Radiomics and radiogenomics in lung cancer: A review for the clinician

Thawani, Rajat; McLane, Michael; Beig, Niha; Ghose, Soumya; Prasanna, Prateek; Velcheti, Vamsidhar; Madabhushi, Anant

doi:10.1016/j.lungcan.2017.10.015

Cited by 368 publications

(286 citation statements)

References 49 publications

Supporting

Mentioning

272

Contrasting

Unclassified

Order By: Relevance

“…First, such mutation status captures only a small degree of tumor heterogeneity and does not provide a complete picture for the assessment of tumor characteristics. Secondly, this method depicts low repeatability and is not feasible for all cases (8).…”

Section: Introductionmentioning

confidence: 99%

“…As a recently developed paradigm of advanced medical image quantification, radiomics has garnered significant interests given its cost-effectiveness and reliability to characterize tumor heterogeneity, and has enabled improved assessment of therapy response and prediction of molecular pathways (10)(11)(12)(13)(14). Accumulating evidence has identified several radiomic features extracted from computed tomography (CT), magnetic resonance (MR) or positron emission tomography (PET) images as highly correlated with genomic parameters in several cancers (8,(15)(16)(17)(18). For NSCLC patients, radiomics studies have shown several CT image-features can predict mutation status in EGFR and KRAS (8).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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...

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…Radiomics uses large amounts of high‐throughput image data to conduct a deeper excavation, prediction, and analysis, which can assist with diagnosis . These results correlate with prognosis, survival prediction, diagnosis of benign and malignant tumors, distant metastases, and tumor gene expression patterns . Thus, the application of radiomics can provide benefits to lung cancer patients.…”

Section: Introductionmentioning

confidence: 99%

“…14,15 These results correlate with prognosis, survival prediction, diagnosis of benign and malignant tumors, distant metastases, and tumor gene expression patterns. [16][17][18][19][20][21][22] Thus, the application of radiomics can provide benefits to lung cancer patients.…”

Section: Introductionmentioning

confidence: 99%

Radiomics for the prediction of EGFR mutation subtypes in non‐small cell lung cancer

Ding

Zhang

et al. 2019

Medical Physics

View full text Add to dashboard Cite

Purpose This retrospective study was designed to investigate the ability of radiomics to predict the mutation status of epidermal growth factor receptor (EGFR) subtypes (19Del and L858R) in patients with non‐small cell lung cancer (NSCLC). Methods In total, 312 patients with NSCLC were included, and 580 radiomic features were extracted from the computed tomography images of each patient. In the training set, univariate analysis was performed on the clinical and radiomic features; logistic regression models were established using a 5‐fold cross validation strategy for the prediction of EGFR subtypes 19Del and L858R. Subsequently, the predictive ability of the joint models was evaluated using the test set. Results The results revealed that the radiomic features specific for EGFR 19Del and L858R were Gabor’s MTRVariance, Gabor’s PTREntropy, and sphericity. Additionally, the respective areas under the receiver operating characteristic curves of the EGFR 19Del and L858R joint models were 0.7925 and 0.7750 for the test set. Conclusions Our study demonstrated the potential for radiomics to predict EGFR 19Del and L858R. Epidermal growth factor receptor 19Del and L858R exhibited distinct imaging phenotypes, which may help to guide the selection of more accurate and personalized treatment programs for patients with NSCLC.

show abstract

“…Six mother wavelet families, that is, Coiflets, Symlets, Daubechies, Biorthogonal, Reverse Biorthogonal, and Fejer‐Korovkin were investigated in this study. We focused on lung cancer because it has attracted increasing attentions in previous radiomics studies as a result of its high mortality and several costly and possibly unnecessary surgical procedures . We hypothesized that optimal mother wavelets could produce features with higher prognostic powers, in terms of their associations with the overall survival time.…”

Section: Introductionmentioning

confidence: 99%

Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition‐based radiomic features

2018

View full text Add to dashboard Cite

show abstract

Radiomics and radiogenomics in lung cancer: A review for the clinician

Cited by 368 publications

References 49 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 the prediction of EGFR mutation subtypes in non‐small cell lung cancer

Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition‐based radiomic features

Contact Info

Product

Resources

About