Clinical trial NCT06717984: AI Model for Classifying Breast Cancer From Histopathology Images
Official title: A Study on Artificial Intelligence Algorithms for Breast Cancer Classification From Histopathology Images
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Breast Cancer
Keywords: Breast Cancer
Histopathology
Deep Learning
Lead sponsor: Bangladesh University of Engineering and Technology
Sponsor class: OTHER
Collaborators: name: National Institute of Cancer Research & Hospital, Bangladesh
First posted: 2024-12-05
Enrollment: 500
Sex: FEMALE
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: The study population includes women who are undergoing breast biopsies or mastectomies due to suspected abnormal cell growth. Participants are drawn from hospitals and clinics where breast cancer screening and diagnostic procedures are conducted. This population includes individuals with a range of breast conditions, from benign abnormalities to noninvasive (in situ) and invasive breast cancers, representing various stages of breast disease for classification and diagnostic purposes.

Brief summary: 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.

Detailed description: Breast cancer, a widespread and potentially fatal illness, emphasizes the urgent requirement for early and precise detection to enhance patient outcomes. The current diagnostic framework, characterized by manual histopathological examination, exhibits inherent drawbacks such as subjectivity and limited throughput. In response, our research aims to transform breast cancer diagnostics by leveraging advanced computational techniques, particularly deep learning. Breast cancer can be classified into two main subtypes: invasive and noninvasive.

Noninvasive breast cancer, often termed in situ breast cancer, is characterized by abnormal cells confined within the milk ducts (ductal carcinoma in situ or DCIS) or lobules (lobular carcinoma in situ or LCIS) without invading surrounding tissues. This type is considered an early stage and typically not life-threatening on its own. Invasive breast cancers are those that spread from the original site (either the milk ducts or the lobules) into the surrounding breast tissue. These constitute approximately 70% of all breast cancer cases and generally have a poorer prognosis compared to the in-situ subtypes.

Medical imaging of breasts can be acquired through various techniques, such as MRI scans, mammography, ultrasound, thermography, computed tomography scans, and histopathology. Among these approaches, the histopathology test serves as the gold standard for the clinical diagnosis of cancer.

The proposed AI model aims to streamline and enhance the analysis of histopathological images for the classification of invasive and noninvasive breast cancer, surpassing conventional methods and providing a reliable means of identifying cancerous regions. This promises to significantly improve the accuracy and efficiency of breast cancer diagnostics, meeting the urgent need for dependable and scalable solutions. By automating the complex process of breast cancer histopathological image analysis, our goal is to democratize screening, making it more accessible and reaching underserved populations. Moreover, our model goes beyond technological innovation; it addresses broader issues of accessibility and scalability, particularly in low-income settings. The research's focus on domain adaptation is crucial, ensuring the model's accuracy and reliability across various health facilities. This involves accommodating differences in resources, equipment, and demographic factors, making it a versatile and adaptable solution for diverse contexts.

In summary, our research not only aims to advance the technological frontier in breast cancer diagnostics but also seeks to drive a transformative change in accessibility, efficiency, and reliability. By developing an AI model that tackles the specific challenges of traditional methods, we aim to make a meaningful impact on breast cancer screening, ultimately leading to early detection, tailored treatment, and improved outcomes for patients worldwide.

Eligibility criteria: * 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.

Primary outcomes:
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).
Secondary outcomes:
Other outcomes:
Locations:
city: Dhaka
country: Bangladesh
facility: National Institute of Cancer Research & Hospital (NICRH)
Study officials:
affiliation: Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka - 1205.
name: Taufiq Hasan, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Department of Histopathology, National Institute of Cancer Research and Hospital (NICRH)
name: Farida Arjuman, FCPS, MCPS
role: PRINCIPAL_INVESTIGATOR
References: