PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma

• Published in: Cancer imaging 2022-01, Vol.22 (1), p.7-7, Article 7
• Main Authors: Zhao, Wenzhe, Huang, Xin, Wang, Geliang, Guo, Jianxin
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Cancer therapies; Care and treatment; Classification; Clinical outcomes; Confidence intervals; Datasets; Decomposition; Discrete Wavelet Transform; Feature extraction; Humans; Image classification; Magnetic Resonance Imaging; Mathematical analysis; Medical prognosis; Metastasis; Patient outcomes; Patients; Performance evaluation; Performance prediction; PET/MR fusion; Positron emission; Positron-Emission Tomography; Radiomics; Redundancy; Retrospective Studies; Sarcoma; Sarcoma - diagnostic imaging; Signatures; Soft Tissue Neoplasms; Soft-tissue sarcoma (STS); Software; Statistical analysis; Texture analysis; Tomography; Wavelet transforms
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-021-00438-y

Various fusion strategies (feature-level fusion, matrix-level fusion, and image-level fusion) were used to fuse PET and MR images, which might lead to different feature values and classification performance. The purpose of this study was to measure the classification capability of features extracted using various PET/MR fusion methods in a dataset of soft-tissue sarcoma (STS). The retrospective dataset included 51 patients with histologically proven STS. All patients had pre-treatment PET and MR images. The image-level fusion was conducted using discrete wavelet transformation (DWT). During the DWT process, the MR weight was set as 0.1, 0.2, 0.3, 0.4, …, 0.9. And the corresponding PET weight was set as 1- (MR weight). The fused PET/MR images was generated using the inverse DWT. The matrix-level fusion was conducted by fusing the feature calculation matrix during the feature extracting process. The feature-level fusion was conducted by concatenating and averaging the features. We measured the predictive performance of features using univariate analysis and multivariable analysis. The univariate analysis included the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. The multivariable analysis was used to develop the signatures by jointing the maximum relevance minimum redundancy method and multivariable logistic regression. The area under the ROC curve (AUC) value was calculated to evaluate the classification performance. By using the univariate analysis, the features extracted using image-level fusion method showed the optimal classification performance. For the multivariable analysis, the signatures developed using the image-level fusion-based features showed the best performance. For the T1/PET image-level fusion, the signature developed using the MR weight of 0.1 showed the optimal performance (0.9524(95% confidence interval (CI), 0.8413-0.9999)). For the T2/PET image-level fusion, the signature developed using the MR weight of 0.3 showed the optimal performance (0.9048(95%CI, 0.7356-0.9999)). For the fusion of PET/MR images in patients with STS, the signatures developed using the image-level fusion-based features showed the optimal classification performance than the signatures developed using the feature-level fusion and matrix-level fusion-based features, as well as the single modality features. The image-level fusion method was more recommended to fuse PET/MR images in future radiomics studies.