Shape Of Features Research Articles

Recovery experiences among mental health service users going through the Balancing Everyday Life™ intervention – A deductive qualitative study

Background The occupational therapy intervention Balancing Everyday Life (BEL)TM aims to support mental health service users towards improved occupational balance and personal recovery. Yet, no research has specifically addressed recovery experiences among BELTM participants. Aim To investigate how the recovery process was experienced by mental health services users who had participated in BELTM. Methods The study was based on qualitative interviews with 11 participants. A deductive content analysis was performed based on the CHIME framework, a research-based tool for characterising the recovery process. Results All categories and most subcategories described in the CHIME framework could be found in the participants’ experiences. The most prominent categories were Sense of connectedness and Empowerment. Most subcategories were identified as well. Additionally, two subcategories not covered in CHIME were distinguished – occupational balance, and self-esteem and self-confidence – which may be specific to an occupational therapy intervention like BELTM. Conclusion The study showed that CHIME was relevant for characterising the recovery process among BELTM participants and identifying the features shaping that process. The findings support CHIME, while also indicating that BELTM offers some additional avenues for personal recovery. Significance An occupational therapy intervention can support mental health service users towards personal recovery.

Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network

ABSTRACT Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues. Individuals frequently struggle with sensory abnormalities, motor deficiencies affecting coordination, and cognitive impairments affecting memory and focus. In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. First, input images are taken from the brain tumour Dataset. To execute this, the input image is pre-processed using Multivariate Fast Iterative Filtering (MFIF) and it reduces the occurrence of over-fitting from the collected dataset; then feature extraction using Self-Supervised Nonlinear Transform (SSNT) to extract essential features like model, shape, and intensity. Then, the proposed PACDNN-COA-BTI-MRI is implemented in Matlab and the performance metrics Recall, Accuracy, F1-Score, Precision Specificity and ROC are analysed. Performance of the PACDNN-COA-BTI-MRI approach attains 16.7%, 20.6% and 30.5% higher accuracy; 19.9%, 22.2% and 30.1% higher recall and 16.7%, 21.9% and 30.8% higher precision when analysed through existing techniques brain tumor identification using MRI-Based Deep Learning Approach for Efficient Classification of Brain Tumor (MRI-DLA-ECBT), MRI-Based Brain Tumor Detection using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques (MRI-BTD-CDMLT) and MRI-Based Brain Tumor Image Detection using CNN-Based Deep Learning Method (MRI-BTID-CNN) methods, respectively.

The Target-Defining Attributes Can Determine the Effects of Attentional Control Settings in Singleton Search Mode.

The attentional control settings (ACSs) can help us efficiently select targets in complex real-world environments. Previous research has shown that category-specific ACS demands more attentional resources than feature-specific ACS. However, comparing natural or alphanumeric categories with color features does not distinguish the effects of processing hierarchy and target-defining properties. The present study employed a spatial cueing paradigm to better understand the effects of target-defining properties and search mode on attentional resources in visual search. The target was defined as a combination of shape feature (shape "X") and color category (green in different shades), which generated shape-specific ACS (sACS) and color-specific ACS (cACS). The degrees of shape matching (SM), color matching (CM), and spatial validity between the cue and target were manipulated. Search modes were manipulated by changing the homogeneity of distractors in either shape or color dimensions. Results show a main effect of CM across all four experiments, indicating that category can tune on attentional capture consistently. Importantly, the analysis between four experiments found different interactions across experiments, suggesting that the singleton search mode can reduce the effects of ACS and increase the interactions with other factors. In conclusion, this study suggests that the effects of ACS on attentional capture are determined by both target-defining properties and search mode, rather than processing hierarchy. The results indicate that attentional processes are highly dynamic and context-dependent, requiring a flexible allocation of resources to effectively prioritize relevant information.

Unconventional Photo‐Control of Structural Features Using Elliptically Polarized Light

AbstractThe holographic interference of either same‐handed or orthogonal polarized beams is commonly employed in the fabrication of optical elements as it enables the development of an ideal sinusoidal surface modulation by inducing the mass migration of photochromic polymer. However, this approach encounters challenges in producing multiplexed optical elements with a complex surface topography, primarily due to only sinusoidal surface topography which limits control over the feature's shape and size. Here, a versatile approach is demonstrated for fabricating spatially multiplexed optical elements using ellipticity‐controlled orthogonally elliptically polarized (OEP) beams that offer distorted square‐like and rhombus‐like pillars on the azopolymer layer by asymmetric azopolymer migration driven by spatially varying ellipticity and azimuth angle of interference beam. These multiplexed distorted shapes enable selective energy transfer during light diffraction and also induce a phase‐shift to polarization states. Ellipticity control of both writing and interference beams provides an additional degree of freedom for structuring the surface topography of optical elements. Furthermore, a quantitative analysis of the multiplexed OEP surface relief is performed to achieve distinct diffraction properties, thereby rendering its suitability for fabricating complex optical elements.

Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms.

Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. A new dataset of high moisture content maize kernel phenotypic features was constructed by extracting seven features (geometric and shape features). Then, the regression model of the kernel (broken and unbroken) weight prediction and the classification model of kernel defect detection were established using the mainstream machine learning algorithm. In this way, the defect rapid identification and accurate weight prediction of broken kernels achieve the purpose of broken rate quantitative detection. The results prove that LGBM (light gradient boosting machine) and RF (random forest) algorithms were suitable for constructing weight prediction models of broken and unbroken kernels, respectively. The r values of the models built by the two algorithms were 0.985 and 0.910, respectively. SVM (support vector machine) algorithms perform well in constructing maize kernel classification models, with more than 95% classification accuracy. A strong linear relationship was observed between the predicted and actual broken rates. Therefore, this method could help to be an accurate, objective, efficient broken rate online detection method for maize harvest.

Semantic Features of Tools Used in The Nyadran Ritual of Tengger Residents in Lumajang

Semantic features are the study of semantics that discusses the shape of an object or object that is characterized by the presence of signs or features and other explanatory components attached to the object or object based on its features. The purpose of this study is to obtain an objective overview of the form, function, and meaning of the semantic features of the tools used in the nyadran ritual . The approach used in this study is an ethnolinguistic approach. The data in this study is in the form of quotations of words, phrases and sentences about the form, function, and meaning of the semantic features of the tools used in the nyadran. ritual. The data sources of this research are traditional leaders and community leaders as well as books and journals that provide information about the semantic features of the tools used in rituals. The instrument used in this study is the researcher as the main instrument coupled with interview guidelines and data description sheets. The data collection technique is carried out using three methods, namely through observation, interviews, and documentation. Data analysis techniques use interactive patterns, namely data reduction, data presentation, and verification. Based on the results of the study, the following findings were obtained. First, the shape of the semantic features of the tools used in the nyadran ritual, namely round, long, and square based on materials, sizes, and colors. Second, the function of the semantic features of the tools used in the nyadran ritual, namely as a substitute for containers for eating, containers for cooking, and containers for storing foodstuffs used on a daily and ritual basis. Third, the meaning of the tools used in the nyadran ritual is different, namely as a symbol of the presence of ancestors, as the pride and honor of a certain person and as a form of respect for people who died unnaturally, which are interpreted in terms of days and rituals. The suggestions that the researcher can convey based on the results of this study are as follows. (1) This research can increase students' knowledge about the shape, function and meaning of an object or object. (2) The results of this study can help teachers in maximizing Indonesian language and literature lessons about the semantic features of the tools used in the nyadran ritual in tengger residents in Lumajang. (3) For future researchers, this research is expected to be a reference for conducting further research.

Multimodal Cross-Lingual Summarization for Videos: A Revisit in Knowledge Distillation Induced Triple-Stage Training Method.

Multimodal summarization (MS) for videos aims to generate summaries from multi-source information (e.g., video and text transcript), showing promising progress recently. However, existing works are limited to monolingual scenarios, neglecting non-native viewers' needs to understand videos in other languages. It stimulates us to introduce multimodal cross-lingual summarization for videos (MCLS), which aims to generate cross-lingual summaries from multimodal input of videos. Considering the challenge of high annotation cost and resource constraints in MCLS, we propose a knowledge distillation (KD) induced triple-stage training method to assist MCLS by transferring knowledge from abundant monolingual MS data to those data with insufficient volumes. In the triple-stage training method, a video-guided dual fusion network (VDF) is designed as the backbone network to integrate multimodal and cross-lingual information through diverse fusion strategies in the encoder and decoder; What's more, we propose two cross-lingual knowledge distillation strategies: adaptive pooling distillation and language-adaptive warping distillation (LAWD), designed for encoder-level and vocab-level distillation objects to facilitate effective knowledge transfer across cross-lingual sequences of varying lengths between MS and MCLS models. Specifically, to tackle lingual sequences of varying lengths between MS and MCLS models. Specifically, to tackle the challenge of unequal length of parallel cross-language sequences in KD, LAWD can directly conduct cross-language distillation while keeping the language feature shape unchanged to reduce potential information loss. We meticulously annotated the How2-MCLS dataset based on the How2 dataset to simulate MCLS scenarios. Experimental results show that the proposed method achieves competitive performance compared to strong baselines, and can bring substantial performance improvements to MCLS models by transferring knowledge from the MS model.

“No Gree for Anybody!”- “Without our compliance, their power means nothing”: unveiling the subtleness in Nigeria’s socio-political activism

ABSTRACT This study employs online archival and interview methods to understand how people on X (formerly Twitter) interpret and construct the ‘No Gree for Anybody’ tweets as a form of digital protest. ‘No Gree for Anybody,’ translating to ‘Do not compromise for anyone’ in Nigerian Pidgin English, became a sort of national anthem on social media, especially on Twitter, amid the socioeconomic challenges in Nigeria. The adoption of this slogan, despite concerns from the Nigerian Police, underscores its influential role as an alternative protest form advocating systemic reform and confronting entrenched power structures. The qualitative data analysis constructed four key themes: Language and Identity, Digital Protest/Mobilization and Collective Identity, Multiple Symbolism of Defiance, and Protests Continuum in Nigeria’s socio-political landscape. These themes provide a nuanced understanding of ‘No Gree for Anybody’ and how its subtleness and non-episodic features shape digital activism and socio-political discourse in Nigeria, highlighting its position in contemporary protest dynamics.

Investigating streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision

Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and deep learning techniques provides a cost-effective alternative for extracting streetscape features. However, prior studies often rely solely on semantic segmentation, overlooking distinctions in feature shapes and contours. This study addresses these limitations by combining semantic segmentation and object detection networks to comprehensively measure streetscape features from Baidu SVIs. Semantic segmentation identifies pixel-level proportions of features such as roads, sidewalks, buildings, fences, trees, and grass, while object detection captures discrete elements like vehicles, pedestrians, and traffic lights. Zero-inflated negative binomial regression models are employed to analyze the impact of these features on three crash types: vehicle-vehicle (VCV), vehicle–pedestrian (VCP), and single-vehicle crashes (SVC). Results show that incorporating streetscape features from combined deep learning methods significantly improves crash prediction. Vehicles have a significant impact on VCV and SVC crashes, whereas pedestrians predominantly affect VCP crashes. Road surfaces, sidewalks, and plants are associated with increased crash risks, while buildings and trees correlate with reduced vehicle crash frequencies. This study highlights the advantages of integrating semantic segmentation and object detection for streetscape analysis and underscores the critical role of environmental characteristics in road traffic crashes. The findings provide actionable insights for urban planning and traffic safety strategies.

Characterizing the Structure of Online Conversations Across Reddit

The proliferation of social media platforms has afforded social scientists unprecedented access to vast troves of data on human interactions, facilitating the study of online behavior at an unparalleled scale. These platforms typically structure conversations as threads, forming tree-like structures known as ''discussion trees.'' This paper examines the structural properties of online discussions on Reddit by analyzing both global (community-level) and local (post-level) attributes of these discussion trees. We conduct a comprehensive statistical analysis of a year's worth of Reddit data, encompassing a quarter of a million posts and several million comments. Our primary objective is to disentangle the relative impacts of global and local properties and evaluate how specific features shape discussion tree structures. The results reveal that both local and global features contribute significantly to explaining structural variation in discussion trees. However, local features, such as post content and sentiment, collectively have a greater impact, accounting for a larger proportion of variation in the width, depth, and size of discussion trees. Our analysis also uncovers considerable heterogeneity in the impact of various features on discussion structures. Notably, certain global features play crucial roles in determining specific discussion tree properties. These features include the subreddit's topic, age, popularity, and content redundancy. For instance, posts in subreddits focused on politics, sports, and current events tend to generate deeper and wider discussion trees. This research enhances our understanding of online conversation dynamics and offers valuable insights for both content creators and platform designers. By elucidating the factors that shape online discussions, our work contributes to ongoing efforts to improve the quality and effectiveness of digital discourse.

Epigenetic dynamics of partially methylated domains in human placenta and trophoblast stem cells

BackgroundThe placenta is essential for nutrient exchange and hormone production between the mother and the developing fetus and serves as an invaluable model for epigenetic research. Most epigenetic studies of the human placenta have used whole placentas from term pregnancies and have identified the presence of partially methylated domains (PMDs). However, the origin of these domains, which are typically absent in most somatic cells, remains unclear in the placental context.ResultsUsing whole-genome bisulfite sequencing and analysis of histone H3 modifications, we generated epigenetic profiles of human cytotrophoblasts during the first trimester and at term, as well as human trophoblast stem cells. Our study focused specifically on PMDs. We found that genomic regions likely to form PMDs are resistant to global DNA demethylation during trophectoderm reprogramming, and that PMDs arise through a slow methylation process within condensed chromatin near the nuclear lamina. In addition, we found significant differences in histone H3 modifications between PMDs in cytotrophoblasts and trophoblast stem cells.ConclusionsOur findings suggest that spatiotemporal genomic features shape megabase-scale DNA methylation patterns, including PMDs, in the human placenta and highlight distinct differences in PMDs between human cytotrophoblasts and trophoblast stem cells. These findings advance our understanding of placental biology and provide a basis for further research into human development and related diseases.

Modelling absorption and emission profiles from accretion disc winds with WINE

Context. Fast and massive winds are ubiquitously observed in the UV and X-ray spectra of active galactic nuclei (AGNs) and other accretion-powered sources. Several theoretical and observational pieces of evidence suggest they are launched at accretion disc scales, carrying significant mass and angular momentum. Thanks to such high-energy output, they may play an important role in transferring the energy released by accretion to the surrounding environment. In the case of AGNs, this process can help to set the so-called co-evolution between an AGN and its host galaxy, which mutually regulates their growth across cosmic time. To precisely assess the effective role of UV and X-ray winds at accretion disc scales, it is necessary to accurately measure their properties, including mass and energy rates. However, this is a challenging task, due to both the limited signal-to-noise ratio of available observations and the limitations of the models currently used in the spectral analysis. Aims. We aim to maximise the scientific return of current and future observations by improving the theoretical modelling of these winds through our Winds in the Ionised Nuclear Environment (WINE) model. WINE is a spectroscopic model specifically designed for disc winds in AGNs and compact accreting sources, which couples photoionisation and radiative transfer with special relativistic effects and a three-dimensional model of the emission profiles. Methods. We explore with WINE the main spectral features associated with the disc winds in AGNs, with a particular emphasis on the detectability of the wind emission in the total transmitted spectrum. We explore the impact of the wind ionisation, column density, velocity field, and geometry in shaping the emission profiles. We simulated observations with the X-ray microcalorimeter Resolve on board the recently launched XRISM satellite and the X-IFU on board the future Athena mission. This allows us to assess the capabilities of these telescopes in the study of disc winds in X-ray spectra of AGNs for the typical physical properties and exposure times of the sources included in the XRISM performance verification phase. Results. The wind kinematic and geometry (together with the ionisation and column density) deeply affect both shape and strength of the wind spectral features. Thanks to this, both Resolve and, on a longer timescale, X-IFU will be able to accurately constrain the main properties of disc winds over a broad range of ionisation, column densities, and covering factors. We also investigate the impact of the spectral energy distribution (SED) on the resulting appearance of the wind. Our findings reveal a dramatic difference in the gas opacity when using a soft, Narrow Line Seyfert 1-like SED compared to a canonical powerlaw SED with a spectral index of Γ ≈ 2.

Functional microstructured surface polishing: mechanism and applications

Functional microstructured surfaces are becoming increasingly important in the fields of optics, microfluidics, and surface engineering. These parts' functional surface roughnesses and microstructure molding accuracies directly affect their final service performance characteristics. To improve the machining qualities of functional microstructured surfaces, subsequent polishing treatment is necessary. This paper discusses the different polishing methods and effects of functional microstructured surface parts. Moreover, the challenges and bottlenecks in polishing functional microstructured surfaces are analyzed and discussed by considering the microstructure feature sizes, shapes, and substrate surface shapes. Then, the advantages, disadvantages, applications, and limitations of functional microstructured surface polishing methods are elucidated. Finally, the development trends of form-preserving polishing methods for functional microstructured surfaces are noted.

From text to insights: leveraging NLP to assess how landscape features shape tourist perceptions and emotions toward traditional villages

Abstract Understanding tourist perceptions and the relationship between landscape features and emotional attitudes in traditional village is crucial for sustainable development. However, quantifying these perceptions over vast spatiotemporal scales is challenging. Additionally, there is a paucity of knowledge on which landscape features influence tourist perceptions within varied samples on a spatial scale. In this study, we analyzed 39,130 online reviews of 57 traditional Chinese villages from 2018 to 2023. Utilizing Word2Vec and machine learning techniques, we identified 14 key landscape feature indicators. These were quantitatively scored using expert evaluations, and their relationship to tourists’ emotional attitudes was determined through linear regression analysis. The analysis revealed that architectural elements, service facilities, convenience, and sanitation are positively associated with tourists’ emotional attitudes towards traditional villages. Contrary to expectations, the frequency of landscape feature mentions did not correlate significantly with emotional attitudes, challenging traditional assumptions about the visibility of landscape features and their impact.

Grayscale Lithography and a Brief Introduction to Other Widely Used Lithographic Methods: A State-of-the-Art Review.

Lithography serves as a fundamental process in the realms of microfabrication and nanotechnology, facilitating the transfer of intricate patterns onto a substrate, typically in the form of a wafer or a flat surface. Grayscale lithography (GSL) is highly valued in precision manufacturing and research endeavors because of its unique capacity to create intricate and customizable patterns with varying depths and intensities. Unlike traditional binary lithography, which produces discrete on/off features, GSL offers a spectrum of exposure levels. This enables the production of complex microstructures, diffractive optical elements, 3D micro-optics, and other nanoscale designs with smooth gradients and intricate surface profiles. GSL plays a crucial role in sectors such as microelectronics, micro-optics, MEMS/NEMS manufacturing, and photonics, where precise control over feature depth, shape, and intensity is critical for achieving advanced functionality. Its versatility and capacity to generate tailored structures make GSL an indispensable tool in various cutting-edge applications. This review will delve into several lithographic techniques, with a particular emphasis on masked and maskless GSL methods. As these technologies continue to evolve, the future of 3D micro- and nanostructure manufacturing will undoubtedly assume even greater significance in various applications.

Symmetry and Meaningfulness in the Spotlight of Expertness

Empirical aesthetics focuses on understanding how perceptual features shape aesthetic preferences, with symmetry being a key aspect. However, recent studies show variation in symmetry preference across samples and stimuli. Our study aims to explore the boundaries of symmetry preference, particularly in relation to meaning, prototypicality and expertise in visual arts. With our stimuli we can test the comparative dominance of these features. In our forced-choice preference task (N = 196), we manipulated images for symmetry, meaning, and prototypicality. Findings reveal that symmetry preference is only remarkable in meaningless images among non-experts. Instead, meaningfulness emerged as a significant factor influencing their aesthetic preferences. Experts show no distinct preference for symmetry or meaningfulness. However, prototypicality is favoured by both groups in meaningful stimuli, regardless of symmetry. These results highlight the dominance of meaning in aesthetic experience and underscore the complex interplay between symmetry, meaning, and expertise in visual arts.

Research on Spatial Data Aggregation Based on Aggregation Degree Function

Abstract. Restricted by the development level of hardware and software technology, computers cannot load massive data at one time, coupled with the fact that the three-dimensional linear scene is characterized by complicated lines, irregular shapes of features and a large number of features. Therefore, it is necessary to seek a three-dimensional spatial data organization and scheduling method applicable to the railroad scene to realize the display and interaction of the scene. In this paper, we propose a data organization method based on the aggregation degree function for the 3D model of features. The feature model is projected to generate an orthographic projection map, the cluster division rule is designed to divide the subsets, the feature model after clustering is aggregated using the feature model aggregation rule, and the aggregated model is displayed according to the aggregation degree function and viewpoint factor. The results of the test applied to the railroad scene show that the 3D spatial data aggregation based on the aggregation degree function can improve the loading speed of the 3D scene, and can effectively support the application of interactive visualization of the railroad 3D scene.

The utility of PET-CT in baseline and sequential characterisation of Pulmonary Pleomorphic Carcinoma

Pulmonary Pleomorphic Carcinomas (PPCs) represent a rare and aggressive subtype of Non-Small Cell Lung Cancer (NSCLC) that can only be definitively diagnosed on a surgical specimen. This study utilised PET-CT to evaluate radiological characteristics of PPCs. This study retrospectively evaluated the radiological characteristics of PCCs diagnosed in St James’s Hospital Dublin between 2012-2023. Computed Tomography (CT) and Positron Emission Tomography (FDG-PET) imaging features (size, location, density, shape, invasion, and growth kinetics) and standard uptake value for each lesion were evaluated. 39 PCCs were identified with a mean age of 66.5 years (range: 49-82 years). FDG-PET was performed in all 39 cases. Tumours demonstrated a high FDG uptake at baseline with a mean (SUV) of 12.6 (range: 1.4 - 36.9). A second interval PET-CT on average 3.3 months after the first in 3 cases demonstrated over 120% increase in SUV. The mean tumour size was 4.3 cm (range: 1.0 - 14.5 cm). Tumours developed rapid interval growth, reaching a mean maximum diameter of 6.6 cm (53.4%) within a mean of 2.1 months. Tumours were predominantly located in the upper lobe (71.8 %) and displayed necrotic features in 53.8 % of cases. 82.1% of tumours invaded the mediastinum. This study describes the largest cohort of Pulmonary Pleomorphic Carcinoma in the literature. Tumours demonstrate a high SUV on baseline imaging and demonstrate rapid growth on interval imaging and central necrosis. Please click on the 'PDF' for the full abstract!

Multi-scale sensing and multi-dimensional feature enhancement for surface defect detection of hot-rolled steel strip

ABSTRACT Surface defect detection of hot-rolled steel strip is a crucial process in its production. Aiming at the problem of poor detection accuracy caused by complex shape of defect features and imbalance between object scale and feature scale, this paper proposes a multi-scale sensing and multi-dimensional feature enhancement algorithm. Firstly, a multi-branch residual aggregation module (MRAM) is proposed, which uses the interactive features of different receptive fields between layers to capture global and local texture features, improving the feature extraction capability for defects of different scales. Then, the multi-dimensional attention enhancement head (MAEH) is proposed, which integrates feature information from different prediction branches and realises feature information enhancement in scale, space and channel dimensions for the aggregated information to improve the accuracy and robustness of steel surface defect detection. The results show that the proposed algorithm achieves 82.7% mAP and 89 FPS on the NEU-DET dataset, and 73.3% mAP and 94 FPS on the GC10-DET dataset. Compared with state-of-the-art algorithms, the proposed algorithm has the highest mAP, strong generalisation ability, and good real-time performance, which proves its effectiveness in detecting hot-rolled steel strip surface defects.

Computed tomography-based radiomics nomogram for prediction of lympho-vascular and perineural invasion in esophageal squamous cell cancer patients: a retrospective cohort study

PurposeLympho-vascular invasion (LVI) and perineural invasion (PNI) have been established as prognostic factors in various types of cancers. The preoperative prediction of LVI and PNI has the potential to guide personalized medicine strategies for patients with esophageal squamous cell cancer (ESCC). This study investigates whether radiomics features derived from preoperative contrast-enhanced CT could predict LVI and PNI in ESCC patients.Methods and materialsA retrospective cohort of 544 ESCC patients who underwent esophagectomy were included in this study. Preoperative contrast-enhanced CT images, pathological results of PNI and LVI, and clinical characteristics were collected. For each patient, the gross tumor volume (GTV-T) and lymph nodes volume (GTV-N) were delineated and four categories of radiomics features (first-order, shape, textural and wavelet) were extracted from GTV-T and GTV-N. The Mann–Whitney U test was used to select significant features associated with LVI and PNI in turn. Subsequently, radiomics signatures for LVI and PNI were constructed using LASSO regression with ten-fold cross-validation. Significant clinical characteristics were combined with radiomics signature to develop two nomogram models for predicting LVI and PNI, respectively. The area under the curve (AUC) and calibration curve were used to evaluate the predictive performance of the models.ResultsThe radiomics signature for LVI prediction consisted of 28 features, while the PNI radiomics signature comprised 14 features. The AUCs of the LVI radiomics signature were 0.77 and 0.74 in the training and validation groups, respectively, while the AUCs of the PNI radiomics signature were 0.69 and 0.68 in the training and validation groups. The nomograms incorporating radiomics signatures and significant clinical characteristics such as age, gender, thrombin time and D-Dimer showed improved predictive performance for both LVI (AUC: 0.82 and 0.80 in the training and validation group) and PNI (AUC: 0.75 and 0.72 in the training and validation groups) compared to the radiomics signature alone.ConclusionThe radiomics features extracted from preoperative contrast-enhanced CT of gross tumor and lymph nodes have demonstrated their potential in predicting LVI and PNI in ESCC patients. Furthermore, the incorporation of clinical characteristics has shown additional value, resulting in improved predictive performance.