Could IVIM and ADC help in predicting the KRAS status in patients with rectal cancer?

Xu, Yanyan; Xu, Qiaoyu; Sun, Hongzan; Liu, Tongxi; Shi, Kun; Wu, Wei

doi:10.1007/s00330-018-5329-y

Cited by 43 publications

(29 citation statements)

References 27 publications

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“…The 75th percentile pixel value of K showed the highest AUC of 0.871 and the sensitivity, specificity, positive predictive value, and negative predictive value were 81.43%, 78.21%, 77.03%, and 82.43%, respectively [31]. Similarly, Xu et al [32] showed that diffusion-weighted MR imaging-derived para-meters can predict KRAS mutation in rectal cancer. They found that the mean apparent diffusion coefficient and D* (pseudo-diffusion coefficient) had moderate diagnostic significance, with respective AUCs of 0.756 and 0.710 [32].…”

Section: Discussionmentioning

confidence: 90%

“…Similarly, Xu et al [32] showed that diffusion-weighted MR imaging-derived para-meters can predict KRAS mutation in rectal cancer. They found that the mean apparent diffusion coefficient and D* (pseudo-diffusion coefficient) had moderate diagnostic significance, with respective AUCs of 0.756 and 0.710 [32]. Yeo et al [33] reported that the perfusion parameters from dynamic contrastenhanced MR imaging were significantly associated with KRAS mutation.…”

Section: Discussionmentioning

confidence: 93%

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Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer

Kim

Lee

et al. 2020

Cancer Res Treat

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Original ArticlePurpose Mutation of the Kirsten Ras (KRAS) oncogene is present in 30%-40% of colorectal cancers and has prognostic significance in rectal cancer. In this study, we examined the ability of radiomics features extracted from T2-weighted magnetic resonance (MR) images to differentiate between tumors with mutant KRAS and wild-type KRAS. Materials and MethodsSixty patients with primary rectal cancer (25 with mutant KRAS, 35 with wild-type KRAS) were retrospectively enrolled. Texture analysis was performed in all regions of interest on MR images, which were manually segmented by two independent radiologists. We identified potentially useful imaging features using the two-tailed t test and used them to build a discriminant model with a decision tree to estimate whether KRAS mutation had occurred. ResultsThree radiomic features were significantly associated with KRAS mutational status (p < 0.05). The mean (and standard deviation) skewness with gradient filter value was significantly higher in the mutant KRAS group than in the wild-type group (2.04±0.94 vs. 1.59±0.69). Higher standard deviations for medium texture (SSF3 and SSF4) were able to differentiate mutant KRAS (139.81±44.19 and 267.12±89.75, respectively) and wild-type KRAS (114.55±29.30 and 224.78±62.20). The final decision tree comprised three decision nodes and four terminal nodes, two of which designated KRAS mutation. The sensitivity, specificity, and accuracy of the decision tree was 84%, 80%, and 81.7%, respectively. ConclusionUsing MR-based texture analysis, we identified three imaging features that could differentiate mutant from wild-type KRAS. T2-weighted images could be used to predict KRAS mutation status preoperatively in patients with rectal cancer.

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 93%

Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer

Kim

Lee

et al. 2020

Cancer Res Treat

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“…In medical imaging aspects, MSCT, MRI, and 18F-FDG-PET/CT are all important examinations for us to have further insight into the role of KRAS mutation in rectal carcinoma. Positive results have already been seen on MRI [5,12,21] and 18F-FDG-PET/CT [11,19] for predicting KRAS mutation in colorectal carcinoma. However, CT predictors of KRAS mutation in rectal carcinoma had not been described previously.…”

Section: Discussionmentioning

confidence: 92%

“…Kenji Kawada et al [10,20], found 18 F-FDG accumulation into metastatic CRC assessed by SUVmax (the maximum standardized uptake value for the primary tumor) was associated with KRAS status. As for MRI, Yanyan Xu et al [21], found that lower mean-ADC (apparent diffusion coe cient) and higher D* (pseudodiffusion coe cient) value on MRI with KRAS mutation in rectal carcinoma. And the study of YU RI SHIN et al [5], showed that KRAS mutation was associated with N stage, gross tumor pattern, axial length of the tumor, and the ratio of the axial to the longitudinal dimensions of the tumor on MRI.…”

Section: Discussionmentioning

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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“…31,32 All these results indicated that histogram analysis can provide more information regarding intratumoral heterogeneity. Second, compared with previous studies that only focused on the exon 2 (codons 12 and 13) of KRAS, 12,14,33 we also investigated the presence of KRAS mutations on codons 61, 117, and 146, because these mutations have the same tumorigenic effects and can affect the response to anti-EGFR targeted therapy.…”

Section: Discussionmentioning

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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Could IVIM and ADC help in predicting the KRAS status in patients with rectal cancer?

Cited by 43 publications

References 27 publications

Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer

Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer

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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