Abstract
BackgroundPrecision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions.ObjectiveThis study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries.MethodsWe extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic’s electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance.ResultsWith 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review.ConclusionsAccurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.
Highlights
Cancer is the second leading cause of death worldwide [1]
We proposed a network-based framework (Figure 1) that represented cancer data using the Fast Healthcare Interoperability Resources (FHIR) standard and Resource Description Framework (RDF) to facilitate the cancer prediction process
We modeled cancer prediction as a multiple-label classification problem, where a given patient was represented with k-dimensional features, and a model categorized the patient into precisely 1 of 9 cancer types: colon cancer (ICD-9: 153.9), pancreas cancer (ICD-9: 157.9), ovary cancer (ICD-9: 183), prostate cancer (ICD-9: 185), connective and other soft tissue cancer (ICD-9: 171.9), thyroid gland cancer (ICD-9: 193), breast cancer (ICD-9: 174.9), liver cancer (ICD-9: 155), and bronchus and lung cancer (ICD-9: 162.9)
Summary
Cancer is the second leading cause of death worldwide [1]. The health burden of cancer in the United States is substantial [2,3], with approximately 1.8 million new diagnoses and an estimated 600,000 deaths in 2020 alone [4]. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions. Objective: This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries. Methods: We extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic’s electronic health records. Results: With 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer
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