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Artificial intelligence in oncology: a revolution in cancer treatment
Cancer is a challenging disease that has affected millions of people worldwide. Finding practical solutions to combat it has been a long-standing effort. Oncology AI holds promise for a new era in cancer treatment. AI's ability to handle large datasets with impressive accuracy sets it apart from traditional analysis methods, making it a game-changer for oncologists and researchers seeking a deeper understanding of cancer's complexities.
Overview AI in Oncology
Artificial intelligence (AI) is significantly transforming the field of oncology, offering promising opportunities to enhance the management of cancer patients. Notably, AI-based devices that have received official approval from the Federal Drug Administration (FDA) are profoundly impacting cancer diagnostics, leading the way in practical clinical applications.
Among the various types of cancer, breast, lung, and prostate cancers are currently reaping the most benefits from AI-based devices. AI algorithms applied in breast cancer screening have improved the accuracy of mammograms, aiding in early detection and reducing false-positive results. In lung cancer, AI-driven image analysis has enabled precise detection and characterization of tumors, leading to better treatment planning. Similarly, AI applications in prostate cancer diagnostics have enhanced the identification of aggressive tumors and improved treatment decision-making.
Summarizing representations of the artificial intelligence-based devices, FDA-approved, expressed by oncology-related specialties (a: cancer radiology 54.9%, pathology 19.7%, artificial intelligence in radiation oncology, 8.5%, gastroenterology 8.5%, clinical oncology 7.0% and gynecology 1.4%) and by tumor types (b: general cancers 33.8%, breast cancer 31.0%, lung cancer 8.5%, prostate cancer 8.5%, colorectal cancer 7.0% and brain tumors 2.8%, others: 6 tumor types, 1.4% each).
Looking into the future, artificial intelligence in oncology holds excellent promise. Multidisciplinary platforms integrating various data sources, such as clinical, imaging, and genomic data, will allow for more comprehensive and precise assessments of a patient's cancer profile. This will enable personalized treatment plans and better patient outcomes.
Moreover, AI will be essential in understanding the significance of all neoplasms, including rare tumors. By analyzing data from diverse cases, AI algorithms can contribute to improved diagnosis and treatment strategies for less common cancer types.
In conclusion, AI is concretely reshaping oncology, offering new opportunities for improving patient management. AI has demonstrated its value in clinical practice with FDA-approved devices already impacting cancer diagnostics.
Enhancing Cancer Genomics with Artificial Intelligence
In oncology, evidence-based scoring systems are vital for cancer management, including risk assessment, diagnosis, treatment, and surveillance. As predictors multiply, computational models get complex, but this complexity can reveal valuable insights. Artificial intelligence, powered by high-performance computing and advanced deep learning, helps synthesize and understand intricate multimodal data interdependencies.
Machine learning and Deep learning algorithms can surpass the limitations of traditional computational methods by learning patterns from whole-blood pan-cancer detection using deep sequencing. For instance, ML methods utilizing whole-transcriptome RNA sequencing data have accurately identified cancerous states, even for rare cancer types, and distinguished them from normal cells. Neural networks applied to transcriptomic data have successfully classified molecular subtypes in various tumors.
In summary, AI is becoming a necessary tool in oncology, facilitating the integration and analysis of complex multimodal data, ultimately leading to improved cancer management and personalized treatment approaches.
Artificial Intelligence Applications in cancer-related image analysis
In oncology radiographic imaging, AI is essential for cancer detection, diagnosis, and management. While earlier computer-aided detection for breast cancer had limited clinical value, AI-driven cancer detection has gained prominence, with FDA-approved breast-imaging algorithms now in clinical use.
According to the WHO, breast cancer is the most common worldwide, with 2.3 million new diagnoses and 685,000 deaths in 2020 alone. However, breast cancer mortality in high-income countries has decreased by 40% since the 1980s due to regular mammography screening. Early detection and treatment are crucial in reducing cancer fatalities, and machine learning skills can help streamline the process of evaluating screening mammograms used by radiologists.
Our team researched to detect breast cancer from screening mammograms, which involved binary classification with highly imbalanced classes. The training dataset had over 53,000 negative and 1,158 positive class samples (cancer cases). This task presented a challenge due to the limited number of positive class samples, making it demanding for machine learning from the outset.
We faced challenges during the project's initial phase and couldn't achieve a satisfactory model despite numerous tests. However, we made significant progress by establishing a robust training pipeline. This pipeline included balancing the positive class, scaling, selecting the suitable model, and post-processing. Ultimately, our decision-making process involved a voting strategy and calculating an average score based on the votes. The solution consisted of four straightforward steps: converting DICOM files to PNG, making inferences using three posterior models with TTA, averaging ensemble probabilities, and applying a threshold.
Conclusion
To sum up, AI's ascendancy over expert-based systems represents a significant boon to the field of oncology, where complexity abounds. Integrating multi omics data, encompassing molecular and health records, enriches our comprehension of patients' conditions. Additionally, including digital pathology and radiomics further expands the horizons of personalized medicine.
To harness the full potential of AI in healthcare, it is imperative to establish effective data storage systems coupled with stringent clinical standards for interoperability. Living databases, capable of continuous updates reflecting patients' evolving health statuses, necessitate ongoing algorithm refinement. Strict model quality metrics serve as the bedrock of reliability.
The applications of AI in radiation oncology span a broad spectrum, ranging from hyper-individualized screening and prevention strategies to personalized treatment dosages and optimized surveillance schedules. As AI technology continues to mature, it promises to enhance the quality of life for cancer patients substantially and heralds a transformation in cancer care delivery.
