The March issue of Cytopathology is now available to view online and the printed issue should be dropping through you letterbox around now.
This month, Cytopathology showcases diverse state of the art technologies and tests that can enhance the role of cytology in clinical practice.
In his review article “The emerging role of deep learning in cytology”, Pranab Dey explains the concept of deep learning before discussing the potential of artificial intelligence in areas such as tumour diagnosis, typing and grading as cytology moves into the digital era.
The range of potentially important analyses for molecular events that can be applied to cytology samples is ever expanding: Davidson et al describe PTPN1 mRNA overexpression in high?grade serous carcinoma, Costa et al assessed DNA ploidy in cervical samples using DNA image cytometry and Geng et al compared the Afirma Gene Expression Classifier with Gene Sequencing Classifier in indeterminate thyroid nodules. As an alternative to conventional immunohistochemistry, Sahu et al evaluate the simultaneous use of multiple antibodies in flow cytometry to detect metastatic carcinomas in effusion samples.
This issue also includes original articles examining the performance and utility of cytology in thyroid nodules, prostatic malignancy and orbital haematolymphoid neoplasms as well as pancreaticobiliary cutaneous and soft tissue tumours!
And for a shorter read, there are four case reports, four enigma portal quizzes and four correspondence articles on a range of interesting cytological entities.
Moving forward, the journal is looking to introduce some exciting new issue sections – we will keep you posted.
Cytopathology continues to welcome manuscript submissions from BAC members and author instructions are available online
If you would like to assist the journal by becoming a reviewer, please get in touch with the Cytopathology office