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

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.

Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

OBJECTIVES: To develop and validate a radiomics predictive model based on pre-treatment multiparameter magnetic resonance imaging (MRI) features and clinical features to predict a pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT). METHODS: One hundred and eighty-six consecutive patients with LARC (training dataset, n = 131; validation dataset, n = 55) were enrolled in our retrospective study. A total of 1,188 imaging features were extracted from pre-CRT T2-weighted (T2-w), contrast-enhanced T1-weighted (cT1-w) and ADC images for each patient. Three steps including least absolute shrinkage and selection operator (LASSO) regression were performed to select key features and build a radiomics signature. Combining clinical risk factors, a radiomics nomogram was constructed. The predictive performance was evaluated by receiver operator characteristic (ROC) curve analysis, and then assessed with respect to its calibration, discrimination and clinical usefulness. RESULTS: Thirty-one of 186 patients (16.7%) achieved pCR. The radiomics signature derived from joint T2-w, ADC, and cT1-w images, comprising 12 selected features, was significantly associated with pCR status and showed better predictive performance than signatures derived from either of them alone in both datasets. The radiomics nomogram, incorporating the radiomics signature and MR-reported T-stages, also showed good discrimination, with areas under the ROC curves (AUCs) of 0.948 (95% CI, 0.907-0.989) and 0.966 (95% CI, 0.924-1.000), as well as good calibration in both datasets. Decision curve analysis confirmed its clinical usefulness. CONCLUSIONS: This study demonstrated that the pre-treatment radiomics nomogram can predict pCR in patients with LARC and potentially guide treatments to select patients for a "wait-and-see" policy. KEY POINTS: • Radiomics analysis of pre-CRT multiparameter MR images could predict pCR in patients with LARC. • Proposed radiomics signature from joint T2-w, ADC and cT1-w images showed better predictive performance than individual signatures. • Most of the clinical characteristics were unable to predict pCR.

Whole-tumour diffusion kurtosis MR imaging histogram analysis of rectal adenocarcinoma: Correlation with clinical pathologic prognostic factors.

OBJECTIVE: To investigate potential relationships between diffusion kurtosis imaging (DKI)-derived parameters using whole-tumour volume histogram analysis and clinicopathological prognostic factors in patients with rectal adenocarcinoma. MATERIAL AND METHODS: 79 consecutive patients who underwent MRI examination with rectal adenocarcinoma were retrospectively evaluated. Parameters D, K and conventional ADC were measured using whole-tumour volume histogram analysis. Student's t-test or Mann-Whitney U-test, receiver operating characteristic curves and Spearman's correlation were used for statistical analysis. RESULTS: Almost all the percentile metrics of K were correlated positively with nodal involvement, higher histological grades, the presence of lymphangiovascular invasion (LVI) and circumferential margin (CRM) (p<0.05), with the exception of between K10th, K90th and histological grades. In contrast, significant negative correlations were observed between 25th, 50th percentiles and mean values of ADC and D, as well as ADC10th, with tumour T stages (p< 0.05). Meanwhile, lower 75th and 90th percentiles of ADC and D values were also correlated inversely with nodal involvement (p< 0.05). Kmean showed a relatively higher area under the curve (AUC) and higher specificity than other percentiles for differentiation of lesions with nodal involvement. CONCLUSION: DKI metrics with whole-tumour volume histogram analysis, especially K parameters, were associated with important prognostic factors of rectal cancer. KEY POINTS: • K correlated positively with some important prognostic factors of rectal cancer. • K mean showed higher AUC and specificity for differentiation of nodal involvement. • DKI metrics with whole-tumour volume histogram analysis depicted tumour heterogeneity.