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

Abstract

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...

Homology-Based Image Processing for Automatic Classification of Histopathological Images of Lung Tissue

“…features of lung cancers [27][28][29], we did not predict them in the current study. Because the classification between LP and AC is directly related to prognosis and survival of lung cancer [23], we believe that our CAD system is useful for evaluating the prognosis and survival of lung cancer.…”

Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images

“…Genotype characterisation, as an essential step for treatment decision, may benefit if more information about the simultaneous pathophysiological processes that occur is combined with traditionally used nodule information. Recent studies have identified other relevant biological structures beyond the nodule that can help the tumour characterisation and contribute to a better understanding of cancer development [ 41 , 42 ]. In fact, lung cancer has been studied as a more extensive clinicopathological phenomenon that involves several other lung structure alterations, and their relationship with cancer development has been identified.…”

“…Radiomics in lung cancer have mostly been based on nodule assessment [ 32 , 63 ]. Recently, a few approaches have tried to use features from other lung structures; however, this information came from semantic annotations provided by radiologists [ 41 , 42 , 59 ]. As AI has shown to be able to extract and use relevant information, the quantitative analysis of the lung cancer performed by radiomic analysis could improve the performance on tumour characterisation—if the data from relevant structures of the lung were taken into consideration and selected by automatic feature learning methods, avoiding the human effort required for semantic annotations and making the process less subjective.…”

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

“…This is linked to the fact that it is often diagnosed in an advanced stage, with 5% or less chance of a 5-year survival [2]. Non-Small-Cell Lung Carcinoma (NSCLC) is the most prevalent histological type of lung cancer, covering about 85% of all lung cancer cases [3], and Epidermal Growth Factor Receptor (EGFR) is the most frequently mutated gene that springs lung cancer of type Adenocarcinoma [4]. The cell surface receptor EGFR is responsible for cell growth and survival and its mutations promote EGFR permanent activation, which contributes to uncontrolled cell division [5,6].…”

“…Combining this signature with a clinical model of EGFR status (AUC = 0.70) significantly improved prediction performance (AUC = 0.75). A XGBoost classifier using only radiomic and semantic features from the nodule obtained an AUC of 0.58 and 0.65, respectively [4].…”