Corino Valentina D A, Bologna Marco, Calareso Giuseppina, Resteghini Carlo, Sdao Silvana, Orlandi Ester, Licitra Lisa, Mainardi Luca, Bossi Paolo
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.
Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy.
J Imaging. 2022 Feb 15;8(2):46. doi: 10.3390/jimaging8020046.
Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST.
Fifty patients were included in the study. For each image (at baseline and after the first IC cycle), 536 radiomic features were extracted as follows: semi-supervised principal component analysis components, explaining 97% of the variance, were used together with a support vector machine (SVM) to develop a radiomic signature. One signature was developed for each sequence (T1-, T2-weighted and ADC). A multiagent decision-making algorithm was used to merge multiple signatures into one score.
The area under the curve (AUC) for mono-modality signatures was 0.79 (CI: 0.65-0.88), 0.76 (CI: 0.62-0.87) and 0.93 (CI: 0.75-1) using T1-, T2-weighted and ADC images, respectively. The fuse signature improved the AUC when an ADC-based signature was added. Radiological prediction using RECIST criteria reached an accuracy of 0.78.
These results suggest the importance of early delta radiomics and of ADC maps to predict the response to IC in sinonasal cancers.
通过从T1加权和T2加权图像以及表观扩散系数（ADC）图中获得的早期差异放射组学，已对鼻窦癌患者的诱导化疗（IC）反应进行了预测，并将结果与通过RECIST进行的早期放射学评估进行了比较。
本研究纳入了50名患者。对于每张图像（基线时和第一个IC周期后），提取了536个放射组学特征，具体如下：使用解释97%方差的半监督主成分分析成分与支持向量机（SVM）一起开发放射组学特征。针对每个序列（T1加权、T2加权和ADC）开发了一个特征。使用多智能体决策算法将多个特征合并为一个分数。
使用T1加权、T2加权和ADC图像时，单模态特征的曲线下面积（AUC）分别为0.79（CI：0.65-0.88）、0.76（CI：0.62-0.87）和0.93（CI：0.75-1）。当添加基于ADC的特征时，融合特征提高了AUC。使用RECIST标准进行的放射学预测准确率达到0.78。
这些结果表明早期差异放射组学和ADC图对预测鼻窦癌对IC的反应具有重要意义。
