In the practice of cytopathology, cytologists frequently encounter a spectrum of benign, normal cells in samples. In fact, these normal cells frequently comprise the greatest proportion of material present on a cytology slide. This is frequently the case in Pap smears of the uterine cervix , urine samples, and lung samples such as bronchial brushings. Normal cytology can often mimic pathology leading to misdiagnoses, especially in cases with reactive and metaplastic changes. Moreover, cytopathology findings of certain neoplasms can also mimic normal cytology.
Today, cytology laboratories are no longer confined to dealing with just exfoliative specimens and superficial aspirations. With interventional radiology as well as endobronchial and endoscopic ultrasound-guided fine needle aspirations (FNA), we increasingly encounter visceral samples. Hence, cytologists are even likely to encounter normal elements from deep-seated organs. Sometimes, unexpected normal elements may be found within cytology specimens because a FNA procedure has contamination or inadvertently sampled a nearby organ or normal anatomical structure. A typical example is the finding of ganglion cells when a FNA is performed targeting a celiac node for cancer staging (Elgarby EA et al. Frequency and characterization of celiac ganglia diagnosed on fine-needle aspiration. Cytojournal. 2015; 12:4).
Despite the importance of knowing the spectrum of normal cytology, there are limited reference materials available on this topic for cytologists. Most cytopathology texts deal with abnormal cytology. Often, the chapters in these books only devote a few sentences about normal cytology (euplasia). Our proposed book intends to fulfil this need. The book will contain a mixture of text and images (atlas). Important aspects related to cytology practice will be highlighted such as clinical relevance, differential diagnoses, mimics and pitfalls. The images will include a variety of cytology specimen preparations (e.g. direct smears, liquid based samples, touch preparations, cell blocks) and stains (e.g. Diff Quik/MGG, Papanicolaou, H&E). In selected cases, the expected immunoprofile of normal cells will be addressed. Each chapter will also include a modest list of helpful and contemporary references.
Author(s): Madelyn Lew, Judy Pang, Liron Pantanowitz
Publisher: Springer
Year: 2023
Language: English
Pages: 173
City: Cham
Foreword
Preface
Acknowledgments
Contents
1: Introduction
Cytology Preparation
Cell Structure
Cell Type and Arrangement
Extracellular Material
References
2: Respiratory System
References
3: Digestive Tract (Oral Cavity, Esophagus, Stomach, Intestines, and Anus)
Oral Cavity
Esophagus
Stomach
Small Intestine
Large Intestine
Anus
References
4: Hepatobiliary System
Liver
Biliary Ducts
Gallbladder
References
5: Exocrine Glands (Salivary Gland and Pancreas)
Salivary Glands
Pancreas
References
6: Endocrine Glands
Thyroid Gland
Parathyroid Glands
Adrenal Glands
References
7: Lymphoid and Hematopoietic Systems (Lymph Nodes, Thymus, Spleen, Bone Marrow)
Lymph Nodes
Spleen
Bone Marrow
Blood and Inflammatory Cells
Thymus
References
8: Urinary Tract
Kidney
Renal Pelvis and Ureters
Urinary Bladder
Urethra
References
9: Female Reproductive System
Ectocervix and Vagina
Endocervix
Endometrium
Inflammatory Elements
Common Microorganisms
Miscellaneous Cellular and Acellular Elements
Fallopian Tubes
Ovaries
References
10: Male Reproductive System
Prostate Gland
Seminal Vesicles
Testis
References
11: Breast
References
12: Musculoskeletal System (Bone, Cartilage, Muscle, Soft Tissue) and Skin
Adipose Tissue
Striated Muscle
Fibrous Connective Tissue
Bone
Cartilage
Synovium
Blood Vessels
Skin
References
13: Body Cavities (Mesothelium, Synovium)
Mesothelium
Synovium
References
14: Central Nervous System, Peripheral Nervous System, and Eye
Central Nervous System
Meninges
Brain Parenchyma: Neurons, Astrocytes, and Oligodendrocytes
Ventricular Lining (Choroid Plexus and Ependymal Cells)
Pituitary Gland
Pineal Gland
Peripheral Nervous System
Ganglia
Peripheral Nerves
Eye
References
Index