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

Abstract

Background 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). Methods 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. Results 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)). Conclusions 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.

Predicting pathological complete response of neoadjuvant radiotherapy and targeted therapy for soft tissue sarcoma by whole-tumor texture analysis of multisequence MRI imaging

“…Texture analysis (TA) can extract and calculate the grayscale changes in pixels or voxels from medical images and analyze quantitative image features to reflect the deep heterogeneity of tumor tissue [23]. It has shown certain value in the pathological grading and prognosis prediction of STS and differentiation of benign and malignant soft tissue masses [24][25][26][27][28]. From the perspective of treatment efficacy, some recent studies have shown good predictive performance [17,29] in predicting the efficacy of neoadjuvant RT and/or chemotherapy in STS through the combination of radiomics features at multiple time points (delta radiomics).…”

Predicting risk of metastases and recurrence in soft-tissue sarcomas via Radiomics and Formal Methods

“…In terms of texture, T2FS and STIR images are considered to be similar, and therefore, they were grouped together under one category. 7 , 21 …”

Radiomics in Oncological PET Imaging: A Systematic Review—Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

“…A total of 12 studies focused 18F-FDG PET radiomics on sarcomas [ 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 ], including 6 on osteosarcoma. On average, 72.4 patients were included (range 35–197), the higher numbers corresponding to studies including sarcomas regardless of their subtypes.…”

“…Few studies evaluated muscolo-skeletal [ 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 ] and skin tumors [ 160 , 161 ]. These are usually aggressive neoplasias, for which new prognostic models derived by PET radiomics and AI analysis could help clinicians and patients to improve survival outcomes.…”