Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases
| Autor: | Netanell Avisdris, Oz Haim, Moran Artzi, Rachel Grossman, Claudia Fanizzi, Francesco DiMeco, Zvi Ram, Ben Shofty, Shani Abramov |
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| Rok vydání: | 2022 |
| Předmět: |
Oncology
medicine.medical_specialty Cancer Research Lung Neoplasms Brain Neoplasms business.industry medicine.disease ErbB Receptors Deep Learning Neurology Egfr mutation Carcinoma Non-Small-Cell Lung Internal medicine Mutation Humans Medicine In patient Prospective Studies Non small cell Neurology (clinical) business Lung cancer Retrospective Studies |
| Zdroj: | Journal of Neuro-Oncology. 157:63-69 |
| ISSN: | 1573-7373 0167-594X |
| DOI: | 10.1007/s11060-022-03946-4 |
| Popis: | PURPOSE: Non-small cell lung cancer (NSCLC), the most prevalent subtype of lung cancer, tends to metastasize to the brain. Between 10-60% of NSCLCs harbor an activating mutation in the epidermal growth factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, due to a high discordance rate between the molecular profile of the primary tumor and the brain metastases (BMs), identifying an individual patient’s EGFR status of the BMs necessitates tissue diagnosis via an invasive surgical procedure. We employed a deep learning (DL) method with the aim of noninvasive detection of the EGFR mutation status in NSCLC BM. METHODS: We retrospectively collected clinical, radiological, and pathological-molecular data of all the NSCLC patients who had been diagnosed with BMs and underwent resection of their BM during 2006-2019. The study population was then divided into 2 groups based upon EGFR mutational status. We further employed a DL technique to classify the 2 groups according to their preoperative magnetic resonance imaging features. Finally, we established the accuracy of our model in predicting EGFR mutation status of BM of NSCLC. RESULTS: Fifty-nine patients were included in the study, 16 patients harbored EGFR mutations. Our model predicted mutational status with mean accuracy of 89.8%, sensitivity of 68.7%, specificity of 97.7%, and a receiver operating characteristic curve )ROC( value of 0.91 across the 5 validation datasets.CONCLUSION: DL based noninvasive molecular characterization is feasible, has high accuracy and should be further validated in large prospective cohorts. |
| Databáze: | OpenAIRE |
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