Prediction of KRAS Mutation in Rectal Cancer Using MRI

Shin, Yu Ri; Kim, Kyung-Ah; Im, Soyoung; Hwang, Seong Su; Kim, Ki Jun

doi:10.21873/anticanres.11039

Cited by 35 publications

(46 citation statements)

References 15 publications

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“…More robust machine learning classifiers have also identified radiomic signatures capable of predicting KRAS (AUC = 0.829), NRAS (AUC = 0.686), and BRAF mutations (AUC = 0.857) in a cohort of 117 CRC patients [171]. Magnetic resonance imaging has also been analyzed where it was observed that, in rectal cancer, KRAS mutations were associated with N stage, axial tumor length, and a polypoid pattern [172]. DCE MR parameters, however, were not associated with either KRAS mutation or microsatellite instability, an important prognostic and predictive biomarker for colorectal cancer [173].…”

Section: Colorectal Carcinomamentioning

confidence: 99%

Radiogenomics: bridging imaging and genomics

et al. 2019

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From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.

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Section: Colorectal Carcinomamentioning

confidence: 99%

Radiogenomics: bridging imaging and genomics

et al. 2019

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“…From1972 to 2005, the rates of rectal cancer have increased from 7.68 and 6.51 to 11.45 and 8.28 per 100,000 in males and females, respectively [4]. Nevertheless, with the improvement of therapeutic regimen, deceasing rectal cancer mortality have been observed [5]. Anti-EGFR (epidermal growth factor receptor) drug is proven effective to rectal cancer.…”

Section: Introductionmentioning

confidence: 99%

“…In our study, CT parameters, especially SRV, have been assessed to predict KRAS mutation. Lymph node enlargement (LNE), distant metastasis, enhanced pattern, CT ratio and the length of tumor were supposed to imply the different invasive behaviors of rectal carcinoma [5]. And the correlation between pathological ndings and KRAS status has also been evaluated.…”

Section: Introductionmentioning

confidence: 99%

Diameter of Superior Rectal Vein – CT Predictor of KRAS mutation in Rectal Carcinoma

Song

Shen

Dong

et al. 2020

Preprint

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Background The purpose of this study was to investigate the feasibility of CT parameters to predict the presence of KRAS mutation in rectal cancer patients. The relationship between KRAS mutation and pathological findings was evaluated simultaneously.Methods 89 patients (29 females, 60 males, age 27–90, mean 59.7 ± 12 years) with pathologically proven rectal cancer were enrolled. KRAS mutation test was completed following surgery. Parameters evaluated on CT included the diameter of superior rectal vein (SRV) and inferior mesenteric vein (IMV), the presence of calcification, ulceration, lymph node enlargement (LNE), distant metastasis, tumor growth pattern (single nodule or annular thickening), enhanced pattern (homogeneous or heterogeneous), CT ratio and the length of tumor (LOT). Pathological findings included lymphovascular emboli, signet ring cell, peripheral fat interval infiltration, focal ulcer, lymph node metastasis, tumor pathological type and differentiation extent. The correlations between KRAS status and CT parameters, KRAS status and pathological findings were investigated. The accuracy of CT characteristics for predicting KRAS mutation was evaluated.Results KRAS mutation was detected in 42 cases. On CT image, the diameter of SRV was significantly increased in KRAS mutation group than in KRAS wild-type group (4.6 ± 0.9 mm vs. 4.2 ± 0.9 mm, p = 0.02). And LNE was more likely to occur in KRAS mutation group (73.3% vs. 26.7%, p = 0.03). There was no significant difference between KRAS mutation group and KRAS wild-type group on the other CT parameters (IMV, calcification, ulcer, distant metastasis, tumor growth pattern, enhanced pattern, CT ratio and LOT). In pathological findings, KRAS mutation was more likely to occur in middle differentiation group (p = 0.03). No significant difference was found between KRAS mutation group and KRAS wild-type group in the presence of lymphovascular emboli, signet ring cell, peripheral fat interval infiltration, focal ulcer, lymph node metastasis, tumor pathological type. The AUC of SRV to predict KRAS mutation was 0.63.Conclusion It was feasible to use the diameter of SRV to predict KRAS mutation in rectal cancer patients, and LNE also can be regarded as an important clue on preoperative CT images.

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“…Several previous studies have mainly focused on the relationship between the tumor metabolism evaluated by positron emission tomography/ computed tomography with 18F‐fluorodeoxyglucose (18F‐FDG PET/CT) and genetic mutations in CRC or rectal cancer; however, with conflicting results . Magnetic resonance imaging (MRI) based on morphologic features and quantitative parameters derived by using diffusion‐weighted imaging (DWI) may help predict mutation status of various tumors . However, conventional DWI using a monoexponential model has the limitation of only assessing the Gaussian water diffusion behavior.…”

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confidence: 99%

“…[12][13][14][15][16] Magnetic resonance imaging (MRI) based on morphologic features and quantitative parameters derived by using diffusion-weighted imaging (DWI) may help predict mutation status of various tumors. [17][18][19][20] However, conventional DWI using a monoexponential model has the limitation of only assessing the Gaussian water diffusion behavior. Diffusion kurtosis imaging (DKI), an advanced non-Gaussian DWI model, can provide more precise information in terms of the heterogeneity and irregularity of tissue components.…”

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confidence: 99%

Diffusion kurtosis imaging‐derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings

Cui

Yang

et al. 2019

Magnetic Resonance Imaging

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Background Although histological examination is the standard method for assessing genetic status, the development of a noninvasive method, which can display the heterogeneity of the whole tumor to supplement genotype analysis, might be important for personalized treatment strategies. Purpose To evaluate the potential role of diffusion kurtosis imaging (DKI)‐derived parameters using histogram analysis derived from whole‐tumor volumes for prediction of the status of KRAS mutations in patients with rectal adenocarcinoma. Study Type Retrospective. Subjects In all, 148 consecutive patients with histologically confirmed rectal adenocarcinoma who were treated at our institution. Sequence DKI was performed with a 3.0 T MRI system using a single‐shot echo‐planar imaging sequence with b values of 0, 700, 1400, and 2100 sec/mm2. Assessment D, K, and apparent diffusion coefficient (ADC) values were measured using whole‐tumor volume histogram analysis and were compared between different KRAS mutations status. Statistical Tests Student's t‐test or Mann–Whitney U‐test, and receiver operating characteristic (ROC) curves were used for statistical analysis. Results All the percentile metrics of ADC and D values were significantly lower in the mutated group than those in the wildtype group (all P < 0.05), except for the minimum value of ADC and D (both P > 0.05), while K‐related percentile metrics were higher in the mutated group compared with those in the wildtype group (all P < 0.05). Regarding the comparison of the diagnostic performance of all the histogram metrics, K75th showed the highest AUC value of 0.871, and the corresponding values for sensitivity, specificity, positive predictive value, and negative predictive value were 81.43%, 78.21%, 77.03%, and 82.43%, respectively. Data Conclusion DKI metrics with whole‐tumor volume histogram analysis is associated with KRAS mutations, and thus may be useful for predicting the KRAS status of rectal cancers for guiding targeted therapy. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:930–939.

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Prediction of KRAS Mutation in Rectal Cancer Using MRI

Abstract: KRAS mutations were associated with N stage, a polypoid pattern, axial tumor length, and the ratio of the axial to the longitudinal dimensions of the tumor.

Cited by 35 publications

References 15 publications

Radiogenomics: bridging imaging and genomics

Radiogenomics: bridging imaging and genomics

Diameter of Superior Rectal Vein – CT Predictor of KRAS mutation in Rectal Carcinoma

Diffusion kurtosis imaging‐derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings

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