The Potential of Radiomics to Differentiate Between Malignant and Benign Bosniak 3 Renal Cysts.

Renal Cyst Complex Clinical Trial

— BIII  

Official title:

The Potential of Radiomics to Differentiate Between Malignant and Benign Bosniak 3 Renal Cysts.

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT03552497
• Other study ID #: BIIIP_18
• Status: Active, not recruiting
• Start date: August 7, 2018
• Est. completion date: January 31, 2021

Study information

• Verified date: April 2020
• Source: Maastricht University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 500
• Est. completion date: January 31, 2021
• Est. primary completion date: December 1, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• Patients who underwent contrast-enhanced CT-Scan, with radiological findings suggestive of Bosniak 3 renal cyst and have available results for pathology analysis of the cyst.

Exclusion Criteria:

• CT-Scans that are reformatted or secondary.

Related Conditions & MeSH terms

• Cysts
• Renal Cyst Complex

Intervention

Diagnostic Test:
• Radiomics
The high-throughput extraction of large amounts of quantitative image features from radiographic medical images

Locations

Country	Name	City	State
Netherlands	Maastricht University Medical Center	Maastricht	Limburg

Sponsors (7)

Lead Sponsor	Collaborator
Maastricht University	Erasmus Medical Center, Memorial Sloan Kettering Cancer Center, Stanford University, University Hospital, Aachen, University of California, San Francisco, University of Sao Paulo General Hospital

Country where clinical trial is conducted

Netherlands, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	malignancy classifier	Machine learning algorithm that can differentiate between malignant and beingn bosniak 3 renal cysts.	1 year