The idea is to assess and identify the cancer that’s of high risk. While the word “biopsy” is enough to send patients into a tizzy, oncologists say it is crucial to correctly identify the cancer stage and “faster growing” ones for appropriate and timely treatment. And to ensure accuracy, researchers in India are now turning to ().
A team of experts from IIT-Kharagpur (IIT-Kgp) and Tata Medical Centre (TMC), Kolkata, has devised a computer-assisted model they say can automatically grade breast cancer aggressiveness, even in remote settings, providing fresh impetus to -based medical technology in India. It also seeks to reduce human error in identifying breast cancer of various levels of aggressiveness to assist in distinguishing normal and low and higher risk malignant tumours. To do that, the team tapped into , a form of concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
Detecting High Risk Cases
“The idea is to assess and identify the cancer that’s of high risk. This software allows accurate identification of the aggressive cancers anywhere, even in the remotest part of the country, allowing faster referral and quicker treatment for patients, irrespective of their geographical location,” Sanjoy Chatterjee, senior clinical oncologist at TMC, told IANS. They were driven by the fact that the precise grouping of aggressiveness (high or low rates of cell growth) of breast cancer remains a challenge at a time when the disease is the top cancer in women worldwide and is increasing, particularly in developing countries like India, where the majority of cases are diagnosed in their late stages.
Support for pathologists
“For best results, it is always desirable to have experienced pathologists in sophisticated laboratories, but it is also important that we recognise that this is not always feasible, especially outside large urban hospitals,” Ahmed pointed out. The clinical decision on breast cancer aggressiveness is mostly made manually based on certain pathological markers as seen in examining a tissue or cell sample under a microscope (called biopsy), they said. “The manual assessment is subjective, and could be error-prone with a steep learning curve and dependent on the intra and inter-observer ambiguities. The algorithm aids pathologists to identify aggressive forms accurately to allow quicker and faster referral and suitable treatment,” said Chakraborty, lead researcher and professor-in-charge of the Biomedical Imaging Informatics (BMI) Laboratory, School of Medical Science & Technology, IIT-Kgp. […]