Magnetic Resonance Imaging Interpretation Research Articles

P.109 Machine learning based approach to improving the prediction of neurological deterioration in mild Degenerative Cervical Myelopathy

Background: Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally, yet clinical guidelines remain unclear on surgical recommendations for patients with mild forms of DCM. This is in part due to limitations in current MR imaging interpretation and complex mechanisms of neurological deterioration. Supervised machine learning (ML) models can help to identify clinical and imaging indicators of deterioration within mild DCM patients. Methods: 127 MRI scans (T2w, Diffusion Tensor Imaging, and Magnetization transfer scans) accompanied by a series of clinical tests underwent a semi-automated analysis to derive quantitative metrics. Random forest classifier, Support Vector Machine, and Logistic Regression models were trained and tested to predict 6-month neurological deterioration within patients. Results: The ML models performed, on average, better than previous studies with a balanced accuracy ranging between 70-75%. “Advanced” imaging metrics such as diffusion tensor imaging and magnetization transfer scans played an important role in improving model accuracy but only when used near the maximally compressed disc level, suggesting that limited yet targetted imaging metrics support ML model performance. Conclusions: The inclusion of specific, targeted imaging and clinical metrics support ML model performance in predicting neurological deterioration within mild DCM patients.

Comparing MRI and arthroscopic appearances of common knee pathologies: A pictorial review

Knee pathology, including anterior cruciate ligament (ACL) tears, meniscal tears, articular cartilage lesions, and intra-articular masses or cysts are common clinical entities treated by orthopedic surgeons with arthroscopic surgery. Preoperatively, magnetic resonance imaging (MRI) is now standard in confirming knee pathology, particularly detecting pathology less evident with history and physical examination alone. The radiologist’s MRI interpretation becomes essential in evaluating intra-articular knee structures. Typically, the radiologist that interprets the MRI does not have the opportunity to view the same pathology arthroscopically. Thus, the purpose of this article is to illustratively reconcile what the orthopedic surgeon sees arthroscopically with what the radiologist sees on magnetic resonance imaging when viewing the same pathology. Correlating virtual and actual images can help better understand pathology, resulting in more accurate MRI interpretations. In this article, we present and review a series of MR and correlating arthroscopic images of ACL tears, meniscal tears, chondral lesions, and intra-articular masses and cysts. Short teaching points are included to highlight the importance of radiological signs and pathological MRI appearance with significant clinical and arthroscopic findings.

A pictorial essay of PI-RADS pearls and pitfalls: toward less ambiguity and better practice.

Prostate Imaging Reporting and Data System (PI-RADS) was designed to standardize the interpretation of multiparametric magnetic resonance imaging (MRI) of the prostate, aiding in assessing the probability of clinically significant prostate cancer. By providing a structured scoring system, it enables better risk stratification, guiding decisions regarding the need for biopsy and subsequent treatment options. In this article, we explore both the strengths and weaknesses of PI-RADS, offering insights into its updated diagnostic performance and clinical applications, while also addressing potential pitfalls using diverse, representative MRI cases.

Axial Spondyloarthritis: Does Magnetic Resonance Imaging Classification Improve Report Interpretation.

The interpretation of magnetic resonance imaging (MRI) reports is crucial for the diagnosis of axial spondyloarthritis, but the subjective nature of narrative reports can lead to varying interpretations. This study presents a validation of a novel MRI reporting system for the sacroiliac joint in clinical practice. A historical review was conducted on 130 consecutive patients referred by 2 rheumatologists for initial MRI assessment of possible axial spondyloarthritis. The original MRI reports were interpreted by the rheumatologists and the radiologist who originally read the images and then categorized according to the novel system. Two musculoskeletal radiologists then reinterpreted the original MRI scans using the new system, and the resulting reports were interpreted and categorized by the same rheumatologists. The quality of the new framework was assessed by comparing the interpretations of both reports. Ninety-two patients met the study criteria. The rheumatologists disagreed on the categorization of the original MRI reports in 12% of cases. The rheumatologists and original radiologists disagreed on the categorization of the initial report in 23.4% of cases. In contrast, there was 100% agreement between the rheumatologists and radiologists on the categorization of the new MRI report. The new MRI categorization system significantly improved the agreement between the clinician and radiologist in report interpretation. The system provided a standard vocabulary for reporting, reduced variability in report interpretation, and may therefore improve clinical decision-making.

AI Applications to Breast MRI: Today and Tomorrow.

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Fixation technique of biodegradable magnesium alloy suture anchor in the rotator cuff repair of the shoulder in a goat model: a technical note

BackgroundShoulder disorders, particularly rotator cuff tears, are prevalent musculoskeletal conditions related to aging. Although the widely used suture anchor technique provides strong mechanical support to the tendon, it is associated with a risk of postoperative tendon retearing. The conventionally used titanium alloys can affect the interpretation of magnetic resonance imaging. Degradable magnesium alloys possess excellent biocompatibility, similar mechanical property to the bone, and stimulating bone formation ability from Mg2+. The purpose of this experiment was to develop innovative magnesium-based suture anchors to enhance rotator cuff repair by improving fixation materials, and to evaluate their feasibility in a goat model.MethodsWe developed fluoridized ZK60 suture anchors as the implantation material for two goats, who underwent rotator cuff repair surgery on both shoulders. Computed tomography (CT) and histological analysis were performed at 12 weeks postoperatively, and the results were compared between the magnesium and titanium alloy groups. Additionally, a hematological examination was conducted, which included assessments of red blood cells, white blood cells, platelets, coagulation function, liver function, kidney function, and magnesium ion concentration.ResultsThe 12-week postoperative CT images showed intact MgF2 ZK60 suture anchors, effectively reconnecting the infraspinatus tendon to the humeral head. The anchors became less visible on CT scans, indicating absorption by surrounding tissues. New bone formation in the MgF2 group surpassed that in the Ti group, demonstrating superior osseointegration. The similarity between cortical bone and magnesium reduced stress-shielding and promoted bone regeneration. Histological analysis revealed successful tendon healing with MgF2 anchors, while the Ti group showed discontinuous interfaces and reduced collagen secretion. Hematological examination showed stable liver, renal function, and magnesium ion levels.ConclusionsThe findings indicate that MgF2-coated suture anchors are feasible for rotator cuff repair and potentially other orthopedic applications. We hope that magnesium alloy anchors can become the solution for rotator cuff tendon repair surgery.

Can an in-person hands-on applicator-based teaching session improve trainee knowledge and comfort with complex gynecologic brachytherapy?

INTRODUCTIONWith the emergence of imaged-based planning and hybrid applicators the complexity of gynecologic brachytherapy has dramatically increased. Despite the known advantages of brachytherapy, notable national declines in utilization of brachytherapy have been documented. Clearly improved education in the sphere of gynecologic brachytherapy is needed. We hypothesize that a hands-on applicator-based training session would improve trainee comfort with gynecologic brachytherapy. METHODS AND MATERIALSAn in-person, applicator-based, hands-on training session was held with trainees from both radiation and gynecologic oncology programs. Trainees practiced assembling and handling applicators while receiving instruction on clinical scenarios in which various applicators are used in gynecologic cancer brachytherapy. Pre- and post-session, participants were administered an objective test of 10 pictorial-based case vignettes to quantify ability to select the correct applicator based on the interpretation of T2-weighted MR images. Participants additionally received a subjective survey to quantify comfort and experience with gynecologic brachytherapy using Likert-type question formatting. RESULTSA total of 14 trainees participated. Most common case volume experience was 0–10 intracavitary (57%), 0–10 hybrid (71%), and 0–10 interstitial (71%). Pre-session, the most common answer to comfort level was “not comfortable still learning” for all brachytherapy types, and most common answer to largest gap in knowledge was all facets of brachytherapy. Average case-based test score was 3.5/10 pre-session versus 5.3/10 post-session (p = 0.028). Post-session, all respondents reported improved comfort level with brachytherapy. Post-session, most common answer to largest gap in knowledge was applicator/patient selection, and applicator/patient selection was also the largest area of identified improvement. 100% of participants felt repeating the session in the future would be helpful. CONCLUSIONSHands-on training with applicators improves both subjective and objective comfort with gynecologic brachytherapy. With 100% of participants requesting to implement this session into resident training, we suggest national opportunities might exist to expand educational processes and improve utilization of complex gynecologic brachytherapy in practice.

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas

PurposePreoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved.MethodsContrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers.ResultsAfter comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71–0.84, 95% CI) based on T1-weighted images, 0.79 (0.72–0.84, 95% CI) for T2-weighted images, 0.88 (0.83–0.92, 95% CI) for CE-T1 images, and 0.88 (0.83–0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66–0.78, 95% CI), 0.84 (0.78–0.89, 95% CI) based on T2-WI, 0.74 (0.67–0.80, 95% CI) for CE-T1, and 0.81 (0.76–0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87.ConclusionsCNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

Arterial Spin-Labeling and DSC Perfusion Metrics Improve Agreement in Neuroradiologists' Clinical Interpretations of Posttreatment High-Grade Glioma Surveillance MR Imaging-An Institutional Experience.

MR perfusion has shown value in the evaluation of posttreatment high-grade gliomas, but few studies have shown its impact on the consistency and confidence of neuroradiologists' interpretation in routine clinical practice. We evaluated the impact of adding MR perfusion metrics to conventional contrast-enhanced MR imaging in posttreatment high-grade glioma surveillance imaging. This retrospective study included 45 adults with high-grade gliomas who had posttreatment perfusion MR imaging. Four neuroradiologists assigned Brain Tumor Reporting and Data System scores for each examination on the basis of the interpretation of contrast-enhanced MR imaging and then after the addition of arterial spin-labeling-CBF, DSC-relative CBV, and DSC-fractional tumor burden. Interrater agreement and rater agreement with a multidisciplinary consensus group were assessed with κ statistics. Raters used a 5-point Likert scale to report confidence scores. The frequency of clinically meaningful score changes resulting from the addition of each perfusion metric was determined. Interrater agreement was moderate for contrast-enhanced MR imaging alone (κ = 0.63) and higher with perfusion metrics (arterial spin-labeling-CBF, κ = 0.67; DSC-relative CBV, κ = 0.66; DSC-fractional tumor burden, κ = 0.70). Agreement between raters and consensus was highest with DSC-fractional tumor burden (κ = 0.66-0.80). Confidence scores were highest with DSC-fractional tumor burden. Across all raters, the addition of perfusion resulted in clinically meaningful interpretation changes in 2%-20% of patients compared with contrast-enhanced MR imaging alone. Adding perfusion to contrast-enhanced MR imaging improved interrater agreement, rater agreement with consensus, and rater confidence in the interpretation of posttreatment high-grade glioma MR imaging, with the highest agreement and confidence scores seen with DSC-fractional tumor burden. Perfusion MR imaging also resulted in interpretation changes that could change therapeutic management in up to 20% of patients.

Mucinous Rectal Adenocarcinoma-Challenges in Magnetic Resonance Imaging Interpretation.

Mucinous rectal cancer (MRC) is defined by the World Health Organization as an adenocarcinoma with greater than 50% mucin content. Classic teaching suggests that it carries a poorer prognosis than conventional rectal adenocarcinoma. This poorer prognosis is thought to be related to mucin dissecting through tissue planes at a higher rate, thus increasing the stage of disease at presentation. Developments in immunotherapy have bridged much of this prognostic gap in recent years. Magnetic resonance imaging is the leading modality in assessing the locoregional spread of rectal cancer. Mucinous rectal cancer carries unique imaging challenges when using this modality. Much of the difficulty lies in the inherent increased T2-weighted signal of mucin on magnetic resonance imaging. This creates difficulty in differentiating mucin from the adjacent background fat, making the detection of both the primary disease process as well as the locoregional spread challenging. Computed tomography scan can act as a valuable companion modality as mucin tends to be more apparent in the background fat. After therapy, diagnostic challenges remain. Mucin is frequently present, and distinguishing cellular from acellular mucin can be difficult. In this article, we will discuss each of these challenges and present examples of such situations and strategies that can be used to overcome them.

Food for Thought: A Review of Neuroradiographic Signs Inspired by Food

Whetting appetites for both knowledge and lunch, there is a rich history of naming signs in radiology after delectable treats. Built to entice even the most discerning palate, this collection of neuroradiologic signs is presented here to prove that learning neuroradiology can be as sweet as it is stimulating. Imaging signs are important to the practice of neuroradiology, a medical subspecialty whose work can influence the decisions of other clinicians. They can assist the physician by associating complex pathologic processes with common, recognizable images from everyday life. Signs may aid the neuroradiologist in the interpretation of MR imaging and other imaging studies by adding confidence to a diagnosis. Sugary treats apparently offer the most inspiration, causing radiology reads to make the ordering physician’s stomach grumble.Learning Objective: To recognize 9 food-inspired radiologic signs and identify the pathology behind them

Diffuse-Type Tenosynovial Giant Cell Tumor: What Are the Important Findings on the Initial and Follow-Up MRI?

Tenosynovial giant cell tumor (TSGCT) is a rare soft tissue tumor that involves the synovial lining of joints, bursae, and tendon sheaths, primarily affecting young patients (usually in the fourth decade of life). The tumor comprises two subtypes: the localized type (L-TSGCT) and the diffuse type (D-TSGCT). Although these subtypes share histological and genetic similarities, they present a different prognosis. D-TSGCT tends to exhibit local aggressiveness and a higher recurrence rate compared to L-TSGCT. Magnetic resonance imaging (MRI) is the preferred diagnostic tool for both the initial diagnosis and for treatment planning. When interpreting the initial MRI of a suspected TSGCT, it is essential to consider: (i) the characteristic findings of TSGCT-evident as low to intermediate signal intensity on both T1- and T2-weighted images, with a blooming artifact on gradient-echo sequences due to hemosiderin deposition; (ii) the possibility of D-TSGCT-extensive involvement of the synovial membrane with infiltrative margin; and (iii) the resectability and extent-if resectable, synovectomy is performed; if not, a novel systemic therapy involving colony-stimulating factor 1 receptor inhibitors is administered. In the interpretation of follow-up MRIs of D-TSGCTs after treatment, it is crucial to consider both tumor recurrence and potential complications such as osteoarthritis after surgery as well as the treatment response after systemic treatment. Given its prevalence in young adult patents and significant impact on patients' quality of life, clinical trials exploring new agents targeting D-TSGCT are currently underway. Consequently, understanding the characteristic MRI findings of D-TSGCT before and after treatment is imperative.

A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS.

MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS. This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model. The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications. Our machine learning model based on radiomics features successfully differentiated prelesions from normal-appearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.

Can a magnetic resonance imaging (MRI) of the temporomandibular joint predict disc perforation?

Background Magnetic resonance imaging (MRI) is widely regarded as the gold standard for diagnosing internal derangement; however, its accuracy in detecting disc perforations varies. Objectives: 1) identify MRI features correlating with disc perforation, 2) identify preoperative variables correlating with disc perforation, and 3) develop a predictive model to ascertain the presence of disc perforation. Materials and Methods This cohort study involved 92 patients who underwent arthroscopic surgery to identify associated variables. Arthroscopically verified disc perforation was considered the primary predictor variable, while MRI interpretation of disc perforation served as the primary outcome variable. Demographic data, patient signs, and symptoms were treated as covariates. Statistical analyses were conducted at a significance level of 5%. Logistic regression models were employed to predict disc perforation, and diagnostic accuracy was assessed using ROC curves. Results Among the 155 joints analyzed (92 patients: 135 females, 20 males), MRI interpretations did not significantly correlate with verified disc perforation (p < 0.218), demonstrating 14.3% sensitivity and 92.5% specificity. Age, symptomatic onset, joint noise type, osteoarthritis diagnosis, altered disc shape, and Wilkes classification exhibited significant associations (p < 0.05). A logistic regression model utilized associated variables for predicting disc perforation. Risk factors included symptom onset time, joint crepitus, osteoarthrosis diagnosis by MRI; joint click was considered protective. Conclusion The model demonstrated high diagnostic accuracy (AUC of 0.836, 95% CI 0.76–0.91). While MRI interpretation was unreliable, our model accurately predicted disc perforation. Identification of these factors could inform surgical decisions

Interpretation of Complete Tumor Response on MRI Following Chemoradiotherapy of Rectal Cancer: Inter-Reader Agreement and Associated Factors in Multi-Center Clinical Practice.

To measure inter-reader agreement and identify associated factors in interpreting complete response (CR) on magnetic resonance imaging (MRI) following chemoradiotherapy (CRT) for rectal cancer. This retrospective study involved 10 readers from seven hospitals with experience of 80-10210 cases, and 149 patients who underwent surgery after CRT for rectal cancer. Using MRI-based tumor regression grading (mrTRG) and methods employed in daily practice, the readers independently assessed mrTRG, CR on T2-weighted images (T2WI) denoted as mrCRT2W, and CR on all images including diffusion-weighted images (DWI) denoted as mrCRoverall. The readers described their interpretation patterns and how they utilized DWI. Inter-reader agreement was measured using multi-rater kappa, and associated factors were analyzed using multivariable regression. Correlation between sensitivity and specificity of each reader was analyzed using Spearman coefficient. The mrCRT2W and mrCRoverall rates varied widely among the readers, ranging 18.8%-40.3% and 18.1%-34.9%, respectively. Nine readers used DWI as a supplement sequence, which modified interpretations on T2WI in 2.7% of cases (36/1341 [149 patients × 9 readers]) and mostly (33/36) changed mrCRT2W to non-mrCRoverall. The kappa values for mrTRG, mrCRT2W, and mrCRoverall were 0.56 (95% confidence interval: 0.49, 0.62), 0.55 (0.52, 0.57), and 0.54 (0.51, 0.57), respectively. No use of rectal gel, larger initial tumor size, and higher initial cT stage exhibited significant association with a higher inter-reader agreement for assessing mrCRoverall (P ≤ 0.042). Strong negative correlations were observed between the sensitivity and specificity of individual readers (coefficient, -0.718 to -0.963; P ≤ 0.019). Inter-reader agreement was moderate for assessing CR on post-CRT MRI. Readers' varying standards on MRI interpretation (i.e., threshold effect), along with the use of rectal gel, initial tumor size, and initial cT stage, were significant factors associated with inter-reader agreement.

The sacroiliac joint across ages - what is normal?

The anatomy of the sacroiliac joint (SIJ) is complex with wide variations inter-individually as well as intra-individually (right versus left) and a frequent occurrence of anatomical variants. Besides, the joints are subject to strain, which may elicit non-inflammatory subchondral changes such as bone marrow edema (BME), sclerosis, and fat deposition simulating inflammatory SIJ changes. Furthermore, normal physiological changes during skeletal maturation can make interpretation of SIJ magnetic resonance imaging in children challenging. Knowledge about the wide range of normal findings is therefore important to avoid misinterpretation of findings as pathological. This review describes the current knowledge about normal SIJ findings across all ages.

A bibliometric analysis of using machine learning and artificial intelligence in prostate cancer detection

Prostate cancer stands as one of the most prevalent cancers globally among men, exhibiting substantial geographical variations in both incidence and mortality. While developed countries bear a higher incidence, developing countries grapple with elevated mortality rates. The heightened mortality in the latter is attributed to variations in practices that impede early diagnosis. In this context, the integration of artificial intelligence (AI) and machine learning (ML) has become increasingly common to improve the diagnostic accuracy of prostate cancer. This review delves into the existing literature to scrutinize the utilization of AI and ML in the diagnosis of prostate cancer. To compile relevant literature, comprehensive searches were conducted on research databases, including SCOPUS, Web of Science, and Google Scholar, to identify articles related to AI or ML (AI/ML) in the diagnosis and management of prostate cancer. Using a screening criterion, 293 reviewed research papers were identified. The two most consistent themes were predictive modeling and the application of AI/ML tools for cancer grading and radiomics. AI and ML enhance diagnostic accuracy by reducing inter-individual variation in Gleason’s scoring and complimenting the interpretation of multiparametric magnetic resonance imaging (mpMRI). A few publications reported the use of AI/ML tools that combine histopathology with MRI signals. The literature surveyed indicates a compelling potential for AI and ML to improve diagnostic accuracy in prostate cancer. Emerging literature suggests the use of a combination of demographic features, clinical data, serological markers, pathological grading and radiological factors, and genomic data to propose an accurate, non-invasive diagnosis of clinically significant prostate cancer.

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol.

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.

Evaluation of 'Normal' Cognitive Functions and Correlation With MRI Volumetry: Towards a Definition of Vascular Cognitive Impairment.

Introduction It is important to establish criteria to define vascular cognitive impairment (VCI) in India as VCI is an image-based diagnosis and magnetic resonance imaging (MRI) changes resulting from age with prevalent vascular risk factors may confound MRI interpretation. The objective of this study was to establish normative community data for MRI volumetryincluding white matter hyperintensity volume (WMHV), correlated with age-stratified cognitive scores and vascular risk factors (VRFs), in adults aged 40 years and above. Methods We screened 2651 individuals without known neurological morbidity, living in Mumbai and nearby rural areas, using validated Marathi translations of Kolkata Cognitive Battery (KCB) and geriatric depression score (GDS). We stratified 1961 persons with GDS ≤9 by age and cognitive score, and randomly selected 10% from each subgroup for MRI brain volumetry. Crude volumes were standardized to reflect percentage of intracranial volume. Results MRI volumetry studies were done in 199 individuals (F/M = 90/109; 73 with body mass index (BMI) ≥25; 44 hypertensives; 29 diabetics; mean cognitive score 76.3). Both grey and white matter volumes decreased with increasing age. WMHV increased with age and hypertension. Grey matter volume (GMV) decreased with increasing WMHV. Positive predictors of cognition included standardized hippocampal volume (HCV), urban living, education, and BMI, while WMHV and age were negative predictors. Urban dwellers had higher cognitive scores than rural, and, paradoxically, smaller HCV. Conclusion In this study of MRI volumetry correlated with age, cognitive scores and VRFs, increasing age and WMHV predicted lower cognitive scores, whereas urban living and hippocampal volume predicted higher scores. Age and WMHV also correlated with decreasing GMV. Further study is warranted into sociodemographic and biological factors that mutually influence cognition and brain volumes, including nutritional and endocrine factors, especially at lower cognitive score bands. In this study, at the lower KCB score bins, the lack of laboratory data pertaining to nutritional and endocrine deficiencies is a drawback that reflects the logistical limitations of screening large populations at the community level.Our volumetric data which is age and cognition stratified, and takes into account the vascular risk factors associated, nevertheless constitutesimportant baseline data for the Indian population. Our findings couldpossibly contribute to the formulation of baseline criteria for defining VCI inIndia and could help in early diagnosis and control of cognitive decline and its key risk factors.

An algorithm for the pre-operative differentiation of benign ovarian tumours based on magnetic resonance imaging interpretation in a regional core hospital: a retrospective study

ObjectiveFor selecting minimally invasive surgery (i.e. laparoscopic ovarian cystectomy) for treating ovarian tumours (OTs) in premenopausal patients, the pre-operative differentiation of benign ovarian tumours (Be-OTs) based on magnetic resonance imaging (MRI) interpretation is important. This paper describes the authors’ 8-year experience of approximately 1000 OT cases, and provides information about a diagnostic algorithm to help other hospitals. Study designThe medical records of 901 patients aged < 50 years with OTs from 1 January 2015–31 March 31 2023 were reviewed. First, the accuracy of pre-operative differentiation between Be-OTs and borderline/malignant ovarian tumours (Bo/Ma-OTs) was compared in each type of OT. Second, to identify the factors influencing differentiation between Be-OTs and Bo/Ma-OTs in 164 serous/mucinous ovarian tumours (SM-OTs), a multi-variate logistic regression analysis was performed to assess the effect of 13 factors, including MRI findings, OT size and tumour markers. ResultsIn the comparison of diagnostic accuracy of pre-operative MRI for each OT type, accuracy was found to be notably high for ovarian endometrial cyst (OEC) (n = 409), ovarian mature cystic teratoma (OMCT) (n = 308), ovarian endometrioid adenocarcinoma (OEA) (n = 6) and ovarian clear cell adenocarcinoma (OCCA) (n = 14). On the other hand, discrepancies between MRI and pathological findings often occurred in SM-OTs, including ovarian serous cystadenoma (n = 86), ovarian mucinous adenocarcinoma (n = 61), ovarian serous adenocarcinoma (n = 12) and ovarian mucinous adenocarcinoma (n = 5). In the multi-variate logistic regression analysis of the latter 164 patients, in addition to MRI findings, OT size and carbohydrate antigen 125 also had an effect to some extent. The combination of MRI interpretation and OT size may enhance differentiation of Be-OTs and Bo/Ma-OTs. ConclusionsAmong four types of OTs (OEC, OMCT, OEA and OCCA), MRI interpretation was able to differentiate between Be-OTs and Bo/Ma-OTs almost perfectly. Additionally, to mitigate the difficulty in differentiating SM-OTs, OT size may be useful in combination with MRI findings, although further accumulation and analysis of OT cases is needed.