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Advances in Pediatric Leukemia
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Advances in Pediatric Leukemia

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Hematology".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2588

Special Issue Editor

Dr. Luciana Vinti

E-Mail Website
Guest Editor
Department of Paediatric Haematology/Oncology, Cell and Gene Therapy, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
Interests: malignant hematology; lymphoma; leukemia
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, many advances have been made in the treatment of pediatric patients with acute lymphoblastic leukemia (ALL), with significant improvements in overall survival and event-free survival. The advances are largely related to an improved understanding of molecular genetics and disease pathogenesis and have led to the development of risk-adapted chemotherapy strategies. New therapeutic approaches, incorporating less intensive chemotherapy regimens with the use of novel targeted agents and immunotherapy, are being investigated to achieve improved long-term survival with fewer long-term comorbidities. Ongoing efforts focus on optimizing treatment options in both the relapsed/refractory setting and in first-line treatment. New therapies recently approved for pediatric ALL have significantly improved response rates and outcomes in patients with relapsed/refractory B-ALL, while the results are less impressive in T-ALL. Progress has also been made in allogeneic stem cell transplantation, with a reduction in transplant-related mortality of recipients.

In this Special Issue, we invite authors to describe prospective and/or retrospective experiences on new therapeutic approaches in ALL (use of targeted tyrosine kinase inhibitors, monoclonal antibodies, antibody-drug conjugates and T-cell-based therapies) and the implementation of strategies that allow for improved overall transplant outcomes (high-resolution typing of donors, choice of conditioning regimen, prophylaxis of graft-versus-host disease and supportive care measures).

Dr. Luciana Vinti
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. Journal of Clinical Medicine 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

  • leukemia
  • pediatric
  • ALL
  • immunotherapy
  • target therapy
  • monoclonal antibodies
  • antibody–drug conjugates
  • T-cell-based therapies

Published Papers (2 papers)

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Research

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24 pages, 2626 KiB  
Article
An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia
by Irfan Al-Hussaini, Brandon White, Armon Varmeziar, Nidhi Mehra, Milagro Sanchez, Judy Lee, Nicholas P. DeGroote, Tamara P. Miller and Cassie S. Mitchell
J. Clin. Med. 2024, 13(6), 1788; https://doi.org/10.3390/jcm13061788 - 20 Mar 2024
Cited by 1 | Viewed by 731
Abstract
Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare [...] Read more.
Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either “high risk” or “low risk” in pediatric ALL (n = 580) and AML (n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL. Full article
(This article belongs to the Special Issue Advances in Pediatric Leukemia)
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Review

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16 pages, 301 KiB  
Review
Antifungal Drug-Drug Interactions with Commonly Used Pharmaceutics in European Pediatric Patients with Acute Lymphoblastic Leukemia
by Beata Sienkiewicz-Oleszkiewicz, Małgorzata Salamonowicz-Bodzioch, Justyna Słonka and Krzysztof Kałwak
J. Clin. Med. 2023, 12(14), 4637; https://doi.org/10.3390/jcm12144637 - 12 Jul 2023
Cited by 1 | Viewed by 1584
Abstract
Leukemia is one of the leading childhood malignancies, with acute lymphoblastic leukemia (ALL) being the most common type. Invasive fungal disease is a concerning problem also at pediatric hemato-oncology units. Available guidelines underline the need for antifungal prophylaxis and give recommendations for proper [...] Read more.
Leukemia is one of the leading childhood malignancies, with acute lymphoblastic leukemia (ALL) being the most common type. Invasive fungal disease is a concerning problem also at pediatric hemato-oncology units. Available guidelines underline the need for antifungal prophylaxis and give recommendations for proper treatment in various clinical scenarios. Nonetheless, antifungal agents are often involved in drug-drug interaction (DDI) occurrence. The prediction of those interactions in the pediatric population is complicated because of the physiological differences in adults, and the lack of pharmacological data. In this review, we discuss the potential DDIs between antifungal agents and commonly used pharmaceutics in pediatric hemato-oncology settings, with special emphasis on the use of liposomal amphotericin B and ALL treatment. We obtained information from Micromedex® and Drugs.com® interaction checking databases and checked the EudraVigilance® database to source the frequency of severe adverse drug reactions that resulted from antifungal drug interactions. Several major DDIs were identified, showing a favorable safety profile of echinocandins and liposomal amphotericin B. Interestingly, although there are numerous available drug interaction checking tools facilitating the identification of potential serious DDIs, it is important to use more than one tool, as the presented searching results may differ between particular checking programs. Full article
(This article belongs to the Special Issue Advances in Pediatric Leukemia)
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