In recent years, people worldwide have made incredible, life-saving achievements with data science, from enabling search and rescue teams to find lost hikers to monitoring and mitigating climate change. In this blog, we focus on major breakthroughs in the prediction, diagnosis, treatment and prevention of cancers using the power of data science.
Cancer is a condition that has affected most people in one way or another. Finding ways to treat it has been the focus of life sciences researchers and pharmaceutical firms for decades. With data, analytics and AI, we are starting results that will have life-changing consequences for millions battling the condition.
Spotting Early Signs with Predictive Analytics
Predictive analytics is transforming oncology by enabling more precise tumor risk assessment. Machine Learning (ML) models show great promise in evaluating mortality risks, allowing clinicians to make better treatment and palliative care decisions. By analyzing the right data on age, gender, ethnicity, family history and lifestyle, AI algorithms can accurately predict patient outcomes, enabling more personalized care strategies.
Through the integration of computer vision and ML, oncologists can extract critical data from a tumor – such as radius, perimeter, compactness and proximity to organs – and identify whether it is benign, malignant or metastatic. Predictive analytics can also be effective in forecasting cancer recurrence by analyzing patterns that signal the reappearance of cancerous cells.
Early detection remains crucial, especially for diseases like breast cancer, which is seen at stage 0 only 20 percent of the time. Predictive analytics can significantly enhance early detection for improved patient outcomes. In one study, an AI-based tool – trained on samples from early-stage breast cancer patients – could successfully spot aggressive and non-aggressive tumors from a series of micrographs 96 percent of the time – a considerable boost over the 70 percent logged by actual physicians.
Treating Patients with the Help of Data Science
Data science is transforming oncology by enabling more precise and effective cancer treatments.
In the US, oncologists are using radiomic data – quantitative insights derived from medical images through advanced algorithms – to determine which lung cancer patients are most likely to benefit from chemotherapy. Radiomics provides a deeper understanding of a tumor’s response to treatment by revealing patterns and characteristics that are invisible to the naked eye, such as heterogeneity within and surrounding the tumor.
Globally, data-driven approaches are also being used to treat other types of cancer. For instance, a deep learning-based chemotherapy recommendation model developed in Korea is advancing personalized treatment for colorectal cancer patients. By analyzing past clinical cases, the model offers tailored treatment recommendations based on each patient’s unique cancer profile and personal characteristics.
Advancing Oncology through Enhanced Data Availability
The scarcity of high-quality data has historically hindered the adoption of data science in oncology. However, this situation is rapidly changing.
Today, research bodies have access to extensive publicly available databases encompassing diverse cancer patient demographics, including variations in race, gender, age and genetics. This wealth of data is fueling the development of groundbreaking personalized cancer therapeutics.
Min Zhang, Associate Director of Data Science at the Purdue University Center for Cancer Research, leads a project that applies a data-driven approach to studying metabolites – sugars, amino acids and other molecules produced by the body’s metabolism – to predict cancer risk. While individual metabolites offer limited insight, Zhang’s team utilizes advanced data science techniques to analyze groups of metabolites, revealing how they collaborate to perform specific functions.
This research has led to the identification of metabolite groups that serve as biomarkers for colorectal cancer, enabling non-invasive screening through blood samples and reducing the need for procedures like colonoscopies. Additionally, Zhang’s team uses ML to study gene regulation throughout cancer progression, providing vital insights into personalized treatment strategies.
As access to comprehensive medical data continues to expand, the role of data science in oncology will only grow, offering new avenues for early detection, personalized treatment and, ultimately, saving more lives.
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