A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types

Abstract

Objective: To evaluate the application value of monoexponential, fractional order calculus (FROC) diffusion models and PET imaging to distinguish between benign and malignant solitary pulmonary lesions (SPLs) and malignant SPLs with different pathological types and explore the correlation between each parameter and Ki67 expression.

Methods: A total of 112 patients were enrolled in this study. Prior to treatment, all patients underwent a dedicated thoracic ¹⁸F-FDG PET/MR examination. Five parameters [including apparent diffusion coefficient (ADC) derived from the monoexponential model; diffusion coefficient (D), a microstructural quantity (μ), and fractional order parameter (β) derived from the FROC model and maximum standardized uptake value (SUVmax) derived from PET] were compared between benign and malignant SPLs and different pathological types of malignant SPLs. Independent sample t test, Mann-Whitney U test, DeLong test and receiver operating characteristic (ROC) curve analysis were used for statistical evaluation. Pearson correlation analysis was used to calculate the correlations between Ki-67 and ADC, D, μ, β, and SUVmax.

Results: The ADC and D values were significantly higher and the μ and SUVmax values were significantly lower in the benign group [1.57 (1.37, 2.05) μm²/ms, 1.59 (1.52, 1.72) μm²/ms, 5.06 (3.76, 5.66) μm, 5.15 ± 2.60] than in the malignant group [1.32 (1.03, 1.51) μm²/ms, 1.43 (1.29, 1.52) μm²/ms, 7.06 (5.87, 9.45) μm, 9.85 ± 4.95]. The ADC, D and β values were significantly lower and the μ and SUVmax values were significantly higher in the squamous cell carcinoma (SCC) group [1.29 (0.66, 1.42) μm²/ms, 1.32 (1.02, 1.42) μm²/ms, 0.63 ± 0.10, 9.40 (7.76, 15.38) μm, 11.70 ± 5.98] than in the adenocarcinoma (AC) group [1.40 (1.28, 1.67) μm²/ms, 1.52 (1.44, 1.64) μm²/ms, 0.70 ± 0.10, 5.99 (4.54, 6.87) μm, 8.76 ± 4.18]. ROC curve analysis showed that for a single parameter, μ exhibited the best AUC value in discriminating between benign and malignant SPLs groups and AC and SCC groups (AUC = 0.824 and 0.911, respectively). Importantly, the combination of monoexponential, FROC models and PET imaging can further improve diagnostic performance (AUC = 0.872 and 0.922, respectively). The Pearson correlation analysis showed that Ki67 was positively correlated with μ value and negatively correlated with ADC and D values (r = 0.402, -0.346, -0.450, respectively).

Conclusion: The parameters D and μ derived from the FROC model were superior to ADC and SUVmax in distinguishing benign from malignant SPLs and adenocarcinoma from squamous cell carcinoma, in addition, the combination of multiple parameters can further improve diagnostic performance. The non-Gaussian FROC diffusion model is expected to become a noninvasive quantitative imaging technique for identifying SPLs.

Keywords: PET/MR; differentiation diagnosis; diffusion-weighted imaging; fractional order calculus; lung cancer; solitary pulmonary lesions.

Figure 1

(A–G) A 51-year-old female patient, with hamartoma in the left lung, (A) is T2 weighted anatomic image, (B) is hematoxylin and eosin (H&E) staining image which confirms this lesion to be hamartoma, (C) is μ map with μ= 3.21 μm, (D) is D map with D = 1.73 μm²/ms, (E) is β map with β= 0.78, (F) is ADC map with ADC = 1.99 μm²/ms, (G) is the fusion image of the SUV map and the attenuation correction map with SUVmax = 0.5.

Figure 3

(A–G) A 67-year-old male patient, with squamous cell carcinoma (SCC) in the right lung, (A) is T2 weighted anatomic image, (B) is hematoxylin and eosin (H&E) staining image which confirms this lesion to be SCC, (C) is μ map with μ= 7.44 μm, (D) is D map with D = 1.42 μm²/ms, (E) is β map with β= 0.68, (F) is ADC map with ADC = 1.29 μm²/ms, (G) is the fusion image of the SUV map and the attenuation correction map with SUVmax = 18.75.

Figure 2

(A–G) A 73-year-old female patient, with adenocarcinoma (AC) in the right lung, (A) is T2 weighted anatomic image, (B) is hematoxylin and eosin (H&E) staining image which confirms this lesion to be AC, (C) is μ map with μ= 4.77 μm, (D) is D map with D = 1.69 μm²/ms, (E) is β map with β = 0.83, (F) is ADC map with ADC = 1.37 μm²/ms, (G) is the fusion image of the SUV map and the attenuation correction map with SUVmax = 3.41.

Figure 4

Boxplots of Diameters, ADC, μ, D, β and SUVmax between the benign and malignant SPLs groups, and between AC and SCC groups. ADC, apparent diffusion coefficient; D, diffusion coefficient; SCC, squamous cell carcinoma; SUVmax, maximum value of standard uptake value.

Figure 5

ROC curves of ADC, μ, D, β, SUVmax and different combinations of μ, D, β, ADC and SUVmax to distinguish between benign and malignant groups (A, C), and between AC and SCC groups (B, D). ROC analysis for differentiation of malignant and benign groups (A, C); ROC analysis for differentiation of adenocarcinoma (AC) and squamous cell carcinoma (SCC) groups(B, D).

Figure 6

Correlation between (A) Ki67 and ADC (r = -0.346, P = 0.002), (B) Ki67 and μ (r = 0.402, P < 0.001), and (C) Ki67 and D (r = -0.450, P < 0.001). D, diffusion coefficient.