Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma.

OBJECTIVE: This study aims to build machine learning-based CT radiomic features to predict patients developing metastasis after osteosarcoma diagnosis. METHODS AND MATERIALS: This retrospective study has included 81 patients with a histopathological diagnosis of osteosarcoma. The entire dataset was divided randomly into training (60%) and test sets (40%). A data augmentation technique for the minority class was performed in the training set, along with feature's selection and model's training. The radiomic features were extracted from CT's image of the local osteosarcoma. Three frequently used machine learning models tried to predict patients with lung metastases (MT) and those without lung metastases (non-MT). According to the higher area under the curve (AUC), the best classifier was chosen and applied in the testing set with unseen data to provide an unbiased evaluation of the final model. RESULTS: The best classifier for predicting MT and non-MT groups used a Random Forest algorithm. The AUC and accuracy results of the test set were bulky (accuracy of 73% [ 95% coefficient interval (CI): 54%; 87%] and AUC of 0.79 [95% CI: 0.62; 0.96]). Features that fitted the model (radiomics signature) derived from Laplacian of Gaussian and wavelet filters. CONCLUSIONS: Machine learning-based CT radiomics approach can provide a non-invasive method with a fair predictive accuracy of the risk of developing pulmonary metastasis in osteosarcoma patients. ADVANCES IN KNOWLEDGE: Models based on CT radiomic analysis help assess the risk of developing pulmonary metastases in patients with osteosarcoma, allowing further studies for those with a worse prognosis.

Osteosarcoma, chondrosarcoma and Ewing sarcoma: Clinical aspects, biomarker discovery and liquid biopsy.

Bone sarcomas, although rare, are associated with significant morbidity and mortality. The most frequent primary bone cancers include osteosarcoma, chondrosarcoma and Ewing sarcoma. The treatment approaches are heterogeneous and mainly chosen based on precise tumour staging. Unfortunately, clinical outcome has not changed significantly in over 30 years and tumour grade is still the best prognosticator of metastatic disease and survival. An option to improve this scenario is to identify molecular biomarkers in the early stage of the disease, or even before the disease onset. Blood-based liquid biopsies are a promising, non-invasive way to achieve this goal and there are an increasing number of studies which investigate their potential application in bone cancer diagnosis, prognosis and personalised therapy. This review summarises the interplay between clinical and molecular aspects of the three main bone sarcomas, alongside biomarker discovery and promising applications of liquid biopsy in each tumour context.

Patient-derived osteosarcoma cells are resistant to methotrexate.

Osteosarcoma is the most common primary bone tumor in children and young adults. The median survival of osteosarcoma patients has not significantly improved since 1990, despite administration of different classes of chemotherapy agents, such as methotrexate, cisplatin and doxorubicin. Cancer stem cells (CSCs) are responsible for the resistance of osteosarcoma to chemotherapy and OCT4, SOX2 and SSEA4 have been used to identify CSCs in osteosarcoma. Here, we used low-passage patient-derived osteosarcoma cells and osteosarcoma cells directly isolated from patients before and after chemotherapy treatments to evaluate the effects of chemotherapy on stem cell markers expression. We demonstrate that primary osteosarcoma cells are resistant to methotrexate treatment and sensitive to cisplatin and doxorubicin in vitro. We also verified that cisplatin and doxorubicin reduce the expression of SOX2 and OCT4 in primary osteosarcoma cells whereas methotrexate does not alter SOX2 and OCT4 expression, however it increases SSEA4 expression in primary osteosarcoma cells. Finally, we found that, although the combination treatment cisplatin plus doxorubicin inhibited the in vivo growth of osteosarcoma cells in NOD-SCID gamma mice subcutaneously injected with SaOs2, the combination treatment cisplatin plus doxorubicin plus methotrexate did not inhibit the in vivo growth of these cells. These observations may provide an explanation for the poor response of osteosarcomas to chemotherapy and point to the need of reevaluating the therapeutic strategies for human osteosarcomas.

Interleukins 1ß and 10 expressions in the periimplant crevicular fluid from patients with untreated periimplant disease.

PURPOSE: To analyze the interleukin (IL)-1ß and IL-10 expressions in periimplant crevicular fluid (PICF) in healthy and diseased regions to elucidate the inflammatory process around implants and its influence on clinical diagnosis. MATERIALS AND METHODS: PICF samples from 30 patients were analyzed for IL-1ß and IL-10 concentrations by enzyme-linked immunosorbent assay. Patients were divided in Groups A (health), B (mucositis), and C (periimplantitis). Plaque accumulation, periodontal phenotype (PP), depth on probing, and history of periodontitis (HP) were evaluated. RESULTS: IL-1ß levels were lower in healthy group compared with Groups B (P < 0.0005) and C (P < 0.001). IL-10 levels were higher in Groups A compared with B (P = 0.033) and C (P = 0.0001). Patients with HP and thin PP had 9 and 4.5 times more chance of presenting disease, respectively. CONCLUSIONS: Lower IL-1ß and higher IL-10 levels characterized healthy periimplant conditions, which demonstrate the anti-inflammatory predominance in sites without disease signs. IL-10 levels decrease significantly according to increase of disease status. Therefore, its levels can differentiate healthy, mucositis, and periimplantitis. Thin PP and HP are associated with periimplant disease. These findings suggest the use of ILs as a biochemical marker for early diagnosis of periimplant disease.

The effect of titanium topography features on mesenchymal human stromal cells' adhesion.

OBJECTIVE: The aim of this study was to evaluate the effect of two kinds of dental implants surfaces with their own characteristics on human marrow stromal cells' adhesion. MATERIAL AND METHODS: Fifty-six titanium discs (28 machined and 28 acid etched) were used. Machined (MS) and acid-etched surfaces (ES) were analyzed using a scanning electron microscope, energy dispersing spectroscopy (EDS), contact angle analysis and human marrow stromal cells' culture. RESULTS: Significant differences were observed in the topography and wetability of the tested surfaces. However, etched surfaces presented a high level of wetability when compared with machined surfaces. Contact angles showed considerable differences between etched and machined surfaces (Friedman test P<0.05). EDS analysis showed the same composition on both the surfaces tested. Counting of adhered cells on both types of surfaces showed that there is no statistical significance in human marrow stromal cells' adhesion after 18 h (Mann-Whitney test P>0.05). CONCLUSION: The present study concludes that modifications on the titanium implant surfaces roughness may promote differences in the morphology of bone marrow stromal cells. Nevertheless, in this microenvironment, no interference in the adhesion phenomenon was noted.