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

doi:10.21037/jtd-22-77

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

J Thorac Dis. 2022 Feb;14(2):443-454. doi: 10.21037/jtd-22-77.

Authors

Guangzheng Li^#¹, Renjun Huang^#¹, Mo Zhu¹, Mingzhan Du², Jingfen Zhu¹, Zongqiong Sun³, Kaili Liu⁴, Yonggang Li¹

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
² Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, China.
³ Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China.
⁴ Department of Oncology, Suzhou Municipal Hospital, Suzhou, China.

^# Contributed equally.

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.

Keywords: Pulmonary neoplasms; diffusion-weighted imaging (DWI); magnetic resonance imaging (MRI).