Impact of signal-to-noise ratio and contrast definition on the sensitivity assessment and benchmarking of fluorescence molecular imaging systems.

Significance: Standardization of fluorescence molecular imaging (FMI) is critical for ensuring quality control in guiding surgical procedures. To accurately evaluate system performance, two metrics, the signal-to-noise ratio (SNR) and contrast, are widely employed. However, there is currently no consensus on how these metrics can be computed. Aim: We aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems. Approach: We quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system. Results: We show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to ∼ 35 dB (SNR), ∼ 8.65 a . u . (contrast), and ∼ 0.67 a . u . (BM score). Conclusions: The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.

Endoscopic detection and diagnosis of gastric cancer using image-enhanced endoscopy: A systematic review and meta-analysis.

Objectives: We aimed to conduct a systematic review and meta-analysis to assess the value of image-enhanced endoscopy including blue laser imaging (BLI), linked color imaging, narrow-band imaging (NBI), and texture and color enhancement imaging to detect and diagnose gastric cancer (GC) compared to that of white-light imaging (WLI). Methods: Studies meeting the inclusion criteria were identified through PubMed, Cochrane Library, and Japan Medical Abstracts Society databases searches. The pooled risk ratio for dichotomous variables was calculated using the random-effects model to assess the GC detection between WLI and image-enhanced endoscopy. A random-effects model was used to calculate the overall diagnostic performance of WLI and magnifying image-enhanced endoscopy for GC. Results: Sixteen studies met the inclusion criteria. The detection rate of GC was significantly improved in linked color imaging compared with that in WLI (risk ratio, 2.20; 95% confidence interval [CI], 1.39-3.25; p < 0.01) with mild heterogeneity. Magnifying endoscopy with NBI (ME-NBI) obtained a pooled sensitivity, specificity, and area under the summary receiver operating curve of 0.84 (95 % CI, 0.80-0.88), 0.96 (95 % CI, 0.94-0.97), and 0.92, respectively. Similarly, ME-BLI showed a pooled sensitivity, specificity, and area under the curve of 0.81 (95 % CI, 0.77-0.85), 0.85 (95 % CI, 0.82-0.88), and 0.95, respectively. The diagnostic efficacy of ME-NBI/BLI for GC was evidently high compared to that of WLI, However, significant heterogeneity among the NBI studies still existed. Conclusions: Our meta-analysis showed a high detection rate for linked color imaging and a high diagnostic performance of ME-NBI/BLI for GC compared to that with WLI.

Characterization of Foodborne Pathogens in Biofilms: A Four-Dimensional Structural Dynamics Approach Using HCS-CLSM.

The functional properties of biofilms are intimately related to their spatial architecture. Structural data are therefore of prime importance to dissect the complex social and survival strategies of biofilms and ultimately to improve their control. Confocal laser scanning microscopy (CLSM) is the most widespread microscopic tool to decipher biofilm structure, enabling noninvasive three-dimensional investigation of their dynamics down to the single-cell scale. The emergence of fully automated high content screening (HCS) systems, associated with large-scale image analysis, has radically amplified the flow of available biofilm structural data. In this contribution, we present a HCS-CLSM protocol used to analyze biofilm four-dimensional structural dynamics at high throughput. Meta-analysis of the quantitative variables extracted from HCS-CLSM will contribute to a better biological understanding of biofilm traits.

Recent advancements in image-enhanced endoscopy in the pancreatobiliary field.

Image-enhanced endoscopy (IEE) has advanced gastrointestinal disease diagnosis and treatment. Traditional white-light imaging has limitations in detecting all gastrointestinal diseases, prompting the development of IEE. In this review, we explore the utility of IEE, including texture and color enhancement imaging and red dichromatic imaging, in pancreatobiliary (PB) diseases. IEE includes methods such as chromoendoscopy, optical-digital, and digital methods. Chromoendoscopy, using dyes such as indigo carmine, aids in delineating lesions and structures, including pancreato-/cholangio-jejunal anastomoses. Optical-digital methods such as narrow-band imaging enhance mucosal details and vessel patterns, aiding in ampullary tumor evaluation and peroral cholangioscopy. Moreover, red dichromatic imaging with its specific color allocation, improves the visibility of thick blood vessels in deeper tissues and enhances bleeding points with different colors and see-through effects, proving beneficial in managing bleeding complications post-endoscopic sphincterotomy. Color enhancement imaging, a novel digital method, enhances tissue texture, brightness, and color, improving visualization of PB structures, such as PB orifices, anastomotic sites, ampullary tumors, and intraductal PB lesions. Advancements in IEE hold substantial potential in improving the accuracy of PB disease diagnosis and treatment. These innovative techniques offer advantages paving the way for enhanced clinical management of PB diseases. Further research is warranted to establish their standard clinical utility and explore new frontiers in PB disease management.

Development of prediction models based on risk scores for clinically significant prostate cancer on MRI/TRUS fusion biopsy.

BACKGROUND: The implementation of population screening for prostate cancer has increased the number of patients with biochemical suspicion. Prediction models may reduce the number of unnecessary biopsies by identifying patients who benefit the most from them. Our aim is to develop a prediction model that is easily applicable in patients with suspicion of prostate cancer in the urology clinic setting to avoid unnecessary biopsies. METHODS: We developed prediction models based on risk scores for the detection of prostate cancer and clinically significant prostate cancer using the TRIPOD guidelines. For this, we conducted an observational and retrospective review of computerised medical records of 204 patients undergoing prostate fusion biopsy between 2018 and 2021. We also reviewed other prediction models for prostate cancer including radiological parameters and targeted sampling of suspicious lesions. RESULTS: A total of 204 patients underwent a biopsy, 138 were diagnosed of prostate cancer, and from them, 60 of clinically significant prostate cancer. Multivariate regression and random forest analysis were performed. Age, PSA density, diameter of the index lesions and PIRADS score on MRI were identified as predictors with an Area Under the Curve ranging between 0.71 and 0.80 and acceptable calibration results. Risk scores may avoid between 21.7% and 48.1% of biopsies. CONCLUSION: Our prediction models are characterised by ease of use and may reduce unnecessary biopsies with satisfactory discrimination and calibration results while bringing benefits to the healthcare system and patients.

Gender and ethnicity bias in generative artificial intelligence text-to-image depiction of pharmacists.

INTRODUCTION: In Australia, 64% of pharmacists are women but continue to be under-represented. Generative artificial intelligence (AI) is potentially transformative but also has the potential for errors, misrepresentations, and bias. Generative AI text-to-image production using DALL-E 3 (OpenAI) is readily accessible and user-friendly but may reinforce gender and ethnicity biases. METHODS: In March 2024, DALL-E 3 was utilized to generate individual and group images of Australian pharmacists. Collectively, 40 images were produced with DALL-E 3 for evaluation of which 30 were individual characters and the remaining 10 images were comprised of multiple characters (N = 155). All images were independently analysed by two reviewers for apparent gender, age, ethnicity, skin tone, and body habitus. Discrepancies in responses were resolved by third-observer consensus. RESULTS: Collectively for DALL-E 3, 69.7% of pharmacists were depicted as men, 29.7% as women, 93.5% as a light skin tone, 6.5% as mid skin tone, and 0% as dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian pharmacists (P < .001). Among the images of individual pharmacists, DALL-E 3 generated 100% as men and 100% were light skin tone. CONCLUSIONS: This evaluation reveals the gender and ethnicity bias associated with generative AI text-to-image generation using DALL-E 3 among Australian pharmacists. Generated images have a disproportionately high representation of white men as pharmacists which is not representative of the diversity of pharmacists in Australia today.

Utilizing diffusion tensor imaging as an image biomarker in exploring the therapeutic efficacy of forniceal deep brain stimulation in a mice model of Alzheimer's disease.

Objective.With prolonged life expectancy, the incidence of memory deficits, especially in Alzheimer's disease (AD), has increased. Although multiple treatments have been evaluated, no promising treatment has been found to date. Deep brain stimulation (DBS) of the fornix area was explored as a possible treatment because the fornix is intimately connected to memory-related areas that are vulnerable in AD; however, a proper imaging biomarker for assessing the therapeutic efficiency of forniceal DBS in AD has not been established.Approach.This study assessed the efficacy and safety of DBS by estimating the optimal intersection volume between the volume of tissue activated and the fornix. Utilizing a gold-electroplating process, the microelectrode's surface area on the neural probe was increased, enhancing charge transfer performance within potential water window limits. Bilateral fornix implantation was conducted in triple-transgenic AD mice (3 × Tg-AD) and wild-type mice (strain: B6129SF1/J), with forniceal DBS administered exclusively to 3 × Tg-AD mice in the DBS-on group. Behavioral tasks, diffusion tensor imaging (DTI), and immunohistochemistry (IHC) were performed in all mice to assess the therapeutic efficacy of forniceal DBS.Main results.The results illustrated that memory deficits and increased anxiety-like behavior in 3 × Tg-AD mice were rescued by forniceal DBS. Furthermore, forniceal DBS positively altered DTI indices, such as increasing fractional anisotropy (FA) and decreasing mean diffusivity (MD), together with reducing microglial cell and astrocyte counts, suggesting a potential causal relationship between revised FA/MD and reduced cell counts in the anterior cingulate cortex, hippocampus, fornix, amygdala, and entorhinal cortex of 3 × Tg-AD mice following forniceal DBS.Significance.The efficacy of forniceal DBS in AD can be indicated by alterations in DTI-based biomarkers reflecting the decreased activation of glial cells, suggesting reduced neural inflammation as evidenced by improvements in memory and anxiety-like behavior.

[Research progress on colorectal cancer identification based on convolutional neural network].

Colorectal cancer (CRC) is a common malignant tumor that seriously threatens human health. CRC presents a formidable challenge in terms of accurate identification due to its indistinct boundaries. With the widespread adoption of convolutional neural networks (CNNs) in image processing, leveraging CNNs for automatic classification and segmentation holds immense potential for enhancing the efficiency of colorectal cancer recognition and reducing treatment costs. This paper explores the imperative necessity for applying CNNs in clinical diagnosis of CRC. It provides an elaborate overview on research advancements pertaining to CNNs and their improved models in CRC classification and segmentation. Furthermore, this work summarizes the ideas and common methods for optimizing network performance and discusses the challenges faced by CNNs as well as future development trends in their application towards CRC classification and segmentation, thereby promoting their utilization within clinical diagnosis.

Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain.

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

Lesion region inpainting: an approach for pseudo-healthy image synthesis in intracranial infection imaging.

The synthesis of pseudo-healthy images, involving the generation of healthy counterparts for pathological images, is crucial for data augmentation, clinical disease diagnosis, and understanding pathology-induced changes. Recently, Generative Adversarial Networks (GANs) have shown substantial promise in this domain. However, the heterogeneity of intracranial infection symptoms caused by various infections complicates the model's ability to accurately differentiate between pathological and healthy regions, leading to the loss of critical information in healthy areas and impairing the precise preservation of the subject's identity. Moreover, for images with extensive lesion areas, the pseudo-healthy images generated by these methods often lack distinct organ and tissue structures. To address these challenges, we propose a three-stage method (localization, inpainting, synthesis) that achieves nearly perfect preservation of the subject's identity through precise pseudo-healthy synthesis of the lesion region and its surroundings. The process begins with a Segmentor, which identifies the lesion areas and differentiates them from healthy regions. Subsequently, a Vague-Filler fills the lesion areas to construct a healthy outline, thereby preventing structural loss in cases of extensive lesions. Finally, leveraging this healthy outline, a Generative Adversarial Network integrated with a contextual residual attention module generates a more realistic and clearer image. Our method was validated through extensive experiments across different modalities within the BraTS2021 dataset, achieving a healthiness score of 0.957. The visual quality of the generated images markedly exceeded those produced by competing methods, with enhanced capabilities in repairing large lesion areas. Further testing on the COVID-19-20 dataset showed that our model could effectively partially reconstruct images of other organs.

Effect of alkali treatment on mechanical and water absorption properties of biodegradable wheat-straw/glass fiber reinforced epoxy hybrid composites: A sustainable alternative for conventional materials.

Fiber-reinforced polymer composites are preferred over conventional materials because of their superior strength and modulus. Previously limited due to high manufacturing costs, synthetic fibers have been replaced by some natural fibers, such as waste wheat straw fibers. Here, epoxy-based polymer composites' mechanical and physical properties have been investigated, focusing on fiber weight ratios for both treated and untreated fiber. The research found that treated fibers display more effective mechanical qualities than untreated fibers, with a higher tensile strength of 54.4 MPa. The untreated Wheat Straw-Glass fiber reinforced composite has a less tensile strength of 26.3 MPa (10 wt% fiber). Pure resin-based composite has the most minor tensile strength at 1.52 MPa. The highest flexural strength obtained for hybrid composite is 88.76 MPa for treated fiber with epoxy resin and 49.6 MPa for untreated 30 wt % fiber. At the same time, the sole epoxy resin composite has the lowest value of 10.60 MPa. Untreated fiber (30 wt%) has the highest impact energy of 8J. Untreated wheat straw fiber absorbs more water due to its hydrophilic nature. In contrast, treated fiber exhibits better bonding and minimal water content, and the sole epoxy resin composite exhibits hydrophobic properties, resulting in less water absorption. The treated fiber displays better bonding than the untreated fiber throughout the SEM analysis. Wheat Straw fiber is mainly used for biodegradable plastic formation, housing construction, building materials, etc.

Automated segmentation of clinical CT scans of the cochlea and analysis of the cochlea's vertical profile.

Purpose: Knowledge of the cochlear anatomy in individual patients is helpful for improving electrode selection and placement during cochlear implantation, as well as in surgical planning. The aim of this study was to develop a model-free automated segmentation algorithm to obtain 3D surfaces from clinical computed tomography (CT) scans that describe an individual's cochlear anatomy and can be used to quantitatively analyze the cochlea's vertical trajectory. Methods: Clinical CT scans were re-oriented and re-sliced to obtain mid-modiolar slices. Using these slices, we segmented the cross-section of the cochlea. Results: 3D surfaces were obtained for the first 1.5 turns of 648 cochleae. Validation of our algorithm against the manually segmented ground truth obtained from 8 micro-CT scans showed good agreement, with 90 % area overlap and an average distance of 0.11 mm between the segmentation contours. The average cochlear duct length for the basal turn was 16.1 mm along the central path and 22.4 mm along the outer wall. The use of an intrinsic, observer-independent coordinate system and principal component analysis enabled unambiguous quantitative evaluation of the vertical trajectory of the cochlea, revealing only a weak correlation between the symmetry of the commonly used basal turn diameters (B-ratio of A and B diameters) and the profile of the vertical trajectory. Conclusion: A model-free segmentation algorithm can achieve similar accuracy as previously published methods relying on statistical shapes. Quantitative analysis of the vertical trajectory can replace the categorization into rollercoaster, sloping, or intermediate vertical trajectory types.

Application of the full-width-at-half-maximum image segmentation method to analyse retinal vascular changes in patients with internal carotid artery stenosis.

Background: To investigate the correlation between retinal vascular changes and ICA stenosis by measuring retinal vessels using full-width-at-half-maximum (FWHM) and intelligent image recognition. Methods: This research selected patients who were admitted to the Vascular Surgery Department of Quzhou People's Hospital from January 2018 to December 2020 and were preparing for Carotid Artery Stenting (CAS). Participants were divided into two groups: without ICA stenosis (Group 0) and with ICA stenosis (Group 1). A total of 109 cases were included in the study, with 50 cases in Group 1 and 59 cases in Group 0. Vascular images of superior temporal zone B of the retina were obtained by spectral domain optical coherence tomography (SD-OCT). The edges of retinal vessels were identified by FWHM. Each vessel of all subjects was measured three times with the FWHM, and the average value was taken to obtain the retinal arteriolar lumen diameter (RALD), retinal arteriolar outer diameter (RAOD), retinal venular lumen diameter (RVLD), and retinal venular outer diameter (RVOD),Arterial Wall Thickness (AWT),Venular Wall Thickness (VWT)=(RVOD-RVLD)/2,Arteriovenous Ratio (AVR) = RAOD/RVOD. Results: We found that compared to Group 0, Group 1 had smaller RALD (P < 0.001) and RAOD (P < 0.001), and wider RVOD (P < 0.001), with thicker VWT (P < 0.001). When compared with the contralateral eye in Group 1, the ipsilateral eye exhibited even smaller RALD,RAOD and AVR (P < 0.001, P < 0.001, P < 0.001). After CAS, the RALD,RAOD and AVR in Group 1 increased (P < 0.001, P < 0.001, P < 0.001),while the RVLD and RVOD decreased (P < 0.05, P < 0.001). Our research reveals a significant correlation between retinal vascular changes and internal ICA stenosis. Conclusion: Utilizing SD-OCT in conjunction with the FWHM,we achieved a non-invasive, intelligent, stable, and precise acquisition of data pertaining to retinal vessels. These findings underscore a significant correlation between alterations in retinal vascular structure and the presence of ICA stenosis, as demonstrated by our research.

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study.

BACKGROUND: Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic detection of NSD has not yet been studied. OBJECTIVE: This research aimed to develop and evaluate a real-time model that can detect probable NSD using cone beam computed tomography (CBCT) images. METHODS: Coronal section images were obtained from 204 full-volume CBCT scans. The scans were classified as normal and deviated by 2 maxillofacial radiologists. The images were then used to train and test the AI model. Mask region-based convolutional neural networks (Mask R-CNNs) comprising 3 different backbones-ResNet50, ResNet101, and MobileNet-were used to detect deviated nasal septum in 204 CBCT images. To further improve the detection, an image preprocessing technique (contrast enhancement [CEH]) was added. RESULTS: The best-performing model-CEH-ResNet101-achieved a mean average precision of 0.911, with an area under the curve of 0.921. CONCLUSIONS: The performance of the model shows that the model is capable of detecting nasal septal deviation. Future research in this field should focus on additional preprocessing of images and detection of NSD based on multiple planes using 3D images.

KnotResolver: Tracking self-intersecting filaments in microscopy using directed graphs.

MOTIVATION: Quantification of microscopy time-series of in vitro reconstituted motor driven microtubule (MT) transport in 'gliding assays' is typically performed using computational object tracking tools. However, these are limited to non-intersecting and rod-like filaments. RESULTS: Here, we describe a novel computational image-analysis pipeline, KnotResolver, to track image time-series of highly curved self-intersecting looped filaments (knots) by resolving cross-overs. The code integrates filament segmentation and cross-over or 'knot' identification based on directed graph representation, where nodes represent cross-overs and edges represent the path connecting them. The graphs are mapped back to contours and the distance to a reference minimized. The accuracy of contour detection is sub-pixel with a robustness to noise. We demonstrate the utility of KnotResolver by automatically quantifying 'flagella-like' curvature dynamics and wave-like oscillations of clamped microtubules in a 'gliding assay'. AVAILABILITY AND IMPLEMENTATION: The MATLAB based source code is released as OpenSource and is available at https://github.com/CyCelsLab/MTKnotResolver. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics.

Boundary-based registration improves sensitivity for detecting hypoperfusion in sporadic frontotemporal lobar degeneration.

Introduction: Frontotemporal lobar degeneration (FTLD) is associated with FTLD due to tau (FTLD-tau) or TDP (FTLD-TDP) inclusions found at autopsy. Arterial Spin Labeling (ASL) MRI is often acquired in the same session as a structural T1-weighted image (T1w), enabling detection of regional changes in cerebral blood flow (CBF). We hypothesize that ASL-T1w registration with more degrees of freedom using boundary-based registration (BBR) will better align ASL and T1w images and show increased sensitivity to regional hypoperfusion differences compared to manual registration in patient participants. We hypothesize that hypoperfusion will be associated with a clinical measure of disease severity, the FTLD-modified clinical dementia rating scale sum-of-boxes (FTLD-CDR). Materials and methods: Patients with sporadic likely FTLD-tau (sFTLD-tau; N = 21), with sporadic likely FTLD-TDP (sFTLD-TDP; N = 14), and controls (N = 50) were recruited from the Connectomic Imaging in Familial and Sporadic Frontotemporal Degeneration project (FTDHCP). Pearson's Correlation Coefficients (CC) were calculated on cortical vertex-wise CBF between each participant for each of 3 registration methods: (1) manual registration, (2) BBR initialized with manual registration (manual+BBR), (3) and BBR initialized using FLIRT (FLIRT+BBR). Mean CBF was calculated in the same regions of interest (ROIs) for each registration method after image alignment. Paired t-tests of CC values for each registration method were performed to compare alignment. Mean CBF in each ROI was compared between groups using t-tests. Differences were considered significant at p < 0.05 (Bonferroni-corrected). We performed linear regression to relate FTLD-CDR to mean CBF in patients with sFTLD-tau and sFTLD-TDP, separately (p < 0.05, uncorrected). Results: All registration methods demonstrated significant hypoperfusion in frontal and temporal regions in each patient group relative to controls. All registration methods detected hypoperfusion in the left insular cortex, middle temporal gyrus, and temporal pole in sFTLD-TDP relative to sFTLD-tau. FTLD-CDR had an inverse association with CBF in right temporal and orbitofrontal ROIs in sFTLD-TDP. Manual+BBR performed similarly to FLIRT+BBR. Discussion: ASL is sensitive to distinct regions of hypoperfusion in patient participants relative to controls, and in patients with sFTLD-TDP relative to sFTLD-tau, and decreasing perfusion is associated with increasing disease severity, at least in sFTLD-TDP. BBR can register ASL-T1w images adequately for controls and patients.

BIOMERO: A scalable and extensible image analysis framework.

In the rapidly evolving field of bioimaging, the integration and orchestration of findable, accessible, interoperable, and reusable (FAIR) image analysis workflows remains a challenge. We introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform; FAIR workflows; and high-performance computing (HPC) environments. BIOMERO facilitates seamless execution of FAIR workflows, particularly for large datasets from high-content or high-throughput screening. BIOMERO empowers researchers by eliminating the need for specialized knowledge, enabling scalable image processing directly from OMERO. BIOMERO notably supports the sharing and utilization of FAIR workflows between OMERO, Cytomine/BIAFLOWS, and other bioimaging communities. BIOMERO will promote the widespread adoption of FAIR workflows, emphasizing reusability, across the realm of bioimaging research. Its user-friendly interface will empower users, including those without technical expertise, to seamlessly apply these workflows to their datasets, democratizing the utilization of AI by the broader research community.

The relationship among body image, psychological distress, and quality of life in young breast cancer patients: a cross-sectional study.

Purpose: The aim of this study is to explore the interrelationships among body image perception, levels of psychological distress, and the quality of life (QOL) experienced by young breast cancer patients. Methods: This study analyzed data from 339 young female breast cancer patients aged between 18 and 40 years (mean age was 33.47 years) from August 2023 to February 2024. Data on demographic characteristics, psychological distress, body image, medical coping, and QOL of young breast cancer patients were collected. Psychological distress, body image, medical coping, and QOL were measured using the Distress Thermometer (DT), Hospital Anxiety and Depression Scale (HADS), Body Image Scale (BIS), Medical Coping Modes Questionnaire (MCMQ), and Functional Assessment of Cancer Therapy-Breast (FACT-B), respectively. Multiple regression analysis was conducted to examine factors influencing QOL. Results: After adjusting for covariates, significant predictors of QOL in young survivors included psychological distress (ß = -3.125; p = 0.002), anxiety and depression (ß = -4.31; p < 0.001), cognitive dimension of body image (ß = -0.218; p = 0.027), behavioral dimension of body image (ß = 0.579; p = 0.047), and confrontational dimension of medical coping (ß = -0.124; p = 0.01). Conclusion: The findings suggest that higher levels of body image concerns and psychological distress are associated with poorer QOL among young female breast cancer patients. Furthermore, breast cancer patients facing with more positive medical coping strategies predicted a higher QOL.