Angela
Treatment planning for esophageal cancer may incorporate individual patient and disease characteristics. More and more artificial intelligence, molecular tests and precision medicine is used in order to better understand the patient’s disease and to select the right treatment for each patient. Even if well-established treatment options are still used a lot, they can be supported by AI, precision medicine and new diagnostic methods.
Understanding Precision Medicine in Esophageal Cancer
By “personalizing” treatment decisions in the context of cancer, Precision medicine uses clinical, molecular, and tumor-specific information to inform treatment decisions, based on information about the patient and his or her tumor. Here, important differences between esophageal cancers arising at same site in the esophagus of different patients can exist.
Determining the best treatment option for a patient can be based upon a variety of different factors, including the type and stage of the individual’s cancer, his or her overall health and how far along his or her cancer has progressed, characteristics of the patient’s tumor(s), previous cancer treatment(s) and/or molecular and/or biomarker information that can provide insight into the biology of the individual’s cancer.
For patients with esophageal cancer, treatment can include surgery, chemotherapy, radiation, immunotherapy and/or targeted therapy. Each of these methods can be used in combination, depending on the individual patient’s needs and circumstances of their cancer.
The Growing Role of Artificial Intelligence
Artificial intelligence (AI) is increasingly used in many areas of cancer research and care. Most importantly, programs can analyze large amounts of information from many sources to find patterns and connections in data that have not previously been established. Algorithms can identify suspicious abnormalities and assist with measuring tumors from medical images such as CT and MRI scans, to mention a few examples. The possible uses for AI-assisted analysis of disease characteristics in cancer are vast and currently being investigated.
Using AI for pathology images of cancerous and normal tissues enables the detection and measurement of key features within the cancerous tissue, assisting the human pathologist in providing a consistent and accurate diagnosis for each case.
Importantly, all data generated by AI systems must be interpreted by a human in a clinical environment and should be used to support, not replace, a doctor’s medical judgement in reaching a final diagnosis and providing treatment to individual patients.
AI and Early Detection
Cancer diagnosis at an early stage of development is crucial for successful cancer treatment of many cancers including the cancer of the esophagus. Symptoms of esophageal cancer treatment are often attributed to other gastrointestinal (stomach) conditions until late in the disease’s development.
In the research community many projects exist to find early cancer detection using AI in medical images, and in pathology, where computer vision can be used to find patterns in images and clinical data. Additionally, to work optimally within a clinical environment, it is often necessary for AI to assist doctors in prioritizing the images and samples that need most to be reviewed. More work is needed to explore these applications and validate their use to improve cancer detection and management.
Molecular Testing and Biomarkers
Tumor samples from patients can be analyzed for specific characteristics of cancer in order to aid in the choice of the best treatment for that patient’s cancer.
For example, it may indicate whether patients could be treated with targeted therapies or with immunotherapy and whether their tumors are likely to respond to treatment or progress despite treatment.
There is increasing interest in finding more parameters which help predict treatment outcome. Combining such new parameters with clinical, imaging and molecular data will allow a more complete understanding of individual tumors.
Improving Treatment Selection
There is also a risk of cancer patients being treated by a “one-size-fits-all” approach and that is something that needs to be avoided by precision medicine.
For example, two individuals with Barrett’s esophageal cancer might both have squamous cell carcinoma of the esophagus but have different tumors, be at different stages of cancer, have different co-morbidities, and have had different treatments before. So, they would receive different treatments.
With increasing amounts of different data types being collected during a cancer patient’s clinical management and follow up, the potential also exists for AI to help treat patients by integrating all this information to search for patterns which may aid in predicting patient response to treatment or disease progression. Important validation is necessary for the use of the new technologies for the individual patient.
Monitoring Response to Treatment
Monitoring Cancer Treatment Response
Patterns can be detected by AI tools to compare images of medical diagnostics from different time points of a patient. These could show the development of a tumor (in size or characteristics). The doctors can thus be informed on time about a reaction to a treatment, about stability of the cancer or about its progression. Images and all relevant molecular and clinical information can be used to better understand the behavior of a cancer during treatment.
Challenges and Limitations
There are many challenges and limitations associated with AI in cancer care. The primary challenge is in obtaining sufficient, high-quality data for the training of AI models, and there are also issues associated with the imaging of patients with cancer, as well as between and within different countries and healthcare systems.
Concerns surrounding patient privacy, data security and the issues of transparency and bias in AI also must be addressed. The model could behave inequitably in patients of different backgrounds if it was only trained on a small data set of patients with a limited range of characteristics. Only after stringent tests have been conducted, followed by clinical validation, can also AI systems become valuable tools for cancer treatment. However, always human experts and professionals have to be involved and make decisions based on the information generated.
Looking Ahead
Combining the uses of AI in medical imaging, with precision medicine in the diagnosis and treatment of esophageal cancer will become increasingly important in the future. Understanding cancer at a more individualized level will become key to the future treatment of all cancers, and in particular, esophageal cancer.
The longer-term potential of these technologies is to provide treatments suited to the individual’s cancer and to better manage the cancer during and after its treatment. AI is being evaluated as a tool that may support aspects of cancer care within multidisciplinary clinical practice. It is important to remember that, for now, the tool will be used by doctors and not replace them entirely and will become an important aid in treatment of esophageal cancer.
Disclaimer: The information provided in this article is intended for educational purposes only and should not be interpreted as medical advice, a clinical guideline, or a recommendation for any specific treatment.
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