Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI.

INTRODUCTION: Classification of clear cell renal cell carcinoma (ccRCC) growth rates in patients with Von Hippel-Lindau (VHL) syndrome has several ramifications for tumor monitoring and surgical planning. Using two separate machine-learning algorithms, we sought to produce models to predict ccRCC growth rate classes based on qualitative MRI-derived characteristics. MATERIAL AND METHODS: We used a prospectively maintained database of patients with VHL who underwent surgical resection for ccRCC between January 2015 and June 2022. We employed a threshold growth rate of 0.5 cm per year to categorize ccRCC tumors into two distinct groups-'slow-growing' and 'fast-growing'. Utilizing a questionnaire of qualitative imaging features, two radiologists assessed each lesion on different MRI sequences. Two machine-learning models, a stacked ensemble technique and a decision tree algorithm, were used to predict the tumor growth rate classes. Positive predictive value (PPV), sensitivity, and F1-score were used to evaluate the performance of the models. RESULTS: This study comprises 55 patients with VHL with 128 ccRCC tumors. Patients' median age was 48 years, and 28 patients were males. Each patient had an average of two tumors, with a median size of 2.1 cm and a median growth rate of 0.35 cm/year. The overall performance of the stacked and DT model had 0.77 ± 0.05 and 0.71 ± 0.06 accuracies, respectively. The best stacked model achieved a PPV of 0.92, a sensitivity of 0.91, and an F1-score of 0.90. CONCLUSION: This study provides valuable insight into the potential of machine-learning analysis for the determination of renal tumor growth rate in patients with VHL. This finding could be utilized as an assistive tool for the individualized screening and follow-up of this population.

Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results.

INTRODUCTION: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI. MATERIAL AND METHODS: We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP). RESULTS: A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72. CONCLUSION: Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers.

Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.

BACKGROUND: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC). PURPOSE: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment. STUDY TYPE: Retrospective analysis of a prospectively maintained cohort. POPULATION: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023. FIELD STRENGTH AND SEQUENCES: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences. ASSESSMENT: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures. STATISTICAL TESTS: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported. RESULTS: The best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported. DATA CONCLUSION: Radiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

The kidney imaging surveillance scoring system (KISSS): using qualitative MRI features to predict growth rate of renal tumors in patients with von-Hippel Lindau (VHL) syndrome.

OBJECTIVE: To determine the reliability of an MRI-based qualitative kidney imaging surveillance scoring system (KISSS) and assess which imaging features predict growth rate (GR) of renal tumors in patients with VHL. MATERIALS AND METHODS: We identified 55 patients with VHL with 128 renal tumors who underwent intervention from 2015 to 2020 at the National Cancer Institute. All patients had 2 preoperative MRIs at least 3 months apart. Two fellowship-trained radiologists scored each tumor on location and MR-sequence-specific imaging parameters from the earlier MRI. Weighted kappa was used to determine the degree of agreement between radiologists for each parameter. GR was calculated as the difference in maximum tumor dimension over time (cm/year). Differences in mean growth rate (MGR) within categories of each imaging variable were assessed by ANOVA. RESULTS: Apart from tumor margin and renal sinus, reliability was at least moderate (K > 0.40) for imaging parameters. Median initial tumor size was 2.1 cm, with average follow-up of 1.2 years. Tumor MGR was 0.42 cm/year. T2 hypointense, mixed/predominantly solid, and high restricted diffusion tumors grew faster. When comparing different combinations of these variables, the model with the lowest mean error among both radiologists utilized only solid/cystic and restricted diffusion features. CONCLUSIONS: We demonstrate a novel MR-based scoring system (KISSS) that has good precision with minimal training and can be applied to other qualitative radiology studies. A subset of imaging variables (T2 intensity; restricted diffusion; and solid/cystic) were independently associated with growth rate in VHL renal tumors, with the combination of the latter two most optimal. Additional validation, including in sporadic RCC population, is warranted.

Preoperative Renal Parenchyma Volume as a Predictor of Kidney Function Following Nephrectomy of Complex Renal Masses.

Background: The von Hippel-Lindau disease (VHL) is a hereditary cancer syndrome with multifocal, bilateral cysts and solid tumors of the kidney. Surgical management may include multiple extirpative surgeries, which ultimately results in parenchymal volume loss and subsequent renal function decline. Recent studies have utilized parenchyma volume as an estimate of renal function prior to surgery for renal cell carcinoma; however, it is not yet validated for surgically altered kidneys with multifocal masses and complex cysts such as are present in VHL. Objective: We sought to validate a magnetic resonance imaging (MRI)-based volumetric analysis with mercaptoacetyltriglycine (MAG-3) renogram and postoperative renal function. Design setting and participants: We identified patients undergoing renal surgery at the National Cancer Institute from 2015 to 2020 with preoperative MRI. Renal tumors, cysts, and parenchyma of the operated kidney were segmented manually using ITK-SNAP software. Outcome measurements and statistical analysis: Serum creatinine and urinalysis were assessed preoperatively, and at 3- and 12-mo follow-up time points. Estimated glomerular filtration rate (eGFR) was calculated using serum creatinine-based CKD-EPI 2021 equation. A statistical analysis was conducted on R Studio version 4.1.1. Results and limitations: Preoperative MRI scans of 113 VHL patients (56% male, median age 48 yr) were evaluated between 2015 and 2021. Twelve (10.6%) patients had a solitary kidney at the time of surgery; 59 (52%) patients had at least one previous partial nephrectomy on the renal unit. Patients had a median of three (interquartile range [IQR]: 2-5) tumors and five (IQR: 0-13) cysts per kidney on imaging. The median preoperative GFR was 70 ml/min/1.73 m2 (IQR: 58-89). Preoperative split renal function derived from MAG-3 studies and MRI split renal volume were significantly correlated (r = 0.848, p < 0.001). On the multivariable analysis, total preoperative parenchymal volume, solitary kidney, and preoperative eGFR were significant independent predictors of 12-mo eGFR. When only considering patients with two kidneys undergoing partial nephrectomy, preoperative parenchymal volume and eGFR remained significant predictors of 12-mo eGFR. Conclusions: A parenchyma volume analysis on preoperative MRI correlates well with renogram split function and can predict long-term renal function with added benefit of anatomic detail and ease of application. Patient summary: Prior to kidney surgery, it is important to understand the contribution of each kidney to overall kidney function. Nuclear medicine scans are currently used to measure split kidney function. We demonstrated that kidney volumes on preoperative magnetic resonance imaging can also be used to estimate split kidney function before surgery, while also providing essential details of tumor and kidney anatomy.

Outcomes of and indications for renal transplantation in patients with von Hippel Lindau disease.

INTRODUCTION AND OBJECTIVE: Von Hippel-Lindau (VHL) is a hereditary cancer syndrome characterized by bilateral, multifocal renal masses. The cumulative impact of extirpative surgery can depreciate renal function and render patients anephric. In the larger end-stage renal disease population, renal transplant offers both excellent quality of life and functional renal replacement. This case control study aims to examine and compare oncologic and functional outcomes of patients who have undergone renal transplant as renal replacement therapy (RRT) to those who remain anephric. METHODS: Patient charts were retrospectively reviewed of patients with germline testing confirmed VHL between 1980 and 2022 for transplant, all prior surgical history (within and outside the NCI), renal function and graft outcomes. Overall survival (OS) was determined from years after radical nephrectomy, and graft time was defined as years of graft function from initial transplant until failure or patient death. Graft survival was determined as time between transplant(s) to last follow up. Kaplan-Meier analysis was conducted to compare graft times of anephric VHL patients to those with transplanted kidneys. RESULTS: A total of 23 VHLD patients were identified as either anephric or candidates for transplant. Out of this cohort, 11 total VHLD received 12 total kidney grafts. Median wait time from nephrectomy to transplant was 22.6 months (IQR: 1.02-40.25 months). Median age at transplant was 32 years (IQR: 23-54 years). OS at 5 and 10 years of anephric patients who did not receive a transplant was 33% and 16.7%, respectively. OS rates of the transplant cohort at 10, 15, and 20 years were 91%, 78%, and 58% years, respectively. Median graft time was 161 months (IQR: 56-214 months). Graft survival at 10, 15, and 20 years was 69.8%, 69.8%, and 26.2%, respectively. CONCLUSIONS: We demonstrate that transplant recipients have decreased mortality with no difference in cancer recurrence compared to those who do not receive renal transplant for RRT. This data can aid in informing providers of the optimal window for early RRT planning in VHL, while also improving patient counseling.

Complications after Nephron-sparing Interventions for Renal Tumors: Imaging Findings and Management.

The two primary nephron-sparing interventions for treating renal masses such as renal cell carcinoma are surgical partial nephrectomy (PN) and image-guided percutaneous thermal ablation. Nephron-sparing surgery, such as PN, has been the standard of care for treating many localized renal masses. Although uncommon, complications resulting from PN can range from asymptomatic and mild to symptomatic and life-threatening. These complications include vascular injuries such as hematoma, pseudoaneurysm, arteriovenous fistula, and/or renal ischemia; injury to the collecting system causing urinary leak; infection; and tumor recurrence. The incidence of complications after any nephron-sparing surgery depends on many factors, such as the proximity of the tumor to blood vessels or the collecting system, the skill or experience of the surgeon, and patient-specific factors. More recently, image-guided percutaneous renal ablation has emerged as a safe and effective treatment option for small renal tumors, with comparable oncologic outcomes to those of PN and a low incidence of major complications. Radiologists must be familiar with the imaging findings encountered after these surgical and image-guided procedures, especially those indicative of complications. The authors review cross-sectional imaging characteristics of complications after PN and image-guided thermal ablation of kidney tumors and highlight the respective management strategies, ranging from clinical observation to interventions such as angioembolization or repeat surgery. Work of the U.S. Government published under an exclusive license with the RSNA. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available in the Online Learning Center. See the invited commentary by Chung and Raman in this issue.

Deep learning-based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI.

BACKGROUND: von Hippel-Lindau syndrome (VHL) is an autosomal dominant hereditary syndrome with an increased predisposition of developing numerous cysts and tumors, almost exclusively clear cell renal cell carcinoma (ccRCC). Considering the lifelong surveillance in such patients to monitor the disease, patients with VHL are preferentially imaged using MRI to eliminate radiation exposure. PURPOSE: Segmentation of kidney and tumor structures on MRI in VHL patients is useful in lesion characterization (e.g., cyst vs. tumor), volumetric lesion analysis, and tumor growth prediction. However, automated tasks such as ccRCC segmentation on MRI is sparsely studied. We develop segmentation methodology for ccRCC on T1 weighted precontrast, corticomedullary, nephrogenic, and excretory contrast phase MRI. METHODS: We applied a new neural network approache using a novel differentiable decision forest, called hinge forest (HF), to segment kidney parenchyma, cyst, and ccRCC tumors in 117 images from 115 patients. This data set represented an unprecedented 504 ccRCCs with 1171 cystic lesions obtained at five different MRI scanners. The HF architecture was compared with U-Net on 10 randomized splits with 75% used for training and 25% used for testing. Both methods were trained with Adam using default parameters ( α = 0.001 , ß 1 = 0.9 , ß 2 = 0.999 $\alpha = 0.001,\ \beta _1 = 0.9,\ \beta _2 = 0.999$ ) over 1000 epochs. We further demonstrated some interpretability of our HF method by exploiting decision tree structure. RESULTS: The HF achieved an average kidney, cyst, and tumor Dice similarity coefficient (DSC) of 0.75 ± 0.03, 0.44 ± 0.05, 0.53 ± 0.04, respectively, while U-Net achieved an average kidney, cyst, and tumor DSC of 0.78 ± 0.02, 0.41 ± 0.04, 0.46 ± 0.05, respectively. The HF significantly outperformed U-Net on tumors while U-Net significantly outperformed HF when segmenting kidney parenchymas ( α < 0.01 $\alpha < 0.01$ ). CONCLUSIONS: For the task of ccRCC segmentation, the HF can offer better segmentation performance compared to the traditional U-Net architecture. The leaf maps can glean hints about deep learning features that might prove to be useful in other automated tasks such as tumor characterization.

Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images.

We demonstrate automated segmentation of clear cell renal cell carcinomas (ccRCC), cysts, and surrounding normal kidney parenchyma in patients with von Hippel-Lindau (VHL) syndrome using convolutional neural networks (CNN) on Magnetic Resonance Imaging (MRI). We queried 115 VHL patients and 117 scans (3 patients have two separate scans) with 504 ccRCCs and 1171 cysts from 2015 to 2021. Lesions were manually segmented on T1 excretory phase, co-registered on all contrast-enhanced T1 sequences and used to train 2D and 3D U-Net. The U-Net performance was evaluated on 10 randomized splits of the cohort. The models were evaluated using the dice similarity coefficient (DSC). Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0.88 and DSC scores of 0.78, 0.40, and 0.46 for segmentation of the kidney, cysts, and tumors, respectively. Our 3D U-Net achieved an average ccRCC lesion detection AUC of 0.79 and DSC scores of 0.67, 0.32, and 0.34 for kidney, cysts, and tumors, respectively. We demonstrated good detection and moderate segmentation results using U-Net for ccRCC on MRI. Automatic detection and segmentation of normal renal parenchyma, cysts, and masses may assist radiologists in quantifying the burden of disease in patients with VHL.

ouR Space Commentary: Wellness Perspective From SABI's Early Career Committee.

ABSTRACT: Radiologists and members-in-training are experiencing higher (and escalating) rates of burnout, resulting in a profound impact on the health of physicians, patients, and the community. Lately, the radiology community has demonstrated a growing awareness of this phenomenon, which has led to emphasis on practicing and promoting wellness. With a myriad of factors contributing to burnout in radiology, a multifaceted approach is pivotal for counteracting burnout and fostering overall well-being, including efforts driven at both organizational and individual levels. This article discusses perspectives from the members of the Early Career Committee at the Society for Advanced Body Imaging (SABI); it explores their beliefs and practical strategies for maintaining personal well-being.

Role of ultra-high b-value DWI in the imaging of hereditary leiomyomatosis and renal cell carcinoma (HLRCC).

PURPOSE: Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome is associated with an aggressive form of renal cell carcinoma with high risk of metastasis, even in small primary tumors with unequivocal imaging findings. In this study, we compare the performance of ultra-high b-value diffusion-weighted imaging (DWI) sequence (b = 2000 s/mm2) to standard DWI (b = 800 s/mm2) sequence in identifying malignant lesions in patients with HLRCC. METHODS: Twenty-eight patients (n = 18 HLRCC patients with 22 lesions, n = 10 controls) were independently evaluated by three abdominal radiologists with different levels of experience using four combinations of MRI sequences in two separate sessions (session 1: DWI with b-800, session 2: DWI with b-2000). T1 precontrast, T2-weighted (T2WI), and apparent diffusion coefficient (ADC) sequences were similar in both sessions. Each identified lesion was subjectively assessed using a six-point cancer likelihood score based on individual sequences and overall impression. RESULTS: The ability to distinguish benign versus malignant renal lesions improved with the use of b-2000 for more experienced radiologists (Reader 1 AUC: Session 1-0.649 and Session 2-0.938, p = 0.017; Reader 2 AUC: Session 1-0.781 and Session 2-0.921, p = 0.157); whereas no improvement was observed for the less experienced reader (AUC: Session 1-0.541 and Session 2-0.607, p = 0.699). CONCLUSION: The inclusion of ultra-high b-value DWI sequence improved the ability of classification of renal lesions in patients with HLRCC for experienced radiologists. Consideration should be given toward incorporation of DWI with b-2000 s/mm2 into existing renal MRI protocols.

An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome.

PURPOSE: Upfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors. MATERIALS AND METHODS: A total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT < = 1 year and DT > 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs). RESULTS: The average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4-90.6] and mean AUC-ROC of 0.795 [0.608-0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56). CONCLUSION: Here we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences.

Treatment Strategies for Hereditary Kidney Cancer: Current Recommendations and Updates.

A subset of renal tumors (5-8%) are associated with syndromes such as von Hippel-Lindau (VHL) syndrome, Birt-Hogg-Dubé syndrome (BHD), tuberous sclerosis complex (TSC), hereditary papillary renal carcinoma (HPRC), hereditary leiomyomatosis and renal cell cancer syndrome (HLRCC), and BRCA1 associated protein (BAP1) tumor predisposition syndrome, succinate dehydrogenase RCC (SDHB/C/D). These syndromes have their specific defined genetic alterations and associated extrarenal manifestations. Due to varying histopathology and aggressiveness of the tumors amongst these syndromes, the management strategies can range from active surveillance to upfront surgical resection. This review delineates specific characteristics of the most common familial renal cancer syndromes and discusses current management strategies.

Morphologic Features of the Distal Femur and Proximal Tibia: A Cross-Sectional Study.

Background The asymmetric medial and lateral condyles of the distal femur and proximal tibia have a direct influence on the biomechanics of knee joint and prostheses design. This study aimed to determine the morphologic data, that is., anteroposterior (AP) and mediolateral (ML) widths, and the radius of curvature (ROC) of the geometric arcs of the distal femur and proximal tibia. Methods One hundred and seventeen adult dry bones (57 femurs and 60 tibias) were studied. Aspect ratios (AP/ML) were calculated. The AP and ML widths were measured using digital Vernier Caliper with a measuring range of 0-150 mm, resolution of 0.01 mm, and accuracy ± 0.02 mm. The geometric arcs of femoral and tibial condyles were divided into three parts namely anterior 1/3rd, distal (femur) or middle (tibia) 1/3rd and posterior 1/3rd and were estimated in the sagittal plane for the femur and transverse plane for tibia using the ROC gauges. Results For the femur, the mean AP length for medial and lateral condyles was 55.62 mm and 57.93 mm, respectively, while the mean ML width was 73.45 mm. For the tibia, the mean AP length for medial condyle (MC) and lateral condyle (LC) was 47.74 mm and 43.46 mm, respectively. The mean aspect ratios for the distal femur and proximal tibia were 1.26 and 1.45, respectively. The mean aspect ratios for MC and LC of the femur were 0.50 and 0.52, respectively, whereas, for tibia, they were 0.61 and 0.71, respectively. The mean ROC for femoral MC - 20.77 mm, 31.42 mm, and 19.68 mm and for LC - 21.48 mm, 64.40 mm and 19.06 mm for the anterior, distal and posterior arcs, respectively. The mean ROC for tibial MC - 22.42 mm, 22.49 mm and 19.94 mm, and LC - 19.92 mm, 21.79 mm and 20.95 mm for the anterior, middle and posterior arcs, respectively. Conclusions The morphologic data accumulated in this study for both the distal femur as well as the proximal tibia would provide guidelines and help the manufacturers of joint prostheses to address the potential for compromised implant fit and re-design and make available 'anatomic' knee prostheses appropriate for the local population which would not only improve function but also prolong the longevity of the prostheses.

Correlation Between the Thickness of Anterolateral Ligament and Lateral Collateral Ligament of the Knee.

Background Persistent anterolateral rotatory instability (ALRI) following the anterior cruciate ligament (ACL) reconstruction has led to a renewed interest in defining the role of anterolateral complex (ALC) of the knee. Methods We explored the anterolateral corner of 34 cadaveric knees to define the anterolateral ligament (ALL) in all its dimensions and measured the thickness of lateral collateral ligament (LCL) at the lateral meniscus level (tLCL) in ALL-intact and ALL-deficient knees. Results ALL was present in 27/34 (79%) of the knees. We found complete ALL in 13 cadavers bilaterally. ALL was absent bilaterally in three cadavers; it was present on one side and absent contralaterally in one cadaver. In ALL-intact knees, the average tLCL was 2.05 mm, whereas, in ALL-deficient knees, it was 2.57 mm. This difference in tLCL was statistically significant. Conclusions Our study adds new data to the recent voluminous research on ALL. We have examined the correlation between the thickness of ALL and LCL and documented alterations in the thickness of LCL in ALL-intact knees. These findings would help in designing reconstructive procedures for the combined ACL injury with ALRI.

Endovascular treatment of arteriovenous graft dysfunction as a result of graft delamination and fracture.

BACKGROUND: Graft thrombosis due to fabric delamination is a rare cause of delayed failure of arteriovenous grafts. Graft delamination is primarily an imaging diagnosis and is confirmed with the help of ultrasound which shows the separation of graft fabric layers. Only two such cases have been described in the literature so far. CASE PRESENTATION: We present a case of upper extremity arteriovenous graft thrombosis in a 79 year old COVID-19 positive patient with end-stage renal disease. The diagnosis was established on ultrasonography which revealed separation of the graft fabric layers with thrombosis within the "false" and "true" lumen of the graft. The patient was managed with angioplasty and embolectomy of the clot material followed by stent-graft placement across the delaminated portion of the graft. Post-procedural angiography confirmed brisk flow across the graft and patient could successfully have subsequent hemodialysis sessions. CONCLUSIONS: Identification of graft delamination as a cause of graft failure is important as its management differs from other conventional causes since it requires stent-grafts to cover the area of delamination to re-establish flow and salvage the AV graft. The recognition of this phenomenon is essential to provide quality care and successful reuse of the AV graft. LEVEL OF EVIDENCE: Level 4, Case Report.

Quadruple Score: A Novel Scoring System for Diagnosing Breast Lump - A Retrospective Pilot Study.

BACKGROUND: Triple test was a breakthrough in the initial screening of breast cancer due to its high sensitivity, but it has few limitations. To overcome those limitations, we developed a novel quadruple score which included physical examination, ultrasound, mammography, and fine-needle aspiration cytology (FNAC). AIM: The aim of this study was to test the sensitivity, specificity, negative predictive value (NPV), and positive predictive value of a novel quadruple score for the initial screening of breast cancer. METHODOLOGY: This retrospective study was conducted on 320 patients having breast lump to the Breast Cancer Outpatient Department of AIIMS, Bhopal. The physical examination findings, breast sonography findings, mammography, and FNAC findings were recorded in the set pro forma, these were later analyzed, and a cumulative scoring was done categorizing patients in the benign, dubious, and malignant category according to the QTS score. These findings were then validated and compared by histopathology. RESULTS: Statistical results show a positive covariance σ suggesting the positive relation between them; also, the correlation coefficient was 0.9996 suggesting a strong linear correlation. On comparing the accuracy of all four variables as physical examination, USG, mammography and FNAC individually and QTS, the accuracy of QTS was highest i.e 99.44%. CONCLUSION: QTS is accurate and reliable diagnostic approach for evaluation of breast lumps. By using the quadruple scoring system, management of palpable breast lump will become more streamlined, providing a platform for managing discordant results, which can be followed universally.

Role of microRNAs in stroke recovery.

Ischemic stroke is an important cause of morbidity and mortality across the globe. It is associated with physical, social, and economic disability. Immediately after the stroke, there is a critical period of spontaneous recovery during which there is maximal return to near normal. Following stroke, there is a period of neurogenesis, angiogenesis, axonal growth, and synaptic plasticity. There have been several studies focusing on neuroprotection and enhancing recovery following stroke. However, translation of these into clinical practice has been disappointing. The role of microRNAs in treatment of cancer has been well documented, but studying their role in stroke recovery has been minimal. MicroRNAs serve as critical mediators of recovery following stroke. In this review article, we discuss the role of microRNAs in stroke recovery.