Abstract
Simple SummaryLow-grade gliomas (LGG) with the 1p/19q co-deletion mutation have been proven to have a better survival prognosis and response to treatment than individuals without the mutation. Identifying this mutation has a vital role in managing LGG patients; however, the current diagnostic gold standard, including the brain-tissue biopsy or the surgical resection of the tumor, remains highly invasive and time-consuming. We proposed a model based on the eXtreme Gradient Boosting (XGBoost) classifier to detect 1p/19q co-deletion mutation using non-invasive medical images. The performance of our model achieved 87% and 82.8% accuracy on the training and external test set, respectively. Significantly, the prediction was based on only seven optimal wavelet radiomics features extracted from brain Magnetic Resonance (MR) images. We believe that this model can address clinicians in the rapid diagnosis of clinical 1p/19q co-deletion mutation, thereby improving the treatment prognosis of LGG patients.The prognosis and treatment plans for patients diagnosed with low-grade gliomas (LGGs) may significantly be improved if there is evidence of chromosome 1p/19q co-deletion mutation. Many studies proved that the codeletion status of 1p/19q enhances the sensitivity of the tumor to different types of therapeutics. However, the current clinical gold standard of detecting this chromosomal mutation remains invasive and poses implicit risks to patients. Radiomics features derived from medical images have been used as a new approach for non-invasive diagnosis and clinical decisions. This study proposed an eXtreme Gradient Boosting (XGBoost)-based model to predict the 1p/19q codeletion status in a binary classification task. We trained our model on the public database extracted from The Cancer Imaging Archive (TCIA), including 159 LGG patients with 1p/19q co-deletion mutation status. The XGBoost was the baseline algorithm, and we combined the SHapley Additive exPlanations (SHAP) analysis to select the seven most optimal radiomics features to build the final predictive model. Our final model achieved an accuracy of 87% and 82.8% on the training set and external test set, respectively. With seven wavelet radiomics features, our XGBoost-based model can identify the 1p/19q codeletion status in LGG-diagnosed patients for better management and address the drawbacks of invasive gold-standard tests in clinical practice.
Highlights
Amongst tumors from the central nervous system (CNS) negatively affecting millions of patients worldwide regardless of their age or gender, brain cancers account for the highest prevalence of more than ninety percent [1]
According to the 2016 World Health Organization (WHO) classification of CNS tumors, gliomas could be divided into diffuse low-grade gliomas (LGGs)
We propose an eXtreme Gradient Boosting (XGBoost) model to tackle the limitations of most models in terms of prolonged runtime, stable performance on different data, and reproducibility under various conditions, which would contribute to targeted decisions and reduce the adverse effects induced by invasive diagnostic methods on LGG patients
Summary
Amongst tumors from the central nervous system (CNS) negatively affecting millions of patients worldwide regardless of their age or gender, brain cancers account for the highest prevalence of more than ninety percent [1]. The criterion of diagnosing LGGs, which make up 30 percent of gliomas [2,3], requires evidence of isocitrate dehydrogenase (IDH). Patients diagnosed with 1p/19q codeletion mutation status LGG significantly had their survival time improved and were more sensitive to therapeutics in terms of chemotherapy and radiotherapy compared with those with 1p/19q non-deleted tumors [9,10,11,12,13]. The manipulation of 1p/19q co-deleted mutation and the early detection of this chromosomal abnormality among patients diagnosed with LGGs is highly appreciated, as this can facilitate short-term and long-term management
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