[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Shao Pengfei\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":157},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,5,0,[8,48,74,99,126],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":18,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":30,"overallStatus":35,"whyStopped":4,"lastUpdateSubmitDate":36,"lastUpdatePostDateStruct":37,"startDateStruct":40,"completionDateStruct":42,"leadSponsor":44,"locationsCount":47},"100603800","application-of-the-off-clamp-and-sutureless-technique-in-robot-assisted-partial-nephrectomy-100603800",false,"NCT07141225","Application of the \"Off-Clamp And Sutureless\" Technique in Robot-Assisted Partial Nephrectomy","Application of the \"Off-Clamp And Sutureless\" Technique in Robot-Assisted Partial Nephrectomy: A Single-Center, Prospective Cohort Study","Inclusion Criteria:\n\n1. Age between 18 and 80 years old, regardless of gender;\n2. Radiological findings demonstrating a tumor size of ≤7 cm;\n3. Imaging studies showing no evidence of collecting system invasion;\n4. Scheduled to undergo robot-assisted partial nephrectomy (RAPN).\n\nExclusion Criteria:\n\n1. Preoperative imaging demonstrating evidence of distant metastasis or lymph node involvement;\n2. Unilateral or bilateral multiple lesions not amenable to simultaneous resection;\n3. History of prior systemic therapy, including molecular targeted therapy, immunotherapy, or chemotherapy;\n4. Intraoperative conversion to radical nephrectomy;\n5. Intraoperative conversion to open surgery.","ALL","18 Years","80 Years",{"count":20,"type":21},190,"ESTIMATED","INTERVENTIONAL",[24],"NA","The detection rate of renal masses smaller than 7 cm has significantly increased in recent years. To preserve postoperative renal function to the greatest extent possible, guidelines from the European Association of Urology (EAU), the National Comprehensive Cancer Network (NCCN), and others have endorsed partial nephrectomy (PN) as the preferred treatment strategy for small renal masses.\n\nIn conventional PN, it is necessary to clamp the renal artery or its branch arteries and employ a double-layer suturing technique to close the resection bed. This controls bleeding, maintains a clear surgical field, and prevents postoperative urinary leakage. The maximum safe duration of warm ischemia to avoid irreversible renal parenchymal damage remains controversial, though most studies indicate a window of 20-30 minutes. Consequently, the \"off-clamp sutureless\" concept has gained prominence. Its core principle is to avoid renal artery clamping and replace suturing with novel haemostatic techniques, thereby maximizing the preservation of healthy renal parenchyma. With the diversification of haemostatic material options and the widespread adoption of robotic-assisted systems, the off-clamp sutureless strategy has become technically feasible for small renal masses with low complexity .\n\nMultiple retrospective studies demonstrate that the off-clamp sutureless technique is non-inferior, offering safety and surgical outcomes comparable to conventional robot-assisted partial nephrectomy (RAPN). However, it is important to note that current research predominantly focuses on tumors ≤4 cm, is largely retrospective, and suffers from limited sample sizes. More robust, evidence-based medical evidence is required to support its application for larger tumors or those with complex anatomy.",[27,28,29],"Renal and Urinary Disorders","Renal Cancer","Renal Particial",[31,32,33,34],"robot-assisted partial nephrectomy","off-clamp","sutureless","postoperative renal function preservation","RECRUITING","2025-08-28",{"date":38,"type":39},"2025-09-05","ACTUAL",{"date":41,"type":39},"2025-06-01",{"date":43,"type":21},"2028-06-30",{"name":45,"class":46},"Shao Pengfei","OTHER",1,{"id":49,"slug":50,"hasResults":11,"nctId":51,"briefTitle":52,"officialTitle":53,"acronym":4,"eligibilityCriteria":54,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":18,"enrollmentInfo":55,"targetDuration":4,"studyType":22,"phases":57,"briefSummary":58,"conditions":59,"keywords":61,"overallStatus":35,"whyStopped":4,"lastUpdateSubmitDate":65,"lastUpdatePostDateStruct":66,"startDateStruct":68,"completionDateStruct":70,"leadSponsor":72,"locationsCount":73},"100594494","using-3d-kidney-model-based-on-artificial-intelligence-to-assist-partial-nephrectomy-a-prospective-validation-study-100594494","NCT07020169","Using 3D Kidney Model Based on Artificial Intelligence to Assist Partial Nephrectomy: A Prospective Validation Study","Artificial Intelligence-Driven 3D Kidney Model for Real-Time Augmented Reality and Surgical Navigation in Minimally Invasive (Robotic\u002FLaparoscopic) Partial Nephrectomy: A Prospective Validation Study","Inclusion Criteria:\n\n* Ages 18-80 years, regardless of gender\n* Written informed consent obtained from the patient or legally authorized representative after full protocol disclosure\n* Preoperative imaging (CT\u002FMRI) confirming clinical stage T1a or select T1b renal tumors suitable for partial nephrectomy (R.E.N.A.L. nephrometry score ≤10)\n* Localized renal tumors without lymph node\u002Fdistant metastasis per NCCN Guidelines® (v2023)\n* Elective minimally invasive partial nephrectomy (laparoscopic\u002Frobotic) after comprehensive surgical counseling\n\nExclusion Criteria:\n\n* Multifocal renal tumors (bilateral or unilateral)\n* Prior systemic anticancer therapy (targeted agents\u002Fimmunotherapy\u002Fchemotherapy) within 6 months\n* Absolute surgical contraindications (e.g., ASA class ≥IV, uncontrolled coagulopathy)\n* Intraoperative conversion to radical nephrectomy or open approach\n* Postoperative adjuvant therapy during protocol-defined follow-up (12 months)\n* Major comorbidities (e.g., NYHA class III\u002FIV heart failure, eGFR \\\u003C30 mL\u002Fmin\u002F1.73m²) affecting outcome assessment\n* Concurrent enrollment in interventional clinical trials\n* Investigator-determined ineligibility based on risk-benefit analysis",{"count":56,"type":21},232,[24],"The goal of this study is to develop a real-time artificial intelligence-driven 3D kidney model to assist robotic or laparoscopic partial nephrectomy:\n\n• Can this AI-powered model optimize the workflow of partial nephrectomy and enhance surgical benefits?",[60],"Renal Cell Cancer",[62,63,64],"partial nephrectomy","Articicial Intelligence","Intraoperative Real-Time Navigation","2025-06-12",{"date":67,"type":39},"2025-06-13",{"date":69,"type":39},"2025-05-01",{"date":71,"type":21},"2027-03-01",{"name":45,"class":46},2,{"id":75,"slug":76,"hasResults":11,"nctId":77,"briefTitle":78,"officialTitle":79,"acronym":4,"eligibilityCriteria":80,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":18,"enrollmentInfo":81,"targetDuration":83,"studyType":84,"phases":4,"briefSummary":85,"conditions":86,"keywords":87,"overallStatus":35,"whyStopped":4,"lastUpdateSubmitDate":91,"lastUpdatePostDateStruct":92,"startDateStruct":94,"completionDateStruct":96,"leadSponsor":98,"locationsCount":73},"100573839","the-value-of-a-convolutional-neural-network-based-renal-artery-perfusion-model-in-predicting-renal-function-after-partial-nephrectomy-a-prospective-study-100573839","NCT06751498","The Value of a Convolutional Neural Network-Based Renal Artery Perfusion Model in Predicting Renal Function After Partial Nephrectomy: A Prospective Study","The Value of a Renal Artery Perfusion Model Based on Convolutional Neural Network in Predicting Renal Function After Partial Nephrectomy: A Prospective, Single-Center Study","Inclusion Criteria:\n\n* people with stage cT1 renal tumors confirmed by preoperative CT or MR\n* people who are proposed to undergoing partial nephrectomy\n* localized renal tumors without lymph node and distant metastases as defined by NCCN guidelines\n* ECOG score of 0 or 1\n* Life expectancy greater than 10 years\n\nExclusion Criteria:\n\n* people with surgically unresectable lesions\n* people with Abnormal preoperative renal function, eGFR(estimated by CKD-EPI)\\\u003C90ml\u002Fmin\u002F1.73m2\n* people who receive preoperative molecular targeted therapy, immunotherapy, chemotherapy\n* people with any contraindications to surgery\n* people who convert to radical nephrectomy during surgery\n* people who receive molecular targeted therapy, immunotherapy or chemotherapy during the postoperative follow-up period\n* people with serious systemic disease",{"count":82,"type":21},300,"1 Year","OBSERVATIONAL","The goal of this observational study is to develop a CNN-based machine module to predict postoperative fractional renal function in people who are proposed to undergo partial nephrectomy. The main question it aims to answer is:\n\n• Does this machine learning model accurately predict renal function after partial nephrectomy?",[60],[63,88,89,62,90],"convolutional neural network","renal arterial perfusion model","fractional renal function","2025-04-14",{"date":93,"type":39},"2025-04-17",{"date":95,"type":39},"2025-01-01",{"date":97,"type":21},"2028-01-01",{"name":45,"class":46},{"id":100,"slug":101,"hasResults":11,"nctId":102,"briefTitle":103,"officialTitle":104,"acronym":4,"eligibilityCriteria":105,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":106,"targetDuration":108,"studyType":84,"phases":4,"briefSummary":109,"conditions":110,"keywords":113,"overallStatus":35,"whyStopped":4,"lastUpdateSubmitDate":119,"lastUpdatePostDateStruct":120,"startDateStruct":122,"completionDateStruct":123,"leadSponsor":125,"locationsCount":47},"100571028","optimising-renal-tumour-management-through-artificial-intelligence-modules-100571028","NCT06714916","Optimising Renal Tumour Management Through Artificial Intelligence Modules","Mutimodal Artificial Intelligence for Optimising Renal Tumour Management: Diagnosis, Surgery and Prognosis","Inclusion Criteria:\n\n* Patients with renal tumor which can be treated by surgery;\n* Complete CECT within 30 days before surgery;\n* Patients who fully understand this study and sign the informed consent;\n\nExclusion Criteria:\n\n* Patients with any item missing from the baseline clinical and pathological information;\n* Patients who has already metastasized by the time the tumor is discovered;\n* Previous treatment in any form, including surgery, targeted therapy and immunotherapy;",{"count":107,"type":21},2100,"5 Years","The goal of this observational study is to improve the management of people with renal tumour by multimodal artificial intelligence(AI). It will also measure the accuracy of the predictions from AI models. The main questions it aims to answer are:\n\n1. whether the AI module can accurately provide tumor-related information such as Benign or malignant, subtypes, grading, stage, etc. by learning from preoperative CT images.\n2. whether the AI module can help clinicians find out the most suitable surgical programme for people with renal tumor.\n3. whether the AI module can integrate CT images and pathology slides, offering supplementary prognostic information to improve postoperative survival.\n\nParticipants who complete a CT(usually Contrast-enhanced CT, CECT) examination and undergo radical or partial nephrectomy will carry out active surveillance and record postoperative survival data for 5 years.",[111,112,60],"Renal Neoplasms","Pathology",[63,114,115,116,117,118],"Renal tumors","Prediction Model","Surgery","Radiomics","Pathomics","2025-03-16",{"date":121,"type":39},"2025-03-19",{"date":95,"type":39},{"date":124,"type":21},"2033-12-31",{"name":45,"class":46},{"id":127,"slug":128,"hasResults":11,"nctId":129,"briefTitle":130,"officialTitle":131,"acronym":4,"eligibilityCriteria":132,"healthyVolunteers":133,"sex":134,"minAge":135,"maxAge":4,"enrollmentInfo":136,"targetDuration":4,"studyType":22,"phases":138,"briefSummary":139,"conditions":140,"keywords":145,"overallStatus":148,"whyStopped":4,"lastUpdateSubmitDate":149,"lastUpdatePostDateStruct":150,"startDateStruct":152,"completionDateStruct":154,"leadSponsor":156,"locationsCount":47},"100567017","artificial-intelligence-models-for-precision-prediction-and-treatment-of-prostate-cancer-100567017","NCT06662708","Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer","Accurate Prediction and Treatment of Prostate Cancer by Artificial Intelligence Model-based Whole Slide Images and MRIs","Inclusion Criteria:\n\n* Patients with suspected PCa (elevated PSA or suspicious positive lesions on ultrasound or MRI results);\n\nExclusion Criteria:\n\n* Previous treatment of the prostate in any form, including surgery, radiotherapy\u002Fchemotherapy, endocrine therapy, targeted therapy and immunotherapy;\n* Patients with any item missing from the baseline clinical and pathological information;\n* Patients with a history of other malignancies, serious comorbidities or other health problems;\n* Unable to provide\u002Fsign an informed consent form;\n* Patients who, in the judgement of the investigator, are deemed unfit to participate in this clinical trial;",true,"MALE","30 Years",{"count":137,"type":21},200,[24],"The aim of this clinical trial is whether artificial intelligence models can be used for accurate clinical preoperative diagnosis and postoperative diagnosis of pathological findings, and will also measure the accuracy of the predictions made by the artificial intelligence models.The main target questions addressed by the model building are:\n\n1. whether the AI model can learn from preoperative MRI and postoperative Whole Slide Images so as to accurately predict information such as benignness or malignancy, aggressiveness, grading, subtypes, genes, etc. for participants suspected of having prostate cancer preoperatively\u002Fpuncturally.\n2. whether the AI model is capable of learning postoperative macropathology slides to enable outcome diagnosis of surgical pathology slides in new participants.\n\nParticipants will:\n\n1. complete an MRI examination and have their MRI images analysed by the established AI model to make an accurate diagnosis of them.\n2. Based on the diagnosis, if prostate cancer is predicted, they will undergo radical prostate cancer surgery and refine their surgical pathology.",[141,142,143,144,112],"Prostate Cancer","Prostate Intraductal Carcinoma","Prostate Cancer Aggressiveness","Prostate Cancer Stage",[63,146,147,141,115],"Whole Slide Image","mp-MRI","NOT_YET_RECRUITING","2024-10-27",{"date":151,"type":39},"2024-10-29",{"date":153,"type":21},"2024-12-01",{"date":155,"type":21},"2030-12-31",{"name":45,"class":46},""]