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Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer.

التفاصيل البيبلوغرافية
العنوان: Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer.
المؤلفون: Mahajan A; Clatterbridge Centre for Oncology NHS Foundation Trust, L7 8YA Liverpool, UK.; Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., B G; Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Wadhwa S; Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Agarwal U; Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Baid U; Department of Electronics and Telecommunication Engineering, SGGS Institute of Engineering and Technology, Nanded 431606, Maharashtra, India., Talbar S; Department of Electronics and Telecommunication Engineering, SGGS Institute of Engineering and Technology, Nanded 431606, Maharashtra, India., Janu AK; Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Patil V; Department of Medical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Noronha V; Department of Medical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Mummudi N; Department of Radiation Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Tibdewal A; Department of Radiation Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Agarwal JP; Department of Radiation Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Yadav S; Department of Pathology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Kumar Kaushal R; Department of Pathology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Puranik A; Department of Nuclear Medicine, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Purandare N; Department of Nuclear Medicine, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India., Prabhash K; Department of Medical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India.
المصدر: Exploration of targeted anti-tumor therapy [Explor Target Antitumor Ther] 2023; Vol. 4 (4), pp. 657-668. Date of Electronic Publication: 2023 Aug 30.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Open Exploration Country of Publication: United States NLM ID: 101770662 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2692-3114 (Electronic) Linking ISSN: 26923114 NLM ISO Abbreviation: Explor Target Antitumor Ther Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Elmhurst, New York : Open Exploration, [2020]-
مستخلص: 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 (χ 2 )], 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.
Competing Interests: The authors declare that they have no conflicts of interest.
(© The Author(s) 2023.)
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فهرسة مساهمة: Keywords: Non-small cell lung cancer; anaplastic lymphoma kinase; convolutional neural networks; deep learning; epidermal growth factor receptor; machine learning; radiomics; semantics
تواريخ الأحداث: Date Created: 20230925 Latest Revision: 20231020
رمز التحديث: 20231020
مُعرف محوري في PubMed: PMC10511818
DOI: 10.37349/etat.2023.00158
PMID: 37745691
قاعدة البيانات: MEDLINE
الوصف
تدمد:2692-3114
DOI:10.37349/etat.2023.00158