The Potential of Radiomics to Differentiate Between Malignant and Benign Bosniak 3 Renal Cysts. (BIII)
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|ClinicalTrials.gov Identifier: NCT03552497|
Recruitment Status : Recruiting
First Posted : June 12, 2018
Last Update Posted : June 12, 2018
More than 200,000 new cases of renal cancer are diagnosed in the world each year, with more than 63,000 new cases in Europe alone. Of those, renal cell carcinoma (RCC) is the most common type in adults, making up more than 90% of the cases. Deciding on the benign or malignant nature of some RCC on the basis of medical images (CT, MRI, US) is an issue, which often leads to unnecessary surgery, morbidity and costs.
A categorization for renal cysts was introduced in the late 1980s known as the Bosniak classification. The Bosniak classification system classifies them into groups that are benign (I and II) and those that need surgical resection (III and IV), based on specific imaging features. However, defining the malignancy of category III lesions still remains a challenge. Though Bosniak classification for renal cysts is used worldwide and underwent a number of modifications, Bosniak III cysts still have almost a 1:1 chance of being malignant. So the problem is that approximately half of the Bosniak category III cystic lesions prove to be benign after surgery.
The proposed project aims to develop a quantitative image analysis (QIA) based multifactorial decision support system (mDSS) capable of classifying renal cysts with high accuracy into benign or malignant status, thus reducing the amount of unnecessary surgeries performed. Using standard-of-care CT images and clinical parameters, the customized DSS will then guide experts in planning a safe and effective diagnostic and treatment strategy for all RCC patients.
|Condition or disease||Intervention/treatment|
|Renal Cyst Complex||Diagnostic Test: Radiomics|
|Study Type :||Observational|
|Estimated Enrollment :||500 participants|
|Official Title:||The Potential of Radiomics to Differentiate Between Malignant and Benign Bosniak 3 Renal Cysts.|
|Estimated Study Start Date :||June 2018|
|Estimated Primary Completion Date :||June 2019|
|Estimated Study Completion Date :||December 2019|
- Diagnostic Test: Radiomics
The high-throughput extraction of large amounts of quantitative image features from radiographic medical images
- malignancy classifier [ Time Frame: 1 year ]Machine learning algorithm that can differentiate between malignant and beingn bosniak 3 renal cysts.
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03552497
|Contact: Henry Woodruff, PhD||0031615131998||h.woodruff@Maastrichtuniversity.nl|
|Contact: Abdalla Ibrahim, M.Dfirstname.lastname@example.org|
|Maastricht University Medical Center||Recruiting|
|Maastricht, Limburg, Netherlands, 6200 MD|
|Contact: Razvan Miclea, MD email@example.com|