Radiogenomics: bridging imaging and genomics

Bodalal, Zuhir; Trebeschi, Stefano; Nguyen-Kim, Thi Dan Linh; Schats, Winnie; Beets-Tan, Regina

doi:10.1007/s00261-019-02028-w

Cited by 218 publications

(155 citation statements)

References 203 publications

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“…This limitation is common to the studies in this field, since in general datasets are small and based on patients cohorts from only one medical centre. A reproducible and clinically viable predictive model needs a large and heterogeneous cohort of patients and methods capable of coping with the inherent data heterogeneity 14 . So, a reliable model would require a reliable dataset, collected from multiple centres in order to capture the heterogeneity of the population, but under an uniform protocol to avoid any inconsistency during data record.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…These models can either use qualitative features, obtained by semantic annotations from human observers, or use quantitative features, obtained through a radiomic approach, which extracts features directly from the image 13 . Radiogenomics, a specific field within radiomics, is defined by the correlation between quantitative features, directly extracted from radiological images (imaging phenotype), and genetic information (genotype) 14 . Studies in lung cancer have presented the association between EGFR mutation status and quantitative features extracted from computed tomography (CT) scans [14][15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

“…Radiogenomics, a specific field within radiomics, is defined by the correlation between quantitative features, directly extracted from radiological images (imaging phenotype), and genetic information (genotype) 14 . Studies in lung cancer have presented the association between EGFR mutation status and quantitative features extracted from computed tomography (CT) scans [14][15][16][17] . The most recent methods are based on convolutional neural networks, which are end-to-end approaches that allow to automatically learn the whole process, reducing the subjectivity and human effort 14,18 .…”

Section: Introductionmentioning

confidence: 99%

“…Studies in lung cancer have presented the association between EGFR mutation status and quantitative features extracted from computed tomography (CT) scans [14][15][16][17] . The most recent methods are based on convolutional neural networks, which are end-to-end approaches that allow to automatically learn the whole process, reducing the subjectivity and human effort 14,18 . Also, regarding qualitative features, recent works have shown that semantic human annotations of CT scans can be used to train a model to accurately predict EGFR mutation status, although the same was not verifiable for KRAS 19,20 .…”

Section: Introductionmentioning

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. 2019

Preprint

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EGFR and KRAS are the most frequently mutated genes in lung cancer, being active research topics in targeted therapy. 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 true 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. 2/103/10 7/10 is balanced individually for each fold using SMOTE-NC, avoiding data leakage. After parameter optimisation, probabilistic outputs of each model with optimal parameters were analysed using the AUC of Receiver Operating Characteristic (ROC). ROC is a probability curve and AUC represents degree or measure of separability, telling how much model is capable of distinguishing between classes. The ROC curve is plotted with True Positive Rate (TPR) against the False Positive Rate (FPR), usually with TPR on the y-axis and FPR on the x-axis. Experimental DesignWe designed four experiments in order to test and compare which type of input features allow to achieve better performance in gene mutation status prediction. We first trained a model that took nodule-related radiomic features as input. Then, for direct comparison purposes and to allow a modular evaluation, we split the semantic data into three parts: nodule, non-nodule and hybrid. The first one contains only nodular information, the second one contains only information external to the nodule and the third one is the combination of both. The split can be seen in detail in Table 2 of the supplementary material. 10/10

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Pinheiro

Pereira

Dias

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Radiogenomics is an encouraging field that combines genomics and medical imaging techniques, considered as a bridge connecting radiomics with genomics [72], while some challenges still need to be addressed. At present, the application of this technique in PCa is relatively less extensive and in-depth than that in other organs tumor such as brain, lung, or liver [72,73]. Since PCa clinical results are closely related to phosphatase and tensin homolog (PTEN), loss of which is associated with increased clinical aggressive phenotype and mortality, related studies are giving out valuable potential.…”

Section: Radiogenomicsmentioning

confidence: 99%

Radiomics in prostate cancer: basic concepts and current state-of-the-art

2019

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Prostate cancer (PCa) is the second most common type of cancer among males and the fifth major contributor to cancerrelated mortality and morbidity worldwide. Radiomics, as a superior method of mining big data in medical imaging, has enormous potential to assess PCa from diagnosis to prognosis to treatment response, empowering clinical medical strategies accurately, reliably, and effectively. Hence, this article reviews the basic concepts of radiomics and its current state-of-the-art in PCa as well as put forwards the prospects of future directions.

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Improving reproducibility and performance of radiomics in low‐dose CT using cycle GANs

Chen

Wee

Dekker

et al. 2022

J Applied Clin Med Phys

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Background As a means to extract biomarkers from medical imaging, radiomics has attracted increased attention from researchers. However, reproducibility and performance of radiomics in low‐dose CT scans are still poor, mostly due to noise. Deep learning generative models can be used to denoise these images and in turn improve radiomics’ reproducibility and performance. However, most generative models are trained on paired data, which can be difficult or impossible to collect. Purpose In this article, we investigate the possibility of denoising low‐dose CTs using cycle generative adversarial networks (GANs) to improve radiomics reproducibility and performance based on unpaired datasets. Methods and materials Two cycle GANs were trained: (1) from paired data, by simulating low‐dose CTs (i.e., introducing noise) from high‐dose CTs and (2) from unpaired real low dose CTs. To accelerate convergence, during GAN training, a slice‐paired training strategy was introduced. The trained GANs were applied to three scenarios: (1) improving radiomics reproducibility in simulated low‐dose CT images and (2) same‐day repeat low dose CTs (RIDER dataset), and (3) improving radiomics performance in survival prediction. Cycle GAN results were compared with a conditional GAN (CGAN) and an encoder–decoder network (EDN) trained on simulated paired data. Results The cycle GAN trained on simulated data improved concordance correlation coefficients (CCC) of radiomic features from 0.87 (95%CI, [0.833,0.901]) to 0.93 (95%CI, [0.916,0.949]) on simulated noise CT and from 0.89 (95%CI, [0.881,0.914]) to 0.92 (95%CI, [0.908,0.937]) on the RIDER dataset, as well improving the area under the receiver operating characteristic curve (AUC) of survival prediction from 0.52 (95%CI, [0.511,0.538]) to 0.59 (95%CI, [0.578,0.602]). The cycle GAN trained on real data increased the CCCs of features in RIDER to 0.95 (95%CI, [0.933,0.961]) and the AUC of survival prediction to 0.58 (95%CI, [0.576,0.596]). Conclusion The results show that cycle GANs trained on both simulated and real data can improve radiomics’ reproducibility and performance in low‐dose CT and achieve similar results compared to CGANs and EDNs.

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Radiogenomics: bridging imaging and genomics

Cited by 218 publications

References 203 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

Radiomics in prostate cancer: basic concepts and current state-of-the-art

Improving reproducibility and performance of radiomics in low‐dose CT using cycle GANs

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