Artificial intelligence models in chronic lymphocytic leukemia – recommendations toward state-of-the-art

Agius, Rudi; Parviz, Mehdi; Niemann, Carsten Utoft

doi:10.1080/10428194.2021.1973672

Cited by 11 publications

(13 citation statements)

References 72 publications

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“…Several approaches have been developed for assessing the prognosis of newly diagnosed patients with CLL, although the performance of the predictions has not improved over time. 1 , 12 , 13 , 14 , 15 , 16 , 17 , 18 A recently developed laboratory-based prognostic calculator demonstrated superior or equal discriminatory power compared with CLL-IPI for TFS across validation cohorts (0.63-0.72 vs 0.61-0.70), with most estimates within the CI of TTFT in our study. 11 The International Prognostic Score-A, a novel score including genetic and clinical variables for predicting TTFT in early-stage CLL, has exhibited C-statistics (0.66-0.75) similar to those of CLL-IPI in direct comparison and with estimates within our CIs.…”

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confidence: 55%

CLL-IPI applied in Binet A CLL: a nationwide cohort study

et al. 2022

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confidence: 55%

CLL-IPI applied in Binet A CLL: a nationwide cohort study

et al. 2022

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“…Assemblage of novel technologies and computational algorithms, however, promises to reduce this breach and help to infer relevant pathophysiological characteristics of rare diseases, such as FA. On the one hand scRNAseq is producing datasets of single cell resolution transcriptional profiles from multiple healthy and diseased tissues, where rare and small populations can be identified 38,39 ; on the other hand, multiple machine learning algorithms exist that can be implemented to identify patterns on these datasets, or that can be trained with labelled datasets for identification of transcriptional profiles of interest in inquiry datasets 7,[40][41][42] .…”

Section: Discussionmentioning

confidence: 99%

Prediction of myeloid malignant cells in Fanconi anemia using machine learning

Flores-Mejía,

Siliceo-Portugal,

Figueroa

et al. 2024

Preprint

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Fanconi anemia (FA) is an inherited bone marrow failure syndrome with cancer predisposition. Most FA patients develop aplastic anemia during childhood and have an extremely high cumulative risk to develop cancer during their lifespan. Myeloid malignancy is one of the main tumor risks for patients with FA, including high-risk myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). Although bone marrow transplantation is the treatment of choice for FA patients that develop aplastic anemia, patients with a more stable bone marrow remain at a high risk of presenting MDS/AML and should be monitored for appearance of myeloid malignant clones. Markers for an as-early-as-possible identification of emerging myeloid malignant cells are needed for the monitoring of patients with FA, since quick medical action after detection of neoplastic transformation is needed.In this work we have leveraged publicly available single cell RNA seq (scRNAseq) datasets of patients with MDS and AML for training deep neural networks (DNN). We have generated two machine learning models aimed to identify myeloid malignant transcriptional profiles in scRNAseq datasets from the bone marrow of patients with FA, one for detection of MDS and a second one for AML. Both predictors displayed high sensitivity, specificity, and accuracy for detection of single cell resolution myeloid malignant transcriptional profiles.Multiple tools for analysis of single cell transcriptional data were implemented to characterize the predicted MDS and AML cells. Our analysis suggests that the predicted MDS and AML cells from FA patients are enriched in the lympho-myeloid-primed progenitor (LMPP) and the granulocyte-monocyte progenitor (GMP) populations. The predicted MDS and AML cells have gene expression and master transcriptional factor profiles that suggest malignant transformation and that differ from the rest of FA cells. Also cues of immune evasion were detected using single cell pathway analysis (SCPA) and cell-cell communication profiles. Next work will be aimed to find potential cell surface markers on the predicted MDS and AML cells as well as to assess our predictions in primary samples from FA patients.

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“…Going forward, the approach of including all available (para) clinical data for the development of predictive models, as employed in CLL-TIM, would eventually allow us to provide decision support based on the prediction of different outcomes during the disease course for individual patients 32 . Consequently, we may be on the right track to identify whom to diagnose with leukemia and whom to diagnose with age-related MBL.…”

Section: Discussionmentioning

confidence: 99%

Pre-diagnostic trajectories of lymphocytosis predict time to treatment and death in patients with chronic lymphocytic leukemia

et al. 2022

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Background The dynamics of pre-diagnostic lymphocytosis in patients with ensuing chronic lymphocytic leukemia (CLL) need to be explored as a better understanding of disease progression may improve treatment options and even lead to disease avoidance approaches. Our aim was to investigate the development of lymphocytosis prior to diagnosis in a population-based cohort of patients with CLL and to assess the prognostic information in these pre-diagnostic measurements. Methods All patients diagnosed with CLL in the Greater Copenhagen area between 2008 and 2016 were included in the study. Pre-diagnostic blood test results were obtained from the Copenhagen Primary Care Laboratory Database encompassing all blood tests requested by Copenhagen general practitioners. Using pre-diagnostic measurements, we developed a model to assess the prognosis following diagnosis. Our model accounts for known prognostic factors and corresponds to lymphocyte dynamics after diagnosis. Results We explore trajectories of lymphocytosis, associated with known recurrent mutations. We show that the pre-diagnostic trajectories are an independent predictor of time to treatment. The implementation of pre-diagnostic lymphocytosis slope groups improved the model predictions (compared to CLL-IPI alone) for treatment throughout the period. The model can manage the heterogeneous data that are to be expected from the real-world setting and adds further prognostic information. Conclusions Our findings further knowledge of the development of CLL and may eventually make prophylactic measures possible.

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Artificial intelligence models in chronic lymphocytic leukemia – recommendations toward state-of-the-art

Cited by 11 publications

References 72 publications

CLL-IPI applied in Binet A CLL: a nationwide cohort study

CLL-IPI applied in Binet A CLL: a nationwide cohort study

Prediction of myeloid malignant cells in Fanconi anemia using machine learning

Pre-diagnostic trajectories of lymphocytosis predict time to treatment and death in patients with chronic lymphocytic leukemia

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