Ct-based intratumoral and peritumoral radiomics for predicting prognosis in osteosarcoma: A multicenter study

Radiomics-based machine learning in prediction of response to neoadjuvant chemotherapy in osteosarcoma: A systematic review and meta-analysis

BACKGROUND AND AIMS

Osteosarcoma (OS) is the most common primary bone malignancy, and neoadjuvant chemotherapy (NAC) improves survival rates. However, OS heterogeneity results in variable treatment responses, highlighting the need for reliable, non-invasive tools to predict NAC response. Radiomics-based machine learning (ML) offers potential for identifying imaging biomarkers to predict treatment outcomes. This systematic review and meta-analysis evaluated the accuracy and reliability of radiomics models for predicting NAC response in OS.

METHODS

A systematic search was conducted in PubMed, Embase, Scopus, and Web of Science up to November 2024. Studies using radiomics-based ML for NAC response prediction in OS were included. Pooled sensitivity, specificity, and AUC for training and validation cohorts were calculated using bivariate random-effects modeling, with clinical-combined models analyzed separately. Quality assessment was performed using the QUADAS-2 tool, radiomics quality score (RQS), and METRICS scores.

RESULTS

Sixteen studies were included, with 63 % using MRI and 37 % using CT. Twelve studies, comprising 1639 participants, were included in the meta-analysis. Pooled metrics for training cohorts showed an AUC of 0.93, sensitivity of 0.89, and specificity of 0.85. Validation cohorts achieved an AUC of 0.87, sensitivity of 0.81, and specificity of 0.82. Clinical-combined models outperformed radiomics-only models. The mean RQS score was 9.44 ± 3.41, and the mean METRICS score was 60.8 % ± 17.4 %.

CONCLUSION

Radiomics-based ML shows promise for predicting NAC response in OS, especially when combined with clinical indicators. However, limitations in external validation and methodological consistency must be addressed.

A CT-based radiomics model for predicting progression-free survival in patients with epithelial ovarian cancer

PURPOSE

This study aimed to develop and validate a CT-based radiomics nomogram for predicting the progression-free survival (PFS) of epithelial ovarian cancer (EOC).

MATERIALS AND METHODS

A total of 144 EOC patients were retrospectively enrolled from two hospitals and The Cancer Genome Atlas and The Cancer Imaging Archive, divided into a training set (n = 101) and a test set (n = 43) using a 7:3 ratio. Radiomic features were extracted from contrast enhanced CT images. The radiomics score (rad-score) was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression. Clinical semantic features with P < 0.05 in multivariate Cox regression were combined with rad-score to develop radiomics nomogram. The predictive performance of the nomogram was assessed using the concordance index (C-index) and calibration curves.

RESULTS

Multivariate Cox regression analysis revealed that the International Federation of Obstetrics and Gynecology stage and residual tumor are significant predictors of PFS. Twelve radiomic features were selected by LASSO Cox regression. The combined model demonstrated superior predictive performance, with a C-index of 0.78 (95% CI: 0.689-0.889) in the training set, and 0.73 (95% CI: 0.572-0.886) in the test set. The combined model outperformed the clinical and radiomics models in predicting 1-, 3-, and 5-year PFS, with area under curves of 0.850 (95% CI: 0.722-0.943), 0.828 (95% CI: 0.722-0.901), and 0.845 (95% CI: 0.722-0.943), respectively. Calibration curves of the radiomic nomogram for prediction of 1-year, 3-year, 5-year PFS showed excellent calibrations in both training and test sets.

CONCLUSION

The combined model integrating rad-score and clinical semantic features effectively evaluates PFS in EOC patients. The radiomics nomogram provides a non-invasive, simple, and feasible method to predict PFS in EOC patients, which may facilitate clinical decision-making.

CLEAR guideline for radiomics: Early insights into current reporting practices endorsed by EuSoMII

PURPOSE

This study aims to evaluate current reporting practices in radiomics research, with a focus on CheckList for EvaluAtion of Radiomics research (CLEAR).

METHODS

We conducted a citation search using Google Scholar to collect original research articles on radiomics citing the CLEAR guideline up to June 17, 2024. We examined the adoption of the guideline, adherence scores per publication, item-wise adherence rates, and self-reporting practices. An expert panel from the European Society of Medical Imaging Informatics Radiomics Auditing Group conducted a detailed item-by-item confirmation analysis of the self-reported CLEAR checklists.

RESULTS

Out of 100 unique citations from 104 records, 48 original research papers on radiomics were included. The overall adoption rate in the literature was 2 %. Among the citing articles, 94 % (45/48) adopted CLEAR for reporting purposes, applying it to both hand-crafted radiomics (89 %) and deep learning (24 %). Self-reported checklists were included in 58 % (26/45) of these papers. Median study-wise adherence score for self-reported data was 91 % (interquartile range = 18 %). Mean confirmed adherence score was 66 % (standard deviation = 14 %). Difference between these scores was statistically significant, (mean = 21 %; standard deviation = 11 %), p < 0.001. Using an arbitrary 50 % adherence cut-off, the number of items with poor adherence increased from 3 to 15 after confirmation analysis, mostly comprised of open science-related items. In addition, several items were frequently misreported.

CONCLUSION

This study revealed significant discrepancies between self-reported and confirmed adherence to the CLEAR guideline in radiomics research, indicating a need for improved reporting accuracy and verification practices.

CT-derived Radiomics Predicts the Efficacy of Tyrosine Kinase Inhibitors in Osteosarcoma Patients with Pulmonary Metastasis

BACKGROUND

To construct and validate the CT-based radiomics model for predicting the tyrosine kinase inhibitors (TKIs) effects in osteosarcoma (OS) patients with pulmonary metastasis.

METHODS

OS patients with pulmonary metastasis treated with TKIs were randomly separated into training and testing cohorts (2:1 ratio). Radiomic features were extracted from the baseline unenhanced chest CT images. The random survival forest (RSF) and Kaplan-Meier survival analyses were performed to construct and evaluate radiomics signatures (R-model-derived). The univariant and multivariant Cox regression analyses were conducted to establish clinical (C-model) and combined models (RC-model). The discrimination abilities, goodness of fit and clinical benefits of the three models were assessed and validated in both training and testing cohorts.

RESULTS

A total of 90 patients, 57 men and 33 women, with a mean age of 18 years and median progression-free survival (PFS) of 7.2 months, were enrolled. The R-model was developed with nine radiomic features and demonstrated significant predictive and prognostic values. In both training and testing cohorts, the time-dependent area under the receiver operating characteristic curves (AUC) of the R-model and RC-model exhibited obvious superiority over C-model. The calibration and decision curve analysis (DCA) curves indicated that the accuracy of the R-model was comparable to RC-model, which exhibited significantly better performance than C-model.

CONCLUSIONS

The R-model showed promising potential as a predictor for TKI responses in OS patients with pulmonary metastasis. It can potentially identify pulmonary metastatic OS patients most likely to benefit from TKIs treatment and help guide optimized clinical decisions.