Clinical trial · Observational
AI Model for Classifying Breast Cancer From Histopathology Images
A Study on Artificial Intelligence Algorithms for Breast Cancer Classification From Histopathology Images
- 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)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Biopsy, Mastectomy, Histopathology | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Women who undergo biopsy for suspected abnormal cell growth in the breast
- description
- The cohort includes women who have undergone a biopsy due to suspected abnormal cell growth in the breast. This cohort captures a wide range of potential diagnoses, including benign conditions, noninvasive (in situ) breast cancers, and invasive breast cancers. All participants have histopathological samples collected for analysis, which serve as the basis for determining the presence and type of abnormal cell growth. The cohort will be studied using deep learning techniques to classify the biopsy samples into specific categories (normal, benign, in situ, or invasive), with the goal of improving diagnostic accuracy and efficiency in detecting breast cancer. By focusing on women undergoing biopsy, this study aims to address the diagnostic challenges faced in distinguishing between various breast tissue abnormalities, contributing to earlier detection and better clinical outcomes.
- interventionNames
- Diagnostic Test: Biopsy, Mastectomy, Histopathology
Primary outcomes (1)
- measure
- Accuracy of Deep Learning Model in Classifying Breast Tissue as Normal Benign, In Situ, or Invasive.
- timeFrame
- Measured at the time of histopathological image analysis (within 1 week of biopsy or mastectomy).
- description
- The primary outcome of this study is the diagnostic accuracy of the deep learning model in classifying histopathological images from breast biopsies into three categories: benign, in situ (noninvasive), and invasive breast cancer. Accuracy will be measured by comparing the model's predictions to the ground truth diagnoses determined by expert pathologists.
Eligibility
Eligibility (as posted)
- Sex
- Female
Show eligibility criteria text
* Inclusion criteria: * Female patients of any age can be selected as subjects. * Individuals willing to participate in breast cancer screening. * Availability for biopsy examination. * Women with no current or prior diagnosis of breast cancer. * Availability of relevant medical records for confirmation and comparison purposes. * Exclusion criteria: * Pregnant women are excluded due to potential impacts on screening results and the necessity for special considerations during pregnancy. * Individuals with severe medical conditions or circumstances that may render histopathologic examination inappropriate or unsafe are excluded. * Patients with conditions that could interfere with the accuracy of screening results are excluded. * Follow-up screenings are not included in this study.
References
Publications (0)
Data not yet available