Evidence map›Paper›PMID 36060592›Full record

ArticleQuantitative imaging in medicine and surgery2022

Intravoxel incoherent motion diffusion-weighted imaging for early assessment of combined anti-angiogenic/chemotherapy for colorectal cancer liver metastases.

Huita Wu, Bangkai Li, Zike Yang, Haonan Ji, Yifang Guo, Jianzhong Lin, Xin Wang

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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

7 authors.

Huita Wu *Department of Oncology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Bangkai Li *Emergency Department (Outpatient Chemotherapy Center), The Third Affiliated Hospital of Kunming Medical University/Yunnan Cancer Hospital, Kunming, China.
Zike Yang *Department of Oncology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Haonan JiDepartment of Oncology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Yifang GuoDepartment of Oncology and Hematology, Xiamen Haicang Hospital, Xiamen, China.
Jianzhong LinDepartment of Magnetic Resonance Imaging, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Xin WangDepartment of Oncology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: To explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the early assessment of colorectal cancer liver metastases (CRLM). Methods: A total of 34 patients with pathologically confirmed unresectable CRLM were enrolled. All participants uniformly received capecitabine and oxaliplatin (CAPOX) plus bevacizumab chemotherapy as standard first-line treatment for advanced colorectal cancer (CRC). Participants underwent 1.5-T conventional magnetic resonance imaging (MRI) and IVIM-DWI sequence scans with 9 b values (0 to 1,000 s/mm Results: According to the RECIST v. 1.1 criteria, the 34 participants were divided into a response group (n=16) and a non-response group (n=18). In the response group, the f value was significantly lowered (P=0.001) and the D value was significantly increased after treatment (P=0.002). In the non-response group, the D value was increased slightly after treatment (P=0.039), and there was no significant difference in the f value and the D* value. In addition, the f value at baseline was significantly greater in the response group than in the non-response group (0.221±0.033 Conclusions: The IVIM-DWI parameters (f values and D values) provided effective assessment of the therapeutic effect of CAPOX combined with bevacizumab in patients with CRLM at an early stage, and the f value of the pre-treatment tumor area was shown to be useful for predicting the treatment response of patients.

Indexed as

bevacizumabColorectal cancer (CRC)intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI)liver metastasisresponse assessment

Identifiers

PMID36060592
PMCPMC9403579

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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.