Last Updated | March 8, 2024
Role of Artificial Intelligence in Personalized AI Cancer Treatment
In the relentless fight against cancer, personalized medicine has emerged as a promising approach. This treatment strategy takes into account a patient’s unique genetic makeup and lifestyle factors to provide more targeted and effective therapies.
In recent years, AI cancer treatment has also gained prominence as a key component of personalized medicine. Particularly the role of artificial intelligence in cancer treatment has shown great potential for improving cancer treatment.
What Is AI Cancer Treatment OR Theranostics?
You might be familiar with the terms “diagnosis” and “therapy” when we talk about healthcare, so you are no stranger to the term “theranostics.”
It’s the collective term for “diagnosis” and “therapeutics” used in precision medicine, which aims to offer personalized treatment for each patient’s unique needs, particularly in cancer treatment.
The beauty of theranostics lies in the ability to scan, diagnose, treat tumors, and monitor the response of the treatment – all at once. It is a systematic procedure where each phase plays a crucial role in providing customized management for various diseases.
The Diagnostic Agent
The first step in personalized treatment is the diagnostic agent. It’s a radioactive drug used to identify the presence of specific tumors. This biomarker, containing tracers, is administered to the patient in regulated doses.
Using advanced medical imaging techniques, like PET, CT, and MRI, for AI cancer treatment, healthcare professionals can then identify the affected regions from the levels of tracers absorbed.
The Therapeutic Agent
Once tumors are identified, the next step in AI cancer treatment is using a therapeutic agent, which is another radioactive biomarker. This agent targets and destroys the cancerous cells highlighted during imaging.
Sometimes, the diagnostic agent serves the purpose of both diagnosis and treatment in cancer management.
AI in cancer treatment has long been used in the treatment of various cancers such as prostate, thyroid, lung, and gastric cancer. Although it is a promising breakthrough in the field of nuclear medicine, it’s still an area of continual research and development.
Transforming Cancer Treatment with AI
What is AI in cancer treatment? Achieving personalized medicine that considers an individual’s genetic, lifestyle and other health risk factors comes with its own set of challenges.
In high-risk diseases, precision in diagnosis and time are the two constant constraints where conventional methods, such as manual lesion identification, may miss out on crucial information obtained from imaging and can adversely impact patient care.
Here, AI creates cancer treatment by enabling routine and reliable personalization of radiopharmaceutical therapies (RPTs). But wait, wouldn’t we need a vast dataset to develop such extensive systems of AI for cancer treatment that not only diagnose but also provide patient-specific treatment recommendations?
Fortunately, the advancements in AI and machine learning in healthcare have enabled the processing of vast amounts of data with speed and accuracy, making it possible to develop personalized AI cancer treatment plans for patients.
Researchers from the University of Toronto and Insilico Medicine made a breakthrough. By using an AI-powered database called AlphaFold to create a drug that could potentially treat hepatocellular carcinoma (HCC) or liver cancer.AI develops cancer treatment in 30 days with end-to-end AI platform in order to generate novel therapeutics to tackle diseases with high unmet needs.
Here are the top potential areas for the integration of AI:
Quantitative Imaging
Since AI cancer treatment heavily relies on essential information about the patient’s condition, including tumor location, size, and metabolic activity from the images, Computer-Aided Diagnosis (CAD) can offer image enhancement to reveal hidden details.
It can also provide high-quality quantitative information by combining images from multiple modalities. For instance, fused images of PET and CT provide precise location and size of lesions, which might be noisy in conventional standalone CT or PET scans.
Image segmentation and classification can help differentiate between organs and tumors, flag regions of high risk, and classify malignant vs. benign tumors for early diagnosis.
Deep learning algorithms such as CNNs and RNNs have been extensively used in medical imaging to achieve this purpose.
Therapeutic Optimization and Personalization
Drug discovery is one of the top AI applications in healthcare for AI cancer treatment. It is a time-consuming and expensive process, which can be significantly reduced with the use of AI algorithms such as reinforcement learning and generative adversarial networks.
It can assist in optimizing treatment regimens by integrating patient-specific data from clinical trials. Additionally, it modifies radiation doses for minimal side effects, predicts oncological response over time, and offers valuable insights into disease progression using machine learning techniques such as pattern recognition and predictive modeling.
Drug Discovery and Repurposing
A significant part of AI-powered cancer treatment relies on drug discovery. AI can aid in identifying potential drug candidates by analyzing vast molecular datasets and predicting the interactions between the targeted disease and a potential drug. Moreover, it can identify suitable patient groups for clinical trials to speed up the R&D process.
Benefits of AI in Cancer Treatment
As AI cancer treatment continues to change precision medicine, several benefits of AI in healthcare for cancer treatment become evident. Some of those benefits are:
- Improved diagnostic accuracy and efficiency
- Personalized treatment plans based on individual patient data
- Faster drug discovery and development process
- Enhanced therapeutic optimization and patient outcomes
- Cost-effectiveness in terms of time, resources, and overall healthcare expenses.
Given the cost of AI in healthcare, it was previously inaccessible. However, with more research and development in the field, AI cancer treatment is becoming more accessible and affordable for patients across the globe.
Application of Artificial Intelligence and Machine Learning for Precision Medicine
Here are some applications of AI-developed cancer treatment by combining artificial intelligence and machine learning that have improved precision medicine for AI cancer treatment:
Genomic Sequencing and Analysis
Genomic sequencing is a crucial step in precision medicine, as it provides valuable insights into an individual’s genetic makeup. With the help of AI algorithms, genomic data can be analyzed more efficiently to identify potential biomarkers and disease risk factors.
Predictive Analytics
Integrating electronic health records (EHRs) with AI and machine learning algorithms can offer predictive analytics for disease diagnosis and risk assessment. It can also assist in identifying patterns that may lead to more targeted treatments based on specific patient characteristics.
Virtual Assistants and Chatbots
Virtual assistants and chatbots powered by AI are becoming increasingly popular in healthcare. They can assist patients with symptom assessment and medication reminders. They can even provide personalized treatment recommendations based on patient data.
Overall, AI in Cancer treatment examples and applications is rapidly advancing. This allows for more precise and personalized cancer treatment. As technology continues to improve, we can expect even more significant advancements in AI-powered precision medicine.
AI-Powered Treatment for Prostate Cancer: A Case Study
Prostate cancer is the most frequent malignancy among males. In the United States, 3,343,976 men were estimated to be living with prostate cancer in 2020, while 288,300 men were predicted to receive a prostate cancer diagnosis in 2023.
Prostate cancer is the fourth most frequent cancer worldwide in terms of diagnoses. Conventional imaging techniques, such as PET scans, have difficulty detecting prostate cancer because they frequently overlook tiny lesions or recurrences, particularly when PSMA levels–the diagnostic agent for prostate cancer–are low.
Manual interpretation of these scans becomes time-consuming and prone to variability between observers, leading to inconsistent diagnoses.
aPROMISE™, a promising AI solution, detected a greater number of lesions and showed significant upstaging in patients with regional tumors compared to the conventional PET scanning technique.
Providing precise anatomical segmentation aided in accurate tumor localization within the prostate gland and surrounding structures. The targeted therapies were then designed to kill cancer cells in identified regions.
Thus, such software can act as a clinical decision support tool for personalized treatment planning, optimizing efficacy while minimizing treatment-related toxicity.
Conclusion
There is a long way to go in improving AI cancer treatment of various types of cancer. AI in breast cancer treatment and other most prevalent such as lung, bronchus, prostate, and colorectal. AI and cancer treatment in 2024 are expected to bring forth further advancements. It can help revolutionize precision medicine, accelerating diagnosis and treatment for complex conditions like cancer.
Developing an AI treatment for cancer as a software tool to manage complete theranostic workflows can boost efficiency within a unified platform. This would allow for seamless integration and analysis of patient-specific data to deliver personalized treatment plans quickly and accurately.
As research in AI and precision medicine continues, we can expect more innovative developments to improve patients’ therapeutic outcomes worldwide.
About the Author
Shalin Amir Ali
Software Engineer
