Increased interest in the opportunities provided by artificial intelligence and machine learning has spawned a new field of health-care research. The new tools under development are targeting many aspects of medical practice, including changes to the practice of pathology and laboratory medicine. Optimal design in these powerful tools requires cross-disciplinary literacy, including basic knowledge and understanding of critical concepts that have traditionally been unfamiliar to pathologists and laboratorians. This review provides definitions and basic knowledge of machine learning categories (supervised, unsupervised, and reinforcement learning), introduces the underlying concept of the bias-variance trade-off as an important foundation in supervised machine learning, and discusses approaches to the supervised machine learning study design along with an overview and description of common supervised machine learning algorithms (linear regression, logistic regression, Naive Bayes, k-nearest neighbor, support vector machine, random forest, convolutional neural networks).
This study demonstrates that a dose of filgrastim 5 micrograms/kg/d administered as a daily SC bolus injection has a similar efficacy and safety profile compared with the 10-microgram/kg/d dose administered as a SC continuous infusion. The lower dose of filgrastim has potential cost-saving implications in terms of both the dose of drug administered and the ease of administration. Based on these findings, the recommended dose of filgrastim after ABMT should be 5 micrograms/kg/d.
Classical Hodgkin lymphoma (CHL) is recognized as a B-cell neoplasm arising from germinal center or postgerminal center B-cells. The hallmark of CHL is the presence of CD30 (+) Hodgkin and Reed-Sternberg (HRS) cells with dim expression of PAX5. Nearly all of the HRS cells are positive for PAX5. However, a small minority of HRS cells may lack PAX5 expression, which can cause a diagnostic dilemma. Herein we describe two cases of PAX5-negative CHL and review of the English literature on this very rare entity. It is crucial to be aware of this phenomenon, which in some cases may lead to misdiagnosis and may ultimately adversely affect patient's management.
Background:
Herein, we present the second documented case of a rare pediatric lower extremity unilateral unilocular cutaneous BPDCN in a relatively asymptomatic Hispanic child who had unusual initial transient improvement from antibiotic treatment.
Case presentation:
A well-nourished 12-year-old Hispanic male with no significant past medical history developed a sizable progressively enlarging lesion on his right inner calf of approximately two months duration after a ground level fall. Review of systems noted night sweats. All imaging findings were unremarkable except for subcutaneous soft tissue ill-defined infiltrative swelling. Routine laboratory findings were non-contributory. The differential of chronic atypical infectious etiology was initially favored. Interestingly, it did have transient improvement with drainage and antibiotic treatment (Cephalexin/Keflex, 500mg). Approximately two months later, the lesional appearance worsened and patient was eventually diagnosed with blastic plasmacytoid dendritic cell neoplasm.
Conclusion:
Pediatric blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and aggressive neoplastic process involving precursor plasmacytoid dendritic cells. Diagnostic evaluation of this intricate heterogeneous entity necessitates the incorporation of various clinical and laboratory findings. Given the challenging nature of this entity, it is imperative to arrive at timely diagnosis with tissue biopsy and initiate appropriate prompt management.
The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http://journals.sagepub.com/doi/10.1177/2374289517715040 .1
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