Evidence map›Paper›PMID 39228692›Full record

ArticleInternational journal of particle therapy2024

Clinical Translation of a Deep Learning Model of Radiation-Induced Lymphopenia for Esophageal Cancer.

Zongsheng Hu, Radhe Mohan, Yan Chu, Xiaochun Wang, Peter S N van Rossum, Yiqing Chen, Madison E Grayson, Angela G Gearhardt, Clemens Grassberger, Degui Zhi and 3 more

Abstract read
In one paragraph

Article in International journal of particle therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Zongsheng HuDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Radhe MohanDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Yan ChuDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Xiaochun WangDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Peter S N van RossumAmsterdam UMC, Amsterdam, the Netherlands.
Yiqing ChenDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Madison E GraysonDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Angela G GearhardtDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Clemens GrassbergerDepartment of Radiation Oncology, University of Washington, Seattle, Washington, USA.
Degui ZhiThe University of Texas Health Science Center at Houston, Houston, Texas, USA.
Brian P HobbsDepartment of Population Health, The University of Texas at Austin, Austin, Texas, USA.
Steven H LinDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Wenhua CaoDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
Project 3: Enhanced Sensitivity of Tumors to Proton Beam Therapy: Mechanisms and Biomarkers.P01CA261669 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI HONG, THEODORE S · 2021 to 2025
$14.0M
NCI NIH HHS P01 CA261669NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

Purpose: Radiation-induced lymphopenia is a common immune toxicity that adversely impacts treatment outcomes. We report here our approach to translate a deep-learning (DL) model developed to predict severe lymphopenia risk among esophageal cancer into a strategy for incorporating the immune system as an organ-at-risk (iOAR) to mitigate the risk. Materials and Methods: We conducted "virtual clinical trials" utilizing retrospective data for 10 intensity-modulated radiation therapy (IMRT) and 10 passively-scattered proton therapy (PSPT) esophageal cancer patients. For each patient, additional treatment plans of the modality other than the original were created employing standard-of-care (SOC) dose constraints. Predicted values of absolute lymphocyte count (ALC) nadir for all plans were estimated using a previously-developed DL model. The model also yielded the relative magnitudes of contributions of iOARs dosimetric factors to ALC nadir, which were used to compute iOARs dose-volume constraints, which were incorporated into optimization criteria to produce "IMRT-enhanced" and "intensity-modulated proton therapy (IMPT)-enhanced" plans. Results: Model-predicted ALC nadir for the original IMRT (IMRT-SOC) and PSPT plans agreed well with actual values. IMPT-SOC showed greater immune sparing vs IMRT and PSPT. The average mean body doses were 13.10 Gy vs 7.62 Gy for IMRT-SOC vs IMPT-SOC for patients treated with IMRT-SOC; and 8.08 Gy vs 6.68 Gy for PSPT vs IMPT-SOC for patients treated with PSPT. For IMRT patients, the average predicted ALC nadir of IMRT-SOC, IMRT-enhanced, IMPT-SOC, and IMPT-enhanced was 281, 327, 351, and 392 cells/µL, respectively. For PSPT patients, the average predicted ALC nadir of PSPT, IMPT-SOC, and IMPT-enhanced was 258, 316, and 350 cells/µL, respectively. Enhanced plans achieved higher predicted ALC nadir, with an average improvement of 40.8 cells/µL (20.6%). Conclusion: The proposed DL model-guided strategy to incorporate the immune system as iOAR in IMRT and IMPT optimization has the potential for radiation-induced lymphopenia mitigation. A prospective clinical trial is planned.

Indexed as

Deep learningEsophageal cancerProton therapyRadiation-induced lymphopenia

Identifiers

PMID39228692
PMCPMC11369390

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.