Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

Reuzé, Sylvain; Schernberg, Antoine; Orlhac, Fanny; Sun, Roger; Chargari, Cyrus; Dercle, Laurent; Deutsch, Éric; Buvat, Irène; Robert, Charlotte

doi:10.1016/j.ijrobp.2018.05.022

Cited by 91 publications

(91 citation statements)

References 174 publications

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“…Radiomics/genomics studies suffer from several challenges, and a robust framework for clinical decision making is highly desired (7,43,44). As an approved guideline, standardization efforts (IBSI in particular) (36) have sought to address the challenge of reproducing and validating reported findings by comparing and standardizing definitions and implementation of several image-feature sets between participating institutions.…”

Section: Discussionmentioning

confidence: 99%

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Shiri¹,

Maleki²,

Hajianfar³

et al. 2020

Mol Imaging Biol

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Conflicts of Interest:The authors declare no potential conflicts of interest. Abstract PurposeConsiderable progress has been made in assessment and management of NSCLC patients based on mutation status in the EGFR and KRAS. At the same time, NSCLC management through KRAS and EGFR mutation profiling faces some challenges. In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC cancer patients based on low dose CT, diagnostic CT, PET modalities radiomic features and machine learning (ML) algorithms. MethodsOur study involved NSCLC cancer patient with 186 PET, and 175 low dose CT and CTD images. More than twenty thousand radiomic features from different image-feature sets were extracted. Conventional clinically used standard uptake value (SUV) parameters were also obtained for PET images. Feature value was normalized to obtain Z-scores, followed by student t-test students for comparison, high correlated features were eliminated and the False discovery rate (FDR) correction were performed and q-value were reported for univariate analysis. Six feature selection methods and twelve classifiers were used to predict gene status in patient. We performed 10-fold cross-validation for model tuning to improve robustness and all model evaluation was reported on independent validation sets (68 patients). The mean area under the receiver operator characteristic (ROC) curve (AUC) was obtained for performance evaluation. ResultsThe best predictive power of conventional PET parameters was achieved by SUVpeak (AUC: 0.69, P-value = 0.0002) and MTV (AUC: 0.55, P-value = 0.0011) for EGFR and KRAS, respectively. Univariate analysis of extracted radiomics features improved prediction power up to AUC: 75 (q-value: 0.003, Short Run Emphasis feature of GLRLM from LOG preprocessed image of PET with sigma value 1.5) and AUC: 0.71 (q-value 0.00005, The Large Dependence Low Gray Level Emphasis from GLDM in LOG preprocessed image of CTD sigma value 5) for EGFR and KRAS, respectively. Furthermore, the machine learning algorithm improved the perdition power up to AUC: 0.82 for EGFR (LOG preprocessed of PET image set with sigma 3 with VT feature selector and SGD classifier) and AUC: 0.83 for KRAS (CT image set with sigma 3.5 with SM feature selector and SGD classifier). ConclusionOur findings demonstrated that non-invasive and reliable radiomics analysis can be successfully used to predict EGFR and KRAS mutation status in NSCLC patients. We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients, and showed that they have more predictive power than conventional imaging parameters.This study was conducted on 211 NSCLC cancer patients with available imaging and genomic data. Imaging modalities, including diagnostic CT (CTD) and PET/CT (i.e., low-dose CT (CT) used for PET attenuation correction and the PET image) for all patients were obtained (29-32) . All images we...

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Section: Discussionmentioning

confidence: 99%

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Shiri¹,

Maleki²,

Hajianfar³

et al. 2020

Mol Imaging Biol

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“…Common approaches use univariate or multivariate regression analysis for the feature selection, thanks to statistical tests such as Student t-test or Mann-Whitney Utest. 2,15 Dimensionality reduction algorithms such as principal component analysis (PCA), are designed to convert a set of possibly correlated features into a set of new uncorrelated ones. 16 The least absolute shrinkage and selection operator (LASSO) Cox regression model is also a suitable alternative for the regression of high-dimensional data.…”

Section: Radiomics: From Data To Clinical Practicementioning

confidence: 99%

“…By its ability to capture tumour heterogeneity through multimodal images, radiomics could therefore be extremely helpful. 15 Most DP clinical trials have been conducted using functional imaging with PET to delineate the biological target volume, [47][48][49][50] based on SUV thresholds or manual delineations. Although the number of patients included is still limited and the clinical benefit of PET-based dose painting still needs to be confirmed, the concept is promising.…”

Section: Radiomics For Delineation and Dose Prescriptionmentioning

confidence: 99%

Radiomics for radiation oncologists: are we ready to go?

et al. 2020

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Radiomics have emerged as an exciting field of research over the past few years, with very wide potential applications in personalised and precision medicine of the future. Radiomics-based approaches are still however limited in daily clinical practice in oncology. This review focus on how radiomics could be incorporated into the radiation therapy pipeline, and globally help the radiation oncologist, from the tumour diagnosis to follow-up after treatment. Radiomics could impact on all steps of the treatment pipeline, once the limitations in terms of robustness and reproducibility are overcome. Major ongoing efforts should be made to collect and share data in the most standardised manner possible.

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“…These models include supervised and nonsupervised approaches. 76 Unsupervised analysis does not provide an outcome variable but rather summary information of the data. The most frequently used graphic display is a heat map, which simultaneously reveals cluster structures in a data matrix.…”

Section: Analytical Toolsmentioning

confidence: 99%

NCTN Assessment on Current Applications of Radiomics in Oncology

Nie

Al‐Hallaq

et al. 2019

International Journal of Radiation Oncology*Biology*Physics

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Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In NotedAn online CME test for this article can be taken at https:// academy.astro.org.

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Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

Cited by 91 publications

References 174 publications

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Radiomics for radiation oncologists: are we ready to go?

NCTN Assessment on Current Applications of Radiomics in Oncology

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