# The value of spectral CT quantitative parameters in predicting Ki-67 level in ovarian cancer

**Authors:** Siwen Pang, Meng Wu, Haijia Yu, Tiantian Ma, Jianhua Liu, Siwen Liu

PMC · DOI: 10.3389/fonc.2025.1576690 · Frontiers in Oncology · 2025-05-14

## TL;DR

This study shows that spectral CT can predict Ki-67 levels in ovarian cancer, helping guide treatment decisions.

## Contribution

The study demonstrates that spectral CT quantitative parameters can predict Ki-67 expression levels in ovarian cancer.

## Key findings

- Spectral CT parameters like A-sIC, A-sZeff, and D-Zeff showed significant differences between high and low Ki-67 groups.
- A combined model of five spectral CT parameters achieved 93.1% sensitivity and 60.9% specificity in predicting Ki-67 levels.
- Spectral CT provides reliable imaging evidence for therapeutic treatment options in ovarian cancer.

## Abstract

The objective of this study is to evaluate the predictive value of quantitative parameters from spectral CT for Ki-67 expression in ovarian cancer (OC).

Spectral CT imaging data from 39 patients with ovarian cancer by pathology, encompassing 52 lesions overall, were collected retrospectively and split into two groups based on immunohistochemical results. Tumor solid components in arterial, venous, and delayed phases can be measured using post-processing software to obtain the quantitative parameters of spectral CT. An independent sample t-test was implemented for evaluating spectral CT parameters between two groups, and a Spearman correlation coefficient was applied among all participants to estimate the relationship between spectral parameters and Ki-67 levels. Moreover, an examination of the receiver operating characteristic (ROC) curve was conducted to assess the diagnostic efficacy of the significantly different parameters between the two groups.

The Ki-67 high-level group includes 22 patients and 29 lesions, while the Ki-67 low-level group contains 17 patients and 23 lesions. The A-sIC, A-sZeff, V-IC, D-Zeff, and D-sZeff values in the Ki-67 high-level group were greater than those in the Ki-67 low-level group (P =0.028, AUC = 0.705; P < 0.001, AUC = 0.742; P = 0.047, AUC = 0.657; P = 0.014, AUC = 0.665; and P = 0.006, AUC = 0.675, respectively). For correlation analysis, A-IC, A-sIC, A-Zeff, A-sZeff, A-λHU, D-IC, D-Zeff, and D-sZeff were positively correlated with Ki-67 levels, with correlation coefficients ranging from 0.277 to 0.417, P<0.05. Through multiple logistic regression, the combined model that included 5 quantitative parameters showed the highest diagnostic performance, with a sensitivity of 93.10%, a specificity of 60.90%, and an AUC value of 0.808.

Spectral CT provides multi-parametric imaging data and is useful in predicting Ki-67 expression in ovarian cancer, delivering comprehensive and reliable imaging evidence for the formulation of therapeutic treatment options.

## Linked entities

- **Proteins:** Mki67 (antigen identified by monoclonal antibody Ki 67)
- **Diseases:** ovarian cancer (MONDO:0005140)

## Full-text entities

- **Diseases:** Tumor (MESH:D009369), ovarian cancer (MESH:D010051)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12116321/full.md

## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12116321/full.md

## References

38 references — full list in the complete paper: https://tomesphere.com/paper/PMC12116321/full.md

---
Source: https://tomesphere.com/paper/PMC12116321