Zymogen granule protein 16B (ZG16B) is a druggable epigenetic target to modulate the mammary extracellular matrix

High tissue density of the mammary gland is considered a pro-tumorigenic factor, hence suppressing the stimuli that induce matrix buildup carries the potential for cancer interception. We found that in non-malignant mammary epithelial cells the combination of the chemopreventive agents bexarotene (Bex) and carvedilol (Carv) suppresses the zymogen granule protein 16B (ZG16B, PAUF) through an interaction of ARID1A with a proximal enhancer. Bex + Carv also reduced ZG16B levels in vivo in normal breast tissue and MDA-MB231 tumor xenografts. The relevance of ZG16B is underscored by ongoing clinical trials targeting ZG16B in pancreatic cancers, but its role in breast cancer development is unclear. In immortalized mammary epithelial cells, secreted recombinant ZG16B stimulated mitogenic kinase phosphorylation, detachment and mesenchymal characteristics, and promoted proliferation, motility and clonogenic growth. Highly concerted induction of specific laminin, collagen and integrin isoforms indicated a shift in matrix properties toward increased density and cell-matrix interactions. Exogenous ZG16B alone blocked Bex + Carv-mediated control of cell growth and migration, and antagonized Bex + Carv-induced gene programs regulating cell adhesion and migration. In breast cancer cells ZG16B induced colony formation and anchorage-independent growth, and stimulated migration in a PI3K/Akt-dependent manner. In contrast, Bex + Carv inhibited colony formation, reduced Ki67 levels, ZG16B expression and glucose uptake in MDA-MB231 xenografts. These data establish ZG16B as a druggable pro-tumorigenic target in breast cell transformation and suggest a key role of the matrisome network in rexinoid-dependent antitumor activity.

Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review

OBJECTIVE

Explainable Artificial Intelligence (XAI) is increasingly recognized as a crucial tool in cancer care, with significant potential to enhance diagnosis, prognosis, and treatment planning. However, the holistic integration of XAI across all stages of cancer care remains underexplored. This review addresses this gap by systematically evaluating the role of XAI in these critical areas, identifying key challenges and emerging trends.

MATERIALS AND METHODS

Following the PRISMA guidelines, a comprehensive literature search was conducted across Scopus and Web of Science, focusing on publications from January 2020 to May 2024. After rigorous screening and quality assessment, 69 studies were selected for in-depth analysis.

RESULTS

The review identified critical gaps in the application of XAI within cancer care, notably the exclusion of clinicians in 83% of studies, which raises concerns about real-world applicability and may lead to explanations that are technically sound but clinically irrelevant. Additionally, 87% of studies lacked rigorous evaluation of XAI explanations, compromising their reliability in clinical practice. The dominance of post-hoc visual methods like SHAP, LIME and Grad-CAM reflects a trend toward explanations that may be inherently flawed due to specific input perturbations and simplifying assumptions. The lack of formal evaluation metrics and standardization constrains broader XAI adoption in clinical settings, creating a disconnect between AI development and clinical integration. Moreover, translating XAI insights into actionable clinical decisions remains challenging due to the absence of clear guidelines for integrating these tools into clinical workflows.

CONCLUSION

This review highlights the need for greater clinician involvement, standardized XAI evaluation metrics, clinician-centric interfaces, context-aware XAI systems, and frameworks for integrating XAI into clinical workflows for informed clinical decision-making and improved outcomes in cancer care.

Leveraging pre-trained machine learning models for islet quantification in type 1 diabetes

Human islets display a high degree of heterogeneity in terms of size, number, architecture, and endocrine cell-type compositions. An ever-increasing number of immunohistochemistry-stained whole slide images (WSIs) are available through the online pathology database of the Network for Pancreatic Organ donors with Diabetes (nPOD) program at the University of Florida (UF). We aimed to develop an enhanced machine learning-assisted WSI analysis workflow to utilize the nPOD resource for analysis of endocrine cell heterogeneity in the natural history of type 1 diabetes (T1D) in comparison to donors without diabetes. To maximize usability, the user-friendly open-source software QuPath was selected for the main interface. The WSI data were analyzed with two pre-trained machine learning models (i.e., Segment Anything Model (SAM) and QuPath's pixel classifier), using the UF high-performance-computing cluster, HiPerGator. SAM was used to define precise endocrine cell and cell grouping boundaries (with an average quality score of 0.91 per slide), and the artificial neural network-based pixel classifier was applied to segment areas of insulin- or glucagon-stained cytoplasmic regions within each endocrine cell. An additional script was developed to automatically count CD3+ cells inside and within 20 μm of each islet perimeter to quantify the number of islets with inflammation (i.e., CD3+ T-cell infiltration). Proof-of-concept analysis was performed to test the developed workflow in 12 subjects using 24 slides. This open-source machine learning-assisted workflow enables rapid and high throughput determinations of endocrine cells, whether as single cells or within groups, across hundreds of slides. It is expected that the use of this workflow will accelerate our understanding of endocrine cell and islet heterogeneity in the context of T1D endotypes and pathogenesis.

Deep learning radiomics on grayscale ultrasound images assists in diagnosing benign and malignant of BI-RADS 4 lesions

This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breast lesions were included, comprising 183 benign lesions and 199 malignant lesions that were collected and confirmed through clinical pathology or biopsy. The enrolled patients were randomly allocated into two groups: a training cohort and an independent test cohort, maintaining a ratio of 7:3.We created a model called CLDLR that utilizes clinical parameters and DLR to diagnose both BBL and MBL through grayscale ultrasound images. In order to assess the practicality of the CLDLR model, two rounds of evaluations were conducted by radiologists. The CLDLR model demonstrates the highest diagnostic performance in predicting benign and malignant BI-RADS 4 lesions, with areas under the receiver operating characteristic curve (AUC) of 0.988 (95% confidence interval : 0.949, 0.985) in the training cohort and 0.888 (95% confidence interval : 0.829, 0.947) in the testing cohort.The CLDLR model outperformed the diagnoses made by the three radiologists in the initial assessment of the testing cohorts. By utilizing AI scores from the CLDLR model and heatmaps from the DLR model, the diagnostic performance of all radiologists was further enhanced in the testing cohorts. Our study presents a noninvasive imaging biomarker for the prediction of benign and malignant BI-RADS 4 lesions. By comparing the results from two rounds of assessment, our AI-assisted diagnostic tool demonstrates practical value for radiologists with varying levels of experience.

Correlation between SWE parameters and histopathological features and immunohistochemical biomarkers in invasive breast cancer

OBJECTIVES

Shear wave elastography (SWE) is a novel quantitative elastography technique that can assess the hardness of different tissues. This study introduces a novel shear wave parameter-frequency of mass characteristic (fmass)-and investigates its correlation, along with other shear wave parameters, with the histopathological features and immunohistochemical (IHC) biomarkers of invasive breast cancer (IBC). The study aims to explore whether SWE can provide useful information for IBC treatment and prognosis.

METHODS

With the pathological results as the gold standard, 258 malignant breast lesions were collected, and all patients underwent conventional ultrasound and SWE examinations. The SWE parameters [maximum elastic value (Emax), minimum elastic value (Emin), mean elastic value (Emean), standard deviation of elastic value of the whole lesion (Esd)] and fmass] in the transverse and longitudinal orthogonal sections were measured, and their correlations with the prognostic factors of IBC [including tumor diameters, axillary lymph node (ALN) metastasis, lymphatic vessel invasion (LVI), calcification, histological type, histological grade, and IHC biomarkers (ER, PR, HER-2, Ki-67), and molecular subtypes] were analyzed. The correlations between the SWE parameters of the transverse and longitudinal sections of the tumors with different prognostic factors and the above indicators were analyzed. At the same time, the receiver operating characteristic (ROC) curve was used to analyze the efficacy of fmass in predicting ER and PR expression.

RESULTS

Emean, Emax, Esd, and fmass were correlated with tumor diameters; Emean, Emax and Esd were correlated with histological types and histological grades. Emax and Esd were correlated with ALN metastasis, LVI and pathological types. In the IHC biomarker-labeled masses, fmass was correlated with ER and PR (both P<0.05), and Emean, Emax, and Esd were correlated with HER-2 and Ki-67 (all P<0.05). Emean, Emax, and fmass were all correlated with breast cancer subtypes (all P<0.05), and Emean and Emax were higher in Luminal B [HER-2(+)] breast cancer, while fmass was lower in HER-2(+) and triple-negative breast cancer. Among the statistically significant prognostic factors, the P values of the transverse sections of the masses were all less than or equal to those of the longitudinal sections. The AUC of fmass in the transverse sections of the masses for predicting ER and PR expression were 0.73 (95% CI 0.65 to 0.80) and 0.67 (95% CI 0.60 to 0.74), respectively, with the optimal cut-off values being 76.50 and 60.66, the sensitivities being 72.45% and 81.98%, the specificities being 66.13% and 45.35%, and the accuracies being 70.93% and 69.77%, respectively. The AUC of fmass in the longitudinal sections of the masses for predicting ER and PR expression were 0.74 (95% CI 0.67 to 0.81) and 0.65 (95% CI 0.58 to 0.72), respectively, with the optimal cut-off values being 131.8 and 137.5, the sensitivities being 69.90% and 66.28%, the specificities being 72.58% and 60.47%, and the accuracies being 70.54% and 64.34%, respectively. The fmass in the transverse sections of the masses was more statistically significant.

CONCLUSIONS

The poor prognosis factors of IBC are related to high Emean, Emin, Emax, Esd, and low fmass. The fmass can predict the expression of ER and PR, and the transverse cut data are more meaningful. SWE is helpful for predicting the invasiveness of IBC.

CTRP13 attenuates atherosclerosis by inhibiting endothelial cell ferroptosis via activating GCH1

C1q/TNF-related protein 13 (CTRP13) is a secreted adipokine that has been shown to play an important role in a variety of cardiovascular diseases. However, the effect of CTRP13 on ferroptosis of endothelial cells and its underlying mechanism remain unclear. In the present study, we analyzed the effects of CTRP13 on endothelial dysfunction in high-lipid-induced ApoE-/- mice and ox-LDL-induced mouse aortic endothelial cells (MAECs). In vivo experiment: Male ApoE-/- mice fed high fat were given C1ql3 gene overexpression adeno-associated virus. The atherosclerotic plaque size, lipid content, collagen fiber proportion and iron deposition level were measured. In vitro, CTRP13 combined with ox-LDL was used to pretreat MAECs to detect cell survival rate, lipid peroxidation, iron ion deposition and mitochondrial level. In this study, CTRP13 was found to inhibit ferroptosis of endothelial cells, demonstrated by up-regulated the expression of ferroptosis protective protein glutathione Peroxidase 4 (GPX4), and decreased the expression of acyl-CoA synthetase long-chain family member 4 (ACSL4) protein. Mechanistically, gtp cyclohydrolase 1(GCH1) silencing or tetrahydrobiopterin (BH4) inhibiting may counteract the protective effect of CTRP13 on ox-LDL-induced ferroptosis of endothelial cells, which is characterized by decreased cell activity, mitochondrial damage, increased iron ion deposition and lipid peroxidation, decreased GPX4 expression, and increased ACSL4 expression. This study demonstrated for the first time that CTRP13 can improve mitochondrial oxidative stress, inhibit ferroptosis of endothelial cells and improve endothelial cell dysfunction by activating the GCH1/BH4 signaling pathway, thereby inhibiting the progression of atherosclerosis.

Incremental Value of Shear Wave Elastography and Contrast-Enhanced Ultrasound in the Differential Diagnosis of Breast Non-Mass-Like Lesions

Objective

To analyse the parameters of shear wave elastography (SWE) and contrast-enhanced ultrasound (CEUS) in breast non-mass-like lesions (NMLs) and to evaluate the added diagnostic value of SWE and CEUS when combined with B-mode ultrasound (US) for differentiating NMLs.

Methods

A total of 118 NMLs from 115 patients underwent US, SWE, and CEUS examinations. The SWE parameter with the highest areas under the receiver operating characteristic (ROC) curves (Az) and independent variables of CEUS obtained by logistic regression were used to adjust the BI-RADS-US (Breast Imaging Reporting and Data System for Ultrasound) classification. The adjusted BI-RADS risk stratification was then compared with the original classification. Additionally, the diagnostic effectiveness of US+SWE, US+CEUS, and US+SWE+CEUS combinations was calculated and compared.

Results

The "stiff rim sign" was used as the optimal SWE indicator for BI-RADS adjustment. CEUS diagnostic criteria for adjustment included enhancement intensity, enhancement size, and the presence of radial or penetrating vessels. The Az values of US+SWE+CEUS and US+CEUS combinations were significantly higher than that of US alone (P<0.05). However, there was no significant difference in the Az value of US+SWE and US (P = 0.072). US+SWE+CEUS combination showed significantly higher Az values compared to other combinations (P<0.05), and achieved the highest sensitivity and specificity.

Conclusion

Adding SWE and CEUS to conventional US enhances diagnostic accuracy for NMLs, offering a meaningful incremental value for BI-RADS classification in the assessment of NMLs.

Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences

Background

Histological grade is an acknowledged prognostic factor for breast cancer, essential for determining clinical treatment strategies and prognosis assessment. Our study aims to establish intra- and peritumoral radiomics models using T2WI and DWI MR sequences for predicting the histological grade of breast cancer.

Methods

700 breast cancer cases who had MRI scans before surgery were included. The intratumoral region (ITR) of interest was manually delineated, while the peritumoral region (PTR-3 mm) was automatically obtained by expanding the ITR by 3 mm. Radiomics features were extracted using the intra- and peritumoral images from T2WI and DWI sequences on breast MRI. Then, the key features with the strongest predictivity of histological grade were selected. Finally, 9 predictive radiomics models were established based on T2WI-ITR, T2WI-3mmPTR, DWI-ITR, DWI-3mmPTR, T2WI-ITR + 3mmPTR, DWI-ITR + 3mmPTR, (T2WI + DWI)-ITR, (T2WI + DWI)-3mmPTR and (T2WI + DWI)-ITR + 3mmPTR.

Results

The (T2WI + DWI)-ITR + 3mmPTR contained 13 DWI features which included a shape feature, a texture feature, and 11 filtered features, as well as 10 T2WI features, all of which were filtered features. Among the 9 models, the combined models showed better performance than the single models in both the training and test sets, especially for the (T2WI + DWI)-ITR + 3mmPTR radiomics model. The (T2WI + DWI)-ITR + 3mmPTR radiomics model achieved a sensitivity, specificity, accuracy, and AUC of 80.4%, 72.4%, 75.0%, and 0.860 in the training set, and 68.9%, 70.5%, 70.0%, and 0.781 in the test set. Decision curve analysis (DCA) showed that the (T2WI + DWI)-ITR + 3mmPTR model had the greatest net clinical benefit compared to the other models.

Conclusion

The intra- and peritumoral radiomics methodologies using T2WI and DWI MR sequences could be utilized to assess histological grade for breast cancer, particularly with the (T2WI + DWI)-ITR + 3mmPTR radiomics model demonstrating significant potential for clinical application.

DNA double-strand break repair capacity and normal tissue toxicity induced by radiotherapy

Radiation therapy is used in the treatment of various cancers, and advancements in irradiation techniques have further expanded its applicability. For radiation oncologists, predicting adverse events remains a critical challenge, even with these technological advancements. Although numerous studies have been conducted to predict individual radiosensitivity, no biomarkers have been clinically applied thus far. This review focuses on γ-H2AX foci and chromosomal aberrations, providing an overview of their association with normal tissue toxicities.

An effective and open source interactive 3D medical image segmentation solution

3D medical image segmentation is a key step in numerous clinical applications. Even though many automatic segmentation solutions have been proposed, it is arguably that medical image segmentation is more of a preference than a reference as inter- and intra-variability are widely observed in final segmentation output. Therefore, designing a user oriented and open-source solution for interactive annotation is of great value for the community. In this paper, we present an effective interactive segmentation method that employs an adaptive dynamic programming approach to incorporates users' interactions efficiently. The method first initializes an segmentation through a feature-based geodesic computation. Then, the segmentation is further refined by using an efficient updating scheme requiring only local computations when new user inputs are available, making it applicable to high resolution images and very complex structures. The proposed method is implemented as a user-oriented software module in 3D Slicer. Our approach demonstrates several strengths and contributions. First, we proposed an efficient and effective 3D interactive algorithm with the adaptive dynamic programming method. Second, this is not just a presented algorithm, but also a software with well-designed GUI for users. Third, its open-source nature allows users to make customized modifications according to their specific requirements.

A novel artificial intelligence model for measuring fetal intracranial markers during the first trimester based on two-dimensional ultrasound image

OBJECTIVE

To establish reference ranges of fetal intracranial markers during the first trimester and develop the first novel artificial intelligence (AI) model to measure key markers automatically.

METHODS

This retrospective study used two-dimensional (2D) ultrasound images from 4233 singleton normal fetuses scanned at 11+0-13+6 weeks of gestation at the Affiliated Suzhou Hospital of Nanjing Medical University from January 2018 to July 2022. We analyzed 10 key markers in three important planes of the fetal head. Based on these, reference ranges of 10 fetal intracranial markers were established and an AI model was developed for automated marker measurement. AI and manual measurements were compared to evaluate differences, correlations, consistency, and time consumption based on mean error, Pearson correlation analysis, intraclass correlation coefficients (ICCs), and average measurement time.

RESULTS

The results of AI and manual methods had strong consistency and correlation (all ICC values >0.75, all r values >0.75, and all P values <0.001). The average absolute error of both only ranged from 0.124 to 0.178 mm. AI achieved a 100% detection rate for abnormal cases. Additionally, the average measurement time of AI was only 0.49 s, which was more than 65 times faster than the manual measurement method.

CONCLUSION

The present study first established the normal standard reference ranges of fetal intracranial markers based on a large Chinese population data set. Furthermore, the proposed AI model demonstrated its capability to measure multiple fetal intracranial markers automatically, serving as a highly effective tool to streamline sonographer tasks and mitigate manual measurement errors, which can be generalized to first-trimester scanning.

Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study

Background

Ovarian cancer has the highest mortality rate among gynaecological malignancies and is initially screened using ultrasound. Owing to the high complexity of ultrasound images of ovarian masses and the anatomical characteristics of the deep pelvic cavity, subjective assessment requires extensive experience and skill. Therefore, detecting the ovaries and ovarian masses and diagnose ovarian cancer are challenging. In the present study, we aimed to develop an automated deep learning framework, the Ovarian Multi-Task Attention Network (OvaMTA), for ovary and ovarian mass detection, segmentation, and classification, as well as further diagnosis of ovarian masses based on ultrasound screening.

Methods

Between June 2020 and May 2022, the OvaMTA model was trained, validated and tested on a training and validation cohort including 6938 images and an internal testing cohort including 1584 images which were recruited from 21 hospitals involving women who underwent ultrasound examinations for ovarian masses. Subsequently, we recruited two external test cohorts from another two hospitals. We obtained 1896 images between February 2024 and April 2024 as image-based external test dataset, and further obtained 159 videos for the video-based external test dataset between April 2024 and May 2024. We developed an artificial intelligence (AI) system (termed OvaMTA) to diagnose ovarian masses using ultrasound screening. It includes two models: an entire image-based segmentation model, OvaMTA-Seg, for ovary detection and a diagnosis model, OvaMTA-Diagnosis, for predicting the pathological type of ovarian mass using image patches cropped by OvaMTA-Seg. The performance of the system was evaluated in one internal and two external validation cohorts, and compared with doctors' assessments in real-world testing. We recruited eight physicians to assess the real-world data. The value of the system in assisting doctors with diagnosis was also evaluated.

Findings

In terms of segmentation, OvaMTA-Seg achieved an average Dice score of 0.887 on the internal test set and 0.819 on the image-based external test set. OvaMTA-Seg also performed well in ovarian mass detection from test images, including healthy ovaries and masses (internal test area under the curve [AUC]: 0.970; external test AUC: 0.877). In terms of classification diagnosis prediction, OvaMTA-Diagnosis demonstrated high performance on image-based internal (AUC: 0.941) and external test sets (AUC: 0.941). In video-based external testing, OvaMTA recognised 159 videos with ovarian masses with AUC of 0.911, and is comparable to the performance of senior radiologists (ACC: 86.2 vs. 88.1, p = 0.50; SEN: 81.8 vs. 88.6, p = 0.16; SPE: 89.2 vs. 87.6, p = 0.68). There was a significant improvement in junior and intermediate radiologists who were assisted by AI compared to those who were not assisted by AI (ACC: 80.8 vs. 75.3, p = 0.00015; SEN: 79.5 vs. 74.6, p = 0.029; SPE: 81.7 vs. 75.8, p = 0.0032). General practitioners assisted by AI achieved an average performance of radiologists (ACC: 82.7 vs. 81.8, p = 0.80; SEN: 84.8 vs. 82.6, p = 0.72; SPE: 81.2 vs. 81.2, p > 0.99).

Interpretation

The OvaMTA system based on ultrasound imaging is a simple and practical auxiliary tool for screening for ovarian cancer, with a diagnostic performance comparable to that of senior radiologists. This provides a potential tool for screening ovarian cancer.

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 12090020, 82071929, and 12090025) and the R&D project of the Pazhou Lab (Huangpu) (Grant No. 2023K0605).

Expansive Receptive Field and Local Feature Extraction Network: Advancing Multiscale Feature Fusion for Breast Fibroadenoma Segmentation in Sonography

Fibroadenoma is a common benign breast disease that affects women of all ages. Early diagnosis can greatly improve the treatment outcomes and reduce the associated pain. Computer-aided diagnosis (CAD) has great potential to improve diagnosis accuracy and efficiency. However, its application in sonography is limited. A network that utilizes expansive receptive fields and local information learning was proposed for the accurate segmentation of breast fibroadenomas in sonography. The architecture comprises the Hierarchical Attentive Fusion module, which conducts local information learning through channel-wise and pixel-wise perspectives, and the Residual Large-Kernel module, which utilizes multiscale large kernel convolution for global information learning. Additionally, multiscale feature fusion in both modules was included to enhance the stability of our network. Finally, an energy function and a data augmentation method were incorporated to fine-tune low-level features of medical images and improve data enhancement. The performance of our model is evaluated using both our local clinical dataset and a public dataset. Mean pixel accuracy (MPA) of 93.93% and 86.06% and mean intersection over union (MIOU) of 88.16% and 73.19% were achieved on the clinical and public datasets, respectively. They are significantly improved over state-of-the-art methods such as SegFormer (89.75% and 78.45% in MPA and 83.26% and 71.85% in MIOU, respectively). The proposed feature extraction strategy, combining local pixel-wise learning with an expansive receptive field for global information perception, demonstrates excellent feature learning capabilities. Due to this powerful and unique local-global feature extraction capability, our deep network achieves superior segmentation of breast fibroadenoma in sonography, which may be valuable in early diagnosis.

Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks.

Machine learning radiomics based on intra and peri tumor PA/US images distinguish between luminal and non-luminal tumors in breast cancers

PURPOSE

This study aimed to evaluate a radiomics model using Photoacoustic/ultrasound (PA/US) imaging at intra and peri-tumoral area to differentiate Luminal and non-Luminal breast cancer (BC) and to determine the optimal peritumoral area for accurate classification.

MATERIALS AND METHODS

From February 2022 to April 2024, this study continuously collected 322 patients at Shenzhen People's Hospital, using standardized conditions for PA/US imaging of BC. Regions of interest were delineated using ITK-SNAP, with peritumoral regions of 2 mm, 4 mm, and 6 mm automatically expanded using code from the Pyradiomic package. Feature extraction was subsequently performed using Pyradiomics. The study employed Z-score normalization, Spearman correlation for feature correlation, and LASSO regression for feature selection, validated through 10-fold cross-validation. The radiomics model integrated intra and peri-tumoral area, evaluated by receiver operating characteristic curve(ROC), Calibration and Decision Curve Analysis(DCA).

RESULTS

We extracted and selected features from intratumoral and peritumoral PA/US images regions at 2 mm, 4 mm, and 6 mm. The comprehensive radiomics model, integrating these regions, demonstrated enhanced diagnostic performance, especially the 4 mm model which showed the highest area under the curve(AUC):0.898(0.78-1.00) and comparably high accuracy (0.900) and sensitivity (0.937). This model outperformed the standalone clinical model and combined clinical-radiomics model in distinguishing between Luminal and non-Luminal BC, as evidenced in the test set results.

CONCLUSION

This study developed a radiomics model integrating intratumoral and peritumoral at 4 mm region PA/US model, enhancing the differentiation of Luminal from non-Luminal BC. It demonstrated the diagnostic utility of peritumoral characteristics, reducing the need for invasive biopsies and aiding chemotherapy planning, while emphasizing the importance of optimizing tumor surrounding size for improved model accuracy.

Optimizing anterior urethral stricture assessment: leveraging AI-assisted three-dimensional sonourethrography in clinical practice

PURPOSE

This investigation sought to validate the clinical precision and practical applicability of AI-enhanced three-dimensional sonographic imaging for the identification of anterior urethral stricture.

METHODS

The study enrolled 63 male patients with diagnosed anterior urethral strictures alongside 10 healthy volunteers to serve as controls. The imaging protocol utilized a high-frequency 3D ultrasound system combined with a linear stepper motor, which enabled precise and rapid image acquisition. For image analysis, an advanced AI-based segmentation process using a modified U-net algorithm was implemented to perform real-time, high-resolution segmentation and three-dimensional reconstruction of the urethra. A comparative analysis was performed against the surgically measured stricture lengths. Spearman's correlation analysis was executed to assess the findings.

RESULTS

The AI model completed the entire processing sequence, encompassing recognition, segmentation, and reconstruction, within approximately 5 min. The mean intraoperative length of urethral stricture was determined to be 14.4 ± 8.4 mm. Notably, the mean lengths of the urethral strictures reconstructed by manual and AI models were 13.1 ± 7.5 mm and 13.4 ± 7.2 mm, respectively. Interestingly, no statistically significant disparity in urethral stricture length between manually reconstructed and AI-reconstructed images was observed. Spearman's correlation analysis underscored a more robust association of AI-reconstructed images with intraoperative urethral stricture length than manually reconstructed 3D images (0.870 vs. 0.820). Furthermore, AI-reconstructed images provided detailed views of the corpus spongiosum fibrosis from multiple perspectives.

CONCLUSIONS

The research heralds the inception of an innovative, efficient AI-driven sonographic approach for three-dimensional visualization of urethral strictures, substantiating its viability and superiority in clinical application.

3D bioprinted breast cancer model reveals stroma-mediated modulation of extracellular matrix and radiosensitivity

Deciphering breast cancer treatment resistance remains hindered by the lack of models that can successfully capture the four-dimensional dynamics of the tumor microenvironment. Here, we show that microextrusion bioprinting can reproducibly generate distinct cancer and stromal compartments integrating cells relevant to human pathology. Our findings unveil the functional maturation of this millimeter-sized model, showcasing the development of a hypoxic cancer core and an increased surface proliferation. Maturation was also driven by the presence of cancer-associated fibroblasts (CAF) that induced elevated microvascular-like structures complexity. Such modulation was concomitant to extracellular matrix remodeling, with high levels of collagen and matricellular proteins deposition by CAF, simultaneously increasing tumor stiffness and recapitulating breast cancer fibrotic development. Importantly, our bioprinted model faithfully reproduced response to treatment, further modulated by CAF. Notably, CAF played a protective role for cancer cells against radiotherapy, facilitating increased paracrine communications. This model holds promise as a platform to decipher interactions within the microenvironment and evaluate stroma-targeted drugs in a context relevant to human pathology.

Deep learning model for diagnosis of thyroid nodules with size less than 1 cm: A multicenter, retrospective study

Objective

To develop a ultrasound images based dual-channel deep learning model to achieve accurate early diagnosis of thyroid nodules less than 1 cm.

Methods

A dual-channel deep learning model called thyroid nodule transformer network (TNT-Net) was proposed. The model has two input channels for transverse and longitudinal ultrasound images of thyroid nodules, respectively. A total of 9649 nodules from 8455 patients across five hospitals were retrospectively collected. The data were divided into a training set (8453 nodules, 7369 patients), an internal test set (565 nodules, 512 patients), and an external test set (631 nodules, 574 patients).

Results

TNT-Net achieved an area under the curve (AUC) of 0.953 (95 % confidence interval (CI): 0.934, 0.969) on the internal test set and 0.941 (95 % CI: 0.921, 0.957) on the external test set, significantly outperforming traditional deep convolutional neural network models and single-channel swin transformer model, whose AUCs ranged from 0.800 (95 % CI: 0.759, 0.837) to 0.856 (95 % CI: 0.819, 0.881). Furthermore, feature heatmap visualization showed that TNT-Net could extract richer and more energetic malignant nodule patterns.

Conclusion

The proposed TNT-Net model significantly improved the recognition capability for thyroid nodules with size less than 1 cm. This model has the potential to reduce overdiagnosis and overtreatment of such nodules, providing essential support for precise management of thyroid nodules while complementing fine-needle aspiration biopsy.

Enhancing image reconstruction in photoacoustic imaging using spatial coherence mean-to-standard-deviation factor beamforming

In photoacoustic imaging (PAI), a delay-and-sum (DAS) beamforming reconstruction algorithm is widely used due to its ease of implementation and fast execution. However, it is plagued by issues such as high sidelobe artifacts and low contrast, that significantly hinder the ability to differentiate various structures in the reconstructed images. In this study, we propose an adaptive weighting factor called spatial coherence mean-to-standard deviation factor (scMSF) in DAS, which is extended into the spatial frequency domain. By combining scMSF with a minimum variance (MV) algorithm, the clutter level is reduced, thereby enhancing the image contrast. Quantitative results obtained from the phantom experiment demonstrate that our proposed method improves contrast ratio (CR) by 30.15 dB and signal-to-noise ratio (SNR) by 8.62 dB compared to DAS while also improving full-width at half maxima (FWHM) by 56%. From the in-vivo experiments, the scMSF-based reconstruction image exhibits a higher generalized contrast-to-noise ratio (gCNR), indicating improved target detectability with a 25.6% enhancement over DAS and a 22.5% improvement over MV.

Application of imaging technology for the diagnosis of malignancy in the pancreaticobiliary duodenal junction (Review)

The pancreaticobiliary duodenal junction (PBDJ) is the connecting area of the pancreatic duct, bile duct and duodenum. In a broad sense, it refers to a region formed by the head of the pancreas, the pancreatic segment of the common bile duct and the intraduodenal segment, the descending and the horizontal part of the duodenum, and the soft tissue around the pancreatic head. In a narrow sense, it refers to the anatomical Vater ampulla. Due to its complex and variable anatomical features, and the diversity of pathological changes, it is challenging to make an early diagnosis of malignancy at the PBDJ and define the histological type. The unique anatomical structure of this area may be the basis for the occurrence of malignant tumors. Therefore, understanding and subclassifying the anatomical configuration of the PBDJ is of great significance for the prevention and treatment of malignant tumors at their source. The present review comprehensively discusses commonly used imaging techniques and other new technologies for diagnosing malignancy at the PBDJ, offering evidence for physicians and patients to select appropriate examination methods.

Assessment of Tail-Cutting in Frozen Albacore (Thunnus alalunga) Through Ultrasound Inspection and Chemical Analysis

Fat content is the main criterion for evaluating albacore quality. However, no reports exist on the accuracy of the tail-cutting method, a method used to assess the fat content of albacore. Here, we evaluated this method by comparing it with chemical analysis and ultrasound inspection. We measured the actual fat content in albacore using chemical analysis and compared the results with those obtained using the tail-cutting method. Significant discrepancies (99% CI, t-test) were observed in fat content among the tail-cutting samples. Using chemical analysis as the ground truth, the accuracy of tail-cutting from two different companies was 70.0% for company A and 51.9% for company B. An ultrasound inspection revealed that a higher fat content reduced the amplitude of ultrasound signals with statistical significance (99% CI, t-test). Finally, machine learning algorithms were used to enforce the ultrasound inspection. The best combination of ultrasound inspection and a machine learning algorithm achieved an 84.2% accuracy for selecting fat-rich albacore, which is better than tail-cutting (73.6%). Our findings suggested that ultrasound inspection could be a valuable and non-destructive method for estimating the fat content of albacore, achieving better accuracy than the traditional tail-cutting method.

Biomimetic mesenchymal stem cell membrane-coated nanoparticle delivery of MKP5 inhibits hepatic fibrosis through the IRE/XBP1 pathway

Hepatic fibrosis is a common disease with high morbidity and mortality rates. The complex and poorly understood mechanisms underlying hepatic fibrosis represent a significant challenge for the development of more effective therapeutic strategies. MKP5 is a potential regulator of multiple fibrotic diseases. However, its precise role and mechanism of action in hepatic fibrosis remains unclear. This study identified a reduction in MKP5 expression in fibrotic liver tissues of mice treated with CCl4 and observed that MKP5 knockout mice exhibited a more pronounced development of hepatic fibrosis. In addition, RNA-seq data indicated activation of protein processing in the endoplasmic reticulum signalling pathway in fibrotic liver tissues of mice lacking MKP5. Mechanistically, MKP5 inhibits the activation of hepatic stellate cells (HSCs) and hepatocyte apoptosis through the regulation of the IRE/XBP1 pathway. Based on these findings, we developed PLGA-MKP5 nanoparticles coated with a mesenchymal stem cell membrane (MSCM). Our results demonstrated that MSCM-PLGA-MKP5 was most effective in attenuating hepatic inflammation and fibrosis in murine models by modulating the IRE/XBP1 axis. This study contributes to the current understanding of the pathogenesis of hepatic fibrosis, suggesting that the targeted delivery of MKP5 via a nano-delivery system may represent a promising therapeutic approach to treat hepatic fibrosis.

Multimodal deep learning approaches for precision oncology: a comprehensive review

The burgeoning accumulation of large-scale biomedical data in oncology, alongside significant strides in deep learning (DL) technologies, has established multimodal DL (MDL) as a cornerstone of precision oncology. This review provides an overview of MDL applications in this field, based on an extensive literature survey. In total, 651 articles published before September 2024 are included. We first outline publicly available multimodal datasets that support cancer research. Then, we discuss key DL training methods, data representation techniques, and fusion strategies for integrating multimodal data. The review also examines MDL applications in tumor segmentation, detection, diagnosis, prognosis, treatment selection, and therapy response monitoring. Finally, we critically assess the limitations of current approaches and propose directions for future research. By synthesizing current progress and identifying challenges, this review aims to guide future efforts in leveraging MDL to advance precision oncology.

Diagnostic value of arterial spin labeling for Alzheimer's disease: A systematic review and meta-analysis

BACKGROUND

Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI) technique that offers a non-invasive approach for measuring cerebral blood perfusion (CBF). CBF serves as a marker of neuronal activity, and ASL has demonstrated the potential to detect reductions in CBF associated with early-stage neurodegenerative diseases like Alzheimer's disease (AD). Consequently, ASL has garnered growing interest as a potential diagnostic tool for AD. Despite the promise of ASL for diagnosing AD, there is a paucity of data regarding the pooled specificity and sensitivity of this technique in this context. The purpose of this systematic review and meta-analysis is to identify the accuracy of ASL in the diagnosis of AD with international clinical diagnosis as the gold standard.

METHODS

Four English databases and four Chinese databases were searched from their inception to 30 November 2023. Two independent reviewers extracted relevant information from the eligible articles, while the quality assessment of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The meta-analysis was carried out using the area under the Receiver Operator Characteristic (ROC) curves (AUC) and sensitivity and specificity values. Meta-DiSc 1.4 was used to perform the statistical analysis. STATA 16.0 was used to perform publication bias and sensitivity analysis.

RESULTS

Of 844 relevant articles retrieved, 10 studies involving 494 participants (AD patients = 262, healthy controls = 232) met the inclusion criteria and were included in the meta-analysis. However, the quality of studies was low based on QUADAS-2. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of ASL for diagnosing AD was 0.83 (95% CI: 0.78-0.87), 0.81 (95% CI: 0.76-0.86), 4.52 (95% CI: 3.40-6.00), 0.22 (95% CI: 0.17-0.28), and 19.31(95% CI: 12.30-30.31), respectively. The pooled AUC = 0.8932. There was low heterogeneity across the included studies. Finally, sensitivity analysis suggested that the results were reliable.

CONCLUSION

ASL is an effective and accurate method for the diagnosis of AD. However, due to the limited quantity and quality of the included studies, the above conclusions need to be verified by more studies.

PROSPERO REGISTRATION

PROSPERO registration number: CRD42023484059.

Relationship Between Ultrasound Diagnosis, Symptoms and Pain Scale Score on Examination in Patients with Uterosacral Ligament Endometriosis

Background/Objectives: This study investigated patient pain descriptors for transvaginal ultrasound (TVS) diagnostic evaluation of endometriosis for uterosacral ligaments (USLs), including correlation between USL thickness and site-specific tenderness (SST). It further investigated if SST could positively assist diagnosing endometriosis on TVS. Methods: TVS images and SST pain descriptors were collected from 42 patients. SST was evaluated by applying sonopalpation during TVS. The images were presented to six observers for diagnosis based on established USL criteria. Following this, they were given the SST pain scores and asked to reevaluate their diagnosis to assess if the pain scores impacted their decision. Results: An independent t-test showed that the patients with an endometriosis history had higher pain scores overall (7.2 ± 0.59) compared to the patients with no history (0.34 ± 0.12), t (40) = 8.8673. Spearman's correlation showed a strong correlation to the pain scale score for clinical symptoms (r = 0.74), endometriosis diagnosis (r = 0.78), USL thickness (r = 0.74), and when USL nodules were identified (r = 0.70). Paired t-tests showed that the observers demonstrated a higher ability to correctly identify endometriosis with the pain scale information (33 ± 8.83) as opposed to not having this information (29.67 ± 6.31), which was a statistically significant change of 3.33, t (5) = 2.7735. Conclusions: Patients with an endometriosis history have significantly higher pain scores on TVS compared to patients with no endometriosis history. A strong correlation was shown between SST pain scores and patient symptoms, USL thickness, and USL nodules. Inclusion of SST alongside TVS imaging shows promise, with these results demonstrating a higher ability to diagnose endometriosis with additional SST pain scale information.

AS2LS: Adaptive Anatomical Structure-Based Two-Layer Level Set Framework for Medical Image Segmentation

Medical images often exhibit intricate structures, inhomogeneous intensity, significant noise and blurred edges, presenting challenges for medical image segmentation. Several segmentation algorithms grounded in mathematics, computer science, and medical domains have been proposed to address this matter; nevertheless, there is still considerable scope for improvement. This paper proposes a novel adaptive anatomical structure-based two-layer level set framework (AS2LS) for segmenting organs with concentric structures, such as the left ventricle and the fundus. By adaptive fitting region and edge intensity information, the AS2LS achieves high accuracy in segmenting complex medical images characterized by inhomogeneous intensity, blurred boundaries and interference from surrounding organs. Moreover, we introduce a novel two-layer level set representation based on anatomical structures, coupled with a two-stage level set evolution algorithm. Experimental results demonstrate the superior accuracy of AS2LS in comparison to representative level set methods and deep learning methods.

The correlation between multi-mode ultrasonographic features of breast cancer and axillary lymph node metastasis

Objective

This study aimed to explore the correlation between multi-mode ultrasonographic features of breast cancer and axillary lymph node metastasis.

Method

A total of 196 patients with surgically confirmed breast cancer between September 2019 and December 2023 were included. Data on preoperative B-mode ultrasound (US), color Doppler, and shear wave elastography (SWE) features of breast cancer masses were collected and analyzed to determine their correlation with axillary lymph node metastasis. The area under the receiver operating characteristic curve (AUC) of B-mode US, color Doppler, SWE, and the multi-mode predictive model for evaluating axillary lymph node metastasis were compared.

Results

Among the 196 patients, 70 had positive axillary lymph nodes, while 126 had negative axillary lymph nodes. There was no significant difference in the color features between the negative and positive axillary lymph node groups. Multifocality/multicentricity, architectural distortion, microcalcifications, and the "stiff rim" sign in SWE were identified as independent risk factors to predict axillary lymph node metastasis according to binary logistic regression analysis. The AUC of the predictive model based on these independent risk factors was 0.803 (95% CI: 0.739-0.867), which was significantly higher than that of B-mode US or SWE alone.

Conclusion

Multifocality/multicentricity, architectural distortion, microcalcifications, and the "stiff rim" sign in SWE were found to be valuable for predicting axillary lymph node metastasis in patients with breast cancer. The predictive model developed in this study, combining the multi-mode ultrasonographic features of breast cancer masses, could serve as a noninvasive and convenient method to predict axillary lymph node status. This approach could aid in clinical decision-making and individualized treatment to improve the prognosis of breast cancer patients.

B-mode ultrasound-based CAD by learning using privileged information with dual-level missing modality completion

Learning using privileged information (LUPI) has shown its effectiveness to improve the B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) by transferring knowledge from the elasticity ultrasound (EUS). However, LUPI only performs transfer learning between the paired data with shared labels, and cannot handle the scenario of modality imbalance. In order to conduct the supervised transfer learning between the paired ultrasound data together with the additional single-modal BUS images, a novel multi-view LUPI algorithm with Dual-Level Modality Completion, named DLMC-LUPI, is proposed to improve the performance of BUS-based CAD. The DLMC-LUPI implements both image-level and feature-level (dual-level) completions of missing EUS modality, and then performs multi-view LUPI for knowledge transfer. Specifically, in the dual-level modality completion stage, a variational autoencoder (VAE) model for feature generation and a novel generative adversarial network (VAE-based GAN) model for image generation are sequentially trained. The proposed VAE-based GAN can improve the synthesis quality of EUS images by adopting the features generated by VAE from the BUS images as the model constrain to make the features generated from the synthesized EUS images more similar to them. In the multi-view LUPI stage, two feature vectors are generated from the real or pseudo images as two source domains, and then fed them to the multi-view support vector machine plus classifier for model training. The experiments on two ultrasound datasets indicate that the DLMC-LUPI outperforms all the compared algorithms, and it can effectively improve the performance of single-modal BUS-based CAD.

Ultrasound normalized cumulative residual entropy imaging: Theory, methodology, and application

BACKGROUND AND OBJECTIVE

Ultrasound information entropy imaging is an emerging quantitative ultrasound technique for characterizing local tissue scatterer concentrations and arrangements. However, the commonly used ultrasound Shannon entropy imaging based on histogram-derived discrete probability estimation suffers from the drawbacks of histogram settings dependence and unknown estimator performance. In this paper, we introduced the information-theoretic cumulative residual entropy (CRE) defined in a continuous distribution of cumulative distribution functions as a new entropy measure of ultrasound backscatter envelope uncertainty or complexity, and proposed ultrasound CRE imaging for tissue characterization.

METHODS

We theoretically analyzed the CRE for Rayleigh and Nakagami distributions and proposed a normalized CRE for characterizing scatterer distribution patterns. We proposed a method based on an empirical cumulative distribution function estimator and a trapezoidal numerical integration for estimating the normalized CRE from ultrasound backscatter envelope signals. We presented an ultrasound normalized CRE imaging scheme based on the normalized CRE estimator and the parallel computation technique. We also conducted theoretical analysis of the differential entropy which is an extension of the Shannon entropy to a continuous distribution, and introduced a method for ultrasound differential entropy estimation and imaging. Monte-Carlo simulation experiments were performed to evaluate the estimation accuracy of the normalized CRE and differential entropy estimators. Phantom simulation and clinical experiments were conducted to evaluate the performance of the proposed normalized CRE imaging in characterizing scatterer concentrations and hepatic steatosis (n = 204), respectively.

RESULTS

The theoretical normalized CRE for the Rayleigh distribution was π/4, corresponding to the case where there were ≥10 randomly distributed scatterers within the resolution cell of an ultrasound transducer. The theoretical normalized CRE for the Nakagami distribution decreased as the Nakagami parameter m increased, corresponding to that the ultrasound backscattered statistics varied from pre-Rayleigh to Rayleigh and to post-Rayleigh distributions. Monte-Carlo simulation experiments showed that the proposed normalized CRE and differential entropy estimators can produce a satisfying estimation accuracy even when the size of the test samples is small. Phantom simulation experiments showed that the proposed normalized CRE and differential entropy imaging can characterize scatterer concentrations. Clinical experiments showed that the proposed ultrasound normalized CRE imaging is capable to quantitatively characterize hepatic steatosis, outperforming ultrasound differential entropy imaging and being comparable to ultrasound Shannon entropy and Nakagami imaging.

CONCLUSION

This study sheds light on the theory and methodology of ultrasound normalized CRE. The proposed ultrasound normalized CRE can serve as a new, flexible quantitative ultrasound envelope statistics parameter. The proposed ultrasound normalized CRE imaging may find applications in quantified characterization of biological tissues. Our code will be made available publicly at https://github.com/zhouzhuhuang.

Evaluation of Carotid Stenosis in a High-Stroke-Risk Population by Hemodynamic Dual-Parameters Based on Ultrasound Vector Flow Imaging

OBJECTIVE

This study explored the feasibility of using high-frame-rate ultrasound vector flow imaging (VFI) to quantitatively assess hemodynamics in atherosclerotic internal carotid artery stenosis (ICAS) by evaluating dual-parameters, turbulence index (Tur), and wall shear stress (WSS). Their efficacy in evaluating carotid artery stenosis was also analyzed.

METHODS

Fifty-nine patients with ICAS were enrolled. B-mode ultrasound and V Flow (a high-frame-rate VFI) were performed using the Resona R9 system. The stenosis rate was measured in grayscale mode, whereas the time-averaged Tur index, the time-averaged WSS (WSSmean), and maximum WSS (WSSmax) around stenosis were measured. The combined diagnostic efficacy of Tur inand WSS was also investigated.

RESULTS

Compared to proximal to stenosis (Tur index, 2.88% ± 3.65%), highly disordered blood flow was observed in the stenotic (23.17% ± 15.52%, p < 0.001) and distal segment (25.86% ± 17.29%, p < 0.001). WSSmax ([11.91 ± 6.73] vs. [4.43 ± 5.4] Pa, p < 0.001) and WSSmean ([3.42 ± 2.67] vs. [0.86 ± 1.21] Pa, p < 0.001) were significantly bigger in stenotic than those in the distal segment. The differences in the ratio WSSmax/Tur or WSSmean/Tur among different segments around stenosis were statistically significant (p < 0.001). The combination of Tur index and WSS had the best diagnostic performance in ICAS (AUC, 0.899).

CONCLUSION

The application of Tur index and WSS for quantitative assessment of ICAS hemodynamic changes is feasible, with the combined evaluation of these two parameters providing incremental diagnostic value for carotid artery stenosis. VFI-based dual quantitative parameters may offer promising noninvasive diagnostic tools for carotid artery stenosis in high-stroke-risk populations.

Hypoxia-Induced Mitochondrial ROS and Function in Pulmonary Arterial Endothelial Cells

Pulmonary artery endothelial cells (PAECs) are a major contributor to hypoxic pulmonary hypertension (PH) due to the possible roles of reactive oxygen species (ROS). However, the molecular mechanisms and functional roles of ROS in PAECs are not well established. In this study, we first used Amplex UltraRed reagent to assess hydrogen peroxide (H2O2) generation. The result indicated that hypoxic exposure resulted in a significant increase in Amplex UltraRed-derived fluorescence (i.e., H2O2 production) in human PAECs. To complement this result, we employed lucigenin as a probe to detect superoxide (O2-) production. Our assays showed that hypoxia largely increased O2- production. Hypoxia also enhanced H2O2 production in the mitochondria from PAECs. Using the genetically encoded H2O2 sensor HyPer, we further revealed the hypoxic ROS production in PAECs, which was fully blocked by the mitochondrial inhibitor rotenone or myxothiazol. Interestingly, hypoxia caused an increase in the migration of PAECs, determined by scratch wound assay. In contrast, nicotine, a major cigarette or e-cigarette component, had no effect. Moreover, hypoxia and nicotine co-exposure further increased migration. Transfection of lentiviral shRNAs specific for the mitochondrial Rieske iron-sulfur protein (RISP), which knocked down its expression and associated ROS generation, inhibited the hypoxic migration of PAECs. Hypoxia largely increased the proliferation of PAECs, determined using Ki67 staining and direct cell number accounting. Similarly, nicotine caused a large increase in proliferation. Moreover, hypoxia/nicotine co-exposure elicited a further increase in cell proliferation. RISP knockdown inhibited the proliferation of PAECs following hypoxia, nicotine exposure, and hypoxia/nicotine co-exposure. Taken together, our data demonstrate that hypoxia increases RISP-mediated mitochondrial ROS production, migration, and proliferation in human PAECs; nicotine has no effect on migration, increases proliferation, and promotes hypoxic proliferation; the effects of nicotine are largely mediated by RISP-dependent mitochondrial ROS signaling. Conceivably, PAECs may contribute to PH via the RISP-mediated mitochondrial ROS.

The role of ultrasonic cardiogram in the diagnosis of hypertension complicated with type 2 diabetes mellitus

Hypertension diabetes mellitus is one of the serious complications of hypertension. In this study, ambulatory blood pressure monitoring (ABPM) and ultrasonic cardiogram (UCG) were used to investigate the cardiac changes and its influencing factors in hypertensive patients with type 2 diabetes mellitus. The ABPM, UCG, Hemoglobin A1c (HbA1c) and body mass index (BMI) of patients were examined. The comparison of HbA1c, BMI, gender, age, daytime and nighttime blood pressure, left ventricular mass index (LVMI), left ventricular hypertrophy (LVH), isovolumic relaxation time (IVRT) and E/A ratio were made between the two groups. The cardiac function of control group was better than that of group B, while that of group B was better than group A. The cardiac index level in group B was better than that in group A, but lower than that in control group. The LVMI in group A was clearly higher than group B and control group, and the incidence of LVH increased. In group A, the nocturnal systolic blood pressure was higher than control group and group B. Nocturnal diastolic blood pressure, daytime diastolic blood pressure and systolic blood pressure in groups A and B were higher than those the control group. The findings indicated that hypertension complicated with type 2 diabetes mellitus can cause degeneration of the heart, and complicated with type 2 diabetes mellitus can accelerate ventricular remodeling and functional deterioration. Hypertension with type 2 diabetes mellitus are more prone to left ventricular damage.

Single-cell RNA sequencing analysis reveals the role of TXNDC5 in keloid formation

Objective

Thioredoxin domain-containing protein 5 (TXNDC5) is associated with fibrosis in a variety of organs, but its mechanism of action in keloid is unclear. In this study, we aimed to investigate the mechanism of TXNDC5 in keloid.

Material and Methods

Single-cell RNA sequencing data of keloid and normal scar samples obtained from public databases were normalized and clustered using the Seurat package. Pathway enrich analysis was conducted using biological process enrichment analysis and Gene Set Enrichment Analysis (GSEA). In addition, TXNDC5 expression and its effects on migration and invasion of keloid fibroblasts (KFs) were validated based on cell function experiments.

Results

A total of five cell types were obtained. The KF clusters were further clustered into two fibroblast subtypes (Fibroblast cells 1 and Fibroblast cells 2). Biological process enrichment analysis showed that transforming growth factor beta (TGF-β) signaling pathway was enriched in the two fibroblast subtypes. GSEA analysis demonstrated that genes in TGF-β signaling pathway were mainly enriched in Fibroblast cells 1, and that genes involved in cell proliferation, migration, and the TGF-β signaling pathway were all high-expressed in fibroblast cells 1. TXNDC5 was positively correlated with fibroblast proliferation, migration and TGF-β signaling pathway, and AUCell score. The cellular experiment confirmed that the messenger RNA and protein levels of TXNDC5 and TGF-β1 were high-expressed in KFs cells (P<0.001), and that knockdown of TXNDC5 downregulated TGF-β1 expression and inhibited migration and invasion of KFs (P<0.0001).

Conclusion

Our study indicated that TGF-β signaling pathway was enriched in fibroblast cells, and TXNDC5 was positively correlated with proliferation, migration, and TGF-β signaling pathway. Cellular experiment demonstrated that knocking down TXNDC5 downregulated TGF-β1 expression, and suppressed migration and invasion of KFs. The current discoveries provided a new therapeutic strategy for the treatment of keloid.

Association between the triglyceride-glucose index and left ventricular myocardial work indices in patients with coronary artery disease

Background

Triglyceride-glucose (TyG) index, as an effective surrogate marker of insulin resistance, has shown predictive value in the risk of heart failure in patients with coronary artery disease (CAD). This study aims to investigate the correlation between TyG index and myocardial work measurements in CAD, and to explore its role in detecting early subclinical cardiac dysfunction.

Methods

This cross-sectional study included 267 patients diagnosed with CAD and excluding left ventricular myocardial dysfunction in Beijing Hospital. Participants were divided into two groups according to the TyG index level, and myocardial work measurements were compared between groups. The correlation was explored between gradually increased TyG index and subclinical myocardial function in CAD patients.

Results

We observed that TyG index was significantly correlated with the global waste work (GWW), and the value of GWW increased progressively with the elevation of TyG index. After adjusting for the effects of confounding factors, TyG index was still independently associated with GWW.

Conclusion

An elevated TyG index was independently correlated with early subclinical myocardial dysfunction in CAD patients. Our study demonstrated that the strict control of TyG index may be conducive to forestall the progression of clinical heart failure in CAD patients.

Repeatability and reproducibility of artificial intelligence-acquired fetal brain measurements (SonoCNS) in the second and third trimesters of pregnancy

Artificial Intelligence (AI)-based algorithms are increasingly entering clinical practice, aiding in the assessment of fetal anatomy and biometry. One such tool for evaluating the fetal head and central nervous system structures is SonoCNS™, which delineates appropriate planes for measuring head circumference (HC), biparietal diameter (BPD), occipitofrontal diameter (OFD), transcerebellar diameter (TCD), width of the posterior horn of the lateral ventricle (Vp), and cisterna magna (CM) based on a 3D volume acquired at the level of the fetal head's thalamic plane. This study aimed to evaluate the intra- and interobserver variability of measurements obtained using this software. The study included 381 patients, 270 in their second trimester of pregnancy (70%) and 111 in the third trimester. Each patient underwent manual biometric measurements of the aforementioned structures and twice using the SonoCNS software. We calculated the intraobserver variability between the manual measurements and the average of the automated measurements, as well as the interobserver variability for automated measurements. We also compared the median examination time for manual and automated measurements. The interclass correlation coefficients (ICC) for interobserver and intraobserver variability for parameters BPD, HC, and OFD ranged from good to excellent reproducibility in the general population and subgroups (> 0.75). CM and Vp measurements, both in the general population and subgroups, fell into the category of moderate (0.5-0.75) and poor reproducibility (< 0.5). TCD measurements showed moderate (> 0.5) to good reproducibility (0.75-0.9), and OFD showed good and excellent reproducibility. The assessment of the biometry of fetal head structures using SonoCNS took an average of 63 s compared to 14 s for manual measurement (p < 0.001). The SonoCNS™ software is characterized by good to excellent reproducibility and repeatability in the measurement of fetal skull biometry (BPD, HC, and OFD), with poorer performance in measurements of intracranial structures (CM, Vp, TCD). Apart from biometric parameters, the software is useful in clinical practice for delineating appropriate planes from the acquired volume of the fetal head and shortening examination time.

The Utility of a Community-Based Knee Ultrasound in Detecting Meniscal Tears: A Retrospective Analysis in Comparison with MRI

BACKGROUND/OBJECTIVES

MRI is the gold standard for detecting meniscal tears; however, ultrasound may readily detect meniscal changes, obviating the need for MRI. We aim to (1) determine ultrasound sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy in detecting meniscal changes, and (2) describe characteristic meniscal changes in US and their prevalence.

METHODS

A retrospective analysis of knee ultrasound scans for the presence of medial and lateral meniscal tears was conducted. Meniscal changes were characterized into five US appearances (cleft, diminutive, cyst, displaced fragment, and extrusion) by the consensus of two musculoskeletal radiologists. Ultrasound findings were then compared to MRI results.

RESULTS

In total, 249 patients were included. Ultrasound sensitivity, specificity, PPV, NPV, and accuracy for medial meniscal tears were 79%, 97.3%, 95.3%, 86.6%, and 90%, respectively, and for lateral meniscal tears the ultrasound sensitivity, specificity, PPV, NPV, and accuracy were 63%, 99.5%, 96%, 93%, and 93.6%, respectively. The false negative and false positive rates for medial meniscal tears were 13.4% and 4.7%, respectively, and for the lateral meniscus, the false negative and false positive rates were 6.7% and 3.8%, respectively. Meniscal clefts were the most prevalent appearance in the medial meniscus followed by extrusions. Meniscal extrusions were the most prevalent appearance in the lateral meniscus followed by clefts.

CONCLUSIONS

Community-based US is highly accurate in the detection of meniscal tears when compared with MRI, making it a valuable diagnostic imaging tool for detecting meniscal tears in a community setting where accessibility to MRI is limited or if there are MRI contraindications.

Predict value of tumor markers combined with interleukins for therapeutic efficacy and prognosis in ovarian cancer patients

Ovarian cancer (OC) is the most prevalent and fatal malignancy of the female reproductive system, with the majority of patients diagnosed at an advanced stage due to the lack of early screening. Despite surgery and chemotherapy being the standard treatments, overall survival rates have not improved significantly, highlighting the need for new biomarkers for therapeutic efficacy and prognostic evaluation. This study aimed to clarify the application value of tumor markers (TMs), including carbohydrate antigen 125 (CA125), alpha-fetoprotein (AFP), and carcinoembryonic antigen (CEA), combined with interleukins (ILs), such as IL-1β, IL-2, IL-6, IL-8, and IL-10, in the evaluation of therapeutic efficacy and prognosis of OC, and to establish a prediction model. A retrospective analysis was conducted on 184 OC patients treated at the Affiliated Hospital of Henan University of Traditional Chinese Medicine from February 2020 to February 2023. Serum levels of CA125, AFP, and CEA were quantified by chemiluminescence immunoassay, and ILs by enzyme-linked immunosorbent assays. Significant decreases in CA125, AFP, CEA, IL-1β, IL-2, IL-6, and IL-10 levels were observed after treatment (all P<0.001), while IL-8 levels showed no significant change (P=0.597). The death group exhibited notably higher levels of CA125, IL-6, and IL-8 than the survival group (all P<0.001). Cox regression analysis identified CA125, IL-8, histological grading, ascites, intravascular tumor thrombus, and International Federation of Gynecology and Obstetrics (FIGO) staging as independent prognostic factors. The Nomogram model based on these factors showed strong predictive ability in predicting patient mortality with an area under the curve (AUC) of 0.756. In conclusion, the combination of TMs and ILs is valuable in evaluating therapeutic efficacy and prognosis in OC. Dynamic monitoring of CA125, IL-6, and IL-8 can guide clinical treatment adjustments, improving diagnostic accuracy and prognosis reliability.

Video-based AI module with raw-scale and ROI-scale information for thyroid nodule diagnosis

OBJECTIVES

Ultrasound examination is a primary method for detecting thyroid lesions in clinical practice. Incorrect ultrasound diagnosis may lead to delayed treatment or unnecessary biopsy punctures. Therefore, our objective is to propose an artificial intelligence model to increase the precision of thyroid ultrasound diagnosis and reduce puncture rates.

METHODS

We consecutively collected ultrasound recordings from 672 patients with 845 nodules across two Chinese hospitals. This dataset was divided into training, validation, and internal test sets in a ratio of 7:1:2. We constructed and tested six different model variants based on different video feature distillation strategies and whether additional information from ROI (Region of Interest) scales was used. The models' performances were evaluated using the internal test set and an additional external test set containing 126 nodules from a third hospital.

RESULTS

The dual-stream model, which contains both raw-scale and ROI-scale streams with the time dimensional convolution layer, achieved the best performance on both internal and external test sets. On the internal test set, it achieved an AUROC (Area Under Receiver Operating Characteristic Curve) of 0.969 (95 % confidence interval, CI: 0.944-0.993) and an accuracy of 92.6 %, outperforming other variants (AUROC: 0.936-0.955, accuracy: 80.2%-88.3 %) and experienced radiologists (accuracy: 91.9 %). The AUROC of the best model in the external test was 0.931 (95 % CI: 0.890-0.972).

CONCLUSION

Integrating a dual-stream model with additional ROI scale information and the time dimensional convolution layer can improve performance in diagnosing thyroid ultrasound videos.

Prediction of allograft function in pre-transplant kidneys using sound touch elastography (STE): an ex vivo study

BACKGROUND

The purpose of the study was to evaluate renal quality and predict posttransplant graft function using ex vivo sound touch elastography (STE).

METHODS

In this prospective study, 106 donor kidneys underwent ex vivo STE examination and biopsy from March 2022 to August 2023. The mean stiffness of the superficial cortex (STEsc), deep cortex (STEdc), and medulla (STEme) was obtained and synthesized into one index (STE) through the factor analysis method. Additionally, 100 recipients were followed up for 6 months. A random forest algorithm was employed to explore significant predictive factors associated with the Remuzzi score and allograft function. The performance of parameters was evaluated by using the area under the receiver operating characteristic curve (AUC).

RESULTS

STE had AUC values of 0.803 for diagnosing low Remuzzi and 0.943 for diagnosing high Remuzzi. Meanwhile, STE had an AUC of 0.723 for diagnosing moderate to severe ATI. Random forest algorithm identified STE and Remuzzi score as significant predictors for 6-month renal function. The AUC for STE in predicting postoperative allograft function was 0.717, which was comparable with that of the Remuzzi score (AUC = 0.756). Nevertheless, the specificity of STE was significantly higher than that of Remuzzi (0.913 vs 0.652, p < 0.001). Given these promising results, donor kidneys can be transplanted directly without the need for biopsy when STE ≤ 11.741.

CONCLUSIONS

The assessment of kidney quality using ex vivo STE demonstrated significant predictive value for the Remuzzi score and allograft function, which could help avoid unnecessary biopsy.

CRITICAL RELEVANCE STATEMENT

Pre-transplant kidney quality measured with ex vivo STE can be used to assess donor kidney quality and avoid unnecessary biopsy.

KEY POINTS

STE has significant value for diagnosing low Remuzzi and high Remuzzi scores. STE achieved good performance in predicting posttransplant allograft function. Assessment of kidney quality using ex vivo STE could avoid unnecessary biopsies.

Cardiovascular Risk Factors as Independent Predictors of Diabetic Retinopathy in Type II Diabetes Mellitus: The Development of a Predictive Model

Background: Diabetic retinopathy (DR) is a leading cause of blindness in patients with type 2 diabetes mellitus (T2DM). Cardiovascular risk factors, such as hypertension, obesity, and dyslipidemia, may play a crucial role in the development and progression of DR, though the evidence remains mixed. This study aimed to assess cardiovascular risk factors as independent predictors of DR and to develop a predictive model for DR progression in T2DM patients. Methods: A retrospective cross-sectional study was conducted on 377 patients with T2DM who underwent a comprehensive eye exam. Clinical data, including blood pressure, lipid profile, BMI, and smoking status, were collected. DR staging was determined through fundus photography and classified as No DR, Non-Proliferative DR (NPDR), and Mild, Moderate, Severe, or Proliferative DR (PDR). A Multivariate Logistic Regression was used to evaluate the association between cardiovascular risk factors and DR presence. Several machine learning models, including Random Forest, XGBoost, and Support Vector Machines, were applied to assess the predictive value of cardiovascular risk factors and identify key predictors. Model performance was evaluated using accuracy, precision, recall, and ROC-AUC. Results: The prevalence of DR in the cohort was 41.6%, with 34.5% having NPDR and 7.1% having PDR. A multivariate analysis identified systolic blood pressure (SBP), LDL cholesterol, and body mass index (BMI) as independent predictors of DR progression (p < 0.05). The Random Forest model showed a moderate predictive ability, with an AUC of 0.62 for distinguishing between the presence and absence of DR XGBoost showing a better performance, featuring a ROC-AUC of 0.68, while SBP, HDL cholesterol, and BMI were consistently identified as the most important predictors across models. After tuning, the XGBoost model showed a notable improvement, with an ROC-AUC of 0.72. Conclusions: Cardiovascular risk factors, particularly BP and BMI, play a significant role in the progression of DR in patients with T2DM. The predictive models, especially XGBoost, showed moderate accuracy in identifying DR stages, suggesting that integrating these risk factors into clinical practice may improve early detection and intervention strategies for DR.

LeSAM: Adapt Segment Anything Model for Medical Lesion Segmentation

The Segment Anything Model (SAM) is a foundational model that has demonstrated impressive results in the field of natural image segmentation. However, its performance remains suboptimal for medical image segmentation, particularly when delineating lesions with irregular shapes and low contrast. This can be attributed to the significant domain gap between medical images and natural images on which SAM was originally trained. In this paper, we propose an adaptation of SAM specifically tailored for lesion segmentation termed LeSAM. LeSAM first learns medical-specific domain knowledge through an efficient adaptation module and integrates it with the general knowledge obtained from the pre-trained SAM. Subsequently, we leverage this merged knowledge to generate lesion masks using a modified mask decoder implemented as a lightweight U-shaped network design. This modification enables better delineation of lesion boundaries while facilitating ease of training. We conduct comprehensive experiments on various lesion segmentation tasks involving different image modalities such as CT scans, MRI scans, ultrasound images, dermoscopic images, and endoscopic images. Our proposed method achieves superior performance compared to previous state-of-the-art methods in 8 out of 12 lesion segmentation tasks while achieving competitive performance in the remaining 4 datasets. Additionally, ablation studies are conducted to validate the effectiveness of our proposed adaptation modules and modified decoder.

AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673 K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms-the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://www.zongweiz.com/dataset.

CFTR and colorectal cancer susceptibility: an urgent need for further studies

Mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene result in cystic fibrosis, a multiorgan disease characterized by aberrant epithelial cell fluid secretion. Recent studies describe a connection between CFTR malfunctioning and a heightened susceptibility to colorectal cancer (CRC). Here, we outline these links and suggest directions for further studies.

Non-Invasive Ultrasound Diagnostic Techniques for Steatotic Liver Disease and Focal Liver Lesions: 2D, Colour Doppler, 3D, Two-Dimensional Shear Wave Elastography (2D-SWE), and Ultrasound-Guided Attenuation Parameter (UGAP)

We conducted a comprehensive literature review to evaluate the efficacy of combining two-dimensional shear wave elastography (2D-SWE) and ultrasound-guided attenuation parameter (UGAP) in assessing the risk of progressive metabolic dysfunction-associated steatohepatitis (MASH). This narrative review explores the applications of liver ultrasound in diagnosing metabolic liver diseases, focusing on recent advancements in diagnostic techniques for steatotic liver disease (SLD). Liver ultrasound can detect a spectrum of SLD manifestations, from metabolic dysfunction-associated liver disease (MASLD) to fibrosis and cirrhosis. It is also possible to identify inflammation, hepatitis, hepatocellular carcinoma (HCC), and various other liver lesions. Innovative ultrasound applications, including elastography and UGAP, can significantly enhance the diagnostic capabilities of ultrasound in accurately interpreting liver diseases. Understanding the pathogenesis of liver diseases requires a thorough analysis of their etiology and progression in order to develop sound diagnostic and therapeutic approaches. Chronic liver diseases (CLD) vary in origin, with MASLD affecting approximately 20-25% of the general population. The insidious progression of CLD from inflammation to fibrosis and cirrhosis underscores the need for effective early detection methods. This review aims to highlight the evolving role of non-invasive ultrasound-based diagnostic tests in the early detection and staging of liver diseases. By synthesizing current evidence, we aim to provide an updated perspective on the utility of advanced ultrasound techniques in redefining the diagnostic landscape for metabolic liver diseases.

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

BACKGROUND AND OBJECTIVES

Techniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel.

METHODS

To construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images.

RESULTS

Comparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images.

CONCLUSIONS

The cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

Pan-cancer analysis reveals that TK1 promotes tumor progression by mediating cell proliferation and Th2 cell polarization

BACKGROUND

TK1 (Thymidine kinase 1) is a member of the thymidine kinase family and has been observed to be significantly upregulated in a variety of cancer types. However, the exact roles of TK1 in tumor progression and the tumor immune microenvironment are not fully understood. This study aims to investigate the comprehensive involvement of TK1 in pan-cancer through the utilization of bioinformatics analysis, validation of pathological tissue samples, and in vitro experimental investigations.

METHODS

The expression profiles together with diagnostic and prognostic role of TK1 in pan-cancer were investigated though TCGA, TARGET, GTEx, and CPTAC databases. The single-sample gene set enrichment analysis (ssGSEA) and single-cell sequencing datasets were used to examine the relationship between TK1 and immune infiltration. The expression of TK1 were verified in hepatocellular carcinoma (HCC) through qPCR, western blotting and immunohistochemical assays. The proliferative capacity of HCC cell lines was assessed through CCK-8 and colony formation assays, while cytokine levels were measured via ELISA. Furthermore, flow cytometry was utilized to analyze cell cycle distribution and the proportions of Th2 cells.

RESULTS

TK1 was overexpressed in most cancers and demonstrated significant diagnostic and prognostic value. Among the various immune cells in pan-cancer, Th2 cells exhibited the closest association with TK1. Furthermore, the single-cell atlas provided insights into the distribution and proportion of TK1 in immune cells of HCC. In vitro experiments revealed an elevated expression of TK1 in HCC tissue and cell lines, and its role in influencing HCC cell proliferation by regulating G0/G1 phase arrest. Additionally, TK1 in cancer cells was found to potentially modulate Th2 cell polarization through the chemokine CCL5.

CONCLUSION

TK1 holds immense potential as a biomarker for pan-cancer diagnosis and prognosis. Additionally, targeting the expression of TK1 represents a promising therapeutic approach that can enhance the efficacy of current anti-tumor immunotherapy by modulating Th2 cell polarization and multiple mechanisms.

Study on the correlation between shear wave elastography and MRI grading of meniscal degeneration

BACKGROUND

Shear Wave Elastography (SWE) offers quantitative insights into the hardness and elasticity characteristics of tissues. The objective of this study is to investigate the correlation between SWE of the menisci and MRI-assessed degenerative changes in the menisci, with the aim of providing novel reference source for improving non-invasive evaluation of meniscal degenerative alterations.

METHODS

The participants in this study were selected from patients who underwent knee joint MRI scans at our hospital from February 2023 to February 2024. The anterior horns of both the medial and lateral menisci were evaluated using SWE technique. The differences in elastic values of meniscus among different MRI grades were compared. The correlation between elastic values and MRI grades, as well as various parameters, was analyzed. Using MRI Grade 3 as the gold standard, the optimal cutoff value for meniscal tear was determined. The intraclass correlation coefficient (ICC) was employed to evaluate the reliability of repeated measurements performed by the same observer.

RESULTS

A total of 104 female participants were enrolled in this study, with 152 lateral menisci (LM) and 144 medial menisci (MM) assessed. For the male group, 83 individuals were included, with 147 LM and 145 MM evaluated. The results demonstrated statistically significant differences in the elasticity values of the menisci at the same anatomical sites across different MRI grades (P < 0.001). Within the same grade, the MM had higher elasticity values than the LM, showing a statistically significant difference (P < 0.001). The elasticity values of the menisci were higher in males compared to females. There were statistically significant positive correlations between the elasticity values of the menisci and age, BMI, and MRI grade. The ICC for repeated measurements within the observer demonstrated good reliability (> 0.79).

CONCLUSIONS

The meniscal elasticity values measured by SWE exhibit a significant positive correlation with the grades of degeneration assessed by MRI. Furthermore, the elasticity values of the meniscus are found to increase with advancing age and elevated BMI.

Review on Photoacoustic Monitoring after Drug Delivery: From Label-Free Biomarkers to Pharmacokinetics Agents

Photoacoustic imaging (PAI) is an emerging noninvasive and label-free method for capturing the vasculature, hemodynamics, and physiological responses following drug delivery. PAI combines the advantages of optical and acoustic imaging to provide high-resolution images with multiparametric information. In recent decades, PAI's abilities have been used to determine reactivity after the administration of various drugs. This study investigates photoacoustic imaging as a label-free method of monitoring drug delivery responses by observing changes in the vascular system and oxygen saturation levels across various biological tissues. In addition, we discuss photoacoustic studies that monitor the biodistribution and pharmacokinetics of exogenous contrast agents, offering contrast-enhanced imaging of diseased regions. Finally, we demonstrate the crucial role of photoacoustic imaging in understanding drug delivery mechanisms and treatment processes.

DeepPlaq: Dental plaque indexing based on deep neural networks

OBJECTIVES

The selection of treatment for dental plaque is closely related to the condition of the plaque on different teeth. This study validated the ability of CNN models in assessing the dental plaque indices.

MATERIALS AND METHODS

In 70 (20 male and 50 female) healthy adults (18 to 55 years old), frontal and lateral view intraoral images (210) of plaque disclosing agent stained permanent and deciduous dentitions were obtained. A three-stage method was employed, where the You Look Only Once version 8 (YOLOv8) model was first used to detect the target teeth, followed by the prompt-based Segment Anything Model (SAM) segmentation algorithm to segment teeth. A new single-tooth dataset consisting of 1400 photographs was obtained after applying a two-stage method. Finally, a multi-class classification model DeepPlaq was trained and evaluated on the accuracy of dental plaque indexing based on the Quigley-Hein Index (QHI) scoring system. Classification performance was measured using accuracy, recall, precision, and F1-score.

RESULTS

The teeth detector exhibited an accuracy (mean average precision, mAP) of approximately 0.941 ± 0.005 in identifying teeth with plaque disclosing agents. The maximum accuracy attained in the plaque indexing through DeepPlaq was 0.84 (probability that DeepPlaq scored identical to experts), and the smallest average scoring error was less than 0.25 on a 0 to 5 scale for scoring.

CONCLUSIONS

A three-stage approach demonstrated excellent performance in detecting and segmenting target teeth, and DeepPlaq model also showed strong performance in assessing dental plaque indices.

CLINICAL RELEVANCE

Application of artificial intelligence to the evaluation of dental plaque distribution could enhance diagnostic accuracy and treatment efficiency and accuracy.