FDG-PET/MR Imaging for prediction of lung metastases in soft-tissue sarcomas of the extremities by texture analysis and wavelet image fusion
Bibliographic record
Abstract
Soft-tissue sarcoma (STS) of the extremities forms a relatively uncommon yet aggressive group of neoplasms with high metastatic risk of the disease. The vast majority of STS metastases occur in the lungs. Due to the general poor prognosis of patients diagnosed with STS lung metastases, there is a clinical need to identify relevant prognostic factors as early as possible in the course of staging and treatment management. Recent evidence suggests that positron emission tomography (PET) using fluorodeoxyglucose (FDG) and magnetic resonance (MR) imaging texture features have the potential to predict the outcome of tumours through the assessment of their microenvironment heterogeneity characteristics. The goal of this work is therefore to investigate FDG-PET and MR texture features as potential early predictors of lung metastasis risk in STS cancer of the extremities.In this study, a dataset of 35 patients with histologically proven STS of the extremities was retrospectively analyzed. All patients received pre-treatment FDG-PET and MR scans. MR imaging data comprised of T1-weighted, T2 fat-saturation (T2FS) and short tau inversion recovery (STIR) sequences. The median follow-up period was 29 months (range: 4 to 85 months). Thirteen patients from the dataset developed lung metastases. Six texture features from the gray-level co-occurrence matrix (GLCM) were extracted from the FDG-PET, MR and fused FDG-PET/MR scans. In addition, the maximum standard uptake value (SUVmax) of the tumours was included in the feature set. The fusion of FDG-PET and MR scans was carried out using the discrete wavelet transform (DWT) and a band-pass frequencies enhancement technique. Statistical analysis was performed using Spearman's correlation (rho), and multivariable modeling using logistic regression. The prediction performance of the different multivariable models was assessed using bootstrap resampling by calculating the area under the receiver-operating characteristics curve (AUC) and Matthews' correlation coefficient (MCC). The highest univariate prediction of lung metastases was attributed to the SUVmax metric (rho=0.6382, p<0.0001). Most texture features extracted from fused scans had higher Spearman's correlation with lung metastases than those extracted from separate scans. On separate scans, FDG-PET texture features were generally dominant over MR texture features. The highest multivariable prediction of lung metastases was found using fused scans and the following 4-parameters model: 0.94*SUVmax − 0.401*PET-T2FS/STIR--Variance − 6.7*PET-T1--Contrast − 165*PET-T1--Homogeneity + 140. This model reached rho=0.8255, p<0.0001 on the entire dataset and AUC=0.956±0.002, MCC=0.829±0.002 in bootstrap testing sets. Overall, this work indicates the strong potential of FDG-PET and MR texture features for the prediction of lung metastases in STS cancer of the extremities. Substantial prediction improvements were found using texture features from fused scans and multivariable modeling strategies compared to texture features extracted from separate scans and univariate analysis. Potentially, this could improve patient outcomes by allowing better personalization of treatments and the application of pre-emptive strategies to mitigate disease spread.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".