Background Time-dependent diffusion MRI has the potential to help characterize tumor cell properties; however, to the knowledge of the authors, its usefulness for breast cancer diagnosis and prognostic evaluation is unknown. Purpose To investigate the clinical value of time-dependent diffusion MRI-based microstructural mapping for noninvasive prediction of molecular subtypes and pathologic complete response (pCR) in participants with breast cancer. Materials and Methods Participants with invasive breast cancer who underwent pretreatment with time-dependent diffusion MRI between February 2021 and May 2023 were prospectively enrolled. Four microstructural parameters were estimated using the IMPULSED method (a form of time-dependent diffusion MRI), along with three apparent diffusion coefficient (ADC) measurements and a relative ADC diffusion-weighted imaging parameter. Multivariable logistic regression analysis was used to identify parameters associated with each molecular subtype and pCR. A predictive model based on associated parameters was constructed, and its performance was assessed using the area under the receiver operating characteristic curve (AUC) and compared by using the DeLong test. The time-dependent diffusion MRI parameters were validated based on correlation with pathologic measurements. Results The analysis included 408 participants with breast cancer (mean age, 51.9 years ± 9.1 [SD]). Of these, 221 participants were administered neoadjuvant chemotherapy and 54 (24.4%) achieved pCR. The time-dependent diffusion MRI parameters showed reasonable performance in helping to identify luminal A (AUC, 0.70), luminal B (AUC, 0.78), and triple-negative breast cancer (AUC, 0.72) subtypes and high performance for human epidermal growth factor receptor 2 (HER2)-enriched breast cancer (AUC, 0.85), outperforming ADC measurements (all P < .05). Progesterone receptor status (odds ratio [OR], 0.08; P = .02), HER2 status (OR, 3.36; P = .009), and the cellularity index (OR, 0.01; P = .02) were independently associated with the odds of achieving pCR. The combined model showed high performance for predicting pCR (AUC, 0.88), outperforming ADC measurements and the clinical-pathologic model (AUC, 0.73 and 0.79, respectively; P < .001). The time-dependent diffusion MRI-estimated parameters correlated well with the pathologic measurements (n = 100; r = 0.67-0.81; P < .001). Conclusion Time-dependent diffusion MRI-based microstructural mapping was an effective method for helping to predict molecular subtypes and pCR to neoadjuvant chemotherapy in participants with breast cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Partridge and Xu in this issue.

中文翻译：

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时间依赖性弥散 MRI 有助于预测乳腺癌患者新辅助化疗的分子亚型和治疗反应。


背景 时间依赖性弥散 MRI 有可能帮助表征肿瘤细胞特性;然而，据作者所知，其对乳腺癌诊断和预后评估的有用性尚不清楚。目的 探讨基于时间依赖性弥散 MRI 的微结构标测对乳腺癌参与者分子亚型和病理完全缓解 （pCR） 无创预测的临床价值。材料和方法 前瞻性纳入 2021 年 2 月至 2023 年 5 月期间接受时间依赖性弥散 MRI 预处理的浸润性乳腺癌参与者。使用 IMPULSED 方法（一种时间依赖性弥散 MRI 的形式）以及三个表观弥散系数 （ADC） 测量值和一个相对 ADC 弥散加权成像参数估计了四个微观结构参数。采用多变量 logistic 回归分析确定与每种分子亚型和 pCR 相关的参数。构建基于相关参数的预测模型，使用受试者工作特征曲线下面积 （AUC） 评估其性能，并使用 DeLong 检验进行比较。根据与病理测量的相关性验证时间依赖性弥散 MRI 参数。结果 分析包括 408 名乳腺癌参与者 （平均年龄 51.9 岁 ± 9.1 [SD]）。其中，221 名参与者接受了新辅助化疗，54 名 （24.4%） 达到 pCR。时间依赖性弥散 MRI 参数在帮助识别管腔 A （AUC， 0.70）、管腔 B （AUC， 0.78） 和三阴性乳腺癌 （AUC， 0.72） 亚型和富含人表皮生长因子受体 2 （HER2） 的乳腺癌的高性能 （AUC， 0.85），优于 ADC 测量 （均 P < .05）。孕激素受体状态 （比值比 [OR]，0.08;P = .02）、HER2 状态 （OR，3.36;P = .009），细胞分布指数 （OR， 0.01;P = .02） 与达到 pCR 的几率独立相关。组合模型在预测 pCR 方面表现出高性能 （AUC， 0.88），优于 ADC 测量和临床病理模型 （AUC， 分别为 0.73 和 0.79;P < .001）.时间依赖性弥散 MRI 估计参数与病理测量值密切相关 （n = 100;r = 0.67-0.81;P < .001）.结论 基于时间依赖性弥散 MRI 的微观结构映射是帮助预测乳腺癌参与者新辅助化疗的分子亚型和 pCR 的有效方法。© RSNA，2024 年本文提供补充材料。另请参见 Partridge 和 Xu 在本期的社论。