Analysis Of Imaging Data Research Articles

Diagnostic errors in assessing magnetic resonance imaging semiotics of primary extracerebral tumors: a case series

Primary extracerebral tumors are represented by benign and malignant neoplasms of the meninges and cranial nerves. Their presurgical differential diagnosis is based on the analysis of magnetic resonance imaging semiotics. The critically significant aspects for classifying tumors of this group include the following: neoplasm structure, contrast enhancement type, delimiting from the brain tissue, and relationship with the meninges or cranial nerves. Differential diagnosis of extracerebral tumors based on visual analysis of magnetic resonance imaging data is generally not challenging because most tumors have typical magnetic resonance imaging semiotics. However, in cases with atypical magnetic resonance imaging signs, reliable differentiation of tumors can be challenging. Moreover, the greatest complexity is the differentiation of meningioma grades, distinction between solitary fibrous tumors and meningiomas, and identification of the tumor type when localized in the cerebellopontine angles. The case series presented the most typical features leading to errors in the differential diagnosis of primary extracerebral tumors. All the presented tumors were verified with postsurgical histological examination. The analysis of the case reports demonstrates that a review of the combined semiotic signs can lower the number of diagnostic errors.

A hybrid BERT-CPSO model for multi-class depression detection using pure hindi and hinglish multimodal data on social media

Due to the psychological strain that depression causes, there has been a noticeable increase in the number of persons compromising their lives in recent years. Social media platforms provide researchers with an entirely novel viewpoint on identifying individuals who are depressed. Previous research on automatic learning models for depression detection revealed low detection accuracy and an absence of optimizing techniques that could enhance detection accuracy. Furthermore, there is no such dataset, and very little study has been done on the multimodal pure Hindi and code-mixed Hinglish language domains. In light of this, we developed a Hindi dataset and suggested reliable methods for depression detection based on multimodal data, i.e., text and images, using the Hindi and Hinglish languages. This study aims to accomplish three things: first, it will evaluate text data using an effective Bidirectional Encoder Representations from Transformers (BERT) approach and compare it with other transfer learning variants; second, it will analyze image data by suggesting a Convolutional Neural Network (CNN) optimized with a nature-inspired algorithm, namely Particle Swarm Optimization (PSO), or CPSO; and third, it will classify the multimodal data into depressive and non-depressive posts by suggesting a hybrid of the best-performing models on text and images, namely BERT-CPSO (BTCPSO). The results produced with the BERT model showed the best accuracy of 95% for text data, in contrast to RoBERTa, DistilBERT, and XLNet. Further, CPSO outperforms other Machine Learning (ML) and Deep Learning (DL) algorithms for image data with an accuracy of 95%. Additionally, comparing the proposed CPSO with a basic CNN revealed that integrating the PSO technique with CNN increased the model's accuracy in detecting depressed posts by 5%. In conclusion, hybrid BERT-CPSO outperforms other BERT combinations with ML and DL algorithms for multimodal data, achieving 97%, 95%, 98%, and 96%, respectively, in accuracy, recall, precision, and F1-scores. As a result, the findings of comparing the suggested technique with the earlier models show the effectiveness of the approach that has been provided and can help medical professionals diagnose depression with precision.

Deep hashing and attention mechanism-based image retrieval of osteosarcoma scans for diagnosis of bone cancer

BackgroundDue to its intricate nature and substantial data size, microscopic image data of osteosarcoma often present a significant obstacle to the effectiveness of conventional image retrieval methods. Therefore, this study investigates a new approach for medical image retrieval using advanced deep hashing techniques and attention mechanisms to address these challenges more effectively. MethodThe proposed algorithm significantly improves osteosarcoma cell microscopic image retrieval efficiency and accuracy using deep hashing and attention mechanisms. Image preprocessing includes adaptive histogram equalization and dataset augmentation to enhance quality and diversity. Feature extraction employs the WRN-AM model to map high-dimensional features to a low-dimensional hash code space, improving retrieval efficiency. Finally, similarity matching via Hamming distance allows rapid and precise identification of similar images. ResultsThe study shows notable advancements: the WRN-AM model achieves 93.2% classification accuracy and 97.09% mAP using 64-bit hash codes. These findings underscore the technique’s effective performance in extracting and categorizing diverse microscopic cell data efficiently and reliably. ConclusionsThis innovative approach provides a robust solution for retrieving and classifying microscopic data of osteosarcoma cells and other cell types, speeding up clinical diagnosis and medical research. It facilitates quicker access and analysis of patient image data, enhancing diagnostic precision and treatment planning for healthcare professionals. Concurrently, it supports researchers in leveraging medical image data more efficiently, fostering progress and innovation in the medical field.

The application of “transfer learning” in optical microscopy: The petrographic classification of opaque minerals

Abstract The analysis of optical microscopic image data is crucial for the identification and characterization of mineral phases and, thus, directly relevant to the subsequent methodology selections of further detailed petrological exploration. Here, we present a novel application of Swin Transformer, a deep learning algorithm to classify mineral phases such as arsenopyrite, chalcopyrite, gold, pyrite, and stibnite in images captured by optical microscopy. To speed up the training process and improve the generalization capabilities of the investigated model, we adopt the “transfer learning” paradigm by pre-training the algorithm using a large, general-purpose image data set named ImageNet-1k. Furthermore, we compare the performances of the Swin Transformer with those of two well-established Convolutional Neural Networks (CNNs) named MobileNetv2 and ResNet50, respectively. Our results highlight a maximum accuracy of 0.92 for the Swin Transformer, outperforming the CNNs. To provide an interpretation of the trained models, we apply the so-called Class Activation Map (CAM), which indicates a strong global feature extraction ability of the Swin Transformer metal mineral classifier that focuses on distinctive (e.g., colors) and microstructural (e.g., edge shapes) features. The results demonstrate that the deep learning approach can accurately extract all available attributes, which reveals the potential to assist in data exploration and provides an opportunity to carry out spatial quantization at a large scale (centimeters-millimeters). Simultaneously, boosting the learning processes with pre-trained weights can accurately capture relevant attributes in mineral classification, revealing the potential for application in mineralogy and petrology, as well as enabling its use in resource explorations.

Comparative Analysis of RGB and Multispectral UAV Image Data for Leaf Area Index Estimation of Sweet Potato

This study aims to evaluate whether the utilization of a broader array of image types and the extraction of plant canopy using RGB and multispectral cameras mounted on unmanned aerial vehicles (UAVs) can improve leaf area index (LAI) estimation accuracy. The accuracy of LAI estimation for sweet potato was compared across different model types—mono-regression models based on a single image, multivariate regression models based on a single type of image, and multivariate regression models based on multiple image types—by employing 10 types of images based on color, morphological indices, vegetation indices (VIs) from RGB cameras, reflectance, VIs from multispectral cameras, and each of their canopy part image. For each regression model, we compared the estimation accuracy based on the whole image with that based on the canopy part image. For the mono-regression model, EVI2 from the whole image exhibited the lowest test root mean squared error (RMSE) of 0.403, which is attributed to EVI2’s capacity to mitigate the effects of spectral saturation. Contrary to the models with multiple image types that demonstrated improved accuracy, the multivariate regression models based on a single image type did not enhance estimation accuracy; this shows that the use of multiple image types improves the LAI estimation accuracy owing to the synergy of different spectral information from various image types. Plant canopy extraction did not enhance the estimation accuracy in mono-regression models or multivariate regression models based on a single image type; however, it improved the accuracy of multivariate regression models based on multiple image types, provided the image types were appropriately combined. The highest estimation accuracy was achieved by a partial least squares regression model based on VI (from the RGB camera) and reflectance (from the multispectral camera) of the canopy part (test R2 = 0.887, test RMSE = 0.351), suggesting that this is an effective approach for LAI estimation in sweet potato (cultivar Beniharuka). Overall, this study demonstrates that an optimal combination of image type and plant canopy extraction can enhance LAI estimation accuracy in UAV-based monitoring.

DNA Nanolock-Based Logic Gate-Directed Reciprocal Feedback for Stepwise Cell Typing and Combination Treatment.

To achieve accurate molecular diagnosis and early-stage intervention of disease on demand, there is an urgent need for the monitoring of multiple biomarkers and multipath information acquisition in living cells. The DNA combinatorial logic gate is an appropriate strategy for providing a systematic proof of concept with comprehensive information and function. Herein, a modular DNA logic gate nanomachine is designed for sufficient multistep reciprocal cell identification and therapy via the iteration of simple logic operations. In this logic gate system, this main module is constructed by G-quadruplex-locked gold nanocages (AuNCs), serving dual functions of drug encapsulation and cell recognition. The logic system is composed of OR, XNOR, AND, and NOR gates employing two intracellular disease biomarkers (microRNA 21 and microRNA 155) as inputs and the fluorescence signal of doxorubicin (Dox) as an output. The output signals of the four logic gates are iterated to process the imaging analysis data from the complex matrix in the living cell. Via positive and negative reciprocal feedback, the series circuit of different gates enables different functions, including the preliminary screening and the distinction of the cell type. Through the mutual preliminary screening and further proof, this logic system achieves accurate identification of cells, controlled drug release, and photothermal treatment using the AuNC as a photothermal transducer. This DNA logic system broadens the applications of the biocomputing system in disease screening and logic-controlled treatment fields.

A spatial human thymus cell atlas mapped to a continuous tissue axis

T cells develop from circulating precursor cells, which enter the thymus and migrate through specialized subcompartments that support their maturation and selection1. In humans, this process starts in early fetal development and is highly active until thymic involution in adolescence. To map the microanatomical underpinnings of this process in pre- and early postnatal stages, we established a quantitative morphological framework for the thymus—the Cortico-Medullary Axis—and used it to perform a spatially resolved analysis. Here, by applying this framework to a curated multimodal single-cell atlas, spatial transcriptomics and high-resolution multiplex imaging data, we demonstrate establishment of the lobular cytokine network, canonical thymocyte trajectories and thymic epithelial cell distributions by the beginning of the the second trimester of fetal development. We pinpoint tissue niches of thymic epithelial cell progenitors and distinct subtypes associated with Hassall’s corpuscles and identify divergence in the timing of medullary entry between CD4 and CD8 T cell lineages. These findings provide a basis for a detailed understanding of T lymphocyte development and are complemented with a holistic toolkit for cross-platform imaging data analysis, annotation and OrganAxis construction (TissueTag), which can be applied to any tissue.

Retraction Note: Quantum optical sensors and IoT for image data analysis in traffic management

Retraction Note: Quantum optical sensors and IoT for image data analysis in traffic management

Pesticide Residue Coverage Estimation on Citrus Leaf Using Image Analysis Assisted by Machine Learning

Globally, the agricultural industry has benefited from using pesticides to minimize crop losses. Nevertheless, the indiscriminate overuse of pesticides has led to significant risks associated with a detrimental impact on the environment and human health. Therefore, emerging concerns of pesticide residue found in crops, food, and livestock are a pressing issue. To address the above challenges, there have been many efforts made towards implementing machine learning to enable precision agricultural practices to reduce pesticide overuse. As of today, there are no guiding digital tools available for citrus growers to provide pesticide residue leaf coverage analysis after foliar applications. Herein, we are the first to report software assisted by lightweight machine learning (ML) to determine the Kocide 3000 and Oxytetracycline (OTC) residue coverage on citrus leaves based on image data analysis. This tool integrates a foundational Segment Anything Model (SAM) for image preprocessing to isolate the area of interest. In addition, Kocide 3000 and Oxytetracycline (OTC) residue coverage analysis was carried out using a specialized Mask Region-Based Convolutional Neural Network (CNN). This CNN was pre-trained on the MS COCO dataset and fine-tuned by training with acquired datasets in laboratory and field conditions. The developed software demonstrated excellent performance on both pesticides’ accuracy, precision, and recall, and F1 score metrics. In summary, this tool has the potential to assist growers with the decision-making process for controlling pesticide use rate and frequency, minimizing pesticide overuse.

Advanced deep learning approaches for early detection and localization of ocular diseases

Rece with t advancements in modern technology have significantly enhanced the transmission of information, particularly in image processing, utilizing deep learning algorithms. This study aims to propose a a robust deep-learning strategy for detecting and recognizing eye defects and diseases from medical images. We present a detailed practical simulation of hybrid deep learning techniques designed for medical image classification based on multi-descriptor algorithms. The focus is on the classification of eye diseases by applying an advanced deep-learning algorithm to a dataset comprising various pathological eye conditions. Training operations for the proposed algorithm were conducted following the initialization phase, which included the extraction of multi-specification features. This enables the deep learning model to effectively analyze input eye images and accurately diagnose conditions. Our results demonstrate a diagnostic efficiency of 99%, with an error rate not exceeding 0.015%. The findings underscore the high efficiency and accuracy of deep learning algorithms in classifying and analyzing image data, thereby significantly reducing the workload for healthcare professionals.

Intravascular enhancement sign at 3D T1-weighted turbo spin echo sequence is associated with cerebral atherosclerotic stenosis

ObjectiveIntravascular enhancement sign (IVES) at three-dimensional T1-weighted turbo spin echo (3D T1W TSE) sequence may be a simple hemodynamic maker. This study aims to investigate the association between IVES and features of intracranial atherosclerotic stenosis (ICAS). MethodRetrospective analysis of clinical and imaging data of patients who underwent high resolution-vessel wall imaging (HR-VWI) examination from May 2021 to May 2023. The number of IVES vessels and ICAS features at HR-VWI were extracted by two neuroradiologists. Paired comparisons and correlation analysis on these indicators were performed. ResultsA total of 118 patients with ICAS in the first segment of the middle cerebral artery and accompanied by unilateral IVES were enrolled. Compared to the non-IVES side, a higher incidence of ischemic events and intraplaque hemorrhage (IPH), higher degree of vascular stenosis and enhancement, lower remodeling index, and lower signal intensity ratio (SIR) were found in subjects with IVES. In the ICAS with IVES, 79.66 % showed severe stenosis and occlusion; in the ICAS with severe stenosis and occlusion, 89.5 % showed IVES in the distal. A multivariable logistic regression model identified the vascular stenosis degree (OR = 1.922; 95 %CI [1.37–2.692]; P < 0.001), enhanced-degree (OR = 2.486; 95 %CI [1.315–4.698]; P = 0.005), position (OR = 2.869; 95 %CI [1.255–6.560]; P = 0.012), and SIR (OR = 0.032; 95 %CI [0.004–0.275]; P = 0.002) were independent association with the presence of IVES. The area under the curve was 0.911 for the use of IVES vessel quantities to identify severe stenosis and occlusion of arterial lumen. ConclusionThe number of IVES vessels was associated with the local features of ICAS, which may indicate severe stenosis and occlusion in the major branches of the proximal artery.

Cherenkov Imaged Bio-morphological Features Verify Patient Positioning with Deformable Tissue Translocation in Breast Radiotherapy

PurposeAccurate patient positioning is crucial for precise radiotherapy dose delivery, as errors in positioning can profoundly influence treatment outcomes. This study introduces a novel application for loco-regional tissue deformation tracking via Cherenkov image analysis, during fractionated breast cancer radiotherapy. The primary objective of this research was to develop and test an algorithmic method for Cherenkov-based position accuracy quantification, particularly for loco-regional deformations which do not have an ideal method for quantification during radiotherapy. Methods and materialsBio-morphological features in the Cherenkov images such as vessels were segmented. A rigid/non-rigid combined registration technique was employed to pinpoint both inter- and intra-fractional positioning variations. The methodology was tested on an anthropomorphic chest phantom experiment via shifting treatment couch with known distances and inducing respiratory motion, to simulate inter-fraction setup uncertainties and intra-fraction motions respectively. It was then applied to a dataset of fractionated whole breast radiotherapy human imaging (n=10 patients). ResultsThe methodology provides quantified positioning variations, comprising two components: a global shift determined through rigid registration, and a two-dimensional variation map illustrating loco-regional tissue deformation quantified via non-rigid registration. Controlled phantom testing yielded an average accuracy of 0.83 mm for couch translations up to 20 mm in various directions. Analysis of clinical Cherenkov imaging data from ten breast cancer patients compared to their first imaged fraction revealed an inter-fraction setup variation of 3.7 ± 2.4 mm in the global shift and loco-regional deformation up to 3.3 ± 1.9 mm (95th percentile of all regional deformation). ConclusionsThis study introduces the use of Cherenkov visualized bio-morphological features to quantify the global and local variations in patient positioning based on rigid and non-rigid registrations. This new approach demonstrates the feasibility to provide quantitative guidance for inter-/intra-fraction positioning, particularly for the loco-regional deformations that have been unappreciated in current practice with conventional imaging techniques.

Effect of patient specificity on predicting knee cartilage degeneration in obese adults: Musculoskeletal finite-element modeling of data from the CAROT trial.

Obesity is a known risk factor for development of osteoarthritis (OA). Numerical tools like finite-element (FE) models combined with degenerative algorithms have been developed to understand the interplay between OA and obesity. Inthisstudy,we aimed to predict knee cartilage degeneration in a cohort of obese adults to investigate the importance of patient-specific information on degeneration predictions. We used a validated FE modeling approach and three different age-dependent functions (step-wise, exponential, and linear) to simulate cartilage degradation under overloading in the knee joint. Gait motion analysis and magnetic resonance imaging data from 115 obese individuals with knee OA were used for musculoskeletal and FE modeling. Cartilage degeneration predictions were contrasted with Kellgren-Lawrence (KL)and Boston-Leeds Osteoarthritis Knee Score (BLOKS) grades. The findings show that overall, the similarities between numerical predictions and clinical measures were better for the medial (average area under the curve(AUC) = 0.62) compared to the lateral compartment (average AUC = 0.52) of the knee. Classification results for KL grades, full patient-specific models and patient-specific geometry with generic gait data showed higher AUC values (AUC = 0.71 and AUC = 0.68, respectively) compared to generic geometry and patient-specific gait (AUC = 0.48). For BLOKS grades, AUC values for both full patient-specific models and for patient-specific geometry with generic gait locomotion were higher (AUC = 0.66 and AUC = 0.64, respectively) compared to when the generic geometry and patient-specific gait were used (AUC = 0.53). In summary, our study highlights the importance of considering individual information in knee OA prediction. Nevertheless, our findings suggest that personalized gait play a smaller role in the OA prediction and classification capacity than personalized joint geometry.

Introducing Angle B: A Novel Quantitative Approach for Sagittal Alignment Reduction in Atlanto-Axial Dislocation Surgery.

This study aims to establish a quantitative relationship between cervical lordosis (CL) and the rotation angle of the axis for the surgical reduction of atlantoaxial dislocation (AAD) and to explore the potential applications of this relationship in preoperative planning and selection of surgical techniques. To construct the correlation between horizontal gaze and location of axis, we introduced the angle B, defined as the angle between the Frankfort horizontal line and the extension line of the posterior edge of the axis, and explored its application in surgical planning. We retrospectively analyzed imaging data from computed tomography of 23 AAD patients who had undergone posterior reduction and fixation. Theoretical equations were deduced through a series of angular transformations, and linear regression analysis was used to validate our findings. Our results showed a strong linear relationship between the change in CL (ΔCL) and the change in B(ΔB) (y=-1.0402x, coefficient of determination R²=0.978, P<0.001), supporting our deduction that ΔCL=ΔB. Furthermore, we found that the rotation angle of the axis (angle D) was equal to ΔCL. By resolving the atlantoaxial interval into the vertical dimension (h) and horizontal dimension (d), we could calculate the maximum vertical and horizontal distance that the axis could move theoretically according to a patient's maximum ΔCL. This finding supports our theory that the introduction of angle B can provide more precise preoperative planning and surgical technique selection for patients with AAD. By introducing angle B and deducing the equation ΔB=ΔCL=D, we have provided an innovative tool for preoperative planning and surgical technique selection for patients with AAD. This equation not only helps surgeons achieve more precise and effective surgical reductions but also emphasizes the important role of angle B in surgical planning.

Images as data – modelling data interactions in social science and humanities research

PurposeThe expanded reuse of images as research data in the social sciences and humanities necessitates the understanding of scholars’ real-life interactions with the type of data. The aim of this study is to analyse activities constituting image data interactions in social science and humanities research and to provide a model describing the data interaction process.Design/methodology/approachThe study is based on interviews with 21 scholars from various academic backgrounds utilising digital and print images collected from external sources as empirical research data. Qualitative content analyses were executed to analyse image data interactions throughout the research process in three task types: contemporary, historical and computational research.FindingsThe findings further develop the task-based information interaction model (Järvelin et al., 2015) originally created to explain the information interaction process. The enhanced model presents five main image data interaction activities: Data gathering, Forming dataset, Working with data, Synthesizing and reporting and Concluding, with various sub-activities. The findings show the variety of image data interactions in different task types.Originality/valueThe developed model contributes to understanding critical points in image data interactions and provides a model for future research analysing research data interactions. The model may also be used, for example, in designing better research services and infrastructures by identifying support needs throughout the research process.

The diagnosis value of dual-energy computed tomography (DECT) multi-parameter imaging in lung adenocarcinoma and squamous cell carcinoma.

Lung cancer continues to pose a serious risk to human health. With a high mortality rate, non-small cell lung cancer (NSCLC) is the major type of lung cancer, making up to 85% of all cases of lung cancer. Lung adenocarcinoma (AC), and lung squamous cell carcinoma (SC) are the two primary types of NSCLC. Determining the pathological type of NSCLC is important in establishing the most effective treatment method. Dual-energy computed tomography (DECT) multi-parameter imaging is an imaging technology that provides accurate and reliable disease diagnosis, and its uses are utilized for the combined diagnostic efficacy of AC and SC. The purpose of this study was to investigate the diagnostic value of spectral parameters of DECT in efficacy to AC and SC, and their combined diagnostic efficacy was also analyzed. We conducted a retrospective analysis of clinical and imaging data for 36 patients diagnosed with SC and 35 patients with AC. These patients underwent preoperative DECT chest scans, encompassing both arterial and venous phases, at our hospital from December 2020 to April 2022. The tumor diameter, water concentration (WC), iodine concentration (IC), normalized iodine concentration (NIC), Z effective (Zeff), and slope of the curve (K) in lesions were evaluated during two scanning phases in the two separate pathological types of lung cancers. The differences in parameters between these two types of lung cancers were statistically analyzed. In addition, receiver operating characteristic (ROC) curves were performed for these parameters to distinguish between SC and AC. In a univariate analysis involving 71 lung cancer patients, the results from Zeff, IC, NIC, and K from the AC's arterial and venous phase images were more elevated than those from the SC (P < 0.05). In contrast, the WC results were lower than those from SC (P < 0.05). The area under the ROC curve (AUC) for multi-parameter joint prediction typing was 0.831, with a corresponding sensitivity of 63.9% and specificity of 94.3%. It is possible to distinguish between central SC and AC using the spectrum characteristics of DECT-enhanced scanning (Zeff, IC, NIC, K, WC, and tumor diameter). Diagnostic effectiveness can be greatly improved when multiple variables are included.

Mitigation of DMM-induced stripe patterns in synchrotron X-ray radiography through dynamic tilting.

In synchrotron X-ray radiography, achieving high image resolution and an optimal signal-to-noise ratio (SNR) is crucial for the subsequent accurate image analysis. Traditional methods often struggle to balance these two parameters, especially in situ applications where rapid data acquisition is essential to capture specific dynamic processes. For quantitative image data analysis, using monochromatic X-rays is essential. A double multilayer monochromator (DMM) is successfully used for this aim at the BAMline, BESSY II (Helmholtz Zentrum Berlin, Germany). However, such DMMs are prone to producing an unstable horizontal stripe pattern. Such an unstable pattern renders proper signal normalization difficult and thereby causes a reduction of the SNR. We introduce a novel approach to enhance SNR while preserving resolution: dynamic tilting of the DMM. By adjusting the orientation of the DMM during the acquisition of radiographic projections, we optimize the X-ray imaging quality, thereby enhancing the SNR. The corresponding shift of the projection during this movement is corrected in post-processing. The latter correction allows a good resolution to be preserved. This dynamic tilting technique enables the homogenization of the beam profile and thereby effectively reduces noise while maintaining high resolution. We demonstrate that data captured using this proposed technique can be seamlessly integrated into the existing radiographic data workflow, as it does not need hardware modifications to classical X-ray imaging beamline setups. This facilitates further image analysis and processing using established methods.

Schatzker IV tibial plateau fractures involving the posterolateral column: Higher incidence of lateral meniscus and anterior cruciate ligament injuries with suboptimal postoperative outcomes

BackgroundCurrent literature offers scant insights into Schatzker IV tibial plateau fractures (TPFs) that affect the posterolateral (PL) column. This study seeks to elucidate the prevalence of meniscal and ligamentous injuries in the knee, as well as the clinical outcomes, in Schatzker IV TPF cases, both with and without PL column involvement. MethodsWe conducted a retrospective analysis of clinical and imaging data from patients with Schatzker IV TPFs who received surgical treatment at our institution between January 2018 and January 2022. Patient demographics and surgical details were meticulously documented. The study participants were categorized into two groups based on the involvement of the PL column in the Schatzker IV TPF. Group A comprised patients without PL column fractures, whereas Group B included those with such fractures. We compared soft tissue injuries affecting knee stability, namely meniscal and ligamentous injuries, between two groups using Magnetic Resonance Imaging (MRI) and surgical records. Further, Group B was divided into subgroups based on the presence or absence of knee subluxation. Within Group B, we measured the lateral plateau depression (LPD) and lateral plateau widening (LPW). Additionally, we investigated the frequency of meniscus entrapment in patients with PL column fractures. Clinical outcomes were assessed using The American Hospital for Special Surgery (HSS) and Rasmussen scores. ResultsThe study comprised 66 patients with Schatzker IV TPFs. Of these, 12 patients had no PL column fracture, while 54 had a PL column fracture. Group B demonstrated a 57.4 % and 64.8 % incidence of lateral meniscus (LM) and anterior cruciate ligament (ACL) injuries, respectively, significantly higher than that in Group A (25 %, 8.3 %) (p = 0.044, p < 0.001). Within Group B, those with knee subluxation showed a marked increase in LPD (14.5 ± 5.3 mm) and LPW (9.3 ± 4.9 mm), correlating with a higher rate of meniscus entrapment (76.0 %) (p < 0.001). Patients in Group B had lower HSS and Rasmussen scores compared to Group A (p = 0.048, p = 0.006). ConclusionThis investigation reveals that Schatzker IV TPFs involving the PL column are highly associated with increased rates of LM and ACL injuries. Additionally, these fractures correlate with a greater likelihood of knee subluxation and meniscus entrapment, ultimately leading to poorer prognostic outcomes.

Predicting Response to [177Lu]Lu-PSMA Therapy in mCRPC Using Machine Learning.

Radioligandtherapy (RLT) with [177Lu]Lu-PSMA has been newly introduced as a routine treatment for metastatic castration-resistant prostate cancer (mCRPC). However, not all patients can tolerate the entire therapeutic sequence, and in some cases, the treatment may prove ineffective. In real-world conditions, the aim is to distinguish between patients who fully benefit from treatment (those who respond effectively and tolerate the entire therapeutic sequence) and those who do not respond or cannot tolerate the entire sequence. This study explores predictive factors to distinguish between fully beneficial RLT treatment patients (FBTP) and not fully beneficial RLT treatment patients (NFBTP). The objective was to enhance the understanding of predictive factors influencing RLT effectiveness and to highlight the significance of machine learning in optimizing patient selection for treatment planning. Data from 25 mCRPC patients, categorized as FBTP (11) or NFBTP (14) to RLT, were analyzed. The dataset included clinical, imaging, and biological parameters. Data analysis techniques, including exploratory data analysis and feature engineering, were used to develop machine learning models for predicting patient outcomes. Imaging data analysis revealed statistically significant differences in the renal uptake intensity of Choline between the two groups. A discordance of FDG+ and PSMA- was identified as a potential indicator of NFBTP. The integration of biological data enhanced the model's predictive capability, achieving an accuracy of 0.92, a sensitivity of 0.96, and a precision of 0.96. Adding blood parameters like neutrophils, leukocytes, and alkaline phosphatase greatly increased prediction accuracy. This study emphasizes the significance of an integrated approach that merges imaging and biological data, thereby augmenting the predictive accuracy of patient outcomes in RLT with [177Lu]Lu-PSMA. In particular, including Choline PET among the imaging parameters provides unique insights into the predictive factors affecting RLT efficacy. This approach not only deepens the understanding of predictive factors but also underscores the utility of machine learning in refining the patient selection process for optimized treatment planning.

Volcanic tremor associated with successive gas emission activity at a boiling pool: Analyses of seismic array and visible image data recorded at Iwo-Yama in Kirishima Volcanic complex, Japan

Volcanic tremors are often observed during volcanic activity and volcanic eruptions, and their generation processes provide clues for understanding volcanic fluid activity underground and eruption dynamics. However, tremors are characterized by continuous oscillations that mask P- and S-waves; hence few studies have precisely located the source, which is the most fundamental information for understanding the generation mechanism. In this study, we focus on volcanic tremors excited by continuous gas emissions occurring at a vent called Y2a in Iwo-Yama, the Kirishima Volcanic Complex, Japan, to clarify the source process of the tremor as well as gas emission activity. We simultaneously observed the volcanic tremor by deploying a small aperture array consisting of six seismometers and the gas emission activity by using a newly developed visual IoT system that can be operated without commercial electricity. MUSIC analysis locates the tremor at depths ranging from the ground surface to approximately 200 m beneath the Y2a and Y2b vents, which are approximately 30 m apart, for approximately four months from November 2021 to February 2022. The source locations of the tremors in the 2 Hz (1.2–2.6 Hz), 4 Hz (3–4 Hz), and 5 Hz (4–5.5 Hz) ranges show some differences and changes with time. The source location tends to become deeper when the 2 Hz amplitude is large. The infrasound generated by gas emission activity is dominant in the tremor signals, which are recognized in the wave propagation velocity with an acoustic velocity of 330 m/s when the 2 Hz amplitude is small. The visual IoT system succeeded in detecting long-term changes in the gas emission activity, and we found that the 2 Hz amplitude of tremor was well correlated with the amount of hot water in the boiling pool of Y2a, which was controlled by precipitation and evaporation during non-rainy days. From these observations, we infer that the volcanic tremor is generated by resonance of volcanic gas and hot water in a crack-like structure beneath Y2a. The resonance was triggered by the counterforces of the gas emissions in the boiling pool, and the infrasound was dominant during periods of hot water depletion in the boiling pool. Temporal changes in the source depths may be caused by changes in the fluid properties, configuration of the resonator and/or the strengths of the underground sources and infrasound. Our simultaneous observations of seismic array and visual IoT system clarify that even the continuous gas emission activity that looks stable is controlled by external sources such as precipitation.