Imaging-Based Prediction of Ki-67 Expression in Hepatocellular Carcinoma: A Retrospective Study

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2025-02-18 DOI:10.1002/cam4.70562

Chiyu Cai, Liancai Wang, Lianyuan Tao, Hengli Zhu, Yongnian Ren, Deyu Li, Dongxiao Li

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Abstract

Aim

This study aims to develop a non-invasive, preoperative predictive model for Ki-67 expression in HCC patients using enhanced computed tomography (CT) and clinical indicators to improve patient outcomes.

Methods

This retrospective study analyzed 595 post-curative hepatectomy HCC patients. Patients were categorized into high (> 20%) and low (≤ 20%) Ki-67 expression groups based on cellular proliferation levels. Radiomic features were extracted from enhanced CT scans and combined with clinical parameters to develop a predictive model for Ki-67 expression.

Results

Key clinical factors impacting Ki-67 expression in HCC included alpha-fetoprotein (AFP), non-smooth tumor margin, ill-defined pseudo-capsule, and peritumoral star node. From 1441 initially extracted radiomic features, 16 key features were selected using Lasso regression. These features were used to develop a radiomics model, which, when combined with clinical data, yielded an integrated predictive model with high accuracy. The combined model achieved an area under the curve (AUC) of 0.854 in the training group and 0.839 in the validation group. A nomogram based on this model was constructed, and its predictive accuracy was validated through calibration curves and decision curve analysis. A risk scorecard model was also constructed as a practical tool for clinicians to assess the risk level of high Ki-67 expression, facilitating personalized treatment planning. Survival analysis demonstrated significant differences in 3-year overall survival (OS) and progression-free survival (PFS) rates between patients with high and low Ki-67 expression, indicating the model's strong prognostic capability.

Conclusions

This study successfully developed a comprehensive model that integrates radiomic and clinical data for the preoperative prediction of Ki-67 expression in HCC patients.

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目的本研究旨在利用增强型计算机断层扫描（CT）和临床指标，开发一种无创、术前预测 HCC 患者 Ki-67 表达的模型，以改善患者预后。方法这项回顾性研究分析了 595 例治愈性肝切除术后的 HCC 患者。根据细胞增殖水平将患者分为高（> 20%）和低（≤ 20%）Ki-67表达组。从增强 CT 扫描图像中提取放射学特征，并结合临床参数建立 Ki-67 表达预测模型。结果影响HCC中Ki-67表达的主要临床因素包括甲胎蛋白（AFP）、肿瘤边缘不平滑、假包膜不清晰和瘤周星状结节。利用 Lasso 回归法从 1441 个初步提取的放射学特征中选出了 16 个关键特征。这些特征被用于建立放射组学模型，该模型与临床数据相结合，产生了一个具有高准确度的综合预测模型。在训练组和验证组中，综合模型的曲线下面积（AUC）分别为 0.854 和 0.839。根据该模型构建了一个提名图，并通过校准曲线和决策曲线分析验证了其预测准确性。此外，还构建了一个风险记分卡模型，作为临床医生评估 Ki-67 高表达风险水平的实用工具，为个性化治疗方案的制定提供了便利。生存分析表明，Ki-67 高表达和低表达患者的 3 年总生存率（OS）和无进展生存率（PFS）存在显著差异，表明该模型具有很强的预后能力。结论本研究成功建立了一个综合模型，该模型整合了放射学和临床数据，用于术前预测 HCC 患者的 Ki-67 表达。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.