Clinical trial · Observational
Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment
Development of an Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment Based on Multimodal Data Fusion Using Deep Learning Technology
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
To improve the accuracy of risk prediction, screening and treatment outcome of cancer, we aim to establish a medical database that includes standardized and structured clinical diagnosis and treatment information, image features, pathological features, and multi-omics information and to develop a multi-modal data fusion-based technology system using artificial intelligence technology based on database.
Conditions
Conditions (9)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence | — | UNRESOLVED | — |
| Cancer Risk | — | UNRESOLVED | — |
| Cancer Screening | — | UNRESOLVED | — |
| Cancer, Treatment-Related | Malignant Neoplasm | ALIAS | 0.85 |
| Colon Cancer | Malignant Colon Neoplasm | CURATED_EXACT | 0.92 |
| Deep Learning | — | UNRESOLVED | — |
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
| Lung; Node | — | UNRESOLVED | — |
| Stomach Cancer | Malignant Gastric Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (3)
- label
- Lung cancer group
- description
- Participants with lung cancer/pulmonary nodules
- label
- Stomach cancer group
- description
- Participants with Stomach cancer/Stomach lesion
- label
- Colorectal cancer group
- description
- Participants with Colorectal cancer/Colorectal lesion
Primary outcomes (4)
- measure
- The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule)
- timeFrame
- 2022-2026
- description
- The outcome of clinical diagnosis of patients with lung cancer/pulmonary nodular (Benign/Malignant nodule). ① Benign nodule ② Malignant neoplasm/nodule: squamous cell carcinoma, adenocarcinoma, small cell carcinoma, and large cell carcinoma.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: 1. Participants with the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision 2. Participants that have signed informed consent. 3. Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information. 4. Healthy participants with no clinical diagnosis of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision. Exclusion Criteria: 1. Participants with primary clinical and pathological data missing. 2. Participants lost to follow-up. 3. Participants with too poor medical image quality to perform segment and mark ROI accurately
References
Publications (0)
Data not yet available