Apparent Diffusion Coefficient Images Research Articles

Abstract PO2-22-06: MRI response Assessment of Single Fraction Pre-operative Stereotactic Radiotherapy: Preliminary results

Abstract Background: Pre-operative stereotactic breast irradiation (SPBI) improves targeting precision and has the potential to decrease treatment-related toxicity while downstaging or eliminating cancer. Accurate pCR prediction may allow omission of surgery. We are reporting initial interim results of the performance of multiparametric breast MRI in identifying pathologic response rate after single-fraction pre-operative SPBI in early stage estrogen receptor-positive (ER+), HER-2 negative breast cancer. Methods: In an ongoing single center prospective single-arm trial 22 patients with ER+/HER2-negative, cN0, unifocal, invasive breast cancer ≤3 cm underwent single-dose ablative SPBI, followed by definitive surgery per standard of care. Residual disease vs complete imaging response to SPBI was assessed on baseline and post-SPBI/pre-operative breast MRI. Pathologic response was categorized using MDACC Residual Cancer Burden (RCB) Categories. Wilcoxon rank sum test was used to assess the correlation of pathologic complete response (pCR) or RCB status on final path with MRI lesion size, volume, diffusion weighted imaging apparent diffusion coefficient (ADC) value, and when available, presence of residual enhancement on pre-operative MRI. MRI volume and ADC values were obtained using a dedicated CAD software. Area under the receiver operating Curve(AUC) was constructed to assess the diagnostic performance of pre-operative MRI on predicting pathologic response. Results Mean patient age was 64.9 (SD±7.3) years, tumor diameter 1.2(± 0.6) cm, pre-SPBI volume 6.4 ± 9.1 cm3, post SPBI volume 1.8 ± 5.6 cm3, ADC 0.91 ×10-3 mm2/s. At surgical pathology, 6(27.2%) achieved pCR/RCB-0, 9(41%) RCB-I, 7(31.8%) RCB-II. Mean baseline MRI size (1.2 ± 0.7 vs 1.3 ± 0.6, p=0.88), enhancement volumes (6.0±6.2 vs 7.9±9.9 cm3, p=0.59), DWI ADC values (1.1 ± 0.3 vs 0.9 ± 0.2 mm2/s, p-0.48) were not significantly different between tumors that showed pCR vs RCB I-II at surgical pathology. Time elapsed from SBRT to surgery were significantly longer in patients with pCR (264.3 ± 74.2 vs 183.7 ± 68.3 days, p=0.04) On pre-op MRI, mean enhancement volume was smaller in cancers that had pCR, however it did not reach significance (0.1 ± 0.1 vs 2.1 ± 6.0 cm3, p=0.2). Of 18 patients with available pre-operative MRI, residual enhancement was present in 7, of these 6/7(87%) had RCB I-II, and 1/7(13%) RCB-0 at surgical pathology. Conversely, in 11 patients with no residual enhancement on MRI, pathology showed pCR/RCB-0 in 5(45.5%), while 6(54.5%) had RCB I or II. Presence of residual enhancement on pre-operative MRI had an AUC of 0.68 (95%CI 0.43; 0.93, p=0.2) in predicting residual cancer after SPBI. Conclusion: Detecting residual enhancement on pre-operative MRI is a strong indicator of residual disease after SPBI, while lack of enhancement is not a reliable predictor of pCR. Our preliminary results do not indicate an association between baseline MRI enhancement volume, DWI ADC and pathologic response. Citation Format: Baṣak Dogan, Sunati Sahoo, Marilyn Leitch, Prasanna Alluri, Robert Timmerman, Deborah Farr, Asal Rahimi. MRI response Assessment of Single Fraction Pre-operative Stereotactic Radiotherapy: Preliminary results [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-22-06.

Robust Ensemble of Two Different Multimodal Approaches to Segment 3D Ischemic Stroke Segmentation Using Brain Tumor Representation Among Multiple Center Datasets.

Ischemic stroke segmentation at an acute stage is vital in assessing the severity of patients' impairment and guiding therapeutic decision-making for reperfusion. Although many deep learning studies have shown attractive performance in medical segmentation, it is difficult to use these models trained on public data with private hospitals' datasets. Here, we demonstrate an ensemble model that employs two different multimodal approaches for generalization, a more effective way to perform on external datasets. First, after we jointly train a segmentation model on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) MR modalities, the model is inferred on the DWI images. Second, a channel-wise segmentation model is trained by concatenating the DWI and ADC images as input, and then is inferred using both MR modalities. Before training with ischemic stroke data, we utilized BraTS 2021, a public brain tumor dataset, for transfer learning. An extensive ablation study evaluates which strategy learns better representations for ischemic stroke segmentation. In our study, nnU-Net well-known for robustness is selected as our baseline model. Our proposed method is evaluated on three different datasets: the Asan Medical Center (AMC) I and II, and the 2022 Ischemic Stroke Lesion Segmentation (ISLES). Our experiments are widely validated over a large, multi-center, and multi-scanner dataset with a huge amount of 846 scans. Not only stroke lesion models can benefit from transfer learning using brain tumor data, but combining the MR modalities using different training schemes also highly improves segmentation performance. The method achieved a top-1 ranking in the ongoing ISLES'22 challenge and performed particularly well on lesion-wise metrics of interest to neuroradiologists, achieving a Dice coefficient of 78.69% and a lesion-wise F1 score of 82.46%. Also, the method was relatively robust on the AMC I (Dice, 60.35%; lesion-wise F1, 68.30%) and II (Dice; 74.12%; lesion-wise F1, 67.53%) datasets in different settings. The high segmentation accuracy of our proposed method could improve radiologists' ability to detect ischemic stroke lesions in MRI images. Our model weights and inference code are available on https://github.com/MDOpx/ISLES22-model-inference .

399 Source Localization of Burst Suppression in Post-Cardiac Arrest Patients

OBJECTIVES/GOALS: Burst suppression is a neurophysiological marker associated with severe hypoxic-ischemic injury following cardiac arrest. The goal of this study is to identify the anatomical regions of the brain associated with burst suppression post-cardiac arrest. METHODS/STUDY POPULATION: 86 comatose patients post-cardiac arrest admitted to the neurological-ICU from Massachusetts General Hospital and Brigham and Women’s Hospital were included in this study. EEG data after return of spontaneous circulation were preprocessed and artifact was rejected. Burst segments were extracted for source localization analysis from epochs with burst suppression. Four bursts for each patients were manually selected. The source of the bursts were obtained using the Champagne algorithm and mapped on the Desikan-Killiany atlas. The source for each burst was defined as any region of interest (ROI) with power > = 75th percentile relative to all ROIs. The power of the bursts at each source was correlated with the burden of brain injury measured using apparent diffusion coefficient (ADC) per ROI. RESULTS/ANTICIPATED RESULTS: 48 (56%) patients had burst suppression. 5 (10.4%) of patients with burst suppression were independent at the time of hospital discharge. Preliminary analyses was performed on 6 patients (24 bursts in total). ROI’s determined to be sources in a majority of the burst (>=13) were bilateral superior frontal, rostral middle frontal, parstriangularis precentral, superior parietal, inferior parietal, right post central, superior temporal, lateral occipital, and left middle temporal ROI. A lower mean ADC intensity was associated with a higher EEG power in the bilateral superior frontal (r = -0.80, p < 0.0001; r = -0.677, p < 0.001, respectively), left superior parietal (r = -0.53, p = 0.009), left middle temporal (r = -0.43, p = 0.042) ROI. DISCUSSION/SIGNIFICANCE: The source of bursts in patients post-cardiac arrest experiencing burst suppression is not well defined. This study will improve our understanding of how burst suppression is a measure of cortical injury, how it may relate to the burden of injury found on ADC imaging, and patient outcomes.

Identifying pathological groups from MRI in prostate cancer using graph representation learning

Multiparametric magnetic resonance imaging (mpMRI) plays a critical role in prostate cancer (PCa) diagnosis, aiding in clinical trial evaluation and personalized treatment planning. We propose a novel prediction approach based on graph representation learning (GRL) integrating mpMRI images for classifying International Society of Urological Pathology (ISUP) grade groups. We initially constructed a mpMRI image graph representation learning (MMIGRL) model using a graph-based representation fusion approach. After constructing a heterogeneous graph integrating T2-weighted and apparent diffusion coefficient (ADC) images, we applied an adaptive method that dynamically adjusted the edges within the graph to reveal relationships within the mpMRI images. Subsequently, we developed a residual graph convolution network (Res-GCN) model to establish the mapping relationship between the mpMRI fusion graph and ISUP grade groups. The results of 5-fold cross-validation demonstrated excellent overall performance, and the prediction results using independent testing datasets confirmed the model's generalizability. The results of ablation studies highlighted the positive impact of integrating T2-weighted and ADC images and the effectiveness of residual connections in learning hierarchical features within the graph structure. The results of comparative experiments validated the superiority of the proposed MMIGRL model compared with convolutional neural network (CNN) and vision transformer (ViT) models. This approach holds promise for accurate ISUP grade group classification in PCa diagnosis, contributing to improved patient care and clinical decision making.

Multi-task reconstruction network for synthetic diffusion kurtosis imaging: Predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer

PurposeTo assess the feasibility and clinical value of synthetic diffusion kurtosis imaging (DKI) generated from diffusion weighted imaging (DWI) through multi-task reconstruction network (MTR-Net) for tumor response prediction in patients with locally advanced rectal cancer (LARC). MethodsIn this retrospective study, 120 eligible patients with LARC were enrolled and randomly divided into training and testing datasets with a 7:3 ratio. The MTR-Net was developed for reconstructing Dapp and Kapp images from apparent diffusion coefficient (ADC) images. Tumor regions were manually segmented on both true and synthetic DKI images. The synthetic image quality and manual segmentation agreement were quantitatively assessed. The support vector machine (SVM) classifier was used to construct radiomics models based on the true and synthetic DKI images for pathological complete response (pCR) prediction. The prediction performance for the models was evaluated by the receiver operating characteristic (ROC) curve analysis. ResultsThe mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) for tumor regions were 0.212, 24.278, and 0.853, respectively, for the synthetic Dapp images and 0.516, 24.883, and 0.804, respectively, for the synthetic Kapp images. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), and Hausdorff distance (HD) for the manually segmented tumor regions were 0.786, 0.844, 0.755, and 0.582, respectively. For predicting pCR, the true and synthetic DKI-based radiomics models achieved area under the curve (AUC) values of 0.825 and 0.807 in the testing datasets, respectively. ConclusionsGenerating synthetic DKI images from DWI images using MTR-Net is feasible, and the efficiency of synthetic DKI images in predicting pCR is comparable to that of true DKI images.

Predicting long-term outcomes for acute ischemic stroke using multi-model MRI radiomics and clinical variables.

The objective of this study was to create and validate a novel prediction model that incorporated both multi-modal radiomics features and multi-clinical features, with the aim of accurately identifying acute ischemic stroke (AIS) patients who faced a higher risk of poor outcomes. A cohort of 461 patients diagnosed with AIS from four centers was divided into a training cohort and a validation cohort. Radiomics features were extracted and selected from diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images to create a radiomic signature. Prediction models were developed using multi-clinical and selected radiomics features from DWI and ADC. A total of 49 radiomics features were selected from DWI and ADC images by the least absolute shrinkage and selection operator (LASSO). Additionally, 20 variables were collected as multi-clinical features. In terms of predicting poor outcomes in validation set, the area under the curve (AUC) was 0.727 for the DWI radiomics model, 0.821 for the ADC radiomics model, 0.825 for the DWI + ADC radiomics model, and 0.808 for the multi-clinical model. Furthermore, a prediction model was built using all selected features, the AUC for predicting poor outcomes increased to 0.86. Radiomics features extracted from DWI and ADC images can serve as valuable biomarkers for predicting poor clinical outcomes in patients with AIS. Furthermore, when these radiomics features were combined with multi-clinical features, the predictive performance was enhanced. The prediction model has the potential to provide guidance for tailoring rehabilitation therapies based on individual patient risks for poor outcomes.

Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer.

To develop deep-learning radiomics model for predicting biochemical recurrence (BCR) of advanced prostate cancer (PCa) based on pretreatment apparent diffusion coefficient (ADC) maps. Data were collected retrospectively from 131 patients diagnosed with advanced PCa, randomly divided into training (n = 93) and test (n = 38) datasets. Pre-treatment ADC images were segmented using a pre-trained artificial intelligence (AI) model to identify suspicious PCa areas. Three models were constructed, including a clinical model, a conventional radiomics model and a deep-radiomics model. The receiver operating characteristic (ROC), precision-recall (PR) curve and decision curve analysis (DCA) were used to assess predictive performance in test dataset. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were employed to compare the performance enhancement of the deep-radiomics model in relation to the other two models. The deep-radiomics model exhibited a significantly higher area under the curve (AUC) of ROC than the other two (P = 0.033, 0.026), as well as PR curve (AUC difference 0.420, 0.432). The DCA curve demonstrated superior performance for the deep-radiomics model across all risk thresholds than the other two. Taking the clinical model as reference, the NRI and IDI was 0.508 and 0.679 for the deep-radiomics model with significant difference. Compared with the conventional radiomics model, the NRI and IDI was 0.149 and 0.164 for the deep-radiomics model without significant difference. The deep-radiomics model exhibits promising potential in predicting BCR in advanced PCa, compared to both the clinical model and the conventional radiomics model.

MRI-based radiomic features for identifying recurrent prostate cancer after proton radiation therapy.

Magnetic Resonance Imaging (MRI) evaluation of recurrent prostate cancer (PCa) following proton beam therapy is challenging due to radiation-induced tissue changes. This study aimed to evaluate MRI-based radiomic features so as to identify the recurrent PCa after proton therapy. We retrospectively studied 12 patients with biochemical recurrence (BCR) following proton therapy. Two experienced radiologists identified prostate lesions from multi-parametric MRI (mpMRI) images post-proton therapy and marked control regions of interest (ROIs) on the contralateral side of the prostate gland. A total of 210 radiomic features were extracted from lesions and control regions on the T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) image series. Recursive Feature Elimination with Cross-Validation method (RFE-CV) was used for feature selection. A Multilayer Perceptron (MLP) neural network was developed to classify three classes: cancerous, benign, and healthy tissue. The 12-core biopsy results were used as the gold standard for the segmentations. The classifier performance was measured using specificity, sensitivity, the area under receiver operating characteristic curve (AUC), and other statistical indicators. Based on biopsy results, 10 lesions were identified as PCa recurrence while eight lesions were confirmed to be benign. Ten radiomic features (10/210) were selected to build the multi-class classifier. The radiomics classifier gave an accuracy of 0.83 in identifying cancerous, benign, and healthy tissue with a sensitivity of 0.80 and specificity of 0.85. The model yielded an AUC of 0.87, 95% CI [0.72-1.00] in differentiating cancer from the benign and healthy tissues. Our proof-of-concept study demonstrates the potential of using radiomic features as part of the differential diagnosis of PCa on mpMRI following proton therapy. The results need to be validated in a larger cohort.

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.

Value of magnetic resonance imaging radiomics features in predicting histologic grade of invasive ductal carcinoma of the breast.

Breast cancer has the second highest mortality rate of all cancers and occurs mainly in women. To investigate the relationship between magnetic resonance imaging (MRI) radiomics features and histological grade of invasive ductal carcinoma (IDC) of the breast and to evaluate its diagnostic efficacy. The two conventional MRI quantitative indicators, i.e. the apparent diffusion coefficient (ADC) and the initial enhancement rate, were collected from 112 patients with breast cancer. The breast cancer lesions were manually segmented in dynamic contrast-enhanced MRI (DCE-MRI) and ADC images, the differences in radiomics features between Grades I, II and III IDCs were compared and the diagnostic efficacy was evaluated. The ADC values (0.77 ± 0.22 vs 0.91 ± 0.22 vs 0.92 ± 0.20, F= 4.204, p< 0.01), as well as the B_sum_variance (188.51 ± 67.803 vs 265.37 ± 77.86 vs 263.74 ± 82.58, F= 6.040, p< 0.01), L_energy (0.03 ± 0.02 vs 0.13 ± 0.11 vs 0.12 ± 0.14, F= 7.118, p< 0.01) and L_sum_average (0.78 ± 0.32 vs 16.34 ± 4.23 vs 015.45 ± 3.74, F= 21.860, p< 0.001) values of patients with Grade III IDC were significantly lower than those of patients with Grades I and II IDC. The B_uniform (0.15 ± 0.12 vs 0.11 ± 0.04 vs 0.12 ± 0.03, F= 3.797, p< 0.01) and L_SRE (0.85 ± 0.07 vs 0.78 ± 0.03 vs 0.79 ± 0.32, F= 3.024, p< 0.01) values of patients with Grade III IDC were significantly higher than those of patients with Grades I and II IDC. All differences were statistically significant (p< 0.05). The ADC radiomics signature model had a higher area-under-the-curve value in identifying different grades of IDC than the ADC value model and the DCE radiomics signature model (0.869 vs 0.711 vs 0.682). The accuracy (0.812 vs 0.647 vs 0.710), specificity (0.731 vs 0.435 vs 0.342), positive predictive value (0.815 vs 0.663 vs 0.669) and negative predictive value (0.753 vs 0.570 vs 0.718) of the ADC radiomics signature model were all significantly better than the ADC value model and the DCE radiomics signature model. ADC values and breast MRI radiomics signatures are significant in identifying the histological grades of IDC, with the ADC radiomics signatures having greater value.

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.

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, 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 h 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 9 of them showed no significant findings. Among the patients, 10 displayed elevations in both cerebral blood flow(CBF) and cerebral blood volume(CBV), 3 only showed elevation in CBF, and 2 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.