Value of IVIM in Differential Diagnoses between Benign and Malignant Solitary Lung Nodules and Masses: A Meta-analysis

Abstract

Purpose: This study aims to evaluate the accuracy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in distinguishing malignant and benign solitary pulmonary nodules and masses.

Methods: Studies investigating the diagnostic accuracy of IVIM-DWI in lung lesions published through December 2020 were searched. The standardized mean differences (SMDs) of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The sensitivity, specificity, area under the curve (AUC), publication bias, and heterogeneity were then summarized, and the source of heterogeneity and the reliability of combined results were explored by meta-regression and sensitivity analysis.

Results: A total of 16 studies including 714 malignant and 355 benign lesions were included. Significantly lower ADC, D, and f values were found in malignant pulmonary lesions compared to those in benign lesions. The D value showed the best diagnostic performance (sensitivity = 0.90, specificity = 0.71, AUC = 0.91), followed by ADC (sensitivity = 0.84, specificity = 0.75, AUC = 0.88), f (sensitivity = 0.70, specificity = 0.62, AUC = 0.71), and D ^* (sensitivity = 0.67, specificity = 0.61, AUC = 0.67). There was an inconspicuous publication bias in ADC, D, D* and f values, moderate heterogeneity in ADC, and high heterogeneity in D, D*, and f values. Subgroup analysis suggested that both ADC and D values had a significant higher sensitivity in "nodules or masses" than that in "nodules."

Conclusions: The parameters derived from IVIM-DWI, especially the D value, could further improve the differential diagnosis between malignant and benign solitary pulmonary nodules and masses.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier: CRD42021226664.

Keywords: IVIM-DWI; differential diagnosis; lung nodules; magnetic resonance imaging; meta-analysis.

Figure 1

Flowchart detailing the study selection process. Sixteen studies that met the inclusion criteria were finally included. FN, false negative; FP, false positive; TN, true negative; TP, true positive.

Figure 2

(A) Methodological quality summary. (B) Methodological quality graph.

Figure 3

Funnel plots. (A) Apparent diffusion coefficient (ADC value); (B) tissue diffusivity (D value); (C) pseudo-diffusivity (D* value); and (D) perfusion fraction (f value).

Figure 4

Forest plot of the mean value of the apparent diffusion coefficient (ADC) between malignant and benign pulmonary nodules and masses.

Figure 5

Forest plot of the mean value of the tissue diffusivity (D value) between malignant and benign pulmonary nodules and masses.

Figure 6

Forest plot of the mean value of the pseudo diffusivity (D* value) between malignant and benign pulmonary nodules and masses.

Figure 7

Forest plot of the mean value of the perfusion fraction (f value) between malignant and benign pulmonary nodules and masses.

Figure 8

Deeks’ funnel plots regarding the diagnostic performance for the (A) apparent diffusion coefficient (ADC value); (B) tissue diffusivity (D value); (C) pseudo-diffusivity (D^* value); and (D) perfusion fraction (f value).

Figure 9

Summary receiver operating characteristic (SROC) curve of the (A) apparent diffusion coefficient (ADC value); (B) tissue diffusivity (D value); (C) pseudo-diffusivity (D* value); and (D) perfusion fraction (f value).

Figure 10

Fagan’s nomogram of the (A) apparent diffusion coefficient (ADC value); (B) tissue diffusivity (D value); (C) pseudo-diffusivity (D* value); and (D) perfusion fraction (f value).