This World Lung Cancer Day, Give Your AI Diagnostics System a Hug
By Ryan Hennen, VP of US Sales, Optellum
X: @Optellum
LinkedIn: Ryan Hennen
World Lung Cancer Day is an important day to raise awareness about lung cancer, its causes, and the importance of early detection and treatment. Lung cancer is the leading cause of cancer death worldwide, and it is estimated that more than two million Americans are diagnosed with the disease each year. Today provides an opportunity to educate the public about the risk factors for lung cancer, such as smoking and exposure to air pollution, and to encourage people to take steps to reduce their risk. It is also a day to honor and support those who have been affected by lung cancer and to advocate for increased funding for research into new treatments and cures.
Early diagnosis of lung cancer and intervention are crucial. Most patients are diagnosed after symptoms have appeared and the disease has progressed to an advanced stage (Stage III or IV), which explains the current worldwide five-year survival rate of just 20 percent. In contrast, the survival rate for small lung tumors that are treated at Stage 1A is as high as 90 percent. This significant difference highlights a critical need for diagnosis and treatment of lung cancer at the earliest possible stage.
One of best opportunities to diagnose more small, pre-symptomatic lung cancers earlier is presented by the two million patients in the United States every year who have a lung nodule identified incidentally during chest CT scans ordered for other reasons, such as during an ER visit or after a cardiac event.
Current care guidelines mandate follow-up over one to two years to determine whether a nodule is cancerous. However, more than 60 percent of these patients do not receive guideline-recommended follow-up, severely limiting opportunities for early intervention and treatment. Patients who do receive recommended follow-up often require multiple imaging scans and biopsies, and sometimes unnecessary invasive procedures such as surgical biopsies and lung resections, before arriving at a definite diagnosis.
Enter AI-powered diagnostics: a rigorous, tireless expert
The legendary abilities of AI to doggedly apply everything we know about early diagnosis are being applied in the fight against lung cancer. Right now, as you read this article, a highly trained AI is using natural-language processing (NLP) to read dozens of radiology reports. It will identify and track patients who should be assigned special care. Additionally, a clinician will use AI to assign a score to the nodules of interest, which help stratify patients and assists with accurate diagnosis. This, in turn, supports better clinical decision making. Someone’s life is being saved.
The potent combination of NLP and AI-assisted diagnostic tools represent a viable solution for many healthcare systems, enabling the treatment of more early-stage lung cancers without increasing the workload of clinical teams. And, by arriving at the right diagnosis sooner, those hospitals can also minimize unnecessary invasive procedures.
The Interventional Pulmonology team at AtlantiCare’s Lung Nodule Clinic, led by Amit Borah, M.D., interventional pulmonologist, is currently using an AI-powered system to identify and systematically follow up on patients with incidentally detected lung nodules.
The system’s lung cancer prediction capability also helps AtlantiCare clinicians prioritize patients at high risk of having lung cancer. So far, the team has identified approximately 50 out of 300 patients who had lesions that needed close surveillance.
“Early-stage lung cancer symptoms are often vague or mimic those of other illnesses,” said Dr. Borah. “Through this technology, we are detecting suspicious nodules at earlier stages than ever, which is so critical to saving lives.”
The pre-operative AI assessment helps inform clinical decisions for appropriate follow-up interventions using the MONARCH™ robotic bronchoscopy techniques, with the goal of achieving the earliest possible and least invasive treatment. Thanks to this unique combination, the clinician can find patients with the smallest and hardest to reach tumors and deploy the robotic bronchoscope to reach and biopsy the regions highlighted by the early-detection AI.
“We have already identified suspicious tumors in individuals who have no known risk of lung cancer through this technology,” added Borah. “AI will enhance the life-saving progress we’ve experienced since offering robotic bronchoscopy to our patients.”
