":4,"eligibilityCriteria":177,"healthyVolunteers":11,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":178,"targetDuration":4,"studyType":60,"phases":179,"briefSummary":180,"conditions":181,"keywords":183,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":187,"lastUpdatePostDateStruct":188,"startDateStruct":190,"completionDateStruct":192,"leadSponsor":194,"locationsCount":195},"100516277","treatment-recommendations-for-gastrointestinal-cancers-via-large-language-models-100516277","NCT06002425","Treatment Recommendations for Gastrointestinal Cancers Via Large Language Models","Application of Large Language Models in the Recommendation of Treatment Plans for Gastrointestinal Cancers","Inclusion Criteria:\n\n* Age ≥18 years, both male and female.\n* Pathologically confirmed diagnosis of gastrointestinal cancer (gastric cancer or colorectal Cancer).\n* Detailed medical records available prior to treatment (including chief complaint, history of present illness, radiological examinations, pathological examinations, laboratory tests, etc.).\n* Participants will receive complete treatment in the participating hospitals.\n\nExclusion Criteria:\n\n* Participants with cancers other than gastrointestinal cancers.\n* Participants who receive treatment in multiple hospitals.",{"count":101,"type":22},[62],"This study will evaluate the utility of ChatGPT in recommending treatment plans for patients with gastrointestinal cancers, using both retrospective and prospective data.",[182],"Gastrointestinal Neoplasm Malignant",[110,184,185,186],"Large Language Model","Gastrointestinal Cancers","Treatment Recommendation","2023-09-06",{"date":189,"type":40},"2023-09-08",{"date":191,"type":40},"2023-08-29",{"date":193,"type":22},"2028-12-31",{"name":46,"class":47},7,{"id":197,"slug":198,"hasResults":11,"nctId":199,"briefTitle":200,"officialTitle":201,"acronym":4,"eligibilityCriteria":202,"healthyVolunteers":17,"sex":203,"minAge":56,"maxAge":4,"enrollmentInfo":204,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":205,"conditions":206,"keywords":208,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":187,"lastUpdatePostDateStruct":211,"startDateStruct":212,"completionDateStruct":214,"leadSponsor":216,"locationsCount":217},"100516276","quality-control-of-ultrasound-images-during-early-pregnancy-via-ai-100516276","NCT06002412","Quality Control of Ultrasound Images During Early Pregnancy Via AI","Deep Learning-based Quality Control of Ultrasound Images During Early Pregnancy","Inclusion Criteria:\n\n* Women in early pregnancy who have detailed personal information and ultrasound images.\n* The ultrasound images should clearly show the fetus's median sagittal, NT, and choroid plexus views.\n\nExclusion Criteria:\n\n* Ultrasound images from women in mid to late pregnancy.\n* Ultrasound images that are unclear or blurry, making evaluation difficult.\n* Women who did not provide complete personal and medical information during the ultrasound scan.","FEMALE",{"count":101,"type":22},"This research integrates artificial intelligence to enhance early pregnancy ultrasonography quality control, focusing on specific fetal sections. In collaboration with prominent medical institutions, the investigators have amassed extensive fetal ultrasound data. The investigators aim to develop a deep learning model that can accurately identify essential anatomical areas in ultrasound images and evaluate their quality. This tool is expected to significantly decrease misdiagnoses of conditions like Down Syndrome and neural system deformities by ensuring real-time image quality assessment.",[207],"Early Pregnancy",[207,209,210],"Ultrasound","Quality Control",{"date":189,"type":40},{"date":213,"type":40},"2023-09-01",{"date":215,"type":22},"2028-07-30",{"name":46,"class":47},4,{"id":219,"slug":220,"hasResults":11,"nctId":221,"briefTitle":222,"officialTitle":223,"acronym":4,"eligibilityCriteria":224,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":225,"enrollmentInfo":226,"targetDuration":4,"studyType":60,"phases":228,"briefSummary":229,"conditions":230,"keywords":4,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":231,"lastUpdatePostDateStruct":232,"startDateStruct":234,"completionDateStruct":236,"leadSponsor":237,"locationsCount":238},"100442909","gpc3-targeted-fluorescence-image-guided-surgery-of-hepatocellular-carcinoma-100442909","NCT05047510","GPC3 Targeted Fluorescence Image Guided Surgery of Hepatocellular Carcinoma","An Evaluation Study of GPC3 Targeted Fluorescence Imaging to Guide the Surgery of Hepatocellular Carcinoma","Inclusion Criteria:\n\n1. Patients who have been diagnosed with hepatocellular carcinoma.\n2. Planned to receive hepatectomy.\n3. Liver function Child-Pugh A\u002FB.\n4. GPC-3 was validated highly expressed preoperatively.\n5. Aged 18 to 75, and the expected lifetime is longer than 6 months.\n6. Approved to sign the informed consent.\n\nExclusion Criteria:\n\n1. Allergic to IRDye800.\n2. Enrolled in other trials in the past 3 months.\n3. Another malignant tumor was found.\n4. Undesirable function of heart, lung, kidney, or any other organs.\n5. Unable to tolerate a hepatectomy.\n6. The researchers considered inappropriate to be included.","75 Years",{"count":227,"type":22},60,[62],"This study is to evaluate whether intraoperative fluorescence imaging targeting GPC3 can aid improve the surgical accuracy of hepatocellular carcinoma.\n\nThe main purposes of this study include:\n\n① To raise the detection rate of hepatocellular carcinoma intraoperatively using the novel NIR-II fluorescence molecular imaging and the GPC-3 targeted fluorophore.\n\n② To validate the safety and effectiveness of the designed GPC-3 targeted fluorophore for clinical application.",[107],"2023-08-28",{"date":233,"type":40},"2023-08-30",{"date":235,"type":40},"2021-09-10",{"date":75,"type":22},{"name":46,"class":47},2,{"id":240,"slug":241,"hasResults":11,"nctId":242,"briefTitle":243,"officialTitle":244,"acronym":4,"eligibilityCriteria":245,"healthyVolunteers":11,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":246,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":247,"conditions":248,"keywords":250,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":253,"lastUpdatePostDateStruct":254,"startDateStruct":256,"completionDateStruct":258,"leadSponsor":259,"locationsCount":260},"100494753","prediction-of-peritoneal-metastasis-for-gastric-cancer-based-on-radiomics-100494753","NCT05722275","Prediction of Peritoneal Metastasis for Gastric Cancer Based on Radiomics","Prediction of Peritoneal Metastasis for Gastric Cancer Based on Radiomics: a Multi-center Prospective Study","Inclusion Criteria:\n\n* (1) diagnosed advanced gastric cancer (≥cT3) by endoscopy-biopsy pathology, combined with CT and\u002For endoscopic ultrasound;\n* (2) with both enhanced CT and laparoscopy;\n* (3) without typical peritoneal metastasis indications in CT (diffuse omental nodules or omental cake, large amount of ascites, obvious irregular thickening with high peritoneal enhancement);\n* (4) without other evidence of distant metastasis, and no stage IV features on CT.\n\nExclusion Criteria:\n\n* (1) previous abdominal surgery;\n* (2) previous abdominal malignancies or inflammatory diseases;\n* (3) time intervals between CT and laparoscopy longer than 2 weeks;\n* (4) CT image artifacts that undermine peritoneal lesion assessment.",{"count":101,"type":22},"Peritoneal metastasis of gastric cancer is difficult to be detected in time, thus delaying treatment. Based on the conventional CT images of gastric cancer, this study plans to develop, improve and validate an intelligent analysis system based on radiomics. By extracting and combining the radiomics features related to peritoneal metastasis of gastric cancer, the intelligent analysis system could predict the risk of peritoneal metastasis, and provide personalized decision suggestions for the treatment of gastric cancer.",[154,249],"Peritoneal Metastases",[110,251,154,249,252,159],"Classification","Computed Tomography","2023-02-12",{"date":255,"type":40},"2023-02-14",{"date":257,"type":40},"2023-01-01",{"date":193,"type":22},{"name":46,"class":47},13,""]