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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: closed (31 July 2024) | Viewed by 7057

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

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Published Papers (3 papers)

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Research

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13 pages, 2130 KiB  
Article
Exploring Pattern of Relapse in Pediatric Patients with Acute Lymphocytic Leukemia and Acute Myeloid Leukemia Undergoing Stem Cell Transplant Using Machine Learning Methods
by David Shyr, Bing M. Zhang, Gopin Saini and Simon C. Brewer
J. Clin. Med. 2024, 13(14), 4021; https://doi.org/10.3390/jcm13144021 - 10 Jul 2024
Cited by 1 | Viewed by 1303
Abstract
Background. Leukemic relapse remains the primary cause of treatment failure and death after allogeneic hematopoietic stem cell transplant. Changes in post-transplant donor chimerism have been identified as a predictor of relapse. A better predictive model of relapse incorporating donor chimerism has the [...] Read more.
Background. Leukemic relapse remains the primary cause of treatment failure and death after allogeneic hematopoietic stem cell transplant. Changes in post-transplant donor chimerism have been identified as a predictor of relapse. A better predictive model of relapse incorporating donor chimerism has the potential to improve leukemia-free survival by allowing earlier initiation of post-transplant treatment on individual patients. We explored the use of machine learning, a suite of analytical methods focusing on pattern recognition, to improve post-transplant relapse prediction. Methods. Using a cohort of 63 pediatric patients with acute lymphocytic leukemia (ALL) and 46 patients with acute myeloid leukemia (AML) who underwent stem cell transplant at a single institution, we built predictive models of leukemic relapse with both pre-transplant and post-transplant patient variables (specifically lineage-specific chimerism) using the random forest classifier. Local Interpretable Model-Agnostic Explanations, an interpretable machine learning tool was used to confirm our random forest classification result. Results. Our analysis showed that a random forest model using these hyperparameter values achieved 85% accuracy, 85% sensitivity, 89% specificity for ALL, while for AML 81% accuracy, 75% sensitivity, and 100% specificity at predicting relapses within 24 months post-HSCT in cross validation. The Local Interpretable Model-Agnostic Explanations tool was able to confirm many variables that the random forest classifier identified as important for the relapse prediction. Conclusions. Machine learning methods can reveal the interaction of different risk factors of post-transplant leukemic relapse and robust predictions can be obtained even with a modest clinical dataset. The random forest classifier distinguished different important predictive factors between ALL and AML in our relapse models, consistent with previous knowledge, lending increased confidence to adopting machine learning prediction to clinical management. Full article
(This article belongs to the Special Issue Advances in Pediatric Leukemia)
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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 5 | Viewed by 2314
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 5 | Viewed by 2935
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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