A Retrospective Comparative Analysis of Outcomes and Prognostic Factors in Adult and Pediatric Patients with Osteosarcoma

Relative Wash-In Rate in Dynamic Contrast-Enhanced Magnetic Resonance Imaging as a New Prognostic Biomarker for Event-Free Survival in 82 Patients with Osteosarcoma: A Multicenter Study

BACKGROUND

The decreased perfusion of osteosarcoma in dynamic contrast-enhanced (DCE) MRI, reflecting a good histological response to neoadjuvant chemotherapy, has been described.

PURPOSE

In this study, we aim to explore the potential of the relative wash-in rate as a prognostic factor for event-free survival (EFS).

METHODS

Skeletal high-grade osteosarcoma patients, treated in two tertiary referral centers between 2005 and 2022, were retrospectively included. The relative wash-in rate (rWIR) was determined with DCE-MRI before, after, or during the second cycle of chemotherapy (pre-resection). A previously determined cut-off was used to categorize patients, where rWIR < 2.3 was considered poor and rWIR ≥ 2.3 a good radiological response. EFS was defined as the time from resection to the first event: local recurrence, new metastases, or tumor-related death. EFS was estimated using Kaplan-Meier's methodology. Multivariate Cox proportional hazard model was used to estimate the effect of histological response and rWIR on EFS, adjusted for traditional prognostic factors.

RESULTS

Eighty-two patients (median age: 17 years; IQR: 14-28) were included. The median follow-up duration was 11.8 years (95% CI: 11.0-12.7). During follow-up, 33 events occurred. Poor histological response was not significantly associated with EFS (HR: 1.8; 95% CI: 0.9-3.8), whereas a poor radiological response was associated with a worse EFS (HR: 2.4; 95% CI: 1.1-5.0). In a subpopulation without initial metastases, the binary assessment of rWIR approached statistical significance (HR: 2.3; 95% CI: 1.0-5.2), whereas its continuous evaluation demonstrated a significant association between higher rWIR and improved EFS (HR: 0.7; 95% CI: 0.5-0.9), underlining the effect of response to chemotherapy. The 2- and 5-year EFS for patients with a rWIR ≥ 2.3 were 85% and 75% versus 55% and 50% for patients with a rWIR < 2.3.

CONCLUSION

The predicted poor chemo response with MRI (rWIR < 2.3) is associated with shorter EFS even when adjusted for known clinical covariates and shows similar results to histological response evaluation. rWIR is a potential tool for future response-based individualized healthcare in osteosarcoma patients before surgical resection.

The use of weakly supervised machine learning for necrosis assessment in patients with osteosarcoma: A pilot study

Percent necrosis (PN) following chemotherapy is a prognostic factor for survival in osteosarcoma. Pathologists estimate PN by calculating tumor viability over an average of whole-slide images (WSIs). This non-standardized, labor-intensive process requires specialized training and has high interobserver variability. Therefore, we aimed to develop a machine-learning model capable of calculating PN in osteosarcoma with similar accuracy to that of a musculoskeletal pathologist. In this proof-of-concept study, we retrospectively obtained six WSIs from two patients with conventional osteosarcomas. A weakly supervised learning model was trained by using coarse and incomplete annotations of viable tumor, necrotic tumor, and nontumor tissue in WSIs. Weakly supervised learning refers to processes capable of creating predictive models on the basis of partially and imprecisely annotated data. Once "trained," the model segmented areas of tissue and determined PN of the same six WSIs. To assess model fidelity, the pathologist also estimated PN of each WSI, and we compared the estimates using Pearson's correlation and mean absolute error (MAE). MAE was 15% over the six samples, and 6.4% when an outlier was removed, for which the model inaccurately labeled cartilaginous tissue. The model and pathologist estimates were strongly, positively correlated (r = 0.85). Thus, we created and trained a weakly supervised machine learning model to segment viable tumor, necrotic tumor, and nontumor and to calculate PN with accuracy similar to that of a musculoskeletal pathologist. We expect improvement can be achieved by annotating cartilaginous and other mesenchymal tissue for better representation of the histological heterogeneity in osteosarcoma.

Characteristics and prognostic factors of adult patients with osteosarcoma from the SEER database

Osteosarcoma is the most common bone malignancy. There are many studies on the prognostic factors of children and adolescents, but the characteristics and prognostic factors of adult osteosarcoma are rarely studied. The aim of this study was to construct a nomogram for predicting the prognosis of adult osteosarcoma. Information on all osteosarcoma patients aged ≥ 18 years from 2004 to 2015 was downloaded from the surveillance, epidemiology and end results database. A total of 70% of the patients were included in the training set and 30% of the patients were included in the validation set. Univariate log-rank analysis and multivariate cox regression analysis were used to screen independent risk factors affecting the prognosis of adult osteosarcoma. These risk factors were used to construct a nomogram to predict 3-year and 5-year prognosis in adult osteosarcoma. Multivariate cox regression analysis yielded 6 clinicopathological features (age, primary site, tumor size, grade, American Joint Committee on Cancer stage, and surgery) for the prognosis of adult osteosarcoma patients in the training cohort. A nomogram was constructed based on these predictors to assess the prognosis of adult patients with osteosarcoma. Concordance index, receiver operating characteristic and calibration curves analyses also showed satisfactory performance of the nomogram in predicting prognosis. The constructed nomogram is a helpful tool for exactly predicting the prognosis of adult patients with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.

18F-FDG PET/CT in the Management of Osteosarcoma

Osteosarcoma is the most common type of primary malignant bone tumor. ¹⁸F-FDG PET/CT is useful for staging, detecting recurrence, monitoring response to neoadjuvant chemotherapy, and predicting prognosis. Here, we review the clinical aspects of osteosarcoma management and assess the role of ¹⁸F-FDG PET/CT, in particular with regard to pediatric and young adult patients.

Analysis of Clinical Manifestations and Prognostic Factors Affecting Osteosarcoma

Objective

This study is aimed at exploring the clinical manifestations and prognostic factors of osteosarcoma.

Methods

The clinical data of patients with osteosarcoma who were treated in our hospital from January 2018 to March 2020 were selected for retrospective analysis. The general information of the patients, including age, gender, tumor diameter, tumor location, tumor type, surgical method, and Enneking stage, distant disease metastasis, KPS score, and the number of postoperative adjuvant chemotherapy, were grouped by prognosis for statistical analysis. The clinical characteristics, morbidity and mortality, and prognostic factors of patients were statistically analyzed.

Results

Among the 83 patients in this group, there were 52 males and 31 females, 59 tumors > 10 cm in diameter and 24 tumors < 10 cm in diameter, 16 tumors in the upper limbs and 67 tumors in the lower limbs, 25 tumors in osteoblastoma, 16 tumors in chondroblastoma, 42 tumors in fibroblastoma, 62 tumors in stage II, and 21 tumors in stage III of Enneking stage, 10 tumors in distant metastasis, and 10 tumors in distant metastasis. The death rate of this group was 19.28% (16/83). Multifactor regression analysis confirmed that the Enneking stage III, distant metastasis, KPS score < 70, and the number of postoperative adjuvant chemotherapy < 6 were important factors influencing the death of osteosarcoma (P < 0.05).

Conclusion

Enneking stage III, distant metastasis, KPS score < 70, and the number of postoperative adjuvant chemotherapy < 6 are important influencing factors of osteosarcoma death. Clinical practice can take corresponding preventive and control measures according to the existence of these factors to ensure a good prognosis.

Expression of the Circadian Clock Gene ARNTL associated with DNA repair gene and prognosis of patient with osteosarcoma

PURPOSE

The study objects were to explore the correlation between the biological role of clock genes and clinical indicators in patients with osteosarcoma (OS).

METHODS

We acquired the clinical information and RNA sequencing data of OS samples from the TARGET database. The protein-protein interaction (PPI) network and expression correlation analysis of clock genes were performed. Then, the functional enrichment analysis of clock genes was analyzed. The survival analysis of clock genes in patients of OS was carried out by univariate cox regression, Kaplan-Meier (KM) curve and multivariate cox regression methods. Moreover, the spearmen correlation analysis was performed to explore the correlation between clock genes and DNA repair genes in patients with OS.

RESULTS

The PPI network and expression correlation analysis of clock genes indicated that the clock genes were highly correlated with each other. The survival analysis of clock genes found that clock gene ARNTL is a protective factor for the prognosis of patients with OS. We found that ARNTL was positively related to DNA repair genes and was involved in the biological process of DNA damage repair in patients with OS.

CONCLUSIONS

ARNTL may affect the prognosis and chemotherapy response of patients with OS by regulating DNA repair pathways.