NEURORADIOLOGY / ORIGINAL PAPER
Can radiomics in brain magnetic resonance imaging predict the mutational status of primary lung cancer based on brain metastasis?
1
Department of Radiology, Kartal Dr. Lütfi Kırdar City Hospital, Istanbul, Turkey
2
Department of Radiation Oncology, Istanbul University School of Medicine, Istanbul, Turkey
3
Department of Pathology, Kartal Dr. Lütfi Kırdar City Hospital, Istanbul, Turkey
4
Department of Radiation Oncology, Kartal Dr. Lütfi Kırdar City Hospital, Istanbul, Turkey
5
Department of Computer Science Application and Research, Istanbul University, Istanbul, Turkey
Submission date: 2025-06-16
Acceptance date: 2025-06-23
Publication date: 2025-09-30
Pol J Radiol, 2025; 90: 475-483
KEYWORDS
TOPICS
ABSTRACT
Purpose:
Lung cancer is one of the most common types of cancer, and the presence of brain metastases has a significant impact on the clinical course and prognosis. EGFR, BRAF, ALK, and ROS1 mutations have previously been identified in lung cancer, and knowing the tumour mutation status is important for molecular therapy. In our study, we investigated the performance of radiomics in predicting the status of brain metastases detected by brain magnetic resonance imaging (MRI), a noninvasive method, in with brain metastases patients diagnosed with lung cancer.
Material and methods:
Lung cancer cases with brain metastasis in our hospital between 2014 and 2024 were analysed retrospectively. Histopathological data were obtained from tissue biopsy results, and EGFR, BRAF, ALK, and ROS1 mutation status were recorded. A total of N = 84 patients were included in the study, and 107 original radiomics parameters were obtained from the segmentation files extracted from the patient images. Due to the class unbalance, the performance of the model was tested using the stratified folding method.
Results:
Five (6.02%) of the patients had EGFR, 3 (4.17%) had ALK, and 2 (2.78%) had ROS1 mutations. Model 1 used for EGFR mutation prediction showed high performance with 93.82% accuracy, Model 2 used for ALK with 84.76% accuracy, and Model 3 used for ROS1 with 79.33% accuracy.
Conclusions:
Our study showed that EGFR mutations, in particular, can be detected with high accuracy by radiomics in lung cancer patients with brain metastases without additional invasive procedures.
Lung cancer is one of the most common types of cancer, and the presence of brain metastases has a significant impact on the clinical course and prognosis. EGFR, BRAF, ALK, and ROS1 mutations have previously been identified in lung cancer, and knowing the tumour mutation status is important for molecular therapy. In our study, we investigated the performance of radiomics in predicting the status of brain metastases detected by brain magnetic resonance imaging (MRI), a noninvasive method, in with brain metastases patients diagnosed with lung cancer.
Material and methods:
Lung cancer cases with brain metastasis in our hospital between 2014 and 2024 were analysed retrospectively. Histopathological data were obtained from tissue biopsy results, and EGFR, BRAF, ALK, and ROS1 mutation status were recorded. A total of N = 84 patients were included in the study, and 107 original radiomics parameters were obtained from the segmentation files extracted from the patient images. Due to the class unbalance, the performance of the model was tested using the stratified folding method.
Results:
Five (6.02%) of the patients had EGFR, 3 (4.17%) had ALK, and 2 (2.78%) had ROS1 mutations. Model 1 used for EGFR mutation prediction showed high performance with 93.82% accuracy, Model 2 used for ALK with 84.76% accuracy, and Model 3 used for ROS1 with 79.33% accuracy.
Conclusions:
Our study showed that EGFR mutations, in particular, can be detected with high accuracy by radiomics in lung cancer patients with brain metastases without additional invasive procedures.
REFERENCES (46)
1.
Nolan C, Deangelis LM. Overview of metastatic disease of the central nervous system. Handb Clin Neurol 2018; 149: 3-23.
2.
Ali A, Goffin JR, Arnold A, Ellis PM. Survival of patients with non-small-cell lung cancer after a diagnosis of brain metastases. Curr Oncol 2013; 20: e300-e306. DOI: 10.3747/co.20.1481.
3.
Soria JC, Ohe Y, Vansteenkiste J, Reungwetwattana T, Chewaskulyong B, Lee KH, et al. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. N Engl J Med 2018; 378: 113-125.
4.
AACR Project GENIE Consortium. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov 2017; 7: 818-831.
5.
Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med 2009; 361: 947-957.
6.
Rosell R, Carcereny E, Gervais R, Vergnenegre A, Massuti B, Felip E, et al. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): a multicentre, open-label, randomised phase 3 trial. Lancet Oncol 2012; 13: 239-246.
7.
Di Lorenzo R, Ahluwalia MS. Targeted therapy of brain metastases: latest evidence and clinical implications. Ther Adv Med Oncol 2017; 9: 781. DOI: 10.1177/1758834017736252.
8.
Bozzetti C, Tiseo M, Lagrasta C, Nizzoli R, Guazzi A, Leonardi F, et al. Comparison between epidermal growth factor receptor (EGFR) gene expression in primary non-small cell lung cancer (NSCLC) and in fine-needle aspirates from distant metastatic sites. J Thorac Oncol 2008; 3: 18-22.
9.
Kuo MD, Jamshidi N. Behind the numbers: decoding molecular phenotypes with radiogenomics – guiding principles and technical considerations. Radiology 2014; 270: 320-325.
10.
Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5: 4006. DOI: 10.1038/NCOMMS5006.
11.
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48: 441-446.
12.
Coroller TP, Agrawal V, Narayan V, Hou Y, Grossmann P, Lee SW, et al. Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol 2016; 119: 480-486.
13.
Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, Madabhushi A. Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer 2018; 115: 34-41.
14.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278: 563-577.
15.
Bhargava R, Madabhushi A. Emerging themes in image informatics and molecular analysis for digital pathology. Annu Rev Biomed Eng 2016; 18: 387-412.
16.
Galloway MM. Texture analysis using gray level run lengths. Computer Graphics and Image Processing 1975; 4: 172-179.
17.
Wu H, Sun T, Wang J, Li X, Wang W, Huo D, et al. Combination of radiological and gray level co-occurrence matrix textural features used to distinguish solitary pulmonary nodules by computed tomography. J Digit Imaging 2013; 26: 797-802.
18.
Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C, et al. Defining the biological basis of radiomic phenotypes in lung cancer. Elife 2017; 6: e23421. DOI: 10.7554/ELIFE.23421.
19.
Larkin TJ, Canuto HC, Kettunen MI, Booth TC, Hu DE, Krishnan AS, et al. Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment. Magn Reson Med 2014; 71: 402-410.
20.
Van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017; 77: e104-e107. DOI: 10.1158/0008-5472.CAN-17-0339.
21.
Jann B. Relative distribution analysis in Stata. Available at: http://ideas.repec.org/p/bss/w... (Accessed: 06.10.2024).
22.
Szeghalmy S, Fazekas A. A comparative study of the use of stratified cross-validation and distribution-balanced stratified cross-validation in imbalanced learning. Sensors 2023; 23: 2333. DOI: 10.3390/S23042333.
23.
Gomes Mantovani R, Horváth T, Rossi ALD, Cerri R, Barbon S Jr, Vanschoren J, de Carvalho ACPLF. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Min Knowl Discov 2018; 38: 1364-1416.
24.
Nicholson AG, Tsao MS, Beasley MB, Borczuk AC, Brambilla E, Cooper WA, et al. The 2021 WHO classification of lung tumors: impact of advances since 2015. J Thorac Oncol 2022; 17: 362-387.
25.
Mayer C, Ofek E, Fridrich DE, Molchanov Y, Yacobi R, Gazy I, et al. Direct identification of ALK and ROS1 fusions in non-small cell lung cancer from hematoxylin and eosin-stained slides using deep learning algorithms. Mod Pathol 2022; 35: 1882-1887.
26.
Chen BT, Jin T, Ye N, Mambetsariev I, Daniel E, Wang T, et al. Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases. Magn Reson Imaging 2020; 69: 49. DOI: 10.1016/J.MRI.2020.03.002.
27.
Haim O, Abramov S, Shofty B, Fanizzi C, DiMeco F, Avisdris N, et al. Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases. J Neurooncol 2022; 157: 63-69.
28.
Zhao H, Su Y, Wang M, Lyu Z, Xu P, Jiao Y, et al. The machine learning model for distinguishing pathological subtypes of non-small cell lung cancer. Front Oncol 2022; 12. DOI: 10.3389/fonc.2022.875761.
29.
Fang W, Zhang G, Yu Y, Chen H, Liu H. Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs. Biosci Rep 2022; 42: BSR20212416. DOI: 10.1042/BSR20212416.
30.
Li Z, Mao Y, Li H, Yu G, Wan H, Li B. Differentiating brain metastases from different pathological types of lung cancers using texture analysis of T1 postcontrast MR. Magn Reson Med 2016; 76: 1410-1419.
31.
Béresová M, Larroza A, Arana E, Varga J, Balkay L, Moratal D. 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution. MAGMA 2018; 31: 285-294.
32.
Ortiz-Ramon R, Larroza A, Arana E, Moratal D. A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017: 493-496.
33.
Rangachari D, Yamaguchi N, VanderLaan PA, Folch E, Mahadevan A, Floyd SR, et al. Brain metastases in patients with EGFR-mutated or ALK-rearranged non-small-cell lung cancers. Lung Cancer 2015; 88: 108-111.
34.
Rikova K, Guo A, Zeng Q, Possemato A, Yu J, Haack H, et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 2007; 131: 1190-1203.
35.
Bergethon K, Shaw AT, Ou SHI, et al. ROS1 rearrangements define a unique molecular class of lung cancers. J Clin Oncol 2012; 30: 863-870.
36.
Shaw AT, Camidge DR, Engelman JA, Clark J, Tye L, Wilner K, et al. Clinical activity of crizotinib in advanced non-small cell lung cancer (NSCLC) harboring ROS1 gene rearrangement. J Clin Oncol 2012; 10: 15. DOI: 10.1200/JCO.2012.30.15_SUPPL.7508.
37.
Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol 2020; 196: 879-887.
38.
Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res 2021; 10: 1186-1199.
39.
Liu Y, Kim J, Balagurunathan Y, Li Q, Garcia AL, Stringfield O, et al. Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin Lung Cancer 2016; 17: 441-448.e6. DOI: 10.1016/J.CLLC.2016.02.001.
40.
Zhang L, Chen B, Liu X, Song J, Fang M, Hu C, et al. Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer. Transl Oncol 2018; 11: 94-101.
41.
Rizzo S, Petrella F, Buscarino V, De Maria F, Raimondi S, Barberis M, et al. CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer. Eur Radiol 2016; 26: 32-42.
42.
Gevaert O, Echegaray S, Khuong A, Hoang CD, Shrager JB, Jensen KC, et al. Predictive radiogenomics modeling of EGFR mutation status in lung cancer. Sci Rep 2017; 7: 41674. DOI: 10.1038/SREP41674.
43.
Eichler AF, Kahle KT, Wang DL, Joshi VA, Willers H, Engelman JA, et al. EGFR mutation status and survival after diagnosis of brain metastasis in nonsmall cell lung cancer. Neuro Oncol 2010; 12: 1193-1199.
44.
Italiano A, Vandenbos FB, Otto J, Mouroux J, Fontaine D, Marcy PY, et al. Comparison of the epidermal growth factor receptor gene and protein in primary non-small-cell-lung cancer and metastatic sites: implications for treatment with EGFR-inhibitors. Ann Oncol 2006; 17: 981-985.
45.
Lee CC, Soon YY, Tan CL, Koh WY, Leong CN, Tey JCS, Tham IWK. Discordance of epidermal growth factor receptor mutation between primary lung tumor and paired distant metastases in non-small cell lung cancer: a systematic review and meta-analysis. PLoS One 2019; 14: e0218414. DOI: 10.1371/JOURNAL.PONE.0218414.
46.
Ahn SJ, Kwon H, Yang JJ, Park M, Cha YJ, Suh SH, Lee JM, et al. Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer. Sci Rep 2020; 10. DOI: 10.1038/S41598-020-65470-7.
Share
RELATED ARTICLE
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
