Radiogenomics in Colorectal Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Radiomics
2.1. Radiomics Workflow
2.2. Features Extraction
2.3. Radiomics in Colorectal Cancer
3. Genomics and Transcriptomics
4. Radiogenomics in Colorectal Cancer
4.1. 18F-FDG PET
4.2. Magnetic Resonance Imaging
4.3. CT SCAN
5. Limitations of Radiogenomics Studies
6. Discussion and Future Directions
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Year | Author | Study | N | Study Population | Aim | Segmentation | Radiomic Features | Main Results | Internal Validation | External Validation | Conclusions |
---|---|---|---|---|---|---|---|---|---|---|---|
2020 | Arslan | R | 83 | All stages | Prediction of the KRAS status | M | SUVmax | KRAS mutation mean SUVmax (24.0 ± 9.0); KRAS wild type mean SUVmax (17.7 ± 8.2) | N | N | Coexistence of KRAS mutation with higher SUVmax is a negative prognostic factor |
2020 | Popovic | R | 37 | Stage IV | Prediction of KRAS status in CRLM | M/A | SUV metrics corrected for tumor-to-blood standard uptake ratio (SUR) and partial volume effect (PVE) | SUV metrics(AUC 0.69–0.72); SUR metrics(AUC 0.73–0.75) | N | N | Corrected PET standard uptake values (SUV) correlated KRAS status |
2019 | Chen | R | 74 | All stages | Association between radiomics and genetic mutations | M | 63 radiomic features | KRAS predictor histograms (OR 1.99) and contrast (OR 1.52) from GLCM predictors; SRLGE associated TP53 (OR 243); LGZE predictor APC (OR < 0.001) | N | N | PET/CT-derived radiomics can determine KRAS, TP53, and APC genetic alterations |
2018 | Mao | R | 49 | Stage IV | Prediction of KRAS status in CRLM | M | Maximum standardized uptake value (SUVmax); change of SUVmax (DSUVmax); retention index (RI) | SUVearly AUC 0.694 (p = 0.002, 95% CI 0.582–0.807); SUVdelayed AUC 0.760 (p < 0.001, 95% CI 0.658–0.862); DSUVmax AUC 0.757 (p < 0.001, 95% CI 0.654–0.861); RI (%) AUC 0.684 (p = 0.003, 95% CI 0.571–0.797) | N | N | KRAS mutations predictors in CRLM: early and delayed SUVmax, DSUVmax, RI |
2017 | Oner | R | 55 | Na | Prediction of the KRAS status | A | SUVmax, SUVmean, MTV and TLG | SUVmax (AUC 0.54, OR 0.08, 95% CI, 0.38–0.7 p = 0.6); MTV (AUC 0.54, OR 0.08, 95% CI, 0.38–0.6 p = 0.6) | N | N | No significant association between KRAS gene mutations and SUVmax, MTV, TLG, NLR, PLR, CEA, CA 19-9 values |
2016 | Lee | P | 179 | All stages | Predict the KRAS status depending on C-reactive protein (CRP) levels | M/A | Maximum standardized uptake value (SUVmax), peak standardized uptake value (SUVpeak), metabolic tumor volume | None of the PET/CT-related parameters showed significant KRAS prediction; In normal CRP group, mutated KRAS associated with higher SUVmax (OR, 3.3; 95% CI, 1.4–7.4), SUVpeak (OR, 3.8; 95% CI, 1.5–9.3) | N | N | Higher SUVmax and SUVpeak values in KRAS mutated patients |
2016 | Lovinfosse | R | 151 | All stages | Prediction of KRAS, NRAS, BRAF | M | Standardized uptake values (SUVs), volume-based parameters and texture analysis | SUVcov highest AUC (0.65), sensitivity 56%, specificity 64%; SUVmax AUC 0.65 and sensibility 69% specificity 52% | N | N | The accuracy of 18F-FDG PET/CT quantitative metrics could not play a clinical role |
2015 | Chen | R | 103 | All stages | Prediction of TP53, KRAS, APC, BRAF, and PIK3CA | M | SUVmax, and various thresholds of metabolic tumor volume, total lesion glycolysis, and PET/CT-based tumor width (TW) were measured | SUVmax predicting TP53, OR 1.28 (95% CI, 1.01–1.61); TW 40% predicting KRAS, OR 1.15 (95% CI, 1.06–1.24) | N | N | Increased SUVmax and TW40% associated with TP53 and KRAS mutations |
2015 | Kawada | R | 55 | Stage IV | Prediction of the KRAS status | M | SUVmax | SUVmax (cutoff value 6.0) in tumors larger than 10 mm OR 0.78 (95% CI, 0.61–0.99) predicted KRAS status | N | N | 18F-FDG accumulation into metastatic CRC was associated with KRAS status |
2014 | Chen | R | 121 | All stages | Prediction of the KRAS status | A | SUVmax; metabolic tumor volume, total lesion glycolysis, PET/CT-based tumor width | SUVmax OR 1.23 (95% CI, 1.01–1.52); TW 40% OR 1.15 (95% CI, 1.02–1.30). | N | N | SUVmax and TW40% were associated in CRC with KRAS mutations |
2014 | Krikelis | R | 44 | Stage IV | Prediction of the KRAS status | M | SUVmax | No correlation of SUVmax with KRAS status | N | N | No statistically significant correlation between SUVmax values and KRAS mutation status or GLUT1 mRNA levels. |
2014 | Miles | P | 33 | All stages | Prediction of the KRAS status | M | SUVmax, mean of positive pixels [MPP]), blood flow (BF) | The true-positive rate, false-positive rate, and accuracy (95% confidence intervals) of the decision tree were 82.4% (63.9%–93.9%), 0% (0%–10.4%), and 90.1% (79.2%–96.0%), respectively. | Y | N | Combined measurements of tumor 18F-FDG uptake, CT texture, and perfusion has the potential to identify KRAS mutations |
2012 | Kawada | R | 51 | All stages | KRAS/BRAF mutations affect FDG accumulation in CRC | M | Radiomic features | KRAS and BRAF mutations correlated with SUVmax (OR, 1.17; 95% CI, 1.03–1.33), TLR (OR, 1.40; 95% CI, 1.08–1.80) | N | N | FDG accumulation was higher in CRC with KRAS/BRAF mutations |
Year | Author | Study | N | Study Population | Aim | Segmentation | Radiomic Features | Main Results | Internal Validation | External Validation | Conclusion |
---|---|---|---|---|---|---|---|---|---|---|---|
2020 | Wang | R | 306 | Na | Deep model to independently predict the genetic status of KRAS mutations | M | DL model | MBCAM2 model- accuracy 90.50%, sens 92.79%, spec 87.64%, and AUC 96.00% | Y | N | Multi-branch cross attention model outperforms all the methods of DL |
2020 | Oh | R | 60 | All stages | Prediction of KRAS status | M | Three radiomic model | sens (84%), spec (80%), accuracy values (81.7%), AUC (0.884) of the decision tree for the whole dataset | Y | N | Three MRI imaging features that could predict KRAS status |
2020 | Cui | R | 304 + 86 | Na | Prediction of KRAS status | M | Seven radiomics features | Training dataset AUC of 0.722 (95% CI, 0.654–0.790); internal validation AUC 0.682 (95% CI, 0.569–0.794); external validation AUC 0.714 (95% CI, 0.602–0.827) | Y | Y | Moderate performance to predict KRAS status |
2019 | Horvat | R | 65 | Na | Correlations between genetic mutations and radiomics | M | Thirty-four texture features | No associations between clusters/qualitative features and gene mutations (except for PTPRT) | N | N | Associations between quantitative features and genetic mutations; pas de correlations between qualitative features and genetic mutations |
2019 | Cui | R | 148 | Exclusion Stage IV | Prediction of the status of KRAS | M | D, K, and apparent diffusion coefficient (ADC) values | K75th AUC value of 0.871 (0.806–0.920) sensitivity 81.43%, specificity78.21%, positive predictive value 77.03%, negative predictive value 82.43% | N | N | DKI metrics with whole-tumor volume histogram analysis is associated with KRAS mutation |
2019 | XU | R | 158 | Na | Prediction of KRAS status | M | Mean, Variance, Skewness, Entropy, gray-level nonuniformity, run length nonuniformity | texture features AUC (0.703–0.813); ADC values (AUC 0.682, 95% CI: 0.564–0.801), sensitivity (66.67) and specificity (62.12%) | N | N | Mean values (Mean, Variance, Skewness, Entropy, gray-level nonuniformity, run-length nonuniformity) higher in KRASmt group |
2018 | JO | R | 75 | Na | Prediction of KRAS status | M | Tumor length, ADC, relative contrast enhancement | The higher ratio of axial to LTL in the KRAS-mutant group AUC 0.640 (95% CI, 0.520 to 0.747, p = 0.0292), maximum accuracy of 64% | N | N | Ratio of axial to longitudinal tumor lengths predicted KRAS mutation (accuracy of 64%) |
2018 | XU | R | 51 | Na | Prediction of KRAS status | M | Max-ADC, Min-ADC, Mean-ADC, pure diffusion, perfusion fraction, pseudo-diffusion coefficient | Kras status AUC values of Max-ADC, Min- ADC, Mean-ADC, D, f and D* were 0.695, 0.604, 0.756, 0.701, 0.599 and 0.710 | N | N | Lower Max-ADC, Mean-ADC and D and higher D values observed in the KRAS mutant group |
2018 | Meng | R | 345 | Na | Radiomic model’s prediction of biological characteristics | M | DL model | Model Ki-67 (AUC 0.699 95% CI, 0.611–0.786); HER-2 (AUC 0.696, 95% CI, 0.610–0.782) Ki-67; KRAS-2 (AUC0.651, 95% CI, 0.539–0.763), | Y | N | Radiomic signatures correlated to HER-2, KRAS-2 gene status |
2016 | Shin | R | 275 | All stages | Prediction of KRAS status | M | Axial tumor length, ratio of the axial to the longitudinal tumor dimensions | KRASm tumors- longer axial length, larger ratio of the axial to the longitudinal dimensions. | N | N | KRAS status associated with gross tumor pattern, axial length, ratio of the axial to the longitudinal dimensions of the tumor |
2013 | Hong | R | 29 | Na | correlations between parameters of dynamic contrast-enhanced magnetic resonance imaging and prognostic factors | M | Steepest slope (SLP), time to peak (Tp), relative enhancement during a rapid rise (Erise), maximal enhancement (Emax) | Erise was significantly correlated with N stage, and Tp was significantly correlated with histologic grade | N | N | no significant correlations between DCE-MRI parameters and K-ras mutation, microsatellite instability |
Year | Author | Study | N | Study Population | Aim | Segmentation | Radiomic Features | Main Results | Internal Validation | External Validation | Conclusion |
---|---|---|---|---|---|---|---|---|---|---|---|
2020 | HE | R | 117 + 40 | All | Predictive performance by using residual neural network (ResNet) to estimate the KRAS status | M | 4 features radiomics model | Radiomics model training cohort, AUC 0.945 (sens: 0.75; spec: 0.94); testing cohort, AUC 0.818 (sens: 0.70; spec: 0.85). ResNet model AUC 0.90 testing cohort | Y | N | Better prediction of Kras status by residual neural network than radiomics model |
2020 | CHU | R | 99 + 42 | All | Relationship among prognosis, radiomics features, and gene expression | M | 12 radiomics features | Radiomic model training cohort AUC 0.829 (95% CI: 0.750–0.908) testing cohort AUC 0.727 (95% CI: 0.570–0.884) | Y | N | Radiomics model reflected by CXCL8 combined with tumor stage information predict the prognosis |
2020 | Negreros-Osuna | R | 145 | Stage IV | Prediction of BRAF mutation | M | Standard deviation (SD), the mean value of positive pixels (MPP) | Lower SD 22.31 (95% CI: 20.66, 24.62) and MPP 51.54 (95% CI: 47.14, 58.99) in BRAF mutant tumors | N | N | Radiomics texture features predictors of BRAF mutation status and 5-year OS |
2020 | González-Castro | R | 47 | All | Prediction of KRAS status | M | Radiomic model (second-order features) | Neural Networks model sens of 88.9%, spec 75.0%, accuracy of 83% | Y | N | Prediction of KRAS status in CRC |
2020 | Dercle | R | 667 | Stage IV (CRYSTAL trial (NCT00154102)) | Predict tumor sensitiveness to FOLFIRI ± cetuximab. | M | 4 features radiomics model | AUC 0.80 (95% CI: 0.69–0.94) sens 0.80, and spec 0.78); p < 0.001) | Y | N | Performance of the signature outperformed both KRAS-mutational status at baseline |
2019 | Pernicka | R | 139 + 59 | Stage II and III | Prediction of (MSI) status | M | 40 radiomic features | AUC of 0.80 for the training set and 0.79 for the test set (spec = 96.8% and 92.5%, respectively) | Y | N | The combined model performed slightly better than the other models |
2019 | Taguchi | R | 40 | Stage II-IV | Prediction of KRAS status | M | 14 CT radiomics et SUV max | Multivariate support vector machine CT radiomics model AUC of 0.82 superior compared to the SUVmax. | N | N | CT texture analysis was superior to the SUVmax for predicting the KRAS mutation status |
2019 | Wu | R | 102 | Na | Prediction of (MSI) status | M | 6 radiomics features | Training set AUC 0.961 (accuracy: 0.875; sens: 1.000; spec: 0.812); testing set AUC of 0.875 (accuracy: 0.788; sens: 0.909; spec: 0.727) | Y | N | Radiomics analysis of iodine-based material decomposition predict MSI status |
2019 | Fan | R | 119 | Stage II | Prediction of (MSI) status | Semiautomatic | 6 radiomics features | Radiomic model AUC = 0.688; accuracy = 0.713; sens = 0.517; spec = 0.858; clinical model 0.598 AUC value, 0.632accuracy, 0.371 sens, and 0.825 spec; combined model AUC 0.752 (accuracy = 0.765; sens = 0.663; spec = 0.842). | N | N | Better detection of MSI status with combined clinical and radiomics feature model than clinical/radiomics alone |
2019 | Badic | R | 64 | All | Prognostic value of gene expression and radiomics | M | Shape, second and third order texture features | PFS Cox model combining Stage 3, ABCC2 and EntropyGLMC HR 22.8 95% CI 3.7 to 141 p < 0.0001 OS Cox model with Ratio and ALDH1A HR 8.4 95% CI 3.4 to 20.6 p = 0.0005 | N | N | Model combining CE-CT radiomics, gene expression, histopathological examination could provide higher prognostic stratification power |
2018 | YANG | R | 61 + 56 | All | Predict KRAS/NRAS/BRAF mutations | M | 3 radiomics features | Testing cohort AUC 0.869, sens 0.757, and spec 0.833; Validation cohort AUC 0.829, sens 0.686, spec 0.857 | Y | N | Prediction of KRAS/NRAS/BRAF mutations |
2015 | Lubner | R | 77 | Stage IV | CT texture features relate to pathologic features and clinical outcomes | M | First class radiomics | Skewness was negatively associated KRAS mutation (p = 0.02). | N | N | MPP, SD, correlates overall survival |
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Badic, B.; Tixier, F.; Cheze Le Rest, C.; Hatt, M.; Visvikis, D. Radiogenomics in Colorectal Cancer. Cancers 2021, 13, 973. https://doi.org/10.3390/cancers13050973
Badic B, Tixier F, Cheze Le Rest C, Hatt M, Visvikis D. Radiogenomics in Colorectal Cancer. Cancers. 2021; 13(5):973. https://doi.org/10.3390/cancers13050973
Chicago/Turabian StyleBadic, Bogdan, Florent Tixier, Catherine Cheze Le Rest, Mathieu Hatt, and Dimitris Visvikis. 2021. "Radiogenomics in Colorectal Cancer" Cancers 13, no. 5: 973. https://doi.org/10.3390/cancers13050973