Apparent Diffusion Coefficient Images Research Articles

Whole-tumor histogram analysis of postcontrast T1-weighted and apparent diffusion coefficient in predicting the grade and proliferative activity of adult intracranial ependymomas.

To investigate the value of histogram analysis of postcontrast T1-weighted (T1C) and apparent diffusion coefficient (ADC) images in predicting the grade and proliferative activity of adult intracranial ependymomas. Forty-seven adult intracranial ependymomas were enrolled and underwent histogram parameters extraction (including minimum, maximum, mean, 1st percentile (Perc.01), Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, Perc.99, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, and entropy of T1C and ADC) using FireVoxel software. Differences in histogram parameters between grade 2 and grade 3 adult intracranial ependymomas were compared. Receiver operating characteristic curves and logistic regression analyses were conducted to evaluate the diagnostic performance. Spearman's correlation analysis was used to evaluate the relationship between histogram parameters and Ki-67 proliferation index. Grade 3 intracranial ependymomas group showed significantly higher Perc.95, Perc.99, SD, variance, CV, and entropy of T1C; lower minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50 of ADC; and higher CV and entropy of ADC than grade 2 intracranial ependymomas group (all p < 0.05). Entropy (T1C) and Perc.10 (ADC) had a higher diagnostic performance with AUCs of 0.805 and 0.827 among the histogram parameters of T1C and ADC, respectively. The diagnostic performance was improved by combining entropy (T1C) and Perc.10 (ADC), with an AUC of 0.857. Significant correlations were observed between significant histogram parameters of T1C (r = 0.296-0.417, p = 0.001-0.044) and ADC (r = -0.428-0.395, p = 0.003-0.038). Whole-tumor histogram analysis of T1C and ADC may be a promising approach for predicting the grade and proliferative activity of adult intracranial ependymomas.

Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features.

We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats. A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment. Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level. MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.

Imaging Characteristics of Clear Cell Papillary Renal Cell Carcinoma: Identifying the Sheep in Wolf's Clothing.

This study aimed to describe the characteristics of computed tomography (CT) and magnetic resonance imaging (MRI) of clear cell papillary renal cell carcinoma (CCPRCC). This retrospective study comprised 27 patients diagnosed with 29 tumors of CCPRCC. The study was approved by the Medical Ethics Committee and the requirement for the informed consent was waived. The inclusion criteria stipulated pathology-confirmed CCPRCCs with at least one preoperative imaging examination, including CT or MRI. Two experienced radiologists independently analyzed the imaging characteristics, including size, location, growth mode, morphology, texture, density, and enhancement pattern. Paired t-test was used to compare differences in CT Hounsfield unit values and apparent diffusion coefficient (ADC) imaging between the tumor and the renal cortex. The mean age of the 27 patients was 57.0 ± 14.2 years. Nineteen patients underwent CT, while 12 underwent MRI (There are 4 patients underwent not only CT but also MRI). Among the cases, 26 (96%) were single, and 1 (4%) was multiple, consisting of three lesions. Out of the 29 tumors, 15 (52%) were located in the left kidney and 14 (48%) in the right kidney. The mean tumor diameter was 3.3 ± 1.7 cm. Furthermore, 19 (66%), 3 (10%), and 7 (24%) tumors were solid, cystic, mixed solid, and cystic type, respectively. The growth mode was endogenous and exogenous in 8 (28%) and 21 (72%) tumors, respectively. The tumor shape was irregular and round in 5 (17%) and 24 (83%) tumors, respectively. The CT value of the tumor was approximately 33.2 ± 9.8 HU, which was not significantly different from that of the renal cortex(31.1 ± 6.3HU)(p = 0.343). Furthermore, 7 (24%), 12 (41%), and 3 (10%) had calcification, cystic degeneration, and hemorrhage, respectively. In 12 tumors, hypointense and hyperintense were predominant on T1 and T2-weighted images, respectively. The tumor capsule was found at the edge of 12 tumors. The average ADC value of the tumor (1.54 ± 0.74 × 10-3 mm2/s) and that of the renal cortex(1.68 ± 0.63×10-3mm2 /s) was not statistically significantly different (p = 0.260). The enhancement scanning revealed "wash-in and wash-out" enhancement in 19 (68%) tumors, continuous or progressive enhancement in 6 (21%) tumors, and enhanced cystic wall and central separation in 3 (11%) tumors. CCPRCC occurs more likely in middle-aged and elderly individuals, and the tumor is prone to cystic degeneration, with rare bleeding and calcification, and no obvious limitation on MRI diffusion-weighted imaging, which enhancement form performs as mainly "wash-in and washout," but the final diagnosis depends on histopathology.

Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer

AimPreoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer. Materials and methodsA total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n ​= ​159) and a validation set (n ​= ​40). The total incidence of TDs was 37.2 ​% (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI ​+ ​ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores. ResultsThe Rad-score (T2WI ​+ ​ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI ​+ ​ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 ​% in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 ​%, 92.0 ​%, 83.3 ​%, and 82.1 ​%, respectively. ConclusionThe radiomics nomogram based on Rad-score (T2WI ​+ ​ADC) and clinical factors provides a promising and effective method for the preoperative prediction of TDs in patients with rectal cancer.

Values of magnetic resonance imaging apparent diffusion coefficient for prognostic evaluation and pathological typing of patients with breast cancer

Values of magnetic resonance imaging apparent diffusion coefficient for prognostic evaluation and pathological typing of patients with breast cancer

A radiomics approach based on MR imaging for classification of deficiency and excess syndrome of traditional Chinese medicine in prostate cancer

ObjectiveTo explore the potential imaging biomarkers for predicting Traditional Chinese medicine (TCM) deficiency and excess syndrome in prostate cancer (PCa) patients by radiomics approach based on MR imaging. MethodsA total of 121 PCa patients from 2 centers were divided into 1 training cohort with 84 PCa patients and 1 validation cohort with 37 PCa patients. The PCa patients were divided into deficiency and excess syndrome group according to TCM syndrome differentiation. Radiomic features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging and apparent diffusion coefficient images originated from diffusion-weighted imaging. A radiomic signature was constructed after reduction of dimension in training group by the minimum redundancy maximum relevance and the least absolute shrinkage and selection operator. The performance of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve. ResultsThe radiomic scores of PCa with TCM excess syndrome group were statistically higher than those of PCa with TCM deficiency syndrome group among T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models. The area under ROC curves for T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models were 0.824, 0.824, 0.847 in the training cohort and 0.759, 0.750, 0.809 in the validation cohort, respectively. The apparent diffusion coefficient imaging model had the best discrimination in separating patients with TCM excess syndrome and deficiency syndrome, and its accuracy was 0.788, 0.778 in the training and validation cohort, respectively. The calibration curve demonstrated that there was a high consistency between the prediction of radiomic scores and the actual classification of TCM's deficiency and excess syndrome in PCa. ConclusionThe radiomic signature based on MR imaging can be performed as a non-invasive, potential approach to discriminate TCM deficiency syndrome from excess syndrome in PCa, in which apparent diffusion coefficient imaging model has the best diagnostic efficiency.

The very low magnetic resonance imaging apparent diffusion coefficient (ADC) measure of abscess is likely due to pus's specific T2 relaxation time.

The very low magnetic resonance imaging apparent diffusion coefficient (ADC) measure of abscess is likely due to pus's specific T2 relaxation time.

Early Detection of hyperemia with Magnetic Resonance Fluid Attenuation Inversion Recovery Imaging after Superficial Temporal Artery to Middle Cerebral Artery Anastomosis.

Cerebral hyperperfusion syndrome (CHS) manifests as a collection of symptoms brought on by heightened focal cerebral blood flow (CBF), afflicting nearly 30% of patients who have undergone superficial temporal artery (STA)-middle cerebral artery (MCA) anastomosis. The aim of this study was to investigate whether the amalgamation of magnetic resonance imaging (MRI) fluid-attenuated inversion recovery (FLAIR) and apparent diffusion coefficient (ADC) imaging via MRI can discern cerebral hyperemia after STA-MCA anastomosis surgery. A retrospective study was performed of patients who underwent STA-MCA anastomosis due to Moyamoya disease or atherosclerotic steno-occlusive disease. A protocol aimed at preventing CHS was instituted, leveraging the use of MRI FLAIR. Patients underwent MRI diffusion with FLAIR imaging 24 hours after STA-MCA anastomosis. A high signal on FLAIR images signified the presence of hyperemia at the bypass site, triggering a protocol of hyperemia care. All patients underwent hemodynamic evaluations, including perfusion MRI, single-photon emission computed tomography (SPECT), and digital subtraction angiography, both before and after the surgery. If a high signal intensity is observed on MRI FLAIR within 24 hours of the surgery, a repeat MRI is performed to confirm the presence of hyperemia. Patients with confirmed hyperemia are managed according to a protocol aimed at preventing further progression. Out of a total of 162 patients, 24 individuals (comprising 16 women and 8 men) exhibited hyperemia on their MRI FLAIR scans following the procedure. SPECT was conducted on 23 patients, and 11 of them yielded positive results. All 24 patients underwent perfusion MRI, but nine of them showed no significant findings. Among the patients, 10 displayed elevations in both CBF and cerebral blood volume (CBV), three only showed elevation in CBF, and two only showed elevation in CBV. Follow-up MRI FLAIR scans conducted 6 months later on these patients revealed complete normalization of the previously observed high signal intensity, with no evidence of ischemic injury. The study determined that the use of MRI FLAIR and ADC mapping is a competent means of early detection of hyperemia after STA-MCA anastomosis surgery. The protocol established can be adopted by other neurosurgical institutions to enhance patient outcomes and mitigate the hazard of permanent cerebral injury caused by cerebral hyperemia.

Apparent diffusion coefficient histogram analysis for differentiating fibroblastic meningiomas from non-fibroblastic WHO grade 1 meningiomas

PurposeTo investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating fibroblastic meningiomas (FM) from non-fibroblastic WHO grade 1 meningiomas (nFM). MethodsThis retrospective study analyzed the histopathological and diagnostic imaging data of 220 patients with histopathologically confirmed FM and nFM. The whole tumors were delineated on axial ADC images, and histogram parameters (mean, variance, skewness, kurtosis, as well as the 1st, 10th, 50th, 90th, and 99th percentile ADC [ADCp1, ADCp10, ADCp50, ADCp90, and ADCp99, respectively]) were obtained. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating FM from nFM WHO grade 1 meningiomas, and their diagnostic efficacy in differentiating FM from nFM before surgery was assessed using receiver operating characteristic (ROC) curves. ResultsThe mean, variance, ADCp50, ADCp90, and ADCp99 of the FM group were all lower than those of the nFM group (P < 0.05), there was significant difference in location and sex (P < 0.05). Multivariate logistic regression showed ADCp99 (P < 0.001) and location (P = 0.007) were the most valuable parameters in the discrimination of FM and nFM WHO grade 1 meningiomas. The diagnostic efficacy was achieved an AUC of 0.817(95% CI, 0.759–0.866), the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 66.4%, 83.6%, 75.0%, 80.2%, and 71.3%, respectively. ConclusionADC histogram analysis is helpful in noninvasive differentiation of FM and nFM WHO grade 1 meningiomas, and combined ADCp99 and location have the best diagnostic efficacy.

The grade of individual prostate cancer lesions predicted by magnetic resonance imaging and positron emission tomography

BackgroundMultiparametric magnetic resonance imaging (mpMRI) and positron emission tomography (PET) are widely used for the management of prostate cancer (PCa). However, how these modalities complement each other in PCa risk stratification is still largely unknown. We aim to provide insights into the potential of mpMRI and PET for PCa risk stratification.MethodsWe analyzed data from 55 consecutive patients with elevated prostate-specific antigen and biopsy-proven PCa enrolled in a prospective study between December 2016 and December 2019. [68Ga]PSMA-11 PET (PSMA-PET), [11C]Acetate PET (Acetate-PET) and mpMRI were co-registered with whole-mount histopathology. Lower- and higher-grade lesions were defined by International Society of Urological Pathology (ISUP) grade groups (IGG). We used PET and mpMRI data to differentiate between grades in two cases: IGG 3 vs. IGG 2 (case 1) and IGG ≥ 3 vs. IGG ≤ 2 (case 2). The performance was evaluated by receiver operating characteristic (ROC) analysis.ResultsWe find that the maximum standardized uptake value (SUVmax) for PSMA-PET achieves the highest area under the ROC curve (AUC), with AUCs of 0.72 (case 1) and 0.79 (case 2). Combining the volume transfer constant, apparent diffusion coefficient and T2-weighted images (each normalized to non-malignant prostatic tissue) results in AUCs of 0.70 (case 1) and 0.70 (case 2). Adding PSMA-SUVmax increases the AUCs by 0.09 (p < 0.01) and 0.12 (p < 0.01), respectively.ConclusionsBy co-registering whole-mount histopathology and in-vivo imaging we show that mpMRI and PET can distinguish between lower- and higher-grade prostate cancer, using partially discriminative cut-off values.

Abstract 296: Comparison of Prognostic Performance Using Quantitative Analysis of Head Computed Tomography and Apparent Diffusion Coefficient Imaging Before Target Temperature Management in Out-of-Hospital Cardiac Arrest Survivors

Aim: We compared head computed tomography (HCT) and diffusion-weighted magnetic resonance imaging (DW-MRI) findings before target temperature management (TTM) to predict neurological outcomes in out-of-hospital cardiac arrest survivors. Methods: Using prospectively collected data from a TTM registry, we included patients who underwent HCT and DW-MRI within 6 h of return of spontaneous circulation (ROSC). We examined the gray-to-white matter ratio (GWR) on HCT and apparent diffusion coefficient (ADC) values obtained through voxel-based analysis of DW-MRI. We analyzed the mean ADC value and the % voxels of ADC thresholds (percentage of voxels below ADC thresholds per total voxel) at 50-step intervals ranging from 200 to 1200 х 10 -6 mm/s, identifying thresholds that differentiate between good and poor neurological outcomes. Additionally, we analyzed the combination of pupillary light reflex (PLR) and neuron-specific enolase (NSE) values acquired at the same time points to predict the neurological outcomes. The primary outcome measure was the dichotomized cerebral performance category (CPC) at six months, defined as good (CPC 1-2) or poor (CPC 3-5). Results: Of 131 patients (26% female), 74 (57%) had poor outcomes. The time from ROSC to HCT and DW-MRI scans was 1.7h and 3.2h, respectively. The group with a good outcome had higher GWR (1.26 vs. 1.20) and mean ADC (787.10 vs. 697.49) values, but lower values in all ranges (250-1150) of ADC values (all P&lt;0.001). Mean ADC and ADC values in the range of 350-650 demonstrated better predictive performance for poor outcomes than the GWR (P &lt; 0.05). The mean ADC value had the highest sensitivity of 51.3% (95% CI 39.4-63.1; cutoff value ≤ 739.2 х 10 –6 mm 2 /s) when the false positive rate (FPR) was 0%. Furthermore, when combined with PLR and NSE values, the prognostic performance was significantly improved compared to using Mean ADC alone (0.83 vs 0.92; P&lt;0.01). Conclusions: In this cohort study, early voxel-based quantitative ADC analysis after ROSC (i.e., before TTM) was associated with poor neurological outcomes 6 months after cardiac arrest. We found that the Mean ADC value showed the highest sensitivity when the FPR was 0% and that combining the NSE and PLR values resulted in an even better prognostic performance.

Value of radiomics models based on MRI diffusion weighted imaging and apparent diffusion coefficient in differentiating benign and malignant thyroid nodules

Objective: To investigate the value of radiomics models based on magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps in distinguishing benign and malignant thyroid nodules. Methods: A cross-sectional study. Clinical data of 148 thyroid nodules (50 benign, 98 malignant) from 140 patients who underwent thyroid MRI examination in Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences between January 2019 and December 2022 were retrospectively analyzed. The nodules were used as the study units, and a leave-one-out method was used to randomly divide the nodules into a training set and a test set at a 7∶3 ratio. Region of interest was segmented and radiomics features were extracted from the DWI and ADC images. In the training set, feature selection was performed using inter-observer agreement analysis, U-test, least absolute shrinkage and selection operator algorithm, and correlation analysis. Four classifiers, including support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR) were used to build models with the selected features, including the DWI models, ADC models, and combined models. The models were independently tested in the test set. The performance of the radiomics models in distinguishing benign and malignant thyroid nodules was evaluated using the receiver operating characteristic (ROC) curve, with pathological results as the gold standard. Results: Of the 140 patients, there were 40 males and 100 females, with a mean age of (38.4±12.2) years. After feature selection, 11 DWI features and 11 ADC features were used to build the models. In the training set, the AUC values of the combined models were higher than those of the corresponding DWI and ADC models. In the test set, the SVM combined model showed the best predictive performance, with an AUC of 0.873 (95%CI:0.740-0.954), accuracy of 75.6%, sensitivity of 46.7%, specificity of 90.0%, positive predictive value (PPV) of 70.0% and negative predictive value (NPV) of 77.1%, while the RF combined model had an AUC of 0.836 (95%CI:0.695-0.929), accuracy of 77.8%, sensitivity of 40.0%, specificity of 96.7%, PPV of 85.7% and NPV of 76.3%, the KNN combined model had an AUC of 0.832 (95%CI:0.691-0.927), accuracy of 77.8%, sensitivity of 33.3%, specificity of 100%, PPV of 100% and NPV of 75.0%, the LR combined model had an AUC of 0.813 (95%CI:0.669-0.914), accuracy of 77.8%, sensitivity of 60.0%, specificity of 86.7%, PPV of 69.2% and NPV of 81.3%. Conclusions: Radiomics models based on DWI and ADC image features can effectively distinguish benign and malignant thyroid nodules. The SVM combined model had the best prediction performance.

Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability

Image perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test–retest imaging on model reliability is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by the two methods using four different classifiers. A 191-patient public breast cancer dataset with 71 test–retest scans was used with pre-determined 117 training and 74 testing samples. We collected apparent diffusion coefficient images and manual tumor segmentations for radiomic feature extraction. Random translations, rotations, and contour randomizations were performed on the training images, and intra-class correlation coefficient (ICC) was used to filter high repeatable features. We evaluated model reliability in both internal generalizability and robustness, which were quantified by training and testing AUC and prediction ICC. Higher testing performance was found at higher feature ICC thresholds, but it dropped significantly at ICC = 0.95 for the test–retest model. Similar optimal reliability can be achieved with testing AUC = 0.7–0.8 and prediction ICC > 0.9 at the ICC threshold of 0.9. It is recommended to include feature repeatability analysis using image perturbation in any radiomic study when test–retest is not feasible, but care should be taken when deciding the optimal feature repeatability criteria.

Relationship between Eccentricity and Volume Determined by Spectral Algorithms Applied to Spatially Registered Bi-Parametric MRI and Prostate Tumor Aggressiveness: A Pilot Study.

(1) Background: Non-invasive prostate cancer assessments using multi-parametric MRI are essential to the reliable detection of lesions and proper management of patients. While current guidelines call for the administration of Gadolinium-containing intravenous contrast injections, eliminating such injections would simplify scanning and reduce patient risk and costs. However, augmented image analysis is necessary to extract important diagnostic information from MRIs. Purpose: This study aims to extend previous work on the signal to clutter ratio and test whether prostate tumor eccentricity and volume are indicators of tumor aggressiveness using bi-parametric (BP)-MRI. (2) Methods: This study retrospectively processed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRIs (apparent diffusion coefficient, high b-value, and T2 images) were resized, translated, cropped, and stitched to form spatially registered BP-MRIs. The International Society of Urological Pathology (ISUP) grade was used to judge cases of prostate cancer as either clinically significant prostate cancer (CsPCa) (ISUP ≥ 2) or clinically insignificant prostate cancer (CiPCa) (ISUP < 2). The Adaptive Cosine Estimator (ACE) algorithm was applied to the BP-MRIs, followed by thresholding, and then eccentricity and volume computations, from the labeled and blobbed detection maps. Then, univariate and multivariate linear regression fittings of eccentricity and volume were applied to the ISUP grade. The fits were quantitatively evaluated by computing correlation coefficients (R) and p-values. Area under the curve (AUC) and receiver operator characteristic (ROC) curve scores were used to assess the logistic fitting to CsPCa/CiPCa. (3) Results: Modest correlation coefficients (R) (>0.35) and AUC scores (0.70) for the linear and/or logistic fits from the processed prostate tumor eccentricity and volume computations for the spatially registered BP-MRIs exceeded fits using the parameters of prostate serum antigen, prostate volume, and patient age (R~0.17). (4) Conclusions: This is the first study that applied spectral approaches to BP-MRIs to generate tumor eccentricity and volume metrics to assess tumor aggressiveness. This study found significant values of R and AUC (albeit below those from multi-parametric MRI) to fit and relate the metrics to the ISUP grade and CsPCA/CiPCA, respectively.

ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index.

Preoperative differentiation of atypical meningioma (AtM) from transitional meningioma (TrM) is critical to clinical treatment. To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating AtM from TrM and its correlation with the Ki-67 proliferation index (PI). Clinical, imaging, and pathological data of 78 AtM and 80 TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile [ADCp1], 10th percentile [ADCp10], 50th percentile [ADCp50], 90th percentile [ADCp90], and 99th percentile [ADCp99]) were generated. The Mann-Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 PI was analyzed. All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different (P < 0.05). Combined ADC histogram parameters (variance, skewness, kurtosis, ADCp90, and ADCp99) achieved the best diagnostic performance for distinguishing AtM from TrM. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.800%, 76.25%, 67.95%, 70.15%, 70.93%, and 73.61%, respectively. All histogram parameters were negatively correlated with Ki-67 PI (r = -0.012 to -0.293). ADC histogram analysis is a potential tool for non-invasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 PI.

Deep Learning Based Prognostic Prediction in Oropharyngeal Cancer Patients Using Multiparametric MRI Inputs

While prognostic outcomes for oropharyngeal cancer (OPC) patients have improved in recent years, patients still face a non-negligible risk of disease recurrence or death. Accurately predicting post-therapy prognosis would be highly valuable for risk stratification and treatment guidance for OPC patients. Recent studies using PET/CT data have demonstrated the effectiveness of large-scale, end-to-end image-based deep learning (DL) models for predicting progression-free survival (PFS) in OPC patients. Multiparametric MRI (mpMRI), which combines anatomical and functional MRI sequences, has the potential to offer similar results, and has the added advantage of high-frequency longitudinal imaging capabilities, such as through MR-Linac devices. Therefore, this study aimed to develop a DL model using mpMRI data to predict PFS, and to evaluate the impact of anatomical and functional input channels on model performance. From a large-scale head and neck cancer database at MD Anderson Cancer Center, treatment-naïve OPC patients with available pre-radiotherapy mpMRI imaging were selected for this study. mpMRI images used for this study included T2-weighted images (T2) and apparent diffusion coefficient (ADC) maps. PFS event status was defined as having either a local, regional, or distant failure, and/or death; data were right censored if an event had not occurred. Images were resampled to the T2 resolution, normalized to a [-1,1] scale, and cropped to the field of view of the ADC image for use in DL models. A DL convolutional neural network model based on the DenseNet121 architecture from the Medical Open Network for AI (MONAI) Python package using a negative log-likelihood loss function was implemented. The model used mpMRI images as input channels and 20 output channels representing the different time intervals of the predicted PFS conditional probabilities of surviving that time interval; final PFS in days was obtained by summing the cumulative probability of surviving each interval times the interval duration. A 5-fold cross validation approach was used for model training and evaluation. Separate models using only T2, only ADC, and T2 + ADC channel inputs were compared. Model performance was measured using the C-index. Out of 1154 patients, 404 met inclusion criteria. The overall PFS event rate was 16%. Median C-index values from the 5-fold cross validation were 0.62, 0.67, and 0.69 for the ADC, T2, and T2+ADC models, respectively. Using large-scale datasets and open-source DL implementations, we find that OPC PFS prediction models using mpMRI data yield modest but comparable performance to existing models (i.e., state-of-the-art reference performance using PET/CT). Moreover, combining mpMRI channels may increase the performance of models for OPC prognostic prediction. Future work will involve integration of additional timepoints, additional mpMRI images, clinical variables, and saliency maps.

Multiparametric MRI-based radiomics nomogram for predicting the hormone receptor status of HER2-positive breast cancer

To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics nomograms for predicting the hormone receptor (HR) status of HER2-positive breast cancer. Patients with HER2-positive invasive breast cancer were divided randomly into training (68 patients) and validation (30 patients) sets. All were classified as either HR-positive (HR+) or negative (HR-) at histopathology. Two radiologists outlined the three-dimensional (3D) volumetric regions of interest (VOI) on the MRI images. Features (n=1,096) were extracted from the T2-weighted imaging (WI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) images separately. Dimensionality was reduced using feature screening. Binary radiomics prediction models were established using a logistic regression classifier and were validated in the validation set. To construct a nomogram, independent predictors were identified using multivariate logistic regression analysis. The predictive efficacy of the model was assessed using the area under the receiver operating characteristic curve (AUC). Ten radiomics features were obtained after feature dimensionality reduction based on the merged T2WI, ADC, and DCE images. The diagnostic efficacy of the radiomics signature using the three sequences was better than that of any single sequence (training set AUC: 0.797; validation set AUC: 0.75). Using multivariate logistic regression analysis, the independent predictors for identifying HR status were combined radiomics signature and peritumoural oedema. Nomograms constructed by combining the radiomics signature and peritumoural oedema showed good discrimination in both the training and validation sets (AUC: 0.815 and 0. 805, respectively). A multiparametric MRI-based nomogram incorporating the radiomics signature and peritumoural oedema can assess the HR status of HER2-positive breast cancer. The resulting model can improve diagnostic accuracy, improving patient outcomes.

Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram.

The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) histogram parameters in differentiating atypical meningioma (AtM) from anaplastic meningioma (AnM). Clinical and imaging data of 34 AtM and 21 AnM diagnosed by histopathology were retrospectively analyzed. The whole tumor delineation along the tumor edge on ADC images and ADC histogram parameters were automatically generated and comparisons between the two groups using the independent samples t test or Mann-Whitney U test. Univariate and multivariate logistic regression analyses were used to construct the nomogram of the AtM and AnM prediction model, and the model's predictive efficacy was evaluated using calibration and decision curves. Significant differences in the mean, enhancement, perc.01%, and edema were noted between the AtM and AnM groups (P < 0.05). Age, sex, location, necrosis, shape, max-D, variance, skewness, kurtosis, perc.10%, perc.50%, perc.90%, and perc.99% exhibited no significant differences (P > 0.05). The mean and enhancement were independent risk factors for distinguishing AtM from AnM. The area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the nomogram were 0.871 (0.753-0.946), 80.0%, 81.0%, 79.4%, 70.8%, and 87.1%, respectively. The calibration curve demonstrated that the model's probability to predict AtM and AnM was in favorable agreement with the actual probability, and the decision curve revealed that the prediction model possessed satisfactory clinical availability. A logistic regression model nomogram based on conventional MRI features and ADC histogram parameters is potentially useful as an auxiliary tool for the preoperative differential diagnosis of AtM and AnM.

Evaluation of alterations in interstitial fluid dynamics in cases of whole-brain radiation using the diffusion-weighted image analysis along the perivascular space method.

In the current study, we assessed changes in interstitial fluid dynamics resulting after whole-brain radiotherapy using the diffusion-weighted image analysis along the perivascular space (DWI-ALPS) method, which is a simplified variation of the diffusion tensor image ALPS (DTI-ALPS) method using diffusion-weighted imaging (DWI) with orthogonal motion-probing gradients (MPGs). This retrospective study included 47 image sets from 22 patients who underwent whole-brain radiotherapy for brain tumors. The data for the normal control group comprised 105 image sets from 105 participants with no pathological changes. DWI was performed with the three MPGs applied in an orthogonal direction to the imaging plane, and apparent diffusion coefficient images for the x-, y-, and z-axes were retrospectively generated. The ALPS index was calculated to quantify interstitial fluid dynamics. The independent t-test was used to compare the ALPS index between normal controls and patients who underwent whole-brain radiotherapy. Patients were compared in all age groups and individual age groups (20-39, 40-59, and 60-84 years). We also examined the correlation between biologically equivalent doses (BEDs) and the ALPS index, as well as the correlation between white matter hyperintensity and the ALPS index. In the comparison of all age groups, the ALPS index was significantly lower (p <0.001) in the postradiation group (1.32 ± 0.16) than in the control group (1.44 ± 0.17), suggesting that interstitial fluid dynamics were altered in patients following whole-brain radiotherapy. Significant age group differences were found (40-59 years: p <0.01; 60-84 years: p <0.001), along with a weak negative correlation between BEDs (r = -0.19) and significant correlations between white matter hyperintensity and the ALPS index (r = -0.46 for periventricular white matter, r = -0.38 for deep white matter). It was concluded that the ALPS method using DWI with orthogonal MPGs suggest alteration in interstitial fluid dynamics in patients after whole-brain radiotherapy. Further systematic prospective studies are required to investigate their association with cognitive symptoms.

Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study

PurposeProstate cancer (PCa) with high Ki-67 expression and high Gleason Scores (GS) tends to have aggressive clinicopathological characteristics and a dismal prognosis. In order to predict the Ki-67 expression status and the GS in PCa, we sought to construct and verify MRI-based radiomics signatures.Methods and materialsWe collected T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images from 170 PCa patients at three institutions and extracted 321 original radiomic features from each image modality. We used support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression to select the most informative radiomic features and built predictive models using up sampling and feature selection techniques. Using receiver operating characteristic (ROC) analysis, the discriminating power of this feature was determined. Subsequent decision curve analysis (DCA) assessed the clinical utility of the radiomic features. The Kaplan–Meier (KM) test revealed that the radiomics-predicted Ki-67 expression status and GS were prognostic factors for PCa survival.ResultThe hypothesized radiomics signature, which included 15 and 9 selected radiomics features, respectively, was significantly correlated with pathological Ki-67 and GS outcomes in both the training and validation datasets. Areas under the curve (AUC) for the developed model were 0.813 (95% CI 0.681,0.930) and 0.793 (95% CI 0.621, 0.929) for the training and validation datasets, respectively, demonstrating discrimination and calibration performance. The model's clinical usefulness was verified using DCA. In both the training and validation sets, high Ki-67 expression and high GS predicted by radiomics using SVM models were substantially linked with poor overall survival (OS).ConclusionsBoth Ki-67 expression status and high GS correlate with PCa patient survival outcomes; therefore, the ability of the SVM classifier-based model to estimate Ki-67 expression status and the Lasso classifier-based model to assess high GS may enhance clinical decision-making.