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Updated: Sep 11, 2025

Automated Cell Enrichment of Cytomegalovirus-specific T cells for Clinical Applications using the Cytokine-capture System

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Machine Learning Algorithm to Explore Patients With Heterogeneous Treatment Effects of Clinically Significant CMV

Takashi Toya¹, Yujiro Nakajima^2,3, Konan Hara^1,4

¹Hematology Division Tokyo Metropolitan Komagome Hospital Tokyo Japan.

|August 11, 2025

Summary

This summary is machine-generated.

A new machine learning model identifies patient subgroups with varying risks for cytomegalovirus infection and non-relapse mortality after stem cell transplants. This offers a more personalized approach to managing transplant complications.

Keywords:

cytomegalovirus risk factors stem cell transplantation

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Generation of Multivirus-specific T Cells to Prevent/treat Viral Infections after Allogeneic Hematopoietic Stem Cell Transplant

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Area of Science:

Hematology
Transplantation immunology
Computational biology

Background:

Clinically significant cytomegalovirus infection (csCMVi) and non-relapse mortality (NRM) are major concerns post-allogeneic hematopoietic stem cell transplantation (HSCT).
Identifying subpopulations with heterogeneous treatment effects (HTEs) for these complications is crucial but remains unclear.
Existing machine learning (ML) applications in HSCT lack methodological clarity.

Purpose of the Study:

To develop and validate an interpretable ML algorithm for identifying HTEs of csCMVi and NRM after HSCT.
To explore patient subpopulations with distinct risk profiles for these adverse outcomes.
To compare the performance of the developed ML model against conventional statistical methods.

Main Methods:

Developed a novel ML algorithm combining weighting procedures and left-truncated and right-censored trees.
Applied the algorithm to a large-scale Japanese registry dataset of 10,480 HSCT patients.
Evaluated model performance using c-indices and compared it with the Fine-Gray model.

Main Results:

Identified CMV-seropositivity, patient age, and acute graft-versus-host disease as key predictors of csCMVi.
Classified patients into subgroups with csCMVi cumulative incidence ranging from 22.7% to 82.7%.
Classified patients into subgroups with 3-year NRM ranging from 8.0% to 48.5%; ML and Fine-Gray models showed comparable performance.

Conclusions:

Developed a reliable, explainable, and interpretable ML model for exploring HTEs of csCMVi and NRM after HSCT.
The model enables better risk stratification and personalized management strategies for HSCT recipients.
This approach advances the application of ML in understanding and mitigating transplant-related complications.