Toward automatic prediction of <i>EGFR</i> mutation status in pulmonary adenocarcinoma with 3D deep learning

Zhao, Wei; Yang, Jiancheng; Ni, Bingbing; Bi, Dexi; Sun, Yingli; Xu, Min; Zhu, Xiaoxia; Li, Cheng; Jin, Liang; Gao, Pan; Wang, Peijun; Hua, Yanqing; Li, Ming

doi:10.1002/cam4.2233

Cited by 99 publications

(83 citation statements)

References 42 publications

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“…Also, another work based on deep learning techniques with an interpretable visual output, identified that the regions surrounding the nodule were the most relevant for the classification decision 31,44 . In our opinion, it is crucial to emphasise this characteristic, as it might change the direction and broaden the analysis spectrum of future radiogenomics studies, which until now have been mainly focusing on the nodule or in a region of interest (ROI) around it 16,45,46 . Lung cancer is the result of multiple and complex combinations of morphological, molecular and genetic alterations 47 .…”

Section: Discussion and Future Workmentioning

confidence: 99%

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Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS

Pinheiro

Pereira

Dias

et al. 2020

Sci Rep

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EGFR and KRAS are the most frequently mutated genes in lung cancer, being active research topics in targeted therapy. The biopsy is the traditional method to genetically characterise a tumour. However, it is a risky procedure, painful for the patient, and, occasionally, the tumour might be inaccessible. This work aims to study and debate the nature of the relationships between imaging phenotypes and lung cancer-related mutation status. Until now, the literature has failed to point to new research directions, mainly consisting of results-oriented works in a field where there is still not enough available data to train clinically viable models. We intend to open a discussion about critical points and to present new possibilities for future radiogenomics studies. We conducted high-dimensional data visualisation and developed classifiers, which allowed us to analyse the results for EGFR and KRAS biological markers according to different combinations of input features. We show that EGFR mutation status might be correlated to CT scans imaging phenotypes; however, the same does not seem to hold for KRAS mutation status. Also, the experiments suggest that the best way to approach this problem is by combining nodule-related features with features from other lung structures. Lung cancer is the cancer type leading the incidence and mortality rates 1,2. This is linked to the fact that it is often diagnosed in an advanced stage, with 15% or less chance of a 5-year survival 3 , which magnifies the importance of treatments for advanced-stage disease. In Non-small-cell lung cancer (NSCLC), which constitutes 85% of all cases of lung cancer, certain genomic biomarkers are now considered predictive biomarkers and critical for the prognostic 4. Epidermal Growth Factor Receptor (EGFR) and Kristen Rat Sarcoma Viral Oncogene Homolog KRAS are the most frequently mutated gene in lung cancer 5. EGFR mutated is present in 15 to 50% of NSCLC patients from never-smokers 5. The two most common EGFR mutations (deletions in exon 19 and the single amino acid substitution L858R in exon 21) correspond to approximately 85% of the EGFR mutations in NSCLC. The other low frequency mutations include: point mutations, deletions, insertions, and duplications correspond to approximated 15% of EGFR mutations in NSCLC 6. Unlike the previous marker, KRAS is associated with tobacco use, with only 5 to 10% of KRAS-mutant lung cancers arising in never or light smokers 5,7. Surgically treated NSCLC patients with EGFR mutations showed better disease-free survival (DFS) and overall survival (OS) and the opposite was verified for KRAS, with worse DFS and OS 8. For cytotoxic chemotherapy, the role of EGFR and KRAS as a predictive marker is still unclear 9 ; however, it appears that mutant KRAS may predict a lack of response to chemotherapy 10. Regarding target therapy, tumour driver mutations have reliable predictive value and, in fact, they guide treatment decision in clinics 11. EGFR gene is a paradigmatic example, since its activating mutations, namely those loca...

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Section: Discussion and Future Workmentioning

confidence: 99%

“…biomarkers, decreasing multiple undesirable side effects associated with cancer treatment 15 . Several clinical trials have been performed to evaluate the efficacy and safety of treatments for lung cancer patients with EGFR mutations 16 . EGFR is a receptor tyrosine kinase that controls the growth and proliferation of cells.…”

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confidence: 99%

Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS

Pinheiro

Pereira

Dias

et al. 2020

Sci Rep

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“…The AI diagnosis system used in the study was supported and maintained by Diannei Technology Co. Ltd (Shanghai, China). The core diagnosis component of the AI system was 3D DenseSharp Network, a state‐of‐the‐art multitask learning deep neural network based on 3D DenseNets, with classification and segmentation heads for diagnosing and segmenting lung nodules. In the primary study on cross‐modal pathological invasiveness prediction from CT scans, the software developer set up a single‐cohort data set, which involved 651 patients from Huadong Hospital affiliated to Fudan University, Shanghai, China .…”

Section: Methodsmentioning

confidence: 99%

“…Based on this data set, the 3D DenseSharp Network was developed to train and predict the invasiveness labels and lesion segmentation, with a superior performance over radiologists on this task. The AI system used in this study, developed with the 3D deep learning technology, took advantage of a multicohort data set, with 10x more patients than their primary study . Using this AI system, dicom files were analyzed to screen out intrapulmonary subsolid nodules and their three‐dimensional (3D) volumes, radial lines, probability of nodules and malignancy, probable pathological patterns and other parameters.…”

Section: Methodsmentioning

confidence: 99%

Application of artificial intelligence in the diagnosis of multiple primary lung cancer

et al. 2019

Thoracic Cancer

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Artificial intelligence (AI) based on deep learning, convolutional neural networks and big data has been increasingly effective in the diagnosis and treatment of multiple primary pulmonary nodules. In comparison to previous imaging systems, AI measures more objective parameters such as three‐dimensional (3D) volume, probability of malignant nodules, and possible pathological patterns, making the access to the properties of nodules more objective. In our retrospective study, a total of 53 patients with synchronous and metachronous multiple pulmonary nodules were enrolled of which 33 patients were confirmed by pathological tests to have primary binodules, and nine to have primary trinodules. A total of 15 patients had only one focus removed. The statistical results showed that the agreement in the AI diagnosis and postoperative pathological tests was 88.8% in identifying benign or malignant lesions. In addition, the probability of malignancy of benign lesions, preinvasive lesions (AAH, AIS) and invasive lesions (MIA, IA) was totally different (49.40±38.41% vs 80.22±13.55% vs 88.17±17.31%). The purpose of our study was to provide references for the future application of AI in the diagnosis and follow‐up of multiple pulmonary nodules. AI may represent a relevant diagnostic aid that shows more accurate and objective results in the diagnosis of multiple pulmonary nodules, reducing the time required for interpretation of results by directly displaying visual information to doctors and patients and together with the clinical conditions of MPLC patients, offering plans for follow‐up and treatment that may be more beneficial and reasonable for patients. Despite the great application potential in pneumosurgery, further research is needed to verify the accuracy and range of the application of AI.

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“…Medical imaging has been playing an increasing key role in personalized precision medicine. Radiomics has been applied to oncology studies (13) and has been widely used in diagnosis (14), prognostic evaluation (15), prediction of biological behaviors (16), and even genetic prediction (11,17).…”

mentioning

confidence: 99%

Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

Liu¹,

Wang²,

Liu³

et al. 2020

Preprint

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Background Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors.Methods We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase (A-phase), venous phase (V-phase), delayed phase (D-phase), and combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC).Results The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval (CI), 0.640–0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397–0.790), 0.715 (95% CI, 0.506–0.873), and 0.679 (95% CI, 0.468–0.847), respectively, with the validation set.Conclusion The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

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Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning

Cited by 99 publications

References 42 publications

Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS

Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS

Application of artificial intelligence in the diagnosis of multiple primary lung cancer

Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

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