Search Results (48)

Gastric cancer remains the fourth leading cause of cancer-related mortality worldwide. Advanced disease is associated with a poor prognosis, emphasizing the critical importance of early diagnosis through endoscopy. In addition to prognosis, disease extent also plays a pivotal role in guiding management strategies. Therefore, accurate locoregional staging (T and N staging) is vital for optimal prognostic and therapeutic planning. Endoscopic ultrasound (EUS) has long been an essential tool in this regard, with computed tomography (CT) and, more recently, positron emission tomography–computed tomography (PET–CT) serving as alternative imaging modalities. EUS is particularly valuable in the assessment of early gastric cancer, defined as tumor invasion confined to the mucosa or submucosa. These tumors are increasingly managed by endoscopic resection techniques offering improved post-treatment quality of life. EUS has also recently been utilized in the restaging process after neoadjuvant chemotherapy, aiding in the evaluation of tumor resectability and prognosis. Its performance may be further enhanced through the application of emerging techniques such as contrast-enhanced endosonography, EUS elastography, and artificial intelligence systems. In advanced, unresectable disease, complications such as gastric outlet obstruction (GOO) severely impact patient quality of life. In this setting, EUS-guided gastroenterostomy (EUS-GE) offers a less invasive alternative to surgical gastrojejunostomy. This review summarizes and critically analyzes the role of EUS in the context of gastric cancer, highlighting its applications across different stages of the disease and evaluating its performance relative to other diagnostic modalities. Full article

In the era of big data, high-dimensional datasets have become increasingly common in fields such as biometrics, computer vision, and medical imaging. While such data contain abundant information, they are often accompanied by substantial noise, high redundancy, and complex intrinsic structures, posing significant challenges for analysis and modeling. To address these issues, unsupervised feature selection has attracted growing interest due to its ability to handle unlabeled, noisy, and unstructured data. This paper proposes a novel unsupervised feature selection algorithm based on a dual-graph autoencoder (DGA), which combines the powerful data reconstruction capability of autoencoders with the structural preservation strengths of graph regularization. Specifically, the algorithm introduces the ${\mathcal{l}}_{2,1/2}$-norm and ${\mathcal{l}}_{2,1}$-norm constraints on the encoder and decoder weight matrices, respectively, to promote feature sparsity and suppress redundancy. Furthermore, an ${\mathcal{l}}_{2,1/2}$-norm loss term is introduced to enhance robustness against noise and outliers. Two separate adjacency graphs are constructed to capture the local geometric relationships among samples and among features, and their corresponding graph regularization terms are embedded in the training process to retain the intrinsic structure of the data. Experiments on multiple benchmark datasets and [⁶⁸Ga]Ga-Pentixafor PET/CT glioma imaging data demonstrate that the proposed DGA significantly improves clustering performance and accurately identifies features associated with lesion regions. From a clinical perspective, DGA facilitates more accurate lesion characterization and biomarker identification in glioma patients, thereby offering potential utility in aiding diagnosis, treatment planning, and personalized prognosis assessment. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy and is characterized by a very unfavorable prognosis. Rarely, patients may develop synchronous PDAC and another distinct primary pancreatic tumor, such as a pancreatic neuroendocrine tumor. This systematic review consolidates published case reports describing the presentation, imaging characteristics, management, and outcomes of patients with synchronous PDAC and other pancreatic malignancies. Methods: A comprehensive search of PubMed and Scopus identified 26 relevant case reports, with inclusion criteria focused on histologically confirmed synchronous pancreatic tumors and exclusion of metastatic disease. Results: The majority of patients present with two pancreatic lesions, often located in both the body and tail of the pancreas. Diagnostic imaging modalities, such as computed tomography and endoscopic ultrasound, reveal common findings. Tumor markers, particularly CA 19-9, are often elevated and aid in the diagnosis. Surgical approaches also vary according to tumor location and staging, with procedures ranging from Whipple surgery to total pancreatectomy. Chemotherapy is frequently employed postoperatively. Notably, lymph node involvement and larger tumor size are associated with poorer prognoses. Conclusions: In conclusion, these patients may present with a common or non-common clinical picture as well as laboratory and imaging findings, constituting an important and unique diagnostic and therapeutic challenge. Full article

Background/Objectives: Small bowel tumors (SBTs) encompass a diverse range of tumor types, with benign tumors being the most prevalent. However, the incidence of malignant SBTs is increasing, particularly small bowel adenocarcinoma; this poses a diagnostic challenge for clinicians and radiologists due to the varied and nonspecific clinical and radiological presentations associated with SBTs. In fact, SBTs can present differently in emergencies, often mimicking inflammatory diseases or manifesting as complications such as intussusception, small bowel obstruction (SBO), intestinal ischemia, perforation, gastrointestinal bleeding, or metastatic disease. These tumors can remain asymptomatic for extended periods. Methods: We present a pictorial review on the role of imaging in evaluating SBTs, focusing on the emergency setting where diagnosis can be incidental. We also include some representative cases that may be useful for radiologists and residents in clinical practice. Results: Despite these challenges, contrast-enhanced computed tomography (CECT) is usually the best modality to use in emergencies for evaluating SBTs, and in some cases, a diagnosis can be made incidentally. However, when possible, multimodal imaging through cross-sectional imaging remains crucial for the non-invasive diagnosis of SBTs in stable patients, as endoscopic procedures may also be impractical. A complementary CT study with distension using negative oral contrast media, such as water, polyethylene glycol, or mannitol solutions, can improve the characterization of SBTs and rule out multiple SBT locations, particularly in small bowel neuroendocrine tumor (NET) and gastrointestinal tumor (GIST) localization. Positive water-soluble iodine-based oral contrast, such as Gastrografin (GGF), can be used to evaluate and monitor the intestinal lumen during the nonsurgical management of small bowel obstruction (SBO) or in suspected cases of small bowel perforations or the presence of fistulas. Magnetic resonance enterography (MRE) can aid in improving the characterization of SBTs through a multiplanar and multisequence study. Positron emission tomography combined with CT is generally an essential modality in evaluating metastatic disease and staging and assessing tumor prognosis, but it has limitations for indolent lymphoma and small NETs. Conclusions: Therefore, the integration of multiple imaging modalities can improve patient management and provide a preoperative risk assessment with prognostic and predictive indicators. In the future, radiomics could potentially serve as a “virtual biopsy” for SBTs, allowing for better diagnosis and more personalized management in precision medicine. Full article

The approach for the diagnosis and treatment of interstitial lung diseases (ILDs) has changed in recent years, mainly for the identification of new entities, such as interstitial lung abnormalities (ILAs) and progressive pulmonary fibrosis (PPF). Clinicians and radiologists are facing new challenges for the screening, diagnosis, prognosis, and follow-up of ILDs. The detection and classification of ILAs or the identification of fibrosis progression at high-resolution computed tomography (HRCT) is difficult, with high inter-reader variability, particularly for non-expert radiologists. In the last few years, various software has been developed for ILD evaluation at HRCT, with excellent results, equal to or more reliable than humans. AI tools can classify ILDs, quantify the extent, analyze the features hidden from the human eye, predict prognosis, and evaluate the progression of the disease. More advanced tools can incorporate clinical and radiological data to obtain personalized prognosis, with the potential ability to steer treatment decisions. To step forward and implement in daily practice such tools, more collaboration is required to collect more homogeneous clinical and radiological data; furthermore, more robust, prospective trials, with the new AI-derived biomarkers compared with each other, are needed to demonstrate the real reliability of the computer-aided evaluation of ILDs. Full article

Conventional methods for tumor diagnosis suffer from two inherent limitations: they are time-consuming and subjective. Computer-aided diagnosis (CAD) is an important approach for addressing these limitations. Pathology whole-slide images (WSIs) are high-resolution tissue images that have made significant contributions to cancer diagnosis and prognosis assessment. Due to the complexity of WSIs and the availability of only slide-level labels, multiple instance learning (MIL) has become the primary framework for WSI classification. However, most MIL methods fail to capture the interdependence among image patches within a WSI, which is crucial for accurate classification prediction. Moreover, due to the weak supervision of slide-level labels, overfitting may occur during the training process. To address these issues, this paper proposes a dual-attention-based multiple instance learning framework (DAMIL). DAMIL leverages the spatial relationships and channel information between WSI patches for classification prediction, without detailed pixel-level tumor annotations. The output of the model preserves the semantic variations in the latent space, enhances semantic disturbance invariance, and provides reliable class identification for the final slide-level representation. We validate the effectiveness of DAMIL on the most commonly used public dataset, Camelyon16. The results demonstrate that DAMIL outperforms the state-of-the-art methods in terms of classification accuracy (ACC), area under the curve (AUC), and F1-Score. Our model also allows for the examination of its interpretability by visualizing the dual-attention weights. To the best of our knowledge, this is the first attempt to use a dual-attention mechanism, considering both spatial and channel information, for whole-slide image classification. Full article

Head and neck cancer represents the seventh most common neoplasm worldwide, with squamous cell carcinoma being the most represented histologic variant. The rising incidence of the neoplastic pathology of this district, coupled with the drastic changes in its epidemiology over the past decades, have posed significant challenges to physicians worldwide in terms of diagnosis, prognosis, and treatment. In order to meet these challenges, a considerable amount of effort has been spent by the authors of the recent literature to explore new technologies and their possible employment for the better diagnostic and prognostic definition of head and neck squamous cell carcinoma (HNSCC). Among these technologies, a growing interest has been gathering around the possible applications of dual-energy computed tomography (DECT) in head and neck pathology. Dual-energy computed tomography (DECT) utilizes two distinct X-ray energy spectra to obtain two datasets in a single scan, allowing for material differentiation based on unique attenuation profiles. DECT offers key benefits such as enhanced contrast resolution, reduced beam-hardening artifacts, and precise iodine quantification through monochromatic reconstructions. It also creates material decomposition images, like iodine maps, aiding in tumor characterization and therapy assessment. This paper aims to summarize recent findings on the use of DECT in HNSCC, providing a comprehensive overview to aid further research and exploration in the field. Full article

A 13-year-old Maltese dog with an abdominal mass underwent 18F-FDG PET/computed tomography (CT) for tumor localization and metastatic evaluation. PET/CT scans revealed a gastric mass near the esophagogastric junction and demonstrated mean and maximum standardized uptake values (SUVs) of 4.596 and 6.234, respectively, for the abdominal mass. Subsequent surgery incorporated ICG for NIR fluorescence-guided imaging, aiding in precise tumor localization and margin assessment. The excised mass was identified as a low-grade leiomyosarcoma on histopathology. The dog underwent PET/CT imaging six months postoperatively following the excision of the mass, which confirmed the absence of recurrence or residual lesions during follow-up. NIR fluorescence imaging using ICG demonstrated efficacy in real-time tumor visualization and margin assessment, a technique not previously reported in veterinary literature. The PET/CT findings complemented the diagnosis and provided valuable insights into metastasis. The absence of recurrence or complications in postoperative follow-up underscores the potential of these imaging modalities in enhancing surgical precision and improving prognosis in canine gastric tumors. Full article

Background/Objectives: Recent advances in artificial intelligence, particularly in cardiac imaging, can potentially enhance patients’ diagnosis and prognosis and identify novel imaging markers. We propose an automated, computer-aided algorithm utilizing native cardiac computed tomography (CT) imaging to identify myocardial fibrosis. This study aims to evaluate its performance compared to CMR markers of fibrosis in a cohort of patients diagnosed with breast cancer. Methods: The study included patients diagnosed with early HER2+ breast cancer, who presented LV dysfunction (LVEF < 50%) and myocardial fibrosis detected on CMR at the time of diagnosis. The patients were also evaluated by cardiac CT, and the extracted images were processed for the implementation of the automatic, computer-assisted algorithm, which marked as fibrosis every pixel that fell within the range of 60–90 HU. The percentage of pixels with fibrosis was subsequently compared with CMR parameters. Results: A total of eight patients (n = 8) were included in the study. High positive correlations between the algorithm’s result and the ECV fraction (r = 0.59, p = 0.126) and native T1 (r = 0.6, p = 0.112) were observed, and a very high positive correlation with LGE of the LV(g) and the LV-LGE/LV mass percentage (r = 0.77, p = 0.025; r = 0.81, p = 0.015). A very high negative correlation was found with GLS (r = −0.77, p = 0.026). The algorithm presented an intraclass correlation coefficient of 1 (95% CI 0.99–1), p < 0.001. Conclusions: The present pilot study proposes a novel promising imaging marker for myocardial fibrosis, generated by an automatic algorithm based on native cardiac CT images. Full article

Brain abscesses in ruminants often arise from primary infection foci, leading to an unfavorable prognosis for affected animals. This highlights the need for comprehensive studies on brain abscesses across different ruminant species. We retrospectively investigated medical records of epidemiological, clinical, neuroimaging, anatomopathological, and bacteriological findings in six ruminants (three goats, two cows, and one sheep) diagnosed with brain abscesses. All animals studied were female. Apathy (50%), compulsive walking (33%), decreased facial sensitivity (33%), head pressing (33%), seizures (33%), semicomatous mental status (33%), strabismus (33%), unilateral blindness (33%), and circling (33%) represented the most common neurologic signs. Leukocytosis and neutrophilia were the main findings in the hematological evaluation. Cerebrospinal fluid (CSF) analysis revealed predominant hyperproteinorrachia and pleocytosis. In three cases, computed tomography or magnetic resonance imaging were used, enabling the identification of typical abscess lesions, which were subsequently confirmed during postmortem examination. Microbiological culture of the abscess samples and/or CSF revealed bacterial coinfections in most cases. Advanced imaging examinations, combined with CSF analysis, can aid in diagnosis, although confirmation typically relies on postmortem evaluation and isolation of the causative agent. This study contributes to clinicopathological aspects, neuroimages, and bacteriological diagnosis of brain abscesses in domestic ruminants. Full article

Cancer remains a significant global health concern, with increasing genetic and metabolic irregularities linked to its onset. Among various forms of cancer, skin cancer, including squamous cell carcinoma, basal cell carcinoma, and melanoma, is on the rise worldwide, often triggered by ultraviolet (UV) radiation. The propensity of skin cancer to metastasize highlights the importance of early detection for successful treatment. This narrative review explores the evolving role of artificial intelligence (AI) in diagnosing head and neck skin cancers from both radiological and pathological perspectives. In the past two decades, AI has made remarkable progress in skin cancer research, driven by advances in computational capabilities, digitalization of medical images, and radiomics data. AI has shown significant promise in image-based diagnosis across various medical domains. In dermatology, AI has played a pivotal role in refining diagnostic and treatment strategies, including genomic risk assessment. This technology offers substantial potential to aid primary clinicians in improving patient outcomes. Studies have demonstrated AI’s effectiveness in identifying skin lesions, categorizing them, and assessing their malignancy, contributing to earlier interventions and better prognosis. The rising incidence and mortality rates of skin cancer, coupled with the high cost of treatment, emphasize the need for early diagnosis. Further research and integration of AI into clinical practice are warranted to maximize its benefits in skin cancer diagnosis and treatment. Full article

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN–Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively. Full article

Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning has been widely applied to computer-aided diagnosis, with an increasing number of models being used for the analysis of renal pathological images. The diversity of renal pathological images and the imbalance between data acquisition and annotation have placed a significant burden on pathologists trying to perform reliable and timely analysis. Transfer learning based on contrastive pretraining is emerging as a viable solution to this dilemma. By incorporating unlabeled positive pretraining images and a small number of labeled target images, a transfer learning model is proposed for high-accuracy renal pathological image classification tasks. The pretraining dataset used in this study includes 5000 mouse kidney pathological images from the Open TG-GATEs pathological image dataset (produced by the Toxicogenomics Informatics Project of the National Institutes of Biomedical Innovation, Health, and Nutrition in Japan). The transfer training dataset comprises 313 human immunoglobulin A (IgA) chronic nephritis images collected at Fukushima Medical University Hospital. The self-supervised contrastive learning algorithm “Bootstrap Your Own Latent” was adopted for pretraining a residual-network (ResNet)-50 backbone network to extract glomerulus feature expressions from the mouse kidney pathological images. The self-supervised pretrained weights were then used for transfer training on the labeled images of human IgA chronic nephritis pathology, culminating in a binary classification model for supervised learning. In four cross-validation experiments, the proposed model achieved an average classification accuracy of 92.2%, surpassing the 86.8% accuracy of the original RenNet-50 model. In conclusion, this approach successfully applied transfer learning through mouse renal pathological images to achieve high classification performance with human IgA renal pathological images. Full article

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study’s commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification. Full article

Registration involves aligning two or more images by transforming one image into the coordinate system of another. Registration of histopathological slide images is a critical step in many image analysis applications including disease detection, classification, and prognosis. It is very useful in Computer-Aided Diagnosis (CAD) and allows automatic analysis of tissue images, enabling more accurate detection and prognosis than manual analysis. Due to the complexity and heterogeneity of histopathological images, registration is challenging and requires the careful consideration of various factors, such as tissue deformation, staining variation, and image noise. There are different types of registration and this work focuses on feature-based image registration specifically. A qualitative analysis of different feature detection and description methods combined with different outlier rejection methods is conducted. The four feature detection and description methods experimentally analyzed are Oriented FAST and rotated BRIEF (ORB), Binary Robust Invariant Scalable Key points (BRISK), KAZE, and Accelerated KAZE, and the three outlier rejection methods examined are Random Sample Consensus (RANSAC), Graph cut RANSAC (GC-RANSAC), and Marginalizing Sample Consensus (MAGSAC++). The results are visually and quantitively analyzed to select the method that gives the most accurate and robust registration of the histopathological dataset at hand. Several evaluation metrics, the number of key points detected, and a number of inliers are used as parameters for evaluating the performance of different feature detection–description methods and outlier rejection algorithm pairs. Among all the combinations of methods analyzed, BRISK paired with MAGSAC++ generates the most optimal registration results. Full article