Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4).

Deep learning applied to whole-slide histopathology images (WSIs) has the potential to enhance precision oncology and alleviate the workload of experts. However, developing these models necessitates large amounts of data with ground truth labels, which can be both time-consuming and expensive to obtain. Pathology reports are typically unstructured or poorly structured texts, and efforts to implement structured reporting templates have been unsuccessful, as these efforts lead to perceived extra workload. In this study, we hypothesised that large language models (LLMs), such as the generative pre-trained transformer 4 (GPT-4), can extract structured data from unstructured plain language reports using a zero-shot approach without requiring any re-training. We tested this hypothesis by utilising GPT-4 to extract information from histopathological reports, focusing on two extensive sets of pathology reports for colorectal cancer and glioblastoma. We found a high concordance between LLM-generated structured data and human-generated structured data. Consequently, LLMs could potentially be employed routinely to extract ground truth data for machine learning from unstructured pathology reports in the future. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization.

Background The level of background parenchymal enhancement (BPE) at breast MRI provides predictive and prognostic information and can have diagnostic implications. However, there is a lack of standardization regarding BPE assessment. Purpose To investigate how well results of quantitative BPE assessment methods correlate among themselves and with assessments made by radiologists experienced in breast MRI. Materials and Methods In this pseudoprospective analysis of 5773 breast MRI examinations from 3207 patients (mean age, 60 years ± 10 [SD]), the level of BPE was prospectively categorized according to the Breast Imaging Reporting and Data System by radiologists experienced in breast MRI. For automated extraction of BPE, fibroglandular tissue (FGT) was segmented in an automated pipeline. Four different published methods for automated quantitative BPE extractions were used: two methods (A and B) based on enhancement intensity and two methods (C and D) based on the volume of enhanced FGT. The results from all methods were correlated, and agreement was investigated in comparison with the respective radiologist-based categorization. For surrogate validation of BPE assessment, how accurately the methods distinguished premenopausal women with (n = 50) versus without (n = 896) antihormonal treatment was determined. Results Intensity-based methods (A and B) exhibited a correlation with radiologist-based categorization of 0.56 ± 0.01 and 0.55 ± 0.01, respectively, and volume-based methods (C and D) had a correlation of 0.52 ± 0.01 and 0.50 ± 0.01 (P < .001). There were notable correlation differences (P < .001) between the BPE determined with the four methods. Among the four quantitation methods, method D offered the highest accuracy for distinguishing women with versus without antihormonal therapy (P = .01). Conclusion Results of different methods for quantitative BPE assessment agree only moderately among themselves or with visual categories reported by experienced radiologists; intensity-based methods correlate more closely with radiologists' ratings than volume-based methods. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Mann in this issue.

The virtual reference radiologist: comprehensive AI assistance for clinical image reading and interpretation.

OBJECTIVES: Large language models (LLMs) have shown potential in radiology, but their ability to aid radiologists in interpreting imaging studies remains unexplored. We investigated the effects of a state-of-the-art LLM (GPT-4) on the radiologists' diagnostic workflow. MATERIALS AND METHODS: In this retrospective study, six radiologists of different experience levels read 40 selected radiographic [n = 10], CT [n = 10], MRI [n = 10], and angiographic [n = 10] studies unassisted (session one) and assisted by GPT-4 (session two). Each imaging study was presented with demographic data, the chief complaint, and associated symptoms, and diagnoses were registered using an online survey tool. The impact of Artificial Intelligence (AI) on diagnostic accuracy, confidence, user experience, input prompts, and generated responses was assessed. False information was registered. Linear mixed-effect models were used to quantify the factors (fixed: experience, modality, AI assistance; random: radiologist) influencing diagnostic accuracy and confidence. RESULTS: When assessing if the correct diagnosis was among the top-3 differential diagnoses, diagnostic accuracy improved slightly from 181/240 (75.4%, unassisted) to 188/240 (78.3%, AI-assisted). Similar improvements were found when only the top differential diagnosis was considered. AI assistance was used in 77.5% of the readings. Three hundred nine prompts were generated, primarily involving differential diagnoses (59.1%) and imaging features of specific conditions (27.5%). Diagnostic confidence was significantly higher when readings were AI-assisted (p > 0.001). Twenty-three responses (7.4%) were classified as hallucinations, while two (0.6%) were misinterpretations. CONCLUSION: Integrating GPT-4 in the diagnostic process improved diagnostic accuracy slightly and diagnostic confidence significantly. Potentially harmful hallucinations and misinterpretations call for caution and highlight the need for further safeguarding measures. CLINICAL RELEVANCE STATEMENT: Using GPT-4 as a virtual assistant when reading images made six radiologists of different experience levels feel more confident and provide more accurate diagnoses; yet, GPT-4 gave factually incorrect and potentially harmful information in 7.4% of its responses.

Dynamic assessment of scapholunate ligament status by real-time magnetic resonance imaging: an exploratory clinical study.

OBJECTIVE: Clinical-standard MRI is the imaging modality of choice for the wrist, yet limited to static evaluation, thereby potentially missing dynamic instability patterns. We aimed to investigate the clinical benefit of (dynamic) real-time MRI, complemented by automatic analysis, in patients with complete or partial scapholunate ligament (SLL) tears. MATERIAL AND METHODS: Both wrists of ten patients with unilateral SLL tears (six partial, four complete tears) as diagnosed by clinical-standard MRI were imaged during continuous active radioulnar motion using a 1.5-T MRI scanner in combination with a custom-made motion device. Following automatic segmentation of the wrist, the scapholunate and lunotriquetral joint widths were analyzed across the entire range of motion (ROM). Mixed-effects model analysis of variance (ANOVA) followed by Tukey's posthoc test and two-way ANOVA were used for statistical analysis. RESULTS: With the increasing extent of SLL tear, the scapholunate joint widths in injured wrists were significantly larger over the entire ROM compared to those of the contralateral healthy wrists (p<0.001). Differences between partial and complete tears were most pronounced at 5°-15° ulnar abduction (p<0.001). Motion patterns and trajectories were altered. Complete SLL deficiency resulted in complex alterations of the lunotriquetral joint widths. CONCLUSION: Real-time MRI may improve the functional diagnosis of SLL insufficiency and aid therapeutic decision-making by revealing dynamic forms of dissociative instability within the proximal carpus. Static MRI best differentiates SLL-injured wrists at 5°-15° of ulnar abduction.

An MRI Deep Learning Model Predicts Outcome in Rectal Cancer.

Background Deep learning (DL) models can potentially improve prognostication of rectal cancer but have not been systematically assessed. Purpose To develop and validate an MRI DL model for predicting survival in patients with rectal cancer based on segmented tumor volumes from pretreatment T2-weighted MRI scans. Materials and Methods DL models were trained and validated on retrospectively collected MRI scans of patients with rectal cancer diagnosed between August 2003 and April 2021 at two centers. Patients were excluded from the study if there were concurrent malignant neoplasms, prior anticancer treatment, incomplete course of neoadjuvant therapy, or no radical surgery performed. The Harrell C-index was used to determine the best model, which was applied to internal and external test sets. Patients were stratified into high- and low-risk groups based on a fixed cutoff calculated in the training set. A multimodal model was also assessed, which used DL model-computed risk score and pretreatment carcinoembryonic antigen level as input. Results The training set included 507 patients (median age, 56 years [IQR, 46-64 years]; 355 men). In the validation set (n = 218; median age, 55 years [IQR, 47-63 years]; 144 men), the best algorithm reached a C-index of 0.82 for overall survival. The best model reached hazard ratios of 3.0 (95% CI: 1.0, 9.0) in the high-risk group in the internal test set (n = 112; median age, 60 years [IQR, 52-70 years]; 76 men) and 2.3 (95% CI: 1.0, 5.4) in the external test set (n = 58; median age, 57 years [IQR, 50-67 years]; 38 men). The multimodal model further improved the performance, with a C-index of 0.86 and 0.67 for the validation and external test set, respectively. Conclusion A DL model based on preoperative MRI was able to predict survival of patients with rectal cancer. The model could be used as a preoperative risk stratification tool. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Langs in this issue.

Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images.

Background Reducing the amount of contrast agent needed for contrast-enhanced breast MRI is desirable. Purpose To investigate if generative adversarial networks (GANs) can recover contrast-enhanced breast MRI scans from unenhanced images and virtual low-contrast-enhanced images. Materials and Methods In this retrospective study of breast MRI performed from January 2010 to December 2019, simulated low-contrast images were produced by adding virtual noise to the existing contrast-enhanced images. GANs were then trained to recover the contrast-enhanced images from the simulated low-contrast images (approach A) or from the unenhanced T1- and T2-weighted images (approach B). Two experienced radiologists were tasked with distinguishing between real and synthesized contrast-enhanced images using both approaches. Image appearance and conspicuity of enhancing lesions on the real versus synthesized contrast-enhanced images were independently compared and rated on a five-point Likert scale. P values were calculated by using bootstrapping. Results A total of 9751 breast MRI examinations from 5086 patients (mean age, 56 years ± 10 [SD]) were included. Readers who were blinded to the nature of the images could not distinguish real from synthetic contrast-enhanced images (average accuracy of differentiation: approach A, 52 of 100; approach B, 61 of 100). The test set included images with and without enhancing lesions (29 enhancing masses and 21 nonmass enhancement; 50 total). When readers who were not blinded compared the appearance of the real versus synthetic contrast-enhanced images side by side, approach A image ratings were significantly higher than those of approach B (mean rating, 4.6 ± 0.1 vs 3.0 ± 0.2; P < .001), with the noninferiority margin met by synthetic images from approach A (P < .001) but not B (P > .99). Conclusion Generative adversarial networks may be useful to enable breast MRI with reduced contrast agent dose. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bahl in this issue.

Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers.

Background Clinicians consider both imaging and nonimaging data when diagnosing diseases; however, current machine learning approaches primarily consider data from a single modality. Purpose To develop a neural network architecture capable of integrating multimodal patient data and compare its performance to models incorporating a single modality for diagnosing up to 25 pathologic conditions. Materials and Methods In this retrospective study, imaging and nonimaging patient data were extracted from the Medical Information Mart for Intensive Care (MIMIC) database and an internal database comprised of chest radiographs and clinical parameters inpatients in the intensive care unit (ICU) (January 2008 to December 2020). The MIMIC and internal data sets were each split into training (n = 33 893, n = 28 809), validation (n = 740, n = 7203), and test (n = 1909, n = 9004) sets. A novel transformer-based neural network architecture was trained to diagnose up to 25 conditions using nonimaging data alone, imaging data alone, or multimodal data. Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC) analysis. Results The MIMIC and internal data sets included 36 542 patients (mean age, 63 years ± 17 [SD]; 20 567 male patients) and 45 016 patients (mean age, 66 years ± 16; 27 577 male patients), respectively. The multimodal model showed improved diagnostic performance for all pathologic conditions. For the MIMIC data set, the mean AUC was 0.77 (95% CI: 0.77, 0.78) when both chest radiographs and clinical parameters were used, compared with 0.70 (95% CI: 0.69, 0.71; P < .001) for only chest radiographs and 0.72 (95% CI: 0.72, 0.73; P < .001) for only clinical parameters. These findings were confirmed on the internal data set. Conclusion A model trained on imaging and nonimaging data outperformed models trained on only one type of data for diagnosing multiple diseases in patients in an ICU setting. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kitamura and Topol in this issue.

Artificial Intelligence for Clinical Interpretation of Bedside Chest Radiographs.

Background Supine chest radiography for bedridden patients in intensive care units (ICUs) is one of the most frequently ordered imaging studies worldwide. Purpose To evaluate the diagnostic performance of a neural network-based model that is trained on structured semiquantitative radiologic reports of bedside chest radiographs. Materials and Methods For this retrospective single-center study, children and adults in the ICU of a university hospital who had been imaged using bedside chest radiography from January 2009 to December 2020 were reported by using a structured and itemized template. Ninety-eight radiologists rated the radiographs semiquantitatively for the severity of disease patterns. These data were used to train a neural network to identify cardiomegaly, pulmonary congestion, pleural effusion, pulmonary opacities, and atelectasis. A held-out internal test set (100 radiographs from 100 patients) that was assessed independently by an expert panel of six radiologists provided the ground truth. Individual assessments by each of these six radiologists, by two nonradiologist physicians in the ICU, and by the neural network were compared with the ground truth. Separately, the nonradiologist physicians assessed the images without and with preliminary readings provided by the neural network. The weighted Cohen κ coefficient was used to measure agreement between the readers and the ground truth. Results A total of 193 566 radiographs in 45 016 patients (mean age, 66 years ± 16 [SD]; 61% men) were included and divided into training (n = 122 294; 64%), validation (n = 31 243; 16%), and test (n = 40 029; 20%) sets. The neural network exhibited higher agreement with a majority vote of the expert panel (κ = 0.86) than each individual radiologist compared with the majority vote of the expert panel (κ = 0.81 to ≤0.84). When the neural network provided preliminary readings, the reports of the nonradiologist physicians improved considerably (aided vs unaided, κ = 0.87 vs 0.79, respectively; P < .001). Conclusion A neural network trained with structured semiquantitative bedside chest radiography reports allowed nonradiologist physicians improved interpretations compared with the consensus reading of expert radiologists. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

Biomechanical analysis of helical versus straight plating of proximal third humeral shaft fractures.

BACKGROUND: Proximal humeral shaft fractures are surgically challenging and plate osteosynthesis with a long straight plate is one operative treatment option in these patients although endangering the radial nerve distally. Helical plates potentially avoid the radial nerve by twisting around the humeral shaft. Aim of the study was to investigate in a human cadaveric model the biomechanical competence of helical plates versus straight lateral plates used for fixation of proximal third comminuted humeral shaft fractures. METHODS: Eight pairs of humeral cadaveric humeri were instrumented using either a long 90°-helical plate (Group1) or a straight long PHILOS plate (Group2). An unstable proximal humeral shaft fracture was simulated by means of a 5 cm osteotomy gap. All specimens were tested under quasi-static loading in axial compression, internal and external rotation, and bending in four directions. Subsequently, progressively increasing cyclic loading in internal rotation until failure was applied and interfragmentary movements were monitored by motion tracking. RESULTS: During static testing flexion/extension deformation in Group1 was significantly higher, however, varus/valgus deformation as well as shear and torsional displacement under torsional load remained statistically indifferent between both groups. During cyclic testing shear and torsional displacements were both significantly higher in Group1 compared to Group 2. However, cycles to catastrophic failure remained statistically indifferent between the groups. CONCLUSIONS: From a biomechanical perspective, although 90°-helical plating is associated with higher initial stability against varus/valgus collapse and comparable endurance under dynamic loading, it demonstrates lower resistance to flexion/extension and internal rotation with bigger shear interfragmentary displacements versus straight lateral plating and, therefore, cannot be considered as its real alternative. Alternative helical plate designs should be investigated in the future.

Lorentzian-Corrected Apparent Exchange-Dependent Relaxation (LAREX) Ω-Plot Analysis-An Adaptation for qCEST in a Multi-Pool System: Comprehensive In Silico, In Situ, and In Vivo Studies.

Based on in silico, in situ, and in vivo studies, this study aims to develop a new method for the quantitative chemical exchange saturation transfer (qCEST) technique considering multi-pool systems. To this end, we extended the state-of-the-art apparent exchange-dependent relaxation (AREX) method with a Lorentzian correction (LAREX). We then validated this new method with in situ and in vivo experiments on human intervertebral discs (IVDs) using the Kendall-Tau correlation coefficient. In the in silico experiments, we observed significant deviations of the AREX method as a function of the underlying exchange rate (kba) and fractional concentration (fb) compared to the ground truth due to the influence of other exchange pools. In comparison to AREX, the LAREX-based Ω-plot approach yielded a substantial improvement. In the subsequent in situ and in vivo experiments on human IVDs, no correlation to the histological reference standard or Pfirrmann classification could be found for the fb (in situ: τ = −0.17 p = 0.51; in vivo: τ = 0.13 p = 0.30) and kba (in situ: τ = 0.042 p = 0.87; in vivo: τ = −0.26 p = 0.04) of Glycosaminoglycan (GAG) with AREX. In contrast, the influence of interfering pools could be corrected by LAREX, and a moderate to strong correlation was observed for the fractional concentration of GAG for both in situ (τ = −0.71 p = 0.005) and in vivo (τ = −0.49 p < 0.001) experiments. The study presented here is the first to introduce a new qCEST method that enables qCEST imaging in systems with multiple proton pools.

Sodium MRI of human articular cartilage of the wrist: a feasibility study on a clinical 3T MRI scanner.

OBJECTIVE: To measure sodium relaxation times and concentrations in human wrists on a clinical magnetic resonance imaging (MRI) scanner with a density-adapted radial sequence. MATERIALS AND METHODS: Sodium MRI of human wrists was conducted on a 3T MR system using a dual-tuned 1H/23Na surface coil. We performed two studies with 10 volunteers each investigating either sodium T1 (study 1) or sodium T2* (study 2) relaxation times in the radiocarpal joint (RCJ) and midcarpal joint (MCJ). Sodium concentrations of both regions were determined. RESULTS: No differences for transversal of longitudinal relaxation times were found between RCJ and MCJ (T2,s*(RCJ) = (0.9 ± 0.4) ms; T2,s*(MCJ) = (0.9 ± 0.3) ms; T2,l*(RCJ) = (14.9 ± 0.9) ms; T2,l*(MCJ) = (13.9 ± 1.1) ms; T1(RCJ) = (19.0 ± 2.4) ms; T1(MCJ) = (18.5 ± 2.1) ms). Sodium concentrations were (157.7 ± 28.4) mmol/l for study 1 and (159.8 ± 29.1) mmol/l for study 2 in the RCJ, and (172.7 ± 35.6) mmol/l for study 1 and (163.4 ± 26.3) mmol/l for study 2 in the MCJ. CONCLUSION: We successfully determined sodium relaxation times and concentrations of the human wrist on a 3T MRI scanner.

Detection of early cartilage degeneration in the tibiotalar joint using 3 T gagCEST imaging: a feasibility study.

OBJECTIVE: To establish and optimize a stable 3 Tesla (T) glycosaminoglycan chemical exchange saturation transfer (gagCEST) imaging protocol for assessing the articular cartilage of the tibiotalar joint in healthy volunteers and patients after a sustained injury to the ankle. METHODS: Using Bloch-McConnell simulations, we optimized the sequence protocol for a 3 T MRI scanner for maximum gagCEST effect size within a clinically feasible time frame of less than 07:30 min. This protocol was then used to analyze the gagCEST effect of the articular cartilage of the tibiotalar joint of 17 healthy volunteers and five patients with osteochondral lesions of the talus following ankle trauma. Reproducibility was tested with the intraclass correlation coefficient. RESULTS: The mean magnetization transfer ratio asymmetry (MTRasym), i.e., the gagCEST effect size, was significantly lower in patients than in healthy volunteers (0.34 ± 1.9% vs. 1.49 ± 0.11%; p < 0.001 [linear mixed model]). Intra- and inter-rater reproducibility was excellent with an average measure intraclass correlation coefficient (ICC) of 0.97 and a single measure ICC of 0.91 (p < 0.01). DISCUSSION: In this feasibility study, pre-morphological tibiotalar joint cartilage damage was quantitatively assessable on the basis of the optimized 3 T gagCEST imaging protocol that allowed stable quantification gagCEST effect sizes across a wide range of health and disease in clinically feasible acquisition times.

Differentiating rheumatoid and psoriatic arthritis: a systematic analysis of high-resolution magnetic resonance imaging features-preliminary findings.

BACKGROUND: Because of overlapping phenotypical presentations, the diagnostic differentiation of rheumatoid arthritis (RA) and psoriatic arthritis (PsA) remains challenging. Thus, this study aimed to examine the diagnostic value of distinct imaging features obtained by high-resolution 3-T MRI for the diagnostic differentiation. MATERIALS AND METHODS: Seventeen patients with PsA and 28 patients with RA were imaged at high resolution using 3-T MRI scanners and a dedicated 16-channel hand coil. All images were analyzed according to the outcome measures in rheumatology clinical trials' (OMERACT) RAMRIS (Rheumatoid Arthritis Magnetic Resonance Imaging Score) and PsAMRIS (Psoriatic Arthritis Magnetic Resonance Imaging Score) for the presence and intensity of synovitis, flexor tenosynovitis, bone edema, bone erosion, periarticular inflammation, bone proliferation, and joint space narrowing. Next, odds ratios (OR) were calculated to determine the strength of the associations between these imaging features, demographic characteristics, and the outcome RA vs. PsA. RESULTS: PsA could be differentiated from RA by extracapsular inflammatory changes (PsAMRIS sub-score "periarticular inflammation"), with low odds for the presence of RA (OR of 0.06, p < 0.01) at all metacarpophalangeal (MCP) joints. A prediction model informed by the items that were strongest associated with the presence of RA or PsA demonstrated excellent differentiating capability with an area under the curve of 98.1%. CONCLUSION: High-resolution imaging is beneficial for the identification of relevant imaging features that may assist the clinical differentiation of inflammatory conditions of the hand. At the MCP level, extracapsular inflammatory changes were strongly associated with PsA and may consequently allow the imaging differentiation of PsA and RA.

Reduced pre-operative skin oxygen saturation predicts revision after open reduction and internal fixation in calcaneal fractures : A reduced pre-operative oxygen saturation as measured by laser-Doppler spectrophotometry in 8 mm depth is associated with revision surgery after open reduction and internal fixation of calcaneal fractures through an extended lateral approach.

PURPOSE: To assess which pre-operative parameters correlate with wound revisions after an extended lateral approach to the calcaneus. METHODS: Pre-operative laser-Doppler spectrophotometry was applied in patients undergoing open reduction and internal fixation. The number of wound revisions was recorded during pos-toperative follow-up. Spearman rho analysis was used to identify factors associated with wound revision and receiver operator characteristics curves were calculated for the identified factors. RESULTS: Thirty-four patients (29 men, 5 women; 37 calcanei) with a mean patient age of 43 ± 14 years were analyzed. The minimal oxygen saturation value at the five measurement locations as well as the minimal value for flow correlated negatively with wound revisions (p value = 0.025 and 0.038, respectively). The area under the curve for the pre-operative minimal value of oxygen saturation was 0.841 (95%CI 0.64-1.00, p = 0.028), indicating a good accuracy as a test to predict wound revision. CONCLUSION: A pre-operative oxygen saturation of at least 20.5% across five measurement points along the anticipated incision identified all patients not needing a wound revision (negative predictive value 100%). On the other hand, patients with at least one measurement below 20.5% were at risk for wound revision surgery (sensitivity 100%, specificity 48.5%). ClinicalTrials.gov NCT01264146.

Trochanteric Femur Fractures: Application of Skeletal Traction during Surgery Does Not Alter Soft-Tissue Microcirculation.

Background and Objectives: Wound infections provoked by alterations in microcirculation are major complications in the treatment of trochanteric femur fractures. Surgical fracture fixation on a traction table is the gold standard for treatment, but the effect on tissue microcirculation is unknown. Microcirculation could be impaired by the pull on the soft-tissue or by a release of vasoactive factors. We hypothesized that intraoperative traction impairs soft-tissue microcirculation. Materials and Methods: In 22 patients (14 women, eight men), average age 78 years (range 36-96 ± 14), with trochanteric femur fractures, non-invasive laser-Doppler spectrophotometry was used to assess oxygen saturation, hemoglobin content, and blood flow in the skin and subcutaneous tissue before and after application of traction. Measurements were recorded in nine locations around the greater trochanter at a depth of 2, 8, and 15 mm before and after fracture reduction by traction. Results: No differences were found in any depth with traction compared to without (oxygen saturation: p = 0.751, p = 0.308, and p = 0.955, haemoglobin content: p = 0.651, p = 0.928, and p = 0.926, blood flow: p = 0.829, p = 0.866, and p = 0.411). Conclusion: In this pilot study, the application of traction does not affect skin and subcutaneous microcirculation in the surgery of proximal femur fractures.

An MRI-compatible varus-valgus loading device for whole-knee joint functionality assessment based on compartmental compression: a proof-of-concept study.

OBJECTIVE: Beyond static assessment, functional techniques are increasingly applied in magnetic resonance imaging (MRI) studies. Stress MRI techniques bring together MRI and mechanical loading to study knee joint and tissue functionality, yet prototypical axial compressive loading devices are bulky and complex to operate. This study aimed to design and validate an MRI-compatible pressure-controlled varus-valgus loading device that applies loading along the joint line. METHODS: Following the device's thorough validation, we demonstrated proof of concept by subjecting a structurally intact human cadaveric knee joint to serial imaging in unloaded and loaded configurations, i.e. to varus and valgus loading at 7.5 kPa (= 73.5 N), 15 kPa (= 147.1 N), and 22.5 kPa (= 220.6 N). Following clinical standard (PDw fs) and high-resolution 3D water-selective cartilage (WATSc) sequences, we performed manual segmentations and computations of morphometric cartilage measures. We used CT and radiography (to quantify joint space widths) and histology and biomechanics (to assess tissue quality) as references. RESULTS: We found (sub)regional decreases in cartilage volume, thickness, and mean joint space widths reflective of areal pressurization of the medial and lateral femorotibial compartments. DISCUSSION: Once substantiated by larger sample sizes, varus-valgus loading may provide a powerful alternative stress MRI technique.

Differentiation of human cartilage degeneration by functional MRI mapping-an ex vivo study.

OBJECTIVE: To evaluate whether the response to loading of cartilage samples as assessed ex vivo by quantitative MRI (qMRI) mapping techniques can differentiate intact and early degenerative cartilage. METHODS: Upon IRB approval and written informed consent, 59 macroscopically intact osteochondral samples were obtained from the central lateral femoral condyles of patients undergoing total knee replacement. Spatially resolved T1, T2, T2*, and T1ρ maps were generated prior to and during displacement-controlled quasi-static indentation loading to 405 µm (Δ1/2) and 810 µm (Δ1). Upon manual segmentation, absolute qMRI parameters and loading-induced relative changes (δ1/2, δ1) were determined for the entire cartilage sample and distinct zones and regions. Based on their histologically determined degeneration as quantified according to Mankin (Mankin sum scores [MSS], range 0-14), samples were dichotomised into intact (int; MSS 0-4, n = 35) and early degenerative (ed, MSS 5-8, n = 24). RESULTS: For T1ρ, consistent loading-induced increases were found for δ1/2 and δ1. Throughout the entire sample, increases in T1ρ were significantly higher in early degenerative than in intact samples (Δ1/2(ed) = 23.8 [q25 = 18.1, q75 = 29.0] %; Δ1/2(int) = 12.7 [q25 = 5.9, q75 = 19.5] %; p < 0.0005), according to Wilcoxon's signed-rank test). Zonal and regional analysis revealed these changes to be most pronounced in the sub-pistonal area. No significant degeneration-dependent loading-induced changes were found for T1, T2, or T2*. CONCLUSION: Aberrant load-bearing of early degenerative cartilage may be detected using T1ρ mapping as a function of loading. Hence, the diagnostic differentiation of intact versus early degenerative cartilage may allow the reliable identification of early and potentially reversible cartilage degeneration, thereby opening new opportunities for diagnosis and treatment of cartilage pathologies. KEY POINTS: • T1ρ mapping of the cartilage response to loading allows the reliable identification of early degenerative changes ex vivo. • Distinct response-to-loading patterns of cartilage tissue as assessed by functional MRI techniques are associated with biomechanical and histological tissue properties. • Non-invasive functional MR imaging techniques may facilitate the more sensitive monitoring of therapeutic outcomes and treatment strategies.

Comparative Analysis of Multimodal Large Language Model Performance on Clinical Vignette Questions.

This study compares 2 large language models and their performance vs that of competing open-source models.

Bioreactor-Controlled Physoxia Regulates TGF-ß Signaling to Alter Extracellular Matrix Synthesis by Human Chondrocytes.

Culturing articular chondrocytes under physiological oxygen tension exerts positive effects on their extracellular matrix synthesis. The underlying molecular mechanisms which enhance the chondrocytic phenotype are, however, still insufficiently elucidated. The TGF-ß superfamily of growth factors, and the prototypic TGF-ß isoforms in particular, are crucial in maintaining matrix homeostasis of these cells. We employed a feedback-controlled table-top bioreactor to investigate the role of TGF-ß in microtissues of human chondrocytes over a wider range of physiological oxygen tensions (i.e., physoxia). We compared 1%, 2.5%, and 5% of partial oxygen pressure (pO2) to the 'normoxic' 20%. We confirmed physoxic conditions through the induction of marker genes (PHD3, VEGF) and oxygen tension-dependent chondrocytic markers (SOX9, COL2A1). We identified 2.5% pO2 as an oxygen tension optimally improving chondrocytic marker expression (ACAN, COL2A1), while suppressing de-differentiation markers (COL1A1, COL3A1). Expression of TGF-ß isoform 2 (TGFB2) was, relatively, most responsive to 2.5% pO2, while all three isoforms were induced by physoxia. We found TGF-ß receptors ALK1 and ALK5 to be regulated by oxygen tension on the mRNA and protein level. In addition, expression of type III co-receptors betaglycan and endoglin appeared to be regulated by oxygen tension as well. R-Smad signaling confirmed that physoxia divergently regulated phosphorylation of Smad1/5/8 and Smad2/3. Pharmacological inhibition of canonical ALK5-mediated signaling abrogated physoxia-induced COL2A1 and PAI-1 expression. Physoxia altered expression of hypertrophy markers and that of matrix metalloproteases and their activity, as well as expression ratios of specific proteins (Sp)/Krüppel-like transcription factor family members SP1 and SP3, proving a molecular concept of ECM marker regulation. Keeping oxygen levels tightly balanced within a physiological range is important for optimal chondrocytic marker expression. Our study provides novel insights into transcriptional regulations in chondrocytes under physoxic in vitro conditions and may contribute to improving future cell-based articular cartilage repair strategies.

Screw-blade fixation systems in Pauwels three femoral neck fractures: a biomechanical evaluation.

OBJECTIVES: To reduce mechanical complications after osteosynthesis of femoral neck fractures, improved fixation techniques have been developed including blade or screw-anchor devices. This biomechanical study compares different fixation systems used for treatment of unstable femoral neck fractures with evaluation of failure mode, load to failure, stiffness, femoral head rotation, femoral neck shortening and femoral head migration. METHODS: Standardized Pauwels type 3 fractures (AO/OTA 31-B2) with comminution were created in 18 biomechanical sawbones using a custom-made sawguide. Fractures were stabilized using either SHS-Screw, SHS-Blade or Rotationally Stable Screw-Anchor (RoSA). Femurs were positioned in 25 degrees adduction and ten degrees posterior flexion and were cyclically loaded with an axial sinusoidal loading pattern of 0.5 Hz, starting with 300 N, with an increase by 300 N every 2000 cycles until bone-implant failure occurred. RESULTS: Mean failure load for the Screw-Anchor fixation (RoSA) was 5100 N (IQR 750 N), 3900 N (IQR 75 N) for SHS-Blade and 3000 N (IQR 675 N; p = 0.002) for SHS-Screw. For SHS-Screw and SHS-Blade we observed fracture displacement with consecutive fracture collapse as the main reason for failure, whereas RoSA mainly showed a cut-out under high loadings. Mean stiffness at 1800 N was 826 (IQR 431) N/mm for SHS-Screw, 1328 (IQR 441) N/mm for SHS-Blade and 1953 (IQR 617) N/mm for RoSA (p = 0.003). With a load of 1800 N (SHS-Screw 12° vs. SHS-Blade 7° vs. RoSA 2°; p = 0.003) and with 2700 N (24° vs. 15° vs. 3°; p = 0.002) the RoSA implants demonstrated a higher rotational stability and had the lowest femoral neck shortening (p = 0.002), compared with the SHS groups. At the 2700 N load point, RoSA systems showed a lower axial (p = 0.019) and cranial (p = 0.031) femoral head migration compared to the SHS-Screw. CONCLUSIONS: In our study, the new Screw-Anchor fixation (RoSA) was superior to the comparable SHS implants regarding rotational stability and femoral neck shortening. Failure load, stiffness, femoral head migration, and resistance to fracture displacement were in RoSA implants higher than in SHS-Screws, but without significance in comparison to SHS-Blades.