Apparent diffusion coefficient (ADC) measurement in ovarian tumor: Effect of region-of-interest methods on ADC values and diagnostic ability

Clinical Utility of Diffusion-Weighted Imaging in Gynecological Imaging: Revisited

ABSTRACT

Diffusion-weighted imaging (DWI) is an increasingly valuable sequence in daily clinical practice, providing both functional and morphological information. The use of DWI can help quantify diffusion using the apparent diffusion coefficient, which reflects the physiological features of the tissue and tumor microcirculation. This knowledge is crucial for understanding and interpreting gynecological imaging. This article reviews the clinical utility of DWI for gynecological imaging, highlighting its ability to aid in the detection of endometrial and cervical cancers, as well as tumor extension and metastasis. In addition, DWI can easily detect the solid components of ovarian cancer (including dissemination), assist in the diagnosis of adnexal torsion, and potentially show bone marrow status. Apparent diffusion coefficient measurement is useful for differentiating between endometrial lesions, uterine leiomyomas, and sarcomas, and may provide important information for predicting the prognosis of gynecological cancers.

Neoadjuvant chemotherapy induces an elevation of tumour apparent diffusion coefficient values in patients with ovarian cancer

OBJECTIVES

Multiparametric magnetic resonance imaging (mMRI) is the modality of choice in the imaging of ovarian cancer (OC). We aimed to investigate the feasibility of different types of regions of interest (ROIs) in the measurement of apparent diffusion coefficient (ADC) values of diffusion-weighted imaging in OC patients treated with neoadjuvant chemotherapy (NACT).

METHODS

We retrospectively enrolled 23 consecutive patients with advanced OC who had undergone NACT and mMRI. Seventeen of them had been imaged before and after NACT. Two observers independently measured the ADC values in both ovaries and in the metastatic mass by drawing on a single slice of (1) freehand large ROIs (L-ROIs) covering the solid parts of the whole tumour and (2) three small round ROIs (S-ROIs). The side of the primary ovarian tumour was defined. We evaluated the interobserver reproducibility and statistical significance of the change in tumoural pre- and post-NACT ADC values. Each patient's disease was defined as platinum-sensitive, semi-sensitive, or resistant. The patients were deemed either responders or non-responders.

RESULTS

The interobserver reproducibility of the L-ROI and S-ROI measurements ranged from good to excellent (ICC range: 0.71-0.99). The mean ADC values were significantly higher after NACT in the primary tumour (L-ROI p  < 0.001, S-ROIs p < 0.01), and the increase after NACT was associated with sensitivity to platinum-based chemotherapy. The changes in the ADC values of the omental mass were associated with a response to NACT.

CONCLUSION

The mean ADC values of the primary tumour increased significantly after NACT in the OC patients, and the amount of increase in omental mass was associated with the response to platinum-based NACT. Our study indicates that quantitative analysis of ADC values with a single slice and a whole tumour ROI placement is a reproducible method that has a potential role in the evaluation of NACT response in patients with OC.

TRIAL REGISTRATION

Retrospectively registered (institutional permission code: 5302501; date of the permission: 31.7.2020).

Radiomics nomogram in aiding preoperatively dilatation and curettage in differentiating type II and type I endometrial cancer

AIM

To established a radiomics nomogram for improving the dilatation and curettage (D&C) result in differentiating type II from type I endometrial cancer (EC) preoperatively.

MATERIAL AND METHODS

EC patients (n=875) were enrolled retrospectively and divided randomly into a training cohort (n=437) and a test cohort (n=438), according to the ratio of 1:1. Radiomics signatures were extracted and selected from apparent diffusion coefficient (ADC) maps. A multivariate logistic regression analysis was used to identify the independent clinical risk factors. An ADC based-radiomics nomogram was built by integrating the selected radiomics signatures and the independent clinical risk factors. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the radiomics nomogram. The net reclassification index (NRI) and total integrated discrimination index (IDI) were calculated to compare the discrimination performances between the radiomics nomogram and the D&C result.

RESULTS

Receiver operating characteristic (ROC) curves showed that the clinical risk factors, the D&C, and the ADC based-radiomics nomogram yielded areas under the ROC curves (AUCs) of 0.70 (95% CI: 0.64-0.76), 0.85 (95% CI: 0.80-0.89), and 0.93 (95% CI: 0.90-0.96) in the training cohort and 0.64 (95% CI: 0.57-0.71), 0.82 (95% CI: 0.77-0.87) and 0.91 (95% CI: 0.87-0.95) in the test cohort, respectively. The DCA, NRI, and IDI demonstrated the clinically usefulness of the ADC based-radiomics nomogram.

CONCLUSION

The ADC-based radiomics nomogram could be used to improve the D&C result in differentiating type II from type I EC preoperatively.

The Effect of the Energy Release Rate on the Local Damage Evolution in TRIP Steel Composite Reinforced with Zirconia Particles

In this study, the effect of the energy release rate on the transformation-induced plasticity (TRIP) steel composite reinforced with 5 vol% ceramic particles is determined using the crystal plasticity simulation of the coupled brittle-ductile damage model and validated by experimental results. A miniature dog bone tensile sample is subjected to an interrupted in situ quasi-static tensile test up to a true strain of 20.3%. Using the commercial digital image correlation program VEDDAC and the image processing method in MATLAB, the test data are utilized to monitor the progress of local microstrain and damage. The impact of the energy release rate of ceramic particles is investigated by simulation using a coupled crystal plasticity-dislocation density model with ductile-brittle criteria for the corresponding phases. It can be shown that the local deformations predicted by the numerical simulation and the experimental data are qualitatively comparable. The damage pixel of the experiment, smaller E_cr (1.0 × 10⁸), and larger E_cr (1.2 × 10⁸) cases of energy release rates are 4.9%, 4.3%, and 5.1%, respectively. Furthermore, on a global strain of 20.3%, the relative error between simulation and experimental validation of smaller E_cr (1.0 × 10⁸) and larger E_cr (1.2 × 10⁸) cases is 12.2% and 4%, respectively.

Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype

Background

Epithelial ovarian tumors (EOTs) are a group of heterogeneous neoplasms. It is importance to preoperatively differentiate the histologic subtypes of EOTs. Our study aims to investigate the potential of radiomics signatures based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps for categorizing EOTs.

Methods

This retrospectively enrolled 146 EOTs patients [34 with borderline EOT(BEOT), 30 with type I and 82 with type II epithelial ovarian cancer (EOC)]. A total of 390 radiomics features were extracted from DWI and ADC maps. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. A radiomics signature was established using multivariable logistic regression method with 3-fold cross-validation and repeated 50 times. Patients with bilateral lesions were included in the validation cohort and a heuristic selection method was established to select the tumor with maximum probability for final consideration. A nomogram incorporating the radiomics signature and clinical characteristics was also developed. Receiver operator characteristic, decision curve analysis (DCA), and net reclassification index (NRI) were applied to compare the diagnostic performance and clinical net benefit of predictive model.

Results

For distinguishing BEOT from EOC, the radiomics signature and nomogram showed more favorable discrimination than the clinical model (0.915 vs. 0.852 and 0.954 vs. 0.852, respectively) in the training cohort. In classifying early-stage type I and type II EOC, the radiomics signature exhibited superior diagnostic performance over the clinical model (AUC 0.905 vs. 0.735). The diagnostic efficacy of the nomogram was the same as that of the radiomics model with NRI value of -0.1591 (P = 0.7268). DCA also showed that the radiomics model and combined model had higher net benefits than the clinical model.

Conclusion

Radiomics analysis based on DWI, and ADC maps serve as an effective quantitative approach to categorize EOTs.

Discriminating Between Benign and Malignant Solid Ovarian Tumors Based on Clinical and Radiomic Features of MRI

RATIONALE AND OBJECTIVES

To develop and validate a combined model integrating clinical and radiomic features to non-invasive discriminate between the benign and malignant solid ovarian tumors.

MATERIALS AND METHODS

A total of 148 patients with 156 solid ovarian tumors (86 benign and 70 malignant tumors) were included in this study. The dataset was split into the training and the test set with a ratio of 8:2 using stratified random sampling. 12 clinical features and 1612 radiomic features were extracted from each tumor. These features were selected by least absolute shrinkage and selection operator (Lasso). Three classification models were built using extreme gradient boosting (XGB) algorithm: clinical model, radiomic model, combined model. The area under the receiver operating characteristic curve (AUC), accuracy, precision and sensitivity were analyzed to evaluate the performance of these models.

RESULTS

All of the three models obtained good performances in differentiating benign with malignant solid ovarian tumors in both training and test sets. The AUC, accuracy, precision, sensitivity of clinical model and radiomic model in test set were 0.847 (95% confidence interval (CI), 0.707-0.986, p <0.01), 0.774, 0.769, 0.714, and 0.807 (95%CI, 0.652-0.961, p <0.05), 0.677, 0.643, 0.643, respectively. Combined model had the best prediction results, the AUC, accuracy, precision and sensitivity were 0.954 (95%CI, 0.862-1.0, p <0.01), 0.839, 0.909 and 0.714 in test set.

CONCLUSION

Radiomics based on machine learning can be helpful for radiologists in differentiating the benign and malignant solid ovarian tumors.

Reproducibility of apparent diffusion coefficient measurement in normal prostate peripheral zone at 1.5T MRI

PURPOSE

The purpose of this study was to quantify the influence of factors of variability on apparent diffusion coefficient (ADC) estimation in the normal prostate peripheral zone (PZ).

MATERIALS AND METHODS

Fifty healthy volunteers underwent in 2017 (n = 17) or 2020 (n = 33) two-point (0, 800 s/mm²) prostate diffusion-weighted imaging in the morning on 1.5 T scanners A and B from different manufacturers. Additional five-point (50, 150, 300, 500, 800 s/mm²) acquisitions were performed on scanner B in the morning and evening. ADC was measured in PZ at midgland using ADC maps reconstructed with various b-value combinations. ADC distributions from 2017 and 2020 were compared using Wilcoxon rank sum test. ADC obtained in the same volunteers were compared using Bland Altman methodology. The 95% confidence interval upper limit of the repeatability/reproducibility coefficient defined the lowest detectable ADC difference.

RESULTS

Forty-nine participants with a mean age of 24.6 ± 3.8 [SD] years (range: 21-37 years) were finally included. ADC distributions from 2017 and 2020 were not significantly different and were combined. Despite high individual variability, there was no significant bias (10 × 10^-6 mm²/s, P = 0.58) between ADC measurements made on both scanners. On scanner B, differences in lowest b-values chosen within the 0-500 s/mm² range for two-point ADC computation induced significant biases (56-109 × 10^-6 mm²/s, P < 0.0001). ADC was significantly lower in the morning (bias: 33 × 10^-6 mm²/s, P = 0.006). The number of b-values had little influence on ADC values. The lowest detectable ADC difference varied from 85 × 10^-6 to 311 × 10^-6 mm²/s across scanners, b-value combinations and periods of the day.

CONCLUSIONS

The MRI scanner, the lowest b-value used and the period of the day induce substantial variability in ADC computation.

Assessment of placental abruption with diffusion-weighted imaging

AIM

To investigate whether placental abruption without fetal distress could be assessed by apparent diffusion coefficient (ADC) values in magnetic resonance imaging (MRI).

METHODS

We conducted a retrospective case-control study at a single center. ADC values at the lesions of placental abruption in the abruption group (n = 8) were compared to those in the control group (n = 32). In the abruption group, ADC values at the sites of abruption were also compared to those at the nonabruption sites within the same placenta.

RESULTS

The ADC values in the placental area above the abruption site in the abruption group showed lower values than those in the control group when the slice containing the umbilical cord insertion site was set as the reference, and those values were compared in each corresponding slice. Compared with average ADC values, those above the abruption site in the abruption group were also significantly lower than those in the control group (p < 0.001). Furthermore, ADC values at the area above abruption were lower than those at the nonabruption area of all planes in the abruption group.

CONCLUSIONS

ADC values at the lesions above the placental abruption site were reduced compared to those in the normal placenta and those in the nonabruption area. Thus, it would be helpful to understand the pathophysiology of placental abruption in expectant management, although further investigations would be needed.

MRI-Based Multiple Instance Convolutional Neural Network for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors

BACKGROUND

Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT vs. MEOT) is challenging and can significantly impact surgical management.

PURPOSE

To develop a multiple instance convolutional neural network (MICNN) that can differentiate BEOT from MEOT, and to compare its diagnostic performance with that of radiologists.

STUDY TYPE

Retrospective study of eight clinical centers.

SUBJECTS

Between January 2010 and June 2018, a total of 501 women (mean age, 48.93 ± 14.05 years) with histopathologically confirmed BEOT (N = 165) or MEOT (N = 336) were divided into the training (N = 342) and validation cohorts (N = 159).

FIELD STRENGTH/SEQUENCE

Three axial sequences from 1.5 or 3 T scanner were used: fast spin echo T2-weighted imaging with fat saturation (T2WI FS), echo planar diffusion-weighted imaging, and 2D volumetric interpolated breath-hold examination of contrast-enhanced T1-weighted imaging (CE-T1WI) with FS.

ASSESSMENT

Three monoparametric MICNN models were built based on T2WI FS, apparent diffusion coefficient map, and CE-T1WI. Based on these monoparametric models, we constructed an early multiparametric (EMP) model and a late multiparametric (LMP) model using early and late information fusion methods, respectively. The diagnostic performance of the models was evaluated using the receiver operating characteristic (ROC) curve and compared to the performance of six radiologists with varying levels of experience.

STATISTICAL TESTS

We used DeLong test, chi-square test, Mann-Whitney U-test, and t-test, with significance level of 0.05.

RESULTS

Both EMP and LMP models differentiated BEOT from MEOT, with an area under the ROC curve (AUC) of 0.855 (95% CI, 0.795-0.915) and 0.884 (95% CI, 0.831-0.938), respectively. The AUC of the LMP model was significantly higher than the radiologists' pooled AUC (0.884 vs. 0.797).

DATA CONCLUSION

The developed MICNN models can effectively differentiate BEOT from MEOT and the diagnostic performances (AUCs) were more superior than that of the radiologists' assessments.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Diffusion-weighted MRI and PET/CT reproducibility in epithelial ovarian cancers during neoadjuvant chemotherapy

PURPOSE

To investigate the reproducibility of diffusion-weighted (DW) MRI and ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG)-Positron emission tomography/CT (PET/CT) in monitoring response to neoadjuvant chemotherapy in epithelial ovarian cancer.

MATERIALS AND METHODS

Ten women (median age, 67 years; range: 41.8-77.3 years) with stage IIIC-IV epithelial ovarian cancers were included in this prospective trial (NCT02792959) between 2014 and 2016. All underwent initial laparoscopic staging, four cycles of carboplatine-paclitaxel-based chemotherapy and interval debulking surgery. PET/CT and DW-MRI were performed at baseline (C0), after one cycle (C1) and before surgery (C4). Two nuclear physicians and two radiologists assessed five anatomic sites for the presence of ≥1 lesion. Target lesions in each site were defined and their apparent diffusion coefficient (ADC), maximal standardized uptake value (SUV-max), SUV-mean, SUL-peak, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were monitored (i.e., 10 patients ×5 sites ×3 time-points). Their relative early and late changes were calculated. Intra/inter-observer reproducibilities of qualitative and quantitative analysis were estimated with Kappa and intra-class correlation coefficients (ICCs).

RESULTS

For both modalities, inter- and intra-observer agreement percentages were excellent for initial staging but declined later for DW-MRI, leading to lower Kappa values for inter- and intra-observer variability (0.949 and 1 at C0, vs. 0.633 and 0.643 at C4, respectively) while Kappa values remained>0.8 for PET/CT. Inter- and intra-observer ICCs were>0.75 for SUV-max, SUL-peak, SUV-mean and their change regardless the time-point. ADC showed lower ICCs (range: 0.013-0.811). ANOVA found significant influences of the evaluation time, the measurement used (ADC, SUV-max, SUV-mean, SUV-max, SUL-peak, MTV or TLG) and their interaction on ICC values (P=0.0023, P<0.0001 and P =0.0028, respectively).

CONCLUSION

While both modalities demonstrated high reproducibility at baseline, only SUV-max, SUL-peak, SUV-mean and their changes maintained high reproducibility during chemotherapy.

Repeatability and reproducibility of MRI apparent diffusion coefficient applied on four different regions of interest for patients with axial spondyloarthritis and healthy volunteers scanned twice within a week

Objectives:

The apparent diffusion coefficient (ADC) may be used as a biomarker for diagnosis and/or monitoring treatment response in patients with axial spondyloarthritis (axSpA), but this requires reliable ADC measurements. This study assessed test–retest repeatability and reproducibility of ADC measurements using four different region of interest (ROI) settings.

Methods:

In this prospective study, the sacroiliac joints (SIJs) of 25 patients with axSpA and 24 age- and sex-matched healthy volunteers were imaged twice at a mean interval of 6.8 days in a 1.5 T scanner using, multishot echoplanar diffusion-weighted sequences. ADCs at four ROI settings were assessed: 5 mm and 10 mm anatomic band-shaped, 15 mm linear, and 40 mm² circular.

Results:

Intraclass correlation coefficient (ICC) assessments showed that the interstudy repeatability was good for median ADC (ADC_med) and 95th-percentile ADC (ADC₉₅) measurements in patients with axSpA (0.77–0.83 and 0.75–0.83, respectively), but poor-to-moderate in healthy subjects (0.27–0.55 and 0.13–0.37, respectively). For all ROI settings, intrareader reproducibility was excellent for ADC_med-measurements (ICC:0.85–0.99) and moderate-to-excellent for ADC₉₅ measurements (ICC:0.68–0.96). The 5 mm ROI had the least estimated bias and highest level of agreement on Bland–Altman plots. The interreader reproducibility was moderate (ICC:0.71). The 15 mm linear ROI produced significantly greater ADC_med and ADC₉₅ measurements than all other ROI settings (p < 0.01–0.02), except for the circular ROI ADC₉₅ measurements.

Conclusion:

ROI settings influence ADC measurements. Interstudy repeatability of SIJ ADC measurements is independent of ROI settings. However, the 5 mm ROI showed the least bias and random error and seems preferable.

Advances in knowledge:

ADC measurements are affected by ROI settings, and this should be taken into account when assessing ADC maps.

MRI and FDG PET/CT Findings for Borderline Brenner Tumor of the Ovary: A Case Report and Literature Review

The imaging features of borderline Brenner tumor (BT) of the ovary are very limited, especially regarding apparent diffusion coefficient (ADC) value and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET)/CT. We report a case of borderline BT in a 54-year-old woman with diffusion-weighted imaging (DWI) and FDG-PET/CT findings. Furthermore, ADC values and maximum standardized uptake value (SUV max) in the present case were compared with those of an additional 7 cases of benign BT in this institution in addition to literature reviews. Magnetic resonance imaging (MRI) revealed a pelvic unilocular cystic tumor with two solid components. The solid mass showing a low signal intensity (SI) in T2-weighted images (T2WI) and DWI was diagnosed as a benign BT histologically. The papillary tumor adjacent to the solid mass showing intermediate SI in T2WIs and high SI on DWI was a borderline BT. The mean ADC value (×10⁻³ mm²/s) of benign BTs (n = 7) and benign component in this case (n = 1) was 1.13, and the range of ADC values was broad (0.51–1.8). While, the ADC value of borderline Brenner component in this case was 1.10. The mean SUVmax of the benign BTs (n = 4) demonstrated mild FDG uptakes (2.3, range 1.9–2.6) in contrast with moderate FDG uptake (SUVmax: 5.8) of borderline Brenner component in this case and high FDG uptake (SUVmax: 9.6) of a malignant BT in a previous report. ADC values for the solid component of BTs are not useful for differentiating benign from malignant or borderline components, whereas PET/CT could be useful.

Ovarian solid tumors: MR imaging features with radiologic-pathologic correlation

Ovarian solid tumors have variable histological types including benign and malignant tumors. In addition, non-neoplastic lesions sometimes show a tumor-like appearance. It is important to differentiate benign from malignant tumors. In general, low signal intensity (SI) on T2-weighted imaging (T2WI), low SI on diffusion-weighted imaging (DWI), and gradual increased pattern on dynamic contrast-enhanced magnetic resonance (MR) imaging are known to be suggestive of a benign tumor. Conversely, there are some cases in which these rules do not apply. We should, therefore, strive for a greater understanding of these exceptional cases. Several tumors show characteristic findings on MR imaging reflecting pathologic features, which leads to the correct diagnosis. Additionally, MR imaging provides important information other than the nature of tumors, such as secondary uterine changes. Furthermore, clinical findings and laboratory examination data also help in determining the correct diagnosis.

MR imaging findings of low-grade serous carcinoma of the ovary: comparison with serous borderline tumor

PURPOSE

To assess MR imaging findings of low-grade serous carcinoma (LGSC) of the ovary compared with those of serous borderline tumor (SBT).

METHODS

Twenty-four patients with histopathologically proven 7 LGSCs and 25 SBTs who underwent preoperative MR imaging were included. We retrospectively reviewed MR images and compared MR findings between the two pathologies.

RESULTS

The predominantly solid lesions were marginally more frequent in LGSCs than in SBTs (43% vs. 8%, p = 0.057). All predominantly cystic LGSCs were multilocular cystic lesions with mural nodules. Predominantly solid LGSCs exhibited pure solid masses in 2 of 3 and solid masses with intratumoral cysts in 1 of 3. Papillary growth pattern with internal branching was observed only in 18 of 25 SBTs. Signal intensity ratio on T2-weighted images (4.48 ± 1.55 vs. 8.40 ± 3.53, p < 0.01) and apparent diffusion coefficient (ADC) values (1.12 ± 0.21 vs. 1.73 ± 0.27 × 10^-3 mm²/s, p < 0.01) of solid components was significantly lower in LGSCs than in SBTs.

CONCLUSION

Compared with SBTs, lower signal intensity on T2-weighted images and lower ADC values were characteristic features of solid components in LGSCs. Papillary growth pattern with internal branching was not observed in LGSCs.

Apparent diffusion coefficients measured using different regions of interest in differentiating borderline from malignant ovarian tumors

Background

Apparent diffusion coefficients (ADCs) measured using different regions of interest (ROIs) are widely used in differentiating ovarian tumors.

Purpose

To evaluate the diagnostic performance of ADCs with different ROIs in differentiating borderline ovarian tumors (BOTs) from malignant ovarian tumors (MOTs).

Material and Methods

Thirty-five BOTs and 54 MOTs who underwent diffusion-weighted magnetic resonance imaging (MRI) were evaluated retrospectively. ADC values were independently measured using five ROI methods: round; rectangle; hot-spot; edge drawing; and five sample ROIs. The inter- and intraclass correlation coefficients (ICCs), one-way analysis of variance, receiver operating characteristic curve analysis, and unpaired t-tests were used to perform the statistical analyses.

Results

The measurement reproducibility of the minimum ADC and mean ADC values were good or excellent for BOTs and MOTs (ICC = 0.70–0.95). The minimum and mean ADC value by the edge drawing ROI were significantly higher than those of the other ROI methods (both P < 0.05). The area under the curve (AUC) of the minimum ADC value was less than that of the mean ADC value from the five ROI methods, whereas the AUCs of the mean ADC values from the round ROI and five sample ROIs were significantly larger than those of the other ROI methods ( P < 0.05). The minimum and mean ADC values from the five ROI methods showed significant differences between BOTs and MOTs (all P < 0.05).

Conclusion

The ROI shape influences the diagnostic performance of ADC value for differentiating BOTs from MOTs. The mean ADC values from the round ROI and five sample ROIs have better diagnostic efficiency.

Effects of region of interest sizes on apparent diffusion coefficient measurements of pleomorphic adenoma, Warthin tumor, and normal parotid parenchyma

Background

Tumor apparent diffusion coefficient (ADC) measurements may be influenced by region of interest (ROI) sizes; however, this effect has not been systematically studied in parotid tumors. Our purpose was to determine the effects of ROI size on ADC measurements for the differentiation of pleomorphic adenoma (PA), Warthin tumor (WT), and normal parotid parenchyma.

Methods

Sixty-five patients including 37 with PA (lesions, n=37) and 28 with WT (lesions, n=36) were examined with diffusion-weighted imaging (DWI). Participants with normal contralateral parenchyma of the parotid gland constituted the control group (n=56). The mean ADC values and standard deviations (SDs) of the ADC (ADC_SD) of 12 concentric round ROIs (areas: 9, 28, 34, 50, 60, 82, 93, 98, 115, 130, 136, and 149 mm²) for tumors and normal tissue were measured by using custom-made software. Homogeneity index, which was defined by the ADC_SD/mean ADC, was also calculated. One-way repeated analyses of variance (ANOVAs) were performed on the mean ADCs, ADC_SDs, and homogeneity indices of the 12 ROIs in each group. The three parameters at different ROIs among PA, WT, and normal parotid parenchyma were compared using Kruskal-Wallis tests.

Results

There was excellent agreement for the ADC measurements with the 12 ROIs for PA [intraclass correlation coefficient (ICC), 0.98], WT (ICC, 0.99), and normal parotid parenchyma (ICC, 0.95). No significant differences were observed in the mean ADCs of the 12 ROIs for each of the three groups (P=0.744-0.990). Among the three groups, the mean ADC of normal parotid parenchyma [(0.94±0.003)×10^-3 mm²/s] was significantly lower than that of both PA [(1.72±0.01)×10^-3 mm²/s] and WT [(1.16±0.01)×10^-3 mm²/s] in the 12 ROIs, whereas the PA group had the highest mean ADC values. No significant differences were found in the mean ADC_SDs with each ROI between PA and WT (all P>0.017). PAs had lower homogeneity indices compared with WTs and normal parotid parenchyma (all P<0.01).

Conclusions

The effect of ROI size on ADC measurements could be excluded from the differentiation of PA, WT, and normal parotid parenchyma. Homogeneity index was a useful parameter in discriminating between the three groups.

Diffusion-weighted imaging of rectal cancer on repeatability and cancer characterization: an effect of b-value distribution study

BACKGROUND

To explore the effect of b-value distributions on the repeatability and diagnostic performance of the ADC value in rectal cancer patients using multiple b-values and mono-exponential model diffusion-weighted imaging (DWI).

METHODS

Thirty-two preoperative rectal cancer patients, without receiving neoadjuvant therapy, were scanned on a 3 Tesla magnetic resonance imaging scanner using DWI with 10 b-values ranging from 0 to 2000 s/mm². The apparent diffusion coefficient (ADC) value was calculated using a mono-exponential model and 31 b-value combinations consisting of 2 to 10 b-values were explored. Regions of interest with the maximum cross-sectional tumour size were outlined on the ADC map by two independent observers. Intraclass correlation coefficients (ICC), coefficient of variation (CV), and Bland-Altman plots between the two observers were calculated and evaluated to determine repeatability. Areas under receiver operating characteristic curves (AUCs) were evaluated for rectal cancer characterization. Correlations between the mean ADC values and T stage were assessed using the Spearman correlation coefficient (ρ). α (= ICC + AUC + |ρ|- CV - |bias|) was defined and used to assess the optimal b-value distribution.

RESULTS

Postoperative pathology tests revealed 4 patients with T1, 10 patients with T2, and 18 patients with T3 stages. There were no significant difference in age and sex between the two groups (T1-2 vs. T3). Excellent reproducibility was observed for ADC values between two observers with ICC and CV values ranging from 0.920 to 0.998, and 1.475 to 5.568%, respectively. The mean percent difference and ρ between the paired measurements was ranged from - 2.7 to 1.2% and from - 0.759 to - 0.407, respectively. The b-value combinations with the top three α values were b(0, 1000 s/mm²), b(500, 1500, 2000 s/mm²) and b(100, 1000, 1500 s/mm²) for α = 2.581, 2.571 and 2.569, respectively.

CONCLUSIONS

The number of b-values and their distributions influenced the repeatability of the ADC values and their diagnostic performance. The optimal b-value combination was 0 and 1000 s/mm² for DWI examination of rectal cancer patients.

Utility of DWI with quantitative ADC values in ovarian tumors: a meta-analysis of diagnostic test performance

Background Diffusion-weighted imaging (DWI) and quantitative apparent diffusion coefficient (ADC) values are widely used in the differential diagnosis of ovarian tumors. Purpose To assess the diagnostic performance of quantitative ADC values in ovarian tumors. Material and Methods PubMed, Embase, the Cochrane Library, and local databases were searched for studies assessing ovarian tumors using quantitative ADC values. We quantitatively analyzed the diagnostic performances for two clinical problems: benign vs. malignant tumors and borderline vs. malignant tumors. We evaluated diagnostic performances by the pooled sensitivity and specificity values and by summary receiver operating characteristic (SROC) curves. Subgroup analyses were used to analyze study heterogeneity. Results From the 742 studies identified in the search results, 16 studies met our inclusion criteria. A total of ten studies evaluated malignant vs. benign ovarian tumors and six studies assessed malignant vs. borderline ovarian tumors. Regarding the diagnostic accuracy of quantitative ADC values for distinguishing between malignant and benign ovarian tumors, the pooled sensitivity and specificity values were 0.91 and 0.91, respectively. The area under the SROC curve (AUC) was 0.96. For differentiating borderline from malignant tumors, the pooled sensitivity and specificity values were 0.89 and 0.79, and the AUC was 0.91. The methodological quality of the included studies was moderate. Conclusion Quantitative ADC values could serve as useful preoperative markers for predicting the nature of ovarian tumors. Nevertheless, prospective trials focused on standardized imaging parameters are needed to evaluate the clinical value of quantitative ADC values in ovarian tumors.

Characterization of tubo-ovarian abscess mimicking adnexal masses: Comparison between contrast-enhanced CT, 18F-FDG PET/CT and MRI

0BJECTIVE: We compared the diagnostic accuracy of contrast-enhanced computed tomography (CT), fluorine 18-labeled-fludeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/CT and conventional magnetic resonance imaging (MRI) without and with diffusion-weighted imaging (DWI) for characterization of tubo-ovarian abscesses (TOAs) that mimic adnexal tumors.

MATERIALS AND METHODS

We evaluated (retrospectively) 43 patients who underwent contrast-enhanced CT, PET/CT, conventional MRI without and with DWI, and who were found to have TOAs and complex adnexal tumors. All images were evaluated independently by four radiologists using a two-point grading system. Results of contrast-enhanced CT, PET/CT, MRI without DWI, and MRI with DWI were compared for each patient using receiver operating characteristic curves. Sensitivity, specificity, and positive predictive value (PPV) were calculated and compared using the chi-square test.

RESULTS

Sensitivity of MRI with DWI (95%) was significantly higher than that of contrast-enhanced CT (78.6%), PET/CT (86.7%) and MRI without DWI (87.5%). Specificities of these modalities were not significantly different. The PPV of MRI with DWI (100%) was significantly higher than that of the other three modalities (CT, 72.4%; PET/CT 78.5%; MRI without DWI, 81.5%). Overall accuracy of MRI with DWI was significantly higher than that of the other three modalities (CT, 74.4%; PET/CT, 81.4%; MRI without DWI, 83.7%).

CONCLUSION

MRI with DWI shows high accuracy for characterization of complex ovarian lesions, and is the most useful method for differentiation of TOAs from ovarian tumors.

Diffusion-weighted quantitative MRI of pleural abnormalities: Intra- and interobserver variability in the apparent diffusion coefficient measurements

PURPOSE

To assess intra- and interobserver variability in the apparent diffusion coefficient (ADC) measurements of pleural abnormalities.

MATERIALS AND METHODS

Diffusion-weighted magnetic resonance imaging was performed in 34 patients to characterize pleural abnormalities, with a 1.5T unit at b values of 0/150/500/800 sec/mm² . In two sessions held 3 months apart, on perfusion-free ADC maps, two independent readers measured the ADC of pleural abnormalities (two readings for each reader in each case) using different methods of region-of-interest (ROI) positioning. In three methods, freehand ROIs were drawn within tumor boundaries to encompass the entire lesion on one or more axial slices (whole tumor volume [WTV], three slices observer-defined [TSOD], single-slice [SS]), while in two methods one or more ROIs were placed on the more restricted areas (multiple small round ROI [MSR], one small round ROI [OSR]). Measurement variability between readings by each reader (intraobserver repeatability) and between readers in first reading (interobserver repeatability) were assessed using intraclass correlation coefficient (ICC) and coefficient of variation (CoV). Analysis of variance (ANOVA) was performed to compare ADC values between the different methods. The measurement time of each case for all methods in first reading was recorded and compared between methods and readers.

RESULTS

All methods demonstrated good (MSR, OSR) and excellent (WTV, TSOD, SS) intra- and interreader agreement, with best and worst repeatability in WTV (lower ICC, 0.977; higher CoV, 3.5%) and OSR (lower ICC, 0.625; higher CoV, 22.8%), respectively. The lower 95% confidence interval of ICC resulted in fair to moderate agreement for OSR (up to 0.379) and in excellent agreement for WTV, TSV, and SS (up to 0.918). ADC values of OSR and MSR were significantly lower compared to other methods (P < 0.001). The OSR and SS required less measurement time (10 and 21/22 sec, respectively) compared to the others (P < 0.0001), while the WTV required the longest measurement time (132/134 sec) (P < 0.0001).

CONCLUSION

ADC measurements of pleural abnormalities are repeatable. The SS method has excellent repeatability, similar to WTV, but requires significantly less measurement time. Thus, its use should be preferred in clinical practice.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:769-782.

Effects of regions of interest methods on apparent coefficient measurement of the parotid gland in early Sjögren's syndrome at 3T MRI

BACKGROUND

The apparent diffusion coefficient (ADC) has been used to assess parotid gland abnormalities in Sjögren's syndrome (SS) patients; however, few data exist on the influence of region of interest (ROI) methods on ADC measurements.

PURPOSE

To assess the influence of ROI methods on ADC measurement, and their diagnostic ability in detecting parotid gland abnormalities in early SS patients.

MATERIAL AND METHODS

Thirteen early SS patients underwent parotid gland diffusion-weighted imaging scans at a 3.0 T MR unit. Two readers independently measured the parotid gland ADC value using three different ROIs (whole-gland [WG], single-slice [SS], and reader-based circular [RBC]). The ADC value based on three different ROIs (ADC-ROI_WG, ADC-ROI_SS, ADC-ROI_RBC) were compared between the SS group and a matched healthy control (HC) group (n = 19). Receiver operating characteristic (ROC) curves and intra-class correlation coefficients (ICC) were used to determine the diagnostic ability and reproducibility of the parameters.

RESULTS

The ADC-ROI_WG, ADC-ROI_SS, and ADC-ROI_RBC in the SS group were all significantly higher than those in HC group (all P < 0.05). The ADC-ROI_WG showed better diagnostic ability than did ADC-ROI_RBC (P = 0.0200), while no significant difference was found between ADC-ROI_WG and ADC-ROI_SS (P = 0.4636). The ROI_WG method showed the best inter- and intra-reader agreement (ICC, 0.902 and 0.928, respectively), followed by ROI_SS and ROI_RBC.

CONCLUSION

The ROI methods can influence the parotid gland ADC measurements and their diagnostic ability. Considering our results, we suggest using in clinical practice single-slice ROIs to measure the ADC of the parotid gland.

Optimal Experiment Design for Monoexponential Model Fitting: Application to Apparent Diffusion Coefficient Imaging

The monoexponential model is widely used in quantitative biomedical imaging. Notable applications include apparent diffusion coefficient (ADC) imaging and pharmacokinetics. The application of ADC imaging to the detection of malignant tissue has in turn prompted several studies concerning optimal experiment design for monoexponential model fitting. In this paper, we propose a new experiment design method that is based on minimizing the determinant of the covariance matrix of the estimated parameters (D-optimal design). In contrast to previous methods, D-optimal design is independent of the imaged quantities. Applying this method to ADC imaging, we demonstrate its steady performance for the whole range of input variables (imaged parameters, number of measurements, and range of b-values). Using Monte Carlo simulations we show that the D-optimal design outperforms existing experiment design methods in terms of accuracy and precision of the estimated parameters.

Diffusion Weighted Imaging for Differentiating Benign from Malignant Orbital Tumors: Diagnostic Performance of the Apparent Diffusion Coefficient Based on Region of Interest Selection Method

Objective

To evaluate the differences in the apparent diffusion coefficient (ADC) measurements based on three different region of interest (ROI) selection methods, and compare their diagnostic performance in differentiating benign from malignant orbital tumors.

Materials and Methods

Diffusion-weighted imaging data of sixty-four patients with orbital tumors (33 benign and 31 malignant) were retrospectively analyzed. Two readers independently measured the ADC values using three different ROIs selection methods including whole-tumor (WT), single-slice (SS), and reader-defined small sample (RDSS). The differences of ADC values (ADC-ROI_WT, ADC-ROI_SS, and ADC-ROI_RDSS) between benign and malignant group were compared using unpaired t test. Receiver operating characteristic curve was used to determine and compare their diagnostic ability. The ADC measurement time was compared using ANOVA analysis and the measurement reproducibility was assessed using Bland-Altman method and intra-class correlation coefficient (ICC).

Results

Malignant group showed significantly lower ADC-ROI_WT, ADC-ROI_SS, and ADC-ROI_RDSS than benign group (all p < 0.05). The areas under the curve showed no significant difference when using ADC-ROI_WT, ADC-ROI_SS, and ADC-ROI_RDSS as differentiating index, respectively (all p > 0.05). The ROI_SS and ROI_RDSS required comparable measurement time (p > 0.05), while significantly shorter than ROI_WT (p < 0.05). The ROI_SS showed the best reproducibility (mean difference ± limits of agreement between two readers were 0.022 [-0.080–0.123] × 10^-3 mm²/s; ICC, 0.997) among three ROI methods.

Conclusion

Apparent diffusion coefficient values based on the three different ROI selection methods can help to differentiate benign from malignant orbital tumors. The results of measurement time, reproducibility and diagnostic ability suggest that the ROI_SS method are potentially useful for clinical practice.