Health Condition 1: C509- Malignant neoplasm of breast of unspecified site Health Condition 2: C189- Malignant neoplasm of colon, unspecified Health Condition 3: C760- Malignant neoplasm of head, face and neck Health Condition 4: C228- Malignant neoplasm of liver, primary, unspecified as to type Health Condition 5: C399- Malignant neoplasm of lower respiratory tract, part unspecified Health Condition 6: C508- Malignant neoplasm of overlappingsites of breast Health Condition 7: C61- Malignant neo
Conditions
Interventions
Intervention1: Nil: Nil
Sponsors
Canary Oncoceutics India Private Limited
Eligibility
Inclusion criteria
Inclusion criteria: 1. To be eligible for the study, participants must have a confirmed diagnosis of cancer based on histopathological assessment. 2. They must also have archival FFPE tumor tissue available for multi-omics and AI-based analysis, with a minimum tumor nuclei content of 50% to ensure reliable genomic profiling. 3. Clinical data, including demographics, treatment history, and survival outcomes, must be available for retrospective analysis. 4. In case of prospective recruitment, newly diagnosed patients must provide written informed consent for genomic profiling and AI-assisted predictions.
Exclusion criteria
Exclusion criteria: 1. Patients will be excluded if their tumor samples are of insufficient quality or quantity for sequencing and AI-based histopathology analysis. 2. Those who have undergone neoadjuvant chemotherapy or radiotherapy before sample collection will be excluded to avoid confounding genomic alterations. 3. Additionally, samples with artifacts, excessive necrosis, or poor resolution in H&E slides will be removed from analysis. 4. Lastly, genomic samples found to be contaminated or of poor sequencing quality during bioinformatics QC assessments will not be included in the study.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The primary outcomes include AI model accuracy in classifying tumors and predicting genomic alterations, metastasis, and survival.Timepoint: In the first phase, a retrospective analysis will be performed on 50,000 cancer tissue samples across multiple tumor types, where multi-omics profiling and histopathological imaging will be used to discover novel biomarkers. | — |
Secondary
| Measure | Time frame |
|---|---|
| Secondary outcomes include assessing the AI model s ability to predict patient response to chemotherapy, immunotherapy, and targeted therapy. The effectiveness of AI-driven risk stratification will be validated against treatment response rates, progression-free survival, and overall survival.Timepoint: In the second phase, AI model will be trained using data from these retrospective cohorts, allowing for precise classification of tumor subtypes based on their genomic and histological features. The final phase will involve validation using an independent cohort of patients, where AI-driven predictions will be tested against molecular and clinical outcomes. | — |
Countries
India
Contacts
Public ContactDr Ashok Kumar Vaid
Medanta- The Medicity
Outcome results
None listed