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Archives of Pathology & Laboratory... Dec 2023Salivary gland neoplasms are rare lesions in the head and neck (H&N) pathology realm. There are more than 20 malignant and 15 benign salivary gland neoplasms in the 5th... (Review)
Review
CONTEXT.—
Salivary gland neoplasms are rare lesions in the head and neck (H&N) pathology realm. There are more than 20 malignant and 15 benign salivary gland neoplasms in the 5th edition of the World Health Organization classification of H&N tumors. These neoplasms consist of heterogeneous groups of uncommon diseases that make diagnosis and treatment challenging for the clinical team. Using an algorithmic immunohistochemical approach-defined tumor origin and type has proven to be effective and advantageous. Immunohistochemistry may be used as sort of a "diagnostic looking glass," not as a positive or negative type tool, but as an indispensable complement to a hematoxylin-eosin morphologic pattern-based approach. Furthermore, the understanding of the novel discoveries of the salivary gland gene fusions and the molecular aspects of these tumors makes the process easier and improve the diagnosis as well as treatment aspects. This review reflects our experience with more recent diagnostic antibodies, which include MYB RNA, Pan-TRK, PLAG1, LEF1, and NR4A3. Each of these is linked with a specific type of neoplasm; for example, gene fusions involving the PLAG1 and HMGA2 oncogenes are specific for benign pleomorphic adenomas, and MYB is associated with adenoid cystic carcinoma.
OBJECTIVE.—
To review these more recent antibodies, which highly enhance salivary gland neoplasm diagnosis.
DATA SOURCES.—
The study sources involved literature PubMed searches, including multiple review articles, case reports, selected book chapters, and Geisinger Medical Center cases.
CONCLUSIONS.—
Salivary gland tumors are a rare, varied group of lesions in H&N pathology. We need to have continuous readings and revisions of the molecular consequences of these fusion oncoproteins and their subsequent targets, which will eventually lead to the identification of novel driver genes in salivary gland neoplasms.
Topics: Humans; Immunohistochemistry; Salivary Gland Neoplasms; Adenoma, Pleomorphic; Salivary Glands; Carcinoma, Adenoid Cystic; Transcription Factors; Biomarkers, Tumor
PubMed: 37074867
DOI: 10.5858/arpa.2022-0461-RA -
Journal of Medical Ultrasound 2023The main cause of death in traumas is hypovolemic shock. Physical examination is limited to detect hemopericardium, hemoperitoneum, and hemopneumothorax. Computed... (Review)
Review
The main cause of death in traumas is hypovolemic shock. Physical examination is limited to detect hemopericardium, hemoperitoneum, and hemopneumothorax. Computed tomography (CT) is the gold standard for traumatic injury evaluation. However, CT is not always available, is more expensive, and there are transportation issues, especially in hemodynamically unstable patients. In this scenario, a rapid, reproducible, portable, and noninvasive method such as ultrasound emerged, directed for detecting hemopericardium, hemoperitoneum, and hemopneumothorax, in a "point of care" modality, known as the focused assessment with sonography for trauma (FAST) protocol. With decades of experience, spread worldwide, and recommended by the most prestigious trauma care guidelines, FAST is a bedside ultrasound to be performed when accessing circulation issues of trauma patients. It is indicated to hemodynamically unstable patients with blunt abdominal trauma, with penetrating trauma of the thoracoabdominal transition (where there is doubt of penetrating the abdominal cavity) and for any patient with the cause of the instability unknown. There are four regions to be examined in the traditional FAST protocol: pericardium (to detect cardiac tamponade), right upper abdominal quadrant, left upper abdominal quadrant, and pelvis (to detect hemoperitoneum). The called extended FAST (e-FAST) protocol also searches the pleural spaces for hemothorax and pneumothorax. It is important to know the false positives and false negatives of the protocol, as well as its limitations. FAST/e-FAST protocol is designed to provide a simple "yes or no" answer regarding the presence of bleeding. It is not intended to quantify the bleeding nor evaluate organ lesions due to its limited accuracy for these purposes. Moreover, the amount of bleeding and/or the identification of organ lesions will not change patient's management: Hemodynamically unstable patients with positive FAST must go to the operating room without delay. CT should be considered for hemodynamically stable patients.
PubMed: 37576415
DOI: 10.4103/jmu.jmu_12_23 -
Nature Medicine Dec 2023Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is...
Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986-0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.
Topics: Humans; Artificial Intelligence; Deep Learning; Pancreatic Neoplasms; Tomography, X-Ray Computed; Pancreas; Carcinoma, Pancreatic Ductal; Retrospective Studies
PubMed: 37985692
DOI: 10.1038/s41591-023-02640-w