Predicting survival, neurotoxicity and response in B-cell lymphoma patients treated with CAR-T therapy using an imaging features-based model

This study successfully integrates imaging features and clinical variables to predict outcomes in R/R B-cell lymphoma patients undergoing CAR-T. Notably, the identified MTV<sub>total</sub> cut-off effectively stratifies patients, as evidenced by significant differences in OS and PFS. Additionally, the predictive models for neurotoxicity and CAR-T response show promising accuracy. This comprehensive approach holds promise for risk stratification and personalized treatment strategies which may become a helpful tool for optimizing CAR-T outcomes in R/R lymphoma patients.

Authors

Institutions

Publication Details

Journal
EJNMMI Research
Published
2024-11-20
DOI
https://doi.org/10.1186/s13550-024-01172-9
Citations
4
Primary Topic
CAR-T cell therapy research
Type
article
Field-Weighted Citation Impact
1.13

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predicting survival, neurotoxicity and response in B-cell lymphoma patients treated with CAR-T therapy using an imaging features-based model

Manuel Guerreiro, Rosa Dosdá, Ana Benzaquén, Ángel Alberich‐Bayarri et al.
4 citations
EJNMMI Research
CAR-T cell therapy research
1.13
article

Predicting survival, neurotoxicity and response in B-cell lymphoma patients treated with CAR-T therapy using an imaging features-based model

Manuel Guerreiro, Rosa Dosdá, Ana Benzaquén, Ángel Alberich‐Bayarri, Blanca Ferrer Lores, Ana Jiménez-Pastor, Jaime Sanz, Alicia Serrano‐Alcalá, Fuensanta Bellvís–Bataller, Rafael Hernani, Almudena Fuster-Matanzo, Aitana Balaguer‐Roselló, José Luís Piñana, María José Terol, Luis Martí‐Bonmatí, Alejandra Estepa‐Fernández, Glen J. Weiss, Laura Ventura, Ana Saus-Carreres, Alfonso Ortiz-Algarra, Ana Belén Teruel, Alfonso Picó-Peris, Pablo Sopena‐Novales, Juan Pedro Fernández
article en
4 citations

Abstract

This study successfully integrates imaging features and clinical variables to predict outcomes in R/R B-cell lymphoma patients undergoing CAR-T. Notably, the identified MTV<sub>total</sub> cut-off effectively stratifies patients, as evidenced by significant differences in OS and PFS. Additionally, the predictive models for neurotoxicity and CAR-T response show promising accuracy. This comprehensive approach holds promise for risk stratification and personalized treatment strategies which may become a helpful tool for optimizing CAR-T outcomes in R/R lymphoma patients.

EJNMMI ResearchVol. 14(1)
Hospital Universitari i Politècnic La Fe (ES), Hospital Clínico Universitario de Valencia (ES), Quantitative BioSciences (US), Universitat Rovira i Virgili (ES)
Gilead Sciences, Gilead Foundation
Openalex Percentile: Top 18%
CAR-T cell therapy research
1.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.