Newswise — One of the most agonizing experiences a cancer patient suffers is waiting without knowing: waiting for a diagnosis, waiting to get test results back, waiting to learn the outcome of treatment protocols. Now a researcher at The University of Alabama in Huntsville (UAH) is co-author of that studies the use of artificial intelligence (AI) and neuronal networks to significantly cut the time required for medical professionals to classify lesions in breast cancer ultrasound images. Accurate classification is pivotal for early diagnosis and treatment, and a deep-learning (DL) approach can effectively represent and utilize the digital content of images for more precise and rapid medical image analysis.
Dr. Keka Biswas is a lecturer of biological sciences at UAH, a part of The University of Alabama System. Her current focus is examining human cancerous cells in a collaborative approach with biologists, mathematicians and statisticians in the field of mathematical biology.
Recent advances in AI and medical imaging have led to the widespread use of deep-learning technology, particularly in image processing and classification.
“Deep learning is a subfield of machine learning that employs neural network-based models to imitate the human brain’s capacity to analyze huge amounts of complicated data in areas such as image recognition,” Biswas explains. “The applications include cancer subtype discovery, text classification, medical imaging, etc. With AI, you can actually use these advances during surgery to see what stage the cancer is in, and the imaging of it is a much faster turnaround time.”
Breast ultrasound imaging is useful for detecting and distinguishing benign masses from malignant masses, but imaging reporting and data system features are difficult for practitioners and radiologists and also time consuming. The study investigated the relationship of breast cancer imaging features and the need for rapid classification and analysis of precise medical images of breast lesions.
“While progress has been made in the diagnosis, prognosis and treatment of cancer patients, individualized and data-driven care remains a challenge,” the researcher notes. “AI has been used to predict and automate many cancers and has emerged as a promising option for improving health care accuracy and patient outcomes.”
AI applications in oncology include risk assessment, early diagnosis, patient prognosis, estimation and treatment selection, based on deep-learning knowledge. Biswas’ study investigates the relationship between breast cancer imaging features and the roles of inter- and extra-lesional tissues and their impact on refining the performance of deep-learning classification. These advances are all predicated on the different ways benign and malignant tumors affect neighboring tissues, such as the pattern of growth and border irregularities, the penetration degree of the adjacent tissue and tissue-level changes.
“This is where deep learning comes in, looking into the deeper tissues and the outside tissue, and that can give us datasets to work with,” Biswas says.
Researchers use AI and pre-training to obtain “neural network datasets,” collections of data used to train a neural network, a type of machine-learning algorithm inspired by the human brain. The training employs labeled examples that the network learns from to identify patterns and make predictions on new data. A model is first trained on a dataset to learn broad features and patterns, which then serves as a “pre-trained” model that can be further fine-tuned on smaller, task-specific datasets to achieve better performance for a particular research goal – basically leveraging the knowledge gained from the large pre-training dataset to improve the efficiency of training on smaller, specialized datasets.
In 2023, Biswas took a serendipitous trip to a conference in South Africa, where she met Dr. Luminita Moraru, a professor at the University of Galati in Romania who has a similar career focus, while approaching the challenge using different tools.
“I met Dr. Moraru, who was from a department of chemistry at her university where she has a model and stimulation lab,” Biswas says. “But she didn’t have the biological or anatomical background for this kind of research.”
By teaming up, the two researchers found they could complement one another’s skillsets to delve much deeper into data challenges like these. “Then, about the same time, one of my friends got detected with breast cancer, and she had given up hope,” Biswas says. “It affected her tremendously. The thing that was scary was how long it was taking to go through all the pathological testing.”
That’s when events soon took an even more personal turn for the UAH educator.
“This summer [2024], I actually experienced this all for myself,” the researcher says. “I had gone in for a routine exam and told the doctor what my symptoms were, and she said let’s run a biopsy. Two days later I got a call, you need to come in, and I knew something was wrong. You have cancer, my doctor said. Do you know which stage it is in? I asked. She couldn’t tell me a stage, even with the pathological tests that had been done. I have a child, a family. I needed to know which stage it is in, what the treatment is going to be. That took another three to four weeks for the results to come in. The delay was the major thing – do I need surgery? How long will it take?
“It all was a much longer process. My oncologist was very frustrated,” the researcher says. “I had a diagnosis, but none of the imaging was telling us what stage it was in. Whether it has metastasized or not. I needed to be aware of how soon I could recover.”
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