Evidence map›Paper›PMID 30962693›Full record

ArticleOncoTargets and therapy2019

SUVmax and metabolic tumor volume: surrogate image biomarkers of KRAS mutation status in colorectal cancer.

Ying Lv, Xin Wang, Lerong Liang, Lei Wang, Jie Lu

Abstract read
In one paragraph

Article in OncoTargets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Correlation betweenAbdominal radiology (New York) · 2022
    Article
  4. Article
  5. Article
  6. Article
  7. PretreatmentJournal of oncology · 2021
    Article
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

5 authors.

Ying LvDepartment of Gastroenterology, Jinan Central Hospital Affiliated to Shandong University, Jinan 250013, Shandong, People's Republic of China.
Xin WangDepartment of Oncology, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, People's Republic of China.
Lerong LiangDepartment of Oncology, Jinan Central Hospital Affiliated to Shandong University, Jinan 250013, Shandong, People's Republic of China.
Lei WangDepartment of Gastrointestinal Surgery, Jinan Central Hospital Affiliated to Shandong University, Jinan 250013, Shandong, People's Republic of China, wanglei999qq@163.com.
Jie LuDepartment of Neurosurgery, Shandong Province Qianfoshan Hospital of Shandong University, Jinan 250014, Shandong, People's Republic of China, dr_peterluu@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe objective of this study was to explore the association between KRAS mutation status and PET/CT metabolic parameters in colorectal cancer (CRC) patients. MATERIALS AND

methodsOne hundred and sixty-four CRC patients were enrolled in this study and received PET/CT examination before operation, then KRAS mutation status was analyzed through pathologically confirmed CRC samples. The association between tumor clinical characteristics and PET/CT metabolic parameters, including maximum standardized uptake value (SUVmax), SUVmean, and metabolic tumor volume (MTV), and KRAS mutation status was analyzed using chi-squared tests, Mann-Whitney

resultsThe KRAS mutation type patients exhibited high MTV and high SUVmax using a threshold of 17.8 cm

conclusionMTV and SUVmax were associated with KRAS mutation type in CRC patients. PET/CT metabolic parameters can be used for supplementing KRAS mutation status prediction in CRC patients.

Indexed as

18F-FDG PET/CTcolorectal cancerKRAS mutationMTVSUVmaxSUVmean

Identifiers

PMID30962693
PMCPMC6433102

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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.