Characterization of malignancy of adnexal lesions using ADC entropy: Comparison with mean ADC and qualitative DWI assessment

Does volumetric measurement of ADC values achieve higher diagnostic performance in bladder cancer MRI?

BACKGROUND

Apparent diffusion coefficient (ADC) value is an important part of bladder cancer magnetic resonance imaging (MRI) assessment and can predict the aggressive and invasive potentials. There is growing interest in whole tumor volume measurements.

PURPOSE

To investigate if the volumetric ADC measurement method will significantly exceed the diagnostic performance of the selected region of interest (ROI) method in everyday practice.

MATERIAL AND METHODS

A prospective evaluation was carried out of 50 patients with bladder cancer by two radiologists. The mean and the minimum ADC values were measured using both methods. The inter-reader agreement was determined by the intraclass correlation coefficient. The ADC values were compared between different grades, states of muscle invasion, and lympho-vascular invasion (LVI); then, validity was evaluated using receiver operating characteristic (ROC) curves. Areas under the curve (AUC) were then compared for the level of statistical significance.

RESULTS

The inter-observer agreement was excellent for the ADC values using both methods. The volumetric measurement provides higher mean and lower minimum ADC values with statistically significant differences (P <0.00001). The highest diagnostic accuracy for differentiating tumor grade and predicting muscle invasion was for the minimum ADC by a selected ROI. However, the differences between the achieved AUCs were of no statistical significance. None of the ADC values predicted LVI with statistical significance.

CONCLUSION

The selected ROI and volumetric measurement methods of mean and minimum ADC in bladder cancer yield different values, still having comparable diagnostic performance with accurate ROI sampling. The minimum ADC value by ROI is preferred in everyday clinical practice.

Mean ADC values and arterial phase hyperintensity discriminate small (≤ 3 cm) well-differentiated hepatocellular carcinoma from dysplastic nodule

BACKGROUND/AIM

This research endeavor sought to distinguish small (≤ 3 cm) well-differentiated hepatocellular carcinoma (WD-HCC) from dysplastic nodules (DN) by employing traditional imaging features and mean apparent diffusion coefficient (mADC) values derived from diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

In this retrospective analysis, we assessed a cohort of ninety patients with confirmed dysplastic nodules (DNs) (n = 71) or well-differentiated hepatocellular carcinoma (WD-HCC) (n = 41) who had undergone dynamic contrast-enhanced magnetic resonance imaging between March 2018 and June 2021. Multivariable logistic regression analyses were executed to pinpoint characteristics that can effectively differentiate histologic grades. A region-of-interest (ROI) encompassing all lesion voxels was delineated on each slice containing the mass in the ADC map. Subsequently, the whole-lesion mean ADC (mADC) were computed from these delineations. A receiver operating characteristic (ROC) curve was generated to assess the discriminatory efficacy of the mADC values in distinguishing between WD-HCC and DN.

RESULTS

Among the histopathological types from benign to malignant, mADC showed a significant decrease (P < 0.001). The mADCs were effective in distinguishing WD-HCC from DN [AUC, 0.903 (95% CI 0.849-0.958)]. The best cutoffs for the Youden index were 0.0012 mm2/s for mADC, with moderate sensitivity (70.7%) and high specificity (94.4%). MRI features including hyperintensity at arterial phase (odds ratio, 21.2; P = 0.009), mADC < 0.0012 mm2/s (odds ratio, 52.2; P < 0.001) were independent predictors for WD-HCC at multivariable analysis. The AUC value of hyperintensity at arterial phase was 0.857 (95% CI 0.786-0.928). The composite diagnostic criterion of arterial hyperintensity + mADC < 0.0012 mm2/s showed good performance [AUC, 0.926 (95% CI 0.878-0.975)], displaying increased sensitivity compared to individual assessments involving arterial hyperintensity (P = 0.013), mADC < 0.0012 mm2/s (P = 0.004), or LR-5 (P < 0.001), with similar specificity compared to LR-5 (P = 0.193).

CONCLUSION

DN and WD-HCC displayed contrasting diffusion characteristics, attainable to distinguish with satisfactory accuracy. The utilization of arterial phase hyperintensity and mADC < 0.0012 on MRI facilitated the differentiation of WD-HCC from DN.

Quantitative MR texture analysis for the differentiation of uterine smooth muscle tumors with high signal intensity on T2-weighted imaging

The purpose of this study was to distinguish leiomyosarcomas/smooth muscle tumors of uncertain malignant potential (STUMP) from leiomyomas with high signal intensity (SI) on T2-weighted imaging (T2WI) using quantitative MR texture analysis combined with patient characteristics and visual assessment. Thirty-one leiomyomas, 2 STUMPs, and 6 leiomyosarcomas showing high SI on T2WI were included. First, we searched for differences in patient characteristics and visual assessment between leiomyomas and leiomyosarcomas/STUMPs. We also compared the MR texture on T2WI and the apparent diffusion coefficient (ADC) to identify differences between leiomyomas and leiomyosarcomas/STUMPs. In the univariate analysis, significant differences between leiomyomas and leiomyosarcomas/STUMPs were observed in age, menopausal status, margin, hemorrhage, long diameter, T2-variance, T2-volume, ADC-variance, ADC-entropy, ADC-uniformity, ADC-90th and 95th percentile values, and ADC-volume (P < .05, respectively). There were significantly more postmenopausal patients with leiomyosarcomas/STUMPs than with leiomyomas, and leiomyosarcomas/STUMPs had more irregular margins, more frequent presence of hemorrhage and exhibited larger tumor diameters, T2-volume, T2-variance, ADC-volume, ADC-variance, ADC-entropy, and higher ADC-90th and 95th percentile values but lower ADC-uniformity. Multivariate analyses revealed that the independent differentiators were menopausal status, hemorrhage and ADC-entropy (P < .05, respectively). The area under the curve obtained by combining the 3 items was 0.980. The best cutoff value for ADC-entropy was 9.625 (sensitivity: 100%, specificity: 58%). The combination of menopausal status, hemorrhage, and ADC-entropy can help accurately distinguish leiomyosarcomas/STUMPs from leiomyomas with high SI on T2WI; however, external validation in a larger population is required because of the small sample size of our study.

Practical Tips for Reporting Adnexal Lesions Using O-RADS MRI

The Ovarian-Adnexal Reporting and Data System (O-RADS) MRI risk stratification system provides a standardized lexicon and evidence-based risk score for evaluation of adnexal lesions. The goals of the lexicon and risk score are to improve report quality and communication between radiologists and clinicians, reduce variability in the reporting language, and optimize management of adnexal lesions. The O-RADS MRI risk score is based on the presence or absence of specific imaging features, including the lipid content, enhancing solid tissue, number of loculi, and fluid type. The probability of malignancy ranges from less than 0.5% when there are benign features to approximately 90% when there is solid tissue with a high-risk time-intensity curve. This information can aid in optimizing management of patients with adnexal lesions. The authors present an algorithmic approach to the O-RADS MRI risk stratification system and highlight key teaching points and common pitfalls. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)

PURPOSE

To investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC).

METHODS AND MATERIALS

Two hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models.

RESULTS

Sixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05).

CONCLUSION

Radiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT-based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients.

Diffusion-Weighted Magnetic Resonance Imaging in Ovarian Cancer: Exploiting Strengths and Understanding Limitations

Detection, characterization, staging, and response assessment are key steps in the imaging pathway of ovarian cancer. The most common type, high grade serous ovarian cancer, often presents late, so that accurate disease staging and response assessment are required through imaging in order to improve patient management. Currently, computerized tomography (CT) is the most common method for these tasks, but due to its poor soft-tissue contrast, it is unable to quantify early response within lesions before shrinkage is observed by size criteria. Therefore, quantifiable techniques, such as diffusion-weighted magnetic resonance imaging (DW-MRI), which generates high contrast between tumor and healthy tissue, are increasingly being explored. This article discusses the basis of diffusion-weighted contrast and the technical issues that must be addressed in order to achieve optimal implementation and robust quantifiable diffusion-weighted metrics in the abdomen and pelvis. The role of DW-MRI in characterizing adnexal masses in order to distinguish benign from malignant disease, and to differentiate borderline from frankly invasive malignancy is discussed, emphasizing the importance of morphological imaging over diffusion-weighted metrics in this regard. Its key role in disease staging and predicting resectability in comparison to CT is addressed, including its valuable use as a biomarker for following response within individual lesions, where early changes in the apparent diffusion coefficient in peritoneal metastases may be detected. Finally, the task of implementing DW-MRI into clinical trials in order to validate this biomarker for clinical use are discussed, along with the trials that include it within their protocols.

Indirect comparison of the diagnostic performance of 18F-FDG PET/CT and MRI in differentiating benign and malignant ovarian or adnexal tumors: a systematic review and meta-analysis

Objective

To compare the value of fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) and magnetic resonance imaging (MRI) in differentiating benign and malignant ovarian or adnexal tumors.

Materials and methods

English articles reporting on the diagnostic performance of MRI or ¹⁸F-FDG PET/CT in identifying benign and malignant ovarian or adnexal tumors published in PubMed and Embase between January 2000 and January 2021 were included in the meta-analysis. Two authors independently extracted the data. If the data presented in the study report could be used to construct a 2 × 2 contingency table comparing ¹⁸F-FDG PET/CT and MRI, the studies were selected for the analysis. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to evaluate the quality of the included studies. Forest plots were generated according to the sensitivity and specificity of ¹⁸F-FDG PET/CT and MRI.

Results

A total of 27 articles, including 11¹⁸F-FDG PET/CT studies and 17 MRI studies on the differentiation of benign and malignant ovarian or adnexal tumors, were included in this meta-analysis. The pooled sensitivity and specificity for ¹⁸F-FDG PET/CT in differentiating benign and malignant ovarian or adnexal tumors were 0.94 (95% CI, 0.87–0.97) and 0.86 (95% CI, 0.79–0.91), respectively, and the pooled sensitivity and specificity for MRI were 0.92 (95% CI: 0.89–0.95) and 0.85 (95% CI: 0.79–0.89), respectively.

Conclusion

While MRI and ¹⁸F-FDG PET/CT both showed to have high and similar diagnostic performance in the differential diagnosis of benign and malignant ovarian or adnexal tumors, MRI, a promising non-radiation imaging technology, may be a more suitable choice for patients with ovarian or accessory tumors. Nonetheless, prospective studies directly comparing MRI and 18F-FDG PET/CT diagnostic performance in the differentiation of benign and malignant ovarian or adnexal tumors are needed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08815-3.

ACR Appropriateness Criteria® Clinically Suspected Adnexal Mass, No Acute Symptoms

There are approximately 9.1 pelvic surgeries performed for every histologically confirmed adnexal malignancy in the United States, compared to 2.3 surgeries per malignancy (in oncology centers) and 5.9 surgeries per malignancy (in other centers) in Europe. An important prognostic factor in the long-term survival in patients with ovarian malignancy is the initial management by a gynecological oncologist. With high accuracy of imaging for adnexal mass characterization and consequent appropriate triage to subspecialty referral, the better use of gynecologic oncology can improve treatment outcomes. Ultrasound, including transabdominal, transvaginal, and duplex ultrasound, combined with MRI with contrast can diagnose adnexal masses as benign with specific features (ie, functional masses, dermoid, endometrioma, fibroma, pedunculated fibroid, hydrosalpinx, peritoneal inclusion cyst, Tarlov cyst), malignant, or indeterminate. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Pulmonary adenocarcinoma appearing as ground-glass opacity nodules identified using non-enhanced and contrast-enhanced CT texture analysis: A retrospective analysis

The present study aimed to investigate the ability of CT-based texture analysis to differentiate invasive adenocarcinoma (IA) from pre-invasive lesions (PIL) or minimally IA (MIA) appearing as ground-glass opacity (GGO) nodules, and to further compare the performance of non-enhanced CT (NECT) images with that of contrast-enhanced CT (CECT) images. A total of 77 patients with GGO nodules and surgically confirmed pulmonary adenocarcinoma were included in the present retrospective study. Each GGO nodule was manually segmented and its texture features were extracted from NECT and CECT images using in-house developed software coded in MATLAB (MathWorks). The independent-samples t-test was used to select the texture features with statistically significant differences between IA and MIA/PIL. Multivariate logistic regression and receiver operating characteristics (ROC) curve analyses were performed to identify predictive features. Of the 77 GGO nodules, 12 were atypical adenomatous hyperplasia or adenocarcinoma in situ (15.6%), 36 were MIA (46.8%) and 29 were IA (37.7%). IA and MIA/PIL exhibited significant differences in most histogram features and gray-level co-occurrence matrix features (P<0.05). Multivariate logistic regression and ROC curve analyses revealed that smaller energy and higher entropy were significant differentiators of IA from MIA and PIL, irrespective of whether NECT images [area under the curve (AUC): 0.839, 0.859] or CECT images (AUC: 0.818, 0.820) are used. Texture analysis of CT images, regardless of whether NECT or CECT is used, has the potential to distinguish IA from PIL or MIA, particularly the parameters of energy and entropy. Furthermore, NECT images were simpler to obtain and no contrast agent was required; thus, analysis with NECT may be a preferred choice.

Exploratory Study of Apparent Diffusion Coefficient Histogram Metrics in Assessing Pancreatic Malignancy

PURPOSE

To evaluate whole-lesion 3D-histogram apparent diffusion coefficient (ADC) metrics for assessment of pancreatic malignancy.

METHODS

Forty-two pancreatic malignancies (36 pancreatic adenocarcinoma [PDAC], 6 pancreatic neuroendocrine [PanNET]) underwent abdominal magnetic resonance imaging (MRI) with diffusion-weighted imaging before endoscopic ultrasound biopsy or surgical resection. Two radiologists independently placed 3D volumes of interest to derive whole-lesion histogram ADC metrics. Mann-Whitney tests and receiver operating characteristic analyses were used to assess metrics' diagnostic performance for lesion histology, T-stage, N-stage, and grade.

RESULTS

Whole-lesion ADC histogram metrics lower in PDACs than PanNETs for both readers (P ≤ .026) were mean ADC (area under the curve [AUC] = 0.787-0.792), mean of the bottom 10th percentile (mean_0-10) (AUC = 0.787-0.880), mean of the 10th-25th percentile (mean_10-25) (AUC = 0.884-0.917) and mean of the 25th-50th percentile (mean_25-50) (AUC = 0.829-0.829). For mean_10-25 (metric with highest AUC for identifying PDAC), for reader 1 a threshold > 0.94 × 10^-3 mm²/s achieved sensitivity 94% and specificity 83%, and for reader 2 a threshold > 0.82 achieved sensitivity 97% and specificity 67%. Metrics lower in nodal status ≥ N₁ than N₀ for both readers (P ≤ .043) were mean_0-10 (AUC = 0.789-0.822) and mean_10-25 (AUC = 0.800-0.822). For mean_10-25 (metric with highest AUC for identifying N₀), for reader 1 a threshold <1.17 achieved sensitivity 87% and specificity 67%, and for reader 2 a threshold <1.04 achieved sensitivity 87% and specificity 83%. No metric was associated with T-stage (P > .195) or grade (P > .215).

CONCLUSION

Volumetric ADC histogram metrics may serve as non-invasive biomarkers of pancreatic malignancy. Mean_10-25 outperformed standard mean for lesion histology and nodal status, supporting the role of histogram analysis.

Whole-lesion histogram analysis metrics of the apparent diffusion coefficient: a correlation study with histological grade of hepatocellular carcinoma

PURPOSE

The study evaluated the relationship between the histological grade of hepatocellular carcinoma (HCC) and the histogram-derived parameters of apparent diffusion coefficient (ADC) obtained from the whole-lesion assessment of diffusion-weighted magnetic resonance (MR) imaging in the liver.

METHODS

A total of 51 patients were included. The parameters were correlated with the Edmondson-Steiner grades by using the Spearman correlation coefficient (ρ). The differences of ADC parameters between different tumor histological grades were compared using the Mann-Whitney U test. The extent to which each parameter aided in differentiating tumors with poor performance (III, IV) and fair performance (I, II) was assessed by using the area under the receiver operating characteristic curve (Az).

RESULTS

The 25th percentile ADC exhibits the most negative correlation with histological grade (ρ = - 0.397), followed by the 30th percentile ADC (ρ = - 0.395), the minimum ADC value (ρ = - 0.390) and the 20th percentile ADC (ρ = - 0.385), whereas the minimum ADC value yielded the highest Az (0.763) in the discrimination of tumor foci with poor differentiation from fairly differentiated HCCs. The minimum ADC of 4.15 × 10^-3 mm²/s or lower was considered to indicate poorly differentiated performance, and the corresponding sensitivity and specificity were 66.7 and 90.9%, respectively.

CONCLUSION

The 25th percentile ADC showed a stronger correlation with the histological grade of HCC than other ADC parameters, and the minimum ADC value might be an optimal metric for determining poor and fair differentiations of HCC in DWI.

Systematic radiological approach to utero-ovarian pathologies

Ultrasound is the first-line imaging modality for the evaluation of suspected adnexal masses, endometriosis and uterine tumors, whereas MRI is used as a secondary diagnostic tool to better characterize these lesions. The aim of this review is to summarize the latest advances in the imaging of these utero-ovarian pathologies.

Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade

OBJECTIVES

To discriminate low grade (Fuhrman I/II) and high grade (Fuhrman III/IV) clear cell renal cell carcinoma (CCRCC) by using CT-based radiomic features.

METHODS

161 and 99 patients diagnosed with low and high grade CCRCCs from January 2011 to May 2018 were enrolled in this study. 1029 radiomic features were extracted from corticomedullary (CMP), and nephrographic phase (NP) CT images of all patients. We used interclass correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO) regression method to select features, then the selected features were constructed three classification models (CMP, NP and with their combination) to discriminate high and low grades CCRCC. These three models were built by logistic regression method using 5-fold cross validation strategy, evaluated with receiver operating characteristics curve (ROC) and compared using DeLong test.

RESULTS

We found 11 and 24 CMP and NP features were independently significantly associated with the Fuhrman grades. The model of CMP, NP and Combined model using radiomic feature set showed diagnostic accuracy of 0.719 (AUC [area under the curve], 0.766; 95% CI [confidence interval]: 0.709-0.816; sensitivity, 0.602; specificity, 0.838), 0.738 (AUC, 0.818; 95% CI:0.765-0.838; sensitivity, 0.693; specificity, 0.838), 0.777(AUC, 0.822; 95% CI: 0.769-0.866; sensitivity, 0.677; specificity, 0.839). There were significant differences in AUC between CMP model and Combined model (P = 0.0208), meanwhile, the differences between CMP model and NP model, NP model and Combined model reached no significant (P = 0.0844, 0.7915).

CONCLUSIONS

Radiomic features could be used as biomarker for the preoperative evaluation of the CCRCC Fuhrman grades.

Whole-lesion Apparent Diffusion Coefficient First- and Second-Order Texture Features for the Characterization of Rectal Cancer Pathological Factors

OBJECTIVE

The objective of this study was to explore the value of whole-volume apparent diffusion coefficient (ADC) features in characterizing pathologic features of rectal cancer.

METHODS

A total of 50 patients who were diagnosed with rectal cancer via biopsy underwent 3-T pretreatment diffusion-weighted imaging. Apparent diffusion coefficient features, including mean, 10th-90th percentile, Entropy and Entropy(H), derived from whole-lesion volumes were compared between pathologic T1-2 and T3 stages, perineural invasion (PNI) present and absent, lymphangiovascular invasion present and absent, and pathological N0 and N+ stage groups.

RESULTS

Entropy and Entropy(H) were significantly lower in rectal cancers at T1-2 stages than T3. The 90th percentile of rectal cancers with PNI was significantly lower than that of those without PNI. All P < 0.05.

CONCLUSIONS

Whole-lesion ADC Entropy and Entropy(H) have potential in evaluating different T stages, and 90th percentile can be helpful for determining PNI presence of rectal cancers.

MRI texture features may predict differentiation and nodal stage of cervical cancer: a pilot study

Background

Texture analysis in oncological magnetic resonance imaging (MRI) may yield surrogate markers for tumor differentiation and staging, both of which are important factors in the treatment planning for cervical cancer.

Purpose

To identify texture features which may predict tumor differentiation and nodal status in diffusion-weighted imaging (DWI) of cervical carcinoma

Material and Methods

Twenty-three patients were enrolled in this prospective, institutional review board (IRB)-approved study. Pelvic MRI was performed at 3-T including a DWI echo-planar sequence with b-values 40, 300, and 800 s/mm². Apparent diffusion coefficient (ADC) maps were used for region of interest (ROI)-based texture analysis (32 texture features) of tumor, muscle, and fat based on histogram and gray-level matrices (GLM). All features confounded by the ROI size (linear model) were excluded. The remaining features were examined for correlations with histological differentiation (Spearman) and nodal status (Kruskal–Wallis). Hierarchical cluster analysis was used to identify correlations between features. A P value < 0.05 was considered statistically significant.

Results

Mean age was 55 years (range = 37–78 years). Biopsy revealed two well-differentiated, eight moderately differentiated, two moderately to poorly differentiated tumors, and five poorly differentiated tumors. Six tumors could not be graded. Lymph nodes were involved in 11 patients. Three GLM features correlated with the differentiation: LRHGE (ϱ = 0.53, P = 0.03), ZP (ϱ = –0.49, P < 0.05), and SZE (ϱ = –0.51, P = 0.04). Two histogram features, skewness (0.65 vs. 1.08, P = 0.04) and kurtosis (0.53 vs. 1.67, P = 0.02), were higher in patients with positive nodal status. Cluster analysis revealed several co-correlations.

Conclusion

We identified potentially predictive GLM features for histological tumor differentiation and histogram features for nodal cancer stage.

ADC-derived spatial features can accurately classify adnexal lesions

BACKGROUND

The role of quantitative apparent diffusion coefficient (ADC) maps in differentiating adnexal masses is unresolved.

PURPOSE/HYPOTHESIS

To propose an objective diagnostic method devised based on spatial features for predicting benignity/malignancy of adnexal masses in ADC maps.

STUDY TYPE

Prospective.

POPULATION

In all, 70 women with sonographically indeterminate and histopathologically confirmed adnexal masses (38 benign, 3 borderline, and 29 malignant) were considered for this study.

FIELD STRENGTH/SEQUENCE

Conventional and diffusion-weighted magnetic resonance (MR) images (b-values = 50, 400, 1000 s/mm² ) were acquired on a 3T scanner.

ASSESSMENT

For each patient, two radiologists in consensus manually delineated lesion borders in whole ADC map volumes, which were consequently analyzed using spatial models (first-order histogram [FOH], gray-level co-occurrence matrix [GLCM], run-length matrix [RLM], and Gabor filters). Two independent radiologists were asked to identify the attributed (benign/malignant) classes of adnexal masses based on morphological features on conventional MRI.

STATISTICAL TESTS

Leave-one-out cross-validated feature selection followed by cross-validated classification were applied to the feature space to choose the spatial models that best discriminate benign from malignant adnexal lesions. Two schemes of feature selection/classification were evaluated: 1) including all benign and malignant masses, and 2) scheme 1 after excluding endometrioma, hemorrhagic cysts, and teratoma (14 benign, 29 malignant masses). The constructed feature subspaces for benign/malignant lesion differentiation were tested for classification of benign/borderline/malignant and also borderline/malignant adnexal lesions.

RESULTS

The selected feature subspace consisting of RLM features differentiated benign from malignant adnexal masses with a classification accuracy of ∼92%. The same model discriminated benign, borderline, and malignant lesions with 87% and borderline from malignant with 100% accuracy. Qualitative assessment of the radiologists based on conventional MRI features reached an accuracy of 80%.

DATA CONCLUSION

The spatial quantification methodology proposed in this study, which works based on cellular distributions within ADC maps of adnexal masses, may provide a helpful computer-aided strategy for objective characterization of adnexal masses.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1061-1071.

Tumor spatial heterogeneity in myxoid-containing soft tissue using texture analysis of diffusion-weighted MRI

The objective of this study was to examine the tumor spatial heterogeneity in myxoid-containing soft-tissue tumors (STTs) using texture analysis of diffusion-weighted imaging (DWI). A total of 40 patients with myxoid-containing STTs (23 benign and 17 malignant) were included in this study. The region of interest (ROI) was manually drawn on the apparent diffusion coefficient (ADC) map. For texture analysis, the global (mean, standard deviation, skewness, and kurtosis), regional (intensity variability and size-zone variability), and local features (energy, entropy, correlation, contrast, homogeneity, variance, and maximum probability) were extracted from the ADC map. Student’s t-test was used to test the difference between group means. Analysis of covariance (ANCOVA) was performed with adjustments for age, sex, and tumor volume. The receiver operating characteristic (ROC) analysis was performed to compare diagnostic performances. Malignant myxoid-containing STTs had significantly higher kurtosis (P = 0.040), energy (P = 0.034), correlation (P<0.001), and homogeneity (P = 0.003), but significantly lower contrast (P<0.001) and variance (P = 0.001) compared with benign myxoid-containing STTs. Contrast showed the highest area under the curve (AUC = 0.923, P<0.001), sensitivity (94.12%), and specificity (86.96%). Our results reveal the potential utility of texture analysis of ADC maps for differentiating benign and malignant myxoid-containing STTs.

Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness

PURPOSE

To explore the role of whole-volume apparent diffusion coefficient (ADC)-based entropy parameters in the preoperative assessment of gastric cancer's aggressiveness.

MATERIALS AND METHODS

In all, 64 patients with gastric cancers who underwent 3.0T magnetic resonance imaging (MRI) were retrospectively included. Regions of interest were drawn manually using in-house software, around gastric cancer lesions on each slice of the diffusion-weighted images and ADC maps. Entropy-related parameters based on ADC maps were calculated automatically: (1) first-order entropy; (2-5) second-order entropies, including entropy(H)₀ , entropy(H)₄₅ , entropy(H)₉₀ , and entropy(H)₁₃₅ ; (6) entropy(H)_mean ; and (7) entropy(H)_range . Correlations between entropy-related parameters and pathological characteristics were analyzed with the Spearman correlation test. The parameters were compared among different pathological characteristics with independent-samples Kruskal-Wallis or Mann-Whitney U-test. Additionally, diagnostic performances of parameters in differentiating different pathological characteristics were analyzed by receiver operating characteristic (ROC) curve analysis.

RESULTS

All the entropy-related parameters significantly correlated with T, N, and overall stages, especially the first-order entropy (r = 0.588, 0.585, and 0.677, respectively, all P < 0.05). All the entropy-related parameters showed significant differences in gastric cancers at different T, N, and overall stages, as well as at different status of vascular invasion (P < 0.001-0.027). And four parameters, including entropy, entropy(H)₀ , entropy(H)₄₅ , and entropy(H)₉₀ , showed significant differences between gastric cancers with and without perineural invasion (P 0.006-0.040).

CONCLUSION

Entropy-related parameters derived from whole-volume ADC texture analysis could help assess the aggressiveness of gastric cancers via analyzing intratumoral heterogeneity quantitatively, especially the first-order entropy.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:168-175.

Diagnostic accuracy of DWI in patients with ovarian cancer: A meta-analysis

BACKGROUND

Diffusion weighted imaging (DWI) is recently developed for identifying different malignant tumors. In this article the diagnostic accuracy of DWI for ovarian cancer was evaluated by synthesis of published data.

METHODS

A comprehensive literature search was conducted in PubMed/MEDLINE and Embase databases on the diagnostic performance of DWI for ovarian cancer published in English. Methodological quality was evaluated following Quality Assessment for Studies of Diagnostic Accuracy 2 (QUADAS 2) tool. We adopted the summary receiver operating characteristic (SROC) curve to assess the DWI accuracy.

RESULTS

Twelve studies including 1142 lesions were analyzed in this meta-analysis to estimate the pooled Sen (sensitivity), Spe (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), and construct SROC (summary receiver operating characteristics) curve. The pooled Sen and Spe were 0.86 (95% confidence interval [CI], 0.83-0.89) and 0.81 (95%CI, 0.77-0.84), respectively. The pooled PLR and pooled NLR were 5.07 (95%CI, 3.15-8.16) and 0.17 (95%CI, 0.10-0.30), respectively. The pooled diagnostic odds ratio (DOR) was 35.23 (95%CI, 17.21-72.14). The area under the curve (AUC) was 0.9160.

CONCLUSION

DWI had moderately excellent diagnostic ability for ovarian cancer and promised to be a helpful diagnostic tool for patients of ovarian cancer.

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.

Characterization of a rat orthotopic pancreatic head tumor model using three-dimensional and quantitative multi-parametric MRI

The purpose of this study was to investigate the reliability of 3D isotropic MRI and quantitative multi-parametric MRI characterization on an orthotopic pancreatic head tumor model in rats. 3D isotropic T₂ -weighted MRI was performed as a routine for tumor longitudinal follow-up and volume estimation. Common bile duct diameter was measured from 3D multiplanar reconstruction. Quantitative multi-parametric measurements including pixel-wise T₂ , T₁ relaxivity, apparent diffusion coefficient (ADC) and apparent diffusion kurtosis mapping were performed twice throughout tumor growth. Semi-quantitative and quantitative analyses based on an extended Tofts model were applied to region-of-interest-based dynamic contrast-enhanced imaging, followed by contrast ratio measurement on standard contrast-enhanced imaging. Moreover, low-level texture-based analysis was inspected for T₂ , T₁ , ADC and contrast ratio measurements. Results indicated that multi-parametric MRI showed good reproducibility for tumor characterization; the measurements were not affected by tumor growth. Tumor growth was further confirmed with histology examinations. To conclude, state-of-the-art clinical MRI techniques were translated to this preclinical tumor model with high reliability, and have paved the way for translational oncology studies on this tumor model.

Diffusion-weighted imaging of the abdomen: Impact of b-values on texture analysis features

The purpose of this work was to systematically assess the impact of the b-value on texture analysis in MR diffusion-weighted imaging (DWI) of the abdomen. In eight healthy male volunteers, echo-planar DWI sequences at 16 b-values ranging between 0 and 1000 s/mm² were acquired at 3 T. Three different apparent diffusion coefficient (ADC) maps were computed (0, 750/100, 390, 750 s/mm² /all b-values). Texture analysis of rectangular regions of interest in the liver, kidney, spleen, pancreas, paraspinal muscle and subcutaneous fat was performed on DW images and the ADC maps, applying 19 features computed from the histogram, grey-level co-occurrence matrix (GLCM) and grey-level run-length matrix (GLRLM). Correlations between b-values and texture features were tested with a linear and an exponential model; the best fit was determined by the smallest sum of squared residuals. Differences between the ADC maps were assessed with an analysis of variance. A Bonferroni-corrected p-value less than 0.008 (=0.05/6) was considered statistically significant. Most GLCM and GLRLM-derived texture features (12-18 per organ) showed significant correlations with the b-value. Four texture features correlated significantly with changing b-values in all organs (p < 0.008). Correlation coefficients varied between 0.7 and 1.0. The best fit varied across different structures, with fat exhibiting mostly exponential (17 features), muscle mostly linear (12 features) and the parenchymatous organs mixed feature alterations. Two GLCM features showed significant variability in the different ADC maps. Several texture features vary systematically in healthy tissues at different b-values, which needs to be taken into account if DWI data with different b-values are analyzed. Histogram and GLRLM-derived texture features are stable on ADC maps computed from different b-values.

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.

ESUR recommendations for MR imaging of the sonographically indeterminate adnexal mass: an update

Abstract

An update of the 2010 published ESUR recommendations of MRI of the sonographically indeterminate adnexal mass integrating functional techniques is provided. An algorithmic approach using sagittal T2 and a set of transaxial T1 and T2WI allows categorization of adnexal masses in one of the following three types according to its predominant signal characteristics. T1 'bright' masses due to fat or blood content can be simply and effectively determined using a combination of T1W, T2W and FST1W imaging. When there is concern for a solid component within such a mass, it requires additional assessment as for a complex cystic or cystic-solid mass. For low T2 solid adnexal masses, DWI is now recommended. Such masses with low DWI signal on high b value image (e.g. > b 1000 s/mm²) can be regarded as benign. Any other solid adnexal mass, displaying intermediate or high DWI signal, requires further assessment by contrast-enhanced (CE)T1W imaging, ideally with DCE MR, where a type 3 curve is highly predictive of malignancy. For complex cystic or cystic-solid masses, both DWI and CET1W—preferably DCE MRI—is recommended. Characteristic enhancement curves of solid components can discriminate between lesions that are highly likely malignant and highly likely benign.

Key Points

• MRI is a useful complementary imaging technique for assessing sonographically indeterminate masses.

• Categorization allows confident diagnosis in the majority of adnexal masses.

• Type 3 contrast enhancement curve is a strong indicator of malignancy.

• In sonographically indeterminate masses, complementary MRI assists in triaging patient management.

Electronic supplementary material

The online version of this article (doi:10.1007/s00330-016-4600-3) contains supplementary material, which is available to authorized users.

Whole-lesion apparent diffusion coefficient (ADC) metrics as a marker of breast tumour characterization-comparison between ADC value and ADC entropy

OBJECTIVE

To prospectively assess the role of whole-lesion apparent diffusion coefficient (ADC) metrics in the characterization of breast tumours by comparing ADC value with ADC entropy.

METHODS

49 patients with 53 breast lesions underwent phased-array breast coil 1.5-T MRI. Two radiologists experienced in breast MRI, blinded to the final diagnosis, reviewed the ADC maps and placed a volume of interest on all slices including each lesion on the ADC map to obtain whole-lesion mean ADC value and ADC entropy. The mean ADC value and ADC entropy in benign and malignant lesions were compared by the Mann-Whitney U-test. Receiver-operating characteristic analysis was performed to assess the sensitivity and specificity of the two variables in the characterization of the breast lesions.

RESULTS

The benign (n = 19) and malignant lesions (n = 34) had mean diameters of 20.8 mm (10.1-31.5 mm) and 26.4 mm (10.5-42.3 mm), respectively. The mean ADC value of the malignant lesions was significantly lower than that of the benign ones (0.87 × 10^-3 vs 1.49 × 10^-3 mm² s^-1; p < 0.0001). Malignant ADC entropy was higher than benign entropy, without reaching levels of statistical significance (5.4 vs 5.0; p = 0.064). At a mean ADC cut-off value of 1.16 × 10^-3 mm² s^-1, the sensitivity and specificity for diagnosing malignancy became optimal (97.1% and 93.7, respectively) with an area under the curve (AUC) of 0.975. With regard to ADC entropy, the sensitivity and specificity at a cut-off of 5.18 were 67.6 and 68.7%, respectively, with an AUC of 0.664.

CONCLUSION

Whole-lesion mean ADC could be a helpful index in the characterization of suspicious breast lesions, with higher sensitivity and specificity than ADC entropy. Advances in knowledge: Two separate parameters of the whole-lesion histogram were compared for their diagnostic accuracy in characterizing breast lesions. Mean ADC was found to be able to characterize breast lesions, whereas entropy proved to be unable to differentiate benign from malignant breast lesions. It is, however, likely that entropy may distinguish these two groups if a larger cohort were used, or the fact that this may be influenced by the molecular subtypes of breast cancers included.

Assessment of Heterogeneity Difference Between Edge and Core by Using Texture Analysis: Differentiation of Malignant From Inflammatory Pulmonary Nodules and Masses

RATIONALE AND OBJECTIVES

This study aimed to test the hypothesis that the heterogeneity difference between edge and core of lesions by using intensity and entropy features obtained from whole-lesion texture analysis on contrast-enhanced computed tomography (CT) may be useful for differentiation of malignant from inflammatory pulmonary nodules and masses.

MATERIALS AND METHODS

In all, 48 single pulmonary nodules and masses were retrospectively evaluated. All lesions were histologically or clinically confirmed (malignancy: inflammation = 24:20). We utilized a newly introduced texture analysis method based on contrast-enhanced CT (first described by Grove et al.) that automatically divided the whole lesion volume into two regions: edge and core. Mean attenuation value (AV) and entropy of each region and also the whole lesion were evaluated separately. Each texture metric (absolute value for each region, and difference value defined as difference between edge and core) of malignant and inflammatory lesions were compared using Mann-Whitney U test. Individual image parameters were combined by using linear discriminant analysis. Receiver operating characteristic curves were generated to assess each texture metric and their combination for discriminating between the two entities.

RESULTS

Mean AV difference and entropy difference were significantly higher in malignant lesions than in inflammatory lesions (4.71 HU ± 5.06 vs -1.53 HU ± 5.05, P < .001; 0.45 ± 0.23 vs 0.18 ± 0.30, P = .001). Receiver operating characteristic curves for individual mean AV difference and entropy difference provided relatively high values for the area under the curve (0.836 and 0.795, respectively). The combination of mean AV difference, entropy difference, and lesion volume improved the area under the curve to 0.864.

CONCLUSION

Heterogeneity difference between edge and core by using whole-lesion texture analysis on contrast-enhanced CT may be a promising tool for estimating the probability of malignancy in pulmonary nodules and masses.

Diffusion weighted imaging for the differential diagnosis of benign vs. malignant ovarian neoplasms

In order to assess the diagnostic accuracy of diffusion weighted imaging (DWI) in differentiating between benign and malignant ovarian neoplasms, a systemic meta-analysis was conducted. Relevant studies were retrieved from scientific literature databases, including the PubMed, Wiley, EBSCO, Ovid, Web of Science, Wanfang, China National Knowledge Infrastructure and VIP databases. Following a multi-step screening and study selection process, the relevant data was extracted for use in the present study. Statistical analyses were performed using Meta-disc software version 1.4 and STATA statistical software version 12.0. A total of 285 articles were retrieved from the database searches. Following a careful screening process, 10 case-control studies were selected for the present meta-analysis. The 10 studies investigated the efficacy of DWI in diagnosing ovarian neoplasms, and included a combined total of 1,159 subjects, of which 559 patients had malignant lesions and 600 had benign lesions. The results showed that the pooled sensitivity, pooled specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, pooled diagnostic odds ratio (DOR) and area under the curve of the summary receiver operating characteristics curve of DWI for differentiating between benign and malignant ovarian neoplasms were 0.93, 0.89, 7.58, 0.10, 85.33 and 0.95, respectively. A subgroup analysis based on ethnicity revealed no significant difference between Asians and Caucasians. Another subgroup analysis by magnetic resonance imaging (MRI) type showed that the DORs for GE Healthcare Life Sciences and Siemens AG machines were 100.76 [95% confidence interval (CI), 65.28-155.53] and 30.85 (95% CI, 10.40-91.53), respectively; this indicates that the diagnostic efficiency of the GE Healthcare Life Sciences MRI is superior compared with the Siemens AG MRI. The DWI demonstrated an excellent diagnostic performance in discriminating between benign and malignant ovarian neoplasms, and predicted the surgical outcome in ovarian neoplasms.

Update on Imaging of Ovarian Cancer

This review will make familiar with new concepts in ovarian cancer and their impact on radiological practice. Disseminated peritoneal spread and ascites are typical of the most common (70-80 %) cancer type, high-grade serous ovarian cancer. Other cancer subtypes differ in origin, precursors, and imaging features. Expert sonography allows excellent risk assessment in adnexal masses. Owing to its high specificity, complementary MRI improves characterization of indeterminate lesions. Major changes in the new FIGO staging classification include fusion of fallopian tube and primary ovarian cancer and the subcategory stage IIIA1 for retroperitoneal lymph node metastases only. Inguinal lymph nodes, cardiophrenic lymph nodes, and umbilical metastases are classified as distant metastases (stage IVB). In multidisciplinary conferences (MDC), CT has been used to predict the success of cytoreductive surgery. Resectability criteria have to be specified and agreed on in MDC. Limitations in detection of metastases may be overcome using advanced MRI techniques.

Radiomics: a new application from established techniques

The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.

Prostate Cancer: Utility of Whole-Lesion Apparent Diffusion Coefficient Metrics for Prediction of Biochemical Recurrence After Radical Prostatectomy

OBJECTIVE

The purpose of this study was to investigate the additional value of whole-lesion histogram apparent diffusion coefficient (ADC) metrics, when combined with standard pathologic features, in prediction of biochemical recurrence (BCR) after radical prostatectomy for prostate cancer.

MATERIALS AND METHODS

The study included 193 patients (mean age, 61 ± 7 years) who underwent 3-T MRI with DWI (b values, 50 and 1000 s/mm(2)) before prostatectomy. Histogram metrics were derived from 3D volumes of interest encompassing the entire lesion on ADC maps. Pathologic features from radical prostatectomy and subsequent BCR were recorded for each patient. The Fisher exact test and Mann-Whitney test were used to compare ADC-based metrics and pathologic features between patients with and patients without BCR. Stepwise logistic regression analysis was used to construct multivariable models for prediction of BCR, which were assessed by ROC analysis.

RESULTS

BCR occurred in 16.6% (32/193) of patients. Variables significantly associated with BCR included primary Gleason grade, Gleason score, extraprostatic extension, seminal vesicle invasion, positive surgical margin, preoperative prostate-specific antigen level, MRI tumor volume, mean whole-lesion ADC, entropy ADC, and mean ADC of the bottom 10th, 10-25th, and 25-50th percentiles (p ≤ 0.019). Significant independent predictors of BCR at multivariable analysis were primary Gleason grade, extraprostatic extension, mean of the bottom 10th percentile ADC, and entropy ADC (p = 0.002-0.037). The AUC of this multivariable model was 0.94 for prediction of BCR; the AUC of pathologic features alone was 0.89 (p = 0.001).

CONCLUSION

A model integrating whole-lesion ADC metrics had significantly higher performance for prediction of BCR than did standard pathologic features alone and may help guide postoperative prognostic assessments and decisions regarding adjuvant therapy.

The Value of Diffusion-Weighted Imaging in the Differential Diagnosis of Ovarian Lesions: A Meta-Analysis

OBJECTIVES

The ability of contrast-enhanced MRI to distinguish between malignant and benign ovarian masses is limited. The aim of this meta-analysis is to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) in differentiating malignant from benign ovarian masses.

METHODS

A comprehensive literature search was performed in several authoritative databases to identify relevant articles. The weighted mean difference (WMD) and corresponding 95% confidence interval (95% CI) were calculated. We also used subgroup analysis to analyze study heterogeneity, and evaluated publication bias.

RESULTS

The meta-analysis is based on 21 studies, which reported the findings for 731 malignant and 918 benign ovarian masses. There was no significant difference in apparent diffusion coefficient (ADC) values for DWI between benign and malignant lesions (WMD = 0.22, 95% CI = -0.02-0.47, p = 0.08). Subgroup analysis by benign tumor type revealed higher ADC values (or a trend toward higher values) for cysts, cystadenomas and other benign tumors compared to malignant masses (cyst: WMD = 0.54, 95% CI = -0.05-1.12, p = 0.07; cystadenoma: WMD = 0.73, 95% CI = 0.38-1.07, p < 0.0001; other benign tumor: WMD = 0.16, 95% CI = -0.13-0.46, p = 0.28). On the other hand, lower ADC values (or a trend toward lower values) were observed for endometrioma and teratoma compared to malignant masses (endometrioma: WMD = -0.09, 95% CI = -0.47-0.29, p = 0.64; teratoma: WMD = -0.49, 95% CI = -0.85-0.12, p = 0.009). Subgroup analysis by mass property revealed higher ADC values in cystic tumor types than in solid types for both benign and malignant tumors. Significant study heterogeneity was observed. There was no notable publication bias.

CONCLUSIONS

Quantitative DWI is not a reliable diagnostic method for differentiation between benign and malignant ovarian masses. This knowledge is essential in avoiding misdiagnosis of ovarian masses.

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.

Hypovascular hepatic nodules at gadoxetic acid-enhanced MRI: whole-lesion hepatobiliary phase histogram metrics for prediction of progression to arterial-enhancing hepatocellular carcinoma

PURPOSE

To explore whole-lesion histogram analysis of the hepatobiliary phase (HBP) defect in indeterminate hypovascular liver lesions for predicting progression to arterial-enhancing hepatocellular carcinoma (HCC).

METHODS

Twenty patients undergoing gadoxetic acid-enhanced MRI for HCC screening with 12° and 25° flip angle (FA) HBP acquisitions demonstrating an indeterminate lesion showing HBP hypointensity but no arterial enhancement were included. Volumes-of-interest were placed on HBP defects, from which histogram metrics were obtained. Associations between these metrics and progression to arterial-enhancing HCC on follow-up imaging were investigated. Lesions were also assessed for the presence of a signal abnormality on conventional sequences.

RESULTS

40% of lesions progressed to arterial-enhancing HCC; 60% were stable at ≥6 months follow-up. Neither T2-hyperintensity increased diffusion signal nor portal/equilibrium phase washout was different between progressing and nonprogressing lesions (p = 1.0). Among direct signal intensity-based measures (overall mean; mean of bottom 10th, 10-25th, and 25-50th percentiles), area-under-the-curve (AUC) for prediction of progression to arterial-enhancing HCC was consistently higher at 25° (range 0.619-0.657) than at 12° (range 0.512-0.548). However, at both FAs, the four measures with highest AUC were measures related to lesion texture and heterogeneity [standard deviation (SD), coefficient of variation (CV), skewness, and entropy], having AUC of 0.655-0.750 at 12° and 0.686-0.800 at 25. The metric with highest AUC at 12° was SD (AUC = 0.750) and at 25° was CV (AUC = 0.800).

CONCLUSION

Whole-lesion histogram HBP measures of indeterminate hypovascular liver lesions may help predict progression to arterial-enhancing HCC by reflecting greater lesion heterogeneity, particularly at higher FA. Larger studies are therefore warranted.

Added Value of Assessing Adnexal Masses with Advanced MRI Techniques

This review will present the added value of perfusion and diffusion MR sequences to characterize adnexal masses. These two functional MR techniques are readily available in routine clinical practice. We will describe the acquisition parameters and a method of analysis to optimize their added value compared with conventional images. We will then propose a model of interpretation that combines the anatomical and morphological information from conventional MRI sequences with the functional information provided by perfusion and diffusion weighted sequences.

Primary ovarian endometrioid adenocarcinoma: magnetic resonance imaging findings including a preliminary observation on diffusion-weighted imaging

OBJECTIVE

This study aimed to investigate the magnetic resonance imaging (MRI) features of ovarian endometrioid adenocarcinoma (OEC) and to evaluate conventional MRI and diffusion-weighted imaging (DWI) for diagnosing OEC.

MATERIALS AND METHODS

Twenty patients with OEC proven by surgery and pathology underwent MRI. The MRI features of the tumors evaluated included laterality, shape, size, configuration, mural nodules, signal intensity, apparent diffusion coefficient (ADC) values, enhancement, peritoneal implants, ascites, and synchronous primary cancer (SPC) of the ovary and endometrium.

RESULTS

Unilateral ovarian masses were observed in 18 (90%) of the 20 patients with 22 OEC lesions, whereas the remaining 2 (10%) patients had bilateral masses. Oval, lobulated, and irregular shapes were observed in 13 (59%), 6 (27%), and 3 (14%) tumors, respectively. The maximum diameter of the tumors ranged from 3.7 to 22.5 cm, with a mean of 11.2 ± 5.1 cm. Fifteen (68%) masses were mainly cystic with mural nodules, 5 (23%) were mixed cystic-solid, and 2 (9%) were solid. The solid components of tumors showed isointensity (100%) on T1-weighted imaging (T1WI), heterogeneous hyperintensity on T2-weighted imaging (T2WI) (86%), and hyperintensity on DWI (82%), with a mean ADC value of (0.96 ± 0.20) × 10 mm/s. The cystic components showed isointensity or hyperintensity (85%) on T1WI, hyperintensity on T2WI (100%), and hypointensity on DWI (63%), with a mean ADC value of (2.27 ± 0.27) × 10 mm/s. Ten (50%) of the patients were SPC. The mean ADC values of the solid components were (0.85 ± 0.19) × 10 mm/s and (1.08 ± 0.15) × 10 mm/s in only-OEC and SPC, respectively, with a statistically significant difference (P = 0.012).

CONCLUSIONS

Ovarian endometrioid adenocarcinoma usually appears as a large, oval, or lobulated cystic mass with mural nodules. Cystic components show isointensity or hyperintensity on T1WI, solid components and hyperintensity on T2WI and DWI. Synchronous primary cancer of the ovary endometrium is another characteristic feature of OEC.

Primary fallopian tube carcinoma: correlation between magnetic resonance and diffuse weighted imaging characteristics and histopathologic findings

OBJECTIVE

The aim of this study was to investigate the magnetic resonance (MR) and diffusion-weighted (DW) imaging characteristics of primary fallopian tube carcinoma (PFTC).

METHODS

The clinical, MR, and DW imaging characteristics and pathologic findings of 23 patients with 27 tumors were studied retrospectively. The MR and DW imaging appearance of tumors including laterality, size and shape, architecture, signal intensity, apparent diffusion coefficient (ADC) value, enhancement pattern, hydrosalpinx, and intrauterine fluid collection were evaluated and correlated with pathologic findings.

RESULTS

Histopathologically, all 27 tumors were serous carcinoma with a unilateral tumor in 19 patients and bilateral tumors in 4 patients. Thirteen patients (57%) with PFTC were misdiagnosed preoperatively, 10 of which as epithelial ovarian carcinoma. The mean (SD) largest diameter was 61 (7) mm. The tumor shape was fusiform, sausagelike, or serpentine in 19 patients (70%) and nodular or irregular in 8 patients (30%). Twenty (74%) of the 27 tumors were solid, and 7 (26%) were cystic-solid. The solid components showed hypointensity to isointensity on T1-weighted imaging, and isointensity to slight hyperintensity on T2-weighted imaging. There were obvious hyperintensity on DW imaging; obvious hypointensity on ADC maps with a mean (SD) ADC value of 0.79 (0.22) × 10 mm; and mild (8/27, 30%), moderate (13/27, 48%), and marked (6/27, 22%) enhancement on contrast-enhanced imaging. Ipsilateral hydrosalpinx, intrauterine fluid collection, and ascites were found in 14 tumors (52%) and 7 (30%) and 5 (22%) patients, respectively.

CONCLUSIONS

The PFTC has some characteristic MR imaging features. The DW imaging, ADC maps, and ADC values are helpful for the detection and differentiation of PFTC from other pelvic masses.

Entropy of T2-weighted imaging combined with apparent diffusion coefficient in prediction of uterine leiomyoma volume response after uterine artery embolization

RATIONALE AND OBJECTIVES

To determine the potential value of entropy of T2-weighted imaging combined with apparent diffusion coefficient (ADC) before uterine artery embolization (UAE) for prediction of uterine leiomyoma volume reduction (VR) after UAE.

MATERIALS AND METHODS

In this prospective study, 11 patients with uterine leiomyomas who underwent pelvic magnetic resonance imaging including diffusion-weighted imaging before and 6 months after UAE were included. A total number of 16 leiomyomas larger than 2 cm in diameter were evaluated. The volume of each leiomyoma before and after UAE was determined, and the percentage change in volume was calculated. Entropy of T2-weighted imaging and ADC before UAE were assessed. Pearson correction coefficients were calculated between leiomyoma VR after UAE and age, leiomyoma volume, ADC, and entropy, respectively. Multiple regression analysis was performed to investigate the parameters that determine the VR after UAE. Receiver operating characteristic curve analysis was used to determine the sensitivity and specificity of ADC, entropy and the combination of ADC and entropy for predicting volume response.

RESULTS

The mean leiomyoma VR was 58.9% (range 25.8%-95.0%) in the 6-month follow-up. The mean ADC of leiomyomas was 1.37 × 10(-3) mm(2)/s (range 1.05 × 10(-3)-2.32 × 10(-3) mm(2)/s) and the mean entropy of T2-weighted imaging was 5.36 (range 4.62-5.91) before UAE. ADC and entropy were significantly correlated with leiomyoma VR, respectively (r = 0.61, P = .012; r = 0.73, P = .001). On multiple regression analysis, a combination of ADC and entropy constituted the best model for determining leiomyoma VR using Akaike information criterion. For predicting ≥50% VR, the optimal cutoff value of ADC was 1.39 × 10(-3) mm(2)/s (sensitivity 45.5%, specificity 80.0%) and the optimal cutoff value of entropy was 5.15 (sensitivity 90.9%, specificity 60.0%). The combination of ADC and entropy (area under the curve [AUC] 0.86) provided better classification accuracy than ADC or entropy alone (AUC 0.69 and 0.82, respectively).

CONCLUSIONS

Pre-UAE entropy of T2-weighted imaging and ADC of leiomyomas were significantly correlated with the leiomyoma VR 6 months after embolization. Higher entropy and higher ADC may be related to greater leiomyoma VR after UAE. A combination of entropy and ADC may have predictive value for leiomyoma VR after UAE.

Whole-lesion apparent diffusion coefficient metrics as a marker of percentage Gleason 4 component within Gleason 7 prostate cancer at radical prostatectomy

PURPOSE

To retrospectively assess the utility of whole-lesion apparent diffusion coefficient (ADC) metrics in characterizing the Gleason 4 component of Gleason 7 prostate cancer (PCa) at radical prostatectomy.

MATERIALS AND METHODS

Seventy patients underwent phased-array coil 3T-magnetic resonance imaging (MRI) before prostatectomy. A uropathologist mapped locations and Gleason 4 percentage (G4%) of Gleason 7 tumors. Two radiologists independently reviewed ADC maps, aware of tumor locations but not G4%, and placed a volume-of-interest (VOI) on all slices including each lesion on the ADC map to obtain whole-lesion mean ADC and ADC entropy. Entropy reflects textural variation and increases with greater macroscopic heterogeneity. Performance for characterizing Gleason 7 tumors was assessed with mixed-model analysis of variance (ANOVA) and logistic regression.

RESULTS

Among 84 Gleason 7 tumors (G4% 5%-85%, median 30%; 59 Gleason 3+4, 25 Gleason 4+3), ADC entropy was significantly higher in Gleason 4+3 than Gleason 3+4 tumors (R1: 5.27 ± 0.61 vs. 4.62 ± 0.78, P = 0.001; R2: 5.91 ± 0.32 vs. 5.57 ± 0.56, P = 0.004); mean ADC was not significantly different between these groups (R1: 0.90 ± 0.15*10(-3) cm(2) /s vs. 0.98 ± 0.21*10(-3) cm(2) /s, P = 0.075; R2: 1.06 ± 0.19*10(-3) cm(2) /s vs. 1.14 ± 0.16*10(-3) cm(2) /s, P = 0.083). The area under the receiver operating characteristic (ROC) curve (AUC) for differentiating groups was significantly higher with ADC entropy than mean ADC for one observer (R1: 0.74 vs. 0.57, P = 0.027; R2: 0.69 vs. 0.61, P = 0.329). For R1, correlation with G4% was moderate for ADC entropy (r = 0.45) and weak for mean ADC (r = -0.25). For R2, correlation with G4% was moderate for ADC entropy (r = 0.41) and mean ADC (r = -0.32). For both readers, ADC entropy (P = 0.028-0.003), but not mean ADC (P = 0.384-0.854), was a significant independent predictor of G4%.

CONCLUSION

Whole-lesion ADC entropy outperformed mean ADC in characterizing Gleason 7 tumors and may help refine prognosis for this heterogeneous PCa subset.

Diffusion MRI and novel texture analysis in osteosarcoma xenotransplants predicts response to anti-checkpoint therapy

Combinations of targeted drugs have been employed to treat sarcomas, however, response rates have not improved notably, therefore emphasizing the need for novel treatments. In addition, imaging approaches to assess therapeutic response is lacking, as currently measurable indices, such as volume and/or diameter, do not accurately correlate with changes in tumor biology. In this study, quantitative and profound analyses of magnetic resonance imaging (MRI) were developed to evaluate these as imaging biomarkers for MK1775 and Gem in an osteosarcoma xenotransplant model at early time-points following treatment. Notably, we showed that Gem and Gem+MK1775 groups had significantly inhibited tumor growth by day 4, which was presaged by elevations in mean ADC by 24 hours post treatment. Significant differences were also observed at later time points for the Gem+MK1775 combination and MK1775 therapy. ADC distribution and entropy (randomness of ADC values) were also elevated by 24 hours following therapy. Immunohistochemistry demonstrated that these treatment-related increases in ADC correlated with apoptosis and observed cell condensations (dense- and exploded bodies). These findings underline the role of ADC as a quantitative imaging biomarker for therapy-induced response and show promising clinical relevance in the sarcoma patient population.