Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction.
Continue reading “Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers”
New Cleveland Clinic-led research shows that artificial intelligence (AI) can use medical scans and health records to personalize the dose of radiation therapy used to treat cancer patients.
Published today in The Lancet Digital Health, the research team developed an AI framework based on patient computerized tomography (CT) scans and electronic health records. This new AI framework is the first to use medical scans to inform radiation dosage, moving the field forward from using generic dose prescriptions to more individualized treatments.
Currently, radiation therapy is delivered uniformly. The dose delivered does not reflect differences in individual tumor characteristics or patient-specific factors that may affect treatment success. The AI framework begins to account for this variability and provides individualized radiation doses that can reduce the treatment failure probability to less than 5 percent. Continue reading “Using artificial intelligence to deliver personalized radiation therapy”
As AI technologies continue to evolve, they may be able to make a significant impact on patient care by reducing the amount of time physicians spend sorting through paperwork and documentation. A new analysis published by the Journal of the American College of Radiology examined this potential at length, focusing on how it applies to radiation oncology.
“The burden of clinical documentation on physicians has substantially increased in recent years due to multiple factors including the introduction of the electronic health record (EHR), the elimination of in-house transcriptionists, and value-based payment programs requiring the reporting of quality metrics,” wrote Join Y. Luh, MD, department of radiation oncology for Providence St. Joseph Health in Eureka, Calif., and colleagues. “Physicians spend an estimated 34% to 78% of their work day creating notes and reviewing medical records in the EHR, costing an estimated $90 to $140 billion in physician time per year.”
So how can AI help? According to Luh and colleagues, these solutions can help automate much of the documentation physicians face, “freeing physician time from clerical tasks, reducing burnout, preserving privacy, and organizing medical data into searchable and useable element.” EHRs, for instance, could seek out every last bit of needed context from a wide variety of places and piece them together into one helpful story that can be read in an instant. Continue reading “How AI can improve patient care in radiation oncology”
Decision support systems for personalized and participatory radiation oncology
A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models ‘learn’ using advanced and innovative information technologies (ideally in a distributed fashion – please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.
Continue reading “Decision Support System – Radiation Oncology”
With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.
Continue reading “Predicting outcomes in radiation oncology – multifactorial decision support systems”
In the field of Radiation Oncology, clinical decision support has at least three main applications:
- The pre-planning prediction of dosimetric tradeoffs to assist physicians, patients and payers alike to make better informed decisions about treatment modality and dose prescription.
- The integration of dosimetric information with orthogonal data (e.g. genomics, imaging, EMR) to build accurate outcomes models of Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP).
- Radiomics, which is a branch of medical imaging analytics that relies upon primary extraction of quantitative imaging features (e.g. texture) to predict various clinical phenomena.
Continue reading “Artificial Intelligence in Radiation Oncology: A Specialty-wide Disruptive Transformation?”