Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer.

OBJECTIVES: Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. METHODS: A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. RESULTS: The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89-9.33% and 14.75-16.67%, respectively, while 95HD and ASD decreased by 33.16-67.81% and 36.05-75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. CONCLUSIONS: Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

Polyvinylpyrrolidone Assisted One-Pot Synthesis of Size-Tunable Cocktail Nanodrug for Multifunctional Combat of Cancer.

Background: The in vivo barriers and multidrug resistance (MDR) are well recognized as great challenges for the fulfillment of antitumor effects of current drugs, which calls for the development of novel therapeutic agents and innovative drug delivery strategies. Nanodrug (ND) combining multiple drugs with distinct modes of action holes the potential to circumvent these challenges, while the introduction of photothermal therapy (PTT) can give further significantly enhanced efficacy in cancer therapy. However, facile preparation of ND which contains dual drugs and photothermal capability with effective cancer treatment ability has rarely been reported. Methods: In this study, we selected curcumin (Cur) and doxorubicin (Dox) as two model drugs for the creation of a cocktail ND (Cur-Dox ND). We utilized polyvinylpyrrolidone (PVP) as a stabilizer and regulator to prepare Cur-Dox ND in a straightforward one-pot method. Results: The size of the resulting Cur-Dox ND can be easily adjusted by tuning the charged ratios. It was noted that both loaded drugs in Cur-Dox ND can realize their functions in the same target cell. Especially, the P-glycoprotein inhibition effect of Cur can synergistically cooperate with Dox, leading to enhanced inhibition of 4T1 cancer cells. Furthermore, Cur-Dox ND exhibited pH-responsive dissociation of loaded drugs and a robust photothermal translation capacity to realize multifunctional combat of cancer for photothermal enhanced anticancer performance. We further demonstrated that this effect can also be realized in 3D multicellular model, which possibly attributed to its superior drug penetration as well as photothermal-enhanced cellular uptake and drug release. Conclusion: In summary, Cur-Dox ND might be a promising ND for better cancer therapy.

Advances in Composite Biofilm Biomimetic Nanodrug Delivery Systems for Cancer Treatment.

Single biofilm biomimetic nanodrug delivery systems based on single cell membranes, such as erythrocytes and cancer cells, have immune evasion ability, good biocompatibility, prolonged blood circulation, and high tumor targeting. Because of the different characteristics and functions of each single cell membrane, more researchers are using various hybrid cell membranes according to their specific needs. This review focuses on several different types of biomimetic nanodrug-delivery systems based on composite biofilms and looks forward to the challenges and possible development directions of biomimetic nanodrug-delivery systems based on composite biofilms to provide reference and ideas for future research.

[Research on Position Verification of Multi-Leaf Collimator (MLC) and Dose Verification Based on Electronic Portal Imaging Device].

Objective: A quality control (QC) system based on the electronic portal imaging device (EPID) system was used to realize the Multi-Leaf Collimator (MLC) position verification and dose verification functions on Primus and VenusX accelerators. Methods: The MLC positions were calculated by the maximum gradient method of gray values to evaluate the deviation. The dose of images acquired by EPID were reconstructed using the algorithm combining dose calibration and dose calculation. The dose data obtained by EPID and two-dimensional matrix (MapCheck/PTW) were compared with the dose calculated by Pinnacle/TiGRT TPS for γ passing rate analysis. Results: The position error of VenusX MLC was less than 1 mm. The position error of Primus MLC was significantly reduced after being recalibrated under the instructions of EPID. For the dose reconstructed by EPID, the average γ passing rates of Primus were 98.86% and 91.39% under the criteria of 3%/3 mm, 10% threshold and 2%/2 mm, 10% threshold, respectively. The average γ passing rates of VenusX were 98.49% and 91.11%, respectively. Conclusion: The EPID-based accelerator quality control system can improve the efficiency of accelerator quality control and reduce the workload of physicists.

Identification of m6A/m5C-related lncRNA signature for prediction of prognosis and immunotherapy efficacy in esophageal squamous cell carcinoma.

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have garnered significant attention in the field of epigenetic research due to their close association with human cancers. This study we focus on elucidating the expression patterns of m6A/m5C-related long non-coding RNAs (lncRNAs) in esophageal squamous cell carcinoma (ESCC) and assessing their prognostic significance and therapeutic potential. Transcriptomic profiles of ESCC were derived from public resources. m6A/m5C-related lncRNAs were obtained from TCGA using Spearman's correlations analysis. The m6A/m5C-lncRNAs prognostic signature was selected to construct a RiskScore model for survival prediction, and their correlation with the immune microenvironment and immunotherapy response was analyzed. A total of 606 m6A/m5C-lncRNAs were screened, and ESCC cases in the TCGA cohort were stratified into three clusters, which showed significantly distinct in various clinical features and immune landscapes. A RiskScore model comprising ten m6A/m5C-lncRNAs prognostic signature were constructed and displayed good independent prediction ability in validation datasets. Patients in the low-RiskScore group had a better prognosis, a higher abundance of immune cells (CD4 + T cell, CD4 + naive T cell, class-switched memory B cell, and Treg), and enhanced expression of most immune checkpoint genes. Importantly, patients with low-RiskScore were more cline benefit from immune checkpoint inhibitor treatment (P < 0.05). Our findings underscore the potential of RiskScore system comprising ten m6A/m5C-related lncRNAs as effective biomarkers for predicting survival outcomes, characterizing the immune landscape, and assessing response to immunotherapy in ESCC.

Prognosis Prediction of Diffuse Large B-cell Lymphoma in 18F-FDG PET images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

NSUN2 promotes lung adenocarcinoma progression through stabilizing PIK3R2 mRNA in an m5C-dependent manner.

It is well known that 5-methylcytosine (m5C) is involved in variety of crucial biological processes in cancers. However, its biological roles in lung adenocarcinoma (LAUD) remain to be determined. The LUAD samples were used to assess the clinical value of NOP2/Sun RNA Methyltransferase 2 (NSUN2). Dot blot was used to determine global m5C levels. ChIP and dual-luciferase assays were performed to investigate the MYC-associated zinc finger protein (MAZ)-binding sites in NSUN2 promoter. RNA-seq was used to explore the downstream molecular mechanisms of NSUN2. Dual luciferase reporter assay, m5C-RIP-qPCR, and mRNA stability assay were conducted to explore the effect of NSUN2-depletion on target genes. Cell viability, transwell, and xenograft mouse model were designed to demonstrate the characteristic of NSUN2 in promoting LUAD progression. The m5C methyltransferase NSUN2 was highly expressed and caused elevated m5C methylation in LUAD samples. Mechanistically, MAZ positively regulated the transcription of NSUN2 and was related to poor survival of LUAD patients. Silencing NSUN2 decreased the global m5C levels, suppressed proliferation, migration and invasion, and inhibited activation of PI3K-AKT signaling in A549 and SPAC-1 cells. Phosphoinositide-3-Kinase Regulatory Subunit 2 (PIK3R2) was upregulated by NSUN2-mediated m5C methylation by enhancing its mRNA stabilization and activated the phosphorylation of the PI3K-AKT signaling. The present study explored the underlying mechanism and biological function of NSUN2-meditated m5C RNA methylation in LUAD. NSUN2 was discovered to facilitate the malignancy progression of LUAD through regulating m5C modifications to stabilize PIK3R2 activating the PI3K-AKT signaling, suggesting that NSUN2 could be a novel biomarker and promising therapeutic target for LUAD patients.

A 4D-CBCT correction network based on contrastive learning for dose calculation in lung cancer.

OBJECTIVE: This study aimed to present a deep-learning network called contrastive learning-based cycle generative adversarial networks (CLCGAN) to mitigate streak artifacts and correct the CT value in four-dimensional cone beam computed tomography (4D-CBCT) for dose calculation in lung cancer patients. METHODS: 4D-CBCT and 4D computed tomography (CT) of 20 patients with locally advanced non-small cell lung cancer were used to paired train the deep-learning model. The lung tumors were located in the right upper lobe, right lower lobe, left upper lobe, and left lower lobe, or in the mediastinum. Additionally, five patients to create 4D synthetic computed tomography (sCT) for test. Using the 4D-CT as the ground truth, the quality of the 4D-sCT images was evaluated by quantitative and qualitative assessment methods. The correction of CT values was evaluated holistically and locally. To further validate the accuracy of the dose calculations, we compared the dose distributions and calculations of 4D-CBCT and 4D-sCT with those of 4D-CT. RESULTS: The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the 4D-sCT increased from 87% and 22.31 dB to 98% and 29.15 dB, respectively. Compared with cycle consistent generative adversarial networks, CLCGAN enhanced SSIM and PSNR by 1.1% (p < 0.01) and 0.42% (p < 0.01). Furthermore, CLCGAN significantly decreased the absolute mean differences of CT value in lungs, bones, and soft tissues. The dose calculation results revealed a significant improvement in 4D-sCT compared to 4D-CBCT. CLCGAN was the most accurate in dose calculations for left lung (V5Gy), right lung (V5Gy), right lung (V20Gy), PTV (D98%), and spinal cord (D2%), with the relative dose difference were reduced by 6.84%, 3.84%, 1.46%, 0.86%, 3.32% compared to 4D-CBCT. CONCLUSIONS: Based on the satisfactory results obtained in terms of image quality, CT value measurement, it can be concluded that CLCGAN-based corrected 4D-CBCT can be utilized for dose calculation in lung cancer.

Nanofabrications of Erythrocyte Membrane-Coated Telmisartan Delivery System Effective for Radiosensitivity of Tumor Cells in Mice Model.

Background: Radiation stimulates the secretion of tumor stroma and induces resistance, recurrence, and metastasis of stromal-vascular tumors during radiotherapy. The proliferation and activation of tumor-associated fibroblasts (TAFs) are important reasons for the production of tumor stroma. Telmisartan (Tel) can inhibit the proliferation and activation of TAFs (resting TAFs), which may promote radiosensitization. However, Tel has a poor water solubility. Methods: In this study, self-assembled telmisartan nanoparticles (Tel NPs) were prepared by aqueous solvent diffusion method to solve the insoluble problem of Tel and achieve high drug loading of Tel. Then, erythrocyte membrane (ECM) obtained by hypotonic lysis was coated on the surface of Tel NPs (ECM/Tel) for the achievement of in vivo long circulation and tumor targeting. Immunofluorescence staining, western blot and other biological techniques were used to investigate the effect of ECM/Tel on TAFs activation inhibition (resting effect) and mechanisms involved. The multicellular spheroids (MCSs) model and mouse breast cancer cells (4T1) were constructed to investigate the effect of ECM/Tel on reducing stroma secretion, alleviating hypoxia, and the corresponding promoting radiosensitization effect in vitro. A mouse orthotopic 4T1 breast cancer model was constructed to investigate the radiosensitizing effect of ECM/Tel on inhibiting breast cancer growth and lung metastasis of breast cancer. Results: ECM/Tel showed good physiological stability and tumor-targeting ability. ECM/Tel could rest TAFs and reduce stroma secretion, alleviate hypoxia, and enhance penetration in tumor microenvironment. In addition, ECM/Tel arrested the cell cycle of 4T1 cells to the radiosensitive G2/M phase. In mouse orthotopic 4T1 breast cancer model, ECM/Tel played a superior role in radiosensitization and significantly inhibited lung metastasis of breast cancer. Conclusion: ECM/Tel showed synergistical radiosensitization effect on both the tumor microenvironment and tumor cells, which is a promising radiosensitizer in the radiotherapy of stroma-vascular tumors.

TXNIP-NLRP3-GSDMD axis-mediated inflammation and pyroptosis of islet ß-cells is involved in cigarette smoke-induced hyperglycemia, which is alleviated by andrographolide.

Epidemiologic surveys have indicated that cigarette smoking is an important risk factor for diabetes, but its mechanisms remain unclear. Andrographolide, an herb traditionally utilized in medicine, provides anti-inflammatory benefits for various diseases. In the present work, 265 patients with Type 2 diabetes (T2D) were investigated, and male C57BL/6 mice were exposed to cigareete smoke (CS) and/or to intraperitoneally injected andrographolide for 3 months. To elucidate the mechanism of CS-induced hyperglycemia and the protective mechanism of andrographolide, MIN6 cells were exposed to cigarette smoke extract (CSE) and/or to andrographolide. Our data from 265 patients with T2D showed that urinary creatinine and serum inflammatory cytokines (interleukin 6 (IL-6), IL-8, IL-1ß, and tumor necrosis factor α (TNF-α)) increased with smoking pack-years. In a mouse model, CS induced hyperglycemia, decreased insulin secretion, and elevated inflammation and pyroptosis in ß-cells of mice. Treatment of mice with andrographolide preserved pancreatic function by reducing the expression of inflammatory cytokines; the expression of TXNIP, NLRP3, cleaved caspase 1, IL-1ß; and the N-terminal of gasdermin D (GSDMD) protein. For MIN6 cells, CSE caused increasing secretion of the inflammatory cytokines IL-6 and IL-1ß, and the expression of TXNIP and pyroptosis-related proteins; however, andrographolide alleviated these changes. Furthermore, silencing of TXNIP showed that the blocking effect of andrographolide may be mediated by TXNIP. In sum, our results indicate that CS induces hyperglycemia through TXNIP-NLRP3-GSDMD axis-mediated inflammation and pyroptosis of islet ß-cells and that andrographolide is a potential therapeutic agent for CS-induced hyperglycemia.

Mitigating misalignment in MRI-to-CT synthesis for improved synthetic CT generation: an iterative refinement and knowledge distillation approach.

Objective.Deep learning has shown promise in generating synthetic CT (sCT) from magnetic resonance imaging (MRI). However, the misalignment between MRIs and CTs has not been adequately addressed, leading to reduced prediction accuracy and potential harm to patients due to the generative adversarial network (GAN)hallucination phenomenon. This work proposes a novel approach to mitigate misalignment and improve sCT generation.Approach.Our approach has two stages: iterative refinement and knowledge distillation. First, we iteratively refine registration and synthesis by leveraging their complementary nature. In each iteration, we register CT to the sCT from the previous iteration, generating a more aligned deformed CT (dCT). We train a new model on the refined 〈dCT, MRI〉 pairs to enhance synthesis. Second, we distill knowledge by creating a target CT (tCT) that combines sCT and dCT images from the previous iterations. This further improves alignment beyond the individual sCT and dCT images. We train a new model with the 〈tCT, MRI〉 pairs to transfer insights from multiple models into this final knowledgeable model.Main results.Our method outperformed conditional GANs on 48 head and neck cancer patients. It reduced hallucinations and improved accuracy in geometry (3% ↑ Dice), intensity (16.7% ↓ MAE), and dosimetry (1% ↑γ3%3mm). It also achieved <1% relative dose difference for specific dose volume histogram points.Significance.This pioneering approach for addressing misalignment shows promising performance in MRI-to-CT synthesis for MRI-only planning. It could be applied to other modalities like cone beam computed tomography and tasks such as organ contouring.

Adipose-derived stem cells repair radiation-induced chronic lung injury via inhibiting TGF-ß1/Smad 3 signaling pathway.

To investigate the effect of adipose-derived stem cells (ASCs) transplantation on radiation-induced lung injury (RILI), Sprague-Dawley rats were divided into phosphate-buffered saline (PBS) group, ASCs group, Radiation + PBS group, and Radiation + ASCs group. Radiation + PBS and Radiation + ASCs groups received single dose of 30 Gy X-ray radiation to the right chest. The Radiation + PBS group received 1 mL PBS suspension and Radiation + ASCs group received 1 mL PBS suspension containing 1 × 107 CM-Dil-labeled ASCs. The right lung tissue was collected on Days 30, 90, and 180 after radiation. Hematoxylin-eosin and Masson staining were performed to observe the pathological changes and collagen fiber content in the lung tissue. Immunohistochemistry (IHC) and western blot (WB) were used to detect levels of fibrotic markers collagen I (Collal), fibronectin (FN), as well as transforming growth factor-ß1 (TGF-ß1), p-Smad 3, and Smad 3. Compared with the non-radiation groups, the radiation groups showed lymphocyte infiltration on Day 30 after irradiation and thickened incomplete alveolar walls, collagen deposition, and fibroplasia on Days 90 and 180. ASCs relieved these changes on Day 180 (Masson staining, P = 0.0022). Compared with Radiation + PBS group, on Day 180 after irradiation, the Radiation + ASCs group showed that ASCs could significantly decrease the expressions of fibrosis markers Collal (IHC: P = 0.0022; WB: P = 0.0087) and FN (IHC: P = 0.0152; WB: P = 0.026) and inhibit the expressions of TGF-ß1 (IHC: P = 0.026; WB: P = 0.0152) and p-Smad 3 (IHC: P = 0.0043; WB: P = 0.0087) in radiation-induced injured lung tissue. These indicated that ASCs could relieve RILI by inhibiting TGF-ß1/Smad 3 signaling pathway.

Application of Deep Learning in Cancer Prognosis Prediction Model.

As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction. Therefore, this article is a systematic review of the latest research on DL in cancer prognosis prediction. First, the data type, construction process, and performance evaluation index of the DL model are introduced in detail. Then, the current mainstream baseline DL cancer prognosis prediction models, namely, deep neural networks, convolutional neural networks, deep belief networks, deep residual networks, and vision transformers, including network architectures, the latest application in cancer prognosis, and their respective characteristics, are discussed. Next, some key factors that affect the predictive performance of the model and common performance enhancement techniques are listed. Finally, the limitations of the DL cancer prognosis prediction model in clinical practice are summarized, and the future research direction is prospected. This article could provide relevant researchers with a comprehensive understanding of DL cancer prognostic models and is expected to promote the research progress of cancer prognosis prediction.

Nondeformed Ultrasound Image Production Method for Ultrasound-Guided Radiotherapy.

Purpose: During ultrasound (US)-guided radiotherapy, the tissue is deformed by probe pressure, and the US image is limited by changes in tissue and organ position and geometry when the US image is aligned with computed tomography (CT) image, leading to poor alignment. Accordingly, a pixel displacement-based nondeformed US image production method is proposed. Methods: The correction of US image deformation is achieved by calculating the pixel displacement of an image. The positioning CT image (CTstd) is used as the gold standard. The deformed US image (USdef) is inputted into the Harris algorithm to extract corner points for selecting feature points, and the displacement of adjacent pixels of feature points in the US video stream is calculated using the Lucas-Kanade optical flow algorithm. The moving least squares algorithm is used to correct USdef globally and locally in accordance with image pixel displacement to generate a nondeformed US image (USrev). In addition, USdef and USrev were separately aligned with CTstd to evaluate the improvement of alignment accuracy through deformation correction. Results: In the phantom experiment, the overall and local average correction errors of the US image under the optimal probe pressure were 1.0944 and 0.7388 mm, respectively, and the registration accuracy of USdef and USrev with CTstd was 0.6764 and 0.9016, respectively. During the volunteer experiment, the correction error of all 12 patients' data ranged from -1.7525 to 1.5685 mm, with a mean absolute error of 0.8612 mm. The improvement range of US and CT registration accuracy, before and after image deformation correction in the 12 patients evaluated by a normalized correlation coefficient, was 0.1232 to 0.2476. Conclusion: The pixel displacement-based deformation correction method can solve the limitation imposed by image deformation on image alignment in US-guided radiotherapy. Compared with USdef, the alignment results of USrev with CT were better.

A transformer-based dual-domain network for reconstructing FOV extended cone-beam CT images from truncated sinograms in radiation therapy.

BACKGROUND AND OBJECTIVE: Cone-beam computed tomography (CBCT) is widely used in clinical radiotherapy, but its small field of view (sFOV) limits its application potential. In this study, a transformer-based dual-domain network (dual_swin), which combined image domain restoration and sinogram domain restoration, was proposed for the reconstruction of complete CBCT images with extended FOV from truncated sinograms. METHODS: The planning CT images with large FOV (LFOV) of 330 patients who received radiation therapy were collected. The synthetic CBCT (sCBCT) images with LFOV were generated from CT images by the trained cycleGAN network, and CBCT images with sFOV were obtained through forward projection, projection truncation, and filtered back projection (FBP), comprising the training and test data. The proposed dual_swin includes sinogram domain restoration, image domain restoration, and FBP layer, and the swin transformer blocks were used as the basic feature extraction module in the network to improve the global feature extraction ability. The proposed dual_swin was compared with the image domain method, the sinogram domain method, the U-Net based dual domain network (dual_Unet), and the traditional iterative reconstruction method based on prior image and conjugate gradient least-squares (CGLS) in the test of sCBCT images and clinical CBCT images. The HU accuracy and body contour accuracy of the predicted images by each method were evaluated. RESULTS: The images generated using the CGLS method were fuzzy and obtained the lowest structural similarity (SSIM) among all methods in the test of sCBCT and clinical CBCT images. The predicted images by the image domain methods are quite different from the ground truth and have low accuracy on HU value and body contour. In comparison with image domain methods, sinogram domain methods improved the accuracy of HU value and body contour but introduced secondary artifacts and distorted bone tissue. The proposed dual_swin achieved the highest HU and contour accuracy with mean absolute error (MAE) of 23.0 HU, SSIM of 95.7%, dice similarity coefficient (DSC) of 99.6%, and Hausdorff distance (HD) of 4.1 mm in the test of sCBCT images. In the test of clinical patients, images that were predicted by dual_swin yielded MAE, SSIM, DSC, and HD of 38.2 HU, 91.7%, 99.0%, and 5.4 mm, respectively. The predicted images by the proposed dual_swin has significantly higher accuracy than the other methods (P < 0.05). CONCLUSIONS: The proposed dual_swin can accurately reconstruct FOV extended CBCT images from the truncated sinogram to improve the application potential of CBCT images in radiotherapy.

Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study.

OBJECTIVE: To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL). METHODS: A total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121. These DLSs were utilized to predict progression-free survival (PFS) and overall survival (OS). Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts. RESULTS: The DLSPFS and DLSOS exhibited significant associations with PFS and OS, respectively (P<0.05) in the training and validation cohorts. The multiparametric models that incorporated DLSs demonstrated superior efficacy in predicting PFS (C-index: 0.866) and OS (C-index: 0.835) compared to competing models in training cohorts. In external validation cohorts, the C-indices for PFS and OS were 0.760 and. 0.770 and 0.748 and 0.766, respectively, indicating the reliable validity of the multiparametric models. The calibration curves displayed good consistency, and the decision curve analysis (DCA) confirmed that the multiparametric models offered more net clinical benefits. CONCLUSIONS: The DLSs were identified as robust prognostic imaging biomarkers for survival in DLBCL patients. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.

Study on injectable silver-incorporated calcium phosphate composite with enhanced antibacterial and biomechanical properties for fighting bone cement-associated infections.

Although commonly used in orthopedic surgery, bone cements often face a high risk of post-operative infection. Developing bone cement with antibacterial capability is an effective path for eliminating implant-associated infections. Herein, the potential of silver ions (Ag+) and silver nanoparticles (AgNPs) in modifying CPC for long-term antibacterial property was investigated. Ag+ ions or AgNPs of various concentrations were incorporated in starch-modified calcium phosphate bone cement (CPB) to obtain Ag+-containing (Ag+@CPB) and AgNPs-containing (AgNP@CPB) bone cements. The results showed that all silver-containing CPBs had setting times of about 25-40 min, compressive strengths of greater than 22 MPa, high cytocompatibility but inhibitory effect on Staphylococcus aureus growth. After soaking for 1 week, the mechanical properties and the cytocompatibility of all cements revealed no significant changes, but only CPB with a relatively high content of Ag+ (H-Ag+@CPB) maintained good antibacterial capability over the tested time period. In addition, all the cements showed high injectability and interdigitating capability in cancellous bone and demonstrated augmentation effect on the cannulated pedicle screws fixation in the Sawbones model. In summary, the sustainable antibacterial capability and enhanced biomechanical properties demonstrated that Ag+ ions were more suitable for the fabrication of antibacterial CPC compared to AgNPs. Also, the H-Ag+@CPB, with good injectability, high cytocompatibility, good interdigitating and biomechanical property in cancellous bone, and sustainable antibacterial effects, bears great potential for the treatments of bone infections or implant-associated infections.

BPAT-UNet: Boundary preserving assembled transformer UNet for ultrasound thyroid nodule segmentation.

BACKGROUND AND OBJECTIVE: Accurate and efficient segmentation of thyroid nodules on ultrasound images is critical for computer-aided nodule diagnosis and treatment. For ultrasound images, Convolutional neural networks (CNNs) and Transformers, which are widely used in natural images, cannot obtain satisfactory segmentation results, because they either cannot obtain precise boundaries or segment small objects. METHODS: To address these issues, we propose a novel Boundary-preserving assembly Transformer UNet (BPAT-UNet) for ultrasound thyroid nodule segmentation. In the proposed network, a Boundary point supervision module (BPSM), which adopts two novel self-attention pooling approaches, is designed to enhance boundary features and generate ideal boundary points through a novel method. Meanwhile, an Adaptive multi-scale feature fusion module (AMFFM) is constructed to fuse features and channel information at different scales. Finally, to fully integrate the characteristics of high-frequency local and low-frequency global, the Assembled transformer module (ATM) is placed at the bottleneck of the network. The correlation between deformable features and features-among computation is characterized by introducing them into the above two modules of AMFFM and ATM. As the design goal and eventually demonstrated, BPSM and ATM promote the proposed BPAT-UNet to further constrain boundaries, whereas AMFFM assists to detect small objects. RESULTS: Compared to other classical segmentation networks, the proposed BPAT-UNet displays superior segmentation performance in visualization results and evaluation metrics. Significant improvement of segmentation accuracy was shown on the public thyroid dataset of TN3k with Dice similarity coefficient (DSC) of 81.64% and 95th percentage of the asymmetric Hausdorff distance (HD95) of 14.06, whereas those on our private dataset were with DSC of 85.63% and HD95 of 14.53, respectively. CONCLUSIONS: This paper presents a method for thyroid ultrasound image segmentation, which achieves high accuracy and meets the clinical requirements. Code is available at https://github.com/ccjcv/BPAT-UNet.

AMPFinder: A computational model to identify antimicrobial peptides and their functions based on sequence-derived information.

Antimicrobial peptides (AMPs) called host defense peptides have existed among all classes of life with 5-100 amino acids generally and can kill mycobacteria, envelop viruses, bacteria, fungi, cancerous cells and so on. Owing to the non-drug resistance of AMP, it has been a wonderful agent to find novel therapies. Therefore, it is urgent to identify AMPs and predict their function in a high-throughput way. In this paper, we propose a cascaded computational model to identify AMPs and their functional type based on sequence-derived and life language embedding, called AMPFinder. Compared with other state-of-the-art methods, AMPFinder obtains higher performance both on AMP identification and AMP function prediction. AMPFinder shows better performance with improvement of F1-score (1.45%-6.13%), MCC (2.92%-12.86%) and AUC (5.13%-8.56%) and AP (9.20%-21.07%) on an independent test dataset. And AMPFinder achieve lower bias of R2 on a public dataset by 10-fold cross-validation with an improvement of (18.82%-19.46%). The comparison with other state-of-the-art methods shows that AMP can accurately identify AMP and its function types. The datasets, source code and user-friendly application are available at https://github.com/abcair/AMPFinder.

Prediction of Axillary Lymph Node Metastatic Load of Breast Cancer Based on Ultrasound Deep Learning Radiomics Nomogram.

Background: Axillary lymph node (ALN) metastatic load is very important in the diagnosis and treatment of breast cancer (BC). We aimed to construct a model for predicting ALN metastatic load using deep learning radiomics (DLR) techniques based on the preoperative ultrasound and clinicopathologic information of patients with stage T1-2 BC. Methods: Retrospective analysis was performed on 176 patients with pathologically confirmed BC in our hospital from February 2018 to April 2020. ALN metastases were divided into a low-load group (< 3 lymph node metastases) and a high-load group (≥ 3 lymph node metastases) according to pathological results. Pyradiomics and pre-trained ResNet50 were used to extract radiomics and deep learning features, respectively. Independent sample T-test, random forest recursive elimination, and Lasso were used to screen the features to construct the deep learning radiomics signature (DLRS). Based on single/multivariate logistic regression analysis results, a DLR nomogram (DLRN) model was constructed by combining valuable clinical features and DLRS. Results: The DLRS was composed of 3 radiomics features and 14 deep learning features and combined with the maximum diameter of lesions to construct the DLRN. The AUCs of the training and test sets were 0.900 (95% CI: 0.853-0.931) and 0.821 (95% CI: 0.769-0.868), respectively. The calibration curve and Hosmer-Lemeshow test confirmed that the DLRN model has a good consistency. The decision curve also confirmed its good clinical practicality. Conclusion: Ultrasound-based DLRN has an excellent performance in predicting ALN load in patients with BC.