Decoding Tumor Phenotypes for ALK, ROS1, and RET Fusions in Lung Adenocarcinoma Using a Radiomics Approach

Yoon, Haesung; Sohn, Insuk; Cho, Jong Ho; Lee, Ho Yun; Kim, Jae‐Hun; Choi, Yoon–La; Kim, Hyeseung; Lee, Genehee; Lee, Kyung Soo; Kim, Young Tae

doi:10.1097/md.0000000000001753

Cited by 110 publications

(83 citation statements)

References 41 publications

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“…In a recent study, 31 of 101 patients were used for validation, and the first 70 for building the model [95]. Contrary to the field of PET/CT, the use of machine-learning techniques including robust feature selection, the combination of features within classifiers and cross-validation or validation in an external cohort are well established in the fields of CT [7, 77, 108, 109] and MR [110–114] radiomics.…”

Section: Promising Clinical Resultsmentioning

confidence: 99%

Characterization of PET/CT images using texture analysis: the past, the present… any future?

Hatt

Tixier

Pierce

et al. 2016

Eur J Nucl Med Mol Imaging

413

383

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After seminal papers over the period 2009 – 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

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Section: Promising Clinical Resultsmentioning

confidence: 99%

Characterization of PET/CT images using texture analysis: the past, the present… any future?

Hatt

Tixier

Pierce

et al. 2016

Eur J Nucl Med Mol Imaging

413

383

View full text Add to dashboard Cite

show abstract

“…Few studies have investigated associations between the tumor imaging phenotype and the underlying molecular landscape (24–31,33). These studies generally had small sample sizes, used subjective observer-dependent imaging descriptors, and did not perform robust external validation.…”

Section: Discussionmentioning

confidence: 99%

“…This association may have clinical impact in selecting patients for targeted therapies. Imaging phenotype associations with other molecular subtypes of NSCLC should to be further investigated in prospective genotype profiling cohorts(33,55). …”

Section: Discussionmentioning

confidence: 99%

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

et al. 2017

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Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n ¼ 353) and verified them in an independent validation cohort (n ¼ 352). All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter).We found a radiomic signature related to radiographic heterogeneity that successfully discriminated between EGFR þ and EGFR Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics. This work has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost.

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“…In particular, tumor size and enhancement texture had associations with transcriptional activities of pathways and miRNA expressions . Radiomic models were implemented for identification of Epithelial Growth Factor Receptor (EGFR) mutant status from CT through multiple logistic regression and pairwise selection and to decode ALK (anaplastic lymphoma kinase), ROS1 (c‐ros oncogene 1), or RET (rearranged during transfection) fusions in lung adenocarcinoma …”

Section: Overview Of Research and Clinical Applications Of Cancer Radmentioning

confidence: 99%

Machine and deep learning methods for radiomics

et al. 2020

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Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three‐dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open‐source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics‐based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.

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Decoding Tumor Phenotypes for ALK, ROS1, and RET Fusions in Lung Adenocarcinoma Using a Radiomics Approach

Abstract: Supplemental Digital Content is available in the text

Cited by 110 publications

References 41 publications

Characterization of PET/CT images using texture analysis: the past, the present… any future?

Characterization of PET/CT images using texture analysis: the past, the present… any future?

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

Machine and deep learning methods for radiomics

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