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Biomedicines
Special Issues
Next Generation Cytopathology: Current Status and Future Prospects
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Next Generation Cytopathology: Current Status and Future Prospects

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 4489

Special Issue Editor

Dr. Chien-Chin Chen

E-Mail Website
Guest Editor
1. Department of Cosmetic Science, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
2. Department of Pathology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, Taiwan
3. Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan, Taiwan
4. Ph.D. Program in Translational Medicine, Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Tainan, Taiwan
Interests: cytopathology; cancer immunology; molecular pathology; medical bioinformatics

Special Issue Information

Dear Colleagues,

Cytopathology is a key specialty in identifying and managing various illnesses, and recent advances have been astounding. The advancement of liquid-based cytology and molecular testing has enabled earlier and more accurate diagnoses and tailored medicines based on genetic abnormalities, improving outcomes for numerous individuals.

In addition, developing new reporting systems in cytopathology with a global agreement is a significant trend that has impacted the field. These systems strive to provide standardized terminology, criteria, and categories for cytological specimen interpretation, which can improve communication between pathologists, physicians, and patients, while also facilitating quality assurance and research.

Looking ahead, the potential for artificial intelligence and machine learning to completely transform the industry is quite remarkable. These technologies have the potential to facilitate quite a few steps that pathologists now conduct, boosting accuracy and efficiency while reducing workload. However, it is crucial to highlight that these technologies do not replace human expertise, and qualified individuals will always be required to analyze and make data-based decisions.

Overall, the area of cytopathology is primed for further advancement and innovation in the following years, with the potential to improve patient outcomes and save lives. This Special Issue, entitled "Cytopathology: Current Status and Future Prospects," aims to recruit high-quality original articles or comprehensive reviews focusing on cytopathology and advances in the following subjects:

  • New insights and experience in international reporting systems;
  • Application of new molecular or ancillary testing in cytopathology;
  • The utilization of digital pathology;
  • Rapid on-site evaluation and potential pitfalls;
  • Machine learning models for cytopathology;
  • Comprehensive comparison for different methods or interobserver variability.

In this Special Issue, global researchers and specialists are welcome to submit your recent research or reviews dedicated to prioritizing future medicine and promoting patient care.

Dr. Chien-Chin Chen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cytology
  • diagnosis
  • digital
  • effusion
  • fine needle aspiration
  • machine learning
  • molecular
  • next-generation sequencing
  • rapid onsite
  • reporting system

Published Papers (3 papers)

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13 pages, 1902 KiB  
Article
Optimal Volume Assessment for Serous Fluid Cytology
by Konstantinos Christofidis, Maria Theochari, Stylianos Mavropoulos Papoudas, Lamprini Kiohou, Stylianos Sousouris, Areti Dimitriadou, Nikolaos Volakakis, Nicoletta Maounis and Panagiota Mikou
Biomedicines 2024, 12(4), 899; https://doi.org/10.3390/biomedicines12040899 - 18 Apr 2024
Viewed by 1006
Abstract
Objective: This study aimed to investigate the optimal volume of serous fluid needed for accurate diagnosis using The International System for Reporting Serous Fluid Cytopathology (TIS), as well as to provide information on the distribution of serous effusion cases in the TIS categories [...] Read more.
Objective: This study aimed to investigate the optimal volume of serous fluid needed for accurate diagnosis using The International System for Reporting Serous Fluid Cytopathology (TIS), as well as to provide information on the distribution of serous effusion cases in the TIS categories (ND: non-diagnostic, NFM: negative for malignancy, AUS: atypia of undetermined significance, SFM: suspicious for malignancy, MAL: malignant) and relevant epidemiological data. Methods: A retrospective analysis of 2340 serous effusion cases (pleural, peritoneal, and pericardial) from two hospitals between 2018 and 2020 was conducted. TIS categories were assigned to each case, and for 1181 cases, these were correlated with the volume of the analyzed fluid. Results: Our study found statistically significant differences in volume distributions between certain TIS categories. Statistically lower volumes were observed in NFM compared to MAL, in UNCERTAIN (ND, AUS, SFM) compared to both MAL and NFM, and in NOT MAL (ND, NFM, AUS, SFM) compared to MAL. However, these differences were not substantial enough to hold any clinical relevance. Conclusions: This study suggests that while fluid volume may slightly influence the TIS category, it does not impact the diagnostic accuracy of serous effusion cytology. Therefore, the ideal serous effusion specimen volume can be defined solely by practical parameters. Full article
(This article belongs to the Special Issue Next Generation Cytopathology: Current Status and Future Prospects)
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11 pages, 1219 KiB  
Article
Evaluation of Fine Needle Aspiration Cytopathology in Salivary Gland Tumors under Milan System: Challenges, Misdiagnosis Rates, and Clinical Recommendations
by Yi-Tien Huang, Chen-Yu Ho, Chun-Yen Ou, Cheng-Chih Huang, Wei-Ting Lee, Shu-Wei Tsai, Heng-Jui Hsu, David Shang-Yu Hung, Chien-Sheng Tsai, Sheen-Yie Fang, Sen-Tien Tsai, Jenn-Ren Hsiao, Chan-Chi Chang and Chien-Chin Chen
Biomedicines 2023, 11(7), 1973; https://doi.org/10.3390/biomedicines11071973 - 12 Jul 2023
Cited by 1 | Viewed by 1177
Abstract
(1) Background: Salivary gland tumors are rare in the head and neck. To determine the need and extent of surgical intervention, fine needle aspiration (FNA) is a widely accepted tool to approach salivary gland lesions. However, the FNA cytology varies between entities, while [...] Read more.
(1) Background: Salivary gland tumors are rare in the head and neck. To determine the need and extent of surgical intervention, fine needle aspiration (FNA) is a widely accepted tool to approach salivary gland lesions. However, the FNA cytology varies between entities, while the lack of uniform terminology makes diagnosis more challenging. Since establishing the Milan system for reporting salivary gland cytopathology (MSRSGC) has become an increasingly accepted reporting standard, further examination and detailed recommendations were needed. (2) Methods: Between April 2013 and October 2021, 375 cases with FNA and salivary gland resection were retrospectively collected. All FNA specimens were reclassified according to the criteria of MSRSGC. After surgical excision, the FNA data were compared with the histological diagnosis to estimate the risk of malignancy (ROM), the risk of neoplasm (RON), and the diagnostic accuracy for each diagnostic category. (3) Results: Our cohort’s distribution of ROM and RON was similar to the MSRSGC’s recommendation. Carcinoma ex pleomorphic adenoma (CXPA) has the highest rate (66.7%) of misdiagnosed as a nonneoplastic lesion or benign salivary gland tumor. Pleomorphic adenoma (PA) and Warthin’s tumor were the most common benign salivary gland tumors, while the cytology diagnosis of Warthin’s tumor seems more challenging than PAs. (4) Conclusions: Despite the convenience and effectiveness of MSRSGC, we suggest close follow-up, re-biopsy, or surgical removal for salivary lesions even in Milan IVA-Benign for possibly missing FNA of malignancy, mixed lesions, or prevention of malignant transformation. Full article
(This article belongs to the Special Issue Next Generation Cytopathology: Current Status and Future Prospects)
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Other

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11 pages, 1659 KiB  
Perspective
Towards Artificial Intelligence Applications in Next Generation Cytopathology
by Enrico Giarnieri and Simone Scardapane
Biomedicines 2023, 11(8), 2225; https://doi.org/10.3390/biomedicines11082225 - 08 Aug 2023
Cited by 7 | Viewed by 1818
Abstract
Over the last 20 years we have seen an increase in techniques in the field of computational pathology and machine learning, improving our ability to analyze and interpret imaging. Neural networks, in particular, have been used for more than thirty years, starting with [...] Read more.
Over the last 20 years we have seen an increase in techniques in the field of computational pathology and machine learning, improving our ability to analyze and interpret imaging. Neural networks, in particular, have been used for more than thirty years, starting with the computer assisted smear test using early generation models. Today, advanced machine learning, working on large image data sets, has been shown to perform classification, detection, and segmentation with remarkable accuracy and generalization in several domains. Deep learning algorithms, as a branch of machine learning, are thus attracting attention in digital pathology and cytopathology, providing feasible solutions for accurate and efficient cytological diagnoses, ranging from efficient cell counts to automatic classification of anomalous cells and queries over large clinical databases. The integration of machine learning with related next-generation technologies powered by AI, such as augmented/virtual reality, metaverse, and computational linguistic models are a focus of interest in health care digitalization, to support education, diagnosis, and therapy. In this work we will consider how all these innovations can help cytopathology to go beyond the microscope and to undergo a hyper-digitalized transformation. We also discuss specific challenges to their applications in the field, notably, the requirement for large-scale cytopathology datasets, the necessity of new protocols for sharing information, and the need for further technological training for pathologists. Full article
(This article belongs to the Special Issue Next Generation Cytopathology: Current Status and Future Prospects)
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