A.I. Can Now Predict Tumor Regrowth in Cancer Patients
Machine learning algorithms take on predictive models for Cancer Recurrence
This is the first article in Benefactor, a new journal under AiSupremacy that will focus on AI healthcare articles and A.I for Good topics generally. I’ve been making some changes to my workflow to try to stimulate more interactivity to know my audience better.
This week I’ve set up a new feature in the margin of the home page, like a guestbook, further dialogue era, a bookmark section and a Newsletter reading room lounge. I hope you can take the time to stop by!
Also try getting a notification instead of checking your Email inbox, I think it’s likely a superior reader experience.
So I’m a bit obsessed with the positive impacts of A.I. in healthcare we are going to experience in the decades ahead. Coupled with advances in genomics, biotechnology and life-augmentation (well-being instead of disease focused) it’s an exciting period.
Do you care about the A.I. for good movement? I most recently covered in the A.I. intersection of Healthcare the following topics:
Using Artificial Intelligence to Diagnose Skin Cancer ~ HERE.
Artificial Intelligence in Autism Detection ~ HERE.
The Future of A.I. in Healthcare. ~ HERE.
Artificial Intelligence is Taking on Parkinson's Disease. ~HERE.
Artificial Intelligence Helps Cut Miss Rate of Colorectal Polyps. ~ HERE.
A.I. Advances in Treatment Of Spinal Cord Injuries and Surgery. ~ HERE.
Future of A.I. in Neurosurgery. ~ HERE.
Artificial Intelligence Could Help Detect Onset of Cardiovascular Disease. ~ HERE.
Can A.I Improve our Breast Cancer Screening?~ HERE.
So finally, let’s get into it:
AI Tool Predicts Cancer Regrowth in Cancer Patients
Lung cancer is the major cause of cancer death worldwide, accounting for slightly more than a fifth (21%) of cancer fatalities in the United Kingdom.
What if an A.I. tool could predict the likelihood of Cancer regrowth even down to the timing?
Doctors and scientists have developed an artificial intelligence tool that can accurately predict how likely tumors are to grow back in cancer patients after they have undergone treatment.
While treatment advances in recent years have boosted survival chances, there remains a risk that the disease might come back. Monitoring patients after treatment is vital to ensuring any cancer recurrence is acted on urgently. If A.I. has a capacity for early detection, how about prevention after treatment?
The breakthrough, described as “exciting” by clinical oncologists, could revolutionize the surveillance of patients, according to the Guardian.
The OCTAPUS-AI study's principal investigator, Dr. Richard Lee, told the Guardian that it might be crucial in not only improving outcomes for cancer patients but also relieving their anxieties, with relapse "a primary cause of anxiety" for many.
The tool could also lead to lowering medical costs, and it’s plain to see why. The AI tool may result in recurrence being recognized earlier in high-risk individuals, ensuring they receive treatment more quickly, but it may also result in fewer unneeded follow-up scans and hospital visits for those at low risk.
Now a world-first study by the Royal Marsden NHS Foundation Trust, the Institute of Cancer Research, London, and Imperial College London has identified a model using machine-learning – a type of AI – that can predict the risk of cancer coming back, and do it better than existing methods.
Increasingly Cancer institutions have an AI Lab Imaging hub, such as the one found here, let’s take a look at what they do exactly:
AI Imaging Hub
The Artificial Intelligence (AI) Imaging Hub is investigating the use of AI in cancer imaging and how it can help clinical teams gather better insight into the biological behaviors of the disease so that tailored treatments can be created and adapted to each patient’s needs.
The AI Imaging Hub team aims to:
Harness the full potential of imaging and AI to transform data into novel insights and clinical decision-making tools to empower patients and improve their outcomes.
Create an exemplar multidisciplinary team for a future-orientated radiology department which is able to deliver the informatics bench to bedside imperative for patient benefit.
Deliver expertise in the development, assessment, validation and commercialization of AI tools in radiology including clinical trial design
“This is an important step forward in being able to use AI to understand which patients are at highest risk of cancer recurrence, and to detect this relapse sooner so that re-treatment can be more effective,” said Dr Richard Lee, a consultant physician in respiratory medicine and early diagnosis at the Royal Marsden NHS Foundation Trust.
A.I. is thus able to improve the personalization of each patient’s cancer journey which could eventually improve survival rates and help Clinicians anticipate most likely courses of development and treatment in the patient experience.
The AI tool may lead to recurrence being detected earlier in patients deemed at high risk, ensuring they receive treatment more urgently, but it could also result in fewer unnecessary follow-up scans and hospital visits for those deemed low risk. It may also help impact and reduce patient anxiety regarding the chances of recurrence.
In the retrospective study, clinicians, scientists, and researchers created a machine learning model to see if it could reliably identify patients with non-small cell lung cancer (NSCLC) who were at risk of recurrence after radiation. Machine learning is a type of AI that allows the software to predict outcomes automatically.
The clinical data had a baseline of 657 patients, to feed their model – and added in data on various prognostic factors to better predict a patient’s chance of recurrence. These included the patient’s age, gender, BMI, smoking status, the intensity of radiotherapy, and their tumou’s characteristics.
Researchers then used the AI model to categorize patients into low and high risk of recurrence, how long a period they might experience before a recurrence, and overall survival two years post treatment.
A.I. Tools can Lead to Improved Prediction of Cancer Recurrence
The tool was found to be more accurate in predicting outcomes than traditional methods.
The results of the study, supported by the Royal Marsden Cancer Charity and the National Institute for Health Research, were published in The Lancet’s eBioMedicine journal. The paper was released on April 2nd, 2022, you can find it here.
The authors made a startling statement that I really noticed. When they said: “As this type of data can be accessed easily, this methodology could be replicated across different health systems.” So as A.I. gets better at early prediction in scans, it can become more capable in helping to predict likely outcomes such as Cancer recurrence, and conceivably, many other disease patterns.
That means A.I. will be significant in allow people to experience healthy well-being for longer periods into old-age that could gradually improve the human life-span globally in the 21ts century. Combined with genomics, biotech and life-sciences promoting longevity, healthcare will mean access to different life-spans across the socio-economic spectrum.
The study was specific to lung cancer and radiation treatment. This robust and ready to use machine learning method, validated and externally tested, sets the stage for future clinical trials entailing quantitative personalized risk-stratification and surveillance following curative-intent radiotherapy for NSCLC.
Why it matters?
The study was the first to compare multiple machine learning algorithms and feature reduction methods using routinely available clinical data and to develop, validate and externally test prediction models for recurrence.
This has significant implications on our well-being since the prevalence of Lung Caner in society is so high. How common is Lung Cancer in the U.S., it’s a pretty high number. This year, an estimated 236,740 adults (117,910 men and 118,830 women) in the United States will be diagnosed with lung cancer. On average, about 19% of people diagnosed with lung cancer will live for at least 5 years. So for most people diagnosed with Lung Caner typically in their early 70s, it’s a death sentence.
This week on my Quantum Foundry Newsletter I’ve added a Journal called “Immortalis”, that will be doing with genomics, biotech and life-science with a focus on longevity startups which are moon-shot companies often with funding from the Billionaire class. You might be interested in the topic.
What do you think?
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