The AI model uses histopathological images for accurate diagnosis (Representational Image)
New Delhi:
Breast cancer accounts for 13.6 percent of all cancer cases (male and female) in India, according to the World Cancer Report 2022 published by IARC (International Agency for Research on Cancer). Among women, it accounts for 26% of all cancer cases. In the United States, breast cancer accounts for about 30% of all new cancer cases among women.
New research shows that Artificial Intelligence (AI) can help combat this threatening disease. Early and accurate diagnosis can be key to treating patients, and a recently developed AI system promises to do so with a near-perfect diagnosis.
A research paper titled “Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology” published in Cancers Journal last month details an AI model that classifies and identifies different types of breast cancer present in a patient, in addition to ruling out malignancy (cancer) first, identifying benign tumors.
The study – carried out by researchers at Northeastern University, Boston, along with the Maine Health Institute for Research – developed an AI model that analyzes high-resolution histopathological (tissue-level microscopic) images of breast tumor tissue.
The AI system, which outperforms previous machine learning (ML) models in the domain by combining the predictions of other ML models, is able to identify and classify the tumor into malignant (cancerous) or benign (non-cancerous) using data histories fed to the model during training.
It was trained on publicly available datasets called BreakHis (Breast Cancer Histopathological Database) and BACH (Breast Cancer Histopathology images). For BACH, microscopic images of breast tissue were meticulously labeled by medical experts, categorizing the images into four classes – Normal, Benign, Carcinoma In Situ and Invasive Carcinoma.
Exemplary microscopy images demonstrating the four classes in the BACH dataset (Image source: Cancers 2024, 16(12), 2222)
And for BreakHis, which consists of 9,109 microscopic images of breast tumor tissue, it was used to categorize benign and malignant tumors into 4 subclasses each – malignant tumors into ductal carcinoma, lobular carcinoma, mucinous carcinoma and papillary carcinoma, and benign tumors into adenosis . , Fibroadenoma, phyllodes tumor and tubular adenoma.
Representative microscopy images of malignant and benign breast tissues from the BreakHis dataset (Image source: Cancers 2024, 16(12), 2222)
Together, the joint ML model has an accuracy of 99.84%. Such a performance metric during the research and development phase shows optimistic promise for real-world application of the technology.
“AI cannot miss a tumor in the biopsy and will not be exhausted after diagnosing 10 or 20 people,” Saeed Amal told Northeastern Global News. Amal is a professor of bioengineering at Northeastern University and leads the ensemble model project.
In addition to diagnosis, AI systems have also made progress in prognosis and predictions related to breast cancer. For example, AI can now predict breast cancer neoadjuvant chemotherapy (NAC) response using hematoxylin and eosin images (common stains in tissue images) from pre-chemotherapy needle biopsies. The AI systems responsible for the same have an accuracy of 95.15 percent and have been detailed in a paper titled “Development of Multiple AI Pipelines Predicting Response to Neoadjuvant Breast Cancer Chemotherapy Using H&E Stained Tissues “, published in May 2023 in the Journal of Pathology.
Furthermore, AI has also made significant progress in identifying lymph node metastases (spread of cancer cells through lymph nodes) and assessing hormonal status, which is important for treating breast cancer. These and many other advances achieved by AI interventions over the years in the fight against breast cancer were stated in a review article published in Diagnostic Pathology in February.
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