Djuričić Goran J, Radulovic Marko, Sopta Jelena P, Nikitović Marina, Milošević Nebojša T
Department of Diagnostic Imaging, University Children's Hospital, University of Belgrade, Belgrade, Serbia.
Institute of Oncology and Radiology of Serbia, Belgrade, Serbia.
Front Oncol. 2017 Oct 19;7:246. doi: 10.3389/fonc.2017.00246. eCollection 2017.
The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.
在诊断时预测诱导化疗反应，通过实现个性化治疗方案的定制，可能会改善骨肉瘤的治疗结果。因此，本研究的目的是探讨骨肉瘤磁共振（MR）图像目前尚未开发的计算分析的预测潜力。在对骨肉瘤诱导化疗前位于股骨远端的原发性骨肉瘤MR图像进行回顾性分析时，采用了分形和灰度共生矩阵（GLCM）算法。然后通过受试者操作特征（ROC）分析和准确性计算，比较预测的和实际的化疗反应结果。Dbin、Λ和SCN是与化疗结果显著相关的标准分形和GLCM特征，但仅在其中一个分析平面上如此。我们新开发的归一化分形维数，称为空间填充率（SFR），具有独立且更好的预测价值，在三个成像平面中的两个平面上实现了预测显著性，准确率为82%，ROC曲线下面积为0.20（95%置信区间0 - 0.41）。总之，作为新设计的分形系数，SFR与标准图像分析特征相比，具有更优的预测性能，这可能是通过补偿MR图像中的肿瘤大小变化来实现 的。
