Editorial for “Preoperative MRI‐Based Radiomics of Brain Metastasis to Assess T790M Resistance Mutation After EGFR‐TKI Treatment in NSCLC”

Non-small-cell lung carcinoma (NSCLC) is one of the most widely prevalent forms of lung cancer, and approximately 40%–50% of such patients suffer from brain metastasis (BM), which further decreases their chances of survival. Modalities such as whole-brain irradiation and stereotactic radiosurgery are available to treat NSCLC patients with BM, but these lead to deterioration of brain functions and do not offer a very satisfactory recovery rate. In the last few years, newer options such as epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKI) have helped improve treatment outcomes drastically. While EGFR-TKI as therapy is clinically effective, the problem is that many patients start to develop resistance due to varied mechanisms after a year of therapy. More than half of these resistance mechanisms occur due to secondary point mutations (T790M) in EGFR. This is one of the reasons why developing a pretreatment biomarker that can detect T790M mutations early would be very helpful. Genetic sequencing and biopsies that have been used to assess T790M mutations previously have many limitations, such as high cost, low quality of samples, and even the risk of additional damage. This is where a noninvasive imaging-based assessment of the T790M mutation would be very effective. In recent times, radiomics advances in clinical imaging have been demonstrated to be highly useful in terms of tumor quantification as well as analysis. Studies have demonstrated the effectiveness of magnetic resonance imaging (MRI) in locating factors associated with EGFR mutations, but no studies have explored the pretreatment imaging-based prediction of T790M mutations. This is why the authors planned this study to analyze correlations of imaging features among BM patients with T790M mutations and analyze if such a radiomics signature could be established as a biomarker for preoperative predictions. The present study was a retrospective audit in which medical records of 80 patients with NSCLC and confirmed BM were taken. All the patients had T1CE, and T2W brain scans recorded pretreatment. The T790M mutation was detected before treatment using genetic sequencing and using blood samples after the treatment. Brain MRI images were used to extract radiomics features, and then models constructed using a POA-based radiomics signature (RS-POA), a TAA-based radiomics signature (RS-TAA), and a multi-region combined radiomics signature. It was seen that the multi-region combined radiomics signature (RS-Com) had higher accuracy in predicting T790 mutations, followed by RS-TAA and RS-POA in order. BM has been found to be different from primary brain tumors as it is not as infiltrative and invades the surrounding areas to a much lesser extent. Findings from a previous study by Hou et al have demonstrated that textural features can be used for the identification of primary as well as acquired T790 mutations, and the present study also reported similar results. Also, the present study reported that textural features were more predictive of secondary mutations. The present study also had limitations in terms of being a singlecenter study and only utilizing T1CE and T2W MRI sequences. Further studies can be planned across multiple centers using additional MRI sequences, including but not limited to FLAIR and DWI. Also, the necrosis/tumor ratio and edema–tumor ratio have been previously reported to be essential imaging features that help determine the prognosis of BM, and these were not analyzed in the study. In conclusion, the authors have provided initial evidence that pretreatment BM radiomics can be utilized as a modality for predicting T790M mutations, and the RS-Com model can be very helpful in guiding the treatment of NSCLC patients with BM.