Standard Apparent Diffusion Coefficient Research Articles

Correlation of ADC values of adult brain tumors with the diagnosis and pathological grade: A cross-sectional multicenter study.

Brain tumors are common, requiring physicians to have a precise understanding of them for accurate diagnosis and treatment. Considering that various histological tumor types present different cellularity, we conducted this research to examine the role of apparent diffusion coefficient (ADC) values in the differential diagnosis and pathologic grading of brain tumor types. In this cross-sectional study, we gathered pathology reports of histological samples of adult brain tumors. The tissue sample of brain tumors were examined histologically by a pathologist. The magnetic resonance imaging data of these patients were interpreted by a neuroradiologist. The measured ADC values and ADC ratios were calculated. Standard mean ADC values were expressed as 10- 6 mm2/s. The findings were compared according to the histological diagnosis of each tumor. Sixty-eight patients were included in the study: 34 (50%) were male, and 34 (50%) were female. The average age of the patients was 51.69 + 16.40 years. In the examination of tumor type, 16 (23.5%) were astrocytoma, 9 (13.2%) were oligodendroglioma, 20 (29.4%) were glioblastoma, 4 (5.9%) were medulloblastoma, and 19 (27.9%) were metastatic tumors. the average value of ADC was statistically significantly different according to the pathological type of tumor (p < 0.001). The two-by-two comparison of average ADC among tumor types revealed significant differences, except for oligodendroglioma and glioblastoma (p-value = 0.87) and glioblastoma and medulloblastoma (p-value = 0.347). The average value of ADC and ADC ratio was statistically significantly different according to the pathological grade of the tumor (p < 0.001). In the two-by-two comparison of average ADC between all pathological grades of the tumor showed a significance difference except for Grade I and Grade II (p-value = 0.355). The mean value of ADC and ADC ratio for glioblastoma and metastatic tumors showed no significant difference. The assessment of brain tumor grade through ADC examination will help to estimate prognosis and devising suitable therapeutic strategies.

Analysis of IVIM Perfusion Fraction Improves Detection of Pancreatic Ductal Adenocarcinoma.

The purpose of this study was to evaluate whether intravoxel incoherent motion (IVIM) parameters can enhance the diagnostic performance of MRI in differentiating normal pancreatic parenchyma from solid pancreatic adenocarcinomas. This study included 113 participants: 66 patients diagnosed with pancreatic adenocarcinoma and 47 healthy volunteers. An MRI was conducted at 1.5 T MR unit, using nine b-values. Postprocessing involved analyzing both conventional monoexponential apparent diffusion coefficient (ADC) and IVIM parameters (diffusion coefficient D-pure molecular diffusion coefficient, perfusion-dependent diffusion coefficient D*-pseudodiffusion coeffitient, and perfusion fraction coefficient (f)) across four different b-value selections. Significantly higher parameters were found in the control group when using high b-values for the pure diffusion analysis and all b-values for the monoexponential analysis. Conversely, in the study group, the parameters were affected by low b-values. Most parameters could differentiate between normal and cancerous tissue, with D* showing the highest diagnostic performance (AUC 98-100%). A marked decrease in perfusion in the patients with pancreatic cancer, indicated by the significant differences in the D* medians between groups, was found. In conclusion, standard ADC maps alone may not suffice for a definitive pancreatic cancer diagnosis, and incorporating IVIM into MRI protocols is recommended, as the reduced tissue perfusion detected by the IVIM parameters is a promising marker for pancreatic adenocarcinoma.

Development of a multiparametric model for predicting the response to neoadjuvant chemotherapy in breast cancer.

Choosing the appropriate treatment early and predicting the efficacy of neoadjuvant chemotherapy (NAC) for locally advanced breast cancer patients are of particular importance for clinicians. Developing and validating a multiparametric model for predicting NAC would be very meaningful for clinical practice. This study included 91 patients with locally advanced breast cancer treated from 2016 to 2020. The correlation between multiparametric characteristics and the efficacy of NAC was examined. The data were randomly divided into training and validation sets. A least absolute shrinkage and selection operator (LASSO) regression analysis was used for the variable screening. A multivariable logistic regression analysis was used to construct the model. Calibration and decision curves were used to assess the performance of the established model. Lymph node metastasis, the first standard apparent diffusion coefficient (ADC) at the baseline, the change in the standard ADC at the first follow-up, the change in tumor volume at the first follow-up, and the clinical stage of the tumor at the baseline were selected for inclusion in the model. In the receiver operating characteristic (ROC) analysis, the areas under the curve (AUCs) were 0.984 [95% confidence interval (CI): 0.958-1] and 0.815 (95% CI: 0.509-1) for the primary and validation cohorts, respectively. The utility of the established model was confirmed by calibration and decision curves, and a nomogram was obtained. A multiparametric model based on clinical-pathological-magnetic resonance imaging (MRI) features was established to predict the effect of NAC in patients with locally advanced breast cancer.

Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm

PurposeOur study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis.MethodsEighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation (DWISD and ADCSD), DWI and ADC signal intensity ratio (DWISIR and ADCSIR), mean ADC and minimum ADC value (ADCmean and ADCmin) and ADC value standard deviation (ADCVSD). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result.ResultsTwo independent predictors of malignant nodules were identified by multivariate analysis: DWISIR (P = 0.007) and ADCmin (P < 0.001). The AUCs for multivariate prediction model, combined DWISIR and ADCmin thresholds model, combined DWISIR and ADCSIR thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined DWISIR and ADCmin threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS.ConclusionQuantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined DWISIR and ADCmin thresholds model significantly reduced the unnecessary biopsy rate.

Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology.

Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

Estimation of effective b-value for a diffusion-weighted double-echo steady-state sequence with bipolar gradients

Diffusion-weighted double-echo steady-state (dwDESS) MRI with bipolar diffusion gradients is a promising candidate to obtain diffusion weighted images (DWI) free of geometric distortions and with low motion sensitivity. However, a wider clinical application of dwDESS is currently hindered as no method is reported to explicitly calculate the effective b-value of the obtained DWI from the diffusion-gradients applied in the sequence. To this end, a previously described signal model was adapted for dwDESS with bipolar diffusion gradients, which allows to estimate an effective b-value, dubbed b’. Evaluation in phantom examinations was performed on a clinical 1.5 T MR system. Experimental results were compared with theoretical predictions, including the apparent diffusion coefficient (ADC) based on b-values from a standard EPI-DWI sequence and ADC’ based on the effective b’ from the dwDESS sequence. The adapted signal model was able to describe the experimental results, and the obtained values of ADC’ were in line with conventional ADC measurements.

Quantitative tumor burden imaging parameters of the spleen at MRI for predicting treatment response in patients with acute leukemia

ObjectivesTo study the value of standardized volume and intravoxel incoherent motion (IVIM) parameters of the spleen based on tumor burden for predicting treatment response in newly diagnosed acute leukemia (AL). MethodsPatients with newly diagnosed AL were recruited and underwent abdominal IVIM diffusion-weighted imaging within one week before the first induction chemotherapy. Quantitative parameters of magnetic resonance imaging (MRI) included the standardized volume (representing volumetric tumor burden) and IVIM parameters (standard apparent diffusion coefficient [sADC]; pure diffusion coefficient [D]; pseudo-diffusion coefficient [D∗]; and pseudo-perfusion fraction [f], representing functional tumor burden) of the spleen. Clinical biomarkers of tumor burden were collected. Patients were divided into complete remission (CR) and non-CR groups according to the treatment response after the first standardized induction chemotherapy, and the MRI and clinical parameters were compared between the two groups. The correlations of MRI parameters with clinical biomarkers were analyzed. Multivariate logistic regression was performed to determine the independent predictors for treatment response. Receiver operating characteristic curves were used to analyze the predicted performance. Results76 AL patients (CR: n = 43; non-CR: n = 33) were evaluated. Standardized spleen volume, sADC, D, f, white blood cell counts, and lactate dehydrogenase were significantly different between CR and non-CR groups (all p < 0.05). Standardized spleen volume, sADC, and D were correlated with white blood cell and lactate dehydrogenase, and f was correlated with lactate dehydrogenase (all p < 0.05). Standardized spleen volume (hazard ratio = 4.055, p = 0.042), D (hazard ratio = 0.991, p = 0.027), and f (hazard ratio = 1.142, p = 0.008) were independent predictors for treatment response, and the combination of standardized spleen volume, D, and f showed more favorable discrimination (area under the curve = 0.856) than individual predictors. ConclusionStandardized volume, D, and f of the spleen could be used to predict treatment response in newly diagnosed AL, and the combination of morphological and functional parameters would further improve the predicted performance. IVIM parameters of the spleen may be viable indicators for evaluating functional tumor burden in AL.

Correlation of IVIM/DKI Parameters with Hypoxia Biomarkers in Fibrosarcoma Murine Models: Direct Control of MRI and Pathological Sections

To investigate whether intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters correlate with hypoxia biomarkers, namely hypoxia inducible factor-1ɑ (HIF-1ɑ), carbonic anhydrase IX (CAIX), and pimonidazole (PIMO), in fibrosarcoma (FS) murine models. A model of 30 FS nude mice was established. All mice underwent magnetic resonance imaging (MRI) scans after which the IVIM (standard apparent diffusion coefficient [standard ADC], pure diffusion coefficient [D], pseudo-diffusion coefficient [D*], and perfusion fraction [f]) and DKI parameters (mean diffusion [MD], mean kurtosis [MK]) were obtained. Based on an MRI-pathology controlled method, correlations between each MRI parameter and hypoxia biomarkers were assessed by Pearson or Spearman tests. An independent sample t-test or Wilcoxon's rank sum test, and receiver operating characteristic curves were used to identify whether MRI parameters could differentiate between high and low expressions of hypoxia biomarkers. The IVIM/DKI parameters showed varying degrees of correlation with HIF-1α, CAIX, and PIMO expression. Among them, the D, f, and MK values could confirm HIF-1α expression, while D, f, and MK values could assess CAIX expression. Finally, standard D and MK values could evaluate PIMO expression levels. IVIM and DKI parameters can be used to reflect hypoxic biomarkers of FS and have the potential to detect tumor hypoxia.

JS09.7.A COMBINATION OF APPARENT DIFFUSION COEFFICIENT AND FET PET COMPARED WITH STANDARD IMAGING FOR QUANTITATIVE TUMOR PRESENCE IN DIFFUSE GLIOMAS

Abstract BACKGROUND Diffuse glioma local treatment is currently guided by standard MRI sequences. Yet, the combination of apparent diffusion coefficient (ADC) and O-(2-[18F]-fluoroethyl)-L-tyrosine positron emission tomography (FET PET) (ADC/FET) detects histopathological qualitative assessed tumor presence more accurately than standard imaging. It is unknown whether this is similar for quantitative assessed tumor presence. This study compares the diagnostic accuracy of standard imaging and ADC/FET for the detection of quantitative assessed tumor presence in diffuse glioma. MATERIAL AND METHODS Fifteen patients with newly diagnosed diffuse glioma were retrospectively included. Pre-operative standard imaging, ADC and FET PET were available. Multiregional image-guided stereotactic biopsies were acquired before craniotomy. Quantitative imaging data from regions-of-interests (ROIs) centered at the biopsy locations was obtained. For each biopsy, sample semi-automatic cellularity and proliferation index, as well as DNA methylation-based tumor purity was determined. Linear mixed models were used to evaluate which modalities were significant independent predictors for each tumor presence marker and fitness was assessed using Bayesian Information Criterium. RESULTS 125 biopsies were analyzed (75 from eight high grade and 50 from seven low grade gliomas). Cellularity was predicted by T1 weighted MRI (p = 0.033), ADC (p = 0.016) and ADC/FET (p &amp;lt;.001), with ADC/FET as best fit. Both proliferation index (p = 0.003) and tumor purity (p &amp;lt;.001) were only predicted by ADC/FET. CONCLUSION Combining ADC and FET PET predicts cellularity, proliferation index and tumor purity better than standard imaging. Further investigation into ADC/FET-guided local treatment of diffuse glioma is therefore needed.

Radiomics model based on intravoxel incoherent motion and diffusion kurtosis imaging for predicting histopathological grade and Ki-67 expression level of soft tissue sarcomas.

Accurate identification of the histopathological grade and the Ki-67 expression level is important in clinical cases of soft tissue sarcomas (STSs). To explore the feasibility of a radiomics model based on intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and diffusion kurtosis imaging (DKI) MRI parameter maps in predicting the histopathological grade and Ki-67 expression level of STSs. In total, 42 patients diagnosed with STSs between May 2018 and January 2020 were selected. The MADC software in Functool of GE ADW 4.7 workstation was used to obtain standard apparent diffusion coefficient (ADC), D, D*, f, mean diffusivity, and mean kurtosis (MK). The histopathological grade and Ki-67 expression level of STSs were identified. The radiomics features of IVIM and DKI parameter maps were used as the dataset. The area under the receiver operating characteristic curve (AUC) and F1-score were calculated. D-SVM achieved the best diagnostic performance for histopathological grade. The AUC in the validation cohort was 0.88 (sensitivity: 0.75 [low level] and 0.83 [high level]; specificity: 0.83 [low level] and 0.75 [high level]; F1-score: 0.75 [low level] and 0.83 [high level]). MK-SVM achieved the best diagnostic performance for Ki-67 expression level. The AUC in the validation cohort was 0.83 (sensitivity: 0.83 [low level] and 0.50 [high level; specificity: 0.50 [low level] and 0.83 [high level]; F1-score: 0.77 [low level] and 0.57 [high level]). The proposed radiomics classifier could predict the pathological grade of STSs and the Ki-67 expression level in STSs.

Using Ultrahigh b -Value Diffusion-Weighted Imaging to Noninvasively Assess Renal Fibrosis in a Rabbit Model of Renal Artery Stenosis.

This study aimed to investigate the feasibility of diffusion-weighted imaging with ultrahigh b values ( ub DWI) for the evaluation of renal fibrosis (RF) induced by renal artery stenosis (RAS) in a rabbit model. Thirty-two rabbits underwent left RAS operation, whereas 8 rabbits received sham surgery. All rabbits underwent ub DWI ( b = 0-4500 s/mm 2 ). The standard apparent diffusion coefficient (ADC st ), molecular diffusion coefficient ( D ), perfusion fraction ( f ), perfusion-related diffusion coefficient ( D *) and ultrahigh apparent diffusion coefficient (ADC uh ) were longitudinally assessed before operation and at weeks 2, 4, and 6 after operation. The degree of interstitial fibrosis and the expression of aquaporin (AQP) 1 and AQP2 were determined through pathological examination. In the stenotic kidney, the ADC st , D , f , and ADC uh values of the renal parenchyma significantly decreased compared with those at baseline (all P < 0.05), whereas the D * values significantly increased after RAS induction ( P < 0.05). The ADC st , D , D *, and f were weakly to moderately correlated with interstitial fibrosis as well as with the expression of AQP1 and AQP2. Furthermore, the ADC uh negatively correlated with interstitial fibrosis ( ρ = -0.782, P < 0.001) and positively correlated with AQP1 and AQP2 expression ( ρ = 0.794, P < 0.001, and ρ = 0.789, P < 0.001, respectively). Diffusion-weighted imaging with ultrahigh b values shows the potential for noninvasive assessment of the progression of RF in rabbits with unilateral RAS. The ADC uh derived from ub DWI could reflect the expression of AQPs in RF.

Comparison of Diffusion Kurtosis Imaging and Standard Mono-Exponential Apparent Diffusion Coefficient in Diagnosis of Significant Prostate Cancer-A Correlation with Gleason Score Assessed on Whole-Mount Histopathology Specimens.

The study was undertaken to compare the diagnostic performance of diffusion kurtosis imaging (DKI) with the standard monoexponential (ME) apparent diffusion coefficient (ADC) model in the detection of significant prostate cancer (PCa), using whole-mount histopathology of radical prostatectomy specimens as a reference standard. 155 patients with prostate cancer had undergone multiparametric magnetic resonance imaging (mpMRI) at 3T before prostatectomy. Quantitative diffusion parameters-the apparent diffusion coefficient corrected for non-Gaussian behavior (Dapp), kurtosis (K), ADC1200, and ADC2000 were correlated with Gleason score and compared between cancerous and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. The mean values of all diffusion parameters (Dapp, K, ADC1200, ADC2000) were significantly different both between malignant and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. Although the kurtosis model was better fitted to DWI data, the diagnostic performance in receiver operating characteristic (ROC) analysis of DKI and the standard ADC model in the detection of significant PCa was similar in the peripheral zone (PZ) and in peripheral and transitional zones (TZ) together. In conclusion, our study was not able to demonstrate a clear superiority of the kurtosis model over standard ADC in the diagnosis of significant PCa in PZ and in both zones combined.

Heterogeneity evaluation of multi-high b-value apparent diffusion coefficient on cerebral ischemia in MCAO rat.

To assess brain damage in a rat model of cerebral ischemia based on apparent diffusion coefficient (ADC) data obtained from multi-high b-values and evaluate the relationship between Aquaporin 4 (AQP4) expression and ADC. Thirty eight male Sprague-Dawley rats were randomized into two groups: (1) sham controls (n = 6) and (2) cerebral ischemia (successful model, n = 19). All rats underwent diffusion-weighted imaging (DWI) with both standard b-values and multi-high b-values (2,500-4,500 s/mm2) using a 3.0-T device. Standard ADC (ADCst) maps and multi-high b-value ADCs (ADCmh) were calculated, respectively. Aquaporin 4 expression was quantified using Western blot. Relative values of ADCst and ADCmh, AQP4 expression were compared between the sham group and the ischemia group. Correlations between ADC values and AQP4 expression were evaluated. At 0.5 h after suture insertion, the value of ADCmh on the lesion was obviously decreased, and there was no difference in lesion volume when compared with ADCst. After reperfusion, besides similar regions where ADCst values decreased, we also found additional large values on ADCmh within the cortex of the ipsilateral side or surrounding the lesion. The lesion evolution of the large value on ADCmh was quite different from other indicators. But the total ADCmh values were still significantly associated with ADCst. The AQP4 protein expression level was appreciably increased after middle cerebral artery occlusion (MCAO), but there was no correlation between AQP4 expression either with ADCmh or ADCst. We found the large values on ADCmh during the progression of cerebral infarction is varied, but there was no correlation between ADCmh values and AQP4 expression. ADCmh may indicate the heterogeneity of ischemia lesions, but the underlying pathological basis should be further explored.

Multiparametric Magnetic Resonance Imaging of the Kidneys: Effects of Regional, Side, and Hydration Variations on Functional Quantifications.

To standardize renal functional magnetic resonance imaging (MRI), it is important to understand the influence of side-to-side variation, regional variation within the organ, and hydration states in MRI and to search for variables that are not affected by those variations. To assess MRI-based biomarkers for characterizing the kidney in healthy volunteers while considering variations in anatomic factors and hydration states. Prospective. Twenty-five healthy volunteers (15 females and 10 males, median age 25 years). 3.0 T intravoxel incoherent motion diffusion-weighted imaging, arterial spinning labeling imaging, blood oxygenation level dependent imaging, and three-dimensional MR elastography. Functional variables were measured before and after water challenge. Regions of interest were manually drawn by two investigators (JC and ZZ, with 8- and 5-year experiences in abdominal radiology) in the cortex, the medulla, and the entire kidney. The medulla/cortex ratio was calculated. Paired t-test or Wilcoxon signed rank test; interobserver correlation coefficient; repeatability coefficients; Spearman's correlation; significance level: P < 0.05. Diffusion parameters were only subject to regional variation. R2*, RBF, and renal stiffness (RS) showed regional variation, side variation, and dependence on hydration states. For each side and hydration state, the cortex showed significantly higher standard apparent diffusion coefficient (sADC), higher true diffusion (D), lower R2*, and lower RS than the medulla. For each region at baseline, the left kidney showed significantly higher R2*, higher RS, and lower renal blood flow (RBF) than the right kidney. For each region and side, RS and RBF increased significantly while R2* decreased significantly after water intake. After introducing the intrinsic regional difference, significantly higher medulla/cortex ratio of RS remained after water intake except for RS@90 Hz in the right kidney. Renal multiparametric MRI quantifications were affected by regional variation, side variation, and hydration states. 2 TECHNICAL EFFICACY: Stage 1.

Abstract 5064: Patient-specific forecasting of prostate cancer growth during active surveillance using an imaging-informed mechanistic model

Abstract Active surveillance (AS) is a suitable management option for many newly-diagnosed prostate cancer (PCa) cases, which usually exhibit low to intermediate clinical risk. In AS, patients are closely monitored via multiparametric magnetic resonance imaging (mpMRI), prostate-specific antigen tests, and biopsies until these reveal an increase in PCa risk that warrants treatment. Thus, AS can contribute to reduce the current overtreatment of PCa and avoid treatment side-effects that may adversely impact patients’ lives without necessarily improving their longevity. However, current AS protocols rely on assessing PCa according to population-based studies, which complicates the design of personalized monitoring plans and the early detection of tumor progression. To address these issues, we propose to predict PCa growth using patient-specific forecasts based on an mpMRI-informed mechanistic model. Here, we present a preliminary study in a cohort of seven PCa patients who enrolled in AS and had three mpMRI scans over a period of 1.4 to 4.9 years. Our mechanistic model describes PCa growth in terms of tumor cell density dynamics, consisting of mobility and proliferation modeled via a diffusion process and a logistic law, respectively. We segment each patient’s prostate geometry on T2-weighted images and estimate tumor cell density from standard apparent diffusion coefficient (ADC) maps. To facilitate PCa modeling, we co-register each patient’s mpMRI datasets with a biomechanical elastic method that aligns the prostate segmentations across the imaging datasets. The model is initialized with the data from the first mpMRI scan. Model calibration relies on a nonlinear least-squares algorithm that minimizes the mismatch between ADC-measured and model-predicted tumor cell density at the second mpMRI date. For validation, we initialize the patient-specific calibrated model with the second mpMRI dataset and forecast PCa growth at the third mpMRI date, which we compare to the corresponding ADC-estimated tumor cell density. Model calibration resulted in a concordance correlation coefficient (CCC) &amp;gt; 0.99 for both tumor volume and global tumor cell count across the cohort. For each patient, the spatial fit of tumor cell density at the second scan date rendered a median Dice score and CCC of 0.79 and 0.52, respectively. Model validation resulted in CCCs of 0.88 and 0.81 for tumor volume and global tumor cell count across the cohort. Comparing the patient-specific ADC-estimated and model forecasts of tumor cell density at the third scan date, we obtained a median Dice score and CCC of 0.65 and 0.46, respectively. Thus, while improvement and further testing in larger cohorts are still needed, these results do suggest that our computational forecasting approach is a promising technology to predict the spatiotemporal growth of PCa during AS on a patient-specific basis. Citation Format: Guillermo Lorenzo, Jon S. Heiselman, Michael A. Liss, Michael I. Miga, Hector Gomez, Thomas E. Yankeelov, Thomas J. Hughes, Alessandro Reali. Patient-specific forecasting of prostate cancer growth during active surveillance using an imaging-informed mechanistic model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5064.

Soft tissue sarcoma: IVIM and DKI parameters correlate with Ki-67 labeling index on direct comparison of MRI and histopathological slices.

To explore the correlation of parameters derived from intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) with Ki-67 labeling index (LI) in soft tissue sarcoma (STS). Forty-one patients with STS underwent IVIM and DKI imaging at 3.0 MRI. The standard apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), mean kurtosis (MK), and mean diffusivity (MD) were compared between Ki-67 low- and high-expression groups by two independent observers. A novel method was used to ensure the topographic correlation of histologic sections and magnetic resonance imaging slices. Receiver operating characteristic (ROC), intraclass correlation coefficient (ICC), and Spearman's rank correlations were performed for statistical analysis. The high-expression group displayed lower standard ADC, D, and MD values and a higher MK value than the low-expression group. No significant differences were found for D∗ and f values. The areas under the curve for standard ADC, D, MD, and MK when discriminating between low- and high-expression groups were 0.736, 0.745, 0.848, and 0.894, respectively. MK was positively correlated with Ki-67 LI (r = 0.809, p < 0.001). Standard ADC, D, and MD were negatively correlated with Ki-67 LI (r = -0.541, -0.556, -0.702, respectively, p < 0.001). IVIM and DKI parameters are correlated with Ki-67 LI. MK may be the optimal imaging biomarker for assessing the Ki-67 expression of STS. • IVIM and DKI parameters are correlated with the expression of Ki-67 in STS. • The MRI-pathology control method ensured a strong correlation between MRI slices and histologic sections, resulting in a robust radiological-pathological correlation.

Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI

BackgroundThis study aims to observe and analyze the effect of diffusion weighted magnetic resonance imaging (MRI) on the patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy.MethodsFifty patients (mean age, 48.7 years) with stage II–III breast cancer who underwent neoadjuvant chemotherapy and preoperative MRI between 2016 and 2020 were retrospectively evaluated. The associations between preoperative breast MRI findings/clinicopathological features and outcomes of neoadjuvant chemotherapy were assessed.ResultsClinical stage at baseline (OR: 0.104, 95% confidence interval (CI) 0.021–0.516, P = 0.006) and standard apparent diffusion coefficient (ADC) change (OR: 9.865, 95% CI 1.024–95.021, P = 0.048) were significant predictive factors of the effects of neoadjuvant chemotherapy. The percentage increase of standard ADC value in pathologic complete response (pCR) group was larger than that in non-pCR group at first time point (P < 0.05). A correlation was observed between the change in standard ADC values and tumor diameter at first follow-up (r: 0.438, P < 0.05).ConclusionsOur findings support that change in standard ADC values and clinical stage at baseline can predict the effects of neoadjuvant chemotherapy for patients with breast cancer in early stage.

Study of Diffusion Weighted Imaging Derived Diffusion Parameters as Biomarkers for the Microenvironment in Gliomas

ObjectivesTo explore the efficacy of diffusion weighted imaging (DWI)-derived metrics under different models as surrogate indicators for molecular biomarkers and tumor microenvironment in gliomas.MethodsA retrospective study was performed for 41 patients with gliomas. The standard apparent diffusion coefficient (ADCst) and ADC under ultra-high b values (ADCuh) (b values: 2500 to 5000 s/mm2) were calculated based on monoexponential model. The fraction of fast diffusion (f), pseudo ADC (ADCfast) and true ADC (ADCslow) were calculated by bi-exponential model (b values: 0 to 2000 s/mm2). The apparent diffusional kurtosis (Kapp) was derived from the simplified diffusion kurtosis imaging (DKI) model (b values: 200 to 3000 s/mm2). Potential correlations between DWI parameters and immunohistological indices (i.e. Aquaporin (AQP)1, AQP4, AQP9 and Ki-67) were investigated and DWI parameters were compared between high- and low-grade gliomas, and between tumor center and peritumor. Receiver operator characteristic (ROC) curve and area under the curve (AUC) were calculated to determine the performance of independent or combined DWI parameters in grading gliomas.ResultsThe ADCslow and ADCuh at tumor center showed a stronger correlation with Ki-67 than other DWI metrics. The ADCst, ADCslow and ADCuh at tumor center presented correlations with AQP1 and AQP4 while AQP9 did not correlate with any DWI metric. Kapp showed a correlation with Ki-67 while no significant correlation with AQPs. ADCst (p < 0.001) and ADCslow (p = 0.001) were significantly lower while the ADCuh (p = 0.006) and Kapp (p = 0.005) were significantly higher in the high-grade than in the low-grade gliomas. ADCst, f, ADCfast, ADCslow, ADCuh, Kapp at the tumor center had significant differences with those in peritumor when the gliomas grade became high (p < 0.05). Involving ADCuh and Kapp simultaneously into an independent ADCst model (AUC = 0.833) could further improve the grading performance (ADCst+ADCuh+Kapp: AUC = 0.923).ConclusionDifferent DWI metrics fitted within different b-value ranges (low to ultra-high b values) have different efficacies as a surrogate indicator for molecular expression or microstructural complexity in gliomas. Further studies are needed to better explain the biological meanings of these DWI parameters in gliomas.

Predicting the consistency of intracranial meningiomas using apparent diffusion coefficient maps derived from preoperative diffusion-weighted imaging.

The consistency of meningiomas is a critical factor affecting the difficulty of resection, operative complications, and operative time. The apparent diffusion coefficient (ADC) is derived from diffusion-weighted imaging (DWI) and is calculated using two optimized b values. While the results of comparisons between the standard ADC and the consistency of meningiomas vary, the shifted ADC has been reported to be strongly correlated with liver stiffness. The purpose of the present prospective cohort study was to determine whether preoperative standard and shifted ADC maps predict the consistency of intracranial meningiomas. Standard (b values 0 and 1000 sec/mm2) and shifted (b values 200 and 1500 sec/mm2) ADC maps were calculated using preoperative DWI in patients undergoing resection of intracranial meningiomas. Regions of interest (ROIs) were placed within the tumor on standard and shifted ADC maps and registered on the navigation system. Tumor tissue located at the registered ROI was resected through craniotomy, and its stiffness was measured using a durometer. The cutoff point lying closest to the upper left corner of a receiver operating characteristic (ROC) curve was determined for the detection of tumor stiffness such that an ultrasonic aspirator or scissors was always required for resection. Each tumor tissue sample with stiffness greater than or equal to or less than this cutoff point was defined as hard or soft tumor, respectively. For 76 ROIs obtained from 25 patients studied, significant negative correlations were observed between stiffness and the standard ADC (ρ = -0.465, p < 0.01) and the shifted ADC (ρ = -0.490, p < 0.01). The area under the ROC curve for detecting hard tumor (stiffness ≥ 20.8 kPa) did not differ between the standard ADC (0.820) and the shifted ADC (0.847) (p = 0.39). The positive predictive value (PPV) for the combination of a low standard ADC and a low shifted ADC for detecting hard tumor was 89%. The PPV for the combination of a high standard ADC and a high shifted ADC for detecting soft tumor (stiffness < 20.8 kPa) was 81%. A combination of standard and shifted ADC maps derived from preoperative DWI can be used to predict the consistency of intracranial meningiomas.

Histogram Analysis Based on Apparent Diffusion Coefficient Maps of Bone Marrow in Multiple Myeloma: An Independent Predictor for High-risk Patients Classified by the Revised International Staging System

The revised International Staging System (R-ISS) is the current risk stratifier for patients with newly diagnosed multiple myeloma (NDMM). We used histogram analysis based on apparent diffusion coefficient (ADC) maps of bone marrow to predict high-risk NDMM patients staged as R-ISS stage III. Sixty-one NDMM patients were recruited prospectively and underwent whole-body diffusion-weighted MRI. Mean ADC and four ADC-based histogram parameters of representative background bone marrow were quantified with TexRAD software, including ADC entropy, ADC standard deviation (SD), ADC skewness and ADC kurtosis. Diagnostic performance to discriminate R-ISS III from I/II disease was evaluated by receiver-operating characteristics curve (ROC). Univariate and multivariate analysis using stepwise logistic regression model was performed to identify predictors for R-ISS III. ADC entropy of background marrow showed the highest areas under the ROC (0.784, sensitivity=93.3%, specificity=63.0%) for the detection of R-ISS stage III disease. Multivariate analysis showed that increased ADC entropy (>3.612) of background marrow can independently predict R-ISS stage III disease in the overall patients (Model 1 corrected for diffuse infiltration [DI] pattern: odds ratio [OR], 10.647; p=0.008; Model 2 corrected for mean ADC: OR, 10.485; p=0.010) and in the subgroup with DI pattern (OR, 7.043; p=0.037). ADC entropy of background marrow may serve as a sensitive imaging biomarker facilitating the detection of high-risk NDMM patients staged as R-ISS stage III. Increased ADC entropy of background marrow can independently predict R-ISS stage III in the overall patients and in the subgroup with DI pattern.