T2w Magnetic Resonance Research Articles

Rare radiological manifestation of enchondromatosis in children: Columnar pattern: A retrospective cohort study.

The columnar cartilage pattern is characterized by parallel aligned cartilage tissue columns related to the physis without matrix calcification separated by the surrounding osseous tissue. Usually, it is seen in patients with multiple enchondromas. The objective of this study was to elucidate the clinical and radiological features of this rare radiological pattern in the physis, which remains unfamiliar to most physician. We retrospectively evaluated the clinical features and imaging findings of 15 patients (9 men and 6 women) who have a columnar pattern with varied spectrum of enchondromatosis. On X-ray and computed tomography (CT) examination, all these lesions were seen as vertical or oblique oriented tubular zones, which have relatively low radiologic density compared with normal bone. The lesions have similar signal characteristics relative to epiphyseal cartilage plates, on T1W and T2W magnetic resonance images. Columnar pattern was observed in different appearances from one single column in one physis to multiple columns in multiple physis. The mean follow-up was 62 months (range: 36-96 months). The mean age was 9.7 (range: 4-14) years at the initial admission. Eight patients had 3 or less affected physis. Five patients had only one affected physis. We defined these patients' group who had up to 3 affected physis as "limited enchondromatosis with columnar pattern (LE-CP)." We observed that most of the columnar cartilage was turning into the normal bone via endochondral ossification. Based on our observations, the columnar pattern is a rare manifestation of the enchondromas. Columnar pattern, along with the related physis, acts as a normal endochondral ossification process, and surgery is not necessary unless there is a risk of fracture or severe deformity. Further awareness of this unique subset of patients may improve our understanding of the disease and lead to better patient outcomes. We have modified non-hereditarily enchondromatosis into 2 categories: limited enchondromatosis with the columnar pattern and multiple enchondromatosis. We believe that LE-CM reflects a developmental anomaly of the physis rather than a true neoplasia, and it acts as a normal endochondral ossification process. Level IV (case series).

Cervical Spinal Cord Morphometrics in Degenerative Cervical Myelopathy: Quantification using Semi-automated Normalized Technique and Correlation with Neurological Dysfunctions

Degenerative cervical myelopathy (DCM) is characterized by spinal cord atrophy. Accurate estimation of spinal cord atrophy is key to the understanding of neurological diseases, including DCM. However, its clinical application is hampered by difficulties in its precise and consistent estimation due to significant variability in spinal cord morphometry along the cervical spine, both within and between individuals. To characterize morphometrics of the compressed spinal cord in DCM patients. We employed our semi-automated analysis framework that incorporates the Spinal Cord Toolbox (SCT) and a normalization approach to effectively address the challenges posed by cord compression in these patients. Additionally, we examined the clinical relevance of these morphometric measures to enhance our understanding of DCM pathophysiology. Prospective study. This study investigated 36 DCM patients and 31 healthy controls (HCs). Clinical scores including 9-hole peg test for hand dexterity, hand grip strength, balance, gait speed, modified Japanese Orthopaedic Association (mJOA) score, and imaging-based spinal cord morphometrics. Using the generic spine acquisition protocol and our semi-automated analysis pipeline, spinal cord morphometrics, including cross-sectional area (CSA), anterior-posterior (AP) and transverse (RL) diameters, eccentricity, and solidity, were estimated from sagittal T2w magnetic resonance imaging (MRI) images using the Spinal Cord Toolbox (SCT). Normalized metrics were extracted from the C1 to C7 vertebral levels and compared between DCM patients and HC. Morphometric data at regions of maximum spinal cord compression (MSCC) were correlated with the clinical scores. A subset of participants underwent follow-up scans at six months to monitor longitudinal changes in spinal cord atrophy. Spinal cord morphometric data were normalized against the healthy population morphometry (PAM50 database) and extracted for all participants. DCM patients showed a notable reduction in CSA, AP, and RL diameter across all vertebral levels compared to HC. MSCC metrics correlated significantly with clinical scores like dexterity, grip strength, and mJOA scores. Longitudinal analysis indicated a decrease in CSA and worsening clinical scores in DCM patients. Our processing pipeline offers a reliable method for assessing spinal cord compression in DCM patients. Normalized spinal cord morphometrics, particularly the CSA could have potential for monitoring DCM disease severity and progression, guiding treatment decisions. Furthermore, to our knowledge our study is the first to apply the generic spinal cord acquisition protocol, ensuring consistent imaging across different MRI scanners and settings. Coupled with our semi-automated analysis pipeline, this protocol is key for the detailed morphometric characterization of compressed spinal cords in patients with DCM, a disease that is both complex and heterogenous. This study was funded by the National Institute of Neurological Disorders and Stroke (NINDS) (K23:NS091430) and (R01: NS129852-01A1).

High baseline perivascular space volume in basal ganglia is associated with attention and executive function decline in Parkinson's disease.

Pathologic perivascular spaces (PVS), the fluid-filled compartments surrounding brain vasculature, may underlie cognitive decline in Parkinson's disease (PD). However, whether this impacts specific cognitive domains has not been investigated. This study examined the relationship of PVS volume at baseline with domain-specific and global cognitive change over 2 years in PD individuals. A total of 39 individuals with PD underwent 3T T1w magnetic resonance imaging to determine PVS volume fraction (PVS volume normalized to total regional volume) within (i) centrum semiovale, (ii) prefrontal white matter (medial orbitofrontal, rostral middle frontal, and superior frontal), and (iii) basal ganglia. A neuropsychological battery included assessment of cognitive domains and global cognitive function at baseline and after 2 years. Higher basal ganglia PVS at baseline was associated with greater decline in attention, executive function, and global cognition scores. While previous reports have associated elevated PVS volume in the basal ganglia with decline in global cognition in PD, our findings show such decline may affect the attention and executive function domains.

Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concern

AbstractThe timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state‐of‐the‐art review followed by a comprehensive evaluation of machine learning models within the randomized neural networks (RNNs) and hyperplane‐based classifiers (HbCs) family to investigate SMC diagnosis thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset, 111 individuals with SMC and 111 healthy older adults are analyzed based on T1W magnetic resonance imaging (MRI) scans, extracting rich features. This analysis is based on baseline structural MRI (sMRI) scans, extracting rich features from gray matter (GM), white matter (WM), Jacobian determinant (JD), and cortical thickness (CT) measurements. In RNNs, deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best classifiers in terms of performance metrics in the identification of SMC. In HbCs, Kernelized pinball general twin support vector machine (Pin‐GTSVM‐K) excels in CT and WM features, whereas Linear Pin‐GTSVM (Pin‐GTSVM‐L) and Linear intuitionistic fuzzy TSVM (IFTSVM‐L) performs well in the JD and GM features sets, respectively. This comprehensive evaluation emphasizes the critical role of feature selection, feature based‐interpretability and model choice in attaining an effective classifier for SMC diagnosis. The inclusion of statistical analyses further reinforces the credibility of the results, affirming the rigor of this analysis. The performance measures exhibit the suitability of this framework in aiding researchers with the automated and accurate assessment of SMC. The source codes of the algorithms and datasets used in this study are available at https://github.com/mtanveer1/SMC.This article is categorized under: Technologies > Classification Technologies > Machine Learning Application Areas > Health Care

Generalizing the Enhanced-Deep-Super-Resolution Neural Network to Brain MR Images: A Retrospective Study on the Cam-CAN Dataset.

The Enhanced-Deep-Super-Resolution (EDSR) model is a state-of-the-art convolutional neural network suitable for improving image spatial resolution. It was previously trained with general-purpose pictures and then, in this work, tested on biomedical magnetic resonance (MR) images, comparing the network outcomes with traditional up-sampling techniques. We explored possible changes in the model response when different MR sequences were analyzed. T1w and T2w MR brain images of 70 human healthy subjects (F:M, 40:30) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) repository were down-sampled and then up-sampled using EDSR model and BiCubic (BC) interpolation. Several reference metrics were used to quantitatively assess the performance of up-sampling operations (RMSE, pSNR, SSIM, and HFEN). Two-dimensional and three-dimensional reconstructions were evaluated. Different brain tissues were analyzed individually. The EDSR model was superior to BC interpolation on the selected metrics, both for two- and three- dimensional reconstructions. The reference metrics showed higher quality of EDSR over BC reconstructions for all the analyzed images, with a significant difference of all the criteria in T1w images and of the perception-based SSIM and HFEN in T2w images. The analysis per tissue highlights differences in EDSR performance related to the gray-level values, showing a relative lack of outperformance in reconstructing hyperintense areas. The EDSR model, trained on general-purpose images, better reconstructs MR T1w and T2w images than BC, without any retraining or fine-tuning. These results highlight the excellent generalization ability of the network and lead to possible applications on other MR measurements.

MRI radiomics captures early treatment response in patient-derived organoid endometrial cancer mouse models.

Radiomics can capture microscale information in medical images beyond what is visible to the naked human eye. Using a clinically relevant mouse model for endometrial cancer, the objective of this study was to develop and validate a radiomic signature (RS) predicting response to standard chemotherapy. Mice orthotopically implanted with a patient-derived grade 3 endometrioid endometrial cancer organoid model (O-PDX) were allocated to chemotherapy (combined paclitaxel/carboplatin, n=11) or saline/control (n=13). During tumor progression, the mice underwent weekly T2-weighted (T2w) magnetic resonance imaging (MRI). Segmentation of primary tumor volume (vMRI) allowed extraction of radiomic features from whole-volume tumor masks. A radiomic model for predicting treatment response was derived employing least absolute shrinkage and selection operator (LASSO) statistics at endpoint images in the orthotopic O-PDX (RS_O), and subsequently applied on the earlier study timepoints (RS_O at baseline, and week 1-3). For external validation, the radiomic model was tested in a separate T2w-MRI dataset on segmented whole-volume subcutaneous tumors (RS_S) from the same O-PDX model, imaged at three timepoints (baseline, day 3 and day 10/endpoint) after start of chemotherapy (n=8 tumors) or saline/control (n=8 tumors). The RS_O yielded rapidly increasing area under the receiver operating characteristic (ROC) curves (AUCs) for predicting treatment response from baseline until endpoint; AUC=0.38 (baseline); 0.80 (week 1), 0.85 (week 2), 0.96 (week 3) and 1.0 (endpoint). In comparison, vMRI yielded AUCs of 0.37 (baseline); 0.69 (w1); 0.83 (week 2); 0.92 (week 3) and 0.97 (endpoint). When tested in the external validation dataset, RS_S yielded high accuracy for predicting treatment response at day10/endpoint (AUC=0.85) and tended to yield higher AUC than vMRI (AUC=0.78, p=0.18). Neither RS_S nor vMRI predicted response at day 3 in the external validation set (AUC=0.56 for both). We have developed and validated a radiomic signature that was able to capture chemotherapeutic treatment response both in an O-PDX and in a subcutaneous endometrial cancer mouse model. This study supports the promising role of preclinical imaging including radiomic tumor profiling to assess early treatment response in endometrial cancer models.

Feasibility and safety of contrast-enhanced magnetic resonance-guided adaptive radiotherapy for upper abdominal tumors: A preliminary exploration

This study investigates the use of contrast-enhanced magnetic resonance (MR) in MR-guided adaptive radiotherapy (MRgART) for upper abdominal tumors. Contrast-enhanced T1-weighted MR (cT1w MR) using half doses of gadoterate was used to guide daily adaptive radiotherapy for tumors poorly visualized without contrast. The use of gadoterate was found to be feasible and safe in 5-fraction MRgART and could improve the contrast-to-noise ratio of MR images. And the use of cT1w MR could reduce the interobserver variation of adaptive tumor delineation compared to plain T1w MR (4.41 vs. 6.58, p < 0.001) and T2w MR (4.41 vs. 7.42, p < 0.001).

Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI

Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.

Cortical morphological networks for profiling autism spectrum disorder using tensor component analysis.

Atypical neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) can alter the cortex morphology at different levels: (i) a low-order level where cortical regions are examined individually, (ii) a high-order level where the relationship between two cortical regions is considered, and (iii) a multi-view high-order level where the relationship between regions is examined across multiple brain views. In this study, we propose to use the emerging multi-view cortical morphological network (CMN), which is derived from T1-w magnetic resonance imaging (MRI), to profile autistic and typical brains and pursue new ways of fingerprinting 'cortical morphology' at the intersection of 'network neuroscience'. Each CMN view models the pairwise morphological dissimilarity at the connection level using a specific cortical attribute (e.g., thickness). Specifically, we set out to identify the inherently most representative morphological connectivities shared across different views of the cortex in both autistic and normal control (NC) populations using tensor component analysis. We thus discover the connectional profiles of both populations shared across different CMNs of the left and right hemispheres, respectively. One of the most representative morphological cortical attributes for assessing the abnormal brain structures in patients with ASD is cortical thickness. The most representative morphological connectivities in multi-view CMN population of normal control and ASD subjects, respectively, and in both left and right hemispheres within the temporal, frontal, and insular lobes of individuals with ASD. These representative connectivities are corresponded to specific clinical features observed in individuals with ASD.

Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model

BackgroundIdentifying axial spondyloarthritis (axSpA) activity early and accurately is essential for treating physicians to adjust treatment plans and guide clinical decisions promptly. The current literature is mostly focused on axSpA diagnosis, and there has been thus far, no study that reported the use of a radiomics approach for differentiating axSpA disease activity. In this study, the aim was to develop a radiomics model for differentiating active from non-active axSpA based on fat-suppressed (FS) T2-weighted (T2w) magnetic resonance imaging (MRI) of sacroiliac joints.MethodsThis retrospective study included 109 patients diagnosed with non-active axSpA (n = 68) and active axSpA (n = 41); patients were divided into training and testing cohorts at a ratio of 8:2. Radiomics features were extracted from 3.0 T sacroiliac MRI using two different heterogeneous regions of interest (ROIs, Circle and Facet). Various methods were used to select relevant and robust features, and different classifiers were used to build Circle-based, Facet-based, and a fusion prediction model. Their performance was compared using various statistical parameters. p < 0.05 is considered statistically significant.ResultsFor both Circle- and Facet-based models, 2284 radiomics features were extracted. The combined fusion ROI model accurately differentiated between active and non-active axSpA, with high accuracy (0.90 vs.0.81), sensitivity (0.90 vs. 0.75), and specificity (0.90 vs. 0.85) in both training and testing cohorts.ConclusionThe multi-ROI fusion radiomics model developed in this study differentiated between active and non-active axSpA using sacroiliac FS T2w-MRI. The results suggest MRI-based radiomics of the SIJ can distinguish axSpA activity, which can improve the therapeutic result and patient prognosis. To our knowledge, this is the only study in the literature that used a radiomics approach to determine axSpA activity.

Increased perivascular space volume in white matter and basal ganglia is associated with cognition in Parkinson’s Disease

Perivascular spaces (PVS), fluid-filled compartments surrounding brain vasculature, are an essential component of the glymphatic system responsible for transport of waste and nutrients. Glymphatic system impairment may underlie cognitive deficits in Parkinson’s disease (PD). Studies have focused on the role of basal ganglia PVS with cognition in PD, but the role of white matter PVS is unknown. This study examined the relationship of white matter and basal ganglia PVS with domain-specific and global cognition in individuals with PD. Fifty individuals with PD underwent 3T T1w magnetic resonance imaging (MRI) to determine PVS volume fraction, defined as PVS volume normalized to total regional volume, within (i) centrum semiovale, (ii) prefrontal white matter (medial orbitofrontal, rostral middle frontal, superior frontal), and (iii) basal ganglia. A neuropsychological battery included assessment of global cognitive function (Montreal Cognitive Assessment, and global cognitive composite score), and cognitive-specific domains (executive function, memory, visuospatial function, attention, and language). Higher white matter rostral middle frontal PVS was associated with lower scores in both global cognitive and visuospatial function. In the basal ganglia higher PVS was associated with lower scores for memory with a trend towards lower global cognitive composite score. While previous reports have shown that greater amount of PVS in the basal ganglia is associated with decline in global cognition in PD, our findings suggest that increased white matter PVS volume may also underlie changes in cognition.

A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes.

Malignant lateral lymph nodes (LLNs) in low, locally advanced rectal cancer can cause (ipsi-lateral) local recurrences ((L)LR). Accurate identification is, therefore, essential. This study explored LLN features to create an artificial intelligence prediction model, estimating the risk of (L)LR. This retrospective multicentre cohort study examined 196 patients diagnosed with rectal cancer between 2008 and 2020 from three tertiary centres in the Netherlands. Primary and restaging T2W magnetic resonance imaging and clinical features were used. Visible LLNs were segmented and used for a multi-channel convolutional neural network. A deep learning model was developed and trained for the prediction of (L)LR according to malignant LLNs. Combined imaging and clinical features resulted in AUCs of 0.78 and 0.80 for LR and LLR, respectively. The sensitivity and specificity were 85.7% and 67.6%, respectively. Class activation map explainability methods were applied and consistently identified the same high-risk regions with structural similarity indices ranging from 0.772-0.930. This model resulted in good predictive value for (L)LR rates and can form the basis of future auto-segmentation programs to assist in the identification of high-risk patients and the development of risk stratification models.

Deep Network-Based Comprehensive Parotid Gland Tumor Detection

Salivary gland tumors constitute 2%-6% of all head and neck tumors and are most common in the parotid gland. Magnetic resonance (MR) imaging is the most sensitive imaging modality for diagnosis. Tumor type, localization, and relationship with surrounding structures are important factors for treatment. Therefore, parotid gland tumor segmentation is important. Specialists widely use manual segmentation in diagnosis and treatment. However, considering the development of artificial intelligence-based models today, it is seen that artificial intelligence-based automatic segmentation models can be used instead of manual segmentation, which is a time-consuming technique. Therefore, we segmented parotid gland tumor (PGT) using deep learning-based architectures in the paper. The dataset used in the study includes 102 T1-w, 102 contrast-enhanced T1-w (T1C-w), and 102 T2-w MR images. After cropping the raw and manually segmented images by experts, we obtained the masks of these images. After standardizing the image sizes, we split these images into approximately 80% training set and 20% test set. Hereabouts, we trained six models for these images using ResNet18 and Xception-based DeepLab v3+. We prepared a user-friendly Graphical User Interface application that includes each of these models. From the results, the accuracy and weighted Intersection over Union values of the ResNet18-based DeepLab v3+ architecture trained for T1C-w, which is the most successful model in the study, are equal to 0.96153 and 0.92601, respectively. Regarding the results and the literature, it can be seen that the proposed system is competitive in terms of both using MR images and training the models independently for T1-w, T1C-w, and T2-w. Expressing that PGT is usually segmented manually in the literature, we predict that our study can contribute significantly to the literature. In this study, we prepared and presented a software application that can be easily used by users for automatic PGT segmentation. In addition to predicting the reduction of costs and workload through the study, we developed models with meaningful performance metrics according to the literature.

Feasibility of online radial magnetic resonance imaging for adaptive radiotherapy of pancreatic tumors.

Online adaptive magnetic resonance (MR)-guided treatment planning for pancreatic tumors on 1.5T systems typically employs Cartesian 3D T 2w magnetic resonance imaging (MRI). The main disadvantage of this sequence is that respiratory motion results in substantial blurring in the abdomen, which can hamper delineation accuracy. This study investigated the use of two motion-robust radial MRI sequences as main delineation scan for pancreatic MR-guided radiotherapy. Twelve patients with pancreatic tumors were imaged with a 3D T 2w scan, a Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) scan (partially overlapping strips), and a 3D Vane scan (stack-of-stars), on a 1.5T MR-Linac under abdominal compression. The scans were assessed by three radiation oncologists for their suitability for online adaptive delineation. A quantitative comparison was made for gradient entropy and the effect of motion on apparent target position. The PROPELLER scans were selected as first preference in 56% of the cases, the 3D T 2w in 42% and the 3D Vane in 3%. PROPELLER scans sometimes contained a large interslice variation which would have compromised delineation. Gradient entropy was significantly higher in 3D T 2w patient scans. The apparent target position was more sensitive to motion amplitude in the PROPELLER scans, but substantial offsets did not occur under 10mm peak-to-peak. PROPELLER MRI may be a superior imaging sequence for pancreatic MRgRT compared to standard Cartesian sequences. The large interslice variation should be mitigated through further sequence optimization before PROPELLER can be adopted for online treatment adaptation.

Comparison of post contrast fluid attenuated inversion recovery, 3D T1-SPACE, and T1W MRI sequences with fat suppression in the diagnosis of infectious meningitis.

To assess the usefulness of post contrast Fluid attenuated inversion recovery (FLAIR), 3D T1-SPACE, and T1W magnetic resonance imaging (MRI) sequences with fat suppression in diagnosis of infectious meningitis. 75 patients with clinical suspicion of meningitis were evaluated with post contrast FLAIR (PC-FLAIR), post contrast T1-SPACE (PC-T1-SPACE), and post contrast T1WI (PC-T1WI). Sensitivity, specificity, positive predictive value, and negative predictive value of individual sequences were assessed. The sensitivity of PC-FLAIR (88.4%) was greater than PC-T1-SPACE (85.5%) and PC-T1WI (82.6%), considering cerebrospinal fluid (CSF) analysis as gold standard (p < 0.05). Kappa inter-rater agreement between two radiologists was 0.921 for PC-T1-SPACE, 0.921 for PC-T1WI, and 1.0 for PC-FLAIR with a p value <0.05. Both PC-T1-SPACE and PC-FLAIR performed equally in sulcal space enhancement. PC-T1-SPACE and PC-T1WI performed better in evaluation of pachymeningeal enhancement, ependymal enhancement in cases of ventriculitis, whereas PC-FLAIR was more sensitive in assessment of basal cistern enhancement and enhancement along the cerebellar folia. Meningeal enhancement could be better appreciated in PC-FLAIR image than PC-T1WI and PC-T1-SPACE. Enhancement in PC-T1-SPACE was comparable to that of PC-T1WI. Being a T1 based spin echo sequence, PC-T1-SPACE has all the advantages of PC-T1WI in addition to its ability to differentiate meningeal enhancement from leptomeningeal vessels. Hence, PC-T1WI can be replaced by PC-T1-SPACE and PC-FLAIR can be added to routine MRI protocol in suspected case of meningitis.

Multi-Class classification of prostate cancer MRI images based on UCLA score using RegNetY-320 model

Prostate cancer (PCa) is the most frequent non-skin cancer in males, and it represents a significant global healthcare problem. Another challenge is the development of an accurate classification model for the detection of PCa. As a result, developing an accurate classification model for PCa is extremely important and has potentially significant clinical implications. These models have the potential to boost the benefits of treatment while also increasing the likelihood of a patient's survival. The PCa is classified in this research based on the T2w magnetic resonance images (MRI), which are collected from the prostate dataset. In this work, a deep learning-based approach RegNetY-320 model is proposed to classify and detect the PCa. The RegNet model is a pre-trained deep transfer learning architecture, which is most commonly used for the image classification process. The proposed model is optimized using various optimizers, including Adam, AdaMax, SGD, RMSprop, Ftrl, and Nadam, and the optimized models are termed model-01 to model-06. For the evaluation, a multiclass classification system was employed to classify 845 patient records from a mpMRI dataset with a unique "UCLA" score of the ROI. Accuracy, precision, sensitivity, specificity, and the F1 score are all calculated based on the classification for the purpose of performance analysis. Finally, in accordance with the findings, the performances of the various proposed models are evaluated in order to determine their validity. When compared among proposed models, the proposed model RegNetY-320_Model-03 optimized using the RMSprop optimizer has obtained greater performance with 95.70 percent accuracy, which is the highest accuracy performance obtained in this research.

APOE-ε4 modulates the association between regional amyloid deposition and cognitive performance in cognitively unimpaired middle-aged individuals

PurposeTo determine whether the APOE-ε4 allele modulates the relationship between regional β-amyloid (Aβ) accumulation and cognitive change in middle-aged cognitively unimpaired (CU) participants.MethodsThe 352 CU participants (mean aged 61.1 [4.7] years) included completed two cognitive assessments (average interval 3.34 years), underwent [18F]flutemetamol Aβ positron emission tomography (PET), T1w magnetic resonance imaging (MRI), as well as APOE genotyping. Global and regional Aβ PET positivity was assessed across five regions-of-interest by visual reading (VR) and regional Centiloids. Linear regression models were developed to examine the interaction between regional and global Aβ PET positivity and APOE-ε4 status on longitudinal cognitive change assessed with the Preclinical Alzheimer’s Cognitive Composite (PACC), episodic memory, and executive function, after controlling for age, sex, education, cognitive baseline scores, and hippocampal volume.ResultsIn total, 57 participants (16.2%) were VR+ of whom 41 (71.9%) were APOE-ε4 carriers. No significant APOE-ε4*global Aβ PET interactions were associated with cognitive change for any cognitive test. However, APOE-ε4 carriers who were VR+ in temporal areas (n = 19 [9.81%], p = 0.04) and in the striatum (n = 8 [4.14%], p = 0.01) exhibited a higher decline in the PACC. The temporal areas findings were replicated when regional PET positivity was determined with Centiloid values. Regionally, VR+ in the striatum was associated with higher memory decline. As for executive function, interactions between APOE-ε4 and regional VR+ were found in temporal and parietal regions, and in the striatum.ConclusionCU APOE-ε4 carriers with a positive Aβ PET VR in regions known to accumulate amyloid at later stages of the Alzheimer’s disease (AD) continuum exhibited a steeper cognitive decline. This work supports the contention that regional VR of Aβ PET might convey prognostic information about future cognitive decline in individuals at higher risk of developing AD.ClinicalTrials.gov Identifier: NCT02485730. Registered 20 June 2015 https://clinicaltrials.gov/ct2/show/NCT02485730 and ClinicalTrials.gov Identifier:NCT02685969. Registered 19 February 2016 https://clinicaltrials.gov/ct2/show/NCT02685969.

Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study

IntroductionBreast cancer is the most common malignancy among women, and its diagnosis relies on medical imaging and the invasive, uncomforted biopsy. Recent advances in quantitative imaging and specifically the application of radiomics has proved to be a very promising technique, facilitating both diagnosis and therapy. The purpose of this study is to assess radiomic features derived from post-contrast T1w Magnetic Resonance Imaging (MRI) sequences and Apparent Diffusion Coefficient (ADC) maps for the evaluation of breast pathologies. MethodsMRI data from 52 women were retrospectively reviewed, involving 54 breast lesions, both malignant and benign. Diffusion Weighted Imaging (DWI) was applied as a standard MRΙ protocol, including dynamic contrast-enhanced (DCE) MRΙ in all cases. All patients were examined on a 1.5T MRI scanner, and 216 features were initially extracted from DCE-MRI images. Histological analysis of the breast lesions was performed, and a comparative analysis of the results was carried out to assess the accuracy of the method. ResultsFollowing surgery and histological analysis, 30 lesions were found to be malignant and 24 benign. Implementation of a Machine Learning (ML) classification algorithm with 5-fold cross-validation resulted in a sensitivity of 70%, specificity of 66%, Negative Predictive Value of 82% and overall accuracy of 67% in differentiating malignancy from benevolence. ConclusionTexture analysis and ML methodology based on the first post-contrast dynamic sequences and ADC maps may be employed to differentiate between malignant and benign breast lesions, offering a promising new tool for diagnostic analysis. Implications for practiceThe results of this study will enhance knowledge around application and performance of radiomics in breast MRI, thus helping MRI radiographers who use AI-enabled technologies to better delineate the pros and cons of these procedures.

A comprehensive evaluation of MR-radiomics role in NAR score prediction in locally advanced rectal cancer

IntroductionNeoadjuvant Rectal Score (NAR score) is a short-term endpoint developed for clinical trials involving rectal cancer. It can be used as an early evaluation of neoadjuvant treatment response. In this study, we aim to develop a machine learning model based on radiomics features extracted from T2w magnetic resonance images (MRI) obtained pre-operatively to predict for two different binary outcomes, NAR>16 and NAR<8. Methods112 unique patients who had undergone neoadjuvant chemoradiation (NAC) followed by total mesorectal excision (TME) in National Cancer Centre Singapore were enrolled in this study. Radiomics features were extracted from three different types of segments that were contoured on the T2w MRI before and after NAC. The segments comprised of gross tumor volume (GTV), clinical tumor volume (CTV) and CTV + GTV (total). From which, five different clinico-radiomics models were constructed namely, 1) clinical + GTV, 2) clinical + CTV, 3) clinical + CTV + GTV, 4) clinical + total and 5) clinical + GTV + CTV + total models. The radiomics features extracted from the pre-MRI were also compared with the delta-radiomics approach in this study. This gives a total of ten clinico-radiomics model for each outcome. A nested 10-fold cross validation approach with the AUC metric were used to determine the optimal model. Calibration curves and feature importance were also calculated for the best performing clinico-radiomics models for each outcome and approach. ResultsThe best NAR<8 pre-MRI and delta clinico-radiomics model models improve the AUC of the clinical model from 0.827 ± 0.206 to 0.868 ± 0.142 and 0.878 ± 0.111 respectively. Both increases are statistically significant (P = 0.032 and P = 0.022). The best NAR>16 pre-MRI and delta clinico-radiomics model models improve the AUC of the clinical model from 0.622 ± 0.146 to 0.749 ± 0.124 and 0.687 ± 0.150 respectively. Only the NAR>16 pre-MRI clinio-radiomics showed a statistically significant increase in AUC (P = 0.041). Out of the four models, only the two NAR>16 clinico-radiomics model are well calibrated. The results also show that the GTV and CTV features are the most important features for NAR<8 model, while the CTV + GTV features are the main driver of NAR>16 pre-MRI clinico-radiomics model. ConclusionWe have carried out for the first time a comprehensive evaluation of MR radiomics models by considering radiomics feature extracted from various segments, pre-MRI versus delta-radiomics and five machine learning algorithms. Our results show that MR radiomics models can value-add to clinical parameters in predicting the NAR score, and it is worthwhile to consider several permutations of the clinico-radiomics models to arrive at the best performing model.

Evaluation of early regression index as response predictor in cervical cancer: A retrospective study on T2 and DWI MR images

Background and PurposeEarly Regression Index (ERITCP) is an image-based parameter based on tumor control probability modelling, that reported interesting results in predicting pathological complete response (pCR) after pre-operative chemoradiotherapy (CRT) in rectal cancer. This study aims to evaluate this parameter for Locally Advanced Cervical Cancer (LACC), considering not only T2-weighted but also diffusion-weighted (DW) Magnetic Resonance (MR) images, comparing it with other image-based parameters such as tumor volumes and apparent coefficient diffusion (ADC). Materials and MethodsA total of 88 patients affected by LACC (FIGO IB2-IVA) and treated with CRT were enrolled. An MRI protocol consisting in two acquisitions (T2-w and DWI) in two times (before treatment and at mid-therapy) was applied.Gross Tumor Volume (GTV) was delineated and ERITCP was calculated for both imaging modalities. Surgery was performed for each patient after nCRT: pCR was considered in case of absence of any residual tumor cells. The predictive performance of ERITCP, GTV volumes (calculated on T2-w and DW MR images) and ADC parameters were evaluated in terms of area (AUC) under the Receiver Operating Characteristic (ROC) curve considering pCR and two-years survival parameters as clinical outcomes. ResultsERITCP and GTV volumes calculated on DW MR images (ERIDWI and Vmid_DWI) significantly predict pCR (AUC = 0.77 and 0.75 respectively) with results superior to those observed considering T2-w MR images or ADC parameters. Significance was also reported in the prediction of 2-years local control and disease free-survival. ConclusionThis study identified ERITCP and Vmid as good predictor of pCR in case of LACC, especially if calculated considering DWI. Using these indicators, it is possible to early identify not responders and modifying the treatment, accordingly.