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.

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.

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.

Will Transformers change gastrointestinal endoscopic image analysis? A comparative analysis between CNNs and Transformers, in terms of performance, robustness and generalization.

Gastrointestinal endoscopic image analysis presents significant challenges, such as considerable variations in quality due to the challenging in-body imaging environment, the often-subtle nature of abnormalities with low interobserver agreement, and the need for real-time processing. These challenges pose strong requirements on the performance, generalization, robustness and complexity of deep learning-based techniques in such safety-critical applications. While Convolutional Neural Networks (CNNs) have been the go-to architecture for endoscopic image analysis, recent successes of the Transformer architecture in computer vision raise the possibility to update this conclusion. To this end, we evaluate and compare clinically relevant performance, generalization and robustness of state-of-the-art CNNs and Transformers for neoplasia detection in Barrett's esophagus. We have trained and validated several top-performing CNNs and Transformers on a total of 10,208 images (2,079 patients), and tested on a total of 7,118 images (998 patients) across multiple test sets, including a high-quality test set, two internal and two external generalization test sets, and a robustness test set. Furthermore, to expand the scope of the study, we have conducted the performance and robustness comparisons for colonic polyp segmentation (Kvasir-SEG) and angiodysplasia detection (Giana). The results obtained for featured models across a wide range of training set sizes demonstrate that Transformers achieve comparable performance as CNNs on various applications, show comparable or slightly improved generalization capabilities and offer equally strong resilience and robustness against common image corruptions and perturbations. These findings confirm the viability of the Transformer architecture, particularly suited to the dynamic nature of endoscopic video analysis, characterized by fluctuating image quality, appearance and equipment configurations in transition from hospital to hospital. The code is made publicly available at: https://github.com/BONS-AI-VCA-AMC/Endoscopy-CNNs-vs-Transformers.

Complementary information mutual learning for multimodality medical image segmentation.

Radiologists must utilize medical images of multiple modalities for tumor segmentation and diagnosis due to the limitations of medical imaging technology and the diversity of tumor signals. This has led to the development of multimodal learning in medical image segmentation. However, the redundancy among modalities creates challenges for existing subtraction-based joint learning methods, such as misjudging the importance of modalities, ignoring specific modal information, and increasing cognitive load. These thorny issues ultimately decrease segmentation accuracy and increase the risk of overfitting. This paper presents the complementary information mutual learning (CIML) framework, which can mathematically model and address the negative impact of inter-modal redundant information. CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering. CIML first decomposes the multimodal segmentation task into multiple subtasks based on expert prior knowledge, minimizing the information dependence between modalities. Furthermore, CIML introduces a scheme in which each modality can extract information from other modalities additively through message passing. To achieve non-redundancy of extracted information, the redundant filtering is transformed into complementary information learning inspired by the variational information bottleneck. The complementary information learning procedure can be efficiently solved by variational inference and cross-modal spatial attention. Numerical results from the verification task and standard benchmarks indicate that CIML efficiently removes redundant information between modalities, outperforming SOTA methods regarding validation accuracy and segmentation effect. To emphasize, message-passing-based redundancy filtering allows neural network visualization techniques to visualize the knowledge relationship among different modalities, which reflects interpretability.

Connexin 43 controls metastatic behavior in triple negative breast cancer through TGFß1-Smad3-intergin αV signaling axis Based on optical image diagnosis.

Abnormal expression of connexin 43 (Cx43) contributes to the development and progression of cancer. However, its regulation is complex and dependent on the environment. The expression of Cx43 in triple-negative cancer lesions was analyzed by immunohistochemistry and optical coherence tomography using experimental models and clinical samples. The model of TGFß1-SMad3-in-αv signal axis was established and verified by experiments. The results show that Cx43 plays a key role in the regulation of triple-negative cancer metastasis. In vivo, over-expressed Cx43 decreased tumor volume and inhibited ITGαV, TGF-ß1, Smad3 and N-cadherin expressions, but enhanced the E-cadherin. Cx43 had the lowest expression in the TNBC samples, especially in lymph node metastatic TNBC patients and had a negative correlation with ITG alpha V, TGF-ß1 and Smad3.The study demonstrated Cx43 controlled metastatic behavior through TGF-ß1 -Smad3-ITG αV signaling axis in MDA-MB-231 cells, providing evidence for Cx43's function in TNBC. The optical image diagnosis method can realize the identification and quantitative evaluation of early cancer triple negative, and provide a new strategy and means for the treatment of cancer triple negative.

Blood Harmonisation of Endoscopic Transsphenoidal Surgical Video Frames on Phantom Models.

Physical phantom models have been integral to surgical training, yet they lack realism and are unable to replicate the presence of blood resulting from surgical actions. Existing domain transfer methods aim to enhance realism, but none facilitate blood simulation. This study investigates the overlay of blood on images acquired during endoscopic transsphenoidal pituitary surgery on phantom models. The process involves employing manual techniques using the GIMP image manipulation application and automated methods using pythons Blend Modes module. We then approach this as an image harmonisation task to assess its practicality and feasibility. Our evaluation uses Structural Similarity Index Measure and Laplacian metrics. The results we obtained emphasize the significance of image harmonisation, offering substantial insights within the surgical field. Our work is a step towards investigating data-driven models that can simulate blood for increased realism during surgical training on phantom models.

Comparison of virtual and true non-contrast images from dual-layer spectral detector computed tomography (CT) in patients with colorectal cancer.

Background: Colorectal cancer (CRC) is commonly assessed by computed tomography (CT), but the associated radiation exposure is a major concern. This study aimed to quantitatively and qualitatively compare the image quality of virtual non-contrast (VNC) images reconstructed from arterial and portal venous phases with that of true non-contrast (TNC) images in patients with CRC to assess the potential of TNC images to replace VNC images, thereby reducing the radiation dose. Methods: A total of 69 patients with postoperative pathologically confirmed CRC at the West China Hospital of Sichuan University between May 2022 and April 2023 were enrolled in this cross-sectional study. The CT protocol included the acquisition of TNC images, arterial and portal venous phase images; the VNC images were reconstructed from the two postcontrast phase images. Several parameters, including the CT attenuation value, absolute attenuation error, imaging noise [standard deviation (SD)], signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were measured in multiple abdominal structures for both the TNC and VNC images. Two blinded readers assessed the subjective image quality using a five-point scale. Interobserver agreement was evaluated using interclass correlation coefficients (ICCs). The paired t-test and Wilcoxon signed-rank test were used to compare the objective and subjective results between the TNC and VNC images. Individual measurements of radiation doses for the TNC scan and contrast scan protocols were recorded. Results: A total of 2,070 regions of interest (ROIs) of the 69 patients were analyzed. Overall, the VNC images exhibited significantly lower attenuation values and SD values than the TNC images in all tissues, except for the abdominal aorta, portal vein, and spleen. The mean absolute attenuation errors between the VNC and TNC images were all less than 10 Hounsfield units (HU). The percentages of absolute attenuation errors less than 5 and 10 HU in the VNC images from the arterial phase (VNCa) were 78.99% and 97.97%, respectively, while those from the portal venous phase (VNCp) were 81.59% and 96.96%, respectively. The absolute attenuation errors between the TNC and VNCa images were smaller than those between the TNC and VNCp images for tumors [VNCaerror: 2.77, interquartile range (IQR) 1.77-4.22; VNCperror: 3.27, IQR 2.68-4.30; P=0.002]. The SNR values and CNR values in the VNC images were significantly higher than those in the TNC images for all tissues, except for the portal vein and spleen. The image quality was rated as excellent (represented by a score of 5) in the majority of the TNC and VNC images; however, the VNC images scored lower than the TNC images. Eliminating the TNC phase resulted in a reduction of approximately 37.99% in the effective dose (ED). Conclusions: The VNC images provided accurate CT attenuation, good image quality, and lower radiation doses than the TNC images in CRC, and the VNCa images showed minimal differences in the CT attenuation of the tumors.

A paradigm shift in oncology imaging: a prospective cross-sectional study to assess low-dose deep learning image reconstruction versus standard-dose iterative reconstruction for comprehensive lesion detection in dual-energy computed tomography.

Background: Low-kiloelectron volt (keV) virtual monochromatic images (VMIs) from low-dose (LD) dual-energy computed tomography (DECT) can enhance lesion contrast but suffer from high image noise. Recently, a deep learning image reconstruction (DLIR) algorithm has been developed and shown significant potential in suppressing image noise and improving image quality. To date, the capacity of LD low-keV thoracic-abdominal-pelvic DECT with DLIR to detect various types of tumor lesions have not been assessed. Hence, this study aimed to evaluate the image quality and lesion detection capabilities of LD VMIs using DLIR with thoracic-abdominal-pelvic DECT versus standard-dose (SD) iterative reconstruction (IR) in oncology patients. Methods: This prospective intraindividual study included 56 oncology patients who received a SD (13.86 mGy) and a consecutive LD (7.15 mGy) thoracic-abdominal-pelvic DECT from April 2022 to July 2023 at The First Affiliated hospital of Zhengzhou University. SD VMIs were reconstructed using IR at 50 keV (SD-IR50 keV), while LD VMIs were processed using DLIR at 50 keV (LD-DL50 keV) and 40 keV (LD-DL40 keV), respectively. Quantitative image parameters [computed tomography (CT) values, image noise, and contrast-to-noise ratios (CNRs)], qualitative metrics (image noise, vessel conspicuity, image contrast, artificial sensation, and overall image quality), and lesion CNRs and conspicuity were compared. The lesion detection rates in the SD-IR50 keV, LD-DL50 keV, and LD-DL40 keV VMIs were assessed according to lesion location (lung, liver, and lymph), type, and size. Repeated measures analysis of variance and the Friedman test were applied for comparing quantitative and qualitative measures, respectively. The Cochran Q test was used for comparing lesion detection rates. Results: Compared to SD-IR50 keV VMIs, LD-DL50 keV VMIs showed similar CT values and image noise (P>0.05), similar (P>0.05) or higher(P<0.05) CNRs, similar (P>0.05) or superior (P<0.05) perceptual image quality, and similar (P>0.05) or higher (P<0.001) lesion CNR and conspicuity. LD-DL40 keV VMIs exhibited higher CT values (by 40.4-47.1%) and CNRs (by 21.8-39.8%) (P<0.001), equivalent image noise, similar (P>0.05) or superior (P<0.05) perceptual image quality except for artificial sensation, and similar (P>0.05) or higher (P<0.001) lesion CNRs (by 16.5-46.3%) and conspicuity. The VMIs of LD-DL50 keV and LD-DL40 keV were consistent with those of SD-IR50 keV in terms of lesion detection capability in pulmonary nodules [SD-IR50 keV vs. LD-DL50 keV vs. LD-DL40 keV: 88/88 (100.0%) vs. 88/88 (100.0%) vs. 88/88 (100.0%); P>0.99], for lymph nodes [125/126 (99.2%) vs. 123/126 (97.6%) vs. 124/126 (98.4%); P>0.05], and high-contrast liver lesions [12/12 (100.0%) vs. 12/12 (100.0%) vs. 12/12 (100.0%); P>0.05], but not for small liver lesions (≤0.5 cm) [63/65 (96.9%) vs. 43/65 (66.2%) vs. 51/65 (78.5%); P<0.05] or low-contrast liver lesions [198/200 (99.0%) vs. 174/200 (87.0%) vs. 183/200 (91.5%); P<0.05]. Conclusions: VMIs at 40 keV with DLIR enables a 50% decrease in the radiation dose while largely maintaining diagnostic capabilities for multidetection of pulmonary nodules, lymph nodes, and liver lesions in oncology patients.

Multidimensional quantitative characterization of periocular morphology: distinguishing esotropia from epicanthus by deep learning network.

Background: Prominent epicanthus could not only diminish the eyes' aesthetics but may be deceptive for its typical appearance of pseudo-esotropia. This study aims to apply a deep learning model to characterize the periocular morphology for preliminary identification. Methods: This prospective study consecutively included 300 subjects visiting the ophthalmology department in a tertiary referral hospital. Children aged 7-18 years with simple epicanthus or concomitant esotropia and healthy volunteers who were age- and gender-matched were eligible for inclusion. Multiple metrics were extracted automatically and manually from facial images to characterize the periocular morphology and binocular symmetry. The dice coefficient (Dice), intraclass correlation coefficient (ICC), and Bland-Altman biases were calculated to evaluate their consistency. The receiver operating characteristic (ROC) curve determined the cut-off values of symmetry indexes (SIs) for distinguishing concomitant esotropia subjects from epicanthus ones. Results: The Dice for eyelid and cornea segmentation were 0.949 and 0.944, respectively. The ICCs of the two measurements ranged from 0.898 to 0.983. Biases ranged from 0.16 to 0.74 mm. The periocular morphology of epicanthus eyes was significantly different from the normal ones, including palpebral fissure width (21.41±1.53 vs. 24.45±1.82 mm; P<0.01), and palpebral fissure height (8.91±1.37 vs. 9.60±1.25 mm; P<0.01). The ROC analysis yielded an area under the curve of 0.971 [95% confidence interval (CI): 0.950-0.991] with SI for distinguishing esotropia subjects. Its optimal cut-off value was 1.296 with 0.920 sensitivity and 0.910 specificity. Conclusions: Our study established a standard deep learning system for characterizing the periocular morphology of epicanthus and esotropia eyes with great accuracy. This objective method could be generalized to other periocular morphological assessments for clinical care.

A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics.

Deep Learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for real-time cancer diagnosis is explored in depth in this work. Real-time medical diagnosis determines the illness or condition that accounts for a patient's symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.

Improved localization and segmentation of spinal bone metastases in MRI with nnUNet radiomics.

Objective: Variability exists in the subjective delineation of tumor areas in MRI scans of patients with spinal bone metastases. This research aims to investigate the efficacy of the nnUNet radiomics model for automatic segmentation and identification of spinal bone metastases. Methods: A cohort of 118 patients diagnosed with spinal bone metastases at our institution between January 2020 and December 2023 was enrolled. They were randomly divided into a training set (n = 78) and a test set (n = 40). The nnUNet radiomics segmentation model was developed, employing manual delineations of tumor areas by physicians as the reference standard. Both methods were utilized to compute tumor area measurements, and the segmentation performance and consistency of the nnUNet model were assessed. Results: The nnUNet model demonstrated effective localization and segmentation of metastases, including smaller lesions. The Dice coefficients for the training and test sets were 0.926 and 0.824, respectively. Within the test set, the Dice coefficients for lumbar and thoracic vertebrae were 0.838 and 0.785, respectively. Strong linear correlation was observed between the nnUNet model segmentation and physician-delineated tumor areas in 40 patients (R 2 = 0.998, P < 0.001). Conclusions: The nnUNet model exhibits efficacy in automatically localizing and segmenting spinal bone metastases in MRI scans.

Automated dose evaluation on daily cone-beam computed tomography for breast cancer patients.

BACKGROUND AND PURPOSE: Our goal was to develop a workflow to automatically evaluate delivered dose on daily cone beam computed tomography (CBCT) in all breast cancer patients to assess dosimetric impact of anatomical changes and guide decision-making for offline plan adaptation. MATERIALS AND METHODS: The workflow automatically processes the daily CBCTs of all breast cancer patients receiving local and locoregional radiotherapy. The planning-CT is registered to the CBCT to create a synthetic CT and propagate contours. A forward dose calculation is performed, and DVH parameters are extracted and printed in a report. We evaluated the workflow on a group level and in a subset of 30 patients on a patient-specific level, including comparison to clinical evaluation on additional planning-CT in 10 patients. RESULTS: 7454 fractions in 647 patients were analyzed over a period of seven months. Median breast clinical target volume V95% was ≥ 95 % for 97 % of the patients. The workflow would have provided useful additional insights for decision-making for the requirement of plan adaptation, based on debatable disagreement with the clinical decision in half of the cases with an additional planning-CT. The workflow also identified cases with suboptimal coverage not identified in the clinical procedure. CONCLUSION: We developed a fully automated workflow for dose evaluation on daily CBCT for local and locoregional breast radiotherapy. We have demonstrated its potential for aiding decision-making for plan adaptation in patients with changing anatomy and its capability to highlight patients that may receive suboptimal treatment and require closer clinical evaluation of treatment quality.

Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis.

Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Utilizing a comprehensive dataset of 12,446 CT whole abdomen and urogram images, this study developed an advanced AI-driven diagnostic system specifically tailored for kidney disease classification. The innovative approach of this study combines the strengths of traditional convolutional neural network architecture (AlexNet) with modern advancements in ConvNeXt architectures. By integrating AlexNet's robust feature extraction capabilities with ConvNeXt's advanced attention mechanisms, the paper achieved an exceptional classification accuracy of 99.85%. A key advancement in this study's methodology lies in the strategic amalgamation of features from both networks. This paper concatenated hierarchical spatial information and incorporated self-attention mechanisms to enhance classification performance. Furthermore, the study introduced a custom optimization technique inspired by the Adam optimizer, which dynamically adjusts the step size based on gradient norms. This tailored optimizer facilitated faster convergence and more effective weight updates, imporving model performance. The model of this study demonstrated outstanding performance across various metrics, with an average precision of 99.89%, recall of 99.95%, and specificity of 99.83%. These results highlight the efficacy of the hybrid architecture and optimization strategy in accurately diagnosing kidney diseases. Additionally, the methodology of this paper emphasizes interpretability and explainability, which are crucial for the clinical deployment of deep learning models.

Leukemia detection and classification using computer-aided diagnosis system with falcon optimization algorithm and deep learning.

Leukemia is a type of blood tumour that occurs because of abnormal enhancement in WBCs (white blood cells) in the bone marrow of the human body. Blood-forming tissue cancer influences the lymphatic and bone marrow system. The early diagnosis and detection of leukaemia, i.e., the accurate difference of malignant leukocytes with little expense at the beginning of the disease, is a primary challenge in the disease analysis field. Despite the higher occurrence of leukemia, there is a lack of flow cytometry tools, and the procedures accessible at medical diagnostics centres are time-consuming. Distinct researchers have implemented computer-aided diagnostic (CAD) and machine learning (ML) methods for laboratory image analysis, aiming to manage the restrictions of late leukemia analysis. This study proposes a new Falcon optimization algorithm with deep convolutional neural network for Leukemia detection and classification (FOADCNN-LDC) technique. The main objective of the FOADCNN-LDC technique is to classify and recognize leukemia. The FOADCNN-LDC technique utilizes a median filtering (MF) based noise removal process to eradicate the image noise. Besides, the FOADCNN-LDC technique employs the ShuffleNetv2 model for the feature extraction process. Moreover, the detection and classification of the leukemia process are performed by utilizing the convolutional denoising autoencoder (CDAE) model. The FOA is implemented to select the hyperparameter of the CDAE model. The simulation process of the FOADCNN-LDC approach is performed on a benchmark medical dataset. The investigational analysis of the FOADCNN-LDC approach highlighted a superior accuracy value of 99.62% over existing techniques.

Exercise interventions for self-perceived body image, self-esteem and self-efficacy in women diagnosed with breast cancer: a systematic review with meta-analysis and meta-regressions.

PURPOSE: To synthesise the effectiveness of exercise interventions on self-perceived body image, self-esteem and self-efficacy in women diagnosed with breast cancer who are undergoing or have completed primary adjuvant treatments. METHODS: A systematic review was conducted with meta-analysis and meta-regressions. Five electronic databases were searched from inception to June 2023, and hand searches were performed to explore the reference lists of similar systematic reviews. The established selection criteria were randomised clinical trials that evaluated any type of physical exercise intervention with self-perceived body image, self-esteem and self-efficacy as outcomes. No restrictions were imposed with respect to the control group. Main characteristics were extracted for each study. Meta-analyses, meta-regressions and sensitivity analyses were performed. The certainty of evidence for each outcome was graded using the GRADE approach. The risk of bias was evaluated using the RoB2 Cochrane tool. RESULTS: Twenty studies, comprising 19 different samples (n = 2030), were included. In general, meta-analysis indicated that physical exercise interventions were not superior to controls for improving self-esteem and body image in women diagnosed with breast cancer. However, subgroup meta-analysis showed a significant difference in self-esteem improvement for resistance exercise (SMD = 0.31; 95% CI = 0.07, 0.55; p = 0.01; I2 = 0%) and supervised exercise (SMD = 0.25; 95% CI = 0.08, 0.42; p = 0.0004; I2 = 0%) compared with controls. Self-efficacy results were scarce and controversial. In addition, serious concerns were mainly detected in terms of the risk of bias and indirectness of the evidence, which caused the certainty of evidence to be very low for all outcomes. CONCLUSION: Supervised exercise and resistance training appear to be effective exercise modalities for improving self-esteem in women diagnosed with breast cancer. In contrast, exercise interventions are not significantly associated with improvements in body image, while results on self-efficacy are controversial. However, due to the study's limitations, further research is needed.

Heterogeneity in immune cell composition is associated with Mycobacterium tuberculosis replication at the granuloma level.

The control of bacterial growth is key to the prevention and treatment of tuberculosis (TB). Granulomas represent independent foci of the host immune response that present heterogeneous capacity for control of bacterial growth. At the whole tissue level, B cells and CD4 or CD8 T cells have an established role in immune protection against TB. Immune cells interact within each granuloma response, but the impact of granuloma immune composition on bacterial replication remains unknown. Here we investigate the associations between immune cell composition, including B cell, CD4, and CD8 T cells, and the state of replicating Mycobacterium tuberculosis (Mtb) within the granuloma. A measure of ribosomal RNA synthesis, the RS ratio®, represents a proxy measure of Mtb replication at the whole tissue level. We adapted the RS ratio through use of in situ hybridization, to identify replicating and non-replicating Mtb within each designated granuloma. We applied a regression model to characterize the associations between immune cell populations and the state of Mtb replication within each respective granuloma. In the evaluation of nearly 200 granulomas, we identified heterogeneity in both immune cell composition and proportion of replicating bacteria. We found clear evidence of directional associations between immune cell composition and replicating Mtb. Controlling for vaccination status and endpoint post-infection, granulomas with lower CD4 or higher CD8 cell counts are associated with a higher percent of replicating Mtb. Conversely, changes in B cell proportions were associated with little change in Mtb replication. This study establishes heterogeneity across granulomas, demonstrating that certain immune cell types are differentially associated with control of Mtb replication. These data suggest that evaluation at the granuloma level may be imperative to identifying correlates of immune protection.

[Effect of Brachytherapy Source Dwell Position on Dose Distribution in Cervical Cancer Therapy].

PURPOSE: To investigate the effect of different source dwell positions on dose distribution in the treatment of cervical cancer with brachytherapy. METHODS: Treatment planning data for cervical cancer patients were used. Treatment plans were created at 1 mm intervals, varying up to 5 mm. For intracavitary brachytherapy and intracavitary and interstitial brachytherapy, the following dose parameters were evaluated: 90% high-risk clinical target volume (HR-CTV D90%), rectum 2 cm3 dose (Rectum D2 cc), small intestine 2 cm3 dose (Small D2 cc), sigmoid colon 2 cm3 dose (Sigmoid D2 cc), bladder 2 cm3 dose (Bladder D2 cc), point A dose. RESULTS: In intracavitary brachytherapy, the HR-CTV D90%, Rectum D2 cc, Small D2 cc, and Sigmoid D2 cc doses increased as the source dwell position changed in the direction. On the other hand, the dose of Bladder D2 cc increased when the source position changed in the outward direction. The same trend was observed in the case of intracavitary and interstitial brachytherapy. CONCLUSION: It was shown that a 1 mm change in the source dwell position can affect the dose by up to 2% or more. The accuracy of the source dwell position is very important and should be checked before using the device.

Deep Learning-Based Denoising Enables High-Quality, Fully Diagnostic Neuroradiological Trauma CT at 25% Radiation Dose.

RATIONALE AND OBJECTIVES: Traumatic neuroradiological emergencies necessitate rapid and accurate diagnosis, often relying on computed tomography (CT). However, the associated ionizing radiation poses long-term risks. Modern artificial intelligence reconstruction algorithms have shown promise in reducing radiation dose while maintaining image quality. Therefore, we aimed to evaluate the dose reduction capabilities of a deep learning-based denoising (DLD) algorithm in traumatic neuroradiological emergency CT scans. MATERIALS AND METHODS: This retrospective single-center study included 100 patients with neuroradiological trauma CT scans. Full-dose (100%) and low-dose (25%) simulated scans were processed using iterative reconstruction (IR2) and DLD. Subjective and objective image quality assessments were performed by four neuroradiologists alongside clinical endpoint analysis. Bayesian sensitivity and specificity were computed with 95% credible intervals. RESULTS: Subjective analysis showed superior scores for 100% DLD compared to 100% IR2 and 25% IR2 (p < 0.001). No significant differences were observed between 25% DLD and 100% IR2. Objective analysis revealed no significant CT value differences but higher noise at 25% dose for DLD and IR2 compared to 100% (p < 0.001). DLD exhibited lower noise than IR2 at both dose levels (p < 0.001). Clinical endpoint analysis indicated equivalence to 100% IR2 in fracture detection for all datasets, with sensitivity losses in hemorrhage detection at 25% IR2. DLD (25% and 100%) maintained comparable sensitivity to 100% IR2. All comparisons demonstrated robust specificity. CONCLUSIONS: The evaluated algorithm enables high-quality, fully diagnostic CT scans at 25% of the initial radiation dose and improves patient care by reducing unnecessary radiation exposure.

Quantification of bone microarchitecture using photon-counting CT at different radiation doses: A comparison with µCT.

PURPOSE: Accurate measurements of trabecular bone microarchitecture are required for a proper assessment of bone fragility. Photon-counting detector CT (PCD-CT) has different technical properties than conventional CT, resulting in higher resolution and thereby potentially enabling in-vivo measurement of trabecular microarchitecture. The purpose of this study was to quantify trabecular bone microarchitectural parameters with PCD-CT at varying radiation doses and compare this to µCT as gold standard. METHOD: Both distal radii, distal tibiae, femoral heads, and two vertebrae were dissected from one human. All specimens were scanned ex-vivo on a PCD-CT system (slice increment 0.1 mm; pixel size 0.1042-0.127 mm) and a µCT system (isotropic voxel size 49-68.4 µm). The radiation doses of the PCD-CT scans were varied from 2.5 to 120 mGy based on the volume CT dose index (CTDIvol32). For the PCD-CT scans, contrast-to-noise ratio and trabecular sharpness were calculated and compared between radiation doses. µCT and PCD-CT scans were registered. The trabecular bone was then segmented from all PCD-CT and µCT scans and split into cubes with 6-mm edge length. For each cube, bone volume over total volume, trabecular thickness, trabecular number, and trabecular heterogeneity were calculated and compared between corresponding PCD-CT and µCT cubes. RESULTS: With increasing dose, contrast-to-noise ratio and trabecular sharpness values increased for the PCD-CT images. Already at the lowest dose, high correlations between the trabecular microarchitectural parameters between µCT and PCD-CT were found (R2 = 0.55-0.95), which improved with increasing radiation dose (R2 = 0.76-0.96 at 20 mGy). CONCLUSIONS: PCD-CT can be used to quantify trabecular bone microarchitecture, with accuracy comparable to µCT and at clinically relevant radiation doses.