The NSH Histology Training Program is intended to create a histology training/onboarding resource for individuals with relevant education and/or experience to become a histotechnician/histotechnologist. This program is incorporates both self-paced didactic training that aligns with the technical skills needed to perform core histology, as well as live support and sessions. 

The program consists of 13 Learning Modules:  Tissue ID, Fixation, Grossing, Processing, Embedding, Microtomy, Staining, H&E Staining, Special Stains, Microscopy, Basics of Immunohistochemistry, Introduction to Advanced Topics (Molecular and Digital Pathology). and Career Pathways. To see all learning objectives, please refer to the content outline. 

This program requires participants to complete all of the assigned activities, discussions, assessments, and lecture content, in addition to attending one live session (offered monthly).  Lecture content and assignments are self-paced you participants have six months to complete the program.  

Lecture/Webinar Hours: ~15 hours
Assignment/Assessment Hours: ~7 hours

TOTAL HOURS: 22 hours
ESTIMATED COMPLETION TIME: 2-4 weeks (Course is available for 6 months from the date of purchase)
CEUs: This histology course is worth 22 continuing education credits.

DOWNLOAD THE FULL COURSE OVERVIEW HERE.

If you are interested in purchasing this product and are from a UN recognized developing nation, you may qualify for a discounted rate.  Please email histo@nsh.org to inquire. 

Malakoplakia is a rare chronic inflammatory disease thought to be the result of defective bacterial phagocytosis and lysosome function, and there is difficulty in accurate diagnosis as a result of non-specific symptoms that mimic other diseases and cancers. This study presents a case of bladder malakoplakia associated with renal failure presenting as a tumor.

CEUs: This histology course is worth 1 continuing education credit. Course is available for 365 days from date of live airing.

In this webinar we concentrate on the application of immunocytochemistry (IHC) techniques within the field of dermatopathology, involving heat mediated antigen retrieval steps, antigen detection systems, signal amplification procedures, and double labelling as well as esoteric IHC techniques involving ‘bleaching’ procedures for the evaluation of melanocytic lesions followed by IHC staining. Consideration will be given to the range and scope of automated IHC staining platforms available. Finally it will be explained how these developments have found a niche within the field of dermatopathology at both the light and electron microscope levels. Emphasis will be given to how best to optimize the amount of useful information that can be obtained from any given sample and will be a focus and goal of this webinar.

CEUs: This histology course is worth 1 continuing education credit. Course is available for 365 days from date of live airing.

This webinar will help participants to understand the complexity and challenges associated with conversion of glass slides into digital images;  to understand the role of image analysis and artificial intelligence in digital pathology;  to understand the crucial role of histotechnologists in the current state of anatomical pathology workflow.

CEUs: This histology course is worth 1 continuing education credit. Course is available for 365 days from date of live airing.

The NSH Workload and Staffing Tool is designed to help laboratories and organizations benchmark their staffing and productivity against other organizations/labs that report similar size and environment.  This tool utilizes the data collected during the 2021 NSH Workload Study.  The study was designed to assess productivity and staffing in the clinical histology laboratory. The Productivity Benchmarking Survey was developed by the NSH Quality Management Committee with the intent to provide usable data for laboratories and organizations to aid in making better staffing decisions.  

This tool provides three unique profile questions that will in turn display the data collected from the 2021 survey that relates to the profile selections.  Additionally, narrative information will be provided and can be downloaded.  

Tissue, blocks, and slides are handled in histology laboratories every day. Procedures and safety measures are put in place to ensure that materials are processed in ways that are consistent with high expectations and quality outcomes. However, it is also important to remember why those specimens are ultimately available: patients. Along with guidelines that histologists need to abide by, maintaining high quality is crucial because of the person behind the specimen. Throughout the presentation, case studies will connect technical expertise back to the patient. Clinical components will be reviewed, such as patient symptoms and subsequent procedures. After tissue samples are retrieved, specimens are sent to histology laboratories for processing, where participants will have the opportunity to take a more investigative look into how specific testing was selected. Diagnostic factors will be discussed and will correlate how histologists contribute to pathologic findings.  Not having the traditional patient interaction can cause the histologist to dissociate the specimen from the patient. By sharing patient cases and reminding laboratory staff how important their role is in the process of making a diagnosis, the significance of having a high quality product will be not only be because a guideline says so, but because a patient behind that sample is counting on it.

CEUs: This histology course is worth 1 continuing education credit. Course is available for 365 days from date of purchase.

Lack of rigor and reproducibility is a major issue in the biomedical research community. A number of studies have demonstrated that published literature lacks sufficient rigor to reproduce key experiments and findings. Nowhere is this more widespread than in the area of histotechnology. This presentation provides a guide to address the lack of detailed reporting of materials and methods in research histology. Our goal is to establish a structured framework that highlights the key factors necessary to document, communicate and reproduce slide-based histotechnology assays.

Presented by L. Chiriboga, E. Chlipala, and Y. Wang

Once a diagnosis of cancer has been established, the tissue specimen containing tumor may be referred for molecular testing (e.g., DNA and RNA sequencing) to detect genomic alterations that can select patients for targeted therapy. However, will these targeted treatments be effective? “Confirmational Proteomics” is an application of immunohistochemistry that can evaluate the tumor “landscape” for overexpression or loss of specific proteins associated with genomic alterations, and which may help guide therapy for patients with certain types of cancer.

Presented by Dr. Richard Cartun, MD

Ulcerative colitis is a chronic disease of the large intestine, in which the lining of the colon becomes inflamed and develops tiny open sores, or ulcers. About 1 million people in the United States have this disease, and it can impact daily living if not treated. This session will explore the histopathology of ulcerative colitis.

Presented by David Krull, HT(ASCP)QIHC, Scientific Leader, GSK

Breast cancer is one of the most common cancers in women. With early diagnosis, some breast cancers are highly curable. However, the concordance rate of breast cancer diagnosis from histology slides by pathologists is unacceptably low. Classifying normal versus tumor breast tissues from breast histology microscopy images is an ideal case to use for deep learning and could help to more reproducibly diagnose breast cancer. This webinar will discuss using 42 combinations of deep learning models, image data preprocessing techniques, and hyperparameter configurations, with accuracy testing of tumor versus normal classification using the Breast Cancer Histology (BACH) dataset. Results of this process will be shared to demonstrate preprocessing and hyperparameter configurations have a direct impact on the performance of deep neural networks for image classification.

Presented by Qiangqiang Gu, MS, PhD Candidate, University of Minnesota