Popliteal lymph nodes in patients with osteosarcoma: are they metastatic?

Cleary, Mark; Fayad, Laura M.; Ahlawat, Shivani

doi:10.1007/s00256-020-03498-6

Cited by 4 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the lack of lymphatic drainage in normal cortical bone and spongy bone, LNM is rare in bone sarcomas ( 35 , 36 ).Regional lymph node involvement in osteosarcoma may be owing to the infiltration of the enlarged tumor parenchyma into the periphery, such as the joint capsule or synovium, leading to dissemination into the lymphatic system ( 37 , 38 ).Our study reported T-stage as a significant predictor of LNM, which was also consistent with previous findings ( 7 ).Meanwhile, because of the fact that the peak incidence of the osteosarcoma is 15–19 years of age, more than 80% of patients achieve limb salvage through surgery ( 4 , 28 , 38 ).In this respect, the surgeons need pay more attention to the status of the regional lymph nodes during the resection of larger osteosarcoma in order to eradicate the sarcoma and preserve the limb. M-stage indicates distant metastasis in osteosarcoma, and it is also a risk factor associated with LNM.…”

Section: Discussionmentioning

confidence: 99%

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

Dong

Wang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Background: This study aimed to construct a clinical prediction model for osteosarcoma patients to evaluate the influence factors for the occurrence of lymph node metastasis (LNM).Methods: In our retrospective study, a total of 1,256 patients diagnosed with chondrosarcoma were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database (training cohort, n = 1,144) and multicenter dataset (validation cohort, n = 112). Both the univariate and multivariable logistic regression analysis were performed to identify the potential risk factors of LNM in osteosarcoma patients. According to the results of multivariable logistic regression analysis, A nomogram were established and the predictive ability was assessed by calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA). Moreover, Kaplan-Meier plot of overall survival (OS) was plot and a web calculator visualized the nomogram.Results: Five independent risk factors [chemotherapy, surgery, lung metastases, lymphatic metastases (M-stage) and tumor size (T-stage)] were identified by multivariable logistic regression analysis. What's more, calibration plots displayed great power both in training and validation group. DCA presented great clinical utility. ROCs curve provided the predictive ability in the training cohort (AUC = 0.805) and the validation cohort (AUC = 0.808). Moreover, patients in LNN group had significantly better survival than that in LNP group both in training and validation group.Conclusion: In this study, we constructed and developed a nomogram with risk factors, which performed well in predicting risk factors of LNM in osteosarcoma patients. It may give a guide for surgeons and oncologists to optimize individual treatment and make a better clinical decision.

show abstract