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

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

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT03552497
Acronym
BIII
Enrollment
500
Registered
2018-06-12
Start date
2018-08-07
Completion date
2021-01-31
Last updated
2020-04-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Renal Cyst Complex

Keywords

Radiomics, Bosniak classification, Renal cell carcinoma, Medical imaging

Brief 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.

Interventions

DIAGNOSTIC_TESTRadiomics

The high-throughput extraction of large amounts of quantitative image features from radiographic medical images

Sponsors

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

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Healthy volunteers
No

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.

Design outcomes

Primary

MeasureTime frameDescription
malignancy classifier1 yearMachine learning algorithm that can differentiate between malignant and beingn bosniak 3 renal cysts.

Countries

Netherlands

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026