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Diagnostics
Special Issues
Advances in Diagnosis and Treatment of Hematological Diseases
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Advances in Diagnosis and Treatment of Hematological Diseases

A Special Issue of Diagnostics (ISSN 2075-4418) belonging to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 1230

Editors

Dr. Mirjana Mitrovic

E-Mail Website
Guest Editor
Clinic of Hematology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
Interests: ITP; non-malignant hematology; cancer-associated thrombosis; acute leukemias
Dr. Nikola Pantic

E-Mail Website
Guest Editor
Clinic of Hematology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
Interests: hematology; acute leukemia; thrombosis; thrombocytopenia; haemostasis

Special Issue Information

Dear Colleagues,

In hematology, diagnostics have long surpassed the mere morphological characterization of disease; the development and widespread availability of methods such as next-generation sequencing (NGS) and next-generation flow (NGF) cytometry have enabled more accurate diagnosis, response assessment, and prognostication of hematological disorders. Furthermore, advances in artificial intelligence and machine learning are increasingly finding applications in modern hematological diagnostics and for evaluating treatment response.

The need for such sophisticated diagnostic approaches has also emerged as a consequence of the remarkable progress achieved in therapeutic strategies for hematological diseases. Assessing molecular markers offers a personalized approach to every patient, as does evaluating deep molecular responses, which is contemporary goal of modern treatment protocols.

The aim of this Special Issue is to gather new information regarding advances in the diagnosis and treatment of both malignant and non-malignant hematological diseases.

We welcome the submission of basic, translational, and clinical original research articles; systematic and narrative reviews; and meta-analyses. Additionally, other types of papers could be submitted following discussion with the editors.

Dr. Mirjana Mitrovic
Dr. Nikola Pantic
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly 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

  • hematology
  • anemia
  • thrombocytopenia
  • bleeding
  • thrombosis
  • hemostasis
  • leukemia
  • lymphoma
  • myeloma
  • diagnosis
  • therapy

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

12 pages, 9413 KB  
Article
Grading of Castleman Disease Histopathology with an Attention-Based Multiple Instance Learning Model
by Muir J. Morrison, Alnoor, Brendan O’Fallon, Ashley Hutchings, Mark Dewey, Paul English, Alexandra E. Rangel, Lauren M. Zuromski, Katie Knight, Anna Bowen, Kiera Kearns, Kristin Shaw, Janani Sankar, Oscar Silva, Peyman Z. Samghabadi, Olga K. Weinberg, Miguel D. Cantu, Yidan Xu-Monette, Archana Agarwal, Timothy M. Hanley, Kristin H. Karner, Madhu Menon, Rodney R. Miles, Jay L. Patel, Anna Shestakova, Peng Li, Nicholas C. Spies, Ken H. Young, David P. Ng and Robert S. Ohgamiadd Show full author list remove Hide full author list
Diagnostics 2026, 16(17), 2750; https://doi.org/10.3390/diagnostics16172750 - 27 Aug 2026
Viewed by 234
Abstract
Background/Objectives: Castleman disease is a rare cytokine-driven lymphoproliferative disorder in which lymph node histopathology provides key diagnostic information. Morphologic features are graded semiquantitatively and often show substantial interobserver variability. Methods: We developed an automated approach to grade six Castleman disease-associated histologic [...] Read more.
Background/Objectives: Castleman disease is a rare cytokine-driven lymphoproliferative disorder in which lymph node histopathology provides key diagnostic information. Morphologic features are graded semiquantitatively and often show substantial interobserver variability. Methods: We developed an automated approach to grade six Castleman disease-associated histologic features on hematoxylin and eosin (H&E) whole slide images (WSIs) using an attention-based multiple instance learning (MIL) model built on a large pathology foundation model encoder. A multi-institution cohort comprised 544 lymph node WSIs, including 397 from cases with Castleman disease or Castleman-like histology and 147 from cases without suspicion for Castleman disease. These case ascertainment categories were not model prediction targets. Slides were assigned ordinal grades (0–3) for regressed germinal centers, follicular dendritic cell prominence, increased vascularity, hyperplastic germinal centers, plasmacytosis, and follicular twinning by hematopathologists. Model performance was assessed on a held-out evaluation set of 142 WSIs. Results: Across the six features, accuracy ranged from 0.52 to 0.68 (mean 0.60). Disagreements were predominantly minor: 96% of predictions were within one grade of the reference. Feature-specific tile contribution heatmaps showed qualitative spatial correspondence with selected plasma cell-rich, vessel-rich, and follicular regions but were not evaluated as quantitative feature localization maps or causal explanations. In a preliminary reader study, concordance with the reference varied widely among hematopathologists (Krippendorff’s alpha 0.20–0.95, mean 0.50), with the model demonstrating concordance in the mid-range (0.56). Conclusions: These findings support the feasibility of automated grading of Castleman disease-associated histologic features for research standardization. The model does not classify Castleman disease, and its potential use as an input to diagnostic or clinical trial workflows requires evaluation in separately designed studies with adjudicated diagnostic labels and integrated clinical data. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Treatment of Hematological Diseases)
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