Leveraging Big Data to Enhance AI in Cancer Detection and Treatment
Integrating AI into the healthcare decision making process helps to revolutionize the sector and result in more accurate and consistent treatment decisions resulting from its virtually limitless ability to discover patterns too complex for humans to see.
The sphere of oncology generates enormous data sets, from unstructured clinical histories to imaging and genomic sequencing data, at various stages of the patient journey. AI can “intelligently” analyze large-scale data batches at faster speeds than traditional methods, which is critical for training the machine learning algorithms which can be foundational for advanced cancer testing and monitoring tools. AI also has tremendous inherent pattern recognition capabilities for efficiently modeling data set complexities. This is vital since it enables deeper, multi-layered understandings of the impact of nuanced molecular signatures in cancer genomics and tumor microenvironments. Discovering a pattern between genes only present in a certain subset of cancer cases or cancer progression patterns can result in a more tailored, patient-specific approach to treatment.
What’s the last word goal? AI-powered cancer tests that support clinical decision-making for doctors and their patients at every step of the cancer journey – from screening and detection, to identifying the fitting treatment, and for monitoring patients’ response to interventions and predicting reoccurrence.
Data Quality and Quantity: The Key to AI Success
Ultimately, an AI algorithm will only be nearly as good as the standard of information that trains it. Poor, incomplete or improperly labeled data can hamstring AI’s ability to seek out the very best patterns (garbage in, garbage out). This is very true for cancer care, where predictive modeling relies on impeccable precision – one gene modification out of 1000’s, for instance, could signal tumor development and inform early detection. Ensuring that top level of quality is time-consuming and dear but leads to raised data, which ends up in optimal testing accuracy. Nonetheless, developing a useful goldmine of information comes with significant challenges. For one, collecting large-scale genomic and molecular data, which might involve tens of millions of information points, is a posh task. It begins with having the very best quality assays that measure these characteristics of cancer with impeccable precision and backbone. The molecular data collected must even be as diverse in geography and patient representation as possible to expand the predictive capability of the training models. It also advantages from constructing long-term multi-disciplinary collaborations and partnerships that may help gather and process raw data for evaluation. Finally, codifying strict ethics standards in data handling is of paramount importance in terms of healthcare information and adhering to strict patient privacy regulations, which might sometimes present a challenge in data collection.
An abundance of accurate, detailed data is not going to only lead to testing capabilities that may find patterns quickly and empower physicians with the very best opportunity to handle the unmet needs for his or her patients but will even improve and advance every aspect of clinical research, especially the urgent seek for higher medicines and biomarkers for cancer.
AI Is Already Showing Promise in Cancer Care and Treatment
More practical ways to coach AI are already being implemented. My colleagues and I are training algorithms from a comprehensive array of information, including imaging results, biopsy tissue data, multiple types of genomic sequencing, and protein biomarkers, amongst other analyses – all of which add as much as massive quantities of coaching data. Our ability to generate data on the dimensions of quadrillions fairly than billions has allowed us to construct a few of the first truly accurate predictive analytics in clinical use, akin to tumor identification for advanced cancers of unknown primary origin or predictive chemotherapy treatment pathways involving subtle genetic variations.
At Caris Life Sciences, we have proven that extensive validation and testing of algorithms are mandatory, with comparisons to real-world evidence playing a key role. For instance, our algorithms trained to detect specific cancers profit from validation against laboratory histology data, while AI predictions for treatment regimens will be cross compared with real-world clinical survival outcomes.
Given the rapid advancements in cancer research, experience suggests that continuous learning and algorithm refinement is an integral a part of a successful AI strategy. As recent treatments are developed and our understanding of the biological pathways driving cancer evolves, updating models with the newest information offers deeper insights and enhances detection sensitivity.
This ongoing learning process highlights the importance of broad collaboration between AI developers and the clinical and research communities. We have found that developing recent tools to research data more rapidly and with greater sensitivity, coupled with feedback from oncologists, is important. Bottom-line: the true measure of an AI algorithm’s success is how accurately it equips oncologists with reliable, predictive insights they need and the way adaptable the AI strategy is to ever-changing treatment paradigms.
Real-World Applications of AI Are Already Increasing Survival Rates and Improving Cancer Management
Advances in data scale and quality have already had measurable impacts by expanding the physician decision-making toolkit, which has had real-world positive results on patient care and survival outcomes. The primary clinically validated AI tool for navigating chemotherapy treatment decisions for a difficult-to-treat metastatic cancer can potentially extend patient survival by 17.5 months, compared to plain treatment decisions made without predictive algorithms1. A distinct AI tool can predict with over 94% accuracy the tumor of origin for dozens of metastatic cancers2 – which is critical to creating an efficient treatment plan. AI algorithms are also predicting how well a tumor will reply to immunotherapy based on every person’s unique tumor genetics. In each of those cases, AI toolkits empower clinical decision-making that improves patient outcomes compared with current standards of care.
Expect An AI Revolution in Cancer
AI is already changing how early we will detect cancer and the way we treat it along the way in which. Cancer management will soon have physicians working side-by-side with integrated AI in real time to treat and monitor patients and stay one step ahead of cancer’s attempts to outwit medicines with mutations. Along with ever-improving predictive models for detecting cancer earlier and providing more practical personalized treatment paradigms, physicians, researchers, and biotech corporations are hard at work today to leverage data and AI analyses to drive recent therapeutic discoveries and molecular biomarkers for tomorrow.
Within the not-too-distant future, these once-impossible advances in AI will reach far beyond cancer care to all disease states, ending an era of uncertainty and making medicine more accurate, more personalized, and more practical.
