Tumor Shape Research Articles

RMCNet: A Liver Cancer Segmentation Network Based on 3D Multi-Scale Convolution, Attention, and Residual Path

Abdominal CT images are important clues for diagnosing liver cancer lesions. However, liver cancer presents challenges such as significant differences in tumor size, shape, and location, which can affect segmentation accuracy. To address these challenges, we propose an end-to-end 3D segmentation algorithm, RMCNet. In the shallow encoding part of RMCNet, we incorporated a 3D multiscale convolution (3D-Multiscale Convolution) module to more effectively extract tumors of varying sizes. Moreover, the convolutional block attention module (CBAM) is used in the encoding part to help the model focus on both the shape and location of tumors. Additionally, a residual path is introduced in each encoding layer to further enrich the extracted feature maps. Our method achieved DSC scores of 76.56% and 72.96%, JCC scores of 75.82% and 71.25%, HD values of 11.07 mm and 17.06 mm, and ASD values of 2.54 mm and 10.51 mm on the MICCAI 2017 Liver Tumor Segmentation public dataset and the 3Dircadb-01 public dataset, respectively. Compared to other methods, RMCNet demonstrates superior segmentation performance and stronger generalization capability.

Super-resolution reconstruction of time-resolved four-dimensional computed tomography (TR-4DCT) with multiple breathing cycles based on TR-4DMRI.

Respiratory motion irregularities in lung cancer patients are common and can be severe during multi-fractional (∼20 mins/fraction) radiotherapy. However, the current clinical standard of motion management is to use a single-breath respiratory-correlated four-dimension computed tomography (RC-4DCT or 4DCT) to estimate tumor motion to delineate the internal tumor volume (ITV), covering the trajectory of tumor motion, as a treatment target. To develop a novel multi-breath time-resolved (TR) 4DCT using the super-resolution reconstruction framework with TR 4D magnetic resonance imaging (TR-4DMRI) as guidance for patient-specific breathing irregularity assessment, overcoming the shortcomings of RC-4DCT, including binning artifacts and single-breath limitations. Six lung cancer patients participated in the IRB-approved protocol study to receive multiple T1w MRI scans, besides an RC-4DCT scan on the simulation day, including 80 low-resolution (lowR: 5×5×5mm3) free-breathing (FB) 3D cine MRFB images in 40s (2Hz) and a high-resolution (highR: 2×2×2mm3) 3D breath-hold (BH) MRBH image for each patient. A CT (1×1×3mm3) image was selected from 10-bin RC-4DCT with minimal binning artifacts and a close diaphragm match (<1cm) to the MRBH image. A mutual-information-based Freeform deformable image registration (DIR) was used to register the CT and MRBH via the opposite directions (namely F1: and F2: ) to establish CT-MR voxel correspondences. An intensity-based enhanced Demons DIR was then applied for , in which the original MRBH was used in D1: , while the deformed MRBH was used in D2: . The deformation vector fields (DVFs) obtained from each DIR were composed to apply to the deformed CT (D1) and original CT (D2) to reconstruct TR-4DCT images. A digital 4D-XCAT phantom at the end of inhalation (EOI) and end of exhalation (EOE) with 2.5cm diaphragmatic motion and three spherical targets (ϕ=2, 3, 4cm) were first tested to reconstruct TR-4DCT. For each of the six patients, TR-4DCT images at the EOI, middle (MID), and EOE were reconstructed with both D1 and D2 approaches. TR-4DCT image quality was evaluated with mean distance-to-agreement (MDA) at the diaphragm compared with MRFB, tumor volume ratio (TVR) referenced to MRBH, and tumor shape difference (DICE index) compared with the selected input CT. Additionally, differences in the tumor center of mass (|∆COMD1-D2|), together with TVR and DICE comparison, was assessed in the D1 and D2 reconstructed TR-4DCT images. In the phantom, TR-4DCT quality is assessed by MDA=2.0±0.8mm at the diaphragm, TVR=0.8±0.0 for all tumors, and DICE=0.83±0.01, 0.85±0.02, 0.88±0.01 for ϕ=2, 3, 4cm tumors, respectively. In six patients, the MDA in diaphragm match is -1.6±3.1mm (D1) and 1.0±3.9mm (D2) between the reconstructed TR-4DCT and lowR MRFB among 18 images (3 phases/patient). The tumor similarity is TVR=1.2±0.2 and DICE=0.70±0.07 for D1 and TVR=1.4±0.3 (D2) and DICE=0.73±0.07 for D2. The tumor position difference is |∆COMD1-D2|=1.2±0.8mm between D1 and D2 reconstructions. The feasibility of super-resolution reconstruction of multi-breathing-cycle TR-4DCT is demonstrated and image quality at the diaphragm and tumor is assessed in both the 4D-XCAT phantom and six lung cancer patients. The similarity of D1 and D2 reconstruction suggests consistent and reliable DIR results. Clinically, TR-4DCT has the potential for breathing irregularity assessment and dosimetry evaluation in radiotherapy.

Comparison of computed tomography findings between perianal tumors originating from the anal sacs and perianal glands in dogs.

Tumors originating from the anal sacs and perianal glands are common tumors of the perianal region. However, differentiation of perianal tumors based on CT findings remains challenging because perianal tumors can show overlapping CT characteristics. Therefore, this retrospective, multi-institutional study aimed to identify the CT findings that can differentiate between tumors originating from the anal sacs and perianal glands in canines. Twenty dogs were included, with nine apocrine gland anal sac adenocarcinomas (AGASACAs), six perianal gland adenomas, three perianal gland carcinomas, and two perianal gland epitheliomas. The tumor CT features included the tumor growth pattern, tumor shape, cystic changes within the tumor, contrast enhancement pattern, displacement of the anus, and identification of the anal sacs. The prevalences of suspected metastatic lymphadenopathy of the iliosacral lymph centers and lung metastases were evaluated. The tumor volumes and values normalized to body weight were also assessed. The tumor growth patterns differed significantly between AGASACAs and perianal gland tumors. AGASACAs mostly showed unilateral growth, whereas perianal gland tumors mostly exhibited bilateral growth. CT features, except for the tumor growth pattern, failed to show significant differences between tumors arising from the anal sacs and those arising from the perianal glands. Although histopathological diagnosis remains the gold standard, this study's findings suggest that CT analysis of tumor growth patterns may help differentiate AGASACAs from perianal gland tumors.

MATHEMATICAL MODELLING OF SURVIVAL IN LOW GRADE GLIOMAS AT MALIGNANT TRANSFORMATION WITH XGBOOST

Abstract AIMS To develop non-linear machine learning models using the XGBoost algorithm to predict a continuous (overall survival (OS) and a binary survival outcome (OS &amp;gt; 5 years) using clinical, molecular and genetic, and radiomic data. METHOD Patients with LGGs treated at a single institution (2005-2020) with histology and MRIs at the time of malignant transformation (MT) were retrospectively included in this study. MRIs underwent in-house tumour segmentation pipeline with radiomic feature extraction of whole-tumour, enhancing, non-enhancing and oedema components, and masked disconnectome map components. Patients were split into training and testing sets for the development of the survival models, which were assessed with mean absolute error (MAE) and root mean square error (RMSE) for the prediction of OS; and receiver operating characteristics analysis for the prediction of OS &amp;gt; 5 years. RESULTS Of 553 patients, 415 patients were included in the training set and 138 patients in the testing set. The XGB Regressor model was able to predict overall survival (OS) from the time of malignant transformation (tMRI) with an MAE of 953 days (RMSE: 1163 days). The XGB Classifier model was able to predict the probability of OS &amp;gt; 5 years from tMRI with an accuracy of 64% (sensitivity: 58%, specificity: 70%). Age, IDH1 mutation, 1p/19q co- deletion, regularity of tumour shape, and disconnectome-related perilesional components were most predictive of survival outcome. CONCLUSION This study has investigated the predictive capabilities of clinical, molecular and genetic, and radiomic data to develop survival analysis models, using XGBoost, to predict OS and OS &amp;gt; 5 years in patients with LGG at tMRI. We corroborate previous findings that age, 1p/19q co-deletion and IDH1 mutation are positive prognosticators for survival. However, further investigation into the radiomics of the disconnectome, especially of the perilesional oedema compartment, presents an intriguing and novel avenue for survival analysis of patients with LGG.

Intratumoral and peritumoral radiomics model for the preoperative prediction of cribriform component in invasive lung adenocarcinoma: a multicenter study.

This study aimed to investigate the predictive value of intratumoral and peritumoral radiomics model for the cribriform component (CC) of invasive lung adenocarcinoma (LUAD). The 144 patients with invasive LUAD from our center were randomly divided into training set (n = 100) and internal validation set (n = 44) in a ratio of 7:3, and 75 patients from center 2 were regarded as the external validation set. Clinical risk factors were examined using univariate and multivariate logistic regression to construct the clinical model. We extracted radiomics features from gross tumor volume (GTV), gross and peritumoral volume (GPTV), and peritumoral volume (PTV), respectively. Radiomics models were constructed with selected features. A combined model based on the optimal Radscore and clinically independent predictors was constructed, and its predictive performance was assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). The area under curves (AUCs) of the GTV model were 0.882 (95% CI 0.817-0.948), 0.794 (95% CI 0.656-0.932), and 0.766 (95% CI 0.657-0.875) in the training, internal validation, and external validation sets, and the PTV model had AUCs of 0.812 (95% CI 0.725-0.899), 0.749 (95% CI 0.597-0.902), and 0.670 (95% CI 0.543-0.798) in the training, internal validation, and external validation sets, respectively. However, the GPTV radiomics model showed better predictive performance compared with the GTV and PTV radiomics models, with the AUCs of 0.950 (95% CI 0.911-0.989), 0.844 (95% CI 0.728-0.959), and 0.815 (95% CI 0.713-0.917) in the training, internal validation and external validation sets, respectively. In the clinical model, tumor shape, lobulation sign and maximal diameter were the independent predictors of CC in invasive LUAD. The combined model including independent clinical predictors and GPTV-Radscore show the considerable instructive to clinical practice, with the AUCs of 0.954(95% CI 0.918-0.990), 0.861(95% CI 0.752-0.970), and 0.794(95% CI 0.690-0.898) in training, internal validation, and external validation sets, respectively. DCA showed that the combined model had good clinical value and correction effect. Radiomics model is a very powerful tool for predicting CC growth pattern in invasive LUAD and can help clinicians make the strategies of treatment and surveillance in patients with invasive LUAD.

LatticeOpt: An automatic tool for planning optimisation of spatially fractionated stereotactic body radiotherapy

PurposeLattice radiotherapy (LRT) is a three dimensional (3D) implementation of spatially fractionated radiation therapy, based on regular spatial distribution of high dose spheres (vertices) inside the target. Due to tumour shape heterogeneity, finding the best lattice arrangement is not trivial.The aim of this study was to develop the LatticeOpt tool to generate the best lattice structures on clinical cases for treatment planning. MethodsDeveloped in MATLAB, LatticeOpt finds the 3D-spatial configurations that maximize the number of vertices within the gross target volume (GTV). If organs at risk (OARs) are considered, it chooses the solution that minimizes the overlapping volume histograms (OVH). Otherwise, the lattice structure with the minimum Hausdorff distance between vertices and GTV boundary is chosen to avoid unpopulated regions.Different lattice structures were created for 20 patients, with (OVHopt) and without (OVHunopt) OVH minimization. Imported into TPS (Eclipse, Varian), corresponding plans were generated and evaluated in terms of OAR mean and maximum doses, GTV vertex coverage and dose gradients, as well as pre-clinical plan dosimetry. ResultsPlans based on an optimized lattice structure (OVHopt, OVHunopt) had similar dose distributions in terms of vertex coverage and gradient index score. OAR sparing was observed in all patients, with a 4 % and 9 % difference for mean and max dose (both p-values <0.01), respectively. The best vertices dimensions and their relative distances were patient dependent. ConclusionsLatticeOpt was able to reduce the time-consuming procedures of LRT, as well as to achieve standardized and reproducible results, useful for multicentre studies.

Ultrasound-based radiomics nomogram for predicting HER2-low expression breast cancer.

Accurate preoperative identification of Human epidermal growth factor receptor 2 (HER2) low expression breast cancer (BC) is critical for clinical decision-making. Our aim was to use machine learning methods to develop and validate an ultrasound-based radiomics nomogram for predicting HER2-low expression in BC. In this retrospective study, 222 patients (108 HER2-0 expression and 114 HER2-low expression) with BC were included. The enrolled patients were randomly divided into a training cohort and a test cohort with a ratio of 8:2. The tumor region of interest was manually delineated from ultrasound image, and radiomics features were subsequently extracted. The features underwent dimension reduction using the least absolute shrinkage and selection operator (LASSO) algorithm, and rad-score were calculated. Five machine learning algorithms were applied for training, and the algorithm demonstrating the best performance was selected to construct a radiomics (USR) model. Clinical risk factors were integrated with rad-score to construct the prediction model, and a nomogram was plotted. The performance of the nomogram was assessed using receiver operating characteristic curve and decision curve analysis. A total of 480 radiomics features were extracted, out of which 11 were screened out. The majority of the extracted features were wavelet features. Subsequently, the USR model was established, and rad-scores were computed. The nomogram, incorporating rad-score, tumor shape, border, and microcalcification, achieved the best performance in both the training cohort (AUC 0.89; 95%CI 0.836-0.936) and the test cohort (AUC 0.84; 95%CI 0.722-0.958), outperforming both the USR model and clinical model. The calibration curves showed satisfactory consistency, and DCA confirmed the clinical utility of the nomogram. The nomogram model based on ultrasound radiomics exhibited high prediction value for HER2-low BC.

Evaluating segment anything model (SAM) on MRI scans of brain tumors

Addressing the challenge of automatically segmenting anatomical structures from brain images has been a long-standing problem, attributed to subject- and image-based variations and constraints in available data annotations. The Segment Anything Model (SAM), developed by Meta, is a foundational model trained to provide zero-shot segmentation outputs with or without interactive user inputs, demonstrating notable performance on various objects and image domains without explicit prior training. This study evaluated SAM’s performance in brain tumor segmentation using two publicly available Magnetic Resonance Imaging (MRI) datasets. The study analyzed SAM’s standalone segmentation as well as its performance when provided user interaction through point prompts and bounding box inputs. SAM exhibited versatility across configurations and datasets, with the bounding box consistently outperforming others in achieving superior localized precision, with average Dice scores of 0.68 for TCGA and 0.56 for BRATS, along with average IoU values of 0.89 and 0.65, respectively, especially for tumors with low-to-medium curvature. Inconsistencies were observed, particularly in relation to variations in tumor size, shape, and textural features. The conclusion drawn from the study is that while SAM can automate medical image segmentation, further training and careful implementation are necessary for diagnostic purposes, especially with challenging cases such as MRI scans of brain tumors.

Incorporating patient-specific information for the development of rectal tumor auto-segmentation models for online adaptive magnetic resonance Image-guided radiotherapy

Background and purposeIn online adaptive magnetic resonance image (MRI)-guided radiotherapy (MRIgRT), manual contouring of rectal tumors on daily images is labor-intensive and time-consuming. Automation of this task is complex due to substantial variation in tumor shape and location between patients. The aim of this work was to investigate different approaches of propagating patient-specific prior information to the online adaptive treatment fractions to improve deep-learning based auto-segmentation of rectal tumors. Materials and methods243 T2-weighted MRI scans of 49 rectal cancer patients treated on the 1.5T MR-Linear accelerator (MR-Linac) were utilized to train models to segment rectal tumors. As benchmark, an MRI_only auto-segmentation model was trained. Three approaches of including a patient-specific prior were studied: 1. include the segmentations of fraction 1 as extra input channel for the auto-segmentation of subsequent fractions, 2. fine-tuning of the MRI_only model to fraction 1 (PSF_1) and 3. fine-tuning of the MRI_only model on all earlier fractions (PSF_cumulative). Auto-segmentations were compared to the manual segmentation using geometric similarity metrics. Clinical impact was assessed by evaluating post-treatment target coverage. ResultsAll patient-specific methods outperformed the MRI_only segmentation approach. Median 95th percentile Hausdorff (95HD) were 22.0 (range: 6.1–76.6) mm for MRI_only segmentation, 9.9 (range: 2.5–38.2) mm for MRI+prior segmentation, 6.4 (range: 2.4–17.8) mm for PSF_1 and 4.8 (range: 1.7–26.9) mm for PSF_cumulative. PSF_cumulative was found to be superior to PSF_1 from fraction 4 onward (p = 0.014). ConclusionPatient-specific fine-tuning of automatically segmented rectal tumors, using images and segmentations from all previous fractions, yields superior quality compared to other auto-segmentation approaches.

End-to-End Fully Automated Lung Cancer Volume Estimation System

The volume of the tumor plays a very crucial role in deciding the stage of lung cancer which in turn helps in deciding the best treatment and its schedule. Currently used computer-based volume estimation techniques are semi-automatic with limited accuracy. For any automatic lung cancer segmentation system, lung CT scans of hundreds of patients are required along with their corresponding annotated segmentation masks. It is difficult to get accurately annotated data as cancer segmentation of CT scans done by the radiologists, is a time-consuming manual process. Also, it is subjective and prone to intra and inter-observer variability. Further, owing to the irregular shape of the cancerous tumor, accurate volume estimation becomes a challenge with regular convolution models. This paper proposes an end-to-end automatic tumor volume estimation model that estimates volume using the GPR (Gaussian Process Regression) interpolation method. The proposed modified cancer segmentation model uses deformable convolutions. This modification offers a higher segmentation accuracy in terms of IoU (Intersection over Union) and clearly defined nodule boundaries with correct retention of the nodule shape. The research was undertaken in collaboration with Nanavati Hospital, Mumbai, and all the models were validated on a real dataset obtained from the hospital. The proposed model gives a mean segmentation IoU (Intersection over Union) of 0.9035 and a volume estimation accuracy of 93.13% which are almost 5% and 3% higher than 0.8548 and 90.51% which are the corresponding results obtained using a standard U-net++ algorithm.

The Impact of Tumor Elongation on Facial Nerve Outcome after Surgery for Koos Grade 3 and 4 Vestibular Schwannomas in the Semi-Sitting Position via the Retrosigmoid Approach.

Background: Facial nerve paralysis is a severe dysfunction after vestibular schwannoma (VS) surgery. Methods: This monocentric study analyzed 61 patients who underwent sporadic VS surgery in a standardized manner. The primary endpoint was the facial nerve outcome (FNO) at 3 months after VS surgery. FNO was dichotomized into "good" (House-Brackmann (HB) score ≤ 2) and "poor" (HB > 2). Results: Poor FNO was observed in 11 patients (18.0%) at 3 months after VS surgery. Radiomic tumor shape features were analyzed, and the AUC of elongation in the prediction of a poor HB at 3 months was 0.70 (95% CI: 0.56-0.85, p = 0.03) and the optimum threshold value (≤/>0.35) yielded a sensitivity and specificity of 64.0% and 75.4%, respectively. Multivariable logistic regression analyses considering the extent of resection (</≥93.4%), preoperative tumor volume (</≥2.6 cm3), age (</≥55), sex (female/male), and elongation (≤/>0.35) revealed that more elongated VSs (≤0.35; OR: 5.8; 95%CI: 1.2-28.2; p = 0.03) and those with an increased EoR (≥93.4%; OR: 6.5; 95%CI: 1.0-42.5; p = 0.05) are independently associated with poorer FNO at 3 months after surgery. Conclusions: Highly elongated VS shape seems to be a risk factor for worsened facial nerve outcome at 3 months after surgery for Koos grade 3 and 4 tumors.

Enhancing brain tumor segmentation in MRI images: A hybrid approach using UNet, attention mechanisms, and transformers

Accurate brain tumor segmentation in MRI images is crucial for effective treatment planning and monitoring. Traditional methods often encounter challenges due to the complexity and variability of tumor shapes and textures. Consequently, there is a growing need for automated solutions to assist healthcare professionals in segmentation tasks, improving efficiency and reducing workload. This study introduces an innovative method for accurately segmenting brain tumors in MRI images by employing a refined 3D UNet model integrated with a Transformer. The goal is to leverage self-attention mechanisms to enhance segmentation capabilities. The proposed model combines Contextual Transformer (CoT) and Double Attention (DA) architectures. CoT is extended to a 3D format and integrated with the baseline model to exploit intricate contextual details in MRI images. DA blocks in skip connections aggregate and distribute long-range features, emphasizing inter-dependencies within an expanded spatial scope. Experimental results demonstrate superior segmentation performance compared to current state-of-the-art methods. With its ability to accurately segment and delineate tumors in 3D, our segmentation model promises to be a powerful tool for medical image processing and performance optimization, saving time for healthcare professionals and healthcare systems.

Breast lesions segmentation from ultrasound images using DeepLabV3 + model with channel and spatial attention mechanism

Segmenting breast tumors in ultrasound images is pivotal for computer-aided diagnosis (CAD) systems aimed at detecting breast cancer. This research contributes directly to the Sustainable Development Goal (SDG), good health and well-being by leveraging innovative deep learning technique to boost the accuracy of breast tumor segmentation. Achieving accurate segmentation is crucial for precise tumor size, shape, and location determination, which in turn facilitates subsequent tumor quantification and classification. However, this task presents challenges, particularly when dealing with small tumors. The complexity arises from factors such as speckle noise, variations in tumor shapes and sizes across patients, and the presence of tumor-like regions in the images. Although deep learning-based methods have shown remarkable success in biomedical image analysis, the current state-of-the-art approaches still struggle to achieve satisfactory performance in segmenting small breast tumors. In this paper, BUS (Breast Ultrasound) dataset was initially applied on state of-the art DeepLabV3 + architecture for the segmentation of breast lesions in ultrasound images. After that to focus on more informative features, a modified DeepLabV3 + has been proposed by integrating a CBAM module in encoder as well as decoder. At the end, comparative analysis of state-of-art DeepLabV3 + model and proposed modified DeepLabV3 + has also been presented in terms of performance metrics like Dice Coefficient, Intersection over Union, Precision, Recall and Specificity. The proposed model demonstrates outstanding performance across different evaluation metrics achieving precision, recall, specificity, dice coefficient and Intersection over Union (IoU) values of 0.974, 0.933, 0.997, 0.951, and 0.933. respectively.

Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis.

Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s.

Application of U-Net Network Utilizing Multiattention Gate for MRI Segmentation of Brain Tumors.

Studies have shown that the type of low-grade glioma is associated with its shape. The traditional diagnostic method involves extraction of the tumor shape from MRIs and diagnosing the type of glioma based on corresponding relationship between the glioma shape and type. This method is affected by the MRI background, tumor pixel size, and doctors' professional level, leading to misdiagnoses and missed diagnoses. With the help of deep learning algorithms, the shape of a glioma can be automatically segmented, thereby assisting doctors to focus more on the diagnosis of glioma and improving diagnostic efficiency. The segmentation of glioma MRIs using traditional deep learning algorithms exhibits limited accuracy, thereby impeding the effectiveness of assisting doctors in the diagnosis. The primary objective of our research is to facilitate the segmentation of low-grade glioma MRIs for medical practitioners through the utilization of deep learning algorithms. In this study, a UNet glioma segmentation network that incorporates multiattention gates was proposed to address this limitation. The UNet-based algorithm in the coding part integrated the attention gate into the hierarchical structure of the network to suppress the features of irrelevant regions and reduce the feature redundancy. In the decoding part, by adding attention gates in the fusion process of low- and high-level features, important feature information was highlighted, model parameters were reduced, and model sensitivity and accuracy were improved. The network model performed image segmentation on the glioma MRI dataset, and the accuracy and average intersection ratio (mIoU) of the algorithm segmentation reached 99.7%, 87.3%, 99.7%, and 87.6%. Compared with the UNet, PSPNet, and Attention UNet network models, this network model has obvious advantages in accuracy, mIoU, and loss convergence. It can serve as a standard for assisting doctors in diagnosis.

Enhancing liver tumor segmentation with UNet-ResNet: Leveraging ResNet’s power

BACKGROUND: Liver cancer poses a significant health challenge due to its high incidence rates and complexities in detection and treatment. Accurate segmentation of liver tumors using medical imaging plays a crucial role in early diagnosis and treatment planning. OBJECTIVE: This study proposes a novel approach combining U-Net and ResNet architectures with the Adam optimizer and sigmoid activation function. The method leverages ResNet’s deep residual learning to address training issues in deep neural networks. At the same time, U-Net’s structure facilitates capturing local and global contextual information essential for precise tumor characterization. The model aims to enhance segmentation accuracy by effectively capturing intricate tumor features and contextual details by integrating these architectures. The Adam optimizer expedites model convergence by dynamically adjusting the learning rate based on gradient statistics during training. METHODS: To validate the effectiveness of the proposed approach, segmentation experiments are conducted on a diverse dataset comprising 130 CT scans of liver cancers. Furthermore, a state-of-the-art fusion strategy is introduced, combining the robust feature learning capabilities of the UNet-ResNet classifier with Snake-based Level Set Segmentation. RESULTS: Experimental results demonstrate impressive performance metrics, including an accuracy of 0.98 and a minimal loss of 0.10, underscoring the efficacy of the proposed methodology in liver cancer segmentation. CONCLUSION: This fusion approach effectively delineates complex and diffuse tumor shapes, significantly reducing errors.

Interlayer information fusion-based and dual-attention improved U-Net for ABVS image sequence intelligent tumor segmentation

Breast cancer is the most common cancer among women. Accurate and intelligent ultrasound image tumor segmentation can help physicians efficiently determine the location, shape and features of tumors and then develop appropriate treatment plans. To this end, we propose a deep learning method combining interlayer information and dual attention improvement for accurate and intelligent segmentation of breast tumors in ABVS image sequences. We extracted the slices containing tumors in ABVS images and formed a three-channel image input network with the current frame and the corresponding before and after frames. The network combines interlayer information during computation, and the before and after frames can guide the current frame image for more accurate tumor segmentation. In addition, we improved the channel attention module by replacing the full connection with a one-dimensional convolution and applying global max pooling on the basis of the original channel attention module, which improved the segmentation performance of the module while reducing the number of parameters. The improved channel attention module was added to the U-Net skip-connection section to assign different weight coefficients to each feature channel according to the characteristics of the input three-channel image. Our method outperformed other mainstream tumor segmentation methods on the dataset in both centers, achieving a Dice similarity coefficient (DSC) of 84.97 %, a Jaccard coefficient (JC) of 74.63 %, a 95 % Hausdorff distance (95HD) of 6.22 mm on the inner center, and DSC of 80.79 %, JC of 69.74 %, 95HD of 9.82 mm on the outer dataset.

Association of clinicopathologic and sonographic features with stromal tumor-infiltrating lymphocytes in triple-negative breast cancer

BackgroundIncreased level of stromal tumor-infiltrating lymphocytes (sTILs) are associated with therapeutic outcomes and prognosis in triple-negative breast cancer (TNBC). This study aimed to investigate the associations of clinicopathologic and sonographic features with sTILs level in TNBC.MethodsThis study included invasive TNBC patients with postoperative evaluation of sTILs after surgical resection. Tumor shape, margin, orientation, echo pattern, posterior features, calcification, and vascularity were retrospectively evaluated. The patients were categorized into high-sTILs (≥ 20%) and low-sTILs (< 20%) level groups. Chi-square or Fisher’s exact tests were used to assess the association of clinicopathologic and sonographic features with sTILs level.ResultsThe 171 patients (mean ± SD age, 54.7 ± 10.3 years [range, 22‒87 years]) included 58.5% (100/171) with low-sTILs level and 41.5% (71/171) with high-sTILs level. The TNBC tumors with high-sTILs level were more likely to be no special type invasive carcinoma (p = 0.008), higher histologic grade (p = 0.029), higher Ki-67 proliferation rate (all p < 0.05), and lower frequency of associated DCIS component (p = 0.026). In addition, the TNBC tumors with high-sTILs level were more likely to be an oval or round shape (p = 0.001), parallel orientation (p = 0.011), circumscribed or micro-lobulated margins (p < 0.001), complex cystic and solid echo patterns (p = 0.001), posterior enhancement (p = 0.002), and less likely to have a heterogeneous pattern (p = 0.001) and no posterior features (p = 0.002).ConclusionsThis preliminary study showed that preoperative sonographic characteristics could be helpful in distinguishing high-sTILs from low-sTILs in TNBC patients.

Application of the bicharacteristic attention residual pyramid for the treatment of brain tumors

Currently, surgery remains the primary treatment for craniocerebral tumors. Before doctors perform surgeries, they need to determine the surgical plan according to the shape, location, and size of the tumor; however, various conditions of different patients make the tumor segmentation task challenging. To improve the accuracy of determining tumor shape and realizing edge segmentation, a U-shaped network combining a residual pyramid module and a dual feature attention module is proposed. The residual pyramid module can enlarge the receptive field, extract multiscale features, and fuse original information, which solves the problem caused by the feature pyramid pooling where the local information is not related to the remote information. In addition, the dual feature attention module is proposed to replace the skip connection in the original U-Net network, enrich the features, and improve the attention of the model to space and channel features with large amounts of information to be used for more accurate brain tumor segmentation. To evaluate the performance of the proposed model, experiments were conducted on the public datasets Kaggle_3M and BraTS2021. Because the model proposed in this study is applicable to two-dimensional image segmentation, it is necessary to obtain the crosscutting images of fair class in the BraTS2021 dataset in advance. Results show that the model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and false negative rate (FNR) on the Kaggle_3M dataset are 0.9395, 0.8812, 0.8958, and 0.007, respectively. The model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and FNR on the BraTS2021 dataset were 0.9375, 0.9072, 0.8981, and 0.0087, respectively. Compared with existing algorithms, all the indicators of the proposed algorithm have been improved, but the proposed model still has certain limitations and has not been applied to actual clinical trials. For specific datasets, the generalization ability of the model needs to be further improved. In the future work, the model will be further improved to address the aforementioned limitations.

Size and shape control of microgel-encapsulating tumor spheroid via a user-friendly solenoid valve-based sorter and its application on precise drug testing

The precision of previous cancer research based on tumor spheroids, especially the microgel-encapsulating tumor spheroids, was limited by the high heterogeneity in the tumor spheroid size and shape. Here, we reported a user-friendly solenoid valve-based sorter to reduce this heterogeneity. The artificial intelligence algorithm was employed to detect and segmentate the tumor spheroids in real-time for the size and shape calculation. A simple off-chip solenoid valve-based sorting actuation module was proposed to sort out target tumor spheroids with the desired size and shape. Utilizing the developed sorter, we successfully uncovered the drug response variations on cisplatin of lung tumor spheroids in the same population but with different sizes and shapes. Moreover, with this sorter, the precision of drug testing on the spheroid population level was improved to a level comparable to the precise but complex single spheroid analysis. The developed sorter also exhibits significant potential for organoid morphology and sorting for precision medicine research.