AI and Cancer Research ── CLL ── 2026-11-05
Chronic lymphocytic leukemia (CLL): treatment, trials and AI
- Abstract
Flow cytometry (FC) is essential for detecting measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL), but its use is limited by the expertise and time required for manual analysis. We developed an artificial intelligence (AI) pipeline, Clustering/Classification of All Events, Dimensionality reduction, Downsampling, and Aberrancy Scaling (CCADDAS), to automatically enhance raw FC files, streamlining CLL MRD detection using a single-tube 10-color panel. FC files from 166 MRD-positive and 61 MRD-negative cases were processed in a cloud environment. Automated steps included error correction (FlowCut), clustering (PARC), dimensionality reduction (UMAP), anomaly detection against negative controls, and cluster-informed downsampling that preserved rare MRD events. A deep neural network trained on expert-defined normal subsets enabled automated gating. AI-enhanced files were analyzed in standard FC software, yielding results highly concordant with conventional expert review (R² = 0.98). Downsampling reduced cellularity by 85% and file size by 78%, while retaining low-level MRD events. An AI-generated aberrancy scale distinguished CLL MRD from background B cells with excellent performance (AUC = 0.98). Manual analysis time decreased from 9.0 to 0.9 min per case (90% reduction). CCADDAS provides a largely unsupervised, software-agnostic method that accelerates and simplifies CLL MRD detection without compromising test performance compared to conventional analysis, enabling broader adoption of FC-based MRD testing.
Journal IF-equivalent: 7.4 (OpenAlex 2-year mean citedness, value as of 2026-11-04, retrieved 2026-11-05; not the official Clarivate IF)Reference: Chiu A, Seheult JN, Shi M, Jevremovic D, Jordan CE, Weybright MJ, et al. Artificial intelligence-enhancement of flow cytometry data accelerates the identification of measurable residual chronic lymphocytic leukemia. Leukemia. 2026 May 26;40(7):1516-26. doi:10.1038/s41375-026-02986-3. PMID: 42192141; PMCID: PMC13322963.Checked: Full text checked - Abstract
Chronic lymphocytic leukaemia (CLL) presents considerable therapeutic obstacles due to the development of treatment-resistant disease, especially with BCL-2 inhibitors like venetoclax, and is among the most challenging haematological malignancies to treat. A critical unmet computational need is the ability to predict patient-specific treatment resistance trajectories and to develop optimal adaptive therapy schedules based on a patient's history and experience with treatments. To address these issues, we have developed TwinRL-Onco, a novel computational framework that fuses digital twin technology, driven by world model-based simulations, with hierarchical reinforcement learning (HRL), to predict in silico treatment response and optimize the adaptive therapy process. TwinRL-Onco consists of three synergistic components: (1) a Variational Recurrent State-Space Model (VRSSM); (2) a hierarchical policy structure with two levels of control; and (3) a resistance trajectory predictor that uses Monte Carlo rollouts generated from the world model to simulate treatment response. TwinRL-Onco achieved an AUROC of 0.917 for 12-month simulated prediction of progression, an improvement of 4.6 percentage points over the DreamerV3 algorithm and 7.0 percentage points over the LSTM-Traj algorithm. TwinRL-Onco is an emphasis on an artificial intelligence-based online environment for hypothesis generation; all on-site metrics indicate inventory-only consistency and need actual clinical validation before any conclusions regarding clinical prediction.
Journal IF-equivalent: 6.5 (OpenAlex 2-year mean citedness, value as of 2026-11-04, retrieved 2026-11-05; not the official Clarivate IF)Reference: Wang Z, Ge C, Hu Y, Bai D, Zhang W. TwinRL-Onco: A World Model-Empowered Digital Twin Framework with Hierarchical Reinforcement Learning for Venetoclax Resistance Trajectory Prediction and Adaptive Therapy Optimization in Chronic Lymphocytic Leukemia. IEEE J Biomed Health Inform. 2026 [Epub ahead of print]. doi:10.1109/jbhi.2026.3696685. PMID: 42184194.Checked: Abstract only - Abstract
We developed a machine learning model and validated it using a large-scale, real-world cohort to predict overall survival in patients with Chronic Lymphocytic Leukemia (CLL). The model captured key survival trends between treatment eras. Individual-level uncertainty was quantified, showing good alignment with observed outcomes. Our findings highlight the potential of predictive modeling to support personalized risk assessment in CLL.
Journal IF-equivalent: 1.0 (OpenAlex 2-year mean citedness, value as of 2026-11-04, retrieved 2026-11-05; not the official Clarivate IF)Reference: Papangelou C, Chatzikonstantinou T, Talimtzi P, Chatzidimitriou A, Minga E. Real-World Application of a Machine Learning Pipeline for Overall Survival in Chronic Lymphocytic Leukemia. Stud Health Technol Inform. 2026 May 21 [Epub ahead of print]. doi:10.3233/shti260276. PMID: 42174951.Checked: Abstract only