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Mahajan A, B G, Wadhwa S, Agarwal U, Baid U, Talbar S, Janu AK, Patil V, Noronha V, Mummudi N, Tibdewal A, Agarwal JP, Yadav S, Kumar Kaushal R, Puranik A, Purandare N, Prabhash K. Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer. Explor Target Antitumor Ther 2023; 4:657-668. [PMID: 37745691 PMCID: PMC10511818 DOI: 10.37349/etat.2023.00158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 04/13/2023] [Indexed: 09/26/2023] Open
Abstract
Aim The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging. Methods Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI). Results In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model. Conclusions Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken.
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Affiliation(s)
- Abhishek Mahajan
- Clatterbridge Centre for Oncology NHS Foundation Trust, L7 8YA Liverpool, UK
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Gurukrishna B
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Shweta Wadhwa
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Ujjwal Agarwal
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Ujjwal Baid
- Department of Electronics and Telecommunication Engineering, SGGS Institute of Engineering and Technology, Nanded 431606, Maharashtra, India
| | - Sanjay Talbar
- Department of Electronics and Telecommunication Engineering, SGGS Institute of Engineering and Technology, Nanded 431606, Maharashtra, India
| | - Amit Kumar Janu
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Vijay Patil
- Department of Medical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Vanita Noronha
- Department of Medical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Naveen Mummudi
- Department of Radiation Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Anil Tibdewal
- Department of Radiation Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - JP Agarwal
- Department of Radiation Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Subash Yadav
- Department of Pathology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Rajiv Kumar Kaushal
- Department of Pathology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Ameya Puranik
- Department of Nuclear Medicine, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Nilendu Purandare
- Department of Nuclear Medicine, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Kumar Prabhash
- Department of Medical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
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