Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL, 33612, USA.
Department of Computer Science & Information Systems, Bradley University, Peoria, IL, 61625, USA.
Sci Rep. 2021 Feb 15;11(1):3785. doi: 10.1038/s41598-021-83271-4.
Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinical need for a non-invasive method to detect sarcomatoid differentiation pre-operatively. We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced arterial-phase T1-weighted fat-saturated, and contrast-enhanced venous-phase T1-weighted fat-saturated MRI images. The SOM was trained on 8 nsRCC and 8 sRCC tumors, and used to compute Activation Maps for each training, validation (3 nsRCC and 3 sRCC), and test (5 nsRCC and 5 sRCC) tumor. The LVQ classifier was trained and optimized on Activation Maps from the 22 training and validation cohort tumors, and tested on Activation Maps of the 10 unseen test tumors. In this preliminary study, the SOM-LVQ model achieved a hold-out testing accuracy of 70% in the task of identifying sarcomatoid differentiation in RCC on standard multiparameter MRI (mpMRI) images. We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis.
肾细胞癌（RCC）中的肉瘤样分化与预后不良相关，需要比无肉瘤样成分的 RCC（nsRCC）更积极的管理。由于怀疑患有肾细胞癌（RCC）的肿瘤通常不会进行活检以进行组织学评估，因此临床上需要一种非侵入性方法来术前检测肉瘤样分化。我们利用无监督自组织映射（SOM）和监督学习向量量化器（LVQ）机器学习来对 T2 加权、非对比 T1 加权脂肪饱和、对比增强动脉期 T1 加权脂肪饱和和对比增强静脉期 T1 加权脂肪饱和 MRI 图像上的 RCC 肿瘤进行分类。SOM 是在 8 个 nsRCC 和 8 个 sRCC 肿瘤上进行训练的，并用于计算每个训练、验证（3 个 nsRCC 和 3 个 sRCC）和测试（5 个 nsRCC 和 5 个 sRCC）肿瘤的激活图。LVQ 分类器是在 22 个训练和验证队列肿瘤的激活图上进行训练和优化的，并在 10 个未见测试肿瘤的激活图上进行测试。在这项初步研究中，SOM-LVQ 模型在标准多参数 MRI（mpMRI）图像上识别 RCC 肉瘤样分化的任务中，获得了 70%的留一测试准确率。我们已经证明了一种联合 SOM-LVQ 机器学习方法，适用于分析有限的 mpMRI 数据集，以进行鉴别诊断任务。
