Apparent Diffusion Coefficient Values Research Articles

The Added Value of Apparent Diffusion Coefficient and Histogram Analysis in Assessing Treatment Response of Locally Advanced Cervical Cancer

Objective The aim of the study is to assess the diagnostic performance of quantitative analysis of diffusion-weighted imaging in assessing treatment response in cervical cancer patients. Methods A retrospective analysis was done for 50 patients with locally advanced cervical cancer who received concurrent chemoradiotherapy and underwent magnetic resonance imaging and diffusion-weighted imaging. Treatment response was classified into 4 categories according to RECIST criteria 6 months after therapy completion. Apparent diffusion coefficient (ADC) values were measured using both region of interest (ROI) ADC and whole lesion (WL) ADC histogram for all cases at both baseline pretreatment and posttreatment Magnetic resonance imaging studies. Changes in ADC values were calculated and compared between groups. Results The percentage change of ROI-ADCmean at a cutoff value of >20 had excellent discrimination of responders versus nonresponders, while the percentage change of WL-ADCmean, ADCmin, and ADCmax at cutoff values of >12.5, >35.8, and > 19.6 had acceptable discrimination of responders versus nonresponders. Logistic regression analysis revealed that only baseline WL ADCmin was a statistically significant independent predictor of response. Cancer cervix patients with baseline ADCmin < or equal to 0.73 have 12.1 times higher odds of exhibiting a response. Conclusions The percentage change of ROI-ADCmean and WL histogram ADCmean values after concurrent chemoradiotherapy can predict response. Pretreatment WL histogram ADCmin was a statistically significant independent predictor of posttherapy response.

Prostate Cancer Risk Prediction Model Using Clinical and Magnetic Resonance Imaging–Related Findings: Impact of Combining Lesions' Locations and Apparent Diffusion Coefficient Values

Objectives The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)–related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance. Methods This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa. Seven clinical, 3 patient-level MRI-related, and 4 lesion-level MRI-related findings were extracted. After feature selection, 2 logistic regression models with and without lesions-level findings were created using data from facility I and II (development cohort). The area under the receiver operating characteristic curve (AUC) between the 2 models was compared in the PI-RADS ≥3 population in the development cohort and Facility III (validation cohort) using the Delong test. Interfacility differences of the selected predictive variables were evaluated using the Kruskal-Wallis test or chi-squared test. Results Selected lesion-level features included the peripheral zone involvement and apparent diffusion coefficient (ADC) values. The model with lesions-level findings had significantly higher AUC than the model without in 655 examinations in the development cohort (0.81 vs 0.79, respectively, P = 0.005), but not in 553 examinations in the validation cohort (0.77 vs 0.76, respectively). Large interfacility differences were seen in the ADC distribution (P < 0.001) and csPCa proportion in PI-RADS 3–5 (P < 0.001). Conclusions Adding lesions-level findings improved the csPCa discrimination in the development but not the validation cohort. Interfacility differences impeded model generalization, including the distribution of reported ADC values and PI-RADS score-level csPCa proportion.

Application of functional magnetic resonance imaging to evaluate renal structure and function in type 2 cardiorenal syndrome.

There is a lack of diagnostic non-invasive imaging technology for assessing the early structural and functional changes of the kidney in type 2 cardiorenal (CRS) patients. This study aims to explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for clinical application in type 2 CRS patients, to provide imaging markers for the assessment of kidney damage. This is a retrospective observational clinical study conducted in Nanjing, China. The clinical characteristics, including age, gender, medical history, laboratory results, and ultrasound and magnetic resonance imaging results were collected from the electronic medical record. Thirty-one patients with type 2 CRS, 20 patients with chronic heart failure (HF) and 20 healthy controls were enrolled and divided into type 2 CRS, HF and control groups. All the participants underwent magnetic resonance imaging (MRI) scanning. The apparent diffusion coefficient (ADC) value and IVIM-DWI parameters including true diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were obtained. The correlation between estimated glomerular filtration rate (eGFR), renal size and imaging parameters was evaluated by Spearman correlation analysis. ADC and D of the renal cortex in patients with type 2 CRS were lower than those in the healthy control group. ADC and f in the HF group were lower than those in the control group. D was positively correlated with the length (r = 0.3752, P = 0.0013) and transverse diameter (r = 0.3258, P = 0.0056) of the kidney. ADC (r = 0.2964, P = 0.0121) and D (r = 0.3051, P = 0.0097) were positively correlated with eGFR. Renal cortical ADC and D values could distinguish type 2 CRS patients from the healthy controls with area under the curve (AUC) of 0.723 and 0.706, respectively. The ADC and D values were not only correlated with renal function, but also had lower levels in type 2 CRS. The IVIM-DWI parameter D was also related to kidney size, but further research is needed to determine whether it can be used as a novel imaging marker for type 2 CRS.

Abstract Sa1101: Association Between Changes in Hypoxic-Ischemic Brain Injury and Neurological Outcomes in Cardiac Arrest Survivors.

Aim: Quantitative analysis of hypoxic-ischemic brain injury (HIBI) using apparent diffusion coefficient (ADC) values from brain magnetic resonance imaging (MRI) is used to predict poor neurological outcomes in out-of-hospital cardiac arrest (OHCA) survivors. Our study aimed to determine the association between changes in HIBI and neurological outcomes in OHCA survivors using two types of MRI. Methods: This observational study used data collected prospectively from a tertiary academic hospital. The study included adult comatose OHCA survivors who underwent targeted temperature management (TTM) and brain MRI within 6 hours (first) and 72–96 hours (second) using the same MR scanner. We analyzed the neurological outcomes based on brain volumes below a specific ADC value using 10-step intervals within the range of 200–1200 × 10 -6 mm 2 /s for the first and second MRIs. ADC-R(x) represents the proportion of total brain volume occupied by voxels, with ADC values ranging from 200 × 10 -6 mm 2 /s to each threshold. Additionally, ADC(x) represents the proportion of total brain volume occupied by voxels that are “threshold - 10” at each threshold. Poor neurological outcomes were categorized as cerebral performance levels 3–5, observed 6 months after OHCA. Results: Overall, 122 patients (25% female) were included in the analysis, of whom 61 (50%) showed poor neurological outcomes. Higher ADC-R(x) were observed in the poor neurological outcome group on both the first and second MRI scans at all ADC intervals (all P < 0.05) (Figure 1). In the Good Neurological Outcome group, the ADC-R values ranged between 370 and 940, with the second MRI value being smaller than that of the first (right shift, P < 0.05). In contrast, the Poor Neurological Outcome group showed ADC-R values ranging between 340 and 1190, with the second MRI value being higher than that of the first (left shift, P < 0.05) (Figure 2). The difference in ADC(x) between the second and first MRI based on neurological outcome showed a positive change in ADC below 620 for a poor neurological outcome and a positive change in ADC above 740 for a good neurological outcome (Figure 3). Conclusions: In this cohort study, we observed that the initial HIBI that occurred during OHCA worsened or returned to normal depending on the neurological outcome after TTM. Prospective multicenter studies are required to generalize our findings.

Diffusion-Weighted Imaging Does Not Differentiate Between Bacterial and Fungal Skull Base Osteomyelitis.

Apparent diffusion coefficient (ADC) value helps in differentiating infections from neoplasms on magnetic resonance imaging (MRI). We investigate the diffusion-weighted images in skull base osteomyelitis (SBO) to evaluate if ADC values can differentiate fungal and bacterial SBO and to analyse the microbiology of all SBO patients. Retrospective observational study. Quaternary care referral centre. A retrospective review of 142 patients diagnosed and treated for SBO patients from January 2010 to May 2023 was done. Chi-square or Fisher's exact test was used to compare ADC values of bacterial and fungal SBO. The most common pathogens isolated were Pseudomonas (42.2%), Aspergillus (30.98%), and S. aureus (23.94%). The average ADC value of affected soft tissues among patients was 1.13 ± 0.26 × 10-3 mm2/s compared to the average ADC value of normal soft tissue, 1.34 ± 0.31 × 10-3 mm2/s. There was no statistical significance when comparing the average ADC values of bacterial and fungal SBO patients (p value = 0.142). This study suggests that though infection due to Pseudomonas was the commonest, it was detected only in 42.2% of patients. More than half of the cases had organisms other than Pseudomonas, demanding the clinician to obtain deeper biopsies early in the course of the disease for microbiological analysis. DWI does not help differentiate bacterial and fungal SBO, again emphasising the need for deeper tissue biopsies in all these patients to assist in the early identification of the pathogen.

Unsupervised clustering analysis-based characterization of spatial profiles of inaccuracy in apparent diffusion coefficient values with varying acquisition plan orientation and diffusion weighting gradient direction - A large multicenter phantom study.

This large multicenter study of 37 magnetic resonance imaging scanners aimed at characterizing, for the first time, spatial profiles of inaccuracy (namely, Δ-profiles) in apparent diffusion coefficient (ADC) values with varying acquisition plan orientation and diffusion weighting gradient direction, using a statistical approach exploiting unsupervised clustering analysis.&#xD;A diffusion-weighted imaging (DWI) protocol (b-value: 0-200-400-600-800-1000 s/mm2) with different combinations of acquisition plan orientation (axial/sagittal/coronal) and diffusion weighting gradient direction (anterior-posterior/left-right/feet-head) was acquired on a standard water phantom. For each acquisition setup, Δ-profiles along the 3 main orthogonal directions were characterized by fitting data with a second order polynomial function (ar2+br+c) . Moreover, for each Δ-profile, the maximum minus minimum of the fitting function (δmax) was calculated. &#xD;The parametersa,b,c, andδmaxshowed some significant variations between scanner systems by different manufacturers or with different static magnetic field strengths, as well as between different acquisition/estimation setups. Unsupervised clustering analysis showed two evident clusters with significantly different values of parameter a (p< 0.0001), which can be grouped by acquisition protocol/Δ-profile direction but not scanner system.&#xD;The results of ∆-profiles confirm an appreciable inter-scanner variability in ADC measurement and corroborate the importance of guarantying the reliability of ADC estimations in clinical or research studies, considering for each scanner system the specific acquisition sequence in terms of acquisition plan orientation and diffusion weighting gradient direction.

Prediction of genetic profile of breast carcinoma on MRI using a combination of DCE‐MRI, DWI, and MR spectroscopy: A prospective observational study

AbstractBackgroundClassification of breast cancer based on gene expression has emerged as the standard approach in its management, owing to the distinct prognoses and treatment responses observed among different subtypes. The aim of this study was to prospectively assess the imaging features of the molecular subtypes of breast cancer using multiparametric magnetic resonance imaging (mMRI) with the combined assessment of dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI), diffusion‐weighted imaging (DWI), and MR spectroscopy (MRS).MethodsThis was a prospective observational single‐center cohort study, which included women with BI‐RADS 4−5 lesions on mammography/ultrasound (US) who subsequently underwent 1.5 T MRI (encompassing DCE‐MRI, DWI, and MRS). The histological subtypes of breast cancer were assessed. Estrogen receptor (ER), progesterone receptor (PR), Ki‐67 status, and human epidermal growth receptor‐2 (HER2) expression, assessed by immunohistochemistry (IHC), defined four molecular subtypes: luminal A, luminal B, HER2‐enriched (Her2en), and triple‐negative breast carcinoma (TNBC). Statistical associations between the four molecular subtypes and MRI features were investigated.ResultsFifty patients were included in the study. Circumscribed margins were significantly correlated with triple‐negative tumors compared to others (78% versus 6%, p &lt; 0.001). Spiculated margins were observed in non‐triple negative tumors. Rim enhancement was significantly correlated to triple‐negative tumors compared to all other subtypes (71.4% versus 25%, p = 0.035). Mean apparent diffusion coefficient (ADC) values were significantly lower for luminal subtypes compared to non‐luminal subtypes (p &lt; 0.001). The total choline (tCho) signal‐to‐noise ratio (SNR) was higher in triple‐negative tumors. A combined algorithm using DCE‐MRI, DWI, and MRS can predict TNBC and Her2en with specificity of 86.6% and 100%, respectively, and sensitivity of 100% and 85.37%, respectively.ConclusionThe combination of mMRI with DCE‐MRI, DWI, and MRS can accurately differentiate the molecular subtypes of breast carcinoma.

Glucose chemical exchange saturation transfer MRI for predicting the histological grade of rectal cancer: a comparative study with amide proton transfer-weighted and diffusion-weighted imaging.

To evaluate the utility of glucose chemical exchange saturation transfer (glucoCEST) MRI with non-contrast injection in predicting the histological grade of rectal cancer. This prospective analysis included 60 patients with preoperative rectal cancer who underwent pelvic glucoCEST, amide proton transfer-weighted imaging (APTWI), and diffusion-weighted imaging (DWI). In total, 21 low-grade and 39 high-grade cases were confirmed by postoperative pathology. The MTRasym (1.2 ppm), MTRasym (3.5 ppm), and apparent diffusion coefficient (ADC) values of lesions between the low-grade and high-grade groups were compared. The area under the receiver operating characteristic curve (AUC) was generated to evaluate the diagnostic performance of each technique. Logistic regression (LR) analysis was applied to determine independent predictors and for multi-parameter combined diagnosis. Elevated MTRasym (1.2 ppm), MTRasym (3.5 ppm) values and lower ADC values were observed in the high-grade group compared with low-grade cases (all p < 0.01). The AUCs of MTRasym (1.2 ppm), MTRasym (3.5 ppm), and ADC for differentiating between low- and high-grade rectal cancer cases were 0.792, 0.839, and 0.855, respectively. The diagnostic performance of the combination of the three indexes was improved (AUC, 0.969; sensitivity, 95.24%; specificity, 87.18%). The good consistency and reliability of the combination of independent predictors were demonstrated by calibration curve analysis and DCA. The glucoCEST MRI without contrast injection, APTWI, and DWI all facilitate the assessment of histological grade in rectal cancer, and the combination of the three can effectively discriminate between high- and low-grade rectal cancer, which is expected to be a promising imaging marker. The glucose chemical exchange saturation transfer MRI method facilitates the assessment of histological grade in rectal cancer and offers additional information to improve the diagnostic performance of amide proton transfer-weighted imaging, and diffusion-weighted imaging. Glucose chemical exchange saturation transfer imaging could differentiate histological grade. Amide proton transfer-weighted and diffusion-weighted were associated with histological grade. The combination of different parameters showed the best diagnostic performance.

Quantitative Estimation of Iron and Fat Content in Prostate Cancer by Multiparametric MRI and Its Application in Optimizing D'Amico Score.

The risk of biochemical recurrence (BCR) in prostate cancer (PCa) is typically assessed using D'Amico score. However, iron and fat content in PCa are closely related to tumor cell proliferation and the risk of BCR may be estimated using multiparametric MRI (mpMRI). To noninvasively estimate fat and iron content in PCa and to evaluate their utility in enhancing D'Amico scores for predicting BCR in PCa patients. Prospective. Forty-eight male patients in the BCR group (age 71.31 ± 5.74 years) and 27 male patients in the non-BCR group (age 70.3 ± 6.04 years). 3.0 T, Turbo-spin echo T2-weighted imaging, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) imaging, Gradient echo Q-Dixon sequence. The mean fat fraction (FF) and T2* values of lesions were extracted from the FF map and the T2* map. Additionally, prostate volume, mean apparent diffusion coefficient (ADC) value, periprostatic fat thickness (PPFT), subcutaneous fat thickness (SFT), blood lipid content, pre- and post-operative prostate-specific antigen (PSA) values were collected. Stepwise-COX regression analysis was employed to identify the significant predictors of BCR, which led to the construction of an improvement-adjusted (IA) model. Then the IA model as well as the D'Amico score were evaluated using C-index and time-dependent AUC, decision-curve analysis, and Kaplan-Meier curve. P < 0.05 was statistically significant. Significant differences were observed in PSA, D'Amico score, ISUP grade, T2*, FF, and ADC values of the lesions in the BCR group compared with the non-BCR group. Mean T2*, FF, and ADC values of the lesions were screened to construct the IA model incorporated into the D'Amico score (IA Model: C-index = 0.749; AUC = 0.812; D'Amico score: C-index = 0.672; AUC = 0.723). This study demonstrated that mpMRI can quantitatively estimate fat and iron within PCa lesions. By integrating ADC, FF, and T2* values into the D'Amico score, the preoperative-risk assessment for BCR can be improved. 2 TECHNICAL EFFICACY: Stage 2.

NIMG-88. EVALUATION OF DIFFUSION AND PERFUSION MRI FEATURES FOR DIFFERENTIATING RECURRENT BRAIN METASTASES AND RADIATION NECROSIS

Abstract BACKGROUND Brain metastases (BM) are a frequent and serious complication of cancer. Differentiating between BM and radiation-induced necrosis (RN) after radiotherapy remains challenging. Diffusion-weighted imaging (DWI) derived apparent diffusion coefficient (ADC) maps and perfusion-weighted imaging (PWI) derived relative cerebral blood volume (rCBV) have the potential to distinguish between BM and RN. Despite the potential of these imaging modalities, previous studies have been limited by the absence of histopathological confirmation. In this study, we utilized a patient cohort that underwent stereotactic biopsy as part of their Laser Interstitial Thermal Therapy (LITT) following suspicious radiographic progression. This allowed us to evaluate the efficacy of ADC and rCBV imaging features with histopathological validation. METHODS In this study, 29 patients underwent MRI scans (3 with 1.5T, 26 with 3T) prior to LITT. Pathology confirmed 11 patients with BM and 18 with RN. Regions of interest (ROIs) in the contrast-enhanced lesion and T2 hyperintensity lesion were identified using post-contrast T1-weighted images and fluid-attenuated inversion recovery (FLAIR) images. Mean values and histogram-based features (standard deviation, 10%, 90%) of lesion volume, ADC, and rCBV within tumor ROIs were compared between the BM and RN using the Wilcoxon rank-sum test. RESULTS Our findings indicated that the mean values of ADC in the enhancing and non-enhancing lesion ROIs did not significantly differ between BM and RN patients. However, in the contrast-enhanced lesion regions, we observed a significant difference in the standard deviation of rCBV (p&amp;lt;0.05), and the 90th percentile value of rCBV also suggested potential differences between BM and RN (p=0.069). In the T2 hyperintensity lesion regions, lesion volume (p&amp;lt;0.05) and the mean of rCBV (p&amp;lt;0.05) show significant differences between BM and RN. CONCLUSION This study demonstrates the potential of using rCBV to aid in the clinical diagnosis of BM. Additionally, our findings indicate that T2 hyperintensity regions, beyond just contrast-enhancing lesions, may provide valuable information for differential diagnosis.

Diagnostic Utility of Diffusion-Weighted Imaging in Distinguishing Common Pediatric Posterior Fossa Tumors: A Single Center Retrospective Study.

Pediatric posterior fossa tumors pose diagnostic challenges due to their diverse histopathological features and variable clinical presentations. Conventional magnetic resonance imaging (MRI) serves as the initial diagnostic tool; however, additional modalities, such as diffusion-weighted imaging (DWI), are essential for refining tumor classification. This retrospective single-center study aimed to evaluate the diagnostic utility of apparent diffusion coefficient (ADC) parameters in distinguishing between the most common pediatric posterior fossa tumors. Fifty-nine patients under the age of 18 (27 females and 32 males) with histopathologically diagnosed primary posterior fossa tumors underwent pre-treatment conventional and diffusion MRI. Apparent diffusion coefficient values were measured from solid tumor regions and normal cerebellar parenchyma, with subsequent calculation of tumor/normal cerebellar ADC ratios. The median ADC values for pilocytic astrocytomas (PAs) were 1786.2 × 10-6 mm2 /s, ependymomas 1144.9 × 10-6 mm2 /s, and for medulloblastomas 666.1 × 10-6 mm2 /s were significantly different (P < .001 for all three). Similarly, the median ADC ratios demonstrated discriminatory potential, with PAs showing the highest ratio (2.46), followed by ependymomas (1.55) and medulloblastomas (0.89) (P < .001 for all three). Receiver operating characteristic analysis revealed distinct ADC cutoffs and ratios for differentiating all tumor types from each other. Despite limitations, such as a small cohort size and different MRI protocols, our results show that ADC metrics are especially useful for distinguishing between the most common pediatric posterior fossa tumors. We recommend that future studies integrate advanced imaging techniques and larger cohorts to improve diagnostic accuracy.

Myocardial fibrosis is associated with brain microstructural alterations in patients with heart failure: A diffusion MRI study

PurposeThis study aimed to examine the changes in brain diffusion in patients with heart failure (HF) by measuring the apparent diffusion coefficient (ADC) and to investigate the relationship between these changes and cardiac injury features. MethodsThe study included 49 patients with HF and 39 healthy controls. Cognitive performance was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). All participants underwent a brain DWI scan, followed by cardiac MR in patients with HF. The ADC values in different regions of the brain were estimated using region-of-interest analysis. Cardiac MR was used to evaluate left ventricular function and the volume of late gadolinium enhancement (LGE). Group differences in ADC values were analysed using generalised linear mixed modelling. Correlation analysis was used to explore the associations between brain diffusion alterations, cognitive performance, and cardiac injury features. ResultsPatients with HF showed significantly increased ADC values in several brain regions, including the frontal, parietal, temporal, occipital lobes, cingulate cortex, hippocampus, cerebellum, and deep nuclei. ADC values in the right hippocampus, right amygdala, and bilateral insula were negatively correlated with MoCA scores. ADC values in the left inferior occipital gyrus (r = 0.570, P<0.001) and lobule VI of the cerebellar hemisphere (r = 0.560, P = 0.001) were correlated with LGE volume. ConclusionThese findings suggest that specific brain regions in patients with HF experience microstructural changes associated with cognitive impairment. Moreover, chronic brain microstructural alterations may be related to myocardial fibrosis in patients with HF.

Assessment and integration of multiparametric MRI for liver fibrosis staging in rat non-alcoholic steatohepatitis: Evaluation of diagnostic efficiency and model interpretation

ObjectivesMulti-parametric MRI techniques, including intravoxel incoherence motion (IVIM), iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL IQ), T2* mapping and T2 mapping, were employed to develop and validate a predictive model for non-alcoholic steatohepatitis (NASH) diagnosis and liver fibrosis (LF) staging in rats. The combined model was interpreted using SHapley Additive exPlanations (SHAP) values for model understanding. Materials and methods160 healthy Sprague-Dawley (SD) rats were divided into control (n = 24) and experimental (n = 136) groups, and the 12-week and 16-week groups were injected intraperitoneally with carbon tetrachloride (CCl4) for 4 weeks, one month before the final feeding period. All rats were subjected to pathological examination to determine LF stage. Upon the study’s completion, 147 SD rats were assessed for liver fibrosis. Results84 SD rats were diagnosed with NASH and 31, 10, and 43 rats were histologically diagnosed with no fibrosis (F0), early LF (F1-F2), and late LF (F3-F4). For diagnosis of NASH and staging of liver fibrosis associated with NASH, a combined multi-parameter magnetic resonance imaging prediction model has a higher area under the ROC curve (AUC) than individual diffusion parameters, especially in advanced stages of fibrosis, with an AUC of 0.929 for the combined model. In SHAP, the free fluid(FF) value contributes most to the model for diagnosing NASH and advanced liver fibrosis, while the T2 value contributes most for diagnosing liver fibrosis and the apparent diffusion coefficient (ADC) value contributes most for diagnosing liver cirrhosis. ConclusionsThe integration of several magnetic resonance imaging methods along with SHAP value analysis provides a more comprehensive and theoretical investigation into the mechanisms and factors contributing to the progression of liver fibrosis in NASH, offering insights from various angles.

Exploring the feasibility of FOCUS DWI with deep learning reconstruction for breast cancer diagnosis: A comparative study with conventional DWI.

This study compared field-of-view (FOV) optimized and constrained undistorted single-shot diffusion-weighted imaging (FOCUS DWI) with deep-learning-based reconstruction (DLR) to conventional DWI for breast imaging. This study prospectively enrolled 49 female patients suspected of breast cancer from July to December 2023. The patients underwent conventional and FOCUS breast DWI and data were reconstructed with and without DLR. Two radiologists independently evaluated three images per patient using a 5-point Likert scale. Objective evaluations, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC), were conducted using manual region of interest-based analysis. The subjective and objective evaluations were compared using the Friedman test. The scores for the overall image quality, anatomical details, lesion conspicuity, artifacts, and distortion in FOCUS-DLR DWI were higher than in conventional DWI (all P < 0.001). The SNR of FOCUS-DLR DWI was higher than that of conventional and FOCUS DWI (both P < 0.001), while FOCUS and conventional DWI were similar (P = 0.096). Conventional, FOCUS, and FOCUS-DLR DWI had similar CNR and ADC values. Our findings indicate that images produced by FOCUS-DLR DWI were superior to conventional DWI, supporting the applicability of this technique in clinical practice. DLR provides a new approach to optimize breast DWI.

Role of Apparent Diffusion Coefficient Value and Apparent Diffusion Coefficient Ratio as Prognostic Factors for Prostate Cancer Aggressiveness.

Prostate cancer is one of the most prevalent cancers in the male population. To determine the aggressiveness of suspected lesions precisely, predictive models are increasingly being developed using quantitative MRI measurements, and particularly the ADC value. This study aimed to determine whether ADC values could be used to establish the aggressiveness of prostate cancer. A retrospective single-center study included 398 patients with prostate cancer who underwent a multiparametric MRI prior to radical prostatectomy. DWI ADC values were measured (µm2/s) using b values of 50 and 1000. The dominant lesion best visualized on MRI was analyzed. The ADC values of the index lesion and reference tissue were compared to tumor aggressivity according to the Gleason grade groups based on radical prostatectomy results. Statistical analysis was performed using the Mann-Whitney U test, Kruskal-Wallis H test, Spearman's rank correlation, and ROC curves. A very strong negative correlation (rs = -0.846, p < 0.001) between ADC and GS was found. ROC analysis revealed an AUC of 0.958 and an ADC threshold value of 758 µm2/s in clinically significant prostate cancer diagnoses using the absolute ADC value, with no advantage of using the ADC ratio over the absolute ADC value being identified. DWI ADC values and the calculated ADC ratio have a significant inverse correlation with GS. The findings indicate a strong capability in determining prostate cancer aggressiveness, as well as the possibility of assisting with assigning PI-RADS categories using ADC as quantitative metrics.

Predictive value of metabolic parameters and apparent diffusion coefficient derived from 18F-FDG PET/MR in patients with non-small cell lung cancer

BackgroundMultiple models intravoxel incoherent motion (IVIM) based 18F-fluorodeoxyglucose positron emission tomography-magnetic resonance(18F-FDG PET/MR) could reflect the microscopic information of the tumor from multiple perspectives. However, its value in the prognostic assessment of non-small cell lung cancer (NSCLC) still needs to be further explored.ObjectiveTo compare the value of 18F-FDG PET/MR metabolic parameters and diffusion parameters in the prognostic assessment of patients with NSCLC.Meterial and methodsChest PET and IVIM scans were performed on 61 NSCLC patients using PET/MR. The maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), diffusion coefficient (D), perfusion fraction (f), pseudo diffusion coefficient (D*) and apparent diffusion coefficient (ADC) were calculated. The impact of SUVmax, MTV, TLG, D, f, D*and ADC on survival was measured in terms of the hazard ratio (HR) effect size. Overall survival time (OS) and progression-free survival time (PFS) were evaluated with the Kaplan–Meier and Cox proportional hazard models. Log-rank test was used to analyze the differences in parameters between groups.Results61 NSCLC patients had an overall median OS of 18 months (14.75, 22.85) and a median PFS of 17 months (12.00, 21.75). Univariate analysis showed that pathological subtype, TNM stage, surgery, SUVmax, MTV, TLG, D, D* and ADC were both influential factors for OS and PFS in NSCLC patients. Multifactorial analysis showed that MTV, D* and ADC were independent predicting factors for OS and PFS in NSCLC patients.ConclusionMTV, D* and ADC are independent predicting factors affecting OS and PFS in NSCLC patients. 18F-FDG PET/MR-derived metabolic parameters and diffusion parameters have clinical value for prognostic assessment of NSCLC patients.

Discrimination Model Construction for Non-Lactational Mastitis and Breast Cancer Based on Imaging Features.

Aims/Background The clinical presentation of non-lactational mastitis (NLM) shares similarities with some symptoms and examination results of breast cancer (BC), which can lead to misdiagnosis or delayed treatment. Current studies on breast lesions mostly focus on the diagnostic performance of a single imaging technique. This study aims to construct a discrimination diagnostic model for NLM and BC based on such imaging features as ultrasound and magnetic resonance imaging (MRI) and to validate the application value of the model, assisting clinicians in improving disease diagnosis and refining medical decisions. Methods This study is a retrospective analysis. Clinical data of 108 patients suspected of NLM based on imaging diagnosis, admitted to The First Affiliated Hospital with Nanjing Medical University between May 2018 and August 2023, were collected. Among them, 94 cases were pathologically confirmed as NLM and 14 cases as BC. Univariate and multivariate logistic regression analyses were performed on the patients' clinical data, ultrasound features, and MRI features to select the risk factors for discriminating NLM and BC, and construct a discrimination model. The discrimination performance of the model was analyzed with the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve. Results In the NLM group, there were 24 cases of granulomatous lobular mastitis (25.53%) and 70 cases of plasma cell mastitis (74.47%). In the BC group, there were 2 cases of infiltrating ductal carcinoma, 2 cases of atypical hyperplasia, 3 cases of papillary carcinoma, and 7 cases of ductal carcinoma in situ. Age, internal blood flow, calcification, edge, enhancement characteristics, apparent diffusion coefficient (ADC) values, and time-intensity curve (TIC) type were independent factors for differentiating NLM and BC (p < 0.05). The ROC analysis showed that the area under the curve of the model for discriminating NLM and BC was 0.920. The DCA results showed that the model had high net benefits for discriminating NLM and BC. The calibration curve analysis showed that the model had good consistency with the actual diagnosis of NLM and BC, with a chi-square value of 4.545 and a p-value of 0.155 according to the Hosmer-Lemeshow test. Conclusion Age, internal blood flow, calcification, edge, enhancement characteristics, ADC, and TIC curve types are important factors in distinguishing NLM and BC, and the model based on the above characteristics to distinguish NLM and BC has a high net benefit in distinguishing the two.

The apparent diffusion coefficient can serve as a predictor of survival in patients with gliomas

Background and purposeMagnetic resonance imaging is indispensable for the preoperative diagnosis of glioma. This study aimed to investigate the role of the apparent diffusion coefficient values as predictors of survival in patients with gliomas.Methods and materialsA retrospective analysis was conducted on 101 patients with gliomas who underwent surgery between 2015 and 2020. Diffusion-weighted MRI was performed before the surgery. The regions of interest were categorized into parenchymal area, non-enhancing peritumoral area, and necrotic or cystic area. All the patients were divided into three subgroups: the parenchyma group, the non-enhancing peritumoral signal abnormality group, and the necrosis or cyst group. Univariate and multivariate analyses were performed using COX regression.ResultsIn the parenchymal group, Ki67, P53, IDH, and the high or low ADC values were identified as independent prognosticators for disease-free survival, while Ki67, IDH, and the high or low ADC values for overall survival. In the non-enhancing peritumoral signal abnormality group, Ki67, P53, IDH, and the ADC parenchymal area/ADC non−enhancing peritumoral area ratio were identified as independent prognostic factors for disease-free survival, while Ki67, IDH, and the ADC parenchymal area/ADC non−enhancing peritumoral area ratio for overall survival. In the necrosis or cyst group, Ki67 was significantly associated with disease-free survival, while Ki67 and the ADC value of the necrotic or cystic area for overall survival.ConclusionsThe ADC values, including the ADC value in the parenchymal area, the ADC parenchymal area/ADC non−enhancing peritumoral area ratio, and the ADC value in the necrotic or cystic area, can serve as an efficient and potential index for predicting the survival of patients with glioma.

Usefulness of apparent diffusion coefficient values for assessment of MRI abnormality at term equivalent age in low-birth-weight infants weighing less than 1500g.

To determine whether apparent diffusion coefficient (ADC) measurements at term equivalent age (TEA) are useful for assessment of major abnormalities revealed by MRI. Of 405 neonates who underwent MRI during the period 2016-2022, 101 low birth weight (LBW) infants (birth weight < 1500g) were imaged at TEA. ADC values were measured in the thalamus, basal ganglia, anterior and posterior centrum semiovale, pons, and cerebellar hemisphere. The ADC values in LBW infants with and without major abnormalities evident on MRI were compared at the above six sites. Abnormal findings included IVH-3 IVH-4, more than six punctate white matter lesion, white matter injury (cystic or non-cystic), and major cerebellar hemorrhage. LBW infants overall (N = 101) and an extreme LBW (ELBW) group (< 1000g) (N = 55) were compared and area under the curve was calculated using ROC analysis. There were no difference in ADC values between LBW infants with and without major abnormalities. In ELBW infants, ADC values in the cerebellum were higher when major abnormalities were present (p = 0.045). ROC analysis yielded AUC < 0.7 for both LBW cases overall and ELBW cases. For LBW infants overall, ADC measurements alone at TEA are not very useful for differentiation between individuals with and without major abnormalities, but ELBW infants with major abnormalitiesshowed higher ADC values in the cerebellum, suggesting that the normal reduction of ADC occurring with maturation between preterm birth and TEA may be impaired.

Correlation of magnetic resonance imaging and computed tomography with biological factor expression and lymph node metastasis in aggressive prostate cancer.

This paper focused on probing the correlation of magnetic resonance imaging (MRI) and computed tomography (CT) manifestations with biological factor expression and lymph node metastasis (LNM) in aggressive prostate cancer (PCa). A total of 136 PCa patients underwent surgical treatment, and received CT and MRI examinations before surgery, whereby the apparent diffusion coefficient (ADC) values of quantitative MRI (qMRI) parameters were obtained. Patients were categorized into the non-aggressive PCa group and aggressive PCa group according to the postoperative pathological results and Gleason scores. The expression of biological factors [prostate-specific antigen (PSA), proliferating cell nuclear antigen (PCNA), p27, and ki-67] in both groups was tested. CT and MRI manifestations of aggressive PCa patients were analyzed. The qMRI parameters, biological factors levels and LNM were compared in two groups; the relationships between CT and MRI manifestations, qMRI parameters and positive expression of biological factors and LNM were probed in two groups. In the aggressive PCa group, MRI and CT presented different degrees of abnormal prostate changes. In the aggressive PCa group, PSA and p27 expression and ADC values were lower, and PCNA and ki-67, and LNM rates were higher. Patients' LNM rate was higher than that of ≤ 2 cm when the tumor diameter was &gt; 2 cm. ADC values were positively correlated with PSA and p27 positive expression, and negatively correlated with PCNA, ki-67 and LNM in the aggressive PCa group. MRI and CT manifestations of aggressive PCa had certain characteristics; MRI manifestations and qMRI possessed a correlation with biological factors and LNM; ADC could be employed to assess the aggressiveness of PCa.