Socio-economic Factors Affect the Outcome of Soft Tissue Sarcoma: an Analysis of SEER Data

Cheung, Min Rex; Kang, Josephine; Ouyang, Daniel; Yeung, Vincent

doi:10.7314/apjcp.2014.15.1.25

Cited by 9 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Socioeconomic factors, such as race and income, were found to contribute to prognosis of STS in this study. Alamanda et al and Cheung et al both found black patients with STS to be at a survival disadvantage, as well as prior authors showing non‐white OS disadvantage among non‐whites in all cancer types . Our model, however, suggested this association was not significant.…”

Section: Discussioncontrasting

confidence: 59%

Time to treatment initiation and survival in adult localized, high‐grade soft tissue sarcoma

Featherall

Curtis²,

Lawrenz³

et al. 2019

Journal of Surgical Oncology

View full text Add to dashboard Cite

Background: Although shorter delays in soft tissue sarcoma (STS) diagnosis may improve overall survival (OS), the influence of time to treatment initiation (TTI) on OS in STS has not been determined.Objective: To determine if TTI influences OS in localized, high-grade STS.Methods: An analysis of the National Cancer Database identified 8648 patients meeting criteria with localized, high-grade STS diagnosed between 2004 and 2012.TTI and secondary variable associations with OS were determined using Kruskal-Wallis tests in univariate analyses, and a Cox regression multivariable model. Results:In a multivariable Cox regression, TTI was associated with OS in a nonlinear fashion with a minimum hazard ratio (HR) demonstrated at 42 days. Secondary variables significantly associated (P < .05) with decreased OS included, advanced age, increased Charlson/Deyo score, nonprivate insurance, axial tumor location, tumor size more than 5 cm, stage III disease, and a nonsurgical treatment modality. Conclusions:Minimum HR was observed at a TTI of 42 days, with HR = 0.64, when compared with TTI = 1 day. Appropriate referrals to a higher volume sarcoma centers may account for these delays and explain a potential OS advantage. This is important in counseling patients, who may seek referral to a higher volume treatment center. K E Y W O R D S overall survival, soft tissue sarcoma, time to treatment initiation, treatment delay | 1249 https://www.facs.org/quality-programs/cancer/ncdb/puf with the permission of the ACS NCDB. ORCID Gannon L. Curtis

show abstract

Section: Discussioncontrasting

confidence: 59%

Time to treatment initiation and survival in adult localized, high‐grade soft tissue sarcoma

Featherall

Curtis²,

Lawrenz³

et al. 2019

Journal of Surgical Oncology

View full text Add to dashboard Cite

show abstract

“…Our study reports no geographical inequalities in the survival of patients with sarcoma, in the context of national organization driven by a reference network. This is an original result since inequalities in sarcoma management and survival have been found in the USA [ 37 , 38 , 39 , 40 ], in England [ 41 ], and in Denmark [ 42 ] based on national cancer databases or registries. While the clinical specificities of rare cancers and the reference centers’ remoteness could have contributed to strengthen the spatial inequalities in cancer management and survival [ 21 ], which have already been proven in France for many cancer locations [ 19 , 20 , 43 ], these results suggest the ability of the reference network organization to address the social and spatial inequalities in cancer management.…”

Section: Discussionmentioning

confidence: 99%

No Geographical Inequalities in Survival for Sarcoma Patients in France: A Reference Networks’ Outcome?

Fayet

Chevreau

Decanter

et al. 2022

Cancers

View full text Add to dashboard Cite

The national reference network NETSARC+ provides remote access to specialized diagnosis and the Multidisciplinary Tumour Board (MTB) to improve the management and survival of sarcoma patients in France. The IGéAS research program aims to assess the potential of this innovative organization to address geographical inequalities in cancer management. Using the IGéAS cohort built from the nationwide NETSARC+ database, the individual, clinical, and geographical determinants of the 3-year overall survival of sarcoma patients in France were analyzed. The survival analysis was focused on patients diagnosed in 2013 (n = 2281) to ensure sufficient hindsight to collect patient follow-up. Our study included patients with bone (16.8%), soft-tissue (69%), and visceral (14.2%) sarcomas, with a median age of 61.8 years. The overall survival was not associated with geographical variables after adjustment for individual and clinical factors. The lower survival in precarious population districts [HR 1.23, 95% CI 1.02 to 1.48] in comparison to wealthy metropolitan areas (HR = 1) found in univariable analysis was due to the worst clinical presentation at diagnosis of patients. The place of residence had no impact on sarcoma patients’ survival, in the context of the national organization driven by the reference network. Following previous findings, this suggests the ability of this organization to go through geographical barriers usually impeding the optimal management of cancer patients.

show abstract

“…32 The SEER database is commonly used in musculoskeletal oncology for large data population studies. 26,[33][34][35][36] undergoing surgery for local control. Institutional patient cases were identified from the orthopedic oncology surgical log at a large, urban, tertiary care sarcoma center.…”

Section: Data Sourcementioning

confidence: 99%

Development of machine learning model algorithm for prediction of 5‐year soft tissue myxoid liposarcoma survival

Kamalapathy

Ramkumar

Karhade

et al. 2021

Journal of Surgical Oncology

View full text Add to dashboard Cite

Background: Predicting survival in myxoid liposarcoma (MLS) patients is very challenging given its propensity to metastasize and the controversial role of adjuvant therapy. The purpose of this study was to develop a machine-learning algorithm for the prediction of survival at five years for patients with MLS and externally validate it using our institutional cohort. Methods: Two databases, the surveillance, epidemiology, and end results program (SEER) database and an institutional database, were used in this study. Five machine learning models were created based on the SEER database and performance was rated using the TRIPOD criteria. The model that performed best on the SEER data was again tested on our institutional database. Results:The net-elastic penalized logistic regression model was the best according to our performance indicators. This model had an area under the curve (AUC) of 0.85 when compared to the SEER testing data and an AUC of 0.76 when tested against institutional database. An application to use this calculator is available at https://sorg-apps.shinyapps.io/myxoid_liposarcoma/. Conclusion:MLS is a soft-tissue sarcoma with adjunct treatment options that are, in part, decided by prognostic survival. We developed the first machine-learning predictive algorithm specifically for MLS using the SEER registry that retained performance during external validation with institutional data.

show abstract

Socio-economic Factors Affect the Outcome of Soft Tissue Sarcoma: an Analysis of SEER Data

Cited by 9 publications

References 31 publications

Time to treatment initiation and survival in adult localized, high‐grade soft tissue sarcoma

Time to treatment initiation and survival in adult localized, high‐grade soft tissue sarcoma

No Geographical Inequalities in Survival for Sarcoma Patients in France: A Reference Networks’ Outcome?

Development of machine learning model algorithm for prediction of 5‐year soft tissue myxoid liposarcoma survival

Contact Info

Product

Resources

About