Digital twins in cancer care
Summary
TLDRThe podcast 'Healthcare Perspectives' by Siemens Healthineers explores the transformative impact of digital twins on cancer care. It discusses how early detection through non-invasive and cost-effective screening can improve prognosis. The episode delves into the potential of digital twins to offer personalized treatment strategies, enhance precision in radiotherapy, and facilitate better decision-making in clinical practice. The technology promises to optimize healthcare systems, improve patient outcomes, and pave the way for a more personalized approach to oncology.
Takeaways
- 🌐 Digital twins have the potential to revolutionize cancer care by providing a more personalized and precise approach to treatment.
- 🔍 Early detection of cancer is crucial, and digital twins can enable earlier and more effective screening methods, improving prognosis and outcomes.
- 📉 The integration of data from digital twins can help decrease the burden on healthcare systems by identifying diseases early and preventing more costly interventions later.
- 🧬 Digital twins can incorporate a wide range of data, including diagnostic imaging, genetic, and molecular information, leading to a more accurate patient representation.
- 💡 The use of digital twins allows clinicians to explore various treatment options and increase precision in treatment strategies, tailoring them to individual patient needs.
- 🛠️ Radiotherapy can benefit from digital twins by providing more precise tumor location data, which is essential for effective treatment planning and minimizing side effects.
- 🩺 Personalized medicine is enhanced by digital twins, as they enable doctors to move beyond population-based guidelines to individualized treatment plans for each patient.
- 🧐 Predictive capabilities of digital twins can help in identifying patient response to treatments before they are administered, potentially avoiding ineffective therapies and associated side effects.
- 🛑 Digital twins offer a 'sandbox' for clinicians to test different treatment protocols and predict outcomes, allowing for the selection of the most efficient treatment for a given patient.
- ✂️ Surgical planning can be aided by digital twins, providing surgeons with a detailed digital representation of the patient's anatomy to simulate and plan surgeries more effectively.
- 🔄 Follow-up care and management of cancer recurrence can be improved with digital twins, allowing for more informed decisions on re-irradiation and other treatments.
Q & A
What is the ideal characteristic of a cancer screening method according to the transcript?
-The ideal cancer screening method should be quick, non-invasive, not expensive, and effective, allowing for easy performance everywhere in the world for early disease prediction.
Why is early detection of breast cancer tumors significant according to the guest Christian?
-Early detection of breast cancer tumors is significant because the smaller the tumor size when detected, the better the prognosis, leading to more effective treatment outcomes.
How does Christian describe the impact of early disease detection on the healthcare system?
-Christian suggests that early detection can ease the burden on the healthcare system by preventing diseases like breast cancer that can be avoided or detected early, which is crucial given the financial constraints many health systems face.
What role do digital twins play in improving cancer care according to the discussion?
-Digital twins can decrease the amount of care necessary, improve outcomes, and free up more time, energy, and money for developments in technology, systems, methods, and diagnostics in oncology.
How can digital twins contribute to more accurate cancer diagnosis?
-Digital twins enable a higher level of data integration, allowing doctors to explore various treatment options and increase precision in treatment by combining individual patient data with big data analysis from thousands of similar cases.
What is the potential of digital twins in terms of personalized therapy for cancer patients?
-Digital twins offer the potential for more personalized therapy by providing detailed, patient-specific information that can help refine decision-making and select the best treatment for an individual patient at a particular moment in time.
How could digital twins assist in radiotherapy treatment planning?
-Digital twins could allow for the collection and analysis of more precise data regarding tumor location, helping to increase tumor control while reducing toxicity by delivering high doses to the tumor while sparing surrounding healthy tissue.
What is the potential benefit of using a digital twin for predicting patient response to treatment?
-A digital twin can predict whether a patient would respond to a treatment before it happens, using the entire clinical and genetic information about the patient, which can help in selecting more efficient therapy options and avoiding ineffective treatments.
How can digital twins assist in surgical planning for liver cancer?
-A digital twin can provide a digital representation of the liver, allowing surgeons to evaluate tumor disposition related to healthy tissue, simulate the resection virtually, and compute the remaining liver function to decide if surgery is safe.
What is the potential impact of digital twins on patient recovery and quality of life?
-By avoiding ineffective therapies and enabling less invasive treatment options, digital twins can contribute to faster patient recoveries, shorter hospital stays, and an improved quality of life.
What does Christian suggest about the future potential of digital twins in managing cancer recurrence?
-Christian suggests that digital twins could estimate the risks of side effects or the possibility of re-irradiation before trying it on the patient, providing more precise predictions for patients and aiding in the development of more transparent and precise therapy strategies.
Outlines
🚀 Introduction to Digital Twins in Cancer Care
The first paragraph introduces the topic of digital twins in the context of cancer care, posing questions about their potential impact and current role in improving patient outcomes. It emphasizes the importance of early, non-invasive, and effective screening methods to detect diseases like cancer at an early stage, which can lead to better treatment results and reduce the burden on healthcare systems.
🌟 Early Detection and the Role of Mammography
In this paragraph, the focus is on the significance of early detection in breast cancer treatment. It discusses the benefits of mammography screening in detecting small tumors that might otherwise go unnoticed, leading to better prognoses. The paragraph also touches on the economic implications of early cancer detection and prevention, highlighting the need for efficient healthcare strategies to manage the financial strain on health systems.
🔬 The Power of Data Integration with Digital Twins
This section delves into the concept of digital twins, emphasizing their potential in integrating vast amounts of clinical data to enhance diagnostic accuracy and treatment precision. It presents a case study of a breast cancer patient, illustrating how digital twins can combine imaging data with big data analysis to provide actionable insights. The paragraph also discusses the benefits of this data integration for clinicians in making more informed decisions.
🛠️ Personalized Therapy and the Evolution of Digital Twins
The fourth paragraph explores the idea of personalized therapy made possible through digital twins. It discusses the evolution of digital twins, which can be enriched with new medical and personal information over time, providing a more accurate representation of individual patients. The potential for more precise radiotherapy planning and the avoidance of ineffective treatments are highlighted, emphasizing the importance of personalized medicine in optimizing patient care.
🛑 Surgical Planning and Follow-up with Digital Twins
This paragraph discusses the application of digital twins in surgical planning, particularly for liver cancer, and their role in follow-up care. It describes how surgeons can use digital twins to evaluate tumor disposition and simulate resection, as well as predict the effectiveness of interventions and potential side effects. The paragraph also touches on the potential of digital twins in managing recurrence and re-irradiation, offering a more transparent and precise approach to cancer treatment.
🌐 Transforming Cancer Care with Digital Twins
The final paragraph wraps up the discussion by highlighting the transformative potential of digital twins in cancer care. It speaks to the ability of digital twins to provide a sandbox for exploring different treatment scenarios and their outcomes, leading to optimized care for patients and healthcare teams. The paragraph also mentions the importance of continued exploration and development in this field, as well as the potential for digital twins to revolutionize the way cancer is treated and managed.
Mindmap
Keywords
💡Digital Twins
💡Mammography
💡Early Detection
💡Precision Medicine
💡Health Economics
💡Radiotherapy
💡Stereotactic Body Radiation Therapy (SBRT)
💡Personalized Therapy
💡Data Integration
💡Quality of Life
💡Re-irradiation
Highlights
The ideal cancer screening should be quick, non-invasive, affordable, and effective for early detection worldwide.
Mammography can detect breast tumors as small as a few millimeters, significantly earlier than when felt by touch.
Early tumor detection improves prognosis and is crucial for easing the burden on healthcare systems.
Digital twins have the potential to decrease necessary care and improve outcomes, freeing up resources.
Data integration with digital twins can provide actionable insights, enhancing the precision of treatment strategies.
Digital twins can integrate a vast amount of clinical data, supporting clinicians with decision making in a broader context.
Digital twins offer personalized therapy options, increasing the precision of treatments like radiotherapy.
Personalized information from digital twins allows for treatment plans tailored to individual patient needs rather than population data.
Digital twins could predict patient response to treatments like SBRT for lung cancer, avoiding ineffective therapies.
Surgeons could use digital twins for precise evaluation and simulation of procedures like liver cancer surgery.
Digital twins enable clinicians to test different treatment protocols in advance, selecting the most efficient for each patient.
By simulating treatment responses, digital twins can help physicians understand potential side effects and limit them.
Digital twins can lead to faster patient recoveries, shorter hospital stays, and an improved quality of life.
Digital twins provide a sandbox for experimenting with different treatment scenarios and observing their outcomes.
Digital twins can assist in managing recurrence by estimating risks and possibilities for re-irradiation.
The implementation of digital patient twins could revolutionize precision medicine and transform cancer care.
Digital twin technology may allow medical professionals to observe treatment scenarios before any physical treatment, enhancing patient outcomes.
Transcripts
Welcome to Healthcare Perspectives, a podcast by Siemens Healthineers about medical breakthroughs
with the power to improve lives everywhere. Today: How can digital twins impact cancer
care? Are they already changing the lives of cancer patients? And what
does this mean for the future of oncology? In a perfect world the screening, I believe should
be quick, it should be non-invasive, it should not be expensive and it should be effective. So that
we have easy to perform everywhere in the world early screening tools for everyone everywhere,
the earlier the disease is predicted the better the treatment outcome would be.
Christian what is your experience with screening patients for breast cancer?
In my practice, every day I have a woman who tells me that her tumor was detected with
mammography screening. The researchers say that if a woman is feeling the tumor in the breast,
the typical average size is about 2.5 centimeters. And if a gynecologist is
detecting the tumor, it's in average, two centimeters, a little bit lower. But with
the mammography, you can detect tumors with, example, three or four millimeters.
And the earlier a tumor is detected the better the prognosis.
It's really the best system to get the data early. Don't wait
until the tumor’s growing and the outcome is worse.
For Christian, treating disease early and effectively is also
about easing the burden on the healthcare system:
It's a great challenge that we prevent diseases like breast cancer, that can be avoided or
detected early. It's important because all the health economic systems are so burdened, so at the
edge in the moment because of lack of financial power, that we have to avoid these diseases.
Earlier detection empowered by digital twins may decrease the amount of care
necessary and improve outcomes. As such, more time, energy, and money is freed up.
We need the money for all the developments and all the new technology systems and
methods and diagnostics, what we are talking about, and this is the only chance we have.
An accurate diagnosis is crucial when creating a treatment strategy for a cancer patient. With
digital twins, the level of data-integration possible could enable doctors to explore a
variety of options and increase precision in treatment. Christian shares his experience here:
Take a case for example, there is a breast cancer patient, 46 years old, breast cancer detected in
an advanced stage with the lymph nodes. This situation is typical for about 20,000 patients
in Germany every year. And then mammography, MR-mammography, sonography, ultrasound were
done and the data were collected. The first step is to get very valid reports about the patients.
The next step is to combine this with big
data analysis from thousands of breast cancer patients worldwide.
Compared with the standard of care, the data integration incorporated
in digital twins could create actionable insights, says Chloe:
In the standard clinical practice,
physicians often read and integrate specific data from medical reports or medical imaging,
and from there they derive a diagnosis that is as robust as possible. And having this in mind,
digital twins can be viewed as the next level of data integration,
where the clinician would have the possibility to create the statistical view of the data that
involves a vast amount of clinical data that would otherwise be impossible for a human to analyze.
And this could support clinicians with decision making by putting
each situation into the bigger picture:
These patient’s clinical conditions at this specific moment in time can be integrated
with diagnostic data from millions of previous cases. And the range of value can be observed
against the healthy population or a group of patients suffering from the same disease.
And by combining all those different data, clinicians could determine more precisely
your particular disease condition or particular medication that is likely to help in this case.
Digital twins are not limited to diagnostic imaging data. One could integrate for example
genetic and molecular information into the twins. As a result, practitioners
would have get access to an even more accurate representation of their patients. Over time the
twins can be enriched with new medical and personal information and evolve further.
We want to represent the anatomy and the physiology of specific patients. All those models,
they need to be personalized using a specific individual. And these models, they should also
evolve over time, the digital twin needs to represent accurately the patient at every point
in time. So that we can update the model and can represent more accurately at every time point.
Perhaps the most incredible potential from the digital twin comes from the opportunity
for more personalized therapy. For Christian as a radiation oncologist, it’s all about the level of
precision that the twin could make possible: In radio therapy we always say: Our beam
is in submillimeters controlled by the radio oncologist. But the main question is how good
is the data where the tumor is located? And I want to go step further. I would say the
cooperation between the radiologist and the radio therapist is essential to make the most
accurate planning. We both need information about the possibilities of the systems and so
we can make a more precise diagnostics and based on this a more precise planning.
In radiotherapy it’s key to be able to increase tumor control while reducing toxicity. It is about
delivering a high dose to the tumor while sparing the healthy tissue surrounding it.
If the digital twin allows us to collect more data,
then it would be such a gain for radiotherapy. We can analyze the
data and answer these questions more precisely than is now possible.
With personalized information, doctors would no longer have to
base therapeutic regimens on population data but adapted to the individual patient needs.
The issue is that those guidelines, they are based on statistics done on the entire population,
but what is best on average might not be the best treatment for a specific individual. So,
because the patient’s condition only determines ease or position on the
guidelines, and therefore the recommended therapy, this is very limited factor,
the detailed knowledge of the patient clinical data, the physiology, can help refining the
decision making to select the best treatment for one patient at a particular moment in time.
Chloe gives an example of a current standard treatment option in the case of lung cancer:
There is one treatment option that is called stereotactic body radiation therapy or SBRT.
We observed that some patients do respond to these treatments and when patients respond to it, it's
a very effective treatment, but some patients are non-responders. And usually when we observe
that they are non-responders, it's often too late to take other action.
If a digital twin would be available for those patients,
doctors could predict whether they would respond to the treatment or not even before it happens!
Using the entire clinical information about the patient, the genetic information,
as well a patient’s specific physiological model can actually help in the patient
selection by differentiating a priori, the patient response pattern, so, by identifying
the patient that would respond to the one that will not respond to the treatment. So,
therefore, such model could help in selecting early other therapy options for the patient
whose digital twin predicted that they will not respond and this would eliminate the need
for therapy change in the hope that the one with the best outcome would come up.
This way, the patient would not suffer from side effects arising from an ineffective
treatment. Furthermore, the twin could help to save the associated costs.
The clinician could use the digital twin to test different treatment protocols in
advance and select the more efficient one for one given patient. So that the
procedure would be tailored precisely for each patient.
That means doctors could even perform multiple treatment scenarios on the twin and predict their
outcomes—before even physically coming into contact with the patient. In the
case of liver cancer, a digital twin could assist in surgical planning. Here’s Chloé:
A surgeon could use a digital twin to navigate the digital representation of the liver before
operating on it. So the cancer surgeon would be able to precisely evaluate our tumor disposition
related to healthy tissue. And you could simulate virtually the resection, for example,
and completely compute the remaining liver function, which is something vital to assess,
to decide if it's safe to go for a surgery or not. Another aspect is to provide the ability
to simulate in advance how an organ would respond to a treatment, if you
change different parameters in the treatment sets. Giving the physician the possibility to
predict the effectiveness of the intervention, to better understand the potential side effects,
and to limit them and to accelerate the operation by avoiding the unnecessary treatment.
By avoiding ineffective therapies and enabling less invasive therapy
options with digital twins, patients could potentially experience faster recoveries,
shorter hospital stays and, in turn, a better quality of life.
It's a Sandbox to look at cause and effect, like an experiment when we change some factors like
therapy regimens or dose in radiotherapy, then the question is what is the difference in months,
years and centuries? And to make it easy to look at these different possibilities and the
different permutations of cases, the digital twin is a wonderful take. It's like a sandbox. It's
like a possibility to starting from different conditions and looking what comes at the end.
And it doesn’t stop there. Also when it comes to follow-up care,
Christian sees potential for a digital twin and explains its application in managing recurrence:
The re-irradiation is also a very interesting object for the digital twin. Because
with a digital twin I will be able to estimate the risks of side effects or the possibility
itself to make a re-irradiation before I try this at the patient, I need every data I can get. It's
so important for the patient to get a second chance, in case of relapse, for longer survival.
Because the life expectancy is increasing, we have more and more cases in oncology
and reoccurrence of tumor will be more and more a challenge. This is clear. And if we have the
possibility to make the therapy strategies more transparent and more precise and combine
this with data, then it's possible to make more precise predictions for these patients.
Practitioners have treated cancer for centuries,
having to wait for physical results to manifest before they knew whether a treatment was effective
or not. With digital twin technology, medical science continues to transform exponentially:
we may have the ability to watch scenarios play out before the patient has a single
treatment in the future. Here’s Christian one last time speaking on the potential:
You can only live one life, it's not possible to say: Oh, stop, rewind like a video recording,
it's not possible. But in case of digital twin you can stop, rewind and start at a certain point.
While we still have quite a way to go before implementing the digital patient
twin, a fully individualized digital avatar of a patient,
preloaded with their entire medical history and continually updated with real-time health data,
it’s incredible to think of the potential the technology has. The digital twin technology can
bring us closer to precision medicine and change cancer care as we know it. If you are interested
in learning more about this topic, check out our episode on patient twinning from September 2022.
From early detection, to diagnosis, to treatment, to follow-up, cancer care could be reframed,
the life of patients and their families could be transformed for the better, the work of Physicians
and care teams could be dramatically optimized. Thank you for listening!
You have been listening to Healthcare Perspectives, a podcast by Siemens Healthineers.
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The opinions expressed by the guests and contributors in this podcast are their
own and do not necessarily reflect the views of Siemens Healthineers.
The statements by Siemens Healthineers’ customers in this podcast are based on results that were
achieved in the customer's unique setting. Because there is no “typical” hospital or laboratory and
many variables exist (e.g., hospital size, samples mix, case mix, level of IT and/or automation
adoption) there can be no guarantee that other customers will achieve the same results.
This podcast describes possible future ideas and concepts. It is not intended to describe
specific performance and/or safety characteristics of currently planned
or future products. Future realization and availability cannot be guaranteed.
Some of the products and applications mentioned in this podcast are currently under development;
they are not for sale. Their future availability cannot be guaranteed.
The information in this podcast is based
on research results that are not commercially available.
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