Gene test 'predicts' lung cancer survival rates
New research which has been published in The Lancet medical journal has revealed that a new genetics-based test is able to predict survival rates in lung cancer patients.
Scientists from the University of California in San Francisco (UCSF) were behind the two international studies and they believe that the molecular gene test is more accurate than current lung cancer diagnostic methods.
It is now hoped the breakthrough will enable doctors to make more informed choices about treatment options.
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There are also aspirations that the gene test could improve the survival chances of thousands of lung cancer patients each year.
The test works by measuring the activity of fourteen genes in cancerous tissue in an attempt to assess how aggressive the tumours will be.
One type of cancer that the gene test works particularly well with is squamous non-small cell lung cancer, often linked to tobacco smoking.
As part of the two independent clinical trials the researchers analysed tissue samples from nearly 1,500 people with early-stage lung cancer in northern California and China.
Both trials revealed that the test could accurately predict whether the odds of death within five years of surgery to remove a lung cancer were low, intermediate, or high.
Commenting on the findings, David Jablons from UCSF, said: “It's quite exciting.
“This has the potential to help hundreds of thousands of people every year survive longer.”
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Introducing Dosis - the AI powered dosing platform
Cloud-based platform Dosis uses AI to help patients and clinicians tailor their medication plans. Shivrat Chhabra, CEO and co-founder, tells us how it works.
When and why was Dosis founded?
Divya, my co-founder and I founded Dosis in 2017 with the purpose of creating a personalised dosing platform. We see personalisation in so many aspects of our lives, but not in the amount of medication we receive. We came across some research at the University of Louisville that personalised the dosing of a class of drugs called ESAs that are used to treat chronic anaemia. We thought, if commercialised, this could greatly benefit the healthcare industry by introducing precision medicine to drug dosing.
The research also showed that by taking this personalised approach, less drugs were needed to achieve the same or better outcomes. That meant that patients were exposed to less medication, so there was a lower likelihood of side effects. It also meant that the cost of care was reduced.
What is the Strategic Anemia Advisor?
Dosis’s flagship product, Strategic Anemia Advisor (SAA), personalises the dosing of Erythropoiesis Stimulating Agents (ESAs). ESAs are a class of drugs used to treat chronic anaemia, a common complication of chronic kidney disease.
SAA takes into account a patient’s previous ESA doses and lab levels, determines the patient’s unique response to the drug and outputs an ESA dose recommendation to keep the patient within a specified therapeutic target range. Healthcare providers use SAA as a clinical decision support tool.
What else is Dosis working on?
In the near term, we are working on releasing a personalised dosing module for IV iron, another drug that’s used in tandem with ESAs to treat chronic anaemia. We’re also working on personalising the dosing for the three drugs used to treat Mineral Bone Disorder. We’re very excited to expand our platform to these new drugs.
What are Dosis' strategic goals for the next 2-3 years?
We strongly believe that personalised dosing will be the standard of care within the next decade, and we’re honored to be a part of making that future a reality. In the next few years, we see Dosis entering partnerships with other companies that operate within value-based care environments, where tools like ours that help reduce cost while maintaining or improving outcomes are extremely useful.
What do you think AI's greatest benefits to healthcare are?
If designed well, AI in healthcare allows for a practical and usable way to deploy solutions that would not be feasible otherwise. For example, it’s possible for someone to manually solve the mathematical equations necessary to personalise drug dosing, but it is just not practical. AI in healthcare offers an exciting path forward for implementing solutions that for so long have appeared impractical or impossible.