Establishment and Clinical Application of AI-based Multimodal Diagnosis System for Ovarian Tumors

NCT06703112 | Not Yet Recruiting DMC

• 1 other identifier: IIT2024-037-002
• Study Type: interventional
• Target: 584
• Locations: 0 countries
• Sites: N/A

Brief Summary

Ovarian tumors are a common disease that threatens women's health. They are insidious in onset, have over ten pathological types, and exhibit diverse biological behaviors, making accurate diagnosis a key factor in clinical decision-making and improving prognosis. Introducing AI technology to establish an auxiliary diagnosis system composed of multi-dimensional clinical data, including medical imaging and tumor markers, will greatly enhance diagnosis efficiency by predicting the pathological types of common ovarian tumors. Our research group has innovatively developed an AI-based ultrasound intelligent auxiliary diagnosis software for ovarian tumors, which has been clinically validated to be effective. This project will build on this by: (1) utilizing a wealth of multi-center retrospective clinical data to combine ultrasound, MRI images, physiological, pathological, and laboratory data to form the first multi-modal ovarian tumor public dataset supporting AI tasks; (2) using convolutional neural network technology to realize multi-modal image multi-classification intelligent recognition on this dataset based on surgical pathology as the standard, and then fuse features at the level of clinical data with the intelligent recognition model to train and validate an auxiliary diagnosis model for predicting the top ten pathological types of ovarian tumors; (3) applying privacy computing and federated learning methods to conduct multi-center, prospective validation and optimization of the above model, ultimately forming a clinical auxiliary diagnosis system that can predict the pathological types of most ovarian tumors and apply it to clinical practice.

Conditions

Ovarian Tumors

Keywords

Ovarian Neoplasms; Artificial Intelligence; Multimodal Imaging; Computer-Assisted Diagnosis; Privacy Computing

Outcome Measures

Primary Outcomes (2)

• Pathological diagnosis

  After the surgical specimen is delivered to the pathology department, the pathologist conducts a gross examination, recording the size, shape, surface characteristics, and cut surface features of the specimen (e.g., the proportion of solid and cystic components of the tumor). The specimen is then processed according to standard protocols to prepare tissue sections, which are subsequently stained. Once staining is complete, the slides are independently examined under a microscope by two pathologists, who evaluate the tumor cell morphology, arrangement, and histological characteristics to determine the pathological nature (benign, borderline, or malignant) and type of the tumor. If there is a discrepancy in the diagnoses, the case is referred to a senior pathologist for review, whose final opinion shall prevail.

  Within one week postoperatively

• AUC （Area Under the ROC Curve）

  In this study, the area under the curve (AUC) is used as the primary evaluation metric to quantify the diagnostic performance of the new prediction model. AUC is based on the receiver operating characteristic (ROC) curve and evaluates the model's ability to distinguish between positive and negative cases at various thresholds by comparing the model's predictions with the gold standard (pathological diagnosis). Significance of AUC: The AUC value ranges from 0.5 to 1.0, with higher values indicating stronger overall discriminative ability of the model. Specific interpretations are as follows: AUC = 0.5: The model has no diagnostic capability. 0.7 ≤ AUC \< 0.9: The model demonstrates good diagnostic performance. AUC ≥ 0.9: The model shows excellent diagnostic performance. AUC \< 0.7: The model's diagnostic capability is considered low. By calculating the AUC value of the new prediction model, this study assesses its ability to distinguish positive cases from negative cases, thereby verif

  All study subjects must complete follow-up within one year after postoperative pathological diagnosis.

Study Arms (1)

ovarian tumors

ACTIVE COMPARATOR

Diagnostic Test: An auxiliary diagnostic model for ovarian tumors

Interventions

An auxiliary diagnostic model for ovarian tumors DIAGNOSTIC_TEST

Through collaboration among gynecological oncology teams from three research centers, the multimodal ovarian tumor auxiliary diagnosis system will undergo multicenter, prospective clinical application, with surgical pathology results used as the gold standard. The diagnostic performance of the multimodal ovarian tumor auxiliary diagnosis system, both overall and across different pathological categories, will be evaluated in the main and subsidiary centers by comparing metrics such as specificity, sensitivity, positive predictive value, negative predictive value, and area under the ROC curve (AUC).

ovarian tumors

Eligibility Criteria

• Sex: female
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• \. Continuous cases admitted for diagnosis of ovarian tumor and preparation for surgical treatment;
• \. Complete imaging data (ultrasound or MRI) and tumor marker results within 3 months before surgery;
• \. Voluntarily sign informed consent.

You may not qualify if:

• \. Patients with non-ovarian origin tumor as surgical pathology;
• \. Repetitive cases;
• \. Cases receiving radiotherapy and chemotherapy;
• \. Recurrent cases; 5. Poor image quality of ovarian lesions;

Sponsors & Collaborators

• Beijing Shijitan Hospital, Capital Medical University: lead
• Beijing Friendship Hospital: collaborator
• Beijing Obstetrics and Gynecology Hospital: collaborator

MeSH Terms

Conditions

Ovarian Neoplasms

Condition Hierarchy (Ancestors)

Endocrine Gland Neoplasms; Neoplasms by Site; Neoplasms; Ovarian Diseases; Adnexal Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Neoplasms, Female; Urogenital Neoplasms; Genital Diseases; Endocrine System Diseases; Gonadal Disorders

Central Study Contacts

lei liu

CONTACT

+86 13718692611; cdcliulei@163.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: The study will develop a multimodal public dataset incorporating ultrasound, MRI images, and clinical data, which will be used to train convolutional neural networks (CNNs) for the intelligent classification of ovarian tumors. The dataset will be constructed using retrospective multicenter data and validated prospectively in clinical settings. To ensure privacy and data security, federated learning will be employed to facilitate collaborative model training across multiple centers without sharing raw patient data. The primary outcome will be the accuracy of AI in predicting common pathological types of ovarian tumors, compared to traditional diagnostic methods.

Study Record Dates

• First Submitted: November 17, 2024
• First Posted: November 25, 2024
• Study Start: June 1, 2025
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: November 25, 2024

Record last verified: 2024-11

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP