Clinical features of multiply recurrent retroperitoneal liposarcoma: A single-center experience

Masaki, Noriyuki; Onozawa, Mizuki; Inoue, Takamitsu; Kurobe, Masayuki; Kawai, Koji; Miyazaki, Jun

doi:10.1016/j.asjsur.2020.10.015

Cited by 15 publications

(20 citation statements)

References 15 publications

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“…7 In fact, as their name implies, WD liposarcomas seem to be the precursor of DD LPS, since their histological appearance is very similar, except for DD LPS, having characteristics of higher-grade tumors, more dense cellular distribution, and more intense staining, for target carcinogenesis-related molecules, such as MDM2, CDK4, YEATS4, and CPM, all presumed to play a central role in the loss of differentiation, and progression from WD to DD liposarcomas. 7,9,10 The presence of distinct areas of non-adipogenic sarcomatous tissue is the histopathological characteristic of DD LPS. 7,9,11 Both WD and DD seem to have poor responses to either chemotherapy or radiotherapy, in contrast with the myxoid and pleomorphic subtypes, that are deemed as good responders.…”

Section: Discussionmentioning

confidence: 99%

“…6,15 In most patients, staging of the suspected LPS requires preoperative ultrasound assessment (depending on accessibility), MRI imaging, and core-needle biopsy retrieval. 9,15 Surgical resection of the malignancy remains the treatment of choice for liposarcomas, according to most major international societies' guidelines. 6,[15][16][17] Resection with clinically negative margins (R0) is the approach of choice, if feasible.…”

Section: Discussionmentioning

confidence: 99%

“…6 Unresectable retroperitoneal or intraabdominal disease follows the same recommendations, except for palliative surgery being performed far more often, due to disease complications. 6,9,16,19,21 Recurrence in liposarcoma patients is frequent. Approximately 50% of patients with grade II/III liposarcomas will develop either local disease recurrence or distant metastatic disease, with a median survival of 12 months.…”

Section: Discussionmentioning

confidence: 99%

“…22 In total, up to 24% of the patients with liposarcoma will have recurrent disease, no matter the grade or subtype, and 70% of the patients with a retroperitoneal liposarcoma will die from recurrence-related adverse effects. 8,9 Risk assessment of patients with primary liposarcoma, concerning future relapses, must include tumor grade, tumor size, and histological subtype. 13,14,23 Complete tumor resection, preferably on a single R0 excision, is the primary prognostic factor, affecting disease recurrence.…”

Section: Discussionmentioning

confidence: 99%

“…7,9,10 The presence of distinct areas of non-adipogenic sarcomatous tissue is the histopathological characteristic of DD LPS. 7,9,11 Both WD and DD seem to have poor responses to either chemotherapy or radiotherapy, in contrast with the myxoid and pleomorphic subtypes, that are deemed as good responders. 12-14 Among them, pleomorphic LPS is the most aggressive subtype, consisting mainly of lipoblasts of high-grade morphology, without resembling any known cell lineage, like the other subtypes.…”

Section: Discussionmentioning

confidence: 99%

See 4 more Smart Citations

Recurrent retroperitoneal liposarcoma: A case report and literature review

Mulita

Verras

Liolis

et al. 2021

Clinical Case Reports

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Recurrent retroperitoneal liposarcoma: A case report and literature review

Mulita

Verras

Liolis

et al. 2021

Clinical Case Reports

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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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Correlation Analysis Between Demographic, Surgical, and Pathological Characteristics with Local Recurrence‐Free Survival for Surgical Resected Retroperitoneal Liposarcoma

Zhao

Gao

et al. 2023

World j. surg.

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Background As the leading cause of mortality for retroperitoneal liposarcoma (RPLS) cases, postoperative recurrence has complicated and unclear risk factors. This study was conducted to explore the correlations between demographic, surgical, and pathological characteristics with local recurrence-free survival (LRFS) for surgical resected RPLS. Methods RPLS cases that underwent radical operation were considered to be included in this analysis. LRFS rates were estimated based on the Kaplan-Meier method and were compared between groups by the log-rank test. Cox proportional hazard regression models were constructed to identified the predictors of LRFS. Subsequently, the independent predictors acquired from multivariate analyses were used to construct a nomogram. Results 348 RPLS cases who underwent radical operation were included. Of the 348 cases, 333 had tumor recurrence or with a follow-up period C5 years. Thus, 296 (88.9%) of the 333 cases had recurrent disease, and the median LRFS duration of 296 recurrence cases was 17.0 (95% confidence interval (CI) 13.2-20.8) months. Multivariate analysis identified the preoperative neutrophil/lymphocyte ratio (NLR), surgical frequency, operative time, tumor shape, histological subtype, and tumor necrosis as independent predictors of LRFS. Based on above independent predictors, a nomogram was constructed to predict the 1-, 3-, and 5-year LRFS of surgical resected RPLS. Conclusion Elevated preoperative NLR, C2nd time surgical frequency, extended operation time, irregular tumor shape, no well-differentiated histological subtype, and tumor necrosis could be used as predictors of LRFS for surgical resected RPLS.

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Clinical features of multiply recurrent retroperitoneal liposarcoma: A single-center experience

Cited by 15 publications

References 15 publications

Recurrent retroperitoneal liposarcoma: A case report and literature review

Recurrent retroperitoneal liposarcoma: A case report and literature review

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

Correlation Analysis Between Demographic, Surgical, and Pathological Characteristics with Local Recurrence‐Free Survival for Surgical Resected Retroperitoneal Liposarcoma

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