Search Results (71)

The gut microbiota has emerged as a key area of biomedical research due to its integral role in maintaining host health and its involvement in the pathogenesis of many systemic diseases. Growing evidence supports the notion that gut dysbiosis contributes significantly to diseases and their progression. An example would be inflammatory bowel disease (IBD), a group of conditions that cause inflammation and swelling of the digestive tract, with the principal types being ulcerative colitis (UC) and Crohn’s disease (CD). Another notable disease with significant association to gut dysbiosis would be colorectal cancer (CRC), a malignancy which typically begins as polyps in the colon or rectum, but has the potential to metastasise to other parts of the body, including the liver and lungs, among others. Concurrently, advances in nanomedicine, an evolving field that applies nanotechnology for disease prevention, diagnosis, and treatment, have opened new avenues for targeted and efficient therapeutic strategies. In this paper, we provide an overview of the gut microbiota and the implications of its dysregulation in human disease. We then review the emerging nanotechnology-based approaches for both therapeutic and diagnostic purposes, with a particular focus on their applications in IBD and CRC. Full article

Background: Exophiala dermatitidis is a dematiaceous, thermotolerant, yeast-like fungus increasingly recognized as an opportunistic pathogen in chronic airway diseases. While commonly associated with cystic fibrosis, its clinical significance in non-cystic fibrosis bronchiectasis (NCFB) remains unclear. Case Presentation: We report the case of a 66-year-old immunocompetent woman with a history of breast cancer in remission and NCFB, who presented with chronic cough and dyspnea. Chest CT revealed bilateral bronchiectasis with new pseudonodular opacities. Bronchoalveolar lavage cultures identified E. dermatitidis, along with Pseudomonas aeruginosa and methicillin-sensitive Staphylococcus aureus. Given clinical stability and the absence of systemic signs, initial therapy included oral voriconazole, levofloxacin, doxycycline, and inhaled amikacin. Despite persistent fungal isolation on repeat bronchoscopy, the patient remained asymptomatic with stable radiologic and functional findings. Antifungal therapy was discontinued, and the patient continued under close monitoring. The patient exhibited clinical and radiological stability despite repeated fungal isolation, reinforcing the hypothesis of persistent colonization rather than active infection. Discussion: This case underscores the diagnostic challenges in distinguishing fungal colonization from true infection in structurally abnormal lungs. In NCFB, disrupted mucociliary clearance and microbial dysbiosis may facilitate fungal persistence, even in the absence of overt immunosuppression. The detection of E. dermatitidis should prompt a comprehensive evaluation, integrating clinical, radiologic, and microbiologic data to guide management. Voriconazole is currently the antifungal agent of choice, though therapeutic thresholds and duration remain undefined. Conclusions: This report highlights the potential role of E. dermatitidis as an under-recognized respiratory pathogen in NCFB and the importance of a multidisciplinary, individualized approach to diagnosis and treatment. This case underscores the need for further research on fungal colonization in NCFB and the development of evidence-based treatment guidelines. Further studies are needed to clarify the pathogenic significance, optimal management, and long-term outcomes of E. dermatitidis in non-CF chronic lung diseases. Full article

Background/Objectives: Patients with inflammatory bowel disease (IBD) are at an increased risk of various cancers; such as colorectal cancer; skin cancer; bile duct cancer; or lymphoma; with IBD itself not being the sole cause. Inappropriate or ineffective IBD therapy with a continuous inflammatory burden within the gut leads to an increased risk of malignancy. Our study aimed to investigate the risk of malignancy in our patient cohort; focusing on concomitant therapy; disease duration; and inflammatory burden. Methods: A total of 333 consecutive adult patients with IBD (Crohn’s disease; ulcerative colitis; and IBD unclassified) were included in this study. Data from patients were collected retrospectively using patient charts. The patients were treated in the gastroenterological outpatient clinic of the University Hospital of Augsburg; Germany; between 1 January 2014 and 31 December 2018. Results: The study group included 333 patients; 32 (9.61%) of whom suffered from malignancy (any form). Men (n = 21; 65.62%) tended to develop malignancy more often than women (n = 11; 34.38%, p = 0.051). It was also observed that the probability of developing cancer was 2.40 times higher in male patients than in female patients in our cohort. However, this trend was non-significant (HR = 2.412; p = 0.075). Furthermore; the probability of developing cancer increased with the increasing age at the time of the first diagnosis of IBD (HR = 1.088; p < 0.025). A total of 20 patients (6.00%) received their cancer diagnosis after being diagnosed with IBD. The majority of those patients had skin (n = 6; 30.00%) or colon cancer (n = 5; 25.00%). Other diseases such as CML; NHL; HL; HCC; liver sarcoma; prostate cancer; breast cancer; seminoma; thyroid cancer (a second cancer in one of the patients); or CUP syndrome/lung cancer were diagnosed in single patients. Patients with IBD and colon cancer (n = 5; 25.00%) shared some of the known risk factors for tumour development; such as a long-lasting IBD (n = 5; 100.00%), diagnosis at a young age (under 30; n = 3; 60.00%), and the coexistence of PSC (n = 1; 20.00%). The cancer prevalence rate was relatively low in our cohort despite the use of diverse biologics and immunosuppressive drugs. Faecal calprotectin was confirmed as a relevant tool for inflammation monitoring in this cohort. Conclusions: In our study cohort; we could show a low prevalence rate of malignancy in IBD. There were more malignancies in men and in patients who were diagnosed with IBD at later ages. It can be observed that the prevalence rate of cancer was relatively low despite the use of diverse biologics and immunosuppressive drugs; which is the major conclusion of this study. Additionally; the known correlation between elevated levels of faecal calprotectin and gut inflammation was confirmed through our statistical analysis. The use of calprotectin as a non-invasive screening tool for gut inflammation is advised. Full article

Objective: Early diagnosis and treatment of metastatic pericardial disease are crucial to prevent the life-threatening complication of cardiac tamponade. Thin Layer Cytology (TLC), a widely adopted technique in cytology, has gained significant acceptance for most specimens. Our study aimed to assess the utility of TLC in diagnosing metastatic neoplasms and their origins in pericardial effusions, as well as monitoring response to chemotherapy. Methods: We examined 184 pericardial fluids collected by pericardiocentesis and processed using the ThinPrep liquid-based technique. Various immunocytochemical markers were used to determine the site of metastatic neoplasms. We also evaluated the response to therapy in 53 patients with lung and breast cancer. Results: Out of 184 specimens, 113 pericardial fluids were diagnosed as positive for malignancy, while 71 were negative. Twenty-three cases of unknown primary site were included in the total positive cases. Ninety cases positive for malignancy had a known primary site of origin, including 31 lung carcinomas, 22 breast carcinomas, 10 ovarian carcinomas, 6 T-cell lymphomas, 3 urinary bladder carcinomas, 4 renal carcinomas, 5 adenocarcinomas of the colon, 5 prostate carcinomas, 2 parotid adenocarcinomas, and 2 melanomas. Regarding the 53 cases with chemotherapy treatment, the cytologic examination of pericardial fluid showed a remarkable reduction in neoplastic burden after the third dose of cisplatin or thiotepa instilled into the pericardial cavity. ThinPrep provided excellent preservation of cytomorphological features, high cellularity per slide, and a clear background. This comprehensive analysis provides crucial information about the types and distribution of cancerous cells present in the samples. Conclusions: Thin Layer Cytology (TLC) is a valuable diagnostic tool for detecting metastatic pericardial malignancy. It allows the examination of exfoliated cells from the pericardial fluid, providing crucial information for diagnosis, management, and monitoring the acute responsiveness to intrapericardial chemotherapy. Immunocytochemistry (IHC) can identify specific markers for various types of cancer, enabling a more accurate diagnosis and guiding further treatment decisions. Full article

This study presents a computer-aided diagnostic (CAD) framework that integrates multi-domain features through a hybrid methodology. The system uses several light deep networks (EfficientNetB0, MobileNet, and ResNet-18), which feature fewer layers and parameters, unlike traditional systems that depend on a single, parameter-complex deep network. Additionally, it employs several handcrafted feature extraction techniques. It systematically assesses the diagnostic power of deep features only, handcrafted features alone, and both deep and handcrafted features combined. Furthermore, it examines the influence of combining deep features from multiple CNNs with distinct handcrafted features on diagnostic accuracy, providing insights into the effectiveness of this hybrid approach for classifying lung and colon cancer. To achieve this, the proposed CAD employs non-negative matrix factorization for lowering the dimension of the spatial deep feature sets. In addition, these deep features obtained from each network are distinctly integrated with handcrafted features sourced from temporal statistical attributes and texture-based techniques, including gray-level co-occurrence matrix and local binary patterns. Moreover, the CAD integrates the deep attributes of the three deep networks with the handcrafted attributes. It also applies feature selection based on minimum redundancy maximum relevance to the integrated deep and handcrafted features, guaranteeing optimal computational efficiency and high diagnostic accuracy. The results indicated that the suggested CAD system attained remarkable accuracy, reaching 99.7% using multi-modal features. The suggested methodology, when compared to present CAD systems, either surpassed or was closely aligned with state-of-the-art methods. These findings highlight the efficacy of incorporating multi-domain attributes of numerous lightweight deep learning architectures and multiple handcrafted features. Full article

Lung cancer remains the most common cancer worldwide, with a limited prognosis despite personalized treatment regimens. Low-dose computed tomography (CT) scanning as a means of early diagnosis has been disappointing due to the high false positive rate. Other non-invasive means of testing need to be developed that offer both timely diagnosis and predict prognosis. Methods: In the course of stool testing in large-scale testing of 2922 patients at increased risk of CRC, we were able to ascertain 112 patients documented to have prospectively been diagnosed with lung cancer. Stool and colonic effluents were tested for p87 with anti-adenoma antibody (Adnab-9) reactivity by ELISA and Western blot. Survival data were obtained where available. Results: Of 112 cancers, approximately 27.6% were squamous (SSC), 17.9% were adenocarcinoma, 8% were small, 6.25% were large cell, 3.57% were designated non-small cell cancer (NSCLC), 0.89% were indeterminate, 0.89% were lepidic spread, 3.57% had metastasis, and in 31.25%, data were unavailable. In total, 49.1% of the lung cancer patients had fecal Adnab-9 testing. Overall, 60% had positive testing compared to 38%, which was significant (OR2.19 [1.06–4.53]; p = 0.045). Cancers with higher lethality were less likely to test positive (approximately 8.5% each for both small and large cell lung cancers) and higher, with 56% for SCC and 25% for adenocarcinoma (0% NSCLC). In the larger groups, overall survival was worse in those testing positive: 474 testing positives versus 844 days in SCC and 54 testing positive versus 749 days in adenocarcinoma patients. Most importantly, the time from a positive test to the clinical diagnosis ranged from 2.72 years for small cell, 3.13 for adenocarcinoma, 5.07 for NSCLC, 6.07 for SSC, and 6.24 for large cell cancer. In excluded cases where cancer in the lung was believed to be metastatic, 83.3% of cancers were positive. Conclusions: At a projected real-world sensitivity of 0.60 and specificity of 0.60, and the ability to predate diagnosis by up to 4.7 years overall, this test could help direct lung cancer screening. In addition, the Adnab-9 testing selectively detects worse tumor types (87.5%) and those with worse prognoses amongst the more common, favorable phenotypes, thus making early diagnosis possible in those patients who stand to benefit most from this strategy. Metastatic lung cancer, also detected by the test, should be identified by the follow-up imaging studies and, therefore, would not be considered to be a major pitfall. Full article

The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and their ineffectiveness in utilising multiscale features. To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. Initially, it extracts deep attributes from two separate layers (pooling and fully connected) of three pre-trained CNNs (MobileNet, ResNet-18, and EfficientNetB0). Second, the system uses the benefits of canonical correlation analysis for dimensionality reduction in pooling layer attributes to reduce complexity. In addition, it integrates the dual-layer features to encapsulate both high- and low-level representations. Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. The CAD is assessed on the LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, and k-nearest neighbours. The experimental results exhibited outstanding performance, attaining 99.8% classification accuracy with cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding the performance of individual CNNs while markedly diminishing computational complexity. The framework’s capacity to sustain exceptional accuracy with a limited feature set renders it especially advantageous for clinical applications where diagnostic precision and efficiency are critical. These findings confirm the efficacy of the multi-CNN, multi-layer methodology in enhancing cancer classification precision while mitigating the computational constraints of current systems. Full article

Background and Aims: A higher incidence of extra-pancreatic malignancies (EPMs) in patients with pancreatic intraductal papillary mucinous neoplasm (IPMN) than in the general population has been shown in several studies. We suppose that EPMs also occur after IPMN has been diagnosed, but few reports have discussed the risk factors that have been identified, except for old age, which was only noted in one study. Our study aims to recognize the distribution of EPMs in Taiwanese patients with a longer duration of follow-up and investigate the risk factors to predict EPMs in IPMN patients. Methods: We retrospectively analyzed 114 patients with pancreatic IPMN from 1 January 2010 to 31 December 2014 in Chang Gung Memorial Hospital. The characteristics of the patients were all recorded. Different EPMs are demonstrated as occurring before, concurrently with, or after IPMN diagnosis. The risk factors were compared between patients with or without an EPM. Results: After an average follow-up duration of 10.45 years, 47 EPMs occurred in 42 patients (36.8%), and over half were found after IPMN was diagnosed (55.3%). The most common EPMs were colon cancer and lung cancer (21.3%). Moreover, cyst size progression was highly associated with EPM occurrence (p = 0.004) and predictive of EPM occurrence after IPMN (p = 0.002), with a cut-off value of 1 cm (accuracy: 79%; sensitivity: 88%; specificity: 58%). Conclusions: Colon cancer and lung cancer account for the majority EPMs in Taiwan. EPMs were also frequently found after IPMN diagnosis when the follow-up duration was prolonged up to 10.45 years. Cyst size progression is a risk factor of EPM after IPMN diagnosis and we suggest a cut-off value of 1 cm for clinical utility. Full article

Histopathological analysis is an essential exam for detecting various types of cancer. The process is traditionally time-consuming and laborious. Taking advantage of deep learning models, assisting the pathologist in the diagnosis process is possible. In this work, a study was carried out based on the DenseNet neural network. It consisted of changing its architecture through combinations of Transformer and MBConv blocks to investigate its impact on classifying histopathological images of penile cancer. Due to the limited number of samples in this dataset, pre-training is performed on another larger lung and colon cancer histopathological image dataset. Various combinations of these architectural components were systematically evaluated to compare their performance. The results indicate significant improvements in feature representation, demonstrating the effectiveness of these combined elements resulting in an F1-Score of up to 95.78%. Its diagnostic performance confirms the importance of deep learning techniques in men’s health. Full article

Background: The global healthcare system faces challenges in diagnosing and managing lung and colon cancers, which are significant health burdens. Traditional diagnostic methods are inefficient and prone to errors, while data privacy and security concerns persist. Objective: This study aims to develop a secure and transparent framework for remote consultation and classification of lung and colon cancer, leveraging blockchain technology and Microsoft Azure cloud services. Dataset and Features: The framework utilizes the LC25000 dataset, containing 25,000 histopathological images, for training and evaluating advanced machine learning models. Key features include secure data upload, anonymization, encryption, and controlled access via blockchain and Azure services. Methods: The proposed framework integrates Microsoft Azure’s cloud services with a permissioned blockchain network. Patients upload CT scans through a mobile app, which are then preprocessed, anonymized, and stored securely in Azure Blob Storage. Blockchain smart contracts manage data access, ensuring only authorized specialists can retrieve and analyze the scans. Azure Machine Learning is used to train and deploy state-of-the-art machine learning models for cancer classification. Evaluation Metrics: The framework’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, demonstrating the effectiveness of the integrated approach in enhancing diagnostic accuracy and data security. Results: The proposed framework achieves an impressive accuracy of 100% for lung and colon cancer classification using DenseNet, ResNet50, and MobileNet models with different split ratios (70–30, 80–20, 90–10). The F1-score and k-fold cross-validation accuracy (5-fold and 10-fold) also demonstrate exceptional performance, with values exceeding 99.9%. Real-time notifications and secure remote consultations enhance the efficiency and transparency of the diagnostic process, contributing to better patient outcomes and streamlined cancer care management. Full article

Background: In recent years, the healthcare field has experienced significant advancements. New diagnostic techniques, treatments, and insights into the causes of various diseases have emerged. Despite these progressions, cancer remains a major concern. It is a widespread illness affecting individuals of all ages and leads to one out of every six deaths. Lung and colon cancer alone account for nearly two million fatalities. Though it is rare for lung and colon cancers to co-occur, the spread of cancer cells between these two areas—known as metastasis—is notably high. Early detection of cancer greatly increases survival rates. Currently, histopathological image (HI) diagnosis and appropriate treatment are key methods for reducing cancer mortality and enhancing survival rates. Digital image processing (DIP) and deep learning (DL) algorithms can be employed to analyze the HIs of five different types of lung and colon tissues. Methods: Therefore, this paper proposes a refined DL model that integrates feature fusion for the multi-classification of lung and colon cancers. The proposed model incorporates three DL architectures: ResNet-101V2, NASNetMobile, and EfficientNet-B0. Each model has limitations concerning variations in the shape and texture of input images. To address this, the proposed model utilizes a concatenate layer to merge the pre-trained individual feature vectors from ResNet-101V2, NASNetMobile, and EfficientNet-B0 into a single feature vector, which is then fine-tuned. As a result, the proposed DL model achieves high success in multi-classification by leveraging the strengths of all three models to enhance overall accuracy. This model aims to assist pathologists in the early detection of lung and colon cancer with reduced effort, time, and cost. The proposed DL model was evaluated using the LC25000 dataset, which contains colon and lung HIs. The dataset was pre-processed using resizing and normalization techniques. Results: The model was tested and compared with recent DL models, achieving impressive results: 99.8% for precision, 99.8% for recall, 99.8% for F1-score, 99.96% for specificity, and 99.94% for accuracy. Conclusions: Thus, the proposed DL model demonstrates exceptional performance across all classification categories. Full article

Lung and colon cancer (LCC) is a dominant life-threatening disease that needs timely attention and precise diagnosis for efficient treatment. The conventional diagnostic techniques for LCC regularly encounter constraints in terms of efficiency and accuracy, thus causing challenges in primary recognition and treatment. Early diagnosis of the disease can immensely reduce the probability of death. In medical practice, the histopathological study of the tissue samples generally uses a classical model. Still, the automated devices that exploit artificial intelligence (AI) techniques produce efficient results in disease diagnosis. In histopathology, both machine learning (ML) and deep learning (DL) approaches can be deployed owing to their latent ability in analyzing and predicting physically accurate molecular phenotypes and microsatellite uncertainty. In this background, this study presents a novel technique called Lung and Colon Cancer using a Swin Transformer with an Ensemble Model on the Histopathological Images (LCCST-EMHI). The proposed LCCST-EMHI method focuses on designing a DL model for the diagnosis and classification of the LCC using histopathological images (HI). In order to achieve this, the LCCST-EMHI model utilizes the bilateral filtering (BF) technique to get rid of the noise. Further, the Swin Transformer (ST) model is also employed for the purpose of feature extraction. For the LCC detection and classification process, an ensemble deep learning classifier is used with three techniques: bidirectional long short-term memory with multi-head attention (BiLSTM-MHA), Double Deep Q-Network (DDQN), and sparse stacked autoencoder (SSAE). Eventually, the hyperparameter selection of the three DL models can be implemented utilizing the walrus optimization algorithm (WaOA) method. In order to illustrate the promising performance of the LCCST-EMHI approach, an extensive range of simulation analyses was conducted on a benchmark dataset. The experimentation results demonstrated the promising performance of the LCCST-EMHI approach over other recent methods. Full article

Vibrio cholerae, a Gram-negative bacterium, is widely known as the cause of cholera, an acute diarrheal disease. While only certain strains are capable of causing cholera, non-O1/non-O139 V. cholerae strains (NOVC) can lead to non-pathogenic colonization or mild illnesses such as gastroenteritis. In immunocompromised patients, however, NOVC can cause severe infections, including rare cases of bacteremia, especially in those with underlying conditions like liver disease, hematologic disorders, and malignancies. This case report presents a rare instance of NOVC bacteremia in a 71-year-old patient with advanced lung cancer, illustrating the clinical presentation, diagnostic challenges, and treatment interventions required. The patient presented with fever, asthenia, and confusion, and was found to have bacteremia caused by NOVC, confirmed through blood cultures and molecular analysis. Treatment with intravenous ceftriaxone and ciprofloxacin led to a rapid clinical improvement and resolution of the infection. This case, along with an overview of similar incidents, underscores the importance of considering NOVC in differential diagnoses for immunocompromised patients presenting with fever, and highlights the necessity of timely diagnosis and targeted antimicrobial therapy to achieve favorable outcomes. Full article

Lung and colon cancers are common types of cancer with significant fatality rates. Early identification considerably improves the odds of survival for those suffering from these diseases. Histopathological image analysis is crucial for detecting cancer by identifying morphological anomalies in tissue samples. Regulations such as the HIPAA and GDPR impose considerable restrictions on the sharing of sensitive patient data, mostly because of privacy concerns. Federated learning (FL) is a promising technique that allows the training of strong models while maintaining data privacy. The use of a federated learning strategy has been suggested in this study to address privacy concerns in cancer categorization. To classify histopathological images of lung and colon cancers, this methodology uses local models with an Inception-V3 backbone. The global model is then updated on the basis of the local weights. The images were obtained from the LC25000 dataset, which consists of five separate classes. Separate analyses were performed for lung cancer, colon cancer, and their combined classification. The implemented model successfully classified lung cancer images into three separate classes with a classification accuracy of 99.867%. The classification of colon cancer images was achieved with 100% accuracy. More significantly, for the lung and colon cancers combined, the accuracy reached an impressive 99.720%. Compared with other current approaches, the proposed framework showed an improved performance. A heatmap, visual saliency map, and GradCAM were generated to pinpoint the crucial areas in the histopathology pictures of the test set where the models focused in particular during cancer class predictions. This approach demonstrates the potential of federated learning to enhance collaborative efforts in automated disease diagnosis through medical image analysis while ensuring patient data privacy. Full article

Background: Metastatic tumors of the oro-facial tissuesare rare, with an incidence ranging between 1% and 8% of all oral malignant tumors. Generally reported with a peak of incidence in the 5–7th decades but possibly occurring at any age, metastases may represent the first sign of an occult cancer or manifest in patients with an already known history of a primary carcinoma, mostly from the lungs, kidney, prostate, and colon/rectum in males, and the uterus, breast, lung, and ovary in females. In the oro-facial tissues, the most involved sites are the oral mucosa, gingiva/jawbones, tongue, and salivary glands. Methods: A broad and deep literature review with a comprehensive analysis of the existing research on oro-facial metastases from renal-cell carcinoma (RCC) was conducted by searching the most used databases, with attention also paid to the clear-cell histological variant, which is the most frequent one. Results: Among the 156 analyzed studies, 206 cases of oro-facial metastases of renal cancer were found in patients with an average age of 60.9 years (145 males, 70.3%; 61 females, 29.6%). In almost 40% of the cases, metastasis represented the first clinical manifestation of the primary tumor, and 122 were histologically diagnosed as clear-cell renal-cell carcinoma (ccRCC) (59.2%). The tongue was involved in most of the cases (55 cases, 26.7%), followed by the gingiva (39 cases, 18.9%), mandible (35 cases, 16.9%), maxilla (23 cases, 11.1%), parotid gland (22 cases, 10.6%), buccal mucosa (11 cases, 5.3%), lips (7 cases, 3.3%), hard palate (6 cases, 2.8%), soft palate, masticatory space, and submandibular gland (2 cases, 0.9%), and lymph nodes, tonsils, and floor of the mouth (1 case, 0.4%). Among the 122 ccRCCs (84 males, 68.8%; 38 females, 31.1%), with an average age of 60.8 years and representing in 33.6% the first clinical manifestation, the tongue remained the most frequent site (31 cases, 25.4%), followed by the gingiva (21 cases, 17.2%), parotid gland (16 cases, 13.1%), mandibular bone (15 cases, 12.2%), maxillary bone (14 cases, 11.4%), buccal mucosa and lips (6 cases, 4.9%), hard palate (5 cases, 4%), submandibular gland and soft palate (2 cases, 1.6%), and lymph nodes, tonsils, oral floor, and masticatory space (1 case, 0.8%). The clinical presentation in soft tissues was mainly represented by a fast-growing exophytic mass, sometimes accompanied by pain, while in bone, it generally presented as radiolucent lesions with ill-defined borders and cortical erosion. Conclusions: The current comprehensive review collected data from the literature about the incidence, site of occurrence, age, sex, and survival of patients affected by oro-facial metastases from renal-cell carcinoma, with particular attention paid to the cases diagnosed as metastases from clear-cell renal-cell carcinoma, which is the most frequent histological variant. Clinical differential diagnosis is widely discussed to provide clinicians with all the useful information for an early diagnosis despite the effective difficulties in recognizing such rare and easily misdiagnosed lesionsTheir early identification represents a diagnostic challenge, especially when the clinical work-up is limited to the cervico–facial region. Nevertheless, early diagnosis and recently introduced adjuvant therapies may represent the key to better outcomes in such patients. Therefore, general guidelines about the clinical and radiological identification of oro-facial potentially malignant lesions should be part of the cultural background of any dentist. Full article