Performance Of Apparent Diffusion Coefficient Research Articles

Transient elastography and diffusion-weighted magnetic resonance imaging for assessment of liver fibrosis in children with chronic hepatitis C.

Chronic hepatitis C (CHC) is a health burden with consequent morbidity and mortality. Liver biopsy is the gold standard for evaluating fibrosis and assessing disease severity and prognostic purposes post-treatment. Noninvasive alternatives for liver biopsy such as transient elastography (TE) and diffusion-weighted magnetic resonance imaging (DW-MRI) are critical needs. To evaluate TE and DW-MRI as noninvasive tools for predicting liver fibrosis in children with CHC. This prospective cross-sectional study initially recruited 100 children with CHC virus infection. Sixty-four children completed the full set of investigations including liver stiffness measurement (LSM) using TE and measurement of apparent diffusion coefficient (ADC) of the liver and spleen using DW-MRI. Liver biopsies were evaluated for fibrosis using Ishak scoring system. LSM and liver and spleen ADC were compared in different fibrosis stages and correlation analysis was performed with histopathological findings and other laboratory parameters. Most patients had moderate fibrosis (73.5%) while 26.5% had mild fibrosis. None had severe fibrosis or cirrhosis. The majority (68.8%) had mild activity, while only 7.8% had moderate activity. Ishak scores had a significant direct correlation with LSM (P = 0.008) and were negatively correlated with both liver and spleen ADC but with no statistical significance (P = 0.086 and P = 0.145, respectively). Similarly, histopathological activity correlated significantly with LSM (P = 0.002) but not with liver or spleen ADC (P = 0.84 and 0.98 respectively). LSM and liver ADC were able to significantly discriminate F3 from lower fibrosis stages (area under the curve = 0.700 and 0.747, respectively) with a better performance of liver ADC. TE and liver ADC were helpful in predicting significant fibrosis in children with chronic hepatitis C virus infection with a better performance of liver ADC.

Comprehensive diagnostic model of metastasis in prostate cancer: Individual and combined bioscore model of ADC value, Gleason score, and PSA

AbstractProstate cancer (PCa) metastasis significantly influences treatment decisions and prognosis. This study evaluated the individual and combined diagnostic ability of apparent diffusion coefficient (ADC), Gleason score and prostate‐specific antigen (PSA) in detecting metastasis. The study included data from 120 biopsy‐confirmed PCa patients treated from 2019 to 2023. Whole‐body MRI images, incorporating high‐resolution T2 and axial DWI sequences, were evaluated by experienced radiologists. Receiver operating characteristic (ROC) curves and logistic regression models were used to assess the diagnostic performance of ADC, Gleason score, and PSA in detecting metastasis. The prevalence of PCa metastasis was 25.0%, with pelvic lymph node metastasis (16.7%) and bony metastasis (12.5%) being most prevalent. Patients with PCa metastasis had significantly lower ADC values, higher Gleason scores, and higher PSA levels compared to those without metastasis. Individually, an ADC cut‐off of ≤549.00 mm2/s was the best marker for detecting metastasis. The combined bioscore model including PSA, ADC values and Gleason score was the best independent predictor, correlating with a 159‐fold increased likelihood of detecting PCa metastasis. This study demonstrated the prognostic ability of PCa markers in detecting metastasis. ADC was an independent, sensitive, specific, and accurate diagnostic marker. The combined bioscore model of ADC, PSA and Gleason score significantly enhanced the identification of patients with PCa metastasis.

The value of apparent diffusion coefficient values in predicting Gleason grading of low to intermediate-risk prostate cancer

ObjectivesTo investigate the diagnostic performance of the apparent diffusion coefficient (ADC) for low to intermediate-risk prostate cancer (PCa), as well as its correlation with the prognostic Gleason score (GS).Materials and methodsRetrospective analysis of MRI images and relevant clinical data from patients with prostate disease. The differences in ADC between different GS groups were compared, and the efficacy of ADC in PCa diagnosis were analyzed. Furthermore, the diagnostic performance of the mean ADC (ADCmean) and minimum ADC (ADCmin) values was compared.ResultsThere were 1414 patients with 1631 lesions. In terms of GS, both ADCmin and ADCmean values of the GS 4 + 3 group were significantly lower than those of the GS 3 + 4 group, GS 3 + 3 group, and the benign group, with all differences being statistically significant (p < 0.01). The AUC values for diagnosing PCa based on ADCmin and ADCmean were 0.914 and 0.944, respectively. The corresponding diagnostic thresholds were 0.703 × 10−3 mm2/s for ADCmin and 0.927 × 10−3 mm2/s for ADCmean. The magnitudes of ADCmin and ADCmean values exhibited a negative correlation with GS values (ρ = −0.750, p < 0.001; ρ = −0.752, p < 0.001).ConclusionsADC values demonstrate an inverse relationship with the invasiveness of PCa, indicating that higher invasiveness is associated with lower ADC values. Additionally, ADC values exhibit high diagnostic potential, sensitivity, and specificity for distinguishing between GS 3 + 4 and GS 4 + 3 lesions. Moreover, the diagnostic value of ADCmean is even more significant, highlighting its crucial role in the diagnosis of low to intermediate-risk PCa.Critical relevance statementADC values are a valuable tool for distinguishing different levels of aggressiveness in PCa. They help in the preoperative assessment of the biological characteristics of PCa, allowing clinicians to develop personalized treatment strategies, effectively mitigating the risk of unnecessary interventions.Key PointsThe preoperative GS is crucial for planning the clinical treatment of PCa.The invasiveness of PCa is inversely correlated with ADC values.ADC values play a crucial role in the accurate preoperative evaluation of low to intermediate-risk PCa, thus aiding clinicians in developing tailored treatment plans.Graphical

The performance of the node reporting and data system 1.0 (Node-RADS) and DWI–MRI in staging patients with cervical carcinoma according to the new FIGO classification (2018)

PurposeTo evaluate the diagnostic accuracy of the Node-RADS score and the utility of apparent diffusion coefficient (ADC) values in predicting metastatic lymph nodes (LNs) involvement in cervical cancer (CC) patients using magnetic resonance imaging (MRI). The applicability of the Node RADS score across three readers with different years of experience in pelvic imaging was also assessed.Material and methodsAmong 140 patients, 68 underwent staging MRI, neoadjuvant chemotherapy and radical surgery, forming the study cohort. Node-RADS scores of the main pelvic stations were retrospectively determined to assess LN metastatic likelihood and compared with the histological findings. Mean ADC, relative ADC (rADC), and correct ADC (cADC) values of LNs classified as Node-RADS ≥ 3 were measured and compared with histological reports, considered as gold standard.ResultsSensitivity, specificity, positive and negative predictive values (PPVs and NPVs), and accuracy were calculated for different Node-RADS thresholds. Node RADS ≥ 3 showed a sensitivity of 92.8% and specificity of 72.5%. Node RADS ≥ 4 yielded a sensitivity of 71.4% and specificity of 100%, while Node RADS 5 yielded 42.9% and 100%, respectively. The diagnostic performance of mean ADC, cADC and rADC values from 78 LNs with Node-RADS score ≥ 3 was assessed, with ADC demonstrating the highest area under the curve (AUC 0.820), compared to cADC and rADC values.ConclusionThe Node-RADS score provides a standardized LNs assessment, enhancing diagnostic accuracy in CC patients. Its ease of use and high inter-observer concordance support its clinical utility. ADC measurement of LNs shows promise as an additional tool for optimizing patient diagnostic evaluation.

Comparison of reduced field-of-view DWI and conventional DWI techniques for the assessment of lumbar bone marrow infiltration in patients with acute leukemia.

To compare the imaging quality, apparent diffusion coefficient (ADC), and the value of assessing bone marrow infiltration between reduced field-of-view diffusion-weighted imaging (r-FOV DWI) and conventional DWI in the lumbar spine of acute leukemia (AL). Patients with newly diagnosed AL were recruited and underwent both r-FOV DWI and conventional DWI in the lumbar spine. Two radiologists evaluated image quality scores using 5-Likert-type scales qualitatively and measured signal-to-noise ratio (SNR), contrast-to-noise (CNR), signal intensity ratio (SIR), and ADC quantitatively. Patients were divided into hypo- and normocellular group, moderately hypercellular group, and severely hypercellular group according to bone marrow cellularity (BMC) obtained from bone marrow biopsies. The image quality parameters and ADC value between the two sequences were compared. One-way analysis of variance followed by LSD post hoc test was used for the comparisons of the ADC values among the three groups. The performance of ADC obtained with r-FOV DWI (ADCr) and conventional DWI(ADCc) in evaluating BMC and their correlations with BMC and white blood cells (WBC) were analyzed and compared. 71 AL patients (hypo- and normocellular: n=20; moderately hypercellular: n=19; severely hypercellular: n=32) were evaluated. The image quality scores, CNR, SIR, and ADC value of r-FOV DWI were significantly higher than those of conventional DWI (all p<0.05), and the SNR of r-FOV DWI was significantly lower (p<0.001). ADCr showed statistical differences in all pairwise comparisons among the three groups (all p<0.05), while ADCc showed significant difference only between hypo- and normocellular group and severely hypercellular group (p=0.014). The performance of ADCr in evaluating BMC (Z=2.380, p=0.017) and its correlations with BMC (Z=-2.008, p = 0.045) and WBC (Z=-2.022, p = 0.043) were significantly higher than those of ADCc. Compared with conventional DWI, r-FOV DWI provides superior image quality of the lumbar spine in AL patients, thus yielding better performance in assessing bone marrow infiltration.

Comparing extracellular volume fraction with apparent diffusion coefficient for the characterization of breast tumors

PurposeTo investigate the feasibility of dual-energy CT (DECT)-derived extracellular volume (ECV) fraction for characterization of breast tumors, compared to apparent diffusion coefficient (ADC) and validated against histopathological findings. Material and methodsThe ECV fraction and ADC were prospectively assessed in patients with breast tumors using chest DECT and breast MRI. The diagnostic performance of ECV fraction and ADC was accessed in predicting breast histopathological subtypes and pathological complete response (pCR) status. Histopathological sections were analyzed by digital image analysis. Pearson’s correlation analysis was used to correlate between DECT and histopathological ECV fractions. ResultsThis study included 271 patients, with 314 breast lesions (61 benign and 253 malignant). The ECV fraction and ADC showed comparable area under the curve (AUC) for distinguishing benign from malignant lesions (p = 0.123) and invasive carcinoma from ductal carcinoma in situ (p = 0.115). There were significant differences in ECV fraction between different hormone receptors and Ki67 states (p = 0.001 ∼ 0.014), while ADC values only differed among various Ki67 states (p < 0.001). The ECV fraction was lower (p = 0.007), ADC was higher (p = 0.013) in pCR than in non-pCR group, with an AUC of 0.748 and 0.730 (p = 0.887), respectively. There was a positive correlation between DECT and histopathological ECV fractions (r = 0.615, p < 0.01). ConclusionsRoutine chest DECT-derived ECV fraction is a viable quantitative imaging biomarker for predicting histopathological subtypes and pCR in patient with breast tumors, and correlated well with histopathology finding.

Differentiating primary central nervous system lymphoma from glioblastoma by time-dependent diffusion using oscillating gradient

BackgroundThis study aimed to elucidate the impact of effective diffusion time setting on apparent diffusion coefficient (ADC)-based differentiation between primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBMs) and to investigate the usage of time-dependent diffusion magnetic resonance imaging (MRI) parameters.MethodsA retrospective study was conducted involving 21 patients with PCNSLs and 66 patients with GBMs using diffusion weighted imaging (DWI) sequences with oscillating gradient spin-echo (Δeff = 7.1 ms) and conventional pulsed gradient (Δeff = 44.5 ms). In addition to ADC maps at the two diffusion times (ADC7.1 ms and ADC44.5 ms), we generated maps of the ADC changes (cADC) and the relative ADC changes (rcADC) between the two diffusion times. Regions of interest were placed on enhancing regions and non-enhancing peritumoral regions. The mean and the fifth and 95th percentile values of each parameter were compared between PCNSLs and GBMs. The area under the receiver operating characteristic curve (AUC) values were used to compare the discriminating performances among the indices.ResultsIn enhancing regions, the mean and fifth and 95th percentile values of ADC44.5 ms and ADC7.1 ms in PCNSLs were significantly lower than those in GBMs (p = 0.02 for 95th percentile of ADC44.5 ms, p = 0.04 for ADC7.1 ms, and p < 0.01 for others). Furthermore, the mean and fifth and 95th percentile values of cADC and rcADC were significantly higher in PCNSLs than in GBMs (each p < 0.01). The AUC of the best-performing index for ADC7.1 ms was significantly lower than that for ADC44.5 ms (p < 0.001). The mean rcADC showed the highest discriminating performance (AUC = 0.920) among all indices. In peritumoral regions, no significant difference in any of the three indices of ADC44.5 ms, ADC7.1 ms, cADC, and rcADC was observed between PCNSLs and GBMs.ConclusionsEffective diffusion time setting can have a crucial impact on the performance of ADC in differentiating between PCNSLs and GBMs. The time-dependent diffusion MRI parameters may be useful in the differentiation of these lesions.

Diffusion-weighted imaging for determining response to neoadjuvant therapy in pancreatic cancer: a systematic review and meta-analysis

ObjectivesTo determine the role of diffusion-weighted imaging (DWI) for predicting response to neoadjuvant therapy (NAT) in pancreatic cancer.Materials and methodsMEDLINE, EMBASE, and Cochrane Library databases were searched for studies evaluating the performance of apparent diffusion coefficient (ADC) to assess response to NAT. Data extracted included ADC pre- and post-NAT, for predicting response as defined by imaging, histopathology, or clinical reference standards. ADC values were compared with standardized mean differences. Risk of bias was assessed using the Quality Assessment of Diagnostic Studies (QUADAS-2).ResultsOf 337 studies, 7 were included in the analysis (161 patients). ADC values reported for the pre- and post-NAT assessments overlapped between responders and non-responders. One study reported inability of ADC increase after NAT for distinguishing responders and non-responders. A correlation with histopathological response was reported for pre- and post-NAT ADC in 4 studies. DWI’s diagnostic performance was reported to be high in three studies, with a 91.6–100% sensitivity and 62.5–94.7% specificity. Finally, heterogeneity and high risk of bias were identified across studies, affecting the domains of patient selection, index test, reference standard, and flow and timing.ConclusionDWI might be useful for determining response to NAT in pancreatic cancer. However, there are still too few studies on this matter, which are also heterogeneous and at high risk for bias. Further studies with standardized procedures for data acquisition and accurate reference standards are needed.Clinical relevance statementDiffusion-weighted MRI might be useful for assessing response to neoadjuvant therapy in pancreatic cancer. However, further studies with robust data are needed to provide specific recommendations for clinical practice.Key Points•The role of DWI with ADC measurements for assessing response to neoadjuvant therapy in pancreatic cancer is still unclear.•Pre- and post-neoadjuvant therapy ADC values overlap between responders and non-responders.•DWI has a reported high diagnostic performance for determining response when using histopathological or clinical reference standards; however, studies are still few and at high risk for bias.

Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value

PurposeDiffusion-weighted imaging (DWI) of the liver suffers from low resolution, noise, and artifacts. This study aimed to investigate the effect of deep learning reconstruction (DLR) on image quality and apparent diffusion coefficient (ADC) quantification of liver DWI at 3 Tesla. MethodIn this prospective study, images of the liver obtained at DWI with b-values of 0 (DWI0), 50 (DWI50) and 800 s/mm2 (DWI800) from consecutive patients with liver lesions from February 2022 to February 2023 were reconstructed with and without DLR (non-DLR). Image quality was assessed qualitatively using Likert scoring system and quantitatively using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and liver/parenchyma boundary sharpness from region-of-interest (ROI) analysis. ADC value of lesion were measured. Phantom experiment was also performed to investigate the factors that determine the effect of DLR on ADC value. Qualitative score, SNR, CNR, boundary sharpness, and apparent diffusion coefficients (ADCs) for DWI were compared using paired t-test and Wilcoxon signed rank test. P < 0.05 was considered statistically significant. ResultsA total of 85 patients with 170 lesions were included. DLR group showed a higher qualitative score than the non-DLR group. for example, with DWI800 the score was 4.77 ± 0.52 versus 4.30 ± 0.63 (P < 0.001). DLR group also showed higher SNRs, CNRs and boundary sharpness than the non-DLR group. DLR reduced the ADC of malignant tumors (1.105[0.904, 1.340] versus 1.114[0.904, 1.320]) (P < 0.001), but there was no significant difference in the diagnostic value of malignancy for DLR and non-DLR groups (P = 57.3). The phantom study confirmed a reduction of ADC in images with low resolution, and a stronger reduction of ADC in heterogeneous structures than in homogeneous ones (P < 0.001). ConclusionsDLR improved image quality of liver DWI. DLR reduced the ADC value of lesions, but did not affect the diagnostic performance of ADC in distinguishing malignant tumors on a 3.0-T MRI system.

Comparison of the Utility of PI-RADS 2.1, ADC Values, and Combined Use of Both, for the Diagnosis of Transition Zone Prostate Cancers.

To assess the performance of apparent diffusion coefficient (ADC; values or category) alone, Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) scoring alone, and the two in combination, to diagnose transition zone prostate cancers (PCas). This retrospective study included 222 patients who underwent multiparametric magnetic resonance imaging of the prostate between May 2020 and December 2022 and who had pathologically confirmed PCa or benign prostatic hyperplasia (BPH). Prostate Imaging Reporting and Data System version 2.1 and ADC (values or category) were used in the assessment of suspicious findings identified in the transition zone. The interobserver agreements for region-of-interest measurements were calculated by intraclass correlation coefficients. Logistic regression analyses were used to determine the performance of PI-RADS v2.1 alone and in combination with ADC (values or category) to diagnose PCa. Receiver operating characteristic curve and DeLong test were used to evaluate the diagnostic performance of the quantitative parameters. A total of 152 patients had BPH, and 70 patients had PCa. For BPH versus PCa, the ADC values of PCa (0.64 × 10 -3 ± 0.16 × 10 -3 mm 2 /s) were significantly lower than BPH (1.06 ± 0.18 × 10 -3 mm 2 /s; P < 0.001). The PI-RADS scores for PCa (5 [interquartile range, 5-5]) were significantly higher than BPH (2 [interquartile range, 2-3]; P < 0.001). For all patients who had PI-RADS 1-5, the combined use of ADC (values or category) together with PI-RADS v2.1 did not perform significantly better than the use of PI-RADS v2.1 alone. The receiver operating characteristic of ADC category in combination with PI-RADS v2.1 score, 0.756 (95% confidence interval, 0.646-0.846), was significantly higher than that for PI-RADS 2.1 alone, 0.631 (95% confidence interval, 0.514-0.738), in PI-RADS 3-4 lesions ( P = 0.047). The ADC category can help to improve the diagnostic performance of PI-RADS v2.1 category 3-4 lesions in diagnosing PCa.

Automated acute ischemic stroke lesion delineation based on apparent diffusion coefficient thresholds.

Automated lesion segmentation is increasingly used in acute ischemic stroke magnetic resonance imaging (MRI). We explored in detail the performance of apparent diffusion coefficient (ADC) thresholding for delineating baseline diffusion-weighted imaging (DWI) lesions. Retrospective, exploratory analysis of the prospective observational single-center 1000Plus study from September 2008 to June 2013 (clinicaltrials.org; NCT00715533). We built a fully automated lesion segmentation algorithm using a fixed ADC threshold (≤620 × 10-6 mm2/s) to delineate the baseline DWI lesion and analyzed its performance compared to manual assessments. Diagnostic capabilities of best possible ADC thresholds were investigated using receiver operating characteristic curves. Influential patient factors on ADC thresholding techniques' performance were studied by conducting multiple linear regression. 108 acute ischemic stroke patients were selected for analysis. The median Dice coefficient for the algorithm was 0.43 (IQR 0.20-0.64). Mean ADC values in the DWI lesion (β = -0.68, p < 0.001) and DWI lesion volumes (β = 0.29, p < 0.001) predicted performance. Optimal individual ADC thresholds differed between subjects with a median of ≤691 × 10-6 mm2/s (IQR ≤660-750 × 10-6 mm2/s). Mean ADC values in the DWI lesion (β = -0.96, p < 0.001) and mean ADC values in the brain parenchyma (β = 0.24, p < 0.001) were associated with the performance of individual thresholds. The performance of ADC thresholds for delineating acute stroke lesions varies substantially between patients. It is influenced by factors such as lesion size as well as lesion and parenchymal ADC values. Considering the inherent noisiness of ADC maps, ADC threshold-based automated delineation of very small lesions is not reliable.

Evaluation of the Diagnostic Performance of F18-Fluorodeoxyglucose-Positron Emission Tomography, Dynamic Susceptibility Contrast Perfusion, and Apparent Diffusion Coefficient in Differentiation between Recurrence of a High-grade Glioma and Radiation Necrosis.

Differentiation between recurrence of brain tumor and radiation necrosis remains a challenge in current neuro-oncology practice despite recent advances in both radiological and nuclear medicine techniques. The purpose of this study was to compare the diagnostic performance of dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI), apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging, and F18-fluorodeoxyglucose-positron emission tomography (F18-FDG-PET) in the differentiation between the recurrence of a high-grade glioma and radiation necrosis. Patients with a diagnosis of high-grade glioma (WHO Grades III and IV) who had undergone surgical resection of the tumor followed by radiotherapy with or without chemotherapy were included in the study. DSC perfusion, diffusion-weighted MRI, and PET scan were acquired on a hybrid PET/MRI scanner. For each lesion, early and delayed tumor-to-brain ratio (TBR), early and delayed maximum standardized uptake value (SUVmax), normalized ADC ratio, and normalized relative cerebral blood volume (rCBV) ratio were calculated and the pattern of lesional enhancement was noted. The diagnosis was finalized with either histopathological examination or the characteristics on follow-up imaging. The statistical analysis using the receiver operator characteristic curves was done to determine the diagnostic performance of DSC perfusion, 18-F FDG-PET, and ADC in differentiation between tumor recurrence and radiation necrosis. Fifty patients were included in the final analysis, 32 of them being men (64%). A cutoff value of early TBR >0.8 (sensitivity of 100% and specificity of 80%), delayed TBR >0.93 (sensitivity of 92.3% and specificity of 80%), early SUVmax >10.2 (sensitivity of 76.9% and specificity of 80%), delayed SUVmax >13.2 (sensitivity of 61.54% and specificity of 100%), normalized rCBV ratio >1.21 (sensitivity of 100% and specificity of 60%), normalized ADC ratio >1.66 (sensitivity of 38.5% and specificity of 80%), and Grade 3 enhancement (sensitivity of 100% and specificity of 60%) were found to differentiate recurrence from radiation necrosis. Early TBR had the highest accuracy (94.44%), while ADC ratio had the lowest accuracy (50%). A combination of early TBR (cutoff value of 0.8), late TBR (cutoff value of 0.93), and rCBV ratio (cutoff value of 1.21) showed a sensitivity of 100%, specificity of 92.3%, positive predictive value of 88.9%, negative predictive value of 93.7%, and an accuracy of 96.6% in discrimination between radiation necrosis and recurrence of tumor. F18-FDG-PET and DSC perfusion can reliably differentiate tumor recurrence from radiation necrosis, with early TBR showing the highest accuracy. ADC demonstrates a low sensitivity, specificity, and accuracy in differentiating radiation necrosis from recurrence. A combination of early TBR, delayed TBR, and rCBV may be more useful in discrimination between radiation necrosis and recurrence of glioma, with this combination showing a better diagnostic performance than individual parameters or any other combination of parameters.

Automated Patient-level Prostate Cancer Detection with Quantitative Diffusion Magnetic Resonance Imaging

BackgroundMultiparametric magnetic resonance imaging (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the subjective Prostate Imaging Reporting and Data System (PI-RADS) system and quantitative apparent diffusion coefficient (ADC) are inconsistent. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted MRI technique that yields a quantitative imaging biomarker for csPCa called the RSI restriction score (RSIrs). ObjectiveTo evaluate RSIrs for automated patient-level detection of csPCa. Design, setting, and participantsWe retrospectively studied all patients (n = 151) who underwent 3 T mpMRI and RSI (a 2-min sequence on a clinical scanner) for suspected prostate cancer at University of California San Diego during 2017–2019 and had prostate biopsy within 180 d of MRI. InterventionWe calculated the maximum RSIrs and minimum ADC within the prostate, and obtained PI-RADS v2.1 from medical records. Outcome measurements and statistical analysisWe compared the performance of RSIrs, ADC, and PI-RADS for the detection of csPCa (grade group ≥2) on the best available histopathology (biopsy or prostatectomy) using the area under the curve (AUC) with two-tailed α = 0.05. We also explored whether the combination of PI-RADS and RSIrs might be superior to PI-RADS alone and performed subset analyses within the peripheral and transition zones. Results and limitationsAUC values for ADC, RSIrs, and PI-RADS were 0.48 (95% confidence interval: 0.39, 0.58), 0.78 (0.70, 0.85), and 0.77 (0.70, 0.84), respectively. RSIrs and PI-RADS were each superior to ADC for patient-level detection of csPCa (p < 0.0001). RSIrs alone was comparable with PI-RADS (p = 0.8). The combination of PI-RADS and RSIrs had an AUC of 0.85 (0.78, 0.91) and was superior to either PI-RADS or RSIrs alone (p < 0.05). Similar patterns were seen in the peripheral and transition zones. ConclusionsRSIrs is a promising quantitative marker for patient-level csPCa detection, warranting a prospective study. Patient summaryWe evaluated a rapid, advanced prostate magnetic resonance imaging technique called restriction spectrum imaging to see whether it could give an automated score that predicted the presence of clinically significant prostate cancer. The automated score worked about as well as expert radiologists’ interpretation. The combination of the radiologists’ scores and automated score might be better than either alone.

Feasibility of evaluating the histologic and genetic subtypes of WHO grade II-IV gliomas by diffusion-weighted imaging

BackgroundTo explore the feasibility of diffusion-weighted imaging (DWI) metrics to predict the histologic subtypes and genetic status of gliomas (e.g., IDH, MGMT, and TERT) noninvasively.MethodsOne hundred and eleven patients with pathologically confirmed WHO grade II-IV gliomas were recruited retrospectively. Apparent diffusion coefficient (ADC) values were measured in solid parts of gliomas on co-registered T2-weighted images and were compared with each other in terms of WHO grading and genotypes using t-tests. Receiver operating characteristic analysis was performed to assess the diagnostic performances of ADC. Subsequently, multiple linear regression was used to find independent variables, which can directly affect ADC values.ResultsThe values of overall mean ADC (omADC) and normalized ADC (nADC) of high grade gliomas and IDH wildtype gliomas were lower than low grade gliomas and IDH mutated gliomas (P < 0.05). nADC values showed better diagnostic performance than omADC in identifying tumor grade (AUC: 0.787 vs. 0.750) and IDH status (AUC: 0.836 vs. 0.777). ADC values had limited abilities in distinguishing TERT status (AUC = 0.607 for nADC and 0.617 for omADC) and MGMT status (AUC = 0.651 for nADC). Only tumor grade and IDH status were tightly associated with ADC values.ConclusionDWI metrics can predict glioma grading and IDH mutation noninvasively, but have limited use in detecting TERT mutation and MGMT methylation.

Diagnostic performance of ADC values and MRI-based radiomics analysis for detecting lymph node metastasis in patients with cervical cancer: A systematic review and meta-analysis

ObjectiveTo evaluate and compare the diagnostic performance of apparent diffusion coefficient (ADC) values and MRI-based radiomics analysis for lymph node metastasis (LNM) detection in patients with cervical cancer (CC). MethodsWe searched relevant databases for studies on ADC values and MRI-based radiomics analysis for LNM detection in CC between January 2001 and December 2021. Methodological quality assessment of risk of bias using Quality Assessment of Diagnostic Accuracy Studies 2 and radiomics quality score (RQS) of the studies was conducted. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR–), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated. Diagnostic performance was compared between the two quantitative analyses using a two-sample Z-test. ResultsIn total, 22 studies including 2314 patients were included. Unclear risk of bias was observed in 4.5–36.4% of the studies. The 8 radiomics studies exhibited a median (interquartile range) RQS of 13.5 (5.5–15.75). The pooled sensitivity, specificity, LR+, LR–, DOR, and AUC of the ADC values vs radiomics analysis were 0.86 vs 0.84, 0.85 vs 0.73, 5.7 vs 3.1, 0.17 vs 0.22, 34 vs 14, and 0.91 vs 0.86, respectively. There was no threshold effect or publication bias, but significant heterogeneity existed among the studies. No significant difference was detected in the diagnostic performance of the two quantitative analyses using the Z-test. ConclusionADC values are more clinically promising because they are more easily accessible and widely applied, and exhibit a non-statistically significant trend to outperform radiomics analysis.

Quantitative differentiation of malignant and benign thyroid nodules with multi-parameter diffusion-weighted imaging

BACKGROUNDThe value of conventional magnetic resonance imaging in the differential diagnosis of thyroid nodules is limited; however, the value of multi-parameter diffusion-weighted imaging (DWI) in the quantitative evaluation of thyroid nodules has not been well determined. AIMTo determine the utility of multi-parametric DWI including mono-exponential, bi-exponential, stretched exponential, and kurtosis models for the differentiation of thyroid lesions.METHODSSeventy-nine patients (62 with benign and 17 with malignant nodules) underwent multi-b value diffusion-weighted imaging of the thyroid. Multiple DWI parameters were obtained for statistical analysis.RESULTSGood agreement was found for diffusion parameters of thyroid nodules. Malignant lesions displayed lower diffusion parameters including apparent diffusion coefficient (ADC), the true diffusion coefficient (D), the perfusion fraction (f), the distributed diffusion coefficient (DDC), the intravoxel water diffusion heterogeneity (α) and kurtosis model-derived ADC (Dapp), and higher apparent diffusional kurtosis (Kapp) than benign entities (all P < 0.01), except for the pseudodiffusion coefficient (D*) (P > 0.05). The area under the ROC curve (AUC) of the ADC(0 and 1000) was not significantly different from that of the ADC(0 and 2000), ADC(0 to 2000), ADC(0 to 1000), D, DDC, Dapp and Kapp (all P > 0.05), but was significantly higher than the AUC of D*, f and α (all P < 0.05) for differentiating benign from malignant lesions.CONCLUSIONMultiple DWI parameters including ADC, D, f, DDC, α, Dapp and Kapp could discriminate benign and malignant thyroid nodules. The metrics including D, DDC, Dapp and Kapp provide additional information with similar diagnostic performance of ADC, combination of these metrics may contribute to differentiate benign and malignant thyroid nodules. The ADC calculated with higher b values may not lead to improved diagnostic performance.

Prediction of tumor budding in patients with rectal adenocarcinoma using b-value threshold map.

To investigate the feasibility of b-value threshold (bThreshold) map in preoperative evaluation of tumor budding (TB) in patients with locally advanced rectal cancer (LARC). Patients with LARC were enrolled and underwent diffusion-weighted imaging (DWI). Contrast-to-noise ratio (CNR) between the lesions and normal tissues was assessed using DWI and bThreshold maps. TB was counted and scored using hematoxylin and eosin staining. Reproducibility for the apparent diffusion coefficient (ADC), bThreshold values, and region-of-interest (ROI) sizes were compared. Differences in ADC and bThreshold values with low-intermediate and high TB grades and the correlations between mean ADC and bThreshold values with TB categories were analyzed. Diagnostic performance of ADC and bThreshold values was assessed using area under the curve (AUC) and decision curve analysis. Fifty-one patients were evaluated. The CNR on bThreshold maps was significantly higher than that on DW images (9.807 ± 4.811 vs 7.779 ± 3.508, p = 0.005). Reproducibility was excellent for the ADC (ICC 0.933; CV 8.807%), bThreshold values (ICC 0.958; CV 7.399%), and ROI sizes (ICC 0.934; CV 8.425%). Significant negative correlations were observed between mean ADC values and TB grades and positive correlations were observed between mean bThreshold values and TB grades (p < 0.05). bThreshold maps showed better diagnostic performance than ADC maps (AUC, 0.914 vs 0.726; p = 0.048). In LARC patients, bThreshold values could distinguish different TB grades better than ADC values, and bThreshold maps may be a preoperative, non-invasive approach to evaluate TB grades. • Compared with diffusion-weighted images, bThreshold maps improved visualization and detection of rectal tumors. • Agreement and diagnostic performance of bThreshold values are superior to apparent diffusion coefficient in assessing tumor budding grades in patients with locally advanced rectal cancer. • bThreshold maps could be used to evaluate tumor budding grades non-invasively before operation.

Value of non-Gaussian diffusion imaging with a fractional order calculus model combined with conventional MRI for differentiating histological types of cervical cancer

ObjectiveThis study aimed to evaluate the value of a fractional order calculus (FROC) model combined with conventional magnetic resonance imaging (MRI) for differentiating cervical adenocarcinoma (CAC) from squamous cell carcinoma (SCC). MethodsDiffusion-weighted imaging (DWI) with 9 b values (0–2000s/mm2) was carried out in 57 cervical cancer patients. Diffusion coefficient (D), fractional order parameter (β), and microstructural quantity (μ) together with apparent diffusion coefficient (ADC) were calculated and compared between the CAC and SCC groups. Conventional MRI features included T2WI signal intensity (SI), unenhanced-T1WI SI, enhanced-T1WI SI, and ∆T1WI SI, which were also compared between the two groups. Receiver operating characteristic (ROC) analysis was employed to assess the performance of FROC parameters, ADC, and conventional MRI features in differentiating CAC from SCC. Resultsβ was significantly lower in the CAC group than in the SCC group (0.682 ± 0.054 vs. 0.723 ± 0.084, P = 0.035), while D and μ were not significantly different between the two groups (D, P = 0.171; μ, P = 0.127). There was no significant difference in the ADC value between the two groups (P = 0.053). In conventional MRI features, enhanced-T1WI SI was significantly higher in the SCC group than in the CAC group (985.78 ± 130.83 vs. 853.92 ± 149.65, P = 0.002). The area under the curve (AUC) of β, ADC, and enhanced-T1WI SI was 0.700, 0.683, and 0.799, respectively. The combination of β, ADC, and enhanced-T1WI SI revealed optimal diagnostic performance in differentiating CAC from SCC (AUC = 0.930), followed by β + enhanced-T1WI SI (AUC = 0.869), ADC+ enhanced-T1WI SI (AUC = 0.817), and β + ADC (AUC = 0.761). ConclusionThe FROC model can serve as a noninvasive and quantitative imaging technique for differentiating CAC from SCC. β combined with ADC and enhanced-T1WI SI had the highest diagnostic efficiency.

Conformance of a 3T radiotherapy MRI scanner to the QIBA Diffusion Profile.

PurposeTo assess the technical performance of the apparent diffusion coefficient (ADC) on a dedicated 3T radiotherapy scanner, using a standardized phantom and sequences. Investigations into factors that could impact the technical performance of ADC in the clinic were also completed, including changing the slice‐encoded imaging direction and the reference sample ADC value.MethodsADC acquisitions were performed monthly on an isotropic diffusion phantom over 1 year. Measurements of ADC %bias, coefficients of variation for short‐/long‐term repeatability and precision (CVST/CVLT and CVP), and b‐value dependency (Dep b ) were calculated. The measurements were then assessed according to the Quantitative Imaging Biomarker Alliance (QIBA) Diffusion Profile specifications.ResultsThe average of all measurements over the year was within Profile recommended ranges. This included when testing was performed in different imaging directions, and on samples that had different ADC reference values (0.4–1.1 μm2/ms). Results in the axial plane for the central water vial included a bias of +0.05%, CVST /CVLT/CVP = 0.1%/ 0.9%/0.4% and Dep b = 0.4%.ConclusionsThe technical performance of ADC on a radiotherapy dedicated MRI scanner over the course of 12 months was considered conformant to the QIBA Profile. Quantifying these metrics and factors that may affect the performance is essential in progressing the use of ADC clinically: ensuring that the observed change of ADC in a tissue is due to a physiological response and not measurement variability.

Impact of Alternate b-Value Combinations and Metrics on the Predictive Performance and Repeatability of Diffusion-Weighted MRI in Breast Cancer Treatment: Results from the ECOG-ACRIN A6698 Trial.

In diffusion-weighted MRI (DW-MRI), choice of b-value influences apparent diffusion coefficient (ADC) values by probing different aspects of the tissue microenvironment. As a secondary analysis of the multicenter ECOG-ACRIN A6698 trial, the purpose of this study was to investigate the impact of alternate b-value combinations on the performance and repeatability of tumor ADC as a predictive marker of breast cancer treatment response. The final analysis included 210 women who underwent standardized 4-b-value DW-MRI (b = 0/100/600/800 s/mm2) at multiple timepoints during neoadjuvant chemotherapy treatment and a subset (n = 71) who underwent test–retest scans. Centralized tumor ADC and perfusion fraction (fp) measures were performed using variable b-value combinations. Prediction of pathologic complete response (pCR) based on the mid-treatment/12-week percent change in each metric was estimated by area under the receiver operating characteristic curve (AUC). Repeatability was estimated by within-subject coefficient of variation (wCV). Results show that two-b-value ADC calculations provided non-inferior predictive value to four-b-value ADC calculations overall (AUCs = 0.60–0.61 versus AUC = 0.60) and for HR+/HER2− cancers where ADC was most predictive (AUCs = 0.75–0.78 versus AUC = 0.76), p < 0.05. Using two b-values (0/600 or 0/800 s/mm2) did not reduce ADC repeatability over the four-b-value calculation (wCVs = 4.9–5.2% versus 5.4%). The alternate metrics ADCfast (b ≤ 100 s/mm2), ADCslow (b ≥ 100 s/mm2), and fp did not improve predictive performance (AUCs = 0.54–0.60, p = 0.08–0.81), and ADCfast and fp demonstrated the lowest repeatability (wCVs = 6.71% and 12.4%, respectively). In conclusion, breast tumor ADC calculated using a simple two-b-value approach can provide comparable predictive value and repeatability to full four-b-value measurements as a marker of treatment response.