Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects

Abstract

Objective The purpose of this paper was to perform a narrative review of current research evidence on conventional computed tomography (CT) imaging features and CT image-based radiomic features for predicting gene mutations in lung adenocarcinoma and discuss how to translate the research findings to guide future practice. Background Lung cancer, especially lung adenocarcinoma, is the leading cause of cancer-related deaths. With advances in the diagnosis and treatment of lung adenocarcinoma with the emergence of molecular testing, the prediction of oncogenes and even drug resistance gene mutations have become key to individualized and precise clinical treatment in order to prolong survival and improve quality of life. The progress of imageological examination includes the development of CT and radiomics are promising quantitative methods for predicting different gene mutations in lung adenocarcinoma, especially common mutations, such as epidermal growth factor receptor (EGFR) mutation, anaplastic lymphoma kinase (ALK) mutation and Kirsten rat sarcoma viral oncogene (KRAS) mutation. Methods The PubMed electronic database was searched along with a set of terms specific to lung adenocarcinoma, radiomics (including texture analysis), CT, computed tomography, EGFR, ALK, KRAS, rearranging transfection (RET) rearrangement and c-ros oncogene 1 (ROS-1), v-raf murine sarcoma viral oncogene homolog B1 (BRAF), and human epidermal growth factor receptor 2 (HER2) mutations et al. This review has been reported in compliance with the Narrative Review checklist guidelines. From each full-text article, information was extracted regarding a set of terms above. Conclusions Research on the application of conventional CT features and CT image-based radiomic features for predicting the gene mutation status of lung adenocarcinoma is still in a preliminary stage. Noninvasively determination of mutation status in lung adenocarcinoma before targeted therapy with conventional CT features and CT image-based radiomic features remains both hopes and challenges. Before radiomics could be applied in clinical practice, more work needs to be done.

Radiomics Signature to Predict Prognosis in Early-Stage Lung Adenocarcinoma (≤3 cm) Patients with No Lymph Node Metastasis

“…Tumor heterogeneity is recognized as an important feature of cancer that may be associated with adverse biological behavior in tumors, and poor prognosis for patients, as cancers with more genomic heterogeneity are more likely to develop resistance to treatment and to metastasize [ 12 , 34 , 35 ]. Radiomics, which involves the extraction of numerous deep imaging features coupled with appropriate statistical analysis, may reflect the expression of genomic and proteogenomic heterogeneity according to the findings of previous studies [ 36 , 37 , 38 ]. However, interpreting the complex associations between radiomics signatures and biological processes remains difficult, and further radio-genomics work is needed to establish the biological underpinnings of tumor heterogeneity and identify potential radiomics-biological correlations [ 39 ].…”

Novel prognostic signature for lung adenocarcinoma based on immune-related mRNA pairs

“…NSCLC is the more common of the two, accounting for approximately 85 % of all cases, with lung adenocarcinoma (LUAD) being the most prevalent subtype [ 6 , 7 ]. Previous reports indicated that the incidence of lung cancer, especially LUAD, in non-smoking women is increasing year by year [ [8] , [9] , [10] ]. While radiotherapy, chemotherapy, and immunotherapy have significantly improved the prognosis and quality of life for LUAD patients, the survival rate for this type of cancer remains relatively low [ 11 ].…”