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NCT06717984
AI Model for Classifying Breast Cancer From Histopathology Images
trial testing Biopsy, Mastectomy, Histopathology in Breast Cancer in 500 participants. Currently enrolling.
11 February 2025
Quick facts
| Lead sponsor | Taufiq Hasan, PhD |
|---|---|
| Status | Recruiting now |
| Study type | OBSERVATIONAL |
| Enrollment | 500 |
| Start date | 11 January 2024 |
| Primary completion | 11 February 2025 |
| Estimated completion | 11 February 2025 |
| Sites | 1 location across Bangladesh |
Drugs / interventions tested
- Biopsy, Mastectomy, Histopathology
Conditions studied
- Breast Cancer — all drugs for Breast Cancer →
Sponsor
Taufiq Hasan, PhD
Who can join
Eligibility, female only, with Breast Cancer. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Breast cancer, a prevalent and potentially fatal disease, underscores the need for early and accurate detection to improve patient outcomes. Traditional histopathological examination, the current gold standard for diagnosis, faces limitations like subjectivity and low efficiency. In response, this research seeks to revolutionize breast cancer diagnostics by using deep learning techniques to classify invasive and noninvasive breast cancer types from histopathological images. Non-invasive cancers, like DCIS and LCIS, are confined to milk ducts or lobules, while invasive cancers spread to surrounding tissue and make up 70% of cases, often leading to poorer outcomes. The proposed AI model aims to enhance diagnostic accuracy and efficiency, surpassing manual methods, and providing a scalable solution for diverse healthcare settings. By automating image analysis, the model seeks to democratize cancer screening, making it accessible in underserved populations and adaptable to different resources and equipment. Ultimately, this research aims to advance breast cancer detection, improve patient care, and contribute to better treatment outcomes globally.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT06717984
- Europe PMC full search
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06717984 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Taufiq Hasan, PhD
- Last refreshed: 5 December 2024
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06717984.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing