Effectiveness of simultaneous multislice accelerated readout-segmented echo planar imaging for the assessment of rectal cancer.

PURPOSE: To investigate the effectiveness of simultaneous multislice (SMS) accelerated readout-segmented echo planar imaging (RESOLVE) DWI for assessing rectal cancer in the clinic. METHOD: Sixty consecutive histologically proven rectal cancer patients were enrolled. They all received MRI examinations, including both SMS-RESOLVE and RESOLVE sequences. Two readers visually assessed the overall image quality, distinction of anatomical structures, lesion conspicuity, and artifacts of two sequences by using a qualitative 4-point Likert scale. The quantitative ADC value, lesion contrast, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and temporal SNR (tSNR) were independently calculated in rectal cancer on the largest slice of the tumor. RESULTS: The scan time was shortened from 3 min and 50 s to 1 min and 47 s. The interobserver agreement of visual and quantitative assessments between the two readers was good overall. There were no differences in overall image quality, lesion conspicuity or artifact scores between the two sequences in both readers (all p > 0.05). The lesion contrast (p = 0.013) was significantly higher in SMS-RESOLVE, and the CNR was similar in the two DWIs. The scores of distinctions of anatomical structures in SMS-RESOLVE were lower (all p < 0.05) in both readers. The SNR of SMS-RESOLVE was lower than that of RESOLVE (p = 0.004), and the tSNR of SMS-RESOLVE was significantly higher (p < 0.001). The ADC value of the tumor was lower in SMS-RESOLVE (p = 0.001), but the ADC values of the normal rectal wall showed no difference between the two DWIs. CONCLUSION: SMS-RESOLVE allowed a substantial reduction in acquisition time while maintaining overall image quality and lesion conspicuity in rectal cancer. It also had a higher contrast of the lesion and a higher temporal SNR.

[Application of MRI-based Radiomics Models in the Assessment of Hepatic Metastasis of Rectal Cancer].

OBEJECTIVE: To explore the clinical value of using radiomics models based on different MRI sequences in the assessment of hepatic metastasis of rectal cancer. METHODS: 140 patients with pathologically confirm edrectal cancer were included in the study. They underwent baseline magnetic resonance imaging (MRI) between April 2015 and May 2018 before receiving any treatment. According to the results of liver biopsy, surgical pathology, and imaging, patients were put into two groups, the patients with hepatic metastasis and those without. T2 weighted images (T2WI), diffusion weighted images (DWI) and apparent diffusion coefficient (ADC) images were used to draw the region of interest (ROI) of primary lesions on consecutive slices on ITK-SNAP. 3-D ROIs were generated and loaded into Artificial Intelligent Kit for extraction of radiomics features and 396 features were extracted for each sequence. The feature data were preprocessed on Python and the samples were oversampled, using Support Vector Machine-Synthetic Minority Over-Sampling Technique (SVM-SMOTE) to balance the number of samples in the group with liver metastasis and the group with no liver metastasis at the end of the follow-up. Then, the samples were divided into the training cohort and the test cohort at a ratio of 2∶1. The logistic regression models were developed with selected radionomic features on R software. The receiver operating characteristics (ROC) curves and calibration curves were used to evaluate the performance of the models. RESULTS: In total, 52 patients with liver metastasis and 88 patients without liver metastasis at the end of follow-up were enrolled. Carcinoembryonic antigen (CEA) and T stage and N stage evaluated on the MRI images showed statistically significant difference between the two groups ( P<0.05). After data preprocessing and selecting, except for 17 non-radiomic features, the model combining T2WI, DWI and ADC features, the model of T2WI features alone, the model of DWI features alone and the model of ADC features alone were developed with 32 features, 10 features, 30 features and 15 features, respectively. The combined model (T2WI+DWI+ADC), the T2WI model, and the ADC model can assess hepatic metastasis accurately, with the area under curve ( AUC) on the train set reaching 93.5%, 89.2%, 90.6% and that of the test set reaching 80.8%, 80.5%, 81.4%, respectively. The combined model did not show a higher AUC than those of the T2WI and ADC alone models. Model based on DWI features has a slightly insufficient AUC of 90.3% in the train set and 75.1% in the test set. The calibration curve showed the smallest fluctuation in the combined model, which is closest fit to the diagonal reference line. The fluctuation in the three independent data set models were similar. The calibration curves of all the four models showed that as the risk increased, the prediction of the models turned from an underestimation to an overestimating the risk. In brief, the combined model showed the best performance, with the best fit to the diagonal reference line in calibration curve and high AUC comparable to the AUC of the T2WI model and ADC model. The performance of T2WI and ADC alone models were second to that of the combined model, while the DWI alone model showed relatively poor performance. CONCLUSION: Radiomics models based on MRI could be effectively used in assessing liver metastasis in rectal cancer, which may help determine clinical staging and treatment.