CT Imaging Of Tumors Research Articles

Deep bone oncology Diagnostics: Computed tomography based Machine learning for detection of bone tumors from breast cancer metastasis

PurposeThe objective of this study is to develop a novel diagnostic tool using deep learning and radiomics to distinguish bone tumors on CT images as metastases from breast cancer. By providing a more accurate and reliable method for identifying metastatic bone tumors, this approach aims to significantly improve clinical decision-making and patient management in the context of breast cancer. MethodsThis study utilized CT images of bone tumors from 178 patients, including 78 cases of breast cancer bone metastases and 100 cases of non-breast cancer bone metastases. The dataset was processed using the Medical Image Segmentation via Self-distilling TransUNet (MISSU) model for automated segmentation. Radiomics features were extracted from the segmented tumor regions using the Pyradiomics library, capturing various aspects of tumor phenotype. Feature selection was conducted using LASSO regression to identify the most predictive features. The model’s performance was evaluated using ten-fold cross-validation, with metrics including accuracy, sensitivity, specificity, and the Dice similarity coefficient. ResultsThe developed radiomics model using the SVM algorithm achieved high discriminatory power, with an AUC of 0.936 on the training set and 0.953 on the test set. The model’s performance metrics demonstrated strong accuracy, sensitivity, and specificity. Specifically, the accuracy was 0.864 for the training set and 0.853 for the test set. Sensitivity values were 0.838 and 0.789 for the training and test sets, respectively, while specificity values were 0.896 and 0.933 for the training and test sets, respectively. These results indicate that the SVM model effectively distinguishes between bone metastases originating from breast cancer and other origins. Additionally, the average Dice similarity coefficient for the automated segmentation was 0.915, demonstrating a high level of agreement with manual segmentations. ConclusionThis study demonstrates the potential of combining CT-based radiomics and deep learning for the accurate detection of bone metastases from breast cancer. The high-performance metrics indicate that this approach can significantly enhance diagnostic accuracy, aiding in early detection and improving patient outcomes. Future research should focus on validating these findings on larger datasets, integrating the model into clinical workflows, and exploring its use in personalized treatment planning.

Primary Leiomyosarcoma of left sided colon: a case report

Leiomyosarcomas are rare smooth muscle tumors that represent 0.1% of all colorectal malignancies. The lack of literature available concerning leiomyosarcomas presents a challenge when diagnosing and treating these tumors, thus it is crucial that we differentiate them from gastrointestinal stromal tumors (GIST), the most common type of mesenchymal neoplasms of the gastrointestinal (GI) tract, especially when considering leiomyosarcoma's high prevalence of recurrence and malignancy. In this article, we present a case of a 69-year-old female with a diagnosis of sigmoid colon leiomyosarcoma. We analyze the tumor's CT imaging findings as well as correlation with the patient's pathological findings including immunostains, size, and mitotic activity, as well as the patient's risk for recurrence.

Rapid response to selpercatinib in RET fusion positive pancreatic neuroendocrine carcinoma confirmed by smartwatch

This case report describes the efficacy of selpercatinib, a selective RET inhibitor, in an unusual case of large-cell neuroendocrine pancreatic carcinoma (LCNEPAC) harboring a CCDC6::RET fusion. A 56-year-old male with a history of multiple lines of systemic therapies exhibited marked clinical amelioration shortly after initiating selpercatinib within the LOXO-RET-17001 study (ClinicalTrials.gov ID: NCT03157128, first posted: 2017-05-17). Data from the patient’s smartwatch suggested early efficacy before conventional methods, such as serum tumor markers and CT imaging confirmed the antitumor activity. This case not only underscores the efficacy of selpercatinib in treating RET fusion-positive rare tumors but also highlights the potential of wearable technology in cancer care. In conclusion, the standard readings from commercially available wearable devices can be useful for the monitoring of treatment response to targeted therapy and may serve as digital biomarkers in clinical trials. This approach marks a significant advancement in patient-centric healthcare, leveraging technology to enhance the effectiveness and precision of treatment evaluation.

MR–CT image fusion method of intracranial tumors based on Res2Net

BackgroundInformation complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation.PurposeThe purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method.MethodsThis paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image.ResultsQualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics.ConclusionThe proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

An automated two-stage approach to kidney and tumor segmentation in CT imaging.

The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment. To enhance accuracy and reduce manual involvement, propose a deep learning-based method for the automatic segmentation of kidneys and kidney tumors in CT images. The proposed method comprises two parts: object detection and segmentation. We first use a model to detect the position of the kidney, then narrow the segmentation range, and finally use an attentional recurrent residual convolutional network for segmentation. Our model achieved a kidney dice score of 0.951 and a tumor dice score of 0.895 on the KiTS19 dataset. Experimental results show that our model significantly improves the accuracy of kidney and kidney tumor segmentation and outperforms other advanced methods. The proposed method provides an efficient and automatic solution for accurately segmenting kidneys and renal tumors on CT images. Additionally, this study can assist radiologists in assessing patients' conditions and making informed treatment decisions.

RadioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer

Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, medical images, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection of patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomic feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomic features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomic features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients’ overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomic feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the association analysis has revealed potential gene mutations and radiomic feature candidates that warrant further investigation in future research endeavors.

An artificial intelligence-assisted framework for fast and automatic radiofrequency ablation planning of liver tumors in CT images

An artificial intelligence-assisted framework for fast and automatic radiofrequency ablation planning of liver tumors in CT images

Radiomics features based on dual-area CT predict the expression levels of fatty acid binding protein 4 and outcome in hepatocellular carcinoma.

To evaluate the predictive value of tumor and peritumor radiomics in the fatty acid binding protein 4 (FABP4) expression levels and overall survival in patients with hepatocellular carcinoma. The genomic data of HCC patients were obtained from The Cancer Genome Atlas. The Dual-area CT images of corresponding patients were downloaded from The Cancer Imaging Archive, for radiomics feature extraction, model construction and prognosis analysis. Simultaneously, using patients from Sichuan Provincial People's Hospital, the prognostic value of the radiomics model in HCC patients was validated. In the TCIA database, the area under the curve (AUC) values of the volumes of interest (VOI)whole model in the training set and internal validation set were 0.812 and 0.754, respectively, and the AUC value of VOIwhole+periphery in the training set and internal validation set were 0.866 and 0.779, respectively. In the VOIwhole and the VOIwhole+periphery model of the independent cohort, there were significant differences in OS between the high and low rad-score groups (P = 0.009, P = 0.021, respectively). Significant positive correlations can be observed between FABP4 expression and correlations with rad-score of VOIwhole model (r = 0.691) and VOIwhole+periphery model (r = 0.732) in the independent cohort. Radiomics models of tumor and peritumor Dual-area CT images could predict stably the expression levels of FABP4 and may be helping in personalized treatment strategies.

Multi-scale consistent self-training network for semi-supervised orbital tumor segmentation.

Segmentation of orbital tumors in CT images is of great significance for orbital tumor diagnosis, which is one of the most prevalent diseases of the eye. However, the large variety of tumor sizes and shapes makes the segmentation task very challenging, especially when the available annotation data islimited. To this end, in this paper, we propose a multi-scale consistent self-training network (MSCINet) for semi-supervised orbital tumor segmentation. Specifically, we exploit the semantic-invariance features by enforcing the consistency between the predictions of different scales of the same image to make the model more robust to size variation. Moreover, we incorporate a new self-training strategy, which adopts iterative training with an uncertainty filtering mechanism to filter the pseudo-labels generated by the model, to eliminate the accumulation of pseudo-label error predictions and increase the generalization of themodel. For evaluation, we have built two datasets, the orbital tumor binary segmentation dataset (Orbtum-B) and the orbital multi-organ segmentation dataset (Orbtum-M). Experimental results on these two datasets show that our proposed method can both achieve state-of-the-art performance. In our datasets, there are a total of 55 patients containing 602 2Dimages. In this paper, we develop a new semi-supervised segmentation method for orbital tumors, which is designed for the characteristics of orbital tumors and exhibits excellent performance compared to previous semi-supervisedalgorithms.

An Intelligent and Soluble Microneedle Composed of Bi/BiVO4 Schottky Heterojunction for Tumor Ct Imaging and Starvation/Gas Therapy-Promoted Synergistic Cancer Treatment.

Phototherapy and sonodynamic therapy (SDT) are widely used for the synergistic treatment of tumors and have received considerable attention. However, an inappropriate tumor microenvironment, including pH, H2O2, oxygen, and glutathione levels, can reduce the therapeutic effects of synergistic phototherapy and SDT. Here, a novel Bi-based soluble microneedle (MN) is designed for the CT imaging of breast tumors and starvation therapy/gas therapy-enhanced phototherapy/SDT. The optimized Bi/BiVO4 Schottky heterojunction serves as the tip of the MN, which not only has excellent photothermal conversion ability and CT contrast properties, but its heterojunction can also avoid the rapid combination of electrons and hole pairs, thereby enhancing the photodynamic/sonodynamic effects. A degradable MN with excellent mechanical properties is fabricated by optimizing the ratios of poly(vinyl alcohol), poly(vinyl pyrrolidone), and sodium hyaluronate. Glucose oxidase (GOx) and diallyl trisulfide are loaded into the MN to achieve tumor starvation and gas therapy, respectively; And the controlled release of GOx and H2S can be achieved under ultrasound or near-infrared laser irradiation. The in vitro and in vivo results demonstrate that this multifunctional MN can achieve high therapeutic efficacy through starvation therapy/gas therapy-enhanced phototherapy/SDT. The designed multifunctional MN provides a prospective approach for synergistic phototherapy and SDT.

Decoupled SculptorGAN Framework for 3D Reconstruction and Enhanced Segmentation of Kidney Tumors in CT Images

Decoupled SculptorGAN Framework for 3D Reconstruction and Enhanced Segmentation of Kidney Tumors in CT Images

3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to represent a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D PDAC, where the contrast is especially poor, owing to the high heterogeneity in both tumor and background tissues. In this study, we developed a new GAN-based model, named 3DGAUnet, for generating realistic 3D CT images of PDAC tumors and pancreatic tissue, which can generate the inter-slice connection data that the existing 2D CT image synthesis models lack. The transition to 3D models allowed the preservation of contextual information from adjacent slices, improving efficiency and accuracy, especially for the poor-contrast challenging case of PDAC. PDAC’s challenging characteristics, such as an iso-attenuating or hypodense appearance and lack of well-defined margins, make tumor shape and texture learning challenging. To overcome these challenges and improve the performance of 3D GAN models, our innovation was to develop a 3D U-Net architecture for the generator, to improve shape and texture learning for PDAC tumors and pancreatic tissue. Thorough examination and validation across many datasets were conducted on the developed 3D GAN model, to ascertain the efficacy and applicability of the model in clinical contexts. Our approach offers a promising path for tackling the urgent requirement for creative and synergistic methods to combat PDAC. The development of this GAN-based model has the potential to alleviate data scarcity issues, elevate the quality of synthesized data, and thereby facilitate the progression of deep learning models, to enhance the accuracy and early detection of PDAC tumors, which could profoundly impact patient outcomes. Furthermore, the model has the potential to be adapted to other types of solid tumors, hence making significant contributions to the field of medical imaging in terms of image processing models.

Multimodal hybrid convolutional neural network based brain tumor grade classification

An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causing damage to the brain or even death. As a result, diagnostic procedures such as computed tomography, magnetic resonance imaging, and positron emission tomography, as well as blood and urine tests, are used to identify brain tumors. However, these methods can be labor-intensive and sometimes yield inaccurate results. Instead of these time-consuming methods, deep learning models are employed because they are less time-consuming, require less expensive equipment, produce more accurate results, and are easy to set up. In this study, we propose a method based on transfer learning, utilizing the pre-trained VGG-19 model. This approach has been enhanced by applying a customized convolutional neural network framework and combining it with pre-processing methods, including normalization and data augmentation. For training and testing, our proposed model used 80% and 20% of the images from the dataset, respectively. Our proposed method achieved remarkable success, with an accuracy rate of 99.43%, a sensitivity of 98.73%, and a specificity of 97.21%. The dataset, sourced from Kaggle for training purposes, consists of 407 images, including 257 depicting brain tumors and 150 without tumors. These models could be utilized to develop clinically useful solutions for identifying brain tumors in CT images based on these outcomes.

Advances in the two-dimensional layer materials for cancer diagnosis and treatment: unique advantages beyond the microsphere.

In recent years, two-dimensional (2D) layer materials have shown great potential in the field of cancer diagnosis and treatment due to their unique structural, electronic, and chemical properties. These non-spherical materials have attracted increasing attention around the world because of its widely used biological characteristics. The application of 2D layer materials like lamellar graphene, transition metal dichalcogenides (TMDs), and black phosphorus (BPs) and so on have been developed for CT/MRI imaging, serum biosensing, drug targeting delivery, photothermal therapy, and photodynamic therapy. These unique applications for tumor are due to the multi-variable synthesis of 2D materials and the structural characteristics of good ductility different from microsphere. Based on the above considerations, the application of 2D materials in cancer is mainly carried out in the following three aspects: 1) In terms of accurate and rapid screening of tumor patients, we will focus on the enrichment of serum markers and sensitive signal transformation of 2D materials; 2) The progress of 2D nanomaterials in tumor MRI and CT imaging was described by comparing the performance of traditional contrast agents; 3) In the most important aspect, we will focus on the progress of 2D materials in the field of precision drug delivery and collaborative therapy, such as photothermal ablation, sonodynamic therapy, chemokinetic therapy, etc. In summary, this review provides a comprehensive overview of the advances in the application of 2D layer materials for tumor diagnosis and treatment, and emphasizes the performance difference between 2D materials and other types of nanoparticles (mainly spherical). With further research and development, these multifunctional layer materials hold great promise in the prospects, and challenges of 2D materials development are discussed.

GMRE-iUnet: Isomorphic Unet fusion model for PET and CT lung tumor images

Lung tumor PET and CT image fusion is a key technology in clinical diagnosis. However, the existing fusion methods are difficult to obtain fused images with high contrast, prominent morphological features, and accurate spatial localization. In this paper, an isomorphic Unet fusion model (GMRE-iUnet) for lung tumor PET and CT images is proposed to address the above problems. The main idea of this network is as following: Firstly, this paper constructs an isomorphic Unet fusion network, which contains two independent multiscale dual encoders Unet, it can capture the features of the lesion region, spatial localization, and enrich the morphological information. Secondly, a Hybrid CNN-Transformer feature extraction module (HCTrans) is constructed to effectively integrate local lesion features and global contextual information. In addition, the residual axial attention feature compensation module (RAAFC) is embedded into the Unet to capture fine-grained information as compensation features, which makes the model focus on local connections in neighboring pixels. Thirdly, a hybrid attentional feature fusion module (HAFF) is designed for multiscale feature information fusion, it aggregates edge information and detail representations using local entropy and Gaussian filtering. Finally, the experiment results on the multimodal lung tumor medical image dataset show that the model in this paper can achieve excellent fusion performance compared with other eight fusion models. In CT mediastinal window images and PET images comparison experiment, AG, EI, QAB/F, SF, SD, and IE indexes are improved by 16.19%, 26%, 3.81%, 1.65%, 3.91% and 8.01%, respectively. GMRE-iUnet can highlight the information and morphological features of the lesion areas and provide practical help for the aided diagnosis of lung tumors.

Differential diagnostic value of tumor markers and contrast-enhanced computed tomography in gastric hepatoid adenocarcinoma and gastric adenocarcinoma.

This study aimed to investigate the effectiveness of tumor markers and contrast-enhanced computed tomography (CE-CT) in differentiating gastric hepatoid adenocarcinoma (GHA) from gastric adenocarcinoma (GA). This retrospective study included 160 patients (44 with GHA vs. 116 with GA) who underwent preoperative CE-CT. Preoperative serum concentrations of tumor biomarkers and CT imaging features were analyzed, including alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), tumor location, growth pattern, size, enhancement pattern, cystic changes, and mass contrast enhancement. Multivariate logistic regression analyses were performed to evaluate useful tumor markers and CT imaging features for differentiating GHA from GA. When compared to GA, GHA showed a higher serum AFP [13.27 ng/ml (5.2-340.1) vs. 2.7 ng/ml (2.2-3.98), P <0.001] and CEA levels [4.07 ng/ml (2.73-12.53) vs. 2.42 ng/ml (1.38-4.31), P <0.001]. CT imaging showed GHA with a higher frequency of tumor location in the gastric antrum (P <0.001). GHA had significantly lower attenuation values at the portal venous phase [PCA, (82.34 HU ± 8.46 vs. 91.02 HU ± 10.62, P <0.001)] and delayed phase [DCA, (72.89 HU ± 8.83 vs. 78.27 HU ± 9.51, P <0.001)] when compared with GA. Multivariate logistic regression analyses revealed that tumor location, PCA, and serum AFP level were independent predictors of differentiation between GHA and GA. The combination of these three predictors performed well in discriminating GHA from GA, with an AUC of 0.903, a sensitivity of 86.36%, and a specificity of 81.90%. Integrated evaluation of tumor markers and CT features, including tumor location, PCA, and serum AFP, allowed for more accurate differentiation of GHA from GA.

Segmentation of tumor region in respiratory disease by extended algorithm

Introduction: The expansion of pulmonary tumors and their alterations take place in a dynamic manner, so that vigorous segmentation of the obtained images is accurately required. Methods: In this research, an extended algorithm in region growing was executed on CT lung tumors to investigate precise tumor region and edges. First, a new threshold via definition of greater target region around the initial tumor was implemented in MATLAB software. Second, nearby points were settled in an array and then these points were updated established upon the tumor growth to delineate the fresh tumor edges. Here, farthest distance from the center of color intensity point of the initial tumor was selected to grow the region in the algorithm. Third, fresh tumor boundary was determined via an interpolation between these fresh points by sketching lines from the tumor midpoint. Then, the edge correction was implemented and the fresh region was attached to the principal region to attain a segmented tumor exterior. Results: The proposed technique enhanced the tumor recognition by 96% and 91% maximum and minimum accuracy, respectively, in comparison with basilar method. In inclusive algorithm, the percentage of conformity had a positive effect on realization of the threshold value and renewal of the relative amount by 13% enhancement over accuracy assessment. Also when compared to basilar algorithm, it was found that at least 12% of the percentage differences in conformity segment the tumor area in lung CT images. The proposed algorithm with sufficient accuracy accelerates the segmentation process to delineate and improve the tumor edges by growing multiple selected regions. The algorithm also guarantees the independence of the results from the starting point. Conclusion: According to the definition of the center of mass of the tumor color intensity, the proposed extended algorithm may be generalized to the 3D images regardless of the matrix size and the image thickness. The combination of techniques such as machine learning is expected to improve segmentation accuracy for different types of nodule and tumor CT images. Implications for practice: Proposed extended algorithm with sufficient accuracy accelerates the segmentation process to delineate and improve the tumor edges by growing multiple selected regions.

Early Stage Liver Disease Prediction using Image Processing

Abstract: Liver diseases have become increasingly common due to sedentary lifestyles and lack of physical activity, particularly in urban areas and metropolitan cities. This has resulted in millions of deaths every year, with liver cancer being a major contributor. However, inaccurate detection of liver tumors has led to many fatalities. Medical image segmentation is a challenging task when it comes to detecting liver tumors in CT images. Therefore, this project aims to improve the accuracy of tumor detection and segmentation using various image processing techniques, such as pre-processing, enhancement, and clustering algorithms. Early detection is critical in preventing liver cancer fatalities, and this project focuses on improving classification performance to aid in early detection. In this paper, we will describe the steps taken to select the best model and develop a necessary system for predicting liver disease.

Early Stage Liver Disease Prediction using Image Processing

Abstract: Liver diseases have become increasingly common due to sedentary lifestyles and lack of physical activity, particularly in urban areas and metropolitan cities. This has resulted in millions of deaths every year, with liver cancer being a major contributor. However, inaccurate detection of liver tumors has led to many fatalities. Medical image segmentation is a challenging task when it comes to detecting liver tumors in CT images. Therefore, this project aims to improve the accuracy of tumor detection and segmentation using various image processing techniques, such as pre-processing, enhancement, and clustering algorithms. Early detection is critical in preventing liver cancer fatalities, and this project focuses on improving classification performance to aid in early detection. In this paper, we will describe the steps taken to select the best model and develop a necessary system for predicting liver disease.

DPA‐UNet rectal cancer image segmentation based on visual attention

SummaryLow segmentation accuracy and background noise exisit in rectal cancer lesion segmentation. To segment colorectal tumors in CT images accurately, we propose an improved U‐Net network for colorectal tumor image segmentation dilated‐pyramid‐attention U‐Net (DPA‐UNet). In this paper, we use the U‐Net network and combine techniques such as dilated convolution, weighted feature pyramid structure (W‐FPN), and convolutional block attention module (CBAM) mechanism. Firstly, CBAM and W‐FPN are combined to extract dense pixel‐level features for pixel labeling. Secondly, after the third network output layer, three serially dilated depth‐separable dilated convolutional layers with dilation rates of 1, 2, and 4, are added respectively to expand the feature map receptive field. Finally, the DPA‐UNet model is compared and analyzed with other new network structures. The experimental results show that DPA‐UNet achieves automatic segmentation of the colorectal cancer image region of interest (ROI).