A prospective, randomized controlled, multicenter clinical study of the effectiveness of deep learning imaging omics model in predicting lymph node metastasis in early cervical cancer

Status

Active, not recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2000039390

Enrollment

Unknown

Registered

2020-10-25

Start date

2021-03-01

Completion date

Unknown

Last updated

2024-01-22

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

Conditions

Cervical Cancer

Interventions

Gold Standard:Pathologic diagnosis of postoperative lymph nodes.

Index test:Group 1: Isothiocyanine

Group 2: The deep neural network model to predict lymph node status in patients with cervical cancer before surgery.

Eligibility

Sex/Gender

Female

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Patients with clinical diagnosis of FIGO IB-IIA cervical cancer (any histological type can be enrolled); 2. The patient is 18 years or older; 3. The original diagnosis and treatment plan included abdominal extensive hysterectomy plus systemic pelvic lymphadenectomy.

Exclusion criteria

Exclusion criteria: 1. Previous history of pelvic lymphadenectomy or pelvic radiotherapy; 2. Significant abnormalities in liver function (MELD score > 10 or equal); 3. Renal function was significantly abnormal (serum creatinine >= 2.0mg/ml); 4. There are other contraindications to surgery; 5. Allergy to isothiocyanine or hypersensitivity to other contrast agents; 5. Is participating in other clinical trials.

Design outcomes

Primary

Measure	Time frame
Accuracy in detecting positive and negative lymph nodes;	—

Countries

China

Contacts

Public ContactDong Taotao

Qilu Hospital of Shandong University

stevendtt@163.com+86 18560081990

Outcome results

None listed

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