Original MRI Research Articles

Development of Prediction Models to Detect the Presence of MGMT Promoter Methylation for Prognosis of Brain Tumor

Methylation of the MGMT promoter is a molecular marker of predictive relevance in brain tumors. Methylation of the MGMT promoter is related to an improved prognosis and may influence therapy decisions. According to the most recent Malaysian National Cancer Registry Report (MNCRR) for 2012–2016, there were 2097 cases of brain tumors in Malaysia overall, with 1117 cases involving men and 908 cases involving women. This research aims to create prediction models for detecting MGMT promoter methylation in brain tumors. So, a dataset from the BraTS (Brain Tumor Segmentation) challenge, which included MRI scans and clinical data for patients with brain tumors, was utilized and explored using Exploratory Data Analysis (EDA) and data visualization techniques. The dataset has 306 methylation cases identified as “1” and 276 unmethylated cases identified as “0”. The average number of scans of train data for modality per patient is between 127 and 171, which provides a wealth of information for pattern learning while the average number of scans of test data for modality per patient is between 124 and 165. In both sets, FLAIR has the least number of files while T2w has the highest number of files among them. Two models of deep learning approaches, ResNet50 and EfficientNetV2, were used to construct and form the prediction models. Various criteria were employed to evaluate the performance of the models. The T2w modality consistently achieved the highest validation accuracy, precision, recall, and F1-score for both the ResNet50 and EfficientNetV2 models. Specifically, the T2w modality achieved a validation accuracy of 0.9453 and a validation loss of 0.2417 for ResNet50. Furthermore, the brain tumor diagnosis interface utilized DICOM images from MRI scans to identify MGMTp methylation status, aiding in therapy effectiveness prediction. Pre-trained ResNet50 model on T2w images was used for classification. The interface displayed the original MRI image, predicted state, and treatment effectiveness indication, while promptly notifying users of invalid or inadequate data for accurate analysis.

Enhanced multistage deep learning for diagnosing anterior disc displacement in the temporomandibular joint using MRI.

This study aimed to propose a new method for the automatic diagnosis of anterior disc displacement of the temporomandibular joint (TMJ) using MRI and deep learning. By using a multistage approach, the factors affecting the final result can be easily identified and improved. This study introduces a multistage automatic diagnostic technique using deep learning. This process involves segmenting the target from MR images, extracting distance parameters, and classifying the diagnosis into 3 classes. MRI exams of 368 TMJs from 204 patients were evaluated for anterior disc displacement. In the first stage, 5 algorithms were used for the semantic segmentation of the disc and condyle. In the second stage, 54 distance parameters were extracted from the segments. In the third stage, a rule-based decision model was developed to link the parameters with the expert diagnosis results. In the first stage, DeepLabV3+ showed the best result (95% Hausdorff distance, Dice coefficient, and sensitivity of 6.47 ± 7.22, 0.84 ± 0.07, and 0.84 ± 0.09, respectively). This study used the original MRI exams as input without preprocessing and showed high segmentation performance compared with that of previous studies. In the third stage, the combination of SegNet and a random forest model yielded an accuracy of 0.89 ± 0.06. An algorithm was developed to automatically diagnose TMJ-anterior disc displacement using MRI. Through a multistage approach, this algorithm facilitated the improvement of results and demonstrated high accuracy from more complex inputs. Furthermore, existing radiological knowledge was applied and validated.

RobU-Net: a heuristic robust multi-class brain tumor segmentation approaches for MRI scans

A tumor is an abnormal growth of cells, either cancerous or benign, that develops in an organ. Early detection and segmentation of brain tumors are crucial for effective treatment, but manual analysis by experts is a labor-intensive and time-consuming process. The proposed solution is a deep learning framework called RobU-Net, a modified U-Net, to improve the accuracy and robustness of MRI image segmentation, specifically designed to handle Rician noise in MRI scans. The study uses various encoder-decoder architectures to segment MRI scans, achieving optimal results that match the ground truth. The approach also employs discrete wavelet transforms for contrast enhancement and tests the framework's robustness. This study used the MRI brain tumor dataset with 3064 slices. The space and time analysis have been carried out using sensitivity (Sn), dice loss (DL), dice coefficient (DC), and Jaccard index (JI). A comprehensive assessment of our proposed framework depicted that it is better than recently existing frameworks. We have found the so far highest Sn, DC, and JI values for noise-impregnated and original MRI datasets using two novice architectures (overall best for A1-E2 and optimum or noisy dataset for A2-E1) as (0.9831, 0.9726, and 0.9468) and (0.9199, 0.9781, and 0.9571).

Structural and functional connectivity in hydrocephalus: a scoping review.

Optimizing the treatment of hydrocephalus remains a major challenge in adult and pediatric neurosurgery. Currently, clinical treatment relies heavily on anatomic imaging of ventricular size and clinical presentation. The emergence of functional and structural brain connectivity imaging has provided the basis for a new paradigm in the management of hydrocephalus. Here we review the pertinent advances in this field. Following PRISMA-ScR guidelines for scoping reviews, we searched PubMed for relevant literature from 1994 to April 2023 using hydrocephalus and MRI-related terms. Included articles reported original MRI data on human subjects with hydrocephalus, while excluding non-English or pre-1994 publications that didn't match the study framework. The review identified 44 studies that investigated functional and/or structural connectivity using various MRI techniques across different hydrocephalus populations. While there is significant heterogeneity in imaging technology and connectivity analysis, there is broad consensus in the literature that 1) hydrocephalus is associated with disruption of functional and structural connectivity, 2) this disruption in cerebral connectivity can be further associated with neurologic compromise 3) timely treatment of hydrocephalus restores both cerebral connectivity and neurologic compromise. The robustness and consistency of these findings vary as a function of patient age, hydrocephalus etiology, and the connectivity region of interest studied. Functional and structural brain connectivity imaging shows potential as an imaging biomarker that may facilitate optimization of hydrocephalus treatment. Future research should focus on standardizing regions of interest as well as identifying connectivity analysis most pertinent to clinical outcome.

Segnet with Unet3+ and EfficientNet: a novel framework of brain tumour segmentation and classification model by multiscale attention-based deep learning techniques with hybrid heuristic improvement using 3D MRI brain images

ABSTRACT An adaptive deep learning is recommended to segment and classify the brain tumor using 3D MRI images. Initially, the original 3D MRI images are gathered and fed into pre-processing, which is accomplished using the 'Contrast Limited Adaptive Histogram Equalization (CLAHE)' technique. The pre-processed image is given as input to 'Multiscale Self-guided Attention based Hybrid Adaptive Networks (MSA-HAN)', where it encompasses Segnet with Unet3+ for segmentation. In this model, certain parameters are optimized using the Position Updating of Black Widow and Shark Smell (PUBWSS). Finally, the segmented region is subjected into the Adaptive Atrous Spatial Pyramid Pooling (ASPP) based EfficientNet (AA-ENet) that considers MobileNet and DenseNet. Thus, the model's performance is evaluated, and results are carried out with diverse measures. The performance of the offered method shows 95.74%, and 95.80% in terms of accuracy and F1-score. Hence, the results declare that the developed model achieves higher segmentation accuracy than other approaches.

Low-field magnetic resonance imaging of sagittal groove disease of the proximal phalanx in non-racing sport horses.

Injuries of the sagittal groove of the proximal phalanx (P1) in equine athletes are considered to predominantly occur due to chronic bone stress overload. To describe the range of abnormalities that is present in the sagittal groove in a large group of horses diagnosed with sagittal groove disease (SGD) on low-field MRI. Retrospective, cross-sectional. Medical records were searched to identify initial MRI images of horses diagnosed with SGD and these were blindly evaluated using a semi-quantitative grading scheme and novel SGD MRI classification system reflecting potential pathways of pathological progression and severity of stress injury. A total of 132 limbs from 111 horses were included in the study; predominantly warmbloods competing in showjumping (n = 83) and dressage (n = 18). SGD MRI classifications were: 0 (normal, n = 0), 1 (small subchondral defect, n = 2), 2 (osseous densification, n = 28), 3 (subchondral microfissure with osseous densification, n = 7), 4 (bone oedema-like signal within the subchondral ± trabecular bone and ± subchondral microfissure or demineralisation, n = 72), 5 (incomplete macrofissure/fracture, n = 23) and 6 (complete fracture, n = 0). Classification 4c (bone oedema-like signal with demineralisation) and 5 had higher proportions in the plantar third of hindlimbs (3% and 10%, respectively) compared with forelimbs (0% and 0%, respectively). SGD classification and extent of bone oedema-like signal were not significantly different between lame (n = 116) and non-lame limbs (n = 16) (both p > 0.05). Periosteal new bone and oedema-like signal were identified (either confidently or suspected) at the dorsoproximal aspect of P1 in 25% and 39% of limbs, respectively. Inclusion via diagnoses in original MRI reports, variable clinical history, small size of some classification groups. The presence or absence of lameness is not a dependable measure of the severity of SGD. The periosteal oedema-like signal of P1 has not previously been described in MRI of SGD and further supports the concept of bone stress injury.

Long-term efficacy of a 2-year MRI treat-to-target strategy on disease activity and radiographic progression in patients with rheumatoid arthritis in clinical remission: 5-year follow-up of the IMAGINE-RA randomised trial

ObjectiveTo investigate whether a 2-year MRI treat-to-target strategy targeting the absence of osteitis combined with clinical remission, compared with a conventional treat-to-target strategy targeting clinical remission only (IMAGINE-rheumatoid arthritis (RA)...

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.

A brain MRI dataset and baseline evaluations for tumor recurrence prediction after Gamma Knife radiotherapy

Prediction and identification of tumor recurrence are critical for brain cancer treatment design and planning. Stereotactic radiation therapy delivered with Gamma Knife has been developed as one of the common treatment approaches combined with others by delivering radiation that targets accurately on the tumor while not affecting nearby healthy tissues. In this paper, we release a fully publicly available brain cancer MRI dataset and the companion Gamma Knife treatment planning and follow-up data for the purpose of tumor recurrence prediction. The dataset contains original patient MRI images, radiation therapy data, and clinical information. Lesion annotations are provided, and inclusive preprocessing steps have been specified to simplify the usage of this dataset. A baseline framework based on a convolutional neural network is proposed companionably with basic evaluations. The release of this dataset will contribute to the future development of automated brain tumor recurrence prediction algorithms and promote the clinical implementations associated with the computer vision field. The dataset is made publicly available on The Cancer Imaging Archive (TCIA) (https://doi.org/10.7937/xb6d-py67).

Feasibility of Using a Single Planning MRI for Two-Insertion Intracavitary Brachytherapy for Cervical Cancer

MRI is an essential tool in treatment planning for cervical cancer patients undergoing definitive chemoradiation involving brachytherapy. However, due access to MRI for many patients may be limited. We sought to examine the feasibility and quality of treatment planning for organs at risk (OAR) and target volumes of a single treatment planning MRI for a planned two-insertion HDR intracavitary cervical brachytherapy. Eighteen patients with cervical cancer who underwent MRI-based treatment planning for intracavitary brachytherapy were selected for analysis. Patients underwent two intracavitary applicator insertions using a tandem and ring applicator (TnR_1 and TnR_2) with both CT (for digitization only) and MRI following each insertion and two fractions delivered with each insertion (total 7 Gy x 4fx). MRI-based TnR_2 plans were retrospectively replanned with the following procedure: 1) register MRI from TnR_1 to TnR_2 CT, 2) transfer cervix contour (Cx_simul) and plan from TnR_1 to TnR_2 CT, simulating blinding from TnR_2 MRI, 3) crop Cx_simul from TnR_2 OARs, 4) adjust plan only to meet OAR constraints or clinical doses if clinical plan (TnR_2 original MRI plan summed with TnR_1 and EBRT doses) violates constraints. Coverage changes (∆_D90%) in actual MRI defined TnR_2 cervix (Cx_real) was compared to clinical plans and evaluated against our clinical target goal: >85 Gy EQD2 (EBRT+BT). Differences between Cx_simul and Cx_real contours were evaluated with Dice Similarity Coefficient (DSC). Changes in bladder, rectum, rectosigmoid, and bowel D2cc was evaluated. Procedure time was recorded to determine time added by MRI (time between CT and final MRI timestamps; n = 29). Plan modification to meet OAR constraints was required for 5/18 plans. Cx_real target coverage from clinical to simulated plans decreased in 7/18 plans (∆_D90% = 1.7±6%). Composite EQD2 was <85 Gy in 2/18 cases, with ∆_D90% of -1.3% (clinical EQD2<85 Gy for this case also) and -7.3%, which was due to inconsistent cervix shape between insertions 1 and 2. Cervix DSC values between Cx_simul and Cx_real ranged from 0.68-0.88 (mean = 0.78 ± 0.06). Bladder, rectum, rectosigmoid, and bowel D2cc increased on average 4 ± 12%, 3 ± 9%, 5 ± 10%, and 5 ± 17%, respectively. Including planning MRI increased procedure time by 41.8 ± 10.4 min. The proposed method of utilizing TnR_1 MRI contours for TnR_2 CT planning reduces procedure time and provides reasonable target coverage for most cases. OAR assessment varies between imaging modality which likely represents variability in OAR position rather than treatment planning modality. Next steps are to increase patients studied, and implement this technique prospectively while waiting for TnR_2 MRI completion and compare the clinical TnR_2 MRI plan with the TnR_2 CT plan created with Cx_simul. This will provide further confidence in validity of the approach and ensure plan quality is maintained prior to full implementation.

Comparing Predicted Toxicities between Hypofractionated Proton and Photon Radiotherapy of Liver Cancer Patients with Different Adaptive Schemes.

With the availability of MRI linacs, online adaptive intensity modulated radiotherapy (IMRT) has become a treatment option for liver cancer patients, often combined with hypofractionation. Intensity modulated proton therapy (IMPT) has the potential to reduce the dose to healthy tissue, but it is particularly sensitive to changes in the beam path and might therefore benefit from online adaptation. This study compares the normal tissue complication probabilities (NTCPs) for liver and duodenal toxicity for adaptive and non-adaptive IMRT and IMPT treatments of liver cancer patients. Adaptive and non-adaptive IMRT and IMPT plans were optimized to 50 Gy (RBE = 1.1 for IMPT) in five fractions for 10 liver cancer patients, using the original MRI linac images and physician-drawn structures. Three liver NTCP models were used to predict radiation-induced liver disease, an increase in albumin-bilirubin level, and a Child-Pugh score increase of more than 2. Additionally, three duodenal NTCP models were used to predict gastric bleeding, gastrointestinal (GI) toxicity with grades >3, and duodenal toxicity grades 2-4. NTCPs were calculated for adaptive and non-adaptive IMRT and IMPT treatments. In general, IMRT showed higher NTCP values than IMPT and the differences were often significant. However, the differences between adaptive and non-adaptive treatment schemes were not significant, indicating that the NTCP benefit of adaptive treatment regimens is expected to be smaller than the expected difference between IMRT and IMPT.

Image segmentation technology based on transformer in medical decision‐making system

AbstractDue to the improvement in computing power and the development of computer technology, deep learning has pene‐trated into various fields of the medical industry. Segmenting lesion areas in medical scans can help clinicians make accurate diagnoses. In particular, convolutional neural networks (CNNs) are a dominant tool in computer vision tasks. They can accurately locate and classify lesion areas. However, due to their inherent inductive bias, CNNs may lack an understanding of long‐term dependencies in medical images, leading to less accurate grasping of details in the images. To address this problem, we explored a Transformer‐based solution and studied its feasibility in medical imaging tasks (OstT). First, we performed super‐resolution reconstruction on the original MRI image of osteosarcoma and improved the texture features of the tissue structure to reduce the error caused by the unclear tissue structure in the image during model training. Then, we propose a Transformer‐based method for medical image segmentation. A gated axial attention model is used, which augments existing architectures by introducing an additional control mechanism in the self‐attention module to improve segmentation accuracy. Experiments on real datasets show that our method outper‐forms existing models such as Unet. It can effectively assist doctors in imaging examinations.

A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images

Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Much research has been conducted to detect it from MRI images through various deep learning approaches.However, the problems of the availability of medical data and preserving the privacy of patients still exists. To mitigate this issue in Alzheimer’s disease detection, we implement the federated approach, which is found to be more efficient, robust, and consistent compared with the conventional approach. For this, we need deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilize two publicly available datasets (OASIS and ADNI) and design various cases to evaluate the performance of the federated approach. The federated approach achieves better accuracy and sensitivity compared with the conventional approaches in most of the cases. Moreover, the robustness of the proposed approach is also found to be better than the conventional approach. In our federated approach, MobileNet, a low-cost transfer learning architecture, achieves the highest 95.24%, 81.94%, and 83.97% accuracy in the OASIS, ADNI, and merged (ADNI + OASIS) test sets, which is much higher than the achieved performance in the conventional approach. Furthermore, in the proposed approach, only the weights of the model are shared, which keeps the original MRI images in their respective hospital or institutions, preserving privacy in the healthcare domain.

Towards a fast PET/MRI protocol for breast cancer imaging: maintaining diagnostic confidence while reducing PET and MRI acquisition times

ObjectivesTo investigate the diagnostic feasibility of a shortened breast PET/MRI protocol in breast cancer patients.MethodsAltogether 90 women with newly diagnosed T1tumor-staged (T1ts) and T2tumor-staged (T2ts) breast cancer were included in this retrospective study. All underwent a dedicated comprehensive breast [18F]FDG-PET/MRI. List-mode PET data were retrospectively reconstructed with 20, 15, 10, and 5 min for each patient to simulate the effect of reduced PET acquisition times. The SUVmax/mean of all malign breast lesions was measured. Furthermore, breast PET data reconstructions were analyzed regarding image quality, lesion detectability, signal-to-noise ratio (SNR), and image noise (IN). The simultaneously acquired comprehensive MRI protocol was then shortened by retrospectively removing sequences from the protocol. Differences in malignant breast lesion detectability between the original and the fast breast MRI protocol were evaluated lesion-based. The 20-min PET reconstructions and the original MRI protocol served as reference.ResultsIn all PET reconstructions, 127 congruent breast lesions could be detected. Group comparison and T1ts vs. T2ts subgroup comparison revealed no significant difference of subjective image quality between 20, 15, 10, and 5 min acquisition times. SNR of qualitative image evaluation revealed no significant difference between different PET acquisition times. A slight but significant increase of IN with decreasing PET acquisition times could be detected. Lesion SUVmax group comparison between all PET acquisition times revealed no significant differences. Lesion-based evaluation revealed no significant difference in breast lesion detectability between original and fast breast MRI protocols.ConclusionsBreast [18F]FDG-PET/MRI protocols can be shortened from 20 to below 10 min without losing essential diagnostic information.Key Points• A highly accurate breast cancer evaluation is possible by the shortened breast [18F]FDG-PET/MRI examination protocol.• Significant time saving at breast [18F]FDG-PET/MRI protocol could increase patient satisfaction and patient throughput for breast cancer patients at PET/MRI.

Time for change? Radiologists highly concordant assessing change in stenoses on follow-up cervical spine MRI.

There is considerable variability among radiologists when grading spinal canal and foraminal stenosis on MRI. However, to date, studies have not evaluated radiologists' agreement when assessing interval change in cervical spine stenoses. The purpose of this study was to evaluate radiologists' concordance for change in cervical spine stenoses on follow-up MRIs, a major indication for these exams. Initial and follow-up cervical MRIs were retrospectively reviewed by three blinded radiologists. Spinal canal and foramina from C1 through T1 were rated for interval change and concordance between the blinded raters was calculated. The original MRI reports were also reviewed for specific language assessing interval change on the follow-up exams. 40 cervical MRI exams and 40 corresponding MRI follow-ups were assessed. Agreement for interval change in spinal canal and foraminal stenosis was near perfect amongst all readers (kappa values of 0.78-0.94). 97% of the original MRI reports used the standard severity scale. 68% of follow-up MRI reports specifically assessed for change. Blinded radiologists had high agreement when assessing for change in spinal canal and foraminal stenosis on follow-up cervical spine MRIs. Because of inter-rater variability in stenosis grading, reports that do not emphasize change assessment, may imply change that is not truly present. For clarity and consistency in reporting of cervical spine stenoses, change assessment should be emphasized and added to structured reporting templates.

PD39-09 IMPACT OF PROSTATE IMAGING QUALITY (PI-QUAL) SCORE ON THE DETECTION OF CLINICALLY SIGNIFICANT PROSTATE CANCER IN MEN UNDERGOING MRI-TARGETED BIOPSY

PD39-09 IMPACT OF PROSTATE IMAGING QUALITY (PI-QUAL) SCORE ON THE DETECTION OF CLINICALLY SIGNIFICANT PROSTATE CANCER IN MEN UNDERGOING MRI-TARGETED BIOPSY

Smoking and secondary ACL rupture are detrimental to knee health post ACL injury—a Bayesian analysis

PurposeTo identify potential prognostic factors for patient-reported outcomes in an Icelandic cohort of ACL injured subjects.MethodsAll knee MRI reports written in Iceland between the years 2001 to 2011 were read to identify individuals with a possible ACL injury. These individuals were contacted and asked to complete an online questionnaire regarding their injury and current knee related health. The questionnaire collected information on years since surgery, injury circumstance, brace use, physiotherapy, ACL surgery, second ACL injury and current smoking status. In addition, the baseline status of their meniscii were assessed from the original MRI report and medical records were used to identify any subsequent, non-ACL surgery. The patient-reported Knee Osteoarthritis and Injury Outcome Score (KOOS) was used assess current knee related health. A Bayesian proportional odds model was used to assess the effect of all potential prognostic factors above as well as age and sex on KOOS outcomes.ResultsA total of 408 subjects completed the questionnaire indicating that they did rupture their ACL. The following variables were associated with worse outcomes across all KOOS subscales: having a subsequent arthroscopy, reinjury to your ACL, and smoking. Having physiotherapy for 9 months was associated with worse KOOS pain scores than having 6 months of physiotherapy. Conversely KOOS pain score tended to be higher if you injured your knee during sports.ConclusionReinjuring your ACL, smoking and having subsequent (non-ACLR) surgery predict your knee related health following an ACL injury. Strategies should be implemented to reduce the risk of secondary ACL injury, and patients should be strongly advised not to smoke.

Fidelity of 3D Printed Brains from MRI Scan in Children with Pathology (Prior Hypoxic Ischemic Injury)

Cortical injury on the surface of the brain in children with hypoxic ischemic injury (HII) can be difficult to demonstrate to non-radiologists and lay people using brain images alone. Three-dimensional (3D) printing is helpful to communicate the volume loss and pathology due to HII in children’s brains. 3D printed models represent the brain to scale and can be held up against models of normal brains for appreciation of volume loss. If 3D printed brains are to be used for formal communication, e.g., with medical colleagues or in court, they should have high fidelity of reproduction of the actual size of patients’ brains. Here, we evaluate the size fidelity of 3D printed models from MRI scans of the brain, in children with prior HII. Twelve 3D prints of the brain were created from MRI scans of children with HII and selected to represent a variety of cortical pathologies. Specific predetermined measures of the 3D prints were made and compared to measures in matched planes on MRI. Fronto-occipital length (FOL) and bi-temporal/bi-parietal diameters (BTD/BPD) demonstrated high interclass correlations (ICC). Correlations were moderate to weak for hemispheric height, temporal height, and pons-cerebellar thickness. The average standard error of measurement (SEM) was 0.48 cm. Our results demonstrate high correlations in overall measurements of each 3D printed model derived from brain MRI scans versus the original MRI, evidenced by high ICC values for FOL and BTD/BPD. Measures with low correlation values can be explained by variability in matching the plane of measurement to the MRI slice orientation.

Retrospective evaluation of national MRI reporting quality for lateral lymph nodes in rectal cancer patients and concordance with prospective re-evaluation following additional training

ObjectivesThe presence and size of lateral lymph nodes (LLNs) are important factors influencing treatment decisions for rectal cancer. Awareness of the clinical relevance and describing LLNs in MRI reports is therefore essential. This study assessed whether LLNs were mentioned in primary MRI reports at a national level and investigated the concordance with standardised re-review.MethodsThis national, retrospective, cross-sectional cohort study included 1096 patients from 60 hospitals treated in 2016 for primary cT3-4 rectal cancer ≤ 8 cm from the anorectal junction. Abdominal radiologists re-reviewed all MR images following a 2-h training regarding LLNs.ResultsRe-review of MR images identified that 41.0% of enlarged (≥ 7 mm) LLNs were not mentioned in primary MRI reports. A contradictory anatomical location was stated for 73.2% of all LLNs and a different size (≥/< 7 mm) for 41.7%. In total, 49.4% of all cases did not mention LLNs in primary MRI reports. Reporting LLNs was associated with stage (cT3N0 44.3%, T3N+/T4 52.8%, p = 0.013), cN stage (N0 44.1%, N1 48.6%, N2 59.5%, p < 0.001), hospital type (non-teaching 34.6%, teaching 52.2%, academic 53.2% p = 0.006) and annual rectal cancer resection volumes (low 34.8%, medium 47.7%, high 57.3% p < 0.001). For LLNs present according to original MRI reports (n = 226), 64.2% also mentioned a short-axis size, 52.7% an anatomical location and 25.2% whether it was deemed suspicious.ConclusionsAlmost half of the primary MRI reports for rectal cancer patients treated in the Netherlands in 2016 did not mention LLNs. A significant portion of enlarged LLNs identified during re-review were also not mentioned originally, with considerable discrepancies for location and size. These results imply insufficient awareness and indicate the need for templates, education and training.

S2888 A Case of a Completely Migrated Gastric Toothpick Caught by EUS

Introduction: Foreign body ingestion is a common cause for medical emergencies. Once they reach the stomach, roughly 80% of foreign bodies pass uneventfully through the GI tract. Sharp objects, such as toothpicks, are more likely to cause injury throughout the GI tract. Toothpick ingestion is associated with high rates of mortality and morbidity, with estimated 79% of ingestions leading to gut perforation. Initial diagnosis can be difficult, as ingestion has been found to mimic other conditions, such as renal colic. Endoscopy is warranted in roughly 20% of cases, with about 1% requiring surgical intervention. Case Description/Methods: A 63-year-old male with a history of HTN and DM presented with a 1-week history of altered mental status, fevers, nausea, vomiting, and melena. CT revealed a linear density extending from the lumen of the distal gastric antrum to the posterior wall of the stomach traversing the pancreas in the expected location of the SMV. Follow up EGD and MRI failed to reveal foreign body. He subsequently developed actinomyces bacteremia, was treated with antibiotics along with dental extraction, and was discharged home. The patient later returned with septic shock, found to have recurrent Actinomyces bacteremia. CT re-demonstrated suspicious thin linear density as well as multiple liver abscesses. Again, EGD failed to reveal foreign body. This time, an EUS was performed, and the foreign body was localized to the antrum, 2.4 mm below the luminal surface. Endoscopic mucosal resection was attempted but was unsuccessful. The patient then underwent an exploratory laparotomy with the retrieval of a wooden toothpick. He discharged in stable condition. Discussion: In this case, original CT scans revealed a possible foreign body, the object being localized as expected near the SMV. Given original negative EGD and MRI, it was felt that CT scan findings represented vasculature. When patient returned in septic shock with recurrent actinomyces bacteremia, a second EGD was performed and was negative. A decision was made at that time to investigate with EUS as a diagnostic tool, which ultimately revealed the location of the foreign body. What makes our case particularly unique is the degree of foreign body migration into the gastric wall, causing difficulty in diagnosis through EGD. This case highlights EUS as an important diagnostic modality in cases of foreign body ingestion.