Characterization of genetic intratumoral heterogeneity of benign and malignant soft tissue tumors for prediction of genetic aberrations by machine learning.

Characterization of genetic intratumoral heterogeneity of benign and malignant soft tissue tumors for prediction of genetic aberrations by machine learning. - STT-AI Study

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00024705

Enrollment

1000

Registered

2023-02-22

Start date

2021-06-06

Completion date

Unknown

Last updated

2025-10-06

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

Conditions

D48.1

Interventions

Group 1: retrospective analysis of data: The collected findings, measurement results and all data collected according to the test plan are entered into the test forms. The number and sequence of the p

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: Patients of the Tumor Orthopedics of the LMU with clinical diagnosis of a soft tissue tumor (malignant / benign) and corresponding radiological examinations as well as histopathological findings/ clinical diagnoses.

Exclusion criteria

Exclusion criteria: - Missing imaging (MRI) - Missing histopathological findings / clinical diagnosis

Design outcomes

Primary

Measure	Time frame
In the future, improved predictive power of benign and malignant soft tissue tumors with respect to their genetic aberrations based on imaging data (MRI images). Sensitivity and specificity will be given between artificial intelligence and histopathology/molecular genetics. A comparison of sensitivity and specificity is made using McNemar's test.	—

Secondary

Measure	Time frame
Prediction of soft tissue tumor prognosis (survival rate, metastasis risk, recurrence rate) by artificial intelligence using image morphological criteria.	—

Countries

Germany

Contacts

Public ContactJens Ricke

Klinik und Poliklinik für Radiologie Klinikum der Universität München Campus Großhadern

Jens.Ricke@med.uni-muenchen.de(089) 4400-4400 72750

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026