Evidence map›Paper›PMID 40460946›Full record

ArticleMagnetic resonance imaging2025

Comprehensive characterization of tumor therapeutic response via simultaneous mapping of cell size, density, and transcytolemmal water exchange.

Diwei Shi, Xiaoxia Wang, Sisi Li, Fan Liu, Xiaoyu Jiang, Li Chen, Jiuquan Zhang, Hua Guo, Junzhong Xu

Abstract read
In one paragraph

Article in Magnetic resonance imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Diwei ShiCenter for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
Xiaoxia WangDepartment of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing 400030, China.
Sisi LiCenter for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
Fan LiuCenter for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
Xiaoyu JiangInstitute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Li ChenCenter for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
Jiuquan ZhangDepartment of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing 400030, China.
Hua GuoCenter for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing 100084, China. Electronic address: huaguo@mail.tsinghua.edu.cn.
Junzhong XuInstitute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA. Electronic address: junzhong.xu@vanderbilt.edu.

Funding

MRI Diffusion in Tumors Using Oscillating GradientsR01CA109106 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI XU, JUNZHONG · 2006 to 2022
$4.0M
MRI of tumor-infiltrating lymphocytes using MRI-cytometryR01CA269620 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI XU, JUNZHONG · 2022 to 2025
$2.3M
in vivo MR characterization of pathological changes in liver microstructuresR01DK135950 · NIDDK · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Xiaoyu Jiang · 2024 to 2026
$1.2M
Differentiation of tumor progression from radiation necrosis using MR cell size imagingR21CA270731 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI XU, JUNZHONG · 2023 to 2024
$440k
NCI NIH HHS R01 CA109106NCI NIH HHS R01 CA269620NCI NIH HHS R21 CA270731NIDDK NIH HHS R01 DK135950
6 · The paper itself

Abstract

The evaluation of tumor response to neoadjuvant chemotherapy is critical for the personalized management of cancer patients, aiming to minimize unnecessary toxicity, costs, and treatment delays. Current imaging techniques primarily depend on detecting tumor volume changes, which reflect downstream effects. In contrast, advanced microstructural diffusion MRI (dMRI) methods offer cellular-level insights but are limited by biased estimates of cell density due to oversimplified biophysical models. We present a novel dMRI-based approach, EXCHANGE, which incorporates transcytolemmal water exchange into a quantitative multi-compartmental biophysical model. This method enables simultaneous mapping of cell size, density, and transcytolemmal water exchange, providing a comprehensive characterization of tumor microstructure. Validation through computer simulations and in vitro studies demonstrated the good accuracy of EXCHANGE-derived metrics. In a proof-of-concept study, EXCHANGE was applied to animal models and patients with triple-negative breast cancer, showcasing its potential to evaluate tumor therapeutic response to neoadjuvant chemotherapy. EXCHANGE offers a unique capability to characterize tumor microstructural properties at the cellular level, paving the way for improved monitoring of treatment response in clinical settings.

Indexed as

Diffusion Magnetic Resonance ImagingTriple Negative Breast NeoplasmsAnimalsCell CountCell Line, TumorCell SizeComputer SimulationFemaleHumansMiceNeoadjuvant TherapyWaterWaterArbitrary gradient waveformDiffusion MRIMicrostructural imagingTime-dependent diffusionTumor responseWater exchange

Identifiers

PMID40460946
PMCPMC12805662

What OpenQuestion holds

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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.