Predicting the Ki-67 proliferation index in cervical cancer: a preliminary comparative study of four non-Gaussian diffusion-weighted imaging models combined with histogram analysis

Abstract

Background: The prognosis for patients with cervical cancer (CC) is strongly correlated with the Ki-67 proliferation index (PI). However, the Ki-67 PI obtained through biopsy has certain limitations. The non-Gaussian distribution diffusion model of magnetic resonance imaging (MRI) may play an important role in characterizing tissue heterogeneity. At present, there are limited data available concerning the prediction of Ki-67 PI using models based on histogram features of non-Gaussian diffusion distribution. This study aimed to determine whether preoperative histogram features from multiple non-Gaussian models of diffusion-weighted imaging can predict the Ki-67 PI in patients with CC.

Methods: Our cross-sectional prospective study recruited a total of 53 patients suspected of having CC who underwent 3.0-T MRI at Sun Yat-sen Memorial Hospital of Sun Yat-sen University between January 2022 and January 2023. Fifteen b values (0-4,000 s/mm²) were used for diffusion-weighted imaging. A total of nine parameters from four non-Gaussian diffusion-weighted imaging models, including continuous-time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM), were used. Whole-tumor volumetric histogram analysis of these parameters was then obtained. In logistic regression, significant histogram characteristics were statistically examined across two groups to build the final prediction model. To assess diagnostic parameters of the proposed model in the diagnosis of the Ki-67 PI, along with the sensitivity, specificity, and diagnostic accuracy of these various parameters from the four models, receiver operating feature analysis was applied.

Results: Among the 53 patients (55.3±9.6 years, ranging from 23 to 79 years) included in the study, 15 had a Ki-67 PI ≤50% and 38 had a Ki-67 PI >50%. Univariable analysis determined that 12 histogram features were statistically different between the two groups. In multivariable logistic regression, we ultimately selected 6 histogram features to construct the final prediction model, with CTRW_α_10^th percentile [odds ratio (OR) =0.955; 95% confidence interval (CI): 0.92-0.99; P=0.019], CTRW_α_robust mean absolute deviation (OR =0.893; 95% CI: 0.81-0.99; P=0.028), and CTRW_α_uniformity (OR =0.000, 95% CI: 0.00-0.90, P=0.047) being the independent predictive variables. The area under the curve of the combined prediction model was 0.845 (95% CI: 0.74-0.95), with a sensitivity of 78.9% (95% CI: 0.63-0.90), a specificity of 86.7% (95% CI: 0.60-0.98), an accuracy of 81.1% (95% CI: 0.68-0.91), a positive predictive value of 93.8% (95% CI: 0.79-0.99), and a negative predictive value of 61.9% (95% CI: 0.38-0.82).

Conclusions: The histogram features of multiple non-Gaussian diffusion-weighted imaging can help to predict the Ki-67 PI of CC, providing a new method for the noninvasive evaluation of critical biological features of CC.

Keywords: Ki-67 proliferation index (Ki-67 PI); cervical cancer (CC); continuous-time random-walk; histogram analysis; non-Gaussian diffusion-weighted imaging.

Figure 1

Workflow for enrollment of patients with CC according to Ki-67 PI value. CC, cervical cancer; MRI, magnetic resonance imaging; PI, proliferation index.

Figure 2

The T2WI, parametric maps, and pathological sections of two patients with cervical cancer. (A-E) Images from a 53-year-old female with cervical squamous cell carcinoma and a Ki-67 proliferation index ≤50%. (F-J) Images from a 65-year-old female with squamous cell carcinoma and a Ki-67 PI >50%. (A,F) Conventional T2-weighted images; the red area in the T2 weighted image represents the lesions contained in the volume of interest. (B-D,G-I) The pseudo-colorized images showing the (B,G) CTRW_α, (C,H) DKI_K, and (D,I) FROC_µ maps. (E,J) S-P-stained sections of cervical cancer (10×). T2WI, T2-weighted imaging; CTRW_α, α value of continuous-time random walk; DKI_K, MK value of diffusion kurtosis imaging; FROC_µ, µ value of fractional order calculus; S-P, streptavidin peroxidase; PI, proliferation index.

Figure 3

The receiver operating characteristic curves of the single mean apparent propagator CTRW_α_10^th percentile, CTRW_α_robust mean absolute deviation, CTRW_α_uniformity, and the combined model. CTRW_α, α value of continuous-time random walk; CI, confidence interval.