Kolli Mahesh, George Agnes, Aoutla Sridevi, Chandrasekar Santosh Kishor, Girivasan Shyam Nikethen, Kolli Ravi Teja
Family Medicine, Apollo Hospitals, Chennai, IND.
Medicine, Apollo Medicals Private Limited, Chennai, IND.
Cureus. 2024 Nov 11;16(11):e73469. doi: 10.7759/cureus.73469. eCollection 2024 Nov.
Neoadjuvant chemotherapy (NAC) is increasingly used for high-risk breast cancer to achieve pathologic complete response (pCR), an indicator of event-free survival and favorable survival outcomes. Integrating MRI and Ki-67 biomarker analysis into predictive models offers a promising approach to optimize NAC response assessment and guide personalized treatment strategies. This study evaluates the validity of combined MRI and Ki-67 metrics for predicting pCR. We conducted a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, including studies on NAC-treated breast cancer patients assessed by MRI and Ki-67. The predictive models were evaluated based on key parameters, including MRI-based tumor size reduction and Ki-67 levels, with outcomes measured by area under the receiver operating characteristic curve (AUC) and calibration metrics. Findings across ten studies consistently show that high Ki-67 levels and significant tumor size reduction on MRI are predictive of pCR, achieving AUCs near 0.90. The analysis highlighted that models integrating MRI with Ki-67 metrics outperformed single-modality approaches, showing enhanced predictive accuracy and calibration. However, high heterogeneity (I² = 77%) was noted, suggesting variability in imaging and Ki-67 assessment protocols across studies. This study underscores the combined utility of MRI and Ki-67 for the non-invasive prediction of pCR, offering both structural and biological insights into tumor responsiveness. The results align with prior research, affirming the role of Radiomic-clinicopathological models in providing a more comprehensive assessment compared to individual markers. Further refinement of imaging and biomarker protocols could improve model reproducibility and applicability. Our findings highlight the robust predictive accuracy of MRI-Ki-67 integrated models for assessing pCR, marking a significant step toward personalized cancer care. Future studies should focus on refining these models with additional biomarkers and standardized protocols, facilitating their integration into routine clinical oncology to enhance treatment decision-making and patient outcomes.
新辅助化疗（NAC）越来越多地用于高危乳腺癌，以实现病理完全缓解（pCR），这是无事件生存和良好生存结果的一个指标。将MRI和Ki-67生物标志物分析整合到预测模型中，为优化NAC反应评估和指导个性化治疗策略提供了一种有前景的方法。本研究评估了联合MRI和Ki-67指标预测pCR的有效性。我们按照系统评价和Meta分析的首选报告项目（PRISMA）指南进行了一项系统评价和Meta分析，纳入了通过MRI和Ki-67评估的接受NAC治疗的乳腺癌患者的研究。基于关键参数评估预测模型，包括基于MRI的肿瘤大小缩小和Ki-67水平，结果通过受试者操作特征曲线（AUC）下面积和校准指标来衡量。十项研究的结果一致表明，高Ki-67水平和MRI上显著的肿瘤大小缩小可预测pCR，AUC接近0.90。分析强调，将MRI与Ki-67指标整合的模型优于单模态方法，显示出更高的预测准确性和校准性。然而，观察到高度异质性（I² = 77%），表明各研究在成像和Ki-67评估方案方面存在差异。本研究强调了MRI和Ki-67在pCR无创预测中的联合效用，为肿瘤反应性提供了结构和生物学方面的见解。结果与先前的研究一致，肯定了影像组学-临床病理模型在提供比单个标志物更全面评估方面的作用。进一步完善成像和生物标志物方案可提高模型的可重复性和适用性。我们的研究结果突出了MRI-Ki-67整合模型在评估pCR方面强大的预测准确性，标志着在个性化癌症治疗方面迈出了重要一步。未来的研究应专注于用额外的生物标志物和标准化方案完善这些模型，促进其整合到常规临床肿瘤学中，以改善治疗决策和患者预后。
