Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review
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ABSTRACT:
SUBMITTER: Ram M
PROVIDER: S-EPMC11337640 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Extreme gradient boosting methods outperform conventional machine-learning models. Here, we have developed the LEukemia Artificial intelligence Program (LEAP) with the extreme gradient boosting decision tree method for the optimal treatment recommendation of tyrosine kinase inhibitors (TKIs) in patients with chronic myeloid leukemia in chronic phase (CML-CP). A cohort of CML-CP patients was randomly divided into training/validation (N = 504) and test cohorts (N = 126). The training/validation cohort was used for 3-fold cross validation to develop the LEAP CML-CP model using 101 variables at diagnosis. The test cohort was then applied to the LEAP CML-CP model and an optimum TKI treatment was suggested for each patient. The area under the curve in the test cohort was 0.81899.Backward multivariate analysis identified age at diagnosis, the degree of comorbidities, and TKI recommended therapy by the LEAP CML-CP model as independent prognostic factors for overall survival. The bootstrapping method internally validated the association of the LEAP CML-CP recommendation with overall survival as an independent prognostic for overall survival. Selecting treatment according to the LEAP CML-CP personalized recommendations, in this model, is associated with better survival probability compared to treatment with a LEAP CML-CP non-recommended therapy. This approach may pave a way of new era of personalized treatment recommendations for patients with cancer.
| S-EPMC9022629 | biostudies-literature
Project description:BackgroundThe International Prognostic Index (IPI) is applied to predict the outcome of chronic lymphocytic leukemia (CLL) with five prognostic factors, including genetic analysis. We investigated whether multiparameter flow cytometry (MPFC) data of CLL samples could predict the outcome by methods of explainable artificial intelligence (XAI). Further, XAI should explain the results based on distinctive cell populations in MPFC dot plots.MethodsWe analyzed MPFC data from the peripheral blood of 157 patients with CLL. The ALPODS XAI algorithm was used to identify cell populations that were predictive of inferior outcomes (death, failure of first-line treatment). The diagnostic ability of each XAI population was evaluated with receiver operating characteristic (ROC) curves.ResultsALPODS defined 17 populations with higher ability than the CLL-IPI to classify clinical outcomes (ROC: area under curve (AUC) 0.95 vs. 0.78). The best single classifier was an XAI population consisting of CD4+ T cells (AUC 0.78; 95% CI 0.70-0.86; p < 0.0001). Patients with low CD4+ T cells had an inferior outcome. The addition of the CD4+ T-cell population enhanced the predictive ability of the CLL-IPI (AUC 0.83; 95% CI 0.77-0.90; p < 0.0001).ConclusionsThe ALPODS XAI algorithm detected highly predictive cell populations in CLL that may be able to refine conventional prognostic scores such as IPI.
| S-EPMC9955184 | biostudies-literature
Project description:BackgroundThere is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health.ObjectiveThis study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care.MethodsWe searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review.ResultsWe identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients.ConclusionsThis scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients' perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.
| S-EPMC10500367 | biostudies-literature
Project description: Not available
| S-EPMC7301058 | biostudies-literature
Project description:Treatment of chronic myeloid leukemia has improved significantly with the introduction of tyrosine kinase inhibitors (TKIs), and treatment guidelines based on numerous clinical trials are available for chronic phase disease. However for CML in the blast phase (CML-BP), prognosis remains poor and treatment options are much more limited. The spectrum of treatment strategies for children and adolescents with CML-BP has largely evolved empirically and includes treatment principles derived from adult CML-BP and pediatric acute leukemia. Given this heterogeneity of treatment approaches, we formed an international panel of pediatric CML experts to develop recommendations for consistent therapy in children and adolescents with this high-risk disease based on the current literature and national standards. Recommendations include detailed information on initial diagnosis and treatment monitoring, differentiation from Philadelphia-positive acute leukemia, subtype-specific selection of induction therapy, and combination with tyrosine kinase inhibitors. Given that allogeneic hematopoietic stem cell transplantation currently remains the primary curative intervention for CML-BP, we also provide recommendations for the timing of transplantation, donor and graft selection, selection of a conditioning regimen and prophylaxis for graft-versus-host disease, post-transplant TKI therapy, and management of molecular relapse. Management according to the treatment recommendations presented here is intended to provide the basis for the design of future prospective clinical trials to improve outcomes for this challenging disease.
| S-EPMC9991904 | biostudies-literature
Project description:Chronic myeloid leukemia (CML) accounts for 2-3% of leukemias in children under 15 and 9% in adolescents aged 15-19. The diagnosis and management of CML in children, adolescents, and young adults have several differences compared to that in adults. This review outlines the diagnosis and management of the underlying disease as well as challenges that can occur when dealing with CML in this patient population.
| S-EPMC6944522 | biostudies-literature
Project description:Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of "black box" in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leukemia (AML) cohort of 319 ex-vivo tumor samples with 122 screened drugs and WES. MOM returned a therapeutic strategy based on the FLT3, CBFβ-MYH11, and NRAS status, which predicted AML patient response to Quizartinib, Trametinib, Selumetinib, and Crizotinib. We successfully validated the results in three different large-scale screening experiments. We believe that XAI will help healthcare providers and drug regulators better understand AI medical decisions.
| S-EPMC9556772 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM.ObjectiveWe aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM.MethodsWe performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed.ResultsThe search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions.ConclusionsEvidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients' values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.
| S-EPMC9399841 | biostudies-literature
Project description:An isodicentric Philadelphia chromosome is an uncommon finding previously described as a secondary chromosomal abnormality in accelerated- or blast-phase of chronic myeloid leukemia (CML) with resistance to imatinib mesylate or dasatinib. Here, we present a case with idic(Ph) chromosome identified at initial diagnosis in a patient with chronic-phase CML.
| S-EPMC4706395 | biostudies-other
Project description:Dasatinib is a dual Abl/Src tyrosine kinase inhibitor (TKI) designed as a prototypic short-acting BCR-ABL-targeted TKI that inhibits BCR-ABL with greater potency compared with imatinib, nilotinib, bosutinib, and ponatinib and has been shown to have potential immunomodulatory effects. Dasatinib is approved for the treatment of all phases of chronic myeloid leukemia (CML) and Philadelphia chromosome-positive acute lymphoblastic leukemia resistant or intolerant to prior imatinib treatment and first-line treatment for CML in chronic phase. In this article, the development of dasatinib as a treatment for patients with CML is reviewed.This is a review of the relevant literature regarding dasatinib development in CML (2003-2013).Dasatinib demonstrates efficacy against most BCR-ABL mutations arising during imatinib therapy and is effective in treating patients with imatinib resistance due to other mechanisms. Randomized trial data show that first-line dasatinib provides superior responses compared with imatinib and enables patients to achieve early, deep responses correlated with improved longer-term outcomes. Dasatinib has a generally acceptable safety profile, with most adverse events (AEs) proving manageable and reversible. Cytopenias are commonly observed with dasatinib, and some nonhematologic AEs including pleural effusion have been consistently reported.Dasatinib is an effective treatment option for patients with CML.
| S-EPMC3825579 | biostudies-other