AI Able To Identify Pre-Malignant Breast Lesions That Will Develop To Advanced Cancer
Source: Thailand Medical News Oct 30, 2019 5 years, 3 weeks, 1 day, 21 hours, 6 minutes ago
Professor
Anant Madabhushi, one of the world’s leading biomedical engineers who has authored over 380 peer-reviewed journal articles, the owner of over 120 patents and is the acclaimed authority on
AI and
computational imaging as well as
personalized diagnostics recently unveiled yet another study done at Case Western Reserve University that could help better determine which patients diagnosed with the pre-malignant
breast cancer commonly as stage 0 are likely to progress to invasive
breast cancer and therefore might benefit from additional therapy over and above surgery alone.
Professor Anant Madabhushi
Once a lumpectomy of breast tissue reveals this pre-cancerous tumor, most women have surgery to remove the remainder of the affected tissue and some are given radiation therapy as well.
Professor
Anant Madabhushi, the F. Alex Nason Professor II of Biomedical Engineering at the Case School of Engineering commented to
Thailand Medical News during his recent visit to Bangkok to attend the ASCO Breakthrough Summit 2019 about this study, "Current testing places patients in high risk, low risk and indeterminate risk but then treats those 'indeterminates' with radiation, anyway. They err on the side of caution, but we're saying that it appears that it should go the other way the middle should be classified with the lower risk. In short, we're probably over-treating patients. That goes against prevailing wisdom, but that's what our analysis is finding."
Stage 0
breast cancer is the most common type of breast cancer and known clinically as
ductal carcinoma in situ (DCIS), indicating that the cancer cell growth starts in the milk ducts.
Typically, about 62,000 cases of DCIS are diagnosed in the United States each year, and globally about 1.2 million cases, accounting for about one of every five new
breast cancer cases, according to the American Cancer Society. People with a type of
breast cancer that has not spread beyond the breast tissue, manage to only live for at least five years after diagnosis, according to the cancer society.
Dr Haojia Li, a graduate student in the Center for &l
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Computational Imaging and
Personalized Diagnostics (
CCIPD) at at Case Western Reserve University and lead researcher under Professor Anant, used a
computer program to analyze the spatial architecture, texture and orientation of the individual cells and nuclei from scanned and digitized lumpectomy tissue samples from 62 DCIS patients.
The result: Both the size and orientation of the tumors characterized as "indeterminate" were actually much closer to those confirmed as low risk for recurrence by an expensive genetic test called Oncotype DX.
Dr Li then validated the features that distinguished the low and high risk Oncotype groups in being able to predict the likelihood of progression from DCIS to invasive
ductal carcinoma in an independent set of 30 patients.
D Li further commented, "This could be a tool for determining who really needs the radiation, or who needs the gene test, both of which is also very expensive."
The research was published in the journal
Breast Cancer Research.
Professor
Anant Madabhushi established the
CCIPD at Case Western Reserve in 2012. The lab now includes nearly 60 researchers. The lab has become a global leader in the detection, diagnosis and characterization of various
cancers and other diseases, including
breast cancer, by meshing medical imaging, machine learning and
artificial intelligence (
AI).
Some of the lab's most recent work, in collaboration with New York University and Yale University, has used
AI to predict which lung cancer patients would benefit from adjuvant chemotherapy based on tissue slide images.
That advancement was named by
Prevention Magazine as one of the top 10 medical breakthroughs of 2018.
In terms of
AI applications for
medical imaging and
personalized diagnostics and
imaging biomarkers , the team from
CCIPD are the world’s leading authorities and also leading innovators with hundreds of projects underway.
Reference: Haojia Li et al. Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings,
Breast Cancer Research (2019).
DOI: 10.1186/s13058-019-1200-6
