Evidence map›Paper›PMID 35280462›Full record

ArticleJournal of thoracic disease2022

Native T1-mapping and diffusion-weighted imaging (DWI) can be used to identify lung cancer pathological types and their correlation with Ki-67 expression.

Guangzheng Li, Renjun Huang, Mo Zhu, Mingzhan Du, Jingfen Zhu, Zongqiong Sun, Kaili Liu, Yonggang Li

Abstract read
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Article in Journal of thoracic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

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17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  13. 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
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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Guangzheng Li *Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Renjun Huang *Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Mo ZhuDepartment of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Mingzhan DuDepartment of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Jingfen ZhuDepartment of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Zongqiong SunDepartment of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China.
Kaili LiuDepartment of Oncology, Suzhou Municipal Hospital, Suzhou, China.
Yonggang LiDepartment of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aimed to explore the value of native T1-mapping and diffusion-weighted imaging (DWI) in differentiating the pathological types and degree of tumor differentiation of lung cancer and their correlation with Ki-67 protein expression. Methods: A total of 78 consecutive lung cancer patients who received chest magnetic resonance imaging (MRI) scans between May 2020 and June 2021 were enrolled in this study. Two radiologists independently analyzed the apparent diffusion coefficient (ADC) and T1 values for each lesion. The intraclass correlation coefficient (ICC) and Bland-Altman plots were generated to assess interobserver agreement of the T1 and ADC mean values in lesions. The difference in ADC and T1 values among different pathological types, as well as between high- and low-differentiated lung cancers were analyzed, and diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve analysis. The correlation between ADC value, T1 value, and Ki-67 protein expression index was determined. Results: The ADC and T1 values showed excellent interobserver agreement (ICC 0.820, 0.942, respectively). There was a significant difference in ADC values between small cell carcinoma and squamous carcinoma (P<0.05), and between small cell carcinoma and adenocarcinoma (P<0.05), but not between squamous carcinoma and adenocarcinoma (P>0.05). A significant difference in T1 values was observed between small cell carcinoma (P<0.05) and adenocarcinoma, and between squamous carcinoma (P<0.05) and adenocarcinoma, but not between squamous carcinoma and small cell carcinoma (P>0.05). There were statistically significant differences in ADC and T1 values between the moderately and highly differentiated group and the poorly differentiated group (P<0.05). ROC curve analysis showed that the T1 combined with ADC value had high diagnostic value for the degree of differentiation of the tumor [area under the curve (AUC) =0.912]. Pearson correlation analysis showed a significant positive correlation between T1 value and Ki-67 index (r=0.66, P<0.001) and a significant negative correlation between ADC value and Ki-67 index (r=-0.45, P<0.01). Conclusions: T1 and ADC values can be used to distinguish the pathological type and differentiation degree of lung cancer.

Indexed as

diffusion-weighted imaging (DWI)magnetic resonance imaging (MRI)Pulmonary neoplasms

Identifiers

PMID35280462
PMCPMC8902102

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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.