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A study of deep learning-based multimodal ultrasound-radiomics to accurately predict non-,micro-,macro- metastasis of central lymph nodes in papillary thyroid microcarcinoma.

A study of deep learning-based multimodal ultrasound-radiomics to accurately predict non-,micro-,macro- metastasis of central lymph nodes in papillary thyroid microcarcinoma.

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300077302
Enrollment
Unknown
Registered
2023-11-03
Start date
2022-12-07
Completion date
Unknown
Last updated
2023-11-06

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

Conditions

Papillary thyroid microcarcinoma

Interventions

Gold Standard:Puncture cytology and surgical histological pathological results
Index test:Deep learning-based multimodal ultrasound-radiomics model

Sponsors

Sun Yat-sen University Cancer Center
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 79 Years

Inclusion criteria

Inclusion criteria: (1)Patients who had undergone B-mode ultrasound, color Doppler ultrasound, SWE elastography before operation. (2)Patients who had undergone thyroid surgery and had definite pathological findings of papillary thyroid microcarcinoma. (3)Patients who had undergone central lymph node dissection and had pathological results of lymph nodes (4)Patients who have complete clinical data.

Exclusion criteria

Exclusion criteria: (1)Patients who were treated for thyroid cancer before. (2)The nodules or lymph nodes on ultrasound images have measurement markers. (3)The quality of ultrasound images was poor.

Design outcomes

Primary

MeasureTime frame
Deep learning-based multimodal ultrasound-radiomics;Accuracy;Sensitivity;Recall rate;ROC Curve;—

Countries

China

Contacts

Public ContactLiu LongZhong

Sun Yat-sen University Cancer Center

liulzh@sysucc.org.cn+86 136 0245 7948

Outcome results

None listed

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