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Development and Validation of a Multi-Omics Nomogram Based on Transfer Learning to Predict Postoperative Efficacy of Thymoma Patients with Myasthenia Gravis

Development and Validation of a Multi-Omics Nomogram Based on Transfer Learning to Predict Postoperative Efficacy of Thymoma Patients with Myasthenia Gravis - A Multi-Omics Nomogram to Predict Postoperative Efficacy of Thymoma Patients with Myasthenia Gravis

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400087232
Enrollment
Unknown
Registered
2024-07-23
Start date
2024-02-01
Completion date
Unknown
Last updated
2024-07-29

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

Conditions

Thymoma

Interventions

Gold Standard:Postoperative pathology clarified the diagnosis of thymoma, and serum antibodies and symptom evaluation clarified the diagnosis of myasthenia gravis.
Index test:Clinical models built from clinical features
radiomics models and deep learning models built from CT image features, and combined nomogram model combining the above models

Sponsors

Xiangya Hospital, Central South University, Changsha, Hunan 410000, People’s Republic of China.
Lead Sponsor

Eligibility

Sex/Gender
All
Age
20 Years to 70 Years

Inclusion criteria

Inclusion criteria: 1. Histologically confirmed thymoma, 2. Typical symptoms of myasthenic weakness, 3. Patients who have undergone a chest CT scan within 2 weeks (extendable up to 1 month) prior to surgery, 4. Patients with complete clinical information.

Exclusion criteria

Exclusion criteria: 1. Cases where CT scans were not performed at our hospital or were conducted outside the study timeframe. 2. Cases with missing follow-up records.

Design outcomes

Primary

MeasureTime frame
area under the receiver operating characteristic curve (AUC);

Countries

China

Contacts

Public ContactRongrong Zhou

Xiangya Hospital, Central South University, Changsha, Hunan 410000, People’s Republic of China.

zhourr@csu.edu.cn+86 138 7589 8127

Outcome results

None listed

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