Optimization of workflow for detection of brain metastases at 3T: is a black-blood MTC prepared 3D T1 used alone robust enough to replace the combination of conventional 3D T1 and the black-blood 3D T1 MTC?

PURPOSE: Sampling perfection with application-optimized contrasts by using different flip angle evolutions (SPACE) is a black-blood 3D T1-weighted (T1w) magnetic resonance imaging (MRI) sequence that has shown robust performance for brain metastases detection. However, this could generate false positive results due to suboptimal blood signal suppression. For that reason, SPACE is used in our institution alongside a non-black-blood T1w sequence: volumetric interpolated breath-hold examination (VIBE). Our study aims to (i) evaluate the diagnostic accuracy of SPACE compared to its use in combination with VIBE, (ii) investigate the effect of radiologist's experience in the sequence's performance, and (iii) analyze causes of discordants results. METHODS: Four hundred seventy-three 3T MRI scans were retrospectively analyzed following a monocentric study design. Two studies were formed: one including SPACE alone and one combining both sequences (SPACE + VIBE, the reference). An experienced neuroradiologist and a radiology trainee independently reviewed the images of each study and reported the number of brain metastases. The sensitivity (Se) and specificity (Sp) of SPACE compared to SPACE + VIBE in metastases detection were reported. Diagnostic accuracy of SPACE compared to SPACE + VIBE was assessed by using McNemar's test. Significance was set at p < 0.05. Cohen's kappa was used for inter-method and inter-observer variability. RESULTS: No significant difference was found between the two methods, with SPACE having a Se > 93% and a Sp > 87%. No effect of readers' experience was disclosed. CONCLUSION: Independently of radiologist's experience, SPACE alone is robust enough to replace SPACE + VIBE for brain metastases detection.

Pre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation.

PURPOSE: The aim of this study was to perform a quantitative quality assurance of diffusion-weighted MRI to assess the variability of the mean apparent diffusion coefficient (ADC) and other radiomic features across the scanners involved in the REGINA trial. MATERIALS AND METHODS: The NIST/QIBA diffusion phantom was acquired on six 3 T scanners from five centres with a rectum-specific diffusion protocol. All sequences were repeated in each scan session without moving the phantom from the table. Linear interpolation to two isotropic voxel spacing (0.9 and 4 mm) was performed as well as the ComBat feature harmonisation method between scanners. The absolute accuracy error was evaluated for the mean ADC. Repeatability and reproducibility within-subject coefficients of variation (wCV) were computed for 142 radiomic features. RESULTS: For the mean ADC, accuracy error ranged between 0.1 % and 8.5 %, repeatability was <1 % and reproducibility was <3 % for diffusivity range between 0.4 and 1.1x10-3mm2/s. For the other radiomic features, wCV was below 10 % for 24 % and 15 % features for repeatability with resampling 0.9 mm and 4 mm, respectively, and 13 % and 11 % feature for reproducibility. ComBat method could improve significantly the wCV compared to reproducibility without ComBat (p-value < 0.001) but variation was still high for most of the features. CONCLUSION: Our study provided the first investigation of feature selection for development of robust predictive models in the REGINA trial, demonstrating the added value of such a quality assurance process to select conventional and radiomic features in prospective multicentre trials.

Radiomics software comparison using digital phantom and patient data: IBSI-compliance does not guarantee concordance of feature values.

INTRODUCTION: Radiomics is a promising imaging-based tool which could enhance clinical observation and identify representative features. To avoid different interpretations, the Image Biomarker Standardisation Initiative (IBSI) imposed conditions for harmonisation. This study evaluates IBSI-compliant radiomics applications against a known benchmark and clinical datasets for agreements. MATERIALS AND METHODS: The three radiomics platforms compared were RadiomiX Research Toolbox, LIFEx v7.0.0, and syngo.via Frontier Radiomics v1.2.5 (based on PyRadiomics v2.1). Basic assessment included comparing feature names and their formulas. The IBSI digital phantom was used for evaluation against reference values. For agreement evaluation (including same software but different versions), two clinical datasets were used: 27 contrast-enhanced computed tomography (CECT) of colorectal liver metastases and 39 magnetic resonance imaging (MRI) of breast cancer, including intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI. The intraclass correlation coefficient (ICC, lower 95% confidence interval) was used, with 0.9 as the threshold for excellent agreement. RESULTS: The three radiomics applications share 41 (3 shape, 8 intensity, 30 texture) out of 172, 84 and 110 features for RadiomiX, LIFEx and syngo.via, respectively, as well as wavelet filtering. The naming convention is, however, different between them. Syngo.via had excellent agreement with the IBSI benchmark, while LIFEx and RadiomiX showed slightly worse agreement. Excellent reproducibility was achieved for shape features only, while intensity and texture features varied considerably with the imaging type. For intensity, excellent agreement ranged from 46% for the DCE maps to 100% for CECT, while this lowered to 44% and 73% for texture features, respectively. Wavelet features produced the greatest variation between applications, with an excellent agreement for only 3% to 11% features. CONCLUSION: Even with IBSI-compliance, the reproducibility of features between radiomics applications is not guaranteed. To evaluate variation, quality assurance of radiomics applications should be performed and repeated when updating to a new version or adding a new modality.

Assessment of response to chemotherapy in pancreatic ductal adenocarcinoma: Comparison between diffusion-weighted MR quantitative parameters and RECIST.

PURPOSE: To prospectively assess chemotherapy-induced changes in pancreatic ductal adenocarcinoma (PDA) with diffusion-weighted (DW)-MR quantitative metrics, including apparent diffusion coefficient (ADC) and histogram-derived parameters, compared with RECIST 1.1. METHODS: 24 patients underwent DW-MR at baseline, week-2 and week-8 after chemotherapy initiation. Tumour diameter was assessed on T2-weighted images. Regions-of-interest (ROI) were drawn on ADC map for ROI-ADC. Volume segmentation (b = 1000 s/mm2 images) provided DW-volume and histogram-derived diffusion parameters (H-ADC, H-D and H-PF). All variables and their relative change were compared to baseline or between responders and non-responders. Discriminant analysis was performed. RESULTS: 15/24 patients were responders. RECIST 1.1 correctly characterized 6/15 responders at week-8. At week-2, in responders DW-volume decreased (P = .002); ROI-ADC mean H-D increased (P = .047; P = .048;). The 25th percentile H-D increased in responders and decreased in non-responders (P = .016; P = .048). At week-8 in responders DW-volume decreased and ROI-ADC mean, 25th, 50th, 75th percentiles of H-ADC and H-D increased (P < .05). No changes were observed in non-responders (P > .05). At week-2, 25th percentile of H-D and H-PF relative change correctly classified 20/24 patients (P = .003); at week-8, DW-volume relative change correctly classified 22/24 patients (P < .0001). CONCLUSIONS: ROI-ADC, DW-volume and histogram-derived diffusion parameters are more accurate to categorize responding and non-responding PDA patients treated with chemotherapy compared with RECIST 1.1.

Total choline quantification measured by 1H MR spectroscopy as early predictor of response after neoadjuvant treatment for locally advanced breast cancer: The impact of immunohistochemical status.

BACKGROUND: Validation of new biomarkers is essential for the early evaluation of neoadjuvant treatments. PURPOSE: To determine whether measurements of total choline (tCho) by 1H spectroscopy could predict morphological or pathological complete response (pCR) of neoadjuvant treatment and whether breast cancer subgroups are related to prediction accuracy. STUDY TYPE: Prospective, nonrandomized, monocentric, diagnostic study. POPULATION: Sixty patients were initially included with 39 women participating in the final cohort. FIELD STRENGTH/SEQUENCE: A 1.5T scanner was used for acquisition and MRS was performed using the syngo GRACE sequence. ASSESSMENT: MRS and MRI examinations were performed at baseline (TP1), 24-72 hours after first chemotherapy (TP2), after the end of anthracycline treatment (TP3), and MRI only after the end of taxane treatment (TP4). Early (EMR) and late (LMR) morphological response were defined as %ΔDmax13 or %ΔDmax14, respectively. Responders were patients with %ΔDmax >30. Pathological complete response (pCR) patients achieved a residual cancer burden score of 0. STATISTICAL TESTS: T-test, receiver operating characteristic (ROC) curves, multiple regression, logistic regression, one-way analysis of variance (ANOVA) analysis were used for the analysis. RESULTS: At TP1 there was a significant difference between response groups for tCho1 concerning EMR prediction (P = 0.05) and pCR (P < 0.05) and for Kep 1 (P = 0.03) concerning LMR prediction. At TP2, no modification of tCho and other parameters could predict response. At TP3, ΔtCho, ΔDmax, and ΔVol could predict LMR (P < 0.05 for all parameters), pCR (P < 0.05 for all parameters), and ΔKtrans could predict only pCR (P = 0.04). Logistic regression at baseline showed the highest area under the curve (AUC) of 0.9 for prediction of pCR. The triple negative (TN) subgroup showed significantly higher tCho at baseline (P = 0.02) and higher ΔtCho levels at TP3 (P < 0.05). DATA CONCLUSION: Baseline measurements of tCho in combination with clinicopathological criteria could predict non-pCR with a high AUC. Furthermore, tCho quantification for prediction of pCR was more sensitive for TN tumors. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2018;48:982-993.

TumourMetrics: a comprehensive clinical solution for the standardization of DCE-MRI analysis in research and routine use.

BACKGROUND: A reliable analysis methodology is needed to provide valuable imaging biomarkers for clinical trials, with particular regards to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) application using pharmacokinetic (PK) model analysis. In order to address this scientific challenge, we provided a comprehensive analysis solution that could overcome the impediments to clinical research and routine use. METHODS: TumourMetrics has been designed to meet the Quantitative Imaging Biomarkers Alliance (QIBA) v.1.0 profile. The quality performance was assessed using the QIBA test data and our customizable numeric phantom. The analysis workflow is made customizable to facilitate standardization of optimized protocol across centers. RESULTS: Our quantification workflow estimated the PK model parameters accurately. The method is robust, almost fully automatic and allows a direct integration of the results into the diagnostic workflow. CONCLUSIONS: The analysis is easy-to-use and accessible for routine implementation of DCE-MRI into clinical practice.

Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy.

OBJECTIVES: To assess whether DCE-MRI pharmacokinetic (PK) parameters obtained before and during chemotherapy can predict pathological complete response (pCR) differently for different breast cancer groups. METHODS: Eighty-four patients who received neoadjuvant chemotherapy for locally advanced breast cancer were retrospectively included. All patients underwent two DCE-MRI examinations, one before (EX1) and one during treatment (EX2). Tumours were classified into different breast cancer groups, namely triple negative (TNBC), HER2+ and ER+/HER2-, and compared with the whole population (WP). PK parameters Ktrans and Ve were extracted using a two-compartment Tofts model. RESULTS: At EX1, Ktrans predicted pCR for WP and TNBC. At EX2, maximum diameter (Dmax) predicted pCR for WP and ER+/HER2-. Both PK parameters predicted pCR in WP and TNBC and only Ktrans for the HER2+. pCR was predicted from relative difference (EX1 - EX2)/EX1 of Dmax and both PK parameters in the WP group and only for Ve in the TNBC group. No PK parameter could predict response for ER+/HER-. ROC comparison between WP and breast cancer groups showed higher but not statistically significant values for TNBC for the prediction of pCR CONCLUSIONS: Quantitative DCE-MRI can better predict pCR after neoadjuvant treatment for TNBC but not for the ER+/HER2- group. KEY POINTS: • DCE-MRI-derived pharmacokinetic parameters can predict response status of neoadjuvant chemotherapy treatment. • Ktrans can better predict pCR for the triple negative group. • No pharmacokinetic parameter could predict response for the ER+/HER2- group.

Pancreatic neuroendocrine tumors: correlation between histogram analysis of apparent diffusion coefficient maps and tumor grade.

PURPOSE: To explore the role of histogram analysis of apparent diffusion coefficient (ADC) MRI maps based on entire tumor volume data in determining pancreatic neuroendocrine tumor (PNT) grade. METHODS AND MATERIALS: Retrospective evaluation of 22 patients with PNTs included low-grade (G1; n = 15), intermediate-grade (G2; n = 4), and high-grade (G3; n = 3) tumors. Regions of interest containing the lesion were drawn on every section of the ADC map containing the tumor and summated to obtain histograms for entire tumor volume. Calculated histographic parameters included mean ADC (mADC), 5th percentile ADC, 10th percentile ADC, 25th percentile ADC, 50th percentile ADC, 75th percentile ADC (ADC75), 90th percentile ADC (ADC90) and 95th percentile ADC (ADC95), skewness and kurtosis. Histogram parameters were correlated with tumor grade by repeated measures analysis of variance with Tukey-Kramer post hoc comparisons. RESULTS: The mADC, ADC75, ADC90, and ADC95 were significantly higher in G1 tumors (1283 ± 267; 1404 ± 300; 1495 ± 318; 1562 ± 347 × 10(-6) mm(2)/s) compared to G2 (892 ± 390; 952 ± 381; 1036 ± 384; 1072 ± 374 × 10(-6) mm(2)/s) and to G3 tumors (733 ± 225; 864 ± 284; 1008 ± 288; 1152 ± 192 × 10(-6) mm(2)/s) (p value <0.05). Skewness and kurtosis were significantly different between G1 (0.041 ± 0.466; 2.802 ± 0.679) and G3 (1.01 ± 1.140; 5.963 ± 4.008) tumors (p value <0.05). Tumor volume (mL) was significantly higher on G3 (55 ± 15.7) compared to G1 (1.9 ± 2.7) and G2 (4.5 ± 3.6) tumors (p value <0.05). In this small sample size, we did not detect statistically significant parameters between G2 (n = 4) and G3 (n = 3) tumors. CONCLUSIONS: Histographic analysis of ADC maps on the basis of the entire tumor volume can be useful in differentiating histologic grades of PNTs.

Quantitative diffusion imaging in breast cancer: a clinical prospective study.

PURPOSE: To study the correlation between apparent diffusion coefficient (ADC) and pathology in patients with undefined breast lesion, to validate how accurately ADC is related to histology, and to define a threshold value of ADC to distinguish malignant from benign lesions. MATERIALS AND METHODS: Seventy-eight patients (110 lesions) were referred for positive or dubious findings. Three-dimensional fast low-angle shot (3D-FLASH) with contrast injection was applied. EPI diffusion-weighted imaging (DWI) with fat saturation was performed, and ROIs were selected on subtraction 3D-FLASH images before and after contrast injection, and copied on an ADC map. Inter- and intraobserver analyses were performed. RESULTS: At pathology 22 lesions were benign, 65 were malignant, and 23 were excluded. The ADCs of malignant and benign lesions were statistically different. In malignant tumors the ADC was (mean +/- SEM) 0.95 +/- 0.027 x 10(-3)mm(2)/second, and in benign tumors it was 1.51 +/- 0.068 x 10(-3)mm(2)/second. According to receiver operating characteristic (ROC) curves, we found a threshold between malignant and benign lesions for highest sensitivity and specificity (both 86%) around 1.13 +/- 0.10 x 10(-3)mm(2)/second. For a threshold of 0.95 +/- 0.10 x 10(-3)mm(2)/second, specificity was 100% but sensitivity was very low. Inter- and intraobserver studies showed good reproducibility. CONCLUSION: The ADC may help to differentiate benign and malignant lesions with good specificity, and may increase the overall specificity of breast MRI.

Contribution of NMR spectroscopy to the differential diagnosis of a recurrent cranial mass 7 years after irradiation for a pediatric ependymoma.

CASE REPORT: We describe the case of a 5-year-old-boy who underwent surgery and focal radiotherapy for an anaplastic ependymoma of the fourth ventricle. One year later, a spinal metastasis was treated the same way. Six years later, a 16-mm lesion was found on a control MRI in the posterior fossa. To help the differential diagnosis between a relapse, a radio-induced modification, and a new tumor, magnetic resonance spectroscopy was performed. The main findings were a peak at the expected resonance frequency of reduced glutathione, a prominent peak of glutamate/glutamine compounds, a low N-acetylaspartate, and the absence of elevated choline. These findings were suggestive of a meningioma, although the latency between irradiation and development of the lesion was quite short. The diagnosis was confirmed by the pathological examination. CONCLUSION: This case exemplifies the fact that magnetic resonance spectroscopy provides useful biochemical information in such a clinical setting.