Characteristics Of Medical Images Research Articles

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.

Current landscape of primary small bowel leiomyosarcoma: cases report and a decade of insights

The incidence of leiomyosarcoma (LMS) is about 4–5/100,000 individuals per year. LMSs occurring in the small bowel are even rarer, and their preoperative diagnosis is very difficult. We described two patients with pathologically confirmed small bowel LMS and analyzed their clinical and medical imaging features. Similar cases reported in English in Pubmed database over the past decade were reviewed and summarized. These tumors were categorized by the growth direction and relationship with the intestinal lumen into three types: intraluminal (n = 10), intermural (n = 3), and extraluminal (n = 7). Notably, among the three types of LMS, the intramural leiomyosarcoma stands out as a noteworthy subtype. Emerging evidence suggests that smaller tumor size (< 5 cm) and the intraluminal type may serve as favorable prognostic indicators, while the extraluminal type is associated with relatively poor prognosis. Furthermore, the integration of imaging features with CA125 and LDH biomarkers holds promise for potential diagnostic value in LMS.

Optimal strategies for modeling anatomy in a hybrid intelligence framework for auto-segmentation of organs.

Organ segmentation is a fundamental requirement in medical image analysis. Many methods have been proposed over the past 6 decades for segmentation. A unique feature of medical images is the anatomical information hidden within the image itself. To bring natural intelligence (NI) in the form of anatomical information accumulated over centuries into deep learning (DL) AI methods effectively, we have recently introduced the idea of hybrid intelligence (HI) that combines NI and AI and a system based on HI to perform medical image segmentation. This HI system has shown remarkable robustness to image artifacts, pathology, deformations, etc. in segmenting organs in the Thorax body region in a multicenter clinical study. The HI system utilizes an anatomy modeling strategy to encode NI and to identify a rough container region in the shape of each object via a non-DL-based approach so that DL training and execution are applied only to the fuzzy container region. In this paper, we introduce several advances related to modeling of the NI component so that it becomes substantially more efficient computationally, and at the same time, is well integrated with the DL portion (AI component) of the system. We demonstrate a 9-40 fold computational improvement in the auto-segmentation task for radiation therapy (RT) planning via clinical studies obtained from 4 different RT centers, while retaining state-of-the-art accuracy of the previous system in segmenting 11 objects in the Thorax body region.

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation.

Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm.

Fully automated diagnosis of thyroid nodule ultrasound using brain-inspired inference

The interpretability of artificial intelligence (AI) based medical diagnostic systems is crucial to make the diagnosis adequately convincible. Deep learning has been extensively investigated and utilized in the area of medical assistance diagnosis in recent decades due to its outstanding performance and objective prediction. However, a huge semantic chasm dividing clinicians and unexplainable deep models emerges. Here we design a brain-inspired inference framework from medical images to explainable features, then to the final diagnostic conclusions. The fast thinking module is responsible for recognizing medical features in ultrasound (US) images, and the slow-thinking module builds a model for inferring from medical features to diagnostic results by constructing a knowledge graph of medical features with tensor decomposition. The whole model infers through intuition and thinking like a human being, and gives the recognized medical image features while inferring the diagnosis, which greatly improves the interpretability of the model. We conducted studies on thyroid cancer diagnoses using US images. The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) characteristics are employed as medical features describing thyroid nodules. Our brain-inspired medical inference framework outperforms commonly used deep learning algorithms, with an AUC score of 0.963 (95% confidential interval (CI)=0.923–1.000) for thyroid US image diagnosis. Results indicate that our framework improves diagnostic objectivity and interpretability while providing performance that is better than deep models. Our proposed brain-inspired medical inference framework could improve the efficiency of diagnosis and our technique is performant, objective and interpretable.

A multi-granularity ensemble algorithm for medical image classification based on broad learning system

Medical image classification is an essential task in the fields of computer-aided diagnosis and medical image analysis. In recent years, researchers have made extensive work on medical image classification by computer vision techniques. However, most of the current work is based on deep learning methods, which still suffer from expensive hardware resources, long time consuming and a lot of parameters to be optimized. In this paper, a multi-granularity ensemble algorithm for medical image classification based on broad learning system is proposed, which is an end-to-end lightweight model. On the one hand, the proposed method is designed to address the problem of weak image feature learning ability of broad learning system. The convolution module with fixed weights based on transfer learning is introduced as a feature extractor to extract fusion features of medical images. On the other hand, the multi-granularity ensemble framework is proposed, which learn the fusion features of medical images from fine-grained to coarse-grained respectively, and the prediction results at different granularity levels are integrated by ensemble learning. In this way, the bottom local features can be sufficiently considered, while the global features can also be taken into account. The experimental results show that on the MedMNIST dataset (containing 10 sub-datasets), the proposed method can shorten the training time by tens of times while having similar accuracy to deep convolutional neural networks. On the ChestXRay2017 dataset, the proposed method can achieve an accuracy of 92.5%, and the training time is also significantly better than other methods.

RED-Net: Residual and Enhanced Discriminative Network for Image Steganalysis in the Internet of Medical Things and Telemedicine.

Internet of Medical Things (IoMT) and telemedicine technologies utilize computers, communications, and medical devices to facilitate off-site exchanges between specialists and patients, specialists, and medical staff. If the information communicated in IoMT is illegally steganography, tampered or leaked during transmission and storage, it will directly impact patient privacy or the consultation results with possible serious medical incidents. Steganalysis is of great significance for the identification of medical images transmitted illegally in IoMT and telemedicine. In this article, we propose a Residual and Enhanced Discriminative Network (RED-Net) for image steganalysis in the internet of medical things and telemedicine. RED-Net consists of a steganographic information enhancement module, a deep residual network, and steganographic information discriminative mechanism. Specifically, a steganographic information enhancement module is adopted by the RED-Net to boost the illegal steganographic signal in texturally complex high-dimensional medical image features. A deep residual network is utilized for steganographic feature extraction and compression. A steganographic information discriminative mechanism is employed by the deep residual network to enable it to recalibrate the steganographic features and drop high-frequency features that are mistaken for steganographic information. Experiments conducted on public and private datasets with data hiding payloads ranging from 0.1bpp/bpnzac-0.5bpp/bpnzac in the spatial and JPEG domain led to RED-Net's steganalysis error PE in the range of 0.0732-0.0010 and 0.231-0.026, respectively. In general, qualitative and quantitative results on public and private datasets demonstrate that the RED-Net outperforms 8 state-of-art steganography detectors.

APPLICATION OF NEW RETRIEVAL METHODS IN MICROSCOPIC MEDICAL IMAGES AND CT IMAGES

Medical image retrieval is of great significance for forming correct medical judgments during the diagnosis and treatment process. In response to the fact that there are rich types of features in medical images, this paper constructs a multi-feature fusion model and applies it to medical image retrieval. This multi-feature fusion model utilizes three features. Color information is characterized using three different moments, texture information is obtained using the LBP (Local Binary Pattern) operator, and shape information is described using Hu moments. After collecting three features of medical images, they are fused into a similarity measure model through a self-set weight structure for comparison in medical image retrieval. Experiments show that our method has satisfactory image retrieval result for medical images such as endoscopic images and computed tomography (CT) images. Despite the continuous improvement of recall indicators, the proposed method still has high precision.

Preoperative multimodal ultrasonic imaging in a case of Peutz-Jeghers syndrome complicated by atypical lobular endocervical glandular hyperplasia: a case report and literature review

BackgroundPeutz-Jeghers syndrome (PJS), an autosomal dominant multiple cancerous disorder, is clinically characterized by mucocutaneous macules and multiple gastrointestinal hamartomatous polyps. Gastric-type endocervical adenocarcinoma (G-EAC), a special subtype of cervical adenocarcinoma with non-specific symptoms and signs, is known to occur in approximately 11% of female patients with PJS.Case presentationHere, we report a case of PJS in a 24-year-old female with multiple mucocutaneous black macules who complained of vaginal discharge and menorrhagia. Moreover, we first described the multimodal ultrasonographical manifestations of PJS-correlated G-EAC. The three-dimensional reconstructed view of G-EAC on 3D realisticVue exhibited a distinctive “cosmos pattern” resembling features on magnetic resonance imaging, and the contrast-enhanced ultrasound displayed a “quick-up and slow-down” pattern of the solid components inside the mixed cervical echoes. We reported the multimodal ultrasonographical characteristics of a case of PJS-related G-EAC, as well as reviewed PJS-related literature and medical imaging features and clinical characteristics of G-EAC to provide insight into the feasibility and potential of utilizing multimodal ultrasonography for the diagnosis of G-EAC.ConclusionsMultimodal ultrasound can visualize morphological features, solid components inside, and blood supplies of the G-EAC lesion and distinguish the G-EAC lesion from normal adjacent tissues. This facilitates preoperative diagnosis and staging of PJS-related G-EAC, thereby aiding subsequent health and reproductive management for patients with PJS.

Research on automatic generation of multimodal medical image reports based on memory driven

The task of automatic generation of medical image reports faces various challenges, such as diverse types of diseases and a lack of professionalism and fluency in report descriptions. To address these issues, this paper proposes a multimodal medical imaging report based on memory drive method (mMIRmd). Firstly, a hierarchical vision transformer using shifted windows (Swin-Transformer) is utilized to extract multi-perspective visual features of patient medical images, and semantic features of textual medical history information are extracted using bidirectional encoder representations from transformers (BERT). Subsequently, the visual and semantic features are integrated to enhance the model's ability to recognize different disease types. Furthermore, a medical text pre-trained word vector dictionary is employed to encode labels of visual features, thereby enhancing the professionalism of the generated reports. Finally, a memory driven module is introduced in the decoder, addressing long-distance dependencies in medical image data. This study is validated on the chest X-ray dataset collected at Indiana University (IU X-Ray) and the medical information mart for intensive care chest x-ray (MIMIC-CXR) released by the Massachusetts Institute of Technology and Massachusetts General Hospital. Experimental results indicate that the proposed method can better focus on the affected areas, improve the accuracy and fluency of report generation, and assist radiologists in quickly completing medical image report writing.

CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer

Background Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of advanced and quantitative medical imaging features. This study develops and validates a radiomic model for predicting TMB level and the response to immunotherapy based on CT features in NSCLC.Method Pre-operative chest CT images of 127 patients with NSCLC were retrospectively studied. The 3D-Slicer software was used to outline the region of interest and extract features from the CT images. Radiomics prediction model was constructed by LASSO and multiple logistic regression in a training dataset. The model was validated by receiver operating characteristic (ROC) curves and calibration curves using external datasets. Decision curve analysis was used to assess the value of the model for clinical application.ResultsA total of 1037 radiomic features were extracted from the CT images of NSCLC patients from TCGA. LASSO regression selected three radiomics features (Flatness, Autocorrelation and Minimum), which were associated with TMB level in NSCLC. A TMB prediction model consisting of 3 radiomic features was constructed by multiple logistic regression. The area under the curve (AUC) value in the TCGA training dataset was 0.816 (95% CI: 0.7109–0.9203) for predicting TMB level in NSCLC. The AUC value in external validation dataset I was 0.775 (95% CI: 0.5528–0.9972) for predicting TMB level in NSCLC, and the AUC value in external validation dataset II was 0.762 (95% CI: 0.5669–0.9569) for predicting the efficacy of immunotherapy in NSCLC.ConclusionThe model based on CT radiomic features helps to achieve cost effective improvement in TMB classification and precise immunotherapy treatment of NSCLC patients.

A Lightweight convolutional medical segmentation algorithm based on ConvNeXt to improve UNet

In recent years, UNet and its derivative networks have gained widespread recognition as major methods of medical image segmentation. However, networks like UNet often struggle with Point-of-Care (POC) healthcare applications due to their high number of parameters and computational complexity. To tackle these challenges, this paper introduces an efficient network designed for medical image segmentation called MCU-Net, which leverages ConvNeXt to enhance UNet. 1) Based on ConvNeXt, MCU-Net proposes the MCU Block, which employs techniques such as large kernel convolution, depth-wise separable convolution, and an inverted bottleneck design. To ensure stable segmentation performance, it also integrates global response normalization (GRN) layers and Gaussian Error Linear Unit (GELU) activation functions. 2) Additionally, MCU-Net introduces an enhanced Multi-Scale Convolution Attention (MSCA) module after the original UNet’s skip connections, emphasizing medical image features and capturing semantic insights across multiple scales. 3)The downsampling process replaces pooling layers with convolutions, and both upsampling and downsampling stages incorporate batch normalization (BN) layers to enhance model stability during training. The experimental results demonstrate that MCU-Net, with a parameter count of 2.19 million and computational complexity of 19.73 FLOPs, outperforms other segmentation models. The overall performance of MCU-Net in medical image segmentation surpasses that of other models, achieving a Dice score of 91.8% and mIoU of 84.7% on the GlaS dataset. When compared to UNet on the BUSI dataset, MCU-Net shows an improvement of 2% in Dice and 2.9% in mIoU.

TumorDet: A Breast Tumor Detection Model Based on Transfer Learning and ShuffleNet

Background: Breast tumor is among the most malignant tumors and early detection can improve patient’s survival rate. Currently, mammography is the most reliable method for diagnosing breast tumor because of high image resolution. Because of the rapid development of medical and artificial intelligence techniques, computer-aided diagnosis technology can greatly improve the detection accuracy of breast tumors and medical imaging has begun to use deep-learning-based approaches. In this study, the TumorDet model is proposed to detect the benign and malignant lesions of breast tumor, which has positive significance for assisting doctors in diagnosis. Objective: We use the proposed TumorDet to analyze and predict breast tumors on the real MRI dataset. Methods: (1) We introduce an adaptive gamma correction (AGC) method to balance brightness equalization and increase the contrast of mammography images; (2) we use the ShuffleNet model to exchange information between different feature layers and extract the hidden high-level features of medical images; and (3) we use the transfer learning method to fine-tune the ShuffleNet model and obtain the optimal parameters. Results: The proposed TumorDet model has shown that accuracy, sensitivity, and specificity reach 90.43%, 89.37%, and 87.81%, respectively. TumorDet performs well in the breast tumor detection task. In addition, we use the proposed TumorDet to conduct experiments on other tasks, such as forest fires, and the robustness of TumorDet is proved by experimental results. Conclusion: TumorDet employs the ShuffleNet model to exchange information between different feature layers without increasing the number of network parameters and applies transfer learning method to further extract the basic features of medical images by fine-tuning. The model is beneficial for the localization and classification of breast tumors and also performs well in forest fire detection.

Research on improved black widow algorithm for medical image denoising

Improving the quality of medical images is crucial for accurate clinical diagnosis; however, medical images are often disrupted by various types of noise, posing challenges to the reliability and diagnostic accuracy of the images. This study aims to enhance the Black Widow optimization algorithm and apply it to the task of denoising medical images to improve both the quality of medical images and the accuracy of diagnostic results. By introducing Tent mapping, we refined the Black Widow optimization algorithm to better adapt to the complex features of medical images. The algorithm's denoising capabilities for various types of noise were enhanced through the combination of multiple filters, all without the need for training each time to achieve preset goals. Simulation results, based on processing a dataset containing 1588 images with Gaussian, salt-and-pepper, Poisson, and speckle noise, demonstrated a reduction in Mean Squared Error (MSE) by 0.439, an increase in Peak Signal-to-Noise Ratio (PSNR) by 4.315, an improvement in Structural Similarity Index (SSIM) by 0.132, an enhancement in Edge-to-Noise Ratio (ENL) by 0.402, and an increase in Edge Preservation Index (EPI) by 0.614. Simulation experiments verified that the proposed algorithm has a certain advantage in terms of computational efficiency. The improvement, incorporating Tent mapping and a combination of multiple filters, successfully elevated the performance of the Black Widow algorithm in medical image denoising, providing an effective solution for enhancing medical image quality and diagnostic accuracy.

Advancements in stone classification: unveiling the beauty of urolithiasis.

Urolithiasis has become increasingly prevalent, leading to higher disability-adjusted life years and deaths. Various stone classification systems have been developed to enhance the understanding of lithogenesis, aid urologists in treatment decisions, and predict recurrence risk. The aim of this manuscript is to provide an overview of different stone classification criteria. Two authors conducted a review of literature on studies relating to the classification of urolithiasis. A narrative synthesis for analysis of the studies was used. Stones can be categorized based on anatomical position, size, medical imaging features, risk of recurrence, etiology, composition, and morphoconstitutional analysis. The first three mentioned offer a straightforward approach to stone classification, directly influencing treatment recommendations. With the routine use of CT imaging before treatment, precise details like anatomical location, stone dimensions, and Hounsfield Units can be easily determined, aiding treatment planning. In contrast, classifying stones based on risk of recurrence and etiology is more complex due to dependencies on multiple variables, including stone composition and morphology. A classification system based on morphoconstitutional analysis, which combines morphological stone appearance and chemical composition, has demonstrated its value. It allows for the rapid identification of crystalline phase principles, the detection of crystalline conversion processes, the determination of etiopathogenesis, the recognition of lithogenic processes, the assessment of crystal formation speed, related recurrence rates, and guidance for selecting appropriate treatment modalities. Recognizing that no single classification system can comprehensively cover all aspects, the integration of all classification approaches is essential for tailoring urolithiasis patient-specific management.

Transfer Learning Strategies for Fine-Tuning Pretrained Convolutional Neural Networks in Medical Imaging

In the area of medical imaging, transfer learning has become a potent technique that uses pretrained Convolutional Neural Networks (CNNs) to improve the performance of particular tasks. An overview of several transfer learning techniques used for optimising pretrained CNNs in the context of medical image analysis is given in this abstract. The size limitations of medical imaging datasets make it difficult to train deep learning models from scratch. Pre-trained CNNs are a good place to start, such as those that have been trained on huge natural picture datasets like ImageNet. When these pre-trained models are applied to medical imaging applications, fine-tuning is frequently used. One common method is feature extraction, where the bottom layers of the pretrained CNN are frozen and operate as feature extractors. Then, for the specific medical task at hand, these features are loaded into a bespoke classifier. The ability of the pretrained network to recognise subtle picture patterns is advantageous in this method. Another strategy is to optimise the CNN architecture as a whole, which enables the model to adjust to the features of medical images. Small learning rates are frequently used in transfer learning techniques to avoid overfitting during fine-tuning. Additionally, to further enhance model generalisation, domain-specific data augmentation is essential. The use of ensemble approaches, which combine several pretrained CNNs, is also investigated. These models are capable of offering various feature representations and improving classification precision. In order to bridge the domain gap between natural photos and medical images, domain adaption techniques are also used. One approach to align feature distributions is by adversarial training, while another is through domain-specific batch normalisation. The feature extraction, network fine-tuning, ensemble approaches, and domain adaptation are all part of transfer learning methodologies for optimising pretrained CNNs in medical imaging. Researchers have made great progress using these techniques in a number of medical image processing tasks, proving the value of transfer learning in this important area.

Review on Pediatric Malignant Focal Liver Lesions with Imaging Evaluation: Part II.

Malignant focal liver lesions (FLLs) represent various kinds of epithelial and mesenchymal tumors. In pediatric patients, the understanding of pediatric liver diseases and associated imaging manifestations is essential for making accurate diagnosis and differential diagnosis. This paper will discuss the latest knowledge of the common pediatric malignant FLLs, including undifferentiated embryonal sarcoma, rhabdomyosarcoma, epithelioid hemangioendothelioma, angiosarcoma, and malignant rhabdoid tumor. Medical imaging features are not only helpful for clinical diagnosis, but can also be useful in the evaluation and follow-up of pre- and post-treatment. The future perspectives of contrast-enhanced ultrasound (CEUS) enhancement patterns of FLLs in pediatric patients are also mentioned.

Review on Pediatric Malignant Focal Liver Lesions with Imaging Evaluation: Part I

Malignant focal liver lesions (FLLs) are commonly reported in adults but rarely seen in the pediatric population. Due to the rarity, the understanding of these diseases is still very limited. In children, most malignant FLLs are congenital. It is very important to choose appropriate imaging examination concerning various factors. This paper will outline common pediatric malignant FLLs, including hepatoblastoma, hepatocellular carcinoma, and cholangiocarcinoma and discuss them against the background of the latest knowledge on comparable/similar tumors in adults. Medical imaging features are of vital importance for the non-invasive diagnosis and follow-up of treatment of FLLs in pediatric patients. The use of CEUS in pediatric patients for characterizing those FLLs that remain indeterminate on conventional B mode ultrasounds may be an effective option in the future and has great potential to be integrated into imaging algorithms without the risk of exposure to ionizing radiation.

Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients

BackgroundThe 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS.MethodsWe retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS.ResultsCompared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p < 0.001) and no significant differences in all the imaging parameters of ultrasound and mammogram. The IHC3 risk and the NPI calculated score significantly correlated with both the binary and quartile categorized 70-GS risk classifications (both p < 0.001). The area under curve (AUC) of receiver-operating curve (ROC) of nomogram for binary risk prediction were 0.826 (C-index 0.903, 0.799–1.000) for training and 0.737 (C-index 0.785, 0.700–0.870) for validation dataset respectively. The AUC of ROC of nomogram for quartile risk prediction was 0.870 (C-index 0.854, 0.746–0.962) for training and 0.592 (C-index 0.769, 0.703–0.835) for testing set. The prediction accuracy of the nomogram for quartile categorized risk groups were 55.0% (likelihood ratio tests, p < 0.001) and 53.3% (p = 0.04) for training and validation, which more than double the baseline probability of 25%.ConclusionsTo our knowledge, we are the first to establish easy-to-use nomograms to predict the individualized binary (high vs low) and the quartile categorized (ultra-high, high, low and ultra-low) risk classification of 70-GS test with fair performance, which might provide information for treatment choice for those who have no access to the 70-GS testing.

CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images.

Medical image segmentation plays a crucial role in clinical diagnosis, treatment planning, and disease monitoring. The automatic segmentation method based on deep learning has developed rapidly, with segmentation results comparable to clinical experts for large objects, but the segmentation accuracy for small objects is still unsatisfactory. Current segmentation methods based on deep learning find it difficult to extract multiple scale features of medical images, leading to an insufficient detection capability for smaller objects. In this paper, we propose a context feature fusion and attention mechanism based network for small target segmentation in medical images called CFANet. CFANet is based on U-Net structure, including the encoder and the decoder, and incorporates two key modules, context feature fusion (CFF) and effective channel spatial attention (ECSA), in order to improve segmentation performance. The CFF module utilizes contextual information from different scales to enhance the representation of small targets. By fusing multi-scale features, the network captures local and global contextual cues, which are critical for accurate segmentation. The ECSA module further enhances the network's ability to capture long-range dependencies by incorporating attention mechanisms at the spatial and channel levels, which allows the network to focus on information-rich regions while suppressing irrelevant or noisy features. Extensive experiments are conducted on four challenging medical image datasets, namely ADAM, LUNA16, Thoracic OAR, and WORD. Experimental results show that CFANet outperforms state-of-the-art methods in terms of segmentation accuracy and robustness. The proposed method achieves excellent performance in segmenting small targets in medical images, demonstrating its potential in various clinical applications.