Intravoxel Incoherent Motion of Colon Cancer Liver Metastases for the Assessment of Response to Antiangiogenic Treatment: Results from a Pilot Study

MRI Apparent Diffusion Coefficient (ADC) as a Biomarker of Tumour Response: Imaging-Pathology Correlation in Patients with Hepatic Metastases from Colorectal Cancer (EORTC 1423)

Background: Tumour apparent diffusion coefficient (ADC) from diffusion-weighted magnetic resonance imaging (MRI) is a putative pharmacodynamic/response biomarker but the relationship between drug-induced effects on the ADC and on the underlying pathology has not been adequately defined. Hypothesis: Changes in ADC during early chemotherapy reflect underlying histological markers of tumour response as measured by tumour regression grade (TRG). Methods: Twenty-six patients were enrolled in the study. Baseline, 14 days, and pre-surgery MRI were performed per study protocol. Surgical resection was performed in 23 of the enrolled patients; imaging-pathological correlation was obtained from 39 lesions from 21 patients. Results: There was no evidence of correlation between TRG and ADC changes at day 14 (study primary endpoint), and no significant correlation with other ADC metrics. In scans acquired one week prior to surgery, there was no significant correlation between ADC metrics and percentage of viable tumour, percentage necrosis, percentage fibrosis, or Ki67 index. Conclusions: Our hypothesis was not supported by the data. The lack of meaningful correlation between change in ADC and TRG is a robust finding which is not explained by variability or small sample size. Change in ADC is not a proxy for TRG in metastatic colorectal cancer.

Automatic segmentation of hepatic metastases on DWI images based on a deep learning method: assessment of tumor treatment response according to the RECIST 1.1 criteria

BACKGROUND

Evaluation of treated tumors according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria is an important but time-consuming task in medical imaging. Deep learning methods are expected to automate the evaluation process and improve the efficiency of imaging interpretation.

OBJECTIVE

To develop an automated algorithm for segmentation of liver metastases based on a deep learning method and assess its efficacy for treatment response assessment according to the RECIST 1.1 criteria.

METHODS

One hundred and sixteen treated patients with clinically confirmed liver metastases were enrolled. All patients had baseline and post-treatment MR images. They were divided into an initial (n = 86) and validation cohort (n = 30) according to the examined time. The metastatic foci on DWI images were annotated by two researchers in consensus. Then the treatment responses were assessed by the two researchers according to RECIST 1.1 criteria. A 3D U-Net algorithm was trained for automated liver metastases segmentation using the initial cohort. Based on the segmentation of liver metastases, the treatment response was assessed automatically with a rule-based program according to the RECIST 1.1 criteria. The segmentation performance was evaluated using the Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD). The area under the curve (AUC) and Kappa statistics were used to assess the accuracy and consistency of the treatment response assessment by the deep learning model and compared with two radiologists [attending radiologist (R1) and fellow radiologist (R2)] in the validation cohort.

RESULTS

In the validation cohort, the mean DSC, VS, and HD were 0.85 ± 0.08, 0.89 ± 0.09, and 25.53 ± 12.11 mm for the liver metastases segmentation. The accuracies of R1, R2 and automated segmentation-based assessment were 0.77, 0.65, and 0.74, respectively, and the AUC values were 0.81, 0.73, and 0.83, respectively. The consistency of treatment response assessment based on automated segmentation and manual annotation was moderate [K value: 0.60 (0.34-0.84)].

CONCLUSION

The deep learning-based liver metastases segmentation was capable of evaluating treatment response according to RECIST 1.1 criteria, with comparable results to the junior radiologist and superior to that of the fellow radiologist.

Intravoxel incoherent motion to assess brain microstructure and perfusion in patients with end-stage renal disease

BACKGROUND AND PURPOSE

This study aimed to investigate the clinical value of intravoxel incoherent motion (IVIM) diffusion-weighted imaging in evaluating the brain microstructure and perfusion changes in end-stage renal disease (ESRD) patients.

METHODS

The routine head MRI sequences and IVIM were performed on 40 ESRD patients and 30 healthy subjects. The IVIM was executed with 10 b-values varying from 0 to 1000 seconds/mm² . All subjects were evaluated on neuropsychological test. Laboratory tests were conducted for ESRD patients.

RESULTS

Compared with the control group, increased slow apparent diffusion coefficient values (ADC_slow ) were found in the left frontal lobe, hippocampus, bilateral temporal lobe, and the right occipital lobe (p < .05), and increased fast ADC values (ADC_fast ) were found in all regions of interest (all p < .001) in ESRD patients. In ESRD patients, ADC_fast in right frontal lobe (p = .041) and insular lobe (p = .045) was negatively correlated with the Montreal Cognitive Assessment score (MoCA), and ADC_fast in the right parietal lobe (p = .009) and hippocampus (p = .041) had positive correlation with hemoglobin levels. Using receiver operating characteristics (ROC) analysis, ADC_fast in the right frontal lobe, insular lobe, hippocampus, and parietal lobe separately showed fair to good efficacy in differentiating ESRD patients from healthy subjects, with the area under the ROC ranging from .853 to .903.

CONCLUSIONS

The microstructure and perfusion of the brain were impaired in ESRD patients. ADC_fast of the right frontal lobe, insular lobe, hippocampus, and parietal lobe could be effective biomarker for evaluating cognitive impairment in ESRD patients.

b value and first-order motion moment optimized data acquisition for repeatable quantitative intravoxel incoherent motion DWI

PURPOSE

To design a b value and first-order motion moment (M₁ ) optimized data acquisition for repeatable intravoxel incoherent motion (IVIM) quantification in the liver.

METHODS

Cramer-Rao lower bound optimization was performed to determine optimal monopolar and optimal 2D samplings of the b-M₁ space based on noise performance. Monte Carlo simulations were used to evaluate the bias and variability in estimates obtained using the proposed optimal samplings and conventional monopolar sampling. Diffusion MRI of the liver was performed in 10 volunteers using 3 IVIM acquisitions: conventional monopolar, optimized monopolar, and b-M₁ -optimized gradient waveforms (designed based on the optimal 2D sampling). IVIM parameter maps of diffusion coefficient, perfusion fraction, and blood velocity SD were obtained using nonlinear least squares fitting. Noise performance (SDs), stability (outlier percentage), and test-retest or scan-rescan repeatability (intraclass correlation coefficients) were evaluated and compared across acquisitions.

RESULTS

Cramer-Rao lower bound and Monte Carlo simulations demonstrated improved noise performance of the optimal 2D sampling in comparison to monopolar samplings. Evaluating the designed b-M₁ -optimized waveforms in healthy volunteers, significant decreases (p < 0.05) in the SDs and outlier percentages were observed for measurements of diffusion coefficient, perfusion fraction, and blood velocity SD in comparison to measurements obtained using monopolar samplings. Good-to-excellent repeatability (intraclass correlation coefficients ≥ 0.77) was observed for all 3 parameters in both the right and left liver lobes using the b-M₁ -optimized waveforms.

CONCLUSIONS

2D b-M₁ -optimized data acquisition enables repeatable IVIM quantification with improved noise performance. 2D acquisitions may advance the establishment of IVIM quantitative biomarkers for liver diseases.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Predicting and Monitoring the Response of Anti-Angiogenic Treatment in the Orthotopic Nude Mouse Model of Lung Adenocarcinoma

BACKGROUND

The treatment efficacy of angiogenesis inhibitor could be underestimated at an early stage based on tumor volume changes. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) can quantitatively assess tumors at the cellular level, but it is unclear whether it can provide useful information for assessing treatment response of anti-angiogenic treatment for lung adenocarcinoma.

PURPOSE

To determine the use of IVIM-DWI for non-invasive monitoring of the early response to anti-angiogenic treatment in the orthotopic transplantation of lung adenocarcinoma model.

STUDY TYPE

Prospective.

POPULATION

Thirty-seven nude mice were randomized into two groups: treatment group (received bevacizumab + cisplatin, N = 20) and control group (received saline, N = 17).

FIELD STRENGTH/SEQUENCE

Single-shot turbo spin-echo (TSE) IVIM-DWI, TSE T2-weighted imaging at 3.0 T.

ASSESSMENT

Tumor volume, IVIM parameters (apparent diffusion coefficient [ADC], diffusivity [D], perfusion fraction [f], and pseudo-diffusion coefficient [D*]) were measured before and 2 hours, 3, 7, 10 and 14 days after treatment. Regions of interest were manually drawn along the inner edge of the tumor by two radiologists with 5 and 10-year experience in magnetic resonance imaging. Pathological examinations (hematoxylin and eosin stain, cluster of differentiation 34) were performed.

STATISTICAL TESTS

Kolmogorov-Smirnov test, repeated-measure two-way analysis of variance test, Mann-Whitney U test, Pearson correlation analysis, receiver operating characteristic curve. P < 0.05 was considered statistically significant.

RESULTS

The tumor volume of the two groups was significantly different only on day 14 (control group vs. treatment group, 43.15 ± 18.28 mm³ vs. 28.41 ± 1.71 mm³ ). ADC_2h , ADC_10d , D_2h , D_7d , D_10d , and D_14d were significantly higher, while f_10d and f_14d were significantly lower in the treatment group compared to those of the control group. Both the △ADC_2h (r = -0.631) and △D_2h (r = -0.700) showed moderate correlations with the relative tumor volume on day 14.

DATA CONCLUSION

IVIM has the potential to predict and monitor the early response to anti-angiogenic treatment, earlier than size changes, for lung adenocarcinoma.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 4.