Effects of marital status on survival of retroperitoneal liposarcomas stratified by age and sex: A population‐based study

Li, Yiding; Wu, Guiling; Zhang, Yujie; Yang, Wanli; Wang, Xiaoqian; Duan, Lili; Niu, Liaoran; Chen, Junfeng; Zhou, Wei; Liu, Jinqiang; Fan, Daiming; Hong, Liu

doi:10.1002/cam4.4962

Cited by 4 publications

(7 citation statements)

References 52 publications

(122 reference statements)

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“…[9][10][11] Therefore, direct comparisons between the current study population cannot be made with other series. However, these results are in agreement with previous studies that focused on the effect of marital status on survival in retroperitoneal liposarcoma 6 and in other malignancies. 2,5 For example, Li et al (SEER, 2004(SEER, -2015 showed that within a cohort of 1211 retroperitoneal liposarcoma patients, 488 (59%) males and 333 (41%) females were married.…”

Section: Male N = 764 (71%)supporting

confidence: 93%

“…For example, Li et al. (SEER, 2004–2015) showed that within a cohort of 1211 retroperitoneal liposarcoma patients, 488 (59%) males and 333 (41%) females were married 6 . Similarly, Collà Ruvolo et al.…”

Section: Discussionmentioning

confidence: 98%

“…2,5 For example, Li et al (SEER, 2004(SEER, -2015 showed that within a cohort of 1211 retroperitoneal liposarcoma patients, 488 (59%) males and 333 (41%) females were married. 6 Similarly, Collà Ruvolo et al identified 3885 (75%) married males and 1639 (45%) married females within 8822 nonmetastatic upper tract urothelial carcinoma patients (SEER, 2004-2016). 5 Second, our analyses demonstrated statistically significant differences between married and unmarried male patients that were not present in their female counterparts.…”

Section: Male N = 764 (71%)mentioning

confidence: 95%

“…However, these results are different from previous studies that addressed the effect of married status on CSM in other primaries. 2,5,6 For example, Li et al (n = 1,211, SEER database, 2004-2015) reported higher CSM rates in divorced, separated, or widowed patients with retroperitoneal liposarcoma relative to their married counterparts. Despite the lack of association between married status and lower CSM in the overall cohort, an association may exist according to sex.…”

Section: Male N = 764 (71%)mentioning

confidence: 99%

“…5 To date, only a single study assessed the effect of marital status on CSM in liposarcoma patients, but it exclusively focused on retroperitoneal location. 6 Therefore, the association between marital status and CSM has never been tested in pelvic liposarcoma, especially according to sex. We addressed this knowledge gap and hypothesized that married status may be associated with lower CSM rates.…”

Section: Introductionmentioning

confidence: 99%

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The effect of married status on cancer‐specific mortality in nonmetastatic pelvic liposarcoma patients according to sex

Baudo,

Morra,

Scheipner

et al. 2023

World j. surg.

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BackgroundIn nonmetastatic pelvic liposarcoma patients, it is unknown whether married status is associated with better cancer‐control outcome defined as cancer‐specific mortality (CSM). We addressed this knowledge gap and hypothesized that married status is associated with lower CSM rates in both male and female patients.MethodsWithin the Surveillance, Epidemiology, and End Results database (2000−2020), nonmetastatic pelvic liposarcoma patients were identified. Kaplan–Meier plots and univariable and multivariable Cox regression models (CRMs) predicting CSM according to marital status were used in the overall cohort and in male and female subgroups.ResultsOf 1078 liposarcoma patients, 764 (71%) were male and 314 (29%) female. Of 764 male patients, 542 (71%) were married. Conversely, of 314 female patients, 192 (61%) were married. In the overall cohort, 5‐year cancer‐specific mortality‐free survival (CSM‐FS) rates were 89% for married versus 83% for unmarried patients (Δ = 6%). In multivariable CRMs, married status did not independently predict lower CSM (hazard ratio [HR]: 0.74, p = 0.06). In males, 5‐year CSM‐FS rates were 89% for married versus 86% for unmarried patients (Δ = 3%). In multivariable CRMs, married status did not independently predict lower CSM (HR: 0.85, p = 0.4). In females, 5‐year CSM‐FS rates were 88% for married versus 79% for unmarried patients (Δ = 9%). In multivariable CRMs, married status independently predicted lower CSM (HR: 0.58, p = 0.03).ConclusionsIn nonmetastatic pelvic liposarcoma patients, married status independently predicted lower CSM only in female patients. In consequence, unmarried female patients should ideally require more assistance and more frequent follow‐up than their married counterparts.

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Section: Male N = 764 (71%)supporting

confidence: 93%

Section: Discussionmentioning

confidence: 98%

Section: Male N = 764 (71%)mentioning

confidence: 95%

Section: Male N = 764 (71%)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The effect of married status on cancer‐specific mortality in nonmetastatic pelvic liposarcoma patients according to sex

Baudo,

Morra,

Scheipner

et al. 2023

World j. surg.

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Effects of marital status on survival of retroperitoneal liposarcomas stratified by age and sex: A population‐based study

Zhang

et al. 2022

Cancer Medicine

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Background Previous studies have shown that marital status is associated with survival in patients with a variety of cancer types, including lung cancer, prostate cancer, and bladder cancer. However, to date, the impact of marital status on the survival of patients with retroperitoneal liposarcomas (RPLs) has not been established. Methods A total of 1211 eligible patients diagnosed with RPLs were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The relationships between marital status and survival in patients with RPLs were assessed. Patients were stratified by age to determine whether an association exists between marital status and age. We also probed the association between marital status and survival in males and females. Results Our findings suggest that divorced, separated, or widowed patients have more advanced cancer stages, and more of these patients do not undergo surgery. Meanwhile, divorced, separated, or widowed patients have worse survival outcomes than married patients (overall survival (OS): HR = 1.66 (95% CI, 1.12, 2.46)); cancer‐specific survival (CSS): HR = 1.90 (95% CI, 1.13, 3.19)). OS does not differ between single patients and married patients (HR = 1.21 [95% CI, 0.81, 1.81]) or CSS (HR = 1.36 [95% CI, 0.80, 2.29]). In addition, these results demonstrate that being divorced, separated, or widowed can play a significant detrimental role in mortality in older and female patients. Conclusion Married patients have earlier disease stages at diagnosis and better survival outcomes than divorced, separated, or widowed patients with RPLs. In addition, this effect is especially pronounced in older people and females.

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Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China

Wang,

Li,

Zeng

et al. 2024

Cancer Medicine

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ObjectiveWe have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results.MethodsWe collected clinicopathological information of RLPS patients from The Surveillance, Epidemiology, and End Results (SEER) database and allocated them into training and validation sets with a 7:3 ratio. Simultaneously, we obtained an external validation cohort from The First Affiliated Hospital of Naval Medical University (Shanghai, China). We performed LASSO regression and multivariate Cox proportional hazards analysis to identify relevant risk factors, which were then combined to develop six machine learning (ML) models: Cox proportional hazards model (Coxph), random survival forest (RSF), ranger, gradient boosting with component‐wise linear models (GBM), decision trees, and boosting trees. The predictive performance of these ML models was evaluated using the concordance index (C‐index), the integrated cumulative/dynamic area under the curve (AUC), and the integrated Brier score, as well as the Cox–Snell residual plot. We also used time‐dependent variable importance, analysis of partial dependence survival plots, and the generation of aggregated survival SHapley Additive exPlanations (SurvSHAP) plots to provide a global explanation of the optimal model. Additionally, SurvSHAP (t) and survival local interpretable model‐agnostic explanations (SurvLIME) plots were used to provide a local explanation of the optimal model.ResultsThe final ML models are consisted of six factors: patient's age, gender, marital status, surgical history, as well as tumor's histopathological classification, histological grade, and SEER stage. Our prognostic model exhibits significant discriminative ability, particularly with the ranger model performing optimally. In the training set, validation set, and external validation set, the AUC for 1, 3, and 5 year OS are all above 0.83, and the integrated Brier scores are consistently below 0.15. The explainability analysis of the ranger model also indicates that histological grade, histopathological classification, and age are the most influential factors in predicting OS.ConclusionsThe ranger ML prognostic model exhibits optimal performance and can be utilized to predict the OS of RLPS patients, offering valuable and crucial references for clinical physicians to make informed decisions in advance.

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Effects of marital status on survival of retroperitoneal liposarcomas stratified by age and sex: A population‐based study

Cited by 4 publications

References 52 publications

The effect of married status on cancer‐specific mortality in nonmetastatic pelvic liposarcoma patients according to sex

The effect of married status on cancer‐specific mortality in nonmetastatic pelvic liposarcoma patients according to sex

Effects of marital status on survival of retroperitoneal liposarcomas stratified by age and sex: A population‐based study

Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China

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