Whole-Lesion DCE-MRI Intensity Histogram Analysis for Diagnosis in Patients with Suspected Lung Cancer

Wei Wu; Shuchang Zhou; Daniel S Hippe; Haining Liu; Yujin Wang; Nina A Mayr; William T C Yuh; Liming Xia; Stephen R Bowen

doi:10.1016/j.acra.2020.01.025

Whole-Lesion DCE-MRI Intensity Histogram Analysis for Diagnosis in Patients with Suspected Lung Cancer

Acad Radiol. 2021 Feb;28(2):e27-e34. doi: 10.1016/j.acra.2020.01.025. Epub 2020 Feb 24.

Authors

Wei Wu¹, Shuchang Zhou², Daniel S Hippe³, Haining Liu³, Yujin Wang², Nina A Mayr⁴, William T C Yuh³, Liming Xia², Stephen R Bowen⁵

Affiliations

¹ Tongji Hospital, Tongji Medical College Affiliated to Huazhong University of Science and Technology, Department of Radiology, Wuhan 430000, Hubei, China; University of Washington School of Medicine, Department of Radiology, Seattle, Washington.
² Tongji Hospital, Tongji Medical College Affiliated to Huazhong University of Science and Technology, Department of Radiology, Wuhan 430000, Hubei, China.
³ University of Washington School of Medicine, Department of Radiology, Seattle, Washington.
⁴ University of Washington School of Medicine, Department of Radiation Oncology, 1959 NE Pacific St, Box 356043, Seattle, Washington.
⁵ University of Washington School of Medicine, Department of Radiology, Seattle, Washington; University of Washington School of Medicine, Department of Radiation Oncology, 1959 NE Pacific St, Box 356043, Seattle, Washington. Electronic address: srbowen@uw.edu.

PMID: 32102748
DOI: 10.1016/j.acra.2020.01.025

Abstract

Rationale and objectives: To explore the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intensity histogram metrics, relative to time intensity curve (TIC)-derived metrics, in patients with suspected lung cancer.

Materials and methods: This retrospective study enrolled 49 patients with suspected lung cancer on routine CT imaging who underwent DCE-MRI scans and had final histopathologic diagnosis. Three TIC-derived metrics (maximum enhancement ratio, peak time [T_max] and slope) and eight intensity histogram metrics (volume, integral, maximum, minimum, median, coefficient of variation [CoV], skewness, and kurtosis) were extracted from DCE-MRI images. TIC-derived and intensity histogram metrics were compared between benignity versus malignancy using the Wilcoxon rank-sum test. Associations between imaging metrics and malignancy risk were assessed by univariate and multivariate logistic regression odds ratios (ORs).

Results: There were 33 malignant lesions and 16 benign lesions based on histopathology. Lower CoV (OR = 0.2 per 1-SD increase, p = 0.0006), lower T_max (OR = 0.4 per 1-SD increase, p = 0.005), and steeper slope (OR = 2.4 per 1-SD increase, p = 0.010) were significantly associated with increased risk of malignancy. Under multivariate analysis, CoV was significantly independently associated with malignancy likelihood after accounting for either T_max (OR = 0.3 per 1-SD increase, p = 0.007) or slope (OR = 0.3 per 1-SD increase, p = 0.011).

Conclusion: This initial study found that DCE-MRI CoV was independently associated with malignancy in patients with suspected lung cancer. CoV has the potential to help diagnose indeterminate pulmonary lesions and may complement TIC-derived DCE-MRI metrics. Further studies are warranted to validate the diagnostic value of DCE-MRI intensity histogram analysis.

Keywords: DCE MRI; Heterogeneity; Lung cancer; Quantitative metric; Voxel Histogram.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Contrast Media
Diagnosis, Differential
Humans
Lung Neoplasms*
Magnetic Resonance Imaging*
Retrospective Studies
Statistics, Nonparametric

Substances

Contrast Media