Clinical-Deep Neural Network and Clinical-Radiomics Nomograms for Predicting the Intraoperative Massive Blood Loss of Pelvic and Sacral Tumors

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies

OBJECTIVE

To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020.

METHODS

A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed.

RESULTS

Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies.

CONCLUSIONS

Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage.

CRITICAL RELEVANCE STATEMENT

An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage.

KEY POINTS

• 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.

Quantile regression-based prediction of intraoperative blood loss in patients with spinal metastases: model development and validation

PURPOSE

To develop and evaluate a quantile regression-based blood loss prediction model for open surgery of spinal metastases.

METHODS

This was a multicenter retrospective cohort study. Over a 11-year period, patients underwent open surgery for spinal metastases at 6 different institutions were reviewed. The outcome measure is intraoperative blood loss (in mL). The effects of baseline, histology of primary tumor and surgical procedure on blood loss were evaluated by univariate and multivariate analysis to determine the predictors. Multivariate ordinary least squares (OLS) regression and 0.75 quantile regression were used to establish two prediction models. The performance of the two models was evaluated in the training set and the test set, respectively.

RESULTS

528 patients were included in this study. Mean age was 57.6 ± 11.2 years, with a range of 20-86 years. Mean blood loss was 1280.1 ± 1181.6 mL, with a range of 10 ~ 10,000 mL. Body mass index (BMI), tumor vascularization, surgical site, surgical extent, total en bloc spondylectomy and microwave ablation use were significant predictors of intraoperative blood loss. Hypervascular tumor, higher BMI, and broader surgical extent were related with massive blood loss. Microwave ablation is more beneficial in surgery with substantial blood loss. Compared to the OLS regression model, the 0.75 quantile regression model may decrease blood loss underestimate.

CONCLUSION

In this study, we developed and evaluated a prediction model for blood loss in open surgery for spinal metastases based on 0.75 quantile regression, which may minimize blood loss underestimate.