People battling any type of cancer may find themselves locked in a race against time. But for those facing lung cancer in particular, time can be especially cruel. Many lung cancer patients go undiagnosed as their cancer grows, unencumbered, for years. When they finally receive a diagnosis for what perhaps seemed for ages like “just a pesky cough,” treatment options are few and the prognosis is grim.
These harsh realities are magnified for people around the world who face financial, geographic and social barriers to accessing quality healthcare and in places without the infrastructure or equipment to handle the burden of care. Even in wealthy nations with advanced diagnostic technologies, overextended healthcare providers struggle to keep patients from falling through the cracks. For these reasons and more, lung cancer is the leading cause of cancer deaths globally.
Against this forbidding backdrop, however, there is hope. For those facing lung cancer — whether they know it yet or not — it comes in the form of advanced digital imaging, artificial intelligence, machine learning algorithms and precision immunotherapies. This group of advanced technologies may offer earlier cancer detection, reveal promising treatment options, and help to reduce the burden on overtaxed healthcare workers and systems.
While these developments are far from a victory lap over lung cancer, Dr. Mathias Goyen, GE HealthCare’s chief medical officer in EMEA, says digital technologies are moving the needle for timely diagnosis, long-term outcomes and equitable access to care. “One of our roles at GE HealthCare is to help the industry with technology and solutions. For example, offering a tool that can serve as a control center that gathers and synthesizes data and streamlines routine tasks, saving time and reducing pressure on clinicians so they can focus more on lung cancer patients and their needs.”
Overcoming barriers to screening
Early screening technology is vital to improving patient outcomes, since timely diagnosis and intervention can lead to better treatment results. But getting people in for screening remains a problem, largely because screening guidelines lag behind the technology. In the U.S., about half of the patients diagnosed with lung cancer do not meet existing criteria for early screening. At the same time, fewer than 6% of those in the U.S. who are eligible actually undergo the potentially life-saving screenings. And in Europe, about 70% of lung cancers in Europe are diagnosed too late for potentially life-saving surgery or treatment. That’s not surprising, given that in France, no early screening program exists, while in Germany, a screening program didn’t exist until July of this year, and it’s still not open to everyone who is at risk. Many of those most likely to develop lung cancer also are suspicious of the healthcare system or do not understand the importance of early screening.
“In the US, we urgently need to change outdated screening and reimbursement guidelines to allow for earlier and expanded lung cancer screenings,” says Dr. Anil Vachani, Co-Director of Lung Cancer Screening and Associate Professor of Medicine, Penn Mediine. “We need AI-enabled diagnostics, virtual care and enhanced community care models to improve early detection and to make sure patients understand the importance of being screened. It’s crucial to capture more patients earlier in their disease.”
Diagnosing even without symptoms
Computer tomography (CT) scans allow clinicians to find and identify lung cancer even before a patient develops symptoms, making it a crucial piece of the early diagnostic puzzle. CT, the top screening tool for lung cancer, provides detailed images of the lungs, helping pinpoint even the tiniest irregularities. It also highlights the size and shape of tumors, making it easier to target therapies, and can reveal enlarged lymph nodes which could be masking cancer.
Smartening up with AI
Tools using artificial intelligence (AI) and machine learning (ML) give physicians a better understanding of an individual’s overall health and help predict which patients will be likely to respond to which treatments. This helps eliminate unnecessary procedures and promote better patient outcomes. AI and ML also can improve healthcare workflows for providers. Using real-time data, AI identifies bottlenecks, risks and barriers related to resources and personnel. It also flags possible actions to help hospital staff prevent delays in the patient journey, reducing strain on healthcare systems, easing clinician burnout and lowering overall costs for lung cancer care.
Advances in AI-powered tools also can help increase access to lung cancer diagnostics and therapies, even in remote areas. AI-enhanced imaging provides real-time guidance, so even inexperienced users can capture diagnostic-quality images, which can then be read at centers sometimes thousands of miles away. This not only broadens access to healthcare, but also decreases time between imaging and diagnosis and speeds up treatment.
Capturing the whole picture
Advances in molecular imaging (MI) also are improving the diagnosis and treatment of lung cancer. Providers use radiopharmaceuticals like fluorine-18 (F18), which latches on to lung cancer cells, with positron emission tomography (PET) or single-photon emission computed tomography (SPECT) scanners. The F18 lights up under the scan, allowing physicians to visualize and characterize tumors. That in turn means more accurate diagnoses and monitoring, and helps providers determine appropriate treatments.
“Radiopharmaceuticals combined with advanced imaging technologies allow us to target individual cancer cells to deliver a much higher dose of radiation than traditional methods, but without harming any of the surrounding tissue,” says Dr. Ilya Gipp, chief medical officer in Oncology, GE HealthCare. “This targeted treatment leads to fewer adverse side effects, giving patients an improved quality of life and lowering overall healthcare costs as a result.”
Targeting the right treatments
Theranostics uses diagnostic techniques and targeted therapies to identify and guide treatment for cancers and other conditions. These tools — which integrate AI with PET and SPECT scans — can analyze complex medical data, such as for lung, including imaging scans, genetic information, lab results and clinical records, to identify lung cancer earlier and more accurately. They also help guide treatments using radiotherapy agents like F18, based on a patient’s genetic make-up, age and stage of disease. Theranostics also allows doctors to more effectively monitor a patient’s progress. This can lead to an overall reduction in scan and diagnosis time, better appointment management efficiency and greater continuity across medical teams.
Summoning the strength within
More and better immunotherapies — treatments that use a patient’s own immune system to fight cancer — are coming onto the market regularly. It might be CAR-T cell therapy, where a patient’s white blood cells are engineered to attack their specific cancer cells, or injected molecules that help the body recognize cancer and stimulate a response against it. GE HealthCare is working to help clinicians capture and integrate more detail on individual lung cancer tumors and better predict the toxicity of immunotherapy. That will help clinicians manage differences across various types of lung cancer, while also driving down the cost of lung cancer care.
“Poor patient treatment adherence is a more serious problem in lung cancer than many other cancers,” says Dr. Fred R. Hirsch, Executive Director at the Center for Thoracic Oncology and Co-Director of the Center of Excellence for Thoracic Oncology at The Tisch Cancer Institute at Mount Sinai (TCI), “The worst thing you can do is start lung cancer patients on the right therapy and have them abandon it. We need more data from patient labs on toxicity earlier in order to keep patients on therapy longer.”
Expanding access to care and relieving overstressed providers to enable better patient outcomes requires investing in new technology. By integrating data across healthcare disciplines, we can empower clinicians and patients with advanced diagnostic and treatment technologies, ultimately enabling earlier diagnosis, personalized treatments and hope for a longer, healthier life beyond lung cancer.
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