Naranjo Isaac Daimiel, Reymbaut Alexis, Brynolfsson Patrik, Lo Gullo Roberto, Bryskhe Karin, Topgaard Daniel, Giri Dilip D, Reiner Jeffrey S, Thakur Sunitha B, Pinker-Domenig Katja
Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th Street, New York, NY 10065, USA.
Department of Radiology, Breast Imaging Service, Guy's and St. Thomas' NHS Trust, Great Maze Pond, London SE1 9RT, UK.
Cancers (Basel). 2021 Mar 31;13(7):1606. doi: 10.3390/cancers13071606.
Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of "size" (1.43 ± 0.54 × 10 mm/s) and higher mean values of "shape" (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of "size" (2.33 ± 0.22 × 10 mm/s) and lower mean values of "shape" (0.27 ± 0.11) corresponding to bin3 ( < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS ( = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.
扩散加权成像（Diffusion-weighted imaging）是一种通过表观扩散系数对乳腺肿瘤进行特征描述的非侵入性功能成像方式。然而，迄今为止，它尚无法直观地反映组织微观结构。在这项经机构审查委员会（IRB）批准的前瞻性研究中，我们对16例疑似乳腺癌患者应用了新型多维扩散（MDD）编码，以评估其在临床环境中进行组织特征描述的潜力。通过定制的MDD序列获取的数据使用一种估计非参数扩散张量分布的算法进行处理。这些分布的统计描述符使我们能够根据反映细胞密度、形状和方向的指标来量化组织组成。此外，来自特定细胞类型的信号分数，如细长细胞（bin1）、各向同性细胞（bin2）和自由水（bin3），也被区分开来。对癌症组织和健康乳腺组织的直方图分析表明，癌症组织中对应于bin1的“大小”平均值较低（1.43±0.54×10 mm/s），“形状”平均值较高（0.47±0.15），而纤维腺性乳腺组织（FGT）中对应于bin3的“大小”平均值较高（2.33±0.22×10 mm/s），“形状”平均值较低（0.27±0.11）（P<0.001）。与原位导管癌（DCIS）和伴有DCIS的浸润性癌相比，浸润性癌在bin1（0.64±0.13对0.4±0.25）和bin3（0.18±0.08对0.42±0.21）的平均信号分数上存在显著差异（P = 0.03）。MDD能够对乳腺癌和健康腺体的组成进行定性和定量评估。
