Evidence map›Paper›PMID 36248958›Full record

ArticleFrontiers in oncology2022

Application of diffusion kurtosis imaging and

Pengyang Feng, Zehua Shao, Bai Dong, Ting Fang, Zhun Huang, Ziqiang Li, Fangfang Fu, Yaping Wu, Wei Wei, Jianmin Yuan and 3 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Correlation ofScientific reports · 2025
    Article
  2. Article
  3. Article
  4. Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Article
  5. Article
  6. Review
  7. 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

13 authors.

Pengyang FengDepartment of Medical Imaging, Henan University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Zehua ShaoHeart Center of Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, China.
Bai DongDepartment of Orthopaedics, Henan University People's Hospital, Zhengzhou, China.
Ting FangDepartment of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Zhun HuangDepartment of Medical Imaging, Henan University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Ziqiang LiDepartment of Medical Imaging, Xinxiang Medical University Henan Provincial People's Hospital, Zhengzhou, China.
Fangfang FuDepartment of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China.
Yaping WuDepartment of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China.
Wei WeiDepartment of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China.
Jianmin YuanCentral Research Institute, United Imaging Healthcare Group, Shanghai, China.
Yang YangBeijing United Imaging Research Institute of Intelligent Imaging, United Imaging Healthcare Group, Beijing, China.
Zhe WangCentral Research Institute, United Imaging Healthcare Group, Shanghai, China.
Meiyun WangDepartment of Medical Imaging, Henan University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung cancer has become one of the deadliest tumors in the world. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for approximately 80%-85% of all lung cancer cases. This study aimed to investigate the value of diffusion kurtosis imaging (DKI), diffusion-weighted imaging (DWI) and 2-[ Methods: Seventy-seven patients with lung lesions were prospectively scanned by hybrid 3.0-T chest Results: The MK and ADC values of the AC group were significantly higher than those of the SCC group (all P< 0.05), and the SUVmax, MTV, and TLG values of the SCC group were significantly higher than those of the AC group (all P<0.05). There was no significant difference in the MD value between the two groups. Moreover, MK, SUVmax, TLG and MTV were independent predictors of the NSCLC subtype, and the combination of these parameters had an optimal diagnostic efficacy (AUC, 0.876; sensitivity, 86.27%; specificity, 80.77%), which was significantly better than that of MK (AUC = 0.758, z = 2.554, P = 0.011), ADC (AUC = 0.679, z = 2.322, P = 0.020), SUVmax (AUC = 0.740, z = 2.584, P = 0.010), MTV (AUC = 0.715, z = 2.530, P = 0.011) or TLG (AUC = 0.716, z = 2.799, P = 0.005). The ROC curve showed that the validation model had high accuracy in identifying AC and SCC (AUC, 0.844; 95% CI, 0.785-0.885);. The SUVmax value was weakly positively correlated with the Ki-67 index (r = 0.340, P< 0.05), the ADC and MD values were weakly negatively correlated with the Ki-67 index (r = -0.256, -0.282, P< 0.05), and the MTV and TLG values were weakly positively correlated with NSCLC stage (r = 0.342, 0.337, P< 0.05). Conclusion: DKI, DWI and

Indexed as

18F-FDG PETdiffusion kurtosis imagingdiffusion-weighted imagingKi-67non-small cell lung cancer

Identifiers

PMID36248958
PMCPMC9562703

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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.