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MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer
Cancer Research ( IF 11.2 ) Pub Date : 2022-08-01 , DOI: 10.1158/0008-5472.can-22-1329
Chengyue Wu 1 , Angela M Jarrett 1, 2 , Zijian Zhou 3 , Nabil Elshafeey 4 , Beatriz E Adrada 5 , Rosalind P Candelaria 5 , Rania M M Mohamed 5 , Medine Boge 5 , Lei Huo 6 , Jason B White 7 , Debu Tripathy 7 , Vicente Valero 7 , Jennifer K Litton 7 , Clinton Yam 7 , Jong Bum Son 3 , Jingfei Ma 3 , Gaiane M Rauch 4, 5 , Thomas E Yankeelov 1, 2, 3, 8, 9, 10
Affiliation  

Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative MRI data with biologically based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 patients with TNBC enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: (i) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and (ii) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P < 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response. Significance: Integrating MRI data with biologically based mathematical modeling successfully predicts breast cancer response to chemotherapy, suggesting digital twins could facilitate a paradigm shift from simply assessing response to predicting and optimizing therapeutic efficacy.

中文翻译:

基于 MRI 的数字模型预测三阴性乳腺癌患者对新辅助化疗的具体治疗反应

三阴性乳腺癌(TNBC)对治疗持续难治,需要改善这种疾病治疗反应的靶向和评估方法。在这里,我们将定量 MRI 数据与基于生物学的数学模型相结合,以准确预测个体 TNBC 对新辅助全身治疗 (NAST) 的反应。具体来说,参加 ARTEMIS 试验 (NCT02276443) 的 56 名 TNBC 患者接受标准护理阿霉素/环磷酰胺 (A/C),然后接受紫杉醇进行 NAST,其中在治疗前采集动态对比增强 MRI 和扩散加权 MRI经过两个和四个空调循环后。建立了一个基于生物学的模型来表征肿瘤细胞运动、增殖和治疗诱导的细胞死亡。使用以下方法研究了两个评估框架:(i) 在两个 A/C 周期之前和之后采集的图像,用于校准和预测 A/C 后的肿瘤状态,以及 (ii) 在两个 A/C 周期之前、之后和四个周期之后采集的图像,用于校准和预测响应遵循 NAST。对于框架 1,预测和测量的患者特异性、A/C 后肿瘤细胞结构和体积变化之间的一致性相关系数分别为 0.95 和 0.94。对于框架 2,基于生物学的模型在区分病理完全缓解 (pCR) 和非 pCR 方面实现了 0.89 的受试者操作特征曲线下面积(敏感性/特异性 = 0.72/0.95),具有统计学优势(P < 0.05) )到四个 A/C 周期后测量的肿瘤体积所达到的值 0.78(敏感性/特异性 = 0.72/0.79)。全面的,该模型成功捕获了 TNBC 对 NAST 反应的患者特异性时空动态,提供了 NAST 反应的高度准确的预测。意义:将 MRI 数据与基于生物学的数学模型相结合,可以成功预测乳腺癌对化疗的反应,这表明数字孪生可以促进从简单评估反应到预测和优化治疗效果的范式转变。
更新日期:2022-08-01
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