Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images

Application of Synthetic MRI for Direct Measurement of Magnetic Resonance Relaxation Time and Tumor Volume at Multiple Time Points after Contrast Administration: Preliminary Results in Patients with Brain Metastasis

Objective

The purpose of this study was to investigate the time-dependent effects of contrast medium on multi-dynamic, multi-echo (MDME) sequence in patients with brain metastases.

Materials and Methods

This study included 7 patients with 15 brain metastases who underwent magnetic resonance (MR) examination which included MDME sequences at 1 minute, 10 minutes and 20 minutes after contrast injection. Two volumes of interests, covering an entire tumor (whole tumor) and the enhancing portion of the tumor, were derived from post-contrast synthetic T1-weighted images. Statistical comparisons were performed for three different time delays for histogram parameters of the longitudinal relaxation rate (R₁) and the transverse relaxation rate (R₂), and lesion volumes.

Results

The mean and the median of R₁ and the mean of R₂ in both the whole tumor and the inner enhancing portion were larger on the 10 minutes delayed images than on the 1 minute or 20 minutes delayed images (mean of R₁ in the whole tumor on the 1 minute, 10 minutes, and 20 minutes delayed images: 1.26 ms, 1.39 ms, and 1.37 ms; mean of R₁ in the inner enhancing portion: 1.43 ms, 1.53 ms and 1.44 ms; all p < 0.017). The volumes of the whole tumor and the inner enhancing portion were significantly larger in the 10 minutes and 20 minutes delayed images than on the 1 minute delayed images (all p < 0.017).

Conclusion

Magnetic resonance relaxation times and the volumes of the whole tumor and the inner enhancing portion were measured larger on the 10 minutes or 20 minutes delayed images than on the 1 minute delayed images. The MDME sequence immediately after contrast injection cannot fully reflect the effects of gadolinium-based contrast agent leakage in the tissue.

Assessment of Therapy Response to Transarterial Radioembolization for Liver Metastases by Means of Post-treatment MRI-Based Texture Analysis

INTRODUCTION

To determine whether post-treatment magnetic resonance imaging (MRI)-based texture analysis of liver metastases (LM) may be suited predicting therapy response to transarterial radioembolization (TARE) during follow-up.

MATERIALS AND METHODS

Thirty-seven patients with LM treated by TARE (mean age 63.4 years) between January 2006 and December 2014 were identified in this retrospective feasibility study. They underwent dynamic contrast-enhanced and hepatocellular phase MRI after TARE (mean 2.2 days). Response was evaluated on follow-up imaging scheduled in intervals of 3 months (median follow-up, 7.3 months) based on response evaluation criteria in solid tumors 1.1 (RECIST 1.1). Results of texture analysis [mean, standard deviation, skewness (s), kurtosis (k), entropy and uniformity] were compared between patients with progressive disease (PD) and patients with stable disease (SD), partial or complete response (PR/CR). Receiver operating characteristics including the area under the curve (AUC) and cutoff values including the sensitivity and specificity were calculated.

RESULTS

According to RECIST 1.1, 24 patients (64.9%) had PD, 8 SD (21.6%) and 5 PR (13.5%). MRI-based texture analysis showed an earlier differentiation between patients with and without PD when compared with RECIST 1.1. Median k (2.88 vs. 2.35) in arterial phase MRI and median s (0.48 vs. 0.25) and k (2.85 vs. 2.25) in venous phase MRI were significantly different (p < 0.05). The AUC for k derived from arterial phase MRI was 0.73 (cutoff = 2.55, sensitivity = 0.83, specificity = 0.62) (p < 0.05). The AUC for s and k in venous phase MRI was 0.76 (cutoff = 0.35, sensitivity = 0.71, specificity = 0.85) (p > 0.05) and 0.83 (cutoff = 2.50, sensitivity = 0.75, specificity = 0.85) (p < 0.05).

CONCLUSION

This study indicates the potential of MRI-based texture analysis at arterial and venous phase MRI for the early prediction of PD after TARE.

LEVEL OF EVIDENCE

IV.

Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation

BACKGROUND

The role of apparent diffusion coefficient (ADC)-based radiomics features in evaluating histopathological grade of cervical cancer is unresolved.

PURPOSE

To determine if there is a difference between radiomics features derived from center-slice 2D versus whole-tumor volumetric 3D for ADC measurements in patients with cervical cancer regarding tumor histopathological grade, and systematically assess the impact of the b value on radiomics analysis in ADC quantifications.

STUDY TYPE

Prospective.

SUBJECTS

In all, 160 patients with histopathologically confirmed squamous cell carcinoma of uterine cervix.

FIELD STRENGTH/SEQUENCE

Conventional and diffusion-weighted MR images (b values = 0, 800, 1000 s/mm² ) were acquired on a 3.0T MR scanner.

ASSESSMENT

Regions of interest (ROIs) were drawn manually along the margin of tumor on each slice, and then the center slice of the tumor was selected with naked eyes in the course of whole-tumor segmentation. A total of 624 radiomics features were derived from T₂ -weighted images and ADC maps. We randomly selected 50 cases and did the reproducibility analysis.

STATISTICAL TESTS

Parameters were compared using Wilcoxon signed rank test, Bland-Altman analysis, t-test, and least absolute shrinkage and selection operator (LASSO) regression with crossvalidation.

RESULTS

In all, 95 radiomics features were insensitive to ROI variation among T₂ images, ADC map of b800, and ADC map of b1000 (P > 0.0002). There was a significant statistical difference between the performances of 2D center-slice and 3D whole-tumor radiomics models in both ADC feature sets of b800 and b1000 (P < 0.0001, P < 0.0001). Compared with ADC features of b800 (0.3758 ± 0.0118), the model of b1000 ADC features appeared to be slightly lower in overall misclassification error (0.3642 ± 0.0162) (P = 0.0076).

DATA CONCLUSION

Several radiomics features extracted from T₂ images and ADC maps were highly reproducible. Whole-tumor volumetric 3D radiomics analysis had a better performance than using the 2D center-slice of tumor in stratifying the histological grade of cervical cancer. A b value of 1000 s/mm² is suggested as the optimal parameter in pelvic DWI scans.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:280-290.

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.

Can the apparent diffusion coefficient differentiate the grade of endometrioid adenocarcinoma and the histological subtype of endometrial cancer?

Background Diffusion-weighted imaging (DWI) provides useful information for the identification of benign and malignant uterine lesions. However, the use of the apparent diffusion coefficient (ADC) for histopathological grading of endometrial cancer is controversial. Purpose To explore the use of ADC values in differentiating the preoperative tumor grading of endometrioid adenocarcinomas and investigate the relationship between the ADC values of endometrial cancer and the histological tumor subtype. Material and Methods We retrospectively evaluated 98 patients with endometrial cancers, including both endometrioid adenocarcinomas (n = 80) and non-endometrioid adenocarcinomas (n = 18). All patients underwent DWI procedures and ADC values were calculated. The Kruskal-Wallis test and the independent samples Mann-Whitney U test were used to compare differences in the ADC values between different tumor grades and different histological subtypes. Results The mean ADC values (ADCmean) for high-grade endometrioid adenocarcinomas were significantly lower than the values for low-grade tumors (0.800 versus 0.962 × 10^-3 mm²/s) ( P = 0.002). However, no significant differences in ADCmean and minimum ADC values (ADCmin) were found between tumor grades (G1, G2, and G3) of endometrial cancer. Compared with endometrioid adenocarcinomas, the adenocarcinoma with squamous differentiation showed lower ADC values (mean/minimum = 0.863/0.636 versus 0.962/0.689 × 10^-3 mm²/s), but the differences were not significant ( P_mean = 0.074, P_min = 0.441). Moreover, ADCmean for carcinosarcomas was significantly higher than the value for G3 non-carcinosarcoma endometrial cancers (1.047 versus 0.823 × 10^-3 mm²/s) ( P = 0.001). Conclusion The ADCmean was useful for identifying high-grade and low-grade endometrioid adenocarcinomas. Additionally, squamous differentiation may decrease ADCmean and ADCmin of endometrioid adenocarcinoma, and carcinosarcomas showed relatively high ADCmean.

Whole-tumor apparent diffusion coefficient (ADC) histogram analysis to differentiate benign peripheral neurogenic tumors from soft tissue sarcomas

BACKGROUND

Apparent diffusion coefficient (ADC) histogram analyses have been used to differentiate tumor grades and predict therapeutic responses in various anatomic sites with moderate success.

PURPOSE

To determine the ability of diffusion-weighted imaging (DWI) with a whole-tumor ADC histogram analysis to differentiate benign peripheral neurogenic tumors (BPNTs) from soft tissue sarcomas (STSs).

STUDY TYPE

Retrospective study, single institution.

SUBJECTS

In all, 25 BPNTs and 31 STSs.

FIELD STRENGTH/SEQUENCE

Two-b value DWI (b-values = 0, 1000s/mm2 ) was at 3.0T.

ASSESSMENT

The histogram parameters of whole-tumor for ADC were calculated by two radiologists and compared between BPNTs and STSs.

STATISTICAL TESTS

Nonparametric tests were performed for comparisons between BPNTs and STSs. P < 0.05 was considered statistically significant. The ability of each parameter to differentiate STSs from BPNTs was evaluated using area under the curve (AUC) values derived from a receiver operating characteristic curve analysis.

RESULTS

The mean ADC and all percentile parameters were significantly lower in STSs than in BPNTs (P < 0.001-0.009), with AUCs of 0.703-0.773. However, the coefficient of variation (P = 0.020 and AUC = 0.682) and skewness (P = 0.012 and AUC = 0.697) were significantly higher in STSs than in BPNTs. Kurtosis (P = 0.295) and entropy (P = 0.604) did not differ significantly between BPNTs and STSs.

DATA CONCLUSION

Whole-tumor ADC histogram parameters except kurtosis and entropy differed significantly between BPNTs and STSs.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

The diagnostic role of diffusion-weighted magnetic resonance imaging in hypopharyngeal carcinoma

The aim of the present study was to assess the role of diffusion-weighted magnetic resonance imaging (DWI) and apparent diffusion coefficient (ADC) values in hypopharyngeal carcinoma. A total of 40 hypopharyngeal carcinoma tissues and 15 benign lesion tissues were retrospectively analyzed. DWI, and T1- and T2-weighted magnetic resonance imaging (MRI) was performed. The sensitivity, specificity and accuracy of conventional MRI were 97.5, 66.7, and 89.1%, respectively. The mean ADC value [diffusion sensitive factor (b)=1,000× sec/mm²) for hypopharyngeal carcinomas was (1.0285±0.0328)×10⁻³ mm²/sec, which was significantly lower than the mean ADC value for benign lesions [(1.5333±0.1061)×10⁻³ mm²/sec; P<0.001]. Receiver operating characteristic (ROC) curve analysis revealed that the area under the curve (AUC) was 0.921 while the optimal threshold for the cut-off point of the ADC was 1.075×10⁻³ mm²/sec. The mean ADC value of the metastatic nodes was (0.9184±0.0538)×10⁻³ mm²/sec, lower than the mean value for the benign nodes [(1.2538±0.1145)×10⁻³ mm²/sec; P=0.005]. Two groups were created according to the mean of the ADC value of hypopharyngeal carcinomas [≤(1.0285±0.0328)×10⁻³ mm²/sec vs. >(1.0285±0.0328)×10⁻³ mm²/sec]. The 2-year survival rates of the two groups were 55.6 and 100.0%, respectively (P=0.024). ADC values may aid in distinguishing hypopharyngeal carcinomas from benign lesions and differentiating metastatic lymph nodes of hypopharyngeal squamous cell carcinomas from reactive cervical lymph nodes. In conclusion, mean ADC values may be useful prognostic factors in univariate analysis of hypopharyngeal carcinoma.

Histogram analysis of stretched-exponential and monoexponential diffusion-weighted imaging models for distinguishing low and intermediate/high gleason scores in prostate carcinoma

BACKGROUND

Noninvasive measures to evaluate the aggressiveness of prostate carcinoma (PCa) may benefit patients.

PURPOSE

To assess the value of stretched-exponential and monoexponential diffusion-weighted imaging (DWI) for predicting the aggressiveness of PCa.

STUDY TYPE

Retrospective study.

SUBJECTS

Seventy-five patients with PCa.

FIELD STRENGTH

3T DWI examinations were performed using b-values of 0, 500, 1000, and 2000 s/mm² .

ASSESSMENT

The research were based on entire-tumor histogram analysis and the reference standard was radical prostectomy.

STATISTICAL TESTS

The correlation analysis was programmed with Spearman's rank-order analysis between the histogram variables and Gleason grade group (GG). Receiver operating characteristic (ROC) regression was used to analyze the ability of these histogram variables to differentiate low-grade (LG) from intermediate/high-grade (HG) PCa.

RESULTS

The percentiles and mean of apparent diffusion coefficient (ADC) and distributed diffusion coefficient (DDC) were correlated with GG (ρ: 0.414-0.593), while there was no significant relation among α value, skewnesses, and kurtosises with GG (ρ:0.034-0.323). HG tumors (ADC:484 ± 136, 592 ± 139, 670 ± 144, 788 ± 146, 895 ± 141 mm² /s; DDC: 410 ± 142, 532 ± 172, 666 ± 193, 786 ± 196, 914 ± 181 mm² /s) had lower values in the 10^th , 25^th , 50^th , 75^th percentiles and means than LG tumors (ADC: 644 ± 779, 737 ± 84, 836 ± 83, 919 ± 82, 997 ± 107 mm² /s; DDC: 552 ± 82, 680 ± 94, 829 ± 112, 931 ± 106, 1045 ± 100 mm² /s). However, there was no difference between LG and HG tumors in α value (0.671 ± 0.041 vs. 0.633 ± 0.114), kurtosises (ADC 0.09 vs. 0.086; DDC -0.033 vs. -0.317), or skewnesses (ADC -0.036 vs. 0.073; DDC -0.063 vs. 0.136). The above statistics were P < 0.01. ADC10 with AUC = 0.840 and DDC10 with AUC = 0.799 were similar in discriminating between LG and HG PCa at P < 0.05.

DATA CONCLUSION

Histogram variables of DDC and ADC may predict the aggressiveness of PCa, while α value does not. The abilities of ADC10 and DDC10 to discriminate LG from HG tumors were similar, and both better than their respective means.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:491-498.

Developing and validating a multivariable prediction model to improve the diagnostic accuracy in determination of cervical versus endometrial origin of uterine adenocarcinomas: A prospective MR study combining diffusion-weighted imaging and spectroscopy

BACKGROUND

A triage test to assist clinical decision-making on choosing primary chemoradiation for cervical carcinomas or primary surgery for endometrial carcinomas is important.

PURPOSE OR HYPOTHESIS

To develop and validate a multiparametric prediction model based on MR imaging and spectroscopy in distinguishing adenocarcinomas of uterine cervical or endometrial origin.

STUDY TYPE

Prospective diagnostic accuracy study.

POPULATION

Eighty-seven women: 25 cervical and 62 endometrial adenocarcinomas divided into training (n = 43; cervical/endometrial adenocarcinomas = 11/32) and validation (n = 44; 14/30) datasets.

FIELD STRENGTH/SEQUENCE

The 3T diffusion-weighted (DW) MR imaging and MR spectroscopy.

ASSESSMENT

Morphology, volumetric DW MR imaging and spectroscopy (MDS) scoring system with total points 0-5, based on presence of the following MR features assessed independently by two radiologists: (a) epicenter at the cervix, (b) rim enhancement, (c) disrupted cervical stromal integrity, (d) mean volumetric apparent diffusion coefficient values (ADCmean) higher than 0.98 × 10-3 mm² /s, (e) fatty acyl δ 1.3 ppm more than 161.92 mM. Histopathology as gold standard.

STATISTICAL TESTS

Logistic regression and receiver operator characteristic (ROC) curves analysis.

RESULTS

For both the training and validation datasets, the MDS score achieved an accuracy of 93.0% and 84.1%, significantly higher than that of morphology (88.4% and 79.5%), ADC value (74.4% and 68.2%), and spectroscopy (81.4% and 68.2%; P < 0.05 for all). The performances of the scoring were superior to the morphology in the training dataset (areas under the receiver operating characteristics curve [AUC] = 0.95 vs. 0.89; P = 0.046), but not in the validation dataset (AUC = 0.90 vs. 0.85; P = 0.289).

DATA CONCLUSION

MDS score has potentials to improve distinguishing adenocarcinomas of cervical or endometrial origin, and warrants large-scale studies for further validation.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1654-1666.

Diffusion-weighted MRI and intravoxel incoherent motion model for diagnosis of pediatric solid abdominal tumors

Background

Pediatric retroperitoneal tumors in the renal bed are often large and heterogeneous, and their diagnosis based on conventional imaging alone is not possible. More advanced imaging methods, such as diffusion‐weighted (DW) MRI and the use of intravoxel incoherent motion (IVIM), have the potential to provide additional biomarkers that could facilitate their noninvasive diagnosis.

Purpose

To assess the use of an IVIM model for diagnosis of childhood malignant abdominal tumors and discrimination of benign from malignant lesions.

Study Type

Retrospective.

Population

Forty‐two pediatric patients with abdominal lesions (n = 32 malignant, n = 10 benign), verified by histopathology.

Field Strength/Sequence

1.5T MRI system and a DW‐MRI sequence with six b‐values (0, 50, 100, 150, 600, 1000 s/mm²).

Assessment

Parameter maps of apparent diffusion coefficient (ADC), and IVIM maps of slow diffusion coefficient (D), fast diffusion coefficient (D*), and perfusion fraction (f) were computed using a segmented fitting model. Histograms were constructed for whole‐tumor regions of each parameter.

Statistical Tests

Comparison of histogram parameters of and their diagnostic performance was determined using Kruskal–Wallis, Mann–Whitney U, and receiver‐operating characteristic (ROC) analysis.

Results

IVIM parameters D* and f were significantly higher in neuroblastoma compared to Wilms' tumors (P < 0.05). The ROC analysis showed that the best diagnostic performance was achieved with D* 90^th percentile (area under the curve [AUC] = 0.935; P = 0.002; cutoff value = 32,376 × 10⁻⁶ mm²/s) and f mean values (AUC = 1.00; P < 0.001; cutoff value = 14.7) in discriminating between neuroblastoma (n = 11) and Wilms' tumors (n = 8). Discrimination between tumor types was not possible with IVIM D or ADC parameters. Malignant tumors revealed significantly lower ADC, D, and higher D* values than in benign lesions (all P < 0.05).

Data Conclusion

IVIM perfusion parameters could distinguish between malignant childhood tumor types, providing potential imaging biomarkers for their diagnosis.

Level of Evidence: 4

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2018;47:1475–1486.

Histogram analysis of apparent diffusion coefficient for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy

Background Apparent diffusion coefficient (ADC) histogram analysis has been widely used in determining tumor prognosis. Purpose To investigate the dynamic changes of ADC histogram parameters during concurrent chemo-radiotherapy (CCRT) in patients with advanced cervical cancers. Material and Methods This prospective study enrolled 32 patients with advanced cervical cancers undergoing CCRT who received diffusion-weighted (DW) magnetic resonance imaging (MRI) before CCRT, at the end of the second and fourth week during CCRT and one month after CCRT completion. The ADC histogram for the entire tumor volume was generated, and a series of histogram parameters was obtained. Dynamic changes of those parameters in cervical cancers were investigated as early biomarkers for treatment response. Results All histogram parameters except AUC_low showed significant changes during CCRT (all P < 0.05). There were three variable trends involving different parameters. The mode, 5th, 10th, and 25th percentiles showed similar early increase rates (33.33%, 33.99%, 34.12%, and 30.49%, respectively) at the end of the second week of CCRT. The pre-CCRT 5th and 25th percentiles of the complete response (CR) group were significantly lower than those of the partial response (PR) group. Conclusion A series of ADC histogram parameters of cervical cancers changed significantly at the early stage of CCRT, indicating their potential in monitoring early tumor response to therapy.

Cancer Metabolism and Tumor Heterogeneity: Imaging Perspectives Using MR Imaging and Spectroscopy

Cancer cells reprogram their metabolism to maintain viability via genetic mutations and epigenetic alterations, expressing overall dynamic heterogeneity. The complex relaxation mechanisms of nuclear spins provide unique and convertible tissue contrasts, making magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) pertinent imaging tools in both clinics and research. In this review, we summarized MR methods that visualize tumor characteristics and its metabolic phenotypes on an anatomical, microvascular, microstructural, microenvironmental, and metabolomics scale. The review will progress from the utilities of basic spin-relaxation contrasts in cancer imaging to more advanced imaging methods that measure tumor-distinctive parameters such as perfusion, water diffusion, magnetic susceptibility, oxygenation, acidosis, redox state, and cell death. Analytical methods to assess tumor heterogeneity are also reviewed in brief. Although the clinical utility of tumor heterogeneity from imaging is debatable, the quantification of tumor heterogeneity using functional and metabolic MR images with development of robust analytical methods and improved MR methods may offer more critical roles of tumor heterogeneity data in clinics. MRI/MRS can also provide insightful information on pharmacometabolomics, biomarker discovery, disease diagnosis and prognosis, and treatment response. With these future directions in mind, we anticipate the widespread utilization of these MR-based techniques in studying in vivo cancer biology to better address significant clinical needs.

Role of Functional Magnetic Resonance Imaging Derived Parameters as Imaging Biomarkers and Correlation with Clinicopathological Features in Carcinoma of Uterine Cervix

INTRODUCTION

Magnetic Resonance Imaging (MRI) is emerging as a powerful tool in the evaluation and management of cervical cancer. The role of Diffusion Weighted Imaging (DWI) with Apparent Diffusion Coefficient (ADC) as a non-invasive imaging biomarker is promising in characterization of the tumour and prediction of response.

AIM

The aim of this study was to evaluate the role of conventional MRI and diffusion weighted MRI in predicting clinicopathological prognostic factors.

MATERIALS AND METHODS

This was a retrospective study. The data of 100 cervical cancer patients who had MRI with DWI was retrieved from the database and analysed. Clinico pathological details were collected from the computerized hospital information system. SPSS version 15.0 was used for statistical analysis.

RESULTS

The mean tumour dimensions on MRI in x, y and z axes were 43.04 mm (±13.93, range: 17-85), 37.05mm (±11.83, range: 9-80) and 39.63 mm (±14.81, range: 14 -76). The mean T₂W MRI based tumour volume (TV) was 48.18 (±34.3, range: 7-206) and on DWI images was 36.68(±33.72, range: 2.5-200). The mean ADC value in patients with squamous cell carcinoma was 0.694 (±0.125, n=88), adenocarcinoma was 0.989 (±0.309, n=6), adenosquamous was 0.894 (±0.324, n=4). There was statistical significant difference in mean ADC between squamous vs. non squamous histology (p = 0.02). The mean ADC values of well differentiated, moderately differentiated, and poorly differentiated tumours were 0.841(±0.227, n= 26), 0.729 (±0.125, n=28), 0.648 (±0.099, n=46) respectively. There was significant statistical difference of mean ADC between well differentiated, moderately differentiated (p=0.020) and poorly differentiated tumours (p=0.0001). Difference between the mean ADC values between the node positive and node negative disease was statistically significant (p=0.0001). There was no correlation between the tumour volumes on T₂W and DWI images and ADC values. Sixteen patients had residual/recurrent disease at a median follow up of 12 months (range: 3-59 months). The mean ADC values in this group was 0.71 (n=16) and was not significantly different from the disease free group (mean ADC =0.72, n=74).

CONCLUSION

Higher ADC values are associated with favourable histology and differentiation. Adenocarcinomas have higher ADC values followed by adenosquamous followed by squamous cell carcinomas. Well differentiated tumours had higher ADC values than moderately followed by poorly differentiated tumours. DWI with ADC have a potential role as an imaging biomarker for prognostication and needs further studies for routine clinical applications.

Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers

BACKGROUND

Whole-lesion apparent diffusion coefficient (ADC) histogram analysis has been introduced and proved effective in assessment of multiple tumors. However, the application of whole-volume ADC histogram analysis in gastrointestinal tumors has just started and never been reported in T and N staging of gastric cancers.

METHODS

Eighty patients with pathologically confirmed gastric carcinomas underwent diffusion weighted (DW) magnetic resonance imaging before surgery prospectively. Whole-lesion ADC histogram analysis was performed by two radiologists independently. The differences of ADC histogram parameters among different T and N stages were compared with independent-samples Kruskal-Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of ADC histogram parameters in differentiating particular T or N stages of gastric cancers.

RESULTS

There were significant differences of all the ADC histogram parameters for gastric cancers at different T (except ADC_min and ADC_max) and N (except ADC_max) stages. Most ADC histogram parameters differed significantly between T1 vs T3, T1 vs T4, T2 vs T4, N0 vs N1, N0 vs N3, and some parameters (ADC_5%, ADC_10%, ADC_min) differed significantly between N0 vs N2, N2 vs N3 (all P < 0.05). Most parameters except ADC_max performed well in differentiating different T and N stages of gastric cancers. Especially for identifying patients with and without lymph node metastasis, the ADC_10% yielded the largest area under the ROC curve of 0.794 (95% confidence interval, 0.677-0.911). All the parameters except ADC_max showed excellent inter-observer agreement with intra-class correlation coefficients higher than 0.800.

CONCLUSION

Whole-volume ADC histogram parameters held great potential in differentiating different T and N stages of gastric cancers preoperatively.

The value of advanced MRI techniques in the assessment of cervical cancer: a review

OBJECTIVES

To assess the value of new magnetic resonance imaging (MRI) techniques in cervical cancer.

METHODS

We searched PubMed and MEDLINE and reviewed articles published from 1990 to 2016 to identify studies that used MRI techniques, such as diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM) and dynamic contrast enhancement (DCE) MRI, to assess parametric invasion, to detect lymph node metastases, tumour subtype and grading, and to detect and predict tumour recurrence.

RESULTS

Seventy-nine studies were included. The additional use of DWI improved the accuracy and sensitivity of the evaluation of parametrial extension. Most studies reported improved detection of nodal metastases. Functional MRI techniques have the potential to assess tumour subtypes and tumour grade differentiation, and they showed additional value in detecting and predicting treatment response. Limitations included a lack of technical standardisation, which limits reproducibility.

CONCLUSIONS

New advanced MRI techniques allow improved analysis of tumour biology and the tumour microenvironment. They can improve TNM staging and show promise for tumour classification and for assessing the risk of tumour recurrence. They may be helpful for developing optimised and personalised therapy for patients with cervical cancer.

TEACHING POINTS

• Conventional MRI plays a key role in the evaluation of cervical cancer. • DWI improves tumour delineation and detection of nodal metastases in cervical cancer. • Advanced MRI techniques show promise regarding histological grading and subtype differentiation. • Tumour ADC is a potential biomarker for response to treatment.

Utility of whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs)

PURPOSE

To evaluate whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs), including in comparison with conventional MRI features.

METHODS

Eighteen branch-duct IPMNs underwent MRI with DWI prior to resection (n = 16) or FNA (n = 2). A blinded radiologist placed 3D volumes-of-interest on the entire IPMN on the ADC map, from which whole-lesion histogram metrics were generated. The reader also assessed IPMN size, mural nodularity, and adjacent main-duct dilation. Benign (low-to-intermediate grade dysplasia; n = 10) and malignant (high-grade dysplasia or invasive adenocarcinoma; n = 8) IPMNs were compared.

RESULTS

Whole-lesion ADC histogram metrics demonstrating significant differences between benign and malignant IPMNs were: entropy (5.1 ± 0.2 vs. 5.4 ± 0.2; p = 0.01, AUC = 86%); mean of the bottom 10th percentile (2.2 ± 0.4 vs. 1.6 ± 0.7; p = 0.03; AUC = 81%); and mean of the 10-25th percentile (2.8 ± 0.4 vs. 2.3 ± 0.6; p = 0.04; AUC = 79%). The overall mean ADC, skewness, and kurtosis were not significantly different between groups (p ≥ 0.06; AUC = 50-78%). For entropy (highest performing histogram metric), an optimal threshold of >5.3 achieved a sensitivity of 100%, a specificity of 70%, and an accuracy of 83% for predicting malignancy. No significant difference (p = 0.18-0.64) was observed between benign and malignant IPMNs for cyst size ≥3 cm, adjacent main-duct dilatation, or mural nodule. At multivariable analysis of entropy in combination with all other ADC histogram and conventional MRI features, entropy was the only significant independent predictor of malignancy (p = 0.004).

CONCLUSION

Although requiring larger studies, ADC entropy obtained from 3D whole-lesion histogram analysis may serve as a biomarker for identifying the malignant potential of IPMNs, independent of conventional MRI features.

Predicting tumor recurrence in patients with cervical carcinoma treated with definitive chemoradiotherapy: value of quantitative histogram analysis on diffusion-weighted MR images

Background Further research is required for evaluating the use of ADC histogram analysis in more advanced stages of cervical cancer treated with definitive chemoradiotherapy (CRT). Purpose To investigate the utility of apparent diffusion coefficient (ADC) histogram derived from diffusion-weighted magnetic resonance images in cervical cancer patients treated with definitive CRT. Material and Methods The clinical and radiological data of 50 patients with histologically proven cervical squamous cell carcinoma treated with definitive CRT were retrospectively analyzed. The impact of clinicopathological factors and ADC histogram parameters on prognostic factors and treatment outcomes was assessed. Results The mean and median ADC values for the cohort were 1.043 ± 0.135 × 10^-3 mm²/s and 1.018 × 10^-3 mm²/s (range, 0.787-1.443 × 10^-3 mm²/s). The mean ADC was significantly lower for patients with advanced stage (≥IIB) or lymph node metastasis compared with patients with stage <IIB or no lymph node metastasis. The mean ADC, 75th percentile ADC (ADC75), 90th percentile ADC (ADC90), and 95th percentile ADC (ADC95) were significantly lower in patients with tumor recurrence compared with patients without recurrence. In multivariate analysis, tumor size, ADC75 and ADC95 were independent prognostic factors for both overall survival and disease-free survival. Conclusion ADC histogram parameters could be markers for disease recurrence and for predicting survival outcomes. ADC75, ADC90, and ADC95 of the primary tumor were significant predictors of disease recurrence in cervical cancer patients treated with definitive CRT.

Whole-Lesion Apparent Diffusion Coefficient-Based Entropy-Related Parameters for Characterizing Cervical Cancers: Initial Findings

RATIONALE AND OBJECTIVES

This study aimed to develop whole-lesion apparent diffusion coefficient (ADC)-based entropy-related parameters of cervical cancer to preliminarily assess intratumoral heterogeneity of this lesion in comparison to adjacent normal cervical tissues.

MATERIALS AND METHODS

A total of 51 women (mean age, 49 years) with cervical cancers confirmed by biopsy underwent 3-T pelvic diffusion-weighted magnetic resonance imaging with b values of 0 and 800 s/mm² prospectively. ADC-based entropy-related parameters including first-order entropy and second-order entropies were derived from the whole tumor volume as well as adjacent normal cervical tissues. Intraclass correlation coefficient, Wilcoxon test with Bonferroni correction, Kruskal-Wallis test, and receiver operating characteristic curve were used for statistical analysis.

RESULTS

All the parameters showed excellent interobserver agreement (all intraclass correlation coefficients  > 0.900). Entropy, entropy(H)₀, entropy(H)₄₅, entropy(H)₉₀, entropy(H)₁₃₅, and entropy(H)_mean were significantly higher, whereas entropy(H)_range and entropy(H)_std were significantly lower in cervical cancers compared to adjacent normal cervical tissues (all P <.0001). Kruskal-Wallis test showed that there were no significant differences among the values of various second-order entropies including entropy(H)_0, entropy(H)₄₅, entropy(H)₉₀, entropy(H)₁₃₅, and entropy(H)_mean. All second-order entropies had larger area under the receiver operating characteristic curve than first-order entropy in differentiating cervical cancers from adjacent normal cervical tissues. Further, entropy(H)₄₅, entropy(H)₉₀, entropy(H)₁₃₅, and entropy(H)_mean had the same largest area under the receiver operating characteristic curve of 0.867.

CONCLUSION

Whole-lesion ADC-based entropy-related parameters of cervical cancers were developed successfully, which showed initial potential in characterizing intratumoral heterogeneity in comparison to adjacent normal cervical tissues.

Apparent diffusion coefficient histogram shape analysis for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy

Background

To explore the role of apparent diffusion coefficient (ADC) histogram shape related parameters in early assessment of treatment response during the concurrent chemo-radiotherapy (CCRT) course of advanced cervical cancers.

Methods

This prospective study was approved by the local ethics committee and informed consent was obtained from all patients. Thirty-two patients with advanced cervical squamous cell carcinomas underwent diffusion weighted magnetic resonance imaging (b values, 0 and 800 s/mm²) before CCRT, at the end of 2nd and 4th week during CCRT and immediately after CCRT completion. Whole lesion ADC histogram analysis generated several histogram shape related parameters including skewness, kurtosis, s-sD_av, width, standard deviation, as well as first-order entropy and second-order entropies. The averaged ADC histograms of 32 patients were generated to visually observe dynamic changes of the histogram shape following CCRT.

Results

All parameters except width and standard deviation showed significant changes during CCRT (all P < 0.05), and their variation trends fell into four different patterns. Skewness and kurtosis both showed high early decline rate (43.10 %, 48.29 %) at the end of 2nd week of CCRT. All entropies kept decreasing significantly since 2 weeks after CCRT initiated. The shape of averaged ADC histogram also changed obviously following CCRT.

Conclusions

ADC histogram shape analysis held the potential in monitoring early tumor response in patients with advanced cervical cancers undergoing CCRT.

Early stage cervical cancer: role of magnetic resonance imaging after conization in determining residual tumor

BACKGROUND

Although magnetic resonance imaging (MRI) is currently indispensable in the management of cervical cancer, its role in determining residual tumor in patients with cervical cancer after conization is not well known.

PURPOSE

To evaluate the value of MRI after conization in determining residual tumor in patients with FIGO stage IA-IB1 cervical cancer.

MATERIAL AND METHODS

In this retrospective study, 55 patients underwent conization followed by preoperative MRI and definitive surgery. Two radiologists evaluated the presence of residual tumor on MRI. MRI and preoperative clinical variables were compared between patients with and without residual tumor at final pathology using Student's t-test or Chi-square test. Association between variables and the presence of residual tumor was assessed using logistic regression analyses and receiver operating characteristic (ROC) curves.

RESULTS

Residual tumor at final pathology was found in 30 (54.5%) patients. Patients with residual tumor were older, had greater SCC antigen, and more frequently had positive conization margins and identifiable tumor on MRI (P < 0.008). Multivariate analysis showed that age (P = 0.008; odds ratio [OR] = 1.140), positive conization margin (P = 0.016; OR = 11.919), and identifiable tumor on MRI (P = 0.038; OR = 6.926) were independently predictive of residual tumor. Areas under the curve (AUCs) calculated with age (0.693), SCC antigen (0.755), and identifiable tumor on MRI (0.727) were greater than lymphovascular space invasion (0.517) and histological subtype (0.520, P ≤ 0.049). Otherwise, there were no significant differences in the AUCs derived from different variables (P = 0.053-0.970).

CONCLUSION

Identifiable tumor on MRI after conization in patients with early stage cervical cancer was an independent predictor of residual tumor at final pathology.

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.

ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma

PURPOSE

To determine the diagnostic performance of apparent diffusion coefficient (ADC) histogram analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI) for differentiating adrenal adenoma from pheochromocytoma.

MATERIALS AND METHODS

We retrospectively evaluated 52 adrenal tumors (39 adenomas and 13 pheochromocytomas) in 47 patients (21 men, 26 women; mean age, 59.3 years; range, 16-86 years) who underwent DW 3.0T MRI. Histogram parameters of ADC (b-values of 0 and 200 [ADC₂₀₀ ], 0 and 400 [ADC₄₀₀ ], and 0 and 800 s/mm² [ADC₈₀₀ ])-mean, variance, coefficient of variation (CV), kurtosis, skewness, and entropy-were compared between adrenal adenomas and pheochromocytomas, using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curves for the histogram parameters were generated to differentiate adrenal adenomas from pheochromocytomas. Sensitivity and specificity were calculated by using a threshold criterion that would maximize the average of sensitivity and specificity.

RESULTS

Variance and CV of ADC₈₀₀ were significantly higher in pheochromocytomas than in adrenal adenomas (P < 0.001 and P = 0.001, respectively). With all b-value combinations, the entropy of ADC was significantly higher in pheochromocytomas than in adrenal adenomas (all P ≤ 0.001), and showed the highest area under the ROC curve among the ADC histogram parameters for diagnosing adrenal adenomas (ADC₂₀₀ , 0.82; ADC₄₀₀ , 0.87; and ADC₈₀₀ , 0.92), with sensitivity of 84.6% and specificity of 84.6% (cutoff, ≤2.82) with ADC₂₀₀ ; sensitivity of 89.7% and specificity of 84.6% (cutoff, ≤2.77) with ADC₄₀₀ ; and sensitivity of 94.9% and specificity of 92.3% (cutoff, ≤2.67) with ADC₈₀₀ .

CONCLUSION

ADC histogram analysis of DW MRI can help differentiate adrenal adenoma from pheochromocytoma.

LEVEL OF EVIDENCE

3 J. Magn. Reson. Imaging 2017;45:1195-1203.

Dynamic Contrast-enhanced MR Imaging in Renal Cell Carcinoma: Reproducibility of Histogram Analysis on Pharmacokinetic Parameters

Pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been increasingly used to evaluate the permeability of tumor vessel. Histogram metrics are a recognized promising method of quantitative MR imaging that has been recently introduced in analysis of DCE-MRI pharmacokinetic parameters in oncology due to tumor heterogeneity. In this study, 21 patients with renal cell carcinoma (RCC) underwent paired DCE-MRI studies on a 3.0 T MR system. Extended Tofts model and population-based arterial input function were used to calculate kinetic parameters of RCC tumors. Mean value and histogram metrics (Mode, Skewness and Kurtosis) of each pharmacokinetic parameter were generated automatically using ImageJ software. Intra- and inter-observer reproducibility and scan–rescan reproducibility were evaluated using intra-class correlation coefficients (ICCs) and coefficient of variation (CoV). Our results demonstrated that the histogram method (Mode, Skewness and Kurtosis) was not superior to the conventional Mean value method in reproducibility evaluation on DCE-MRI pharmacokinetic parameters (K^trans & V_e) in renal cell carcinoma, especially for Skewness and Kurtosis which showed lower intra-, inter-observer and scan-rescan reproducibility than Mean value. Our findings suggest that additional studies are necessary before wide incorporation of histogram metrics in quantitative analysis of DCE-MRI pharmacokinetic parameters.

Value of diffusion-weighted MRI in diagnosis of uterine cervical cancer: a prospective study evaluating the benefits of DWI compared to conventional MR sequences in a 3T environment

BACKGROUND

Imaging of cervical carcinoma remains challenging as local infiltration of surrounding tissues cannot always be discriminated safely. New imaging techniques, like diffusion-weighted imaging (DWI) have emerged, which could lead to a more sensitive tumor detection.

PURPOSE

To evaluate the benefits of DWI for determination of size, local infiltration, and tumor grading, in patients with primary and recurrent cervical cancer.

MATERIAL AND METHODS

In this prospective, study we enrolled 50 patients with primary (n = 35) and recurrent (n = 15) tumors. All patients underwent 3T magnetic resonance imaging (MRI) including conventional (e.g. T1/T2 ± fs ± contrast) sequences and DWI (b-values of 0, 50, 400, 800 s/mm(2)). All images were analyzed by three readers with different experience levels (1, 3, 6 years), who compared image quality, tumor delineation, dimensions, local infiltration, lymph node involvement, and quantified ADC values compared to the histopathological grading.

RESULTS

Additional use of DWI resulted in significantly better (P < 0.001) tumor delineation for the least experienced reader, but not for experienced readers. Tumor dimensions were assessed almost equally (P > 0.05) in conventional sequences and DWI. Use of DWI led to an increase in sensitivity of infiltrated adjacent tissue (from 86% to 90%) and detection of lymph node metastases (from 47% to 67%). Quantitative assessment of carcinomas showed lower ADC values (P < 0.001) with significant inverse correlations between different grading levels.

CONCLUSION

Our study demonstrates the overall benefits using DWI in 3T MRI resulting in a higher reader confidence, sensitivity of tissue infiltration, and tumor-grading for cervical cancer.

CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on unenhanced CT

PURPOSE

The purpose of the paper is to evaluate if CT pixel distribution and texture analysis can identify fat in angiomyolipoma (AML) on unenhanced CT.

METHODS

Thirty-seven patients with 38 AMLs and 75 patients with 83 renal cell carcinomas (RCCs) were evaluated. Region of interest (ROI) was manually placed over renal mass on unenhanced CT. In-house software generated multiple overlapping small-ROIs of various sizes within whole-lesion-ROI. Maximal number of pixels under cutoff attenuation values in the multiple small-ROIs was calculated. Skewness of CT attenuation histogram was calculated from whole-lesion-ROI. Presence of fat in renal mass was also evaluated subjectively. Performance of subjective evaluation and objective methods for identifying fat was compared using McNemar test.

RESULTS

Macroscopic fat was identified in 15/38 AMLs and 1/83 RCCs by both subjective evaluation and by CT negative pixel distribution analysis (p = 1.0). Optimal threshold was ≥6 pixels below -30 HU within 13-pixel-ROI. Skewness of < -0.4 in whole-lesion-ROI identified fat in 10/38 AMLs and 0/83 RCCs. By combining CT negative pixel distribution analysis and skewness, fat was identified in 20/38 AMLs and 1/83 RCCs, but the difference to the subjective method was not statistically significant (p = 0.07).

CONCLUSION

CT negative attenuation pixel distribution analysis does not identify fat in AML beyond subjective evaluation. Addition of skewness by texture analysis may help improve identifying fat in AML.

Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients

GBM is a markedly heterogeneous brain tumor consisting of three main volumetric phenotypes identifiable on magnetic resonance imaging: necrosis (vN), active tumor (vAT), and edema/invasion (vE). The goal of this study is to identify the three glioblastoma multiforme (GBM) phenotypes using a texture-based gray-level co-occurrence matrix (GLCM) approach and determine whether the texture features of phenotypes are related to patient survival. MR imaging data in 40 GBM patients were analyzed. Phenotypes vN, vAT, and vE were segmented in a preprocessing step using 3D Slicer for rigid registration by T1-weighted imaging and corresponding fluid attenuation inversion recovery images. The GBM phenotypes were segmented using 3D Slicer tools. Texture features were extracted from GLCM of GBM phenotypes. Thereafter, Kruskal-Wallis test was employed to select the significant features. Robust predictive GBM features were identified and underwent numerous classifier analyses to distinguish phenotypes. Kaplan-Meier analysis was also performed to determine the relationship, if any, between phenotype texture features and survival rate. The simulation results showed that the 22 texture features were significant with p value <0.05. GBM phenotype discrimination based on texture features showed the best accuracy, sensitivity, and specificity of 79.31, 91.67, and 98.75 %, respectively. Three texture features derived from active tumor parts: difference entropy, information measure of correlation, and inverse difference were statistically significant in the prediction of survival, with log-rank p values of 0.001, 0.001, and 0.008, respectively. Among 22 features examined, three texture features have the ability to predict overall survival for GBM patients demonstrating the utility of GLCM analyses in both the diagnosis and prognosis of this patient population.

Functional MR Imaging in Gynecologic Cancer

Dynamic-contrast enhanced (DCE) and diffusion-weighted (DW) MR imaging are invaluable in the detection, staging, and characterization of uterine and ovarian malignancies, for monitoring treatment response, and for identifying disease recurrence. When used as adjuncts to morphologic T2-weighted (T2-W) MR imaging, these techniques improve accuracy of disease detection and staging. DW-MR imaging is preferred because of its ease of implementation and lack of need for an extrinsic contrast agent. MR spectroscopy is difficult to implement in the clinical workflow and lacks both sensitivity and specificity. If used quantitatively in multicenter clinical trials, standardization of DCE- and DW-MR imaging techniques and rigorous quality assurance is mandatory.

Comparison between borderline ovarian tumors and carcinomas using semi-automated histogram analysis of diffusion-weighted imaging: focusing on solid components

PURPOSE

This study aimed to evaluate whether histogram analysis of the apparent diffusion coefficient (ADC) of a solid tumor component could distinguish borderline ovarian tumors from ovarian carcinoma.

MATERIALS AND METHODS

Sixteen pathologically proven borderline tumors and 21 carcinomas were retrospectively examined. Magnetic resonance (1.5-T) image data sets were coregistered, and the solid components of each tumor were semiautomatically segmented. ADC histograms of the solid components were extracted; modes, minimums, means, and 10th, 25th, 50th, 75th, and 90th percentiles of the histograms were compared between the two tumor types, and receiver-operating characteristic (ROC) analysis was performed.

RESULTS

The mode, minimum, mean, 10th, 25th, 50th, and 75th percentile ADC values of solid components of borderline tumors were significantly larger than those of carcinomas. Among these, the 10th percentile values had the lowest p value (p = 0.0003). At ROC analysis, the area under the curve (AUC) in the 10th percentile was the greatest (0.854), and the best cutoff value in the 10th percentile provided the highest specificity (93.8 %).

CONCLUSIONS

ADC histograms of solid tumor components facilitated the distinction between borderline ovarian tumors and carcinoma. The 10th percentile ADC values had the best diagnostic performance.

Histogram Analysis of Apparent Diffusion Coefficient in Differentiating Pancreatic Adenocarcinoma and Neuroendocrine Tumor

The aim of this study was to investigate whether histogram analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI) can help differentiate pancreatic adenocarcinomas from neuroendocrine tumors.Sixty-four patients with histologically confirmed 53 pancreatic adenocarcinomas or 19 neuroendocrine tumors underwent DW MRI. We evaluated the pixel distribution histogram parameters (mean, skewness, kurtosis, and entropy) of the apparent diffusion coefficient (ADC) values derived from b-values of 0 and 200 (ADC200), 0 and 400 (ADC400), or 0 and 800 (ADC800) s/mm(2). Histogram parameters were compared between pancreatic adenocarcinomas and neuroendocrine tumors, and the diagnostic performance was evaluated by using receiver operating characteristic (ROC) analysis.The mean ADC200 and ADC400 were significantly higher in neuroendocrine tumors than in pancreatic adenocarcinomas (P = 0.001 and P = 0.019, respectively). Pancreatic adenocarcinomas showed significantly higher skewness and kurtosis on ADC400 (P = 0.007 and P = 0.001, respectively) and ADC800 (P = 0.001 and P = 0.001, respectively). With all b-value combinations, the entropy of ADC values was significantly higher in pancreatic adenocarcinomas (P < 0.001 for ADC200; P = 0.001 for ADC400; P < 0.001 for ADC800), and showed the highest area under the ROC curve for diagnosing adenocarcinomas (0.77 for ADC200, 0.76 for ADC400, and 0.78 for ADC800).ADC histogram analysis of DW MRI can help differentiate pancreatic adenocarcinomas from neuroendocrine tumors.

Role of 3T multiparametric-MRI with BOLD hypoxia imaging for diagnosis and post therapy response evaluation of postoperative recurrent cervical cancers

•

In operated cervix cancer, the accuracy of diagnosing vaginal vault/local recurrent lesions was higher at combined multiparametric MR imaging and conventional MR imaging (100%) than at conventional MR imaging (70%) or multiparametric MR imaging (96.7%) alone.

•

We found a significant correlation between percentage tumor regression and pre-treatment parameters: NEI (p = 0.02), the maximum slope (p = 0.04), mADC value (p = 0.001) and amount of hypoxic fraction present in the pretherapy MRI (p = 0.01).

•

Multiparametric and BOLD hypoxia MR Imaging are feasible and reliable in diagnosing post-operative recurrence in cervical cancer and should be applied when there is clinical suspicion of post-operative recurrence.

•

Quantitative image features obtained at multiparametric-MRI with BOLD hypoxia imaging has potential to be an appropriate and reliable biologic target for radiation dose painting to optimize therapy in future.

Objectives

To assess the diagnostic value of multiparametric-MRI (MPMRI) with hypoxia imaging as a functional marker for characterizing and detecting vaginal vault/local recurrence following primary surgery for cervical cancer.

Methods

With institutional review board approval and written informed consent 30 women (median age: 45 years) from October 2009 to March 2010 with previous operated carcinoma cervix and suspected clinical vaginal vault/local recurrence were examined with 3.0T-MRI. MRI imaging included conventional and MPMRI sequences [dynamic contrast enhanced (DCE), diffusion weighted (DW), 1H-MR spectroscopy (1HMRS), blood oxygen level dependent hypoxia imaging (BOLD)]. Two radiologists, blinded to pathologic findings, independently assessed the pretherapy MRI findings and then correlated it with histopathology findings. Sensitivity, specificity, positive predictive value, negative predictive value and their confidence intervals were calculated. The pre and post therapy conventional and MPMRI parameters were analyzed and correlated with response to therapy.

Results

Of the 30 patients, there were 24 recurrent tumors and 6 benign lesions. The accuracy of diagnosing recurrent vault lesions was highest at combined MPMRI and conventional MRI (100%) than at conventional-MRI (70%) or MPMRI (96.7%) alone. Significant correlation was seen between percentage tumor regression and pre-treatment parameters such as negative enhancement integral (NEI) (p = 0.02), the maximum slope (p = 0.04), mADC value (p = 0.001) and amount of hypoxic fraction on the pretherapy MRI (p = 0.01).

Conclusion

Conventional-MR with MPMRI significantly increases the diagnostic accuracy for suspected vaginal vault/local recurrence. Post therapy serial MPMRI with hypoxia imaging follow-up objectively documents the response. MPMRI and BOLD hypoxia imaging provide information regarding tumor biology at the molecular, subcellular, cellular and tissue levels and this information may be used as an appropriate and reliable biologic target for radiation dose painting to optimize therapy in future.

Diffusion volume (DV) measurement in endometrial and cervical cancer: A new MRI parameter in the evaluation of the tumor grading and the risk classification

PURPOSE

A new MRI parameter representative of active tumor burden is proposed: diffusion volume (DV), defined as the sum of all the voxels within a tumor with apparent diffusion coefficient (ADC) values within a specific range. The aims of the study were: (a) to calculate DV on ADC maps in patients with cervical/endometrial cancer; (b) to correlate DV with histological grade (G) and risk classification; (c) to evaluate intra/inter-observer agreement of DV calculation.

MATERIALS AND METHODS

Fifty-three patients with endometrial (n=28) and cervical (n=25) cancers underwent pelvic MRI with DWI sequences. Both endometrial and cervical tumors were classified on the basis of G (G1/G2/G3) and FIGO staging (low/medium/high-risk). A semi-automated segmentation procedure was used to calculate the DV. A freehand closed ROI outlined the whole visible tumor on the most representative slice of ADC maps defined as the slice with the maximum diameter of the solid neoplastic component. Successively, two thresholds were generated on the basis of the mean and standard deviation (SD) of the ADC values: lower threshold (LT="mean minus three SD") and higher threshold (HT="mean plus one SD"). The closed ROI was expanded in 3D, including all the contiguous voxels with ADC values in the range LT-HT × 10-3mm(2)/s. A Kruskal-Wallis test was used to assess the differences in DV among G and risk groups. Intra-/inter-observer variability for DV measurement was analyzed according to the method of Bland and Altman and the intraclass-correlation-coefficient (ICC).

RESULTS

DV values were significantly different among G and risk groups in both endometrial (p<0.05) and cervical cancers (p ≤ 0.01). For endometrial cancer, DV of G1 (mean ± sd: 2.81 ± 3.21 cc) neoplasms were significantly lower than G2 (9.44 ± 9.58 cc) and G3 (11.96 ± 8.0 cc) ones; moreover, DV of low risk cancers (5.23 ± 8.0 cc) were significantly lower than medium (7.28 ± 4.3 cc) and high risk (14.7 ± 9.9 cc) ones. For cervical cancer, DV of G1 (0.31 ± 0.13 cc) neoplasms was significantly lower than G3 (40.68 ± 45.65 cc) ones; moreover, DV of low risk neoplasms (6.98 ± 8.08 cc) was significantly lower than medium (21.7 ± 17.13 cc) and high risk (62.9 ± 51.12 cc) ones and DV of medium risk neoplasms was significantly lower than high risk ones. The intra-/inter-observer variability for DV measurement showed an excellent correlation for both cancers (ICC ≥ 0.86).

CONCLUSIONS

DV is an accurate index for the assessment of G and risk classification of cervical/endometrial cancers with low intra-/inter-observer variability.

High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features

Statistical features are widely used in radiology for tumor heterogeneity assessment using magnetic resonance (MR) imaging technique. In this paper, feature selection based on decision tree is examined to determine the relevant subset of glioblastoma (GBM) phenotypes in the statistical domain. To discriminate between active tumor (vAT) and edema/invasion (vE) phenotype, we selected the significant features using analysis of variance (ANOVA) with p value < 0.01. Then, we implemented the decision tree to define the optimal subset features of phenotype classifier. Naïve Bayes (NB), support vector machine (SVM), and decision tree (DT) classifier were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate vAT from vE. Whole nine features were statistically significant to classify the vAT from vE with p value < 0.01. Feature selection based on decision tree showed the best performance by the comparative study using full feature set. The feature selected showed that the two features Kurtosis and Skewness achieved a highest range value of 58.33–75.00% accuracy classifier and 73.88–92.50% AUC. This study demonstrated the ability of statistical features to provide a quantitative, individualized measurement of glioblastoma patient and assess the phenotype progression.

Diagnostic significance of apparent diffusion coefficient values with diffusion weighted MRI in breast cancer: a meta- analysis

AIMS

Apparent diffusion coefficient (ADC) values of nodes in diffusion-weighted imaging (DWI) are widely used in differentiating metastatic from non-metastatic lymph nodes. The purpose of this meta-analysis was to demonstrate whether DWI could contribute to the precise diagnosis of breast cancer (BC) with and without lymph node metastasis (LNM).

MATERIALS AND METHODS

English and Chinese electronic databases were searched for relevant studies followed by a comprehensive literature search. Two reviewers independently assessed the methodological quality of the included trials based on the quality assessment of diagnostic accuracy studies (QUADAS). Summary odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) were calculated.

RESULTS

Final analysis of 624 BC subjects (patients with LNM = 254, patients without LNM = 370) were incorporated into the current meta-analysis from 9 eligible cohort studies. Combined ORs of ADCs suggested that ADC values in BC patients without LNM were higher than in patients with LNM (OR=0.56, 95%CI: 0.11-1.01, p=0.015). Subgroup analysis stratified by country indicated a low ADC value in BC patients with LNM rather than those without LNM among Chinese (OR=1.27, 95%CI: 0.89-1.66, p<0.001), Italians (OR=0.75, 95%CI: 0.13-1.38, p=0.018), and Egyptians (OR=1.27, 95%CI: 0.71-1.84, p<0.001). The findings of subgroup analysis by MRI machine type revealed that ADC values from diffusion MRI may be potential diagnostic indicators for BC using Non-Philips 1.5T (OR=1.10, 95%CI: 0.84-1.36, p<0.001).

CONCLUSIONS

The main findings of our meta-analysis demonstrated that increased signal intensity on DWI and decreased signals on ADC are helpful in diagnosis of BC patients with or without LNM. DWI could therefore be an important imaging investigation in patients suspected of BC.

Endometrial Cancer: Combined MR Volumetry and Diffusion-weighted Imaging for Assessment of Myometrial and Lymphovascular Invasion and Tumor Grade

PURPOSE

To investigate magnetic resonance (MR) volumetry of endometrial tumors and its association with deep myometrial invasion, tumor grade, and lymphovascular invasion and to assess the value of apparent diffusion coefficient (ADC) histographic analysis of the whole tumor volume for prediction of tumor grade and lymphovascular invasion.

MATERIALS AND METHODS

The institutional review board approved this retrospective study; patient consent was not required. Between May 2010 and May 2012, 70 women (mean age, 64 years; range, 24-91 years) with endometrial cancer underwent preoperative MR imaging, including axial oblique and sagittal T2-weighted, dynamic contrast material-enhanced, and diffusion-weighted imaging. Volumetry of the tumor and uterus was performed during the six sequences, with manual tracing of each section, and the tumor volume ratio (TVR) was calculated. ADC histograms were generated from pixel ADCs from the whole tumor volume. The threshold of TVR associated with myometrial invasion was assessed by using receiver operating characteristic curves. An independent sample Mann Whitney U test was used to compare differences in ADCs, skewness, and kurtosis between tumor grade and the presence of lymphovascular invasion.

RESULTS

No significant difference in tumor volume and TVR was found among the six MR imaging sequences (P = .95 and .86, respectively). A TVR greater than or equal to 25% allowed prediction of deep myometrial invasion with sensitivity of 100% and specificity of 93% (area under the curve, 0.96; 95% confidence interval: 0.86, 0.99) at axial oblique diffusion-weighted imaging. A TVR of greater than or equal to 25% was associated with grade 3 tumors (P = .0007) and with lymphovascular invasion (P < .0001). There was no significant difference in the ADCs between grades 1 and 2 tumors (P > .05). The minimum, 10th, 25th, 50th, 75th, and 90th percentile ADCs were significantly lower in grade 3 tumors than in grades 1 and 2 tumors (P < .02).

CONCLUSION

The combination of whole tumor volume and ADC can be used for prediction of tumor grade, lymphovascular invasion, and depth of myometrial invasion.

Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma

OBJECTIVE

The purpose of this article is to evaluate differences in texture measures on apparent diffusion coefficient (ADC) maps between low- and high-stage clear cell renal cell carcinomas (RCCs).

MATERIALS AND METHODS

In this retrospective study, 61 patients with clear cell RCC at pathologic examination and who underwent preoperative MRI with diffusion-weighted imaging were included. Clear cell RCCs were clinically staged on review of preoperative MRI by a board-certified radiologist blinded to the pathologic findings. Whole lesions were segmented on ADC maps by two readers independently, from which first-order texture features (i.e., mean and skewness) and second-order texture features (i.e., cooccurrence matrix measures) were calculated. Texture metrics were compared between low- and high-stage clear cell RCC.

RESULTS

In 61 patients, there were 62 clear cell RCCs (33 low stage [stages I and II] and 29 high stage [stages III and IV]) at pathologic examination. Staging accuracy of qualitative interpretation was 100% for low-stage lesions and 37.9% (11/29) for high-stage lesions. There was no statistically significant difference in mean ADC between high- and low-stage clear cell RCCs (1.77×10(-3) vs 1.80×10(-3) mm2/s; p=0.7). However, high-stage clear cell RCCs were larger (6.96±2.93 vs 3.49±1.57 cm; p<0.0001) and had statistically significantly (p≤0.0001) higher ADC skewness (0.02±0.33 vs -0.52±0.65) and cooccurrence matrix correlation (0.64±0.11 vs 0.49±0.13). Multivariate logistic regression identified size, skewness, and cooccurrence matrix correlation as significant independent predictors of high stage (AUC=0.92). Interreader correlation in texture metrics ranged from 0.82 to 0.89.

CONCLUSION

First- and second-order ADC texture metrics differ between low- and high-stage clear cell RCCs. A model that includes size and ADC texture measures may help to stage clear cell RCCs noninvasively.

The service encounter in radiology: acing the "moments of truth" to achieve patient-centered care

Radiologists are increasingly recognizing their role as direct service providers to patients and seeking to offer an exceptional patient experience as part of high-quality service delivery. Patients' perceptions of service delivery are derived from the chain of numerous individual real-time encounters that occur throughout their visit. These so-called "moments of truth" define the overall experience and form the lasting impression of the given practice in their mind. Providing excellent service can be difficult to achieve in practice given its intangible nature as well as the heterogeneity and unpredictability of the large number of patients, frontline staff, and environmental circumstances that define the patient experience. Thus, broad commitment and team effort among all members of a radiology practice are required. This article explores important areas to be considered by a radiology practice to ensure positive and meaningful patient experiences. Specific ways in which every member within the practice, including schedulers, receptionists, technologists, nurses, and radiologists, can contribute to achieving high-quality patient service are discussed. Examples of patient-oriented language that may be useful in particular scenarios in radiology practice are given. The role of the practice's physical facility, including all aspects of its aesthetics and amenities, as well as of Internet services, in shaping the patient experience is also described. Throughout this work, a proactive approach to promoting a service-oriented organizational culture is provided. By improving the patient experience, these strategies may serve to enhance patients' perceptions of radiology and radiologists.

Histogram analysis of apparent diffusion coefficient map of diffusion-weighted MRI in endometrial cancer: a preliminary correlation study with histological grade

BACKGROUND

Until now, several investigators have explored the value of diffusion-weighted magnetic resonance imaging (DWI) for the preoperative tumor grading of endometrial cancer. However, the diagnostic value of DWI with quantitative analysis of apparent diffusion coefficient (ADC) has been controversial.

PURPOSE

To explore the role of histogram analysis of ADC maps based on entire tumor volume in determining the grade of endometrial cancer.

MATERIAL AND METHODS

This study was IRB-approved with waiver of informed consent. Thirty-three patients with endometrial cancer underwent DWI (b = 0, 600, 1000 s/mm(2)), and corresponding ADC maps were acquired. Regions of interest (ROIs) were drawn on all slices of the ADC map in which the tumor was visualized including areas of necrosis to derive volume-based histographic ADC data. Histogram parameters (5th-95th percentiles, mean, standard deviation, skewness, kurtosis) were correlated with histological grade using one-way ANOVA with Tukey-Kramer test for post hoc comparisons, and were compared between high (grade 3) and low (grades 1/2) grade using Student t-test. ROC curve analysis was performed to determine the optimum threshold value for each parameter, and their corresponding sensitivity and specificity.

RESULTS

The standard deviation, quartile, 75th, 90th, and 95th percentiles of ADC showed significant differences between grades (P ≤ 0.03 for all) and between high and low grades (P ≤ 0.024 for all). There were no significant correlations between tumor grade and other parameters. ROC curve analysis yielded sensitivities and specificities of 75% and 96%, 62.5% and 92%, 100% and 52%, 100% and 72%, and 100% and 88%, using standard deviation, quartile, 75th, 90th, and 95th percentiles for determining high grade with corresponding areas under the curve (AUCs) of 0.787, 0.792, 0.765, 0.880, and 0.925, respectively.

CONCLUSION

Histogram analysis of ADC maps based on entire tumor volume can be useful for predicting the histological grade of endometrial cancer. The 90th and 95th percentiles of ADC were the most promising parameters for differentiating high from low grade.

Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review

BACKGROUND

Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice.

METHODOLOGY

The Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared.

PRINCIPAL FINDINGS

Of the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description.

CONCLUSIONS

In a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.

Improving tumour heterogeneity MRI assessment with histograms

By definition, tumours are heterogeneous. They are defined by marked differences in cells, microenvironmental factors (oxygenation levels, pH, VEGF, VPF and TGF-α) metabolism, vasculature, structure and function that in turn translate into heterogeneous drug delivery and therapeutic outcome. Ways to estimate quantitatively tumour heterogeneity can improve drug discovery, treatment planning and therapeutic responses. It is therefore of paramount importance to have reliable and reproducible biomarkers of cancerous lesions' heterogeneity. During the past decade, the number of studies using histogram approaches increased drastically with various magnetic resonance imaging (MRI) techniques (DCE-MRI, DWI, SWI etc.) although information on tumour heterogeneity remains poorly exploited. This fact can be attributed to a poor knowledge of the available metrics and of their specific meaning as well as to the lack of literature references to standardised histogram methods with which surrogate markers of heterogeneity can be compared. This review highlights the current knowledge and critical advances needed to investigate and quantify tumour heterogeneity. The key role of imaging techniques and in particular the key role of MRI for an accurate investigation of tumour heterogeneity is reviewed with a particular emphasis on histogram approaches and derived methods.

Intratumoral heterogeneity of breast cancer xenograft models: texture analysis of diffusion-weighted MR imaging

Objective

To investigate whether there is a relationship between texture analysis parameters of apparent diffusion coefficient (ADC) maps and histopathologic features of MCF-7 and MDA-MB-231 xenograft models.

Materials and Methods

MCF-7 estradiol (+), MCF-7 estradiol (-), and MDA-MB-231 xenograft models were made with approval of the animal care committee. Twelve tumors of MCF-7 estradiol (+), 9 tumors of MCF-7 estradiol (-), and 6 tumors in MDA-MB-231 were included. Diffusion-weighted MR images were obtained on a 9.4-T system. An analysis of the first and second order texture analysis of ADC maps was performed. The texture analysis parameters and histopathologic features were compared among these groups by the analysis of variance test. Correlations between texture parameters and histopathologic features were analyzed. We also evaluated the intraobserver agreement in assessing the texture parameters.

Results

MCF-7 estradiol (+) showed a higher standard deviation, maximum, skewness, and kurtosis of ADC values than MCF-7 estradiol (-) and MDA-MB-231 (p < 0.01 for all). The contrast of the MCF-7 groups was higher than that of the MDA-MB-231 (p = 0.004). The correlation (COR) of the texture analysis of MCF-7 groups was lower than that of MDA-MB-231 (p < 0.001). The histopathologic analysis showed that Ki-67_mean and Ki-67_diff of MCF-7 estradiol (+) were higher than that of MCF-7 estradiol (-) or MDA-MB-231 (p < 0.05). The microvessel density (MVD)_mean and MVD_diff of MDA-MB-231 were higher than those of MCF-7 groups (p < 0.001). A diffuse-multifocal necrosis was more frequently found in MDA-MB-231 (p < 0.001). The proportion of necrosis moderately correlated with the contrast (r = -0.438, p = 0.022) and strongly with COR (r = 0.540, p = 0.004). Standard deviation (r = 0.622, r = 0.437), skewness (r = 0.404, r = 0.484), and kurtosis (r = 0.408, r = 0.452) correlated with Ki-67_mean and Ki-67_diff (p < 0.05 for all). COR moderately correlated with Ki-67_diff (r = -0.388, p = 0.045). Skewness (r = -0.643, r = -0.464), kurtosis (r = -0.581, r = -0.389), contrast (r = -0.473, r = -0.549) and COR (r = 0.588, r = 0.580) correlated with MVD_mean and MVD_diff (p < 0.05 for all).

Conclusion

The texture analysis of ADC maps may help to determine the intratumoral spatial heterogeneity of necrosis patterns, amount of cellular proliferation and the vascularity in MCF-7 and MDA-MB-231 xenograft breast cancer models.