Apparent Diffusion Coefficient Data Research Articles

Assessment of the contribution of the ADC value to the Kaiser score in the differential diagnosis of breast lesions with non-mass enhancement morphology on MRI

PurposeTo investigate the effectiveness of diffusion-weighted imaging (DWI) as a supplementary tool to the Kaiser score (KS) in diagnosing breast cancer in non-mass enhancement (NME) lesions using breast magnetic resonance imaging (MRI). MethodsThis single-center, retrospective study analyzed 360 cases with NME on MRI images. Two breast radiologists independently evaluated each lesion using the Kaiser score (KS) and apparent diffusion coefficient (ADC) values, without knowledge of the pathological outcomes. NME lesions with a KS above 4 and an ADC value below 1.3 × 10-3mm2/s were classified as malignant. Inter-rater reliability was determined using Cohen’s Kappa (κ) statistics. The diagnostic performance of KS, DWI, and their combination was assessed by calculating sensitivity, specificity, and the area under the curve (AUC), and the results were compared across the benign and malignant groups. ResultsThe diagnostic performance of KS surpassed that of DWI in predicting the malignancy of NMEs (p = 0.003). The sensitivity of KS alone was 93 %; however, when ADC data was incorporated, the sensitivity decreased to 86 %, with no significant difference observed (p = 0.060). The specificity of the combined KS and ADC (94 %) was significantly higher than that of KS alone (89 %) and DWI alone (73 %) (p < 0.001). ConclusionOur findings indicated that although the combination of KS and ADC increased specificity and reduced unnecessary biopsies, the resulting decrease in sensitivity was unacceptable. Therefore, KS alone is superior to the KS-ADC combination in detecting malignancy in NME lesions.

Usefulness of Diffusion-Weighted Imaging in Evaluating Acute Cellular Rejection and Monitoring Treatment Response in Liver Transplant Recipients.

Acute cellular rejection (ACR) is a significant immune issue among recipients following liver transplantation. Although diffusion-weighted magnetic resonance imaging (DWI) is widely used for diagnosing liver disease, it has not yet been utilized for monitoring ACR in patients after liver transplantation. Therefore, the aim of this study was to evaluate the efficacy of DWI in monitoring treatment response among recipients with ACR. This study enrolled 25 recipients with highly suspected ACR rejection, and all subjects underwent both biochemistry and DWI scans before and after treatment. A pathological biopsy was performed 4 to 24 h after the first MRI examination to confirm ACR and degree of rejection. All patients were followed up and underwent a repeated MRI scan when their liver function returned to the normal range. After data acquisition, the DWI data were post-processed to obtain the apparent diffusion coefficient (ADC) map on a voxel-by-voxel basis. Five regions of interest were identified on the liver parenchyma to measure the mean ADC values from each patient. Finally, the mean ADC values and biochemical markers were statistically compared between ACR and non-ACR groups. A receiver operating characteristic (ROC) curve was constructed to evaluate the performance of the ADC and biochemical data in detecting ACR, and correlation analysis was used to understand the relationship between the ADC values, biochemical markers, and the degree of rejection. The histopathologic results revealed that 20 recipients had ACR, including 10 mild, 9 moderate, and 1 severe rejection. The results demonstrated that the ACR patients had significantly lower hepatic ADC values than those in patients without ACR. After treatment, the hepatic ADC values in ACR patients significantly increased to levels similar to those in non-ACR patients with treatment. The ROC analysis showed that the sensitivity and specificity for detecting ACR were 80% and 95%, respectively. Furthermore, the correlation analysis revealed that the mean ADC value and alanine aminotransferase level had strong and moderate negative correlation with the degree of rejection, respectively (r = -0.72 and -0.47). The ADC values were useful for detecting hepatic ACR and monitoring treatment response after immunosuppressive therapy.

Volumetric tumor segmentation according to diffusion-weighted MRI data in predicting and evaluating the response to chemotherapy in bladder cancer

The purpose of the study was to develop and evaluate the technique for volumetric tumor segmentation based on diffusion-weighted magnetic resonance imaging (DW-MRI) in predicting and assessing the response to chemotherapy in patients with bladder cancer (BC). Material and Methods. We examined 26 patients with morphologically verified transitional cell carcinoma of the bladder. The group was characterized by the presence of one or several tumors with a size of 17 to 46 mm. Before planning chemotherapy according to the M-VAC scheme, a study and post-processing of DW-MRI with volumetric segmentation of lesions, assessment of the volume and apparent diffusion coefficient (ADC) in the entire tumor mass were performed. According to the ADC data, shape of the tumor and its relation to the bladder wall, the coefficient (C) for predicting the response to chemotherapy was calculated. Results. In the cases with a coefficient value below 0.51, a high risk of treatment failure was predicted, at C≥0.74, a positive effect of treatment was predicted. With a value of 0.51≤C&lt;0.74, the prognosis was uncertain, stabilization was more likely. The sensitivity and specificity of the method were 92.3 % and 92.4 %, respectively. Conclusion. The method of volumetric segmentation makes it possible to predict and evaluate bladder cancer response to chemotherapy with a sufficiently high accuracy. The advantages of the method are the possibility of assessing the tumor regardless of the degree of filling of the bladder, with non-contrast MRI, and with large lesions.

Differentiating renal cell carcinoma and oncocytoma with volumetric MRI histogram analysis.

In this study, the utility of histogram parameters derived from diffusion-weighted imaging for differentiate renal cell carcinoma (RCC) from oncocytoma was investigated. This research tracked 126 individuals who were diagnosed with RCC and oncocytoma through histopathological analysis, using magnetic resonance imaging (MRI) assessments from 2015 to 2023. We observed various attributes of these patients, including demographic details, surgical records, pre-surgery MRI results, MRI apparent diffusion coefficient (ADC) histogram analysis, and post-surgery histopathological outcomes. Calculations of ADC measurements such as mean, minimum, and maximum in conjunction with the 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantile points were made. In addition, we also noted the skewness, kurtosis, and variance of these data points. The focus group for this investigation consisted of 75 male and 51 female patients. Out of these, 82 were diagnosed with RCC and 44 with oncocytoma. All ADC parameters including ADCmin, ADCmedian, ADCmean, and ADCmax, including the 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantile divisions among the oncocytoma cohort were observed to be higher than the corresponding ones in the RCC group. A statistically meaningful difference was discovered between the minimum ADC value along with the 5th ranking of ADC measurements (p<0.001), in addition to mean of ADC (p=0.050), and the 10th (p=0.002) and 25th (p=0.015) quantiles of ADC data. When considering the region below the curve (AUC) in ROC analysis, the value of ADCmin was recorded as 0.739, with a sensitivity of 75.0%, and specificity of 68.2%. To distinguish oncocytoma from RCC, it may be useful to conduct a whole-tumor histogram and textural analysis of ADC values.

Application of Apparent Diffusion Coefficient Histogram Metrics for Differentiation of Pediatric Posterior Fossa Tumors : ALarge Retrospective Study and Brief Review of Literature.

This study aimed to evaluate the application of apparent diffusion coefficient (ADC) histogram analysis to differentiate posterior fossa tumors (PFTs) in children. A total of 175 pediatric patients with PFT, including 75pilocytic astrocytomas (PA), 59medulloblastomas, 16ependymomas, and 13atypical teratoid rhabdoid tumors (ATRT), were analyzed. Tumors were visually assessed using DWI trace and conventional MRI images and manually segmented and post-processed using parametric software (pMRI). Furthermore, tumor ADC values were normalized to the thalamus and cerebellar cortex. The following histogram metrics were obtained: entropy, minimum, 10th, and 90th percentiles, maximum, mean, median, skewness, and kurtosis to distinguish the different types of tumors. Kruskal Wallis and Mann-WhitneyU tests were used to evaluate the differences. Finally, receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off values for differentiating the various PFTs. Most ADC histogram metrics showed significant differences between PFTs (p < 0.001) except for entropy, skewness, and kurtosis. There were significant pairwise differences in ADC metrics for PA versus medulloblastoma, PA versus ependymoma, PA versus ATRT, medulloblastoma versus ependymoma, and ependymoma versus ATRT (all p < 0.05). Our results showed no significant differences between medulloblastoma and ATRT. Normalized ADC data showed similar results to the absolute ADC value analysis. ROC curve analysis for normalized ADCmedian values to thalamus showed 94.9% sensitivity (95% CI: 85-100%) and 93.3% specificity (95% CI: 87-100%) for differentiating medulloblastoma from ependymoma. ADC histogram metrics can be applied to differentiate most types of posterior fossa tumors in children.

Volumetric apparent diffusion coefficient histogram analysis of the testes in nonobstructive azoospermia: a noninvasive fingerprint of impaired spermatogenesis?

To explore the association between testicular volumetric apparent diffusion coefficient (ADC) histogram analysis metrics and histologic categories in nonobstructive azoospermia (NOA). The role of ADC histogram analysis in predicting the presence of spermatozoa, prior to testicular sperm extraction (TESE), was also investigated. Forty-one NOA men and 17 age-matched controls underwent scrotal MRI with diffusion-weighted imaging. Histogram analysis of ADC data of the whole testis was performed. Metrics including mean, standard deviation, median, mode, 25th percentile, 75th percentile, skewness, kurtosis, and entropy of volumetric ADC histograms were calculated. Nonparametric statistical tests were used to assess differences in ADC histogram parameters between NOA histologic categories (hypospermatogenesis, severe hypospermatogenesis, early maturation arrest, and Sertoli cell-only syndrome) and normal testes and, between NOA with positive and negative sperm retrieval. Normal testes had a lower mean, median, mode, 25th percentile (p < 0.001), and 75th percentile of ADC (p = 0.001), compared to NOA histologic phenotypes. NOA with hypospermatogenesis had a lower 25th percentile of ADC compared to NOA with severe hypospermatogenesis. Regression analysis revealed that the 25th percentile of ADC had a moderately negative correlation with NOA histologic phenotype. The median ADC proved the most significant metric (p = 0.007) to predict the presence of sperm. Testicular volumetric ADC histogram parameters may contribute in the identification of the subpopulation of NOA men with a specific type of spermatogenic arrest. • Volumetric ADC histogram analysis metrics may be used as noninvasive markers of impaired spermatogenesis in nonobstructive azoospermia. • The 25th percentile of ADC proved useful in discriminating between NOA testes with hypospermatogenesis and severe hypospermatogenesis. • The median ADC proved the most significant parameter to predict the presence of viable spermatozoa prior to TESE.

Longitudinal evaluation of apparent diffusion coefficient values as a predictor of Prostate Cancer Research International Active Surveillance reclassification.

This study aimed to evaluate the effectiveness of apparent diffusion coefficient (ADC) parameters in distinguishing between Prostate Cancer Research International Active Surveillance (PRIAS) non-reclassification and reclassification groups during active surveillance (AS) of prostate cancer. We included 55 patients who fulfilled the PRIAS criteria and underwent ≥ 2 magnetic resonance imaging (MRI) including diffusion-weighted imaging with an interval of ≤ 3years between baseline and second MRI. A mono-exponential fitting model was used to automatically create ADC maps with minimum b-values of 0 and maximum of 2000s/mm2. For detectable lesions on ADC maps, the lesions were manually segmented on each slice of the ADC maps. For undetectable lesions, the corresponding normal-appearing zone of the lobe on each slice of ADC maps was segmented. The ADC data for each slice were summed to obtain the 25th, 50th, and 75th percentile ADC values of the histogram at baseline and second MRI. These ADC parameters at baseline and second MRI, and the changes of ADC parameters from baseline to second MRI were compared between PRIAS non-reclassification and reclassification groups. The PRIAS reclassification group had significantly lower 25th, 50th, and 75th percentile ADC values at second MRI compared to the non-reclassification group. The non-reclassification group had significantly lower changes in ADC values in these percentiles compared to the reclassification group. The ADC parameters at second MRI and the changes from baseline to second MRI may be effective distinguishing factors between PRIAS non-reclassification and reclassification groups.

Diffusion-Weighted Magnetic Resonance Imaging in the Evaluation of Wilms Tumors: Correlation with Histological Data. Pilot Study

The aim of the study is to assess the difference in apparent diffusion coefficient (ADC) values depending on the degrees of malignancy of Wilms’ tumor. The study includes 64 patients with verified Wilms tumor after a course of chemotherapy, before undergoing surgical treatment. The patients were examined using scanners with magnetic field induction of 3.0 and 1,5 T. ADC data collection (mm2/s) was carried out using specialized software. Statistical analysis was performed using the Graphpad Prism software package. Based on the results of this study, average ADC values were obtained for histological types of Wilms’ tumors distributed by clinical risk groups: 0.4 × 10-3 mm2/s — for the low grade of malignancy, 1.1 × 10-3 mm2/s — for the average grade of malignancy and 0.6 × 10-3 mm2/s — for the high grade. In addition, for the average grade of malignancy, the ADC values were divided into groups depending on the cellular composition — 1 ± 0.2 × 10-3 mm2/s — for the regressive and mixed type; 0.9 ± 0.2 × 10-3 mm2/s — for the epithelial type; 1.3 ± 0.4 × 10-3 mm2/s — for the stromal type. Thus, diffusion-weighted MRI can be a useful tool in the initial assessment and differential diagnosis of patients with Wilms tumor.

Exploring Correlation Between CT Number and ADC Value for Understanding Radiation Response

Radiation induced changes in CT number (CTN) have been observed during/after radiation therapy (RT) in various tumors and healthy tissues and have been shown to correlate with radiation response. As CTN is related to the combined radiation attenuations of the intracellular volume (ICV) and the extracellular volume (ECV), the observed CTN changes may be contributed by the radiation induced variations in ICV and ECV, e.g., the ratio of ICV and ECV, in the tumor/tissue. The apparent diffusion coefficient (ADC), derived from diffusion-weighted MRI (DWI) is known to represent cellularity, i.e., the IVC/ECV ratio. The ADC or its change during/after RT has been shown to predict radiation response. CT and ADC images are commonly used in RT, offering necessary and complimentary data, one for anatomic and another for functional information. Based on these observations, we hypothesize that CTN correlates with ADC and this correlation can help understand radiation response in RT. The purpose of this study is to verify this hypothesis by analyzing selected CT and ADC data collected in our clinic.Spleen and kidney were selected as the target organs for this study as they have no fat tissue that can interfere with and add complexity to the analysis. Data for a total of 20 and 15 patients with abdominal/pelvic cancers were used for the spleen and kidney analyses, respectively. The image data included the CT acquired during the standard CT simulation using the same CT scanner at 120 kVp and the ADC maps derived from the DWI acquired immediately before the first fraction using a 1.5T MR-Linac. The time gap between the CT and ADC acquisitions were approximately 3 weeks. Spleen and kidneys (left and right) were contoured on both the ADC and the CT images. The mean values of the ADC and CTN from the voxels within the contours were analyzed using a linear regression method.The CTNs over the patient cohort have a median of 44.7 HU (range 31.5 ∼ 56.9 HU) for spleen, and 29.7 HU (range 23.0 ∼ 38.2 HU) for kidney. Both spleen and kidney data show a strong correlation between CTN and ADC (r = -0.80 for spleen and -0.83 for kidney, P < 0.001 for both). The linear regression lines were ADC = -0.0212 x CTN + 1.86 for the spleen and ADC = -0.0220 x CTN + 2.64 for the kidney. The difference between the y-intercepts may be interpreted as the additional water flow related to the renal filtration. With such an adjustment the data for the two organs can be fitted into one regression line same as that for the spleen.There is a strong correlation between CTN and ADC in kidneys and spleen, suggesting that CTN is related to the ICV/ECV ratio (measured by ADC) and the radiation-induced changes in CTN during RT is related to the ADC changes. Further studies are needed to confirm the CTN and ADC correlation and to use the correlation for understanding radiation response.

The role of apparent diffusion coefficient as a predictive factor for tumor recurrence in patients with cerebellopontine angle epidermoid tumor.

Intracranial epidermoid tumors are slowly growing benign tumors, but due to adjacent critical neurovascular structures, surgical resection is challenging, with the risk of recurrence. The apparent diffusion coefficient (ADC) has been used to evaluate the characteristics of brain tumors, but its utility for intracranial epidermoid tumors has not been specifically explored. This study analyzed the utility of preoperative ADC values in predicting tumor recurrence for patients with intracranial epidermoid tumors. Between 2008 and 2019, 21 patients underwent surgery for cerebellopontine angle (CPA) epidermoid tumor, and their preoperative ADC data were analyzed. The patients were divided into two groups: the recurrence group, defined by regrowth of the remnant tumor or newly developed mass after gross total resection on magnetic resonance imaging (MRI); and the stable group, defined by the absence of growth or evidence of tumor on MRI. Receiver operating characteristic (ROC) analysis was used to obtain the ADC cutoff values for predicting tumor recurrence. The prognostic value of the ADC was assessed using Kaplan-Meier curves. The minimum ADC values were significantly lower in the recurrence group than in the stable tumor group (P = 0.020). ROC analysis showed that a minimum ADC value lower than 804.5 × 10-6 mm2/s could be used to predict higher recurrence risk of CPA epidermoid tumors. Non-total resection and mean and minimum ADC values lower than the respective cutoffs were negative predictors of recurrence-free survival. Minimum ADC values could be useful in predicting the recurrence of CPA epidermoid tumors.

Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms.

To develop and validate deep learning (DL) methods for diagnosing autism spectrum disorder (ASD) based on conventional MRI (cMRI) and apparent diffusion coefficient (ADC) images. A total of 151 ASD children and 151 age-matched typically developing (TD) controls were included in this study. The data from these subjects were assigned to training and validation datasets. An additional 20 ASD children and 25 TD controls were acquired, whose data were utilized in an independent test set. All subjects underwent cMRI and diffusion-weighted imaging examination of the brain. We developed a series of DL models to separate ASD from TD based on the cMRI and ADC data. The seven models used include five single-sequence models (SSMs), one dominant-sequence model (DSM), and one all-sequence model (ASM). To enhance the feature detection of the models, we embed an attention mechanism module. The highest AUC (0.824 ~ 0.850) was achieved when applying the SSM based on either FLAIR or ADC to the validation and independent test sets. A DSM using the combination of FLAIR and ADC showed an improved AUC in the validation (0.873) and independent test sets (0.876). The ASM also showed better diagnostic value in the validation (AUC = 0.838) and independent test sets (AUC = 0.836) compared to the SSMs. Among the models with attention mechanism, the DSM achieved the highest diagnostic performance with an AUC, accuracy, sensitivity, and specificity of 0.898, 84.4%, 85.0%, and 84.0% respectively. This study established the potential of DL models to distinguish ASD cases from TD controls based on cMRI and ADC images. • Deep learning models based on conventional MRI and ADC can be used to diagnose ASD. • The model (DSM) based on the FLAIR and ADC sequence achieved the best diagnostic performance with an AUC of 0.836 in the independent test sets. • The attention mechanism further improved the diagnostic performance of the models.

Determination of Cervical Lymph Nodes Metastasis and Extra Nodal Extension Status by Quantitative Assessment of Border Irregularity and Apparent Diffusion Coefficient in Patients With Tongue Squamous Cell Carcinoma.

The objective of this study was to determine the diagnostic value of quantitative border irregularity assessment and apparent diffusion coefficient (ADC) in patients with squamous cell carcinoma of the tongue (SCCT). Cervical lymph nodes (n = 192) from 63 patients with SCCT were examined preoperatively by magnetic resonance imaging, including routine head and neck sequences, dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, ADC, surface regularity (SR), and visually assessed variables, and evaluated pathologically after surgery. Necrosis, lymphatic hilum, unclear margin, higher SR, long to short axis ratio, and ADC were associated with metastasis in cervical lymph nodes (M-cLNs) and extranodal extension (ENE), and thickened nodal rim with ENE alone. Apparent diffusion coefficient, SR, unclear margin, and visible necrosis were strongly associated with M-cLN, whereas SR, unclear margin, and visible necrosis were associated with ENE status on logistic regression analysis. Quantitative SR and ADC data greatly improved diagnosis of M-cLNs and ENE, relative to visible variables alone in patients with SCCT.

Apparent diffusion coefficient as a prognostic factor in clival chordoma

Clival chordoma is a rare disease with high recurrence rates even after a combination of surgical resection and radiotherapy. Apparent diffusion coefficient (ADC) has been used to evaluate aggressive features of chordoma, but its utility for clival chordoma has not been explored specifically. In this study, the utility of preoperative ADC values was analyzed for predicting tumor progression and recurrence in patients with clival chordoma. Between 2012 and 2019, a total of 30 operated cases were analyzed with available preoperative ADC data. Receiver operating characteristic (ROC) analysis was used to obtain ADC cutoff values for predicting tumor aggressiveness. The mean and minimum ADC values were significantly lower in the aggressive tumor group than in the stable tumor group (both P < 0.001). ROC analysis showed that a mean cutoff ADC value of 1198 × 10−6 mm2/s and minimum ADC value of 895.5 × 10–6 mm2/s could be used to predict aggressive features of clival chordoma. Subtotal resection, partial resection, and mean and minimum ADC values that were lower than cutoff values were negative predictors of overall survival and progression-free survival. In conclusion, mean and minimum ADC values could be useful in predicting aggressiveness of clival chordoma.

Is the Promise of Diffusion-weighed (DWI) MRI based Therapy Response Prediction in Advanced Cervical Cancer Ready for Prime Time in Standard Community Settings? The Issue of Standardized Postprocessing

Functional imaging (DWI) has shown promise in predicting response and tumor control in cervical cancer treated with radiation/chemotherapy, but clinical translation has been limited by complexities in image registration, tumor delineation on apparent diffusion coefficient (ADC) maps, and artifacts. Two methods, scanner-generated auto-coregistration (Coaut) and more resource-intense individualized coregistration (Coind) may influence DWI results. Our purpose was to compare both methods' consistency and accuracy for tumor response prediction and assess potential pitfalls. We analyzed 21 FIGO stage IB2-IVA cervical cancer patients enrolled in a prospective imaging trial who were treated with concurrent chemoradiation. Coaut vs. Coind were studied in each with 3 serial MRIs, pre-therapy (pre), early-therapy (2-wk) and mid-therapy (5-wk) obtained. ADC data sets contained T2- weighted (T2w)-based whole-tumor contouring and were linked to 1-month post-therapy tumor response. For Coind, the scanner-DICOM-generated Coaut were adjusted individually to account for distortion and shifts of the tumor region between T2w and DWI. Intensity histogram tumor features, mean, coefficient of variation, skewness, and kurtosis were extracted from the Coaut and Coind MRI ADC images at each time point and correlated with treatment response. Pairwise consistency of features was evaluated with signed rank testing. Predicted response classification was compared by ROC area-under-the-curve (AUC) and rank sum testing. Pre and 2-wk mean ADC were consistent (<20% variability, p>0.34) between Coaut and Coind in most patients (20/21 each); 5-wk mean ADC was less consistent (18/21). Inconsistencies were due to tumor distortion/shifts (4 patients) and artifact (1). Coind-based pre and 5-wk mean ADC predicted response (AUC 0.81 and 0.82, p = 0.021 and 0.017, respectively); Coaut-based pre mean ADC showed similar performance (AUC = 0.81, p = 0.021) but not 5-wk mean ADC (AUC 0.65, p = NS). Coind-based decrease in 2-wk ADC skewness best predicted response (AUC = 0.84, p = 0.013), outperforming Coaut (AUC 0.71, p = NS). Inconsistencies between scanner automated and individualized co-registration were small in the vast majority of cases for whole-tumor based pre-therapy mean ADC, which predicted response with both methods. Major ADC distortion and ADC- T2w misalignments that were easily identifiable benefited from additional individualized co-registration adjustment. Pre-therapy DWI MRI for response prediction in cervical cancer is readily exportable to community settings using scanner based post-processing. This capability may broaden the application of DWI for treatment of cervical cancer in the wider community. Supported by R01CA155454.

Detection of water-molecular-motion configuration in patients with lupus nephritis: a primary study using diffusion-weighted imaging

BackgroundLupus nephritis (LN) is one of most common types of secondary glomerulonephritis, which is characterized by longitudinal pathological changes. Microstructural lesions of LN will impact the motion of water molecules, which can be detected by diffusion-weighted imaging (DWI). There are few reported measurements of water diffusion in patients with LN, and the nature of water diffusion across the entire depth of the renal parenchyma remains largely unknown.MethodsTwenty adult patients with LN and 11 healthy volunteers underwent DWI inspection. Renal biopsy samples were characterized based on the revised ISN/RPS 2003 classification. The apparent-diffusion coefficient (ADC) was calculated via fitting into a mono-exponential model. To compare the ADC level across the entire renal parenchyma between the two groups, repeated-measures analysis of variance (RM-ANOVA) was performed. ADC data derived from DWI pictures were transformed into tridimensional maps by MATLAB software.ResultsCompared with data from healthy volunteers, lower average ADC values with major undulatory magnitudes were found in patients with LN, especially in the cortical zone. Tridimensional maps of patients with LN displayed geographic terrain-like canyons and/or valleys that were different from the corresponding terrain-like flatlands and/or plateaus in healthy volunteers. A heterogeneity of ADC values was found in bilateral kidneys. Left kidneys predominated higher ADC values in patients with LN. The ADC values across the entire renal parenchyma exhibited statistically significant differences among the three identified pathological subclasses (P < 0.001).ConclusionsAnalysis of the motion of water molecules across the entire renal parenchyma may be helpful for better understanding the pathological conditions of LN, for which microstructural and functional heterogeneity may be detected and visualized via DWI.

Gaussian mixture model-based cluster analysis of apparent diffusion coefficient values: a novel approach to evaluate uterine endometrioid carcinoma grade.

The purpose of our study was to perform Gaussian mixture model (GMM)-based cluster analysis of the apparent diffusion coefficient (ADC) data of patients with endometrioid carcinoma, and to evaluate the relationship between histological grade and the ratios of the different clusters in each patient. This retrospective study enrolled 122 patients (training: n = 63; and validation: n = 59) imaged between May 2015 and February 2020. In the training cohort, manual segmentation was performed on the ADC maps to obtain the ADC data of each patient, and these ADC data were summated to obtain the "All-patient" ADC data. Cluster analysis (three clusters) was performed on this All-patient ADC data, and the ADC ranges of each cluster were defined as follows: cluster 1, 490-699 × 10-6mm2/s; cluster 2, 700-932 × 10-6mm2/s; and cluster 3, over 933 × 10-6mm2/s. In the training and validation cohorts, the ADC data of each patient was classified into three clusters according to these ADC ranges. The cluster ratios of each patient were calculated and compared with histological grade. In the training cohort, a significant positive correlation was found between the cluster 1 ratio and histological grade (ρ = 0.34, p = 0.0059). The cluster 1 ratios of high-grade lesions (grade 3) were significantly higher than those of low-grade lesions (grades 1 and 2) (p = 0.0084). A similar significant positive correlation was found between the cluster 1 ratio and histological grade in the validation cohort (ρ = 0.44, p = 0.0006). The cluster 1 ratio containing voxels with low ADC was significantly correlated with the histological grade of endometrioid carcinoma. • We performed Gaussian mixture model (GMM)-based cluster analysis of the apparent diffusion coefficient (ADC) data of patients with endometrioid carcinoma. • The cluster 1 ratio, which included low ADC values, was significantly positive correlated with histological grade in the training and validation cohorts. • The GMM-based cluster analysis of voxel-based ADC data was effective for grading endometrioid carcinoma.

Diffusion-weighted Imaging Voxelwise-matched Analyses of Lung Cancer at 3.0-T PET/MRI: Reverse Phase Encoding Approach for Echo-planar Imaging Distortion Correction.

Background PET/MRI has drawn increasing interest in thoracic oncology due to the simultaneous acquisition of PET and MRI data. Geometric distortions related to diffusion-weighted imaging (DWI) limit the evaluation of voxelwise multimodal analyses. Purpose To assess the effectiveness of reverse phase encoding in correcting DWI geometric distortion for multimodal PET/MRI voxelwise lung tumor analyses. Materials and Methods In this prospective study, reverse phase encoding method was implemented with 3.0-T PET/MRI to correct geometric distortions related to DWI. The method was validated in dedicated phantom and then applied to 12 consecutive patients (mean age, 66 years ± 13 [standard deviation]; 10 men) suspected of having lung cancer who underwent fluorodeoxyglucose PET/MRI between October 2018 and April 2019. The effects on DWI-related image matching and apparent diffusion coefficient (ADC) regional map computation were assessed. Consequences on multimodal PET/MRI voxelwise lung tumor analyses were evaluated. Spearman correlation coefficients (rs) between the standardized uptake value (SUV) and ADC data corrected for distortion were computed from optimal realigned DWI PET data, along with bootstrap confidence intervals. Results Phantom results showed that in highly distorted areas, correcting the distortion significantly reduced the mean error against the ground truth (-25% ± 10.6 to -18.4% ± 12.6; P < .001) and the number of voxels with more than 20% error (from 85.3% to 31.4%). In the 12 patients, the coregistration of multimodal PET/MRI tumor data was improved by using the reverse phase encoding method (0.4%-44%). In all tumors, voxelwise correlations (rs) between ADC and SUV revealed null or weak monotonic relationships (mean rs of 0.016 ± 0.24 with none above 0.5). Conclusion Reverse phase encoding is a simple-to-implement method for improved diffusion-weighted multimodal PET/MRI voxelwise-matched analyses in lung cancer. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Colletti in this issue.

Turbo Spin-echo Diffusion-weighted Imaging Compared with Single-shot Echo-planar Diffusion-weighted Imaging: Image Quality and Diagnostic Performance When Differentiating between Ductal Carcinoma in situ and Invasive Ductal Carcinoma.

Purpose:To compare the image quality between turbo spin-echo (TSE)-diffusion weighted imaging (DWI) and single-shot echo-planar imaging (EPI)-DWI, and to verify the diagnostic performance of the apparent diffusion coefficient (ADC) parameters of the two techniques by using histogram analysis in terms of differentiation between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) lesions.Methods:Ninety-four women with 94 lesions diagnosed as breast cancer by surgery underwent IRB-approved preoperative magnetic resonance imaging, including TSE and EPI-DWI with b-values of 50 and 850 s/mm2. Twenty lesions were identified as DCIS and 74 as IDC. Image quality [signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and geometric distortion] was evaluated quantitatively and compared between the TSE and EPI-DWI. A histogram analysis of the entire tumor voxel-based ADC data was performed, and the 10th, 25th, 50th, 75th, and 90th percentile values of each technique were compared between DCIS and IDC lesions.Results:The SNR and CNR of TSE-DWI were significantly higher than those of EPI-DWI (P < 0.0001 and < 0.0001). The geometric distortion of TSE-DWI was significantly lower than that of EPI-DWI (P < 0.0001). In TSE-DWI, the 10th, 25th, 50th, and 75th percentile values were significantly different between the DCIS and IDC lesions (P = 0.0010, 0.0004, 0.0008, and 0.0044, respectively). In EPI-DWI, the 50th and 75th percentile values were significantly different between the two groups (P = 0.0009 and 0.0093). There was no significant difference in the area under the curve of the receiver operating characteristic analysis of the 10th, 25th, 50th, and 75th percentile values of TSE-DWI, and the 50th and 75th percentile values of EPI-DWI (P = 0.29).Conclusion:The image quality of TSE-DWI was better than that of EPI-DWI. DCIS lesions were distinguished from IDC lesions with a wider range of percentile values in TSE-DWI than in EPI-DWI, although diagnostic performance was not significantly different between the techniques.

A Potential Biomarker from Diffusion Weighted Imaging and Parametric Response Map Analysis for Treatment Response Prediction in Nasopharyngeal Cancer

Diffusion-weighted imaging (DWI) is an MRI technique which provides functional information of tissue by detecting microscopic motion of water molecules. The change of apparent diffusion coefficient (ADC) derived from DWI was used as an imaging biomarker for treatment response prediction in cancers [1]. However, it was based on whole-tumor analysis which did not reflect heterogeneity within the tumor. To overcome this limitation, a new method called parametric response map (PRM) analysis was proposed to evaluate response by quantifying voxel-wise changes in ADC [2]. Here we investigated the use of PRM analysis on ADC from DWI as an imaging biomarker for treatment response prediction in nasopharyngeal cancer (NPC) patients. We collected thirteen patient datasets including ten complete response (CR) patients and three partial response (PR) patients at King Chulalongkorn Memorial Hospital where one patient dataset consisted of DWI and ADC data acquired before (i.e. pre-treatment) and at five weeks after (i.e. mid-treatment) initiation of chemoradiation therapy. For each dataset, we compared pre-treatment ADC image with co-registered mid-treatment ADC image, and calculated the percentage of voxels with increased ADC values with respect to total voxels within the tumor ROI, defined as PRM+. To validate the feasibility of the PRM biomarker, we computed the mean and standard deviation (SD) of percentage change in tumor volume (%AVol ) and in ADC (%AADC) and PRM+ across CR and PR patients, where tumor response was from 6-month follow-up data using RECIST1.1 guideline. The results showed that %AVol as well as %AADC between both groups was not significantly different. In contrast, PRM+ was significantly different between both groups (p < 0.05, 82.7±7.8% in CR vs 66.7±6.5% in PR). Our results implied that the proposed PRM+ biomarker could be a potential biomarker for early treatment response prediction in NPC patients.

Combining Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Apparent Diffusion Coefficient Maps for a Radiomics Nomogram to Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients.

The objective of this study was to develop a nomogrom for prediction of pathological complete response (PCR) to neoadjuvant chemotherapy in breast cancer patients. Ninety-one patients were analyzed. A total of 396 radiomics features were extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator was selected for data dimension reduction to build a radiomics signature. Finally, the nomogram was built to predict PCR. The radiomics signature of the model that combined DCE-MRI and ADC maps showed a higher performance (area under the receiver operating characteristic curve [AUC], 0.848) than the models with DCE-MRI (AUC, 0.750) or ADC maps (AUC, 0.785) alone in the training set. The proposed model, which included combined radiomics signature, estrogen receptor, and progesterone receptor, yielded a maximum AUC of 0.837 in the testing set. The combined radiomics features from DCE-MRI and ADC data may serve as potential predictor markers for predicting PCR. The nomogram could be used as a quantitative tool to predict PCR.