Data collection
Ethical approval for this two-center retrospective study was provided by our hospital’s institutional review board, which waived the requirement for informed consent. Pre-therapeutic T1-weighted imaging (T1WI) and fat-suppressed T2-weighted imaging (FS-T2WI) of 180 patients with STS were retrospectively analyzed. All patients’ FNCLCC tumor grades had been pathologically confirmed using postoperative specimens from November 2007 to November 2019. The minimum follow up time is 5 mouths. Detailed descriptions of the inclusion and conclusion criteria are shown in Supplementary Material. The patients’ data were gathered from The Affiliated Hospital of Qingdao University (training set, n = 109) and The Third Hospital of Hebei Medical University (external validation set, n = 71). The final pathologic results of the 180 patients with STS are shown in Table 1.
The patients were subsequently grouped into a low-grade group (n = 93) and high-grade group (n = 87) according to their FNCLCC tumor grade. The patients’ basic data are shown in Table 2. The patients comprised 112 male and 68 female patients with a mean age of 52.4 years (range, 1–93 years). Fig. 1 shows a flow chart of the enrolled patients and radiomics implementation.
MRI acquisition and region-of-interest segmentation
All 180 patients underwent MRI scanning using a GE MRI 1.5T, GE Signa HDx 3.0T (GE Medical Systems, Milwaukee, WI, USA), Siemens Skyra 3.0T, Siemens Magnetom Prisma 3.0T (Siemens Healthcare GmbH, Erlangen, Germany), or Philips Achieva 1.5T (Philips Medical Systems, Best, the Netherlands). The following scanning parameters were used: T1WI (repetition time [TR] / echo time [TE], 420–680 ms / 6.1–20 ms); FS-T2WI (TR / TE, 2640–5000 ms/ 30–102 ms,); section spacing, 1 mm; section thickness, 3–4 mm, matrix, 320 × 320; field of view, 200–400 mm Three-dimensional region of interest (3D-ROI) segmentation of all tumors was conducted manually using ITK-SNAP open-source software (v.3.8.0; http://www.itksnap.org). The ROI was outlined according to the contour of the tumor from each transverse layer on preoperative T1WI and FS-T2WI sequences and automatically turned into a 3D-ROI. The 3D-ROI segmentation covered the entire primary tumor and avoided obvious peritumoral edema. Intraobserver and interobserver intraclass correlation coefficients (ICCs) were calculated to test the intraobserver reproducibility and interobserver reliability for the radiomic feature extraction of 40 random patients. Readers 1 and 2 drew the 3D-ROIs, and the next Reader 1 repeated the segmentation after 1 month. The ROI segmentation depicted by Reader 1 were used for further analysis. Intraobserver and interobserver ICCs of >0.75 were included for the subsequent investigation.
Image preprocessing and radiomics feature extraction
Preprocessing procedures were applied to compensate for inhomogeneous intensity caused by different institutions and to decrease the variability of features. A method to decrease the number of gray levels and thus improve the signal-to-noise ratio of the texture calculations results was applied. The 3D-ROIs were then isotropically resampled to a planar resolution (voxel size = 1 × 1 × 1 mm3) using cubic interpolation to standardize the voxel spacing [23, 24].
3D Slicer software (v.4.10.2; https://www.slicer.org/) was implemented for radiomics feature extraction. Using this software, a range of radiomics features was extracted and the intratumoral heterogeneity of the segmented 3D-ROIs was quantitatively expressed by the extracted features. The radiomics features (n = 1130) were respectively derived from T1WI and FS-T2WI sequences from each 3D-ROI, incorporating shape features, first-order features, texture features including the gray-level co-occurrence matrix, gray-level dependence matrix, gray-level size-zone matrix, gray-level run-length matrix, and neighboring gray tone matrix and wavelet decomposition features.
ComBat compensation method
Effects obtained by different MRI scanners and protocols were removed using the ComBat compensation method while retaining its outperforming features in texture patterns, which potentially improved the power and reproducibility of subsequent investigations [25, 26].
Patients’ clinical data and MRI features
Clinical data (age, sex, and TNM stage) and MRI features were analyzed. The TNM stage was determined using the preoperative MRI and computed tomography information. Each musculoskeletal MRI scan was evaluated by two readers who had 7 and 14 years of experience and were blinded to the clinical and histopathological data. A consensus was reached in cases of discrepancy. The recorded data were described in Supplementary Material.
A pathologist (F.H.) with 11 years of experience in soft tissue diseases explained the pathology, incorporating the stage and histologic subtype. The FNCLCC system assigns a score for the tumor’s mitotic index, differentiation, and extent of necrosis, and the tumor grade is obtained by summing these three scores. The pathologic TNM stage was determined based on the guidelines in the American Joint Committee on Cancer (AJCC) Cancer Staging Manual, 8th edition.
Construction of radiomics signature
A subsequent statistical analysis was performed using R software (v3.5.1; https://www.R-project.org). After removing redundant and irrelevant features and retaining the most related features for grading of STS by applying the minimum redundancy maximum relevance algorithm, the 30 best radiomics features were selected and applied to least absolute shrinkage and selection operator (LASSO) regression to generate the radiomics signature[27]. Next, the radiomics features with non-zero coefficients selected from LASSO regression formed three radiomics signatures based on T1WI sequences (RS-T1 model), FS-T2WI sequences (RS-FST2 model), and their combination (RS-Combined model). The radiomics score (rad-score) was calculated according to its linear combination of corresponding LASSO non-zero coefficients.
Development of clinical model and radiomics nomogram
Univariate logistic regression was performed for the clinical risk factors and MRI features used to evaluate the STS grade. The factors with a two-sided P value of <0.05 were then introduced into a multivariate logistic regression. The variables with a two-sided P value of <0.05 from the multivariate analysis were considered potential independent clinical risk factors associated with the histologic grade and were used to compose a clinical model. Ultimately, a clinical model was established. Finally, the significant clinical factors and the optimal radiomics signature were selected and combined in the radiomics nomogram.
Validation of the radiomics nomogram and performance assessment of differentmodels
The radiomics nomogram was assessed for discrimination, calibration, and clinical application [28] in both sets. The discrimination capability of the clinical model, radiomics signatures models and radiomics nomogram to correctly distinguish the grade was quantified by the AUC and accuracy. The Hosmer–Lemeshow test was used to assess the goodness-of-fit with a calibration curve to evaluate the calibration of the nomogram [29]. The external validation set was used to test the radiomics nomogram, and the rad-score was correspondingly calculated using the formula established in the training set.
The AUC between each two of the three models was evaluated using the DeLong test. The clinical application was estimated by a decision curve analysis (DCA) to determine whether the radiomics nomogram can be regarded as robust and effective. The DCA was used to quantitatively calculate the net benefits for a range of different threshold probabilities in the whole cohort.
Follow-up and survival analysis
The patients underwent MRI or computed tomography follow-up examinations every 6 to 12 months for the first 2 years and annually thereafter. Progression-free survival (PFS) was calculated as the survival endpoint for patient outcomes from the time of surgery to the time of radiographic detection of recurrence, time of last follow-up examination, or time of death without evidence of progression. Patients were censored in case of on 30 November 2019.
Survival curves were generated based on Kaplan–Meier survival analysis. Differences in survival curves were assessed by the log-rank test. The pathologic grade results model, radiomics signature model, and radiomics nomogram model were further evaluated for their performance in PFS stratification. We combined the nomogram model with the AJCC staging system (Cancer Staging Manual, 8th edition) to analyze its ability in PFS stratification.
Statistical analysis
R software was used to perform the statistical analysis. A two-sided P value of <0.05 was regarded as statistically significant.
A univariate analysis was performed to evaluate the relationships between the patients’ characteristics. For continuous variables, Student’s t-test or the Mann–Whitney U test was used to determine whether significant inter-group differences existed between the low-grade and high-grade groups; for classified variables, the chi-square test or Fisher’s exact test was performed where appropriate. The packages we used in R software were described in Supplementary Material.