Correlation between Prognostic Factors and the Histopathological Response to Neoadjuvant Chemotherapy in Osteosarcoma: A Retrospective Study

Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram

Objective

To evaluate the value of a nomogram combined MRI Diffusion Weighted Imaging (DWI) and clinical features to predict the treatment response of Neoadjuvant Chemotherapy (NAC) in patients with osteosarcoma.

Methods

A retrospective analysis was conducted on 209 osteosarcoma patients admitted into two bone cancer treatment centers (133 males, 76females; mean age 16.31 ± 11.42 years) from January 2016 to January 2022. Patients were classified as pathological good responders (pGRs) if postoperative histopathological examination revealed ≥90% tumor necrosis, and non-pGRs if <90%. Their clinical features were subjected to univariate and multivariate analysis, and features with statistically significance were utilized to construct a clinical signature using machine learning algorithms. Apparent diffusion coefficient (ADC) values pre-NAC (ADC 0) and post two chemotherapy cycles (ADC 1) were recorded. Regions of interest (ROIs) were delineated from pre-treatment DWI images (b=1000 s/mm²) for radiomic features extraction. Variance thresholding, SelectKBest, and LASSO regression were used to select features with strong relevance, and three machine learning models (Logistic Regression, RandomForest and XGBoost) were used to construct radiomics signatures for predicting treatment response. Finally, the clinical and radiomics signatures were integrated to establish a comprehensive nomogram model. Predictive performance was assessed using ROC curve analysis, with model clinical utility appraised through AUC and decision curve analysis (DCA).

Results

Of the 209patients, 51 (24.4%) were pGRs, while 158 (75.6%) were non-pGRs. No significant ADC1 difference was observed between groups (P>0.05), but pGRs had a higher ADC 0 (P<0.01). ROC analysis indicated an AUC of 0.681 (95% CI: 0.482-0.862) for ADC 0 at the threshold of ≥1.37×10-3 mm²/s, achieving 74.7% sensitivity and 75.7% specificity. The clinical and radiomics models reached AUCs of 0.669 (95% CI: 0.401-0.826) and 0.768 (95% CI: 0.681-0.922) respectively in the test set. The combined nomogram displayed superior discrimination with an AUC of 0.848 (95% CI: 0.668-0.951) and 75.8% accuracy. The DCA suggested the clinical utility of the nomogram.

Conclusion

The nomogram based on combined radiomics and clinical features outperformed standalone clinical or radiomics model, offering enhanced accuracy in evaluating NAC response in osteosarcoma. It held significant promise for clinical applications.

Massage manipulation and progression of osteosarcoma, does it really correlate: a combination of prospective and retrospective cohort study

In Indonesia, the challenge of osteosarcoma progression is further worsened by patients' dependence on traditional massage therapy, low socio-economy, and educational status. This study aims to analyze the differences in the characteristics, laboratory findings, surgery techniques, degree of histopathological necrosis, and metastasis between osteosarcoma patients with and without prior massage manipulation therapy. This research is an analytical observational study with a prospective and retrospective cohort design. Patients were treated and followed for one year to evaluate the occurrence of metastasis. Prospective data was collected through interviews, and secondary data was collected from the patient's medical record. Of 84 subjects analyzed, 69% had a history of massage. There was an increase in LDH and ALP in patients with massage manipulation (p = 0.026). The median time to metastasis from baseline in the massage group (4 months) was statistically significant compared to the non-manipulation group (12 months) (p < 0.0001). This research found that massage therapy significantly increases LDH and ALP levels, making amputations more likely to be performed and a higher risk of metastasis that lowered the survival rate. The onset of metastasis was three times faster in patients with prior massage therapy. Therefore, we strongly recommend against massage manipulation therapy in osteosarcoma patients.

DNA Damage Response Genes in Osteosarcoma

BACKGROUND

Improving the osteosarcoma (OS) patients' survival has long been a challenge, even though the disease's treatment is on the verge of progress. DNA damage response (DDR) has traditionally been associated with carcinogenesis, tumor growth, and genomic instability. No study has used DDR genes as a signature to identify the prognosis of OS. The goal of this work was to find an effective possible DDR gene biomarker for predicting OS prognosis, which may be useful in clinical diagnosis and therapy.

METHODS

To assess gene methylation, univariate and multivariate cox regression analyses were performed on data from OS patients. The data were retrieved from public databases, including the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and the Gene Expression Omnibus (GEO).

RESULTS

The DDR gene signature was chosen, which included seven genes (NHEJ1, RMI2, SWI5, ERCC2, CLK2, POLG, and MLH1). In the TARGET dataset, patients were categorized into two groups: high-risk and low-risk. Patients with a high-risk score revealed a shorter OS rate (hazard ratio (HR): 3.15, 95% confidence interval (CI): 1.38-4.34, P < 0.001) in comparison with the patients with a low-risk score in the TARGET as a training group. The validation of the prognostic signature accuracy was carried out in relapse and validation cohorts (TARGET, n = 75; GSE21257, n = 53). The signature was found to be an independent predictive factor for OS in multivariate cox regression analysis, and a nomogram model was developed to predict an individual's risk of OS. DDR gene signature involved in Fanconi anemia pathway, nonhomologous end-joining pathway, mismatch repair, and nucleotide excision repair pathway.

CONCLUSIONS

Our study suggests that the identified novel DDR genes could be a powerful prognostic tool for prognosis evaluation and a valuable tool in predicting the risk factors in OS patients.