Background: Patients diagnosed with cancer are frequent users of the emergency department (ED). While many visits are unavoidable, a significant portion may be potentially preventable ED visits (PPEDs). Cancer treatments have greatly advanced, whereby patients may present with unique toxicities from targeted therapies and are often living longer with advanced disease. Prior work focused on patients undergoing cytotoxic chemotherapy, and often excluded those on supportive care alone. Other contributors to ED visits in oncology, such as patient-level variables, are less well-established. Finally, prior studies focused on ED diagnoses to describe trends and did not evaluate PPEDs. An updated systematic review was completed to focus on PPEDs, novel cancer therapies, and patient-level variables, including those on supportive care alone. Methods: Three online databases were used. Included publications were in English, from 2012-2022, with sample sizes of ≥50, and reported predictors of ED presentation or ED diagnoses in oncology. Results: 45 studies were included. Six studies highlighted PPEDs with variable definitions. Common reasons for ED visits included pain (66%) or chemotherapy toxicities (69.1%). PPEDs were most frequent amongst breast cancer patients (13.4%) or patients receiving cytotoxic chemotherapy (20%). Three manuscripts included immunotherapy agents, and only one focused on end-of-life patients. Conclusion: This updated systematic review highlights variability in oncology ED visits during the last decade. There is limited work on the concept of PPEDs, patient-level variables and patients on supportive care alone. Overall, pain and chemotherapy toxicities remain key drivers of ED visits in cancer patients. Further work is needed in this realm.

Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions.

Follicular skin disorders, including hidradenitis suppurativa (HS), frequently coexist with systemic autoinflammatory diseases, such as inflammatory bowel disease (IBD) and its subtypes, Crohn’s disease and ulcerative colitis. Previous studies suggest that dysbiosis of the human gut microbiome may serve as a pathogenic link between HS and IBD. However, the role of the microbiome (gut, skin, and blood) in the context of IBD and various follicular disorders remains underexplored. Here, we performed a systematic review to investigate the relationship between follicular skin disorders, IBD, and the microbiome. Of the sixteen included studies, four evaluated the impact of diet on the microbiome in HS patients, highlighting a possible link between gut dysbiosis and yeast-exclusion diets. Ten studies explored bacterial colonization and HS severity with specific gut and skin microbiota, including Enterococcus and Veillonella. Two studies reported on immunological or serological biomarkers in HS patients with autoinflammatory disease, including IBD, and identified common markers including elevated cytokines and T-lymphocytes. Six studies investigated HS and IBD patients concurrently. Our systematic literature review highlights the complex interplay between the human microbiome, IBD, and follicular disorders with a particular focus on HS. The results indicate that dietary modifications hold promise as a therapeutic intervention to mitigate the burden of HS and IBD. Microbiota analyses and the identification of key serological biomarkers are crucial for a deeper understanding of the impact of dysbiosis in these conditions. Future research is needed to more thoroughly delineate the causal versus associative roles of dysbiosis in patients with both follicular disorders and IBD.

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.

Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices.

Regenerative medicine holds significant promise for addressing diseases and irreversible damage that are challenging to treat with conventional methods, making it a prominent research focus in modern medicine. Research on stem cells, a key area within regenerative medicine due to their self-renewal capabilities, is expanding, positioning them as a novel therapeutic option. Stem cells, utilized in various treatments, are categorized based on their differentiation potential and the source tissue. The term ‘stem cell’ encompasses a broad spectrum of cells, which can be derived from embryonic tissues, adult tissues, or generated by reprogramming differentiated cells. These cells, applied across numerous medical disciplines including cardiovascular, neurological, and hematological disorders, as well as wound healing, demonstrate varying therapeutic applications based on their differentiation capacities, each presenting unique advantages and limitations. Nevertheless, the existing literature lacks a comprehensive synthesis examining stem cell therapy and its cellular subtypes across different medical specialties. This review addresses this lacuna by collectively categorizing contemporary stem cell research according to medical specialty and stem cell classification, offering an exhaustive analysis of their respective benefits and constraints, thereby elucidating multifaceted perspectives on the clinical implementation of this therapeutic modality.