Development and validation of a prognostic nomogram for predicting overall survival in patients with large retroperitoneal liposarcoma: a population-based cohort study

Development and validation of a prognostic nomogram for predicting overall survival in patients with large retroperitoneal liposarcoma: a population-based cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of a prognostic nomogram for predicting overall survival in patients with large retroperitoneal liposarcoma: a population-based cohort study Huan Deng, Zhenhua Lu, Bingrui Wang, Yajie wang, Lin Xiao, Yisheng Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5821949/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aimed to show the clinicopathological characteristics of large retroperitoneal liposarcoma (RLS) and to developed a customized nomogram model for those patients. Methods A total of 1735 patients diagnosed with RLS were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Among them, 1113 patients with a maximum tumor diameter greater than 150 mm were included for further analysis. Nomogram models were developed based on lasso and multivariate cox regression analyses. The 166 patients collected from the same period at First Medical Center, Chinese People Liberation Army General Hospital (CPLAGH), were used for external validations. The model was further validated through multiple dimensions. Results Larger tumor size in RLS was associated with worse survival outcomes (hazard ratio [HR] = 0.66, 95% confidence interval [CI]: 0.53–0.81, P < 0.05). Lasso and Cox regression analyses consistently identified age, TNM stage, occurrence pattern, histology, and surgery as important prognostic factors for OS. The constructed model demonstrated robust predictive performance, with better time-ROC (Time-Dependent Receiver Operating Characteristic) for 1-year (83.1%), 3-year (83.8%), and 5-year (81.4%) survival in training cohort. The concordance index (C-index) was approximately 0.80 in both the training and validation cohorts, reflecting excellent discriminatory ability of the model. Survival risk stratification analysis revealed significant differences in survival outcomes between the groups (HR = 4.12 [3.31–5.12], P < 0.001 in training cohort). Decision curve analysis (DCA) confirmed that the nomogram provided greater net benefits across a range of threshold probabilities. Conclusion This study identified important prognostic factors for survival in patients with large RLS and developed a reliable nomogram for predicting OS. The model’s strong predictive performance supports its use in personalized treatment strategies, improving prognosis assessment and clinical decision-making for these patients. retroperitoneal liposarcoma large surgery Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Retroperitoneal liposarcoma (RLS) is a rare malignancy that originates in the retroperitoneal space and represents the predominant form of retroperitoneal sarcoma. It accounts for approximately 0.07–0.2% of all malignancies and 12–40% of all liposarcomas[ 1 ]. According to the WHO (World Health Organization) classification, the histology of RLS can be further categorized into four subtypes: well-differentiated liposarcoma (WDL), dedifferentiated liposarcoma (DDL), myxoid liposarcoma (MLS), and pleomorphic liposarcoma (PLS). Among these, WDL and DDL are the predominant subtypes, comprising about 37–56% of primary retroperitoneal liposarcomas[ 2 ]. The prognosis of RLS is influenced by its histological subtype, with poorly differentiated tumors generally linked to higher rates of local recurrence and distant metastasis[ 3 ]. Although resection remains the primary treatment for RLS, the tumor exhibits a higher tendency for relapse following surgical intervention[ 4 ]. According to the previous literature, the resection margin and histologic subtype are the most important prognostic predictors of RFS and OS for RLS[ 2 , 5 , 6 ]. A substantial body of research indicates that the combined resection of adjacent organs, such as renal and gastrointestinal tissues, can significantly improve local outcomes by reducing the risk of recurrence and enhancing the effectiveness of treatment[ 7 – 9 ]. However, the complex anatomical features of RLS pose significant challenges, often hindering the surgeon's ability to achieve clear surgical margins, which is frequently associated with an unfavorable prognosis.[ 10 ]. Sometimes, complete capsule resection and radical surgical treatment cannot achieve a complete cure in RLS, which is a challenge for surgeons[ 11 ]. For larger RLS tumors, the extent of invasion and treatment challenges differ significantly from those with typical RLS. According to the eighth edition of the AJCC Cancer Staging Manual, tumors larger than 15 cm are classified as the T4 category[ 7 ]. Currently, there is no dedicated research addressing the survival prognosis of patients with T4 stage or the differences in clinical and pathological characteristics compared to those with smaller tumors. In clinical practice, we have observed that tumors with large volumes often invade complex abdominal structures, increasing surgical risks and difficulty[ 12 , 13 ]. The short-term and long-term prognosis of these patients differs significantly from that of those with smaller tumors. In our study, the term “large” was specifically defined as a tumor with a maximal diameter exceeding 150 mm (T4 stage)[ 13 , 14 ]. Compared to other types of RLS, a significant proportion of patients with large tumors have experienced rapid mortality, primarily due to the impacts of local recurrence or distant metastasis. The large tumor volume and its proximity to critical abdominal structures are key factors that distinguish large RLS in this disease, contributing to the increased complexity and difficulty in both diagnosis and treatment. The aim of this study was to comprehensively analyze the clinicopathological features and prognostic outcomes of large RLS. We collected retrospective case data from public SEER databases and conducted multidimensional analyses and validations to provide valuable insights for the clinical treatment and prognosis assessment of patients with large RLS. Additionally, survival and prognostic models were assessed to further elucidate the clinical trajectory of this specific RLS. The findings contribute valuable evidence to support the development of personalized clinical management strategies for RLS. Materials and Methods Patient selection The data utilized in this study were derived from two primary sources. The first source was the public SEER database, accessed through the SEER*Stat software version 8.4.3 provided by the National Cancer Institute ( https://seer.cancer.gov/datasoftware/ ). The second source was cases treated at the First Medical Center, CPLAGH from 2000 to 2020. This study was approved by the Protection of Human Subjects Committee of the CPLAGH. The cases of second source served as external validations. The screening of patients with RLS in the SEER database is shown in Supplementary Fig. 1 . However, cases with distant organ metastasis were missing from the second source. Finally, a total of 166 patients with T4 stage of RLS were collected from the second source. We extracted key demographic information, clinicopathological characteristics, treatment methods, and vital survival status data from the SEER database. The primary endpoints of this study were OS. Important definitions Overall survival (OS) was defined as the time from the surgery date to the time of the last follow-up or death. Primary RLS was defined as the initial diagnosis of an RLS tumor. Recurrent RLS was defined as a RLS tumour that relapsed at least once since the initial diagnosis. The definition of large RLS was specifically defined as a tumor with a maximal diameter exceeding 150 mm (T4 stage)[ 14 ]. WDL and MLS are histological low-grade tumors, and DDL and PLS are histological high-grade tumors[ 15 ]. The occurrence pattern of local or non-local were defined according to SEER manual ( https://seer.cancer.gov/seerstat/variables/seer/lrd-stage/ ) and previous study. Local ocurrence was defined as the tumor's origin site and main growth area are located within an abdominal compartment in this study[ 12 , 13 ]. The TNM stages were confirmed according to AJCC Retroperitoneal Soft Tissue Sarcoma Staging System (8th Edition, 2016)[ 16 ]. Statistical Analysis Categorical data are presented as frequencies (percentages) and compared using either the chi-square test or Fisher's exact test. The normality of continuous variables was assessed using the Shapiro-Wilk test. For variables that did not follow a normal distribution, data are reported as medians with interquartile ranges (IQR, Q1-Q3). Differences between two independent groups were compared using the non-parametric Mann-Whitney U test. The least absolute shrinkage and selection operator (LASSO) used to explore optimal predictors for OS. Selection of the optimal tuning parameter (λ) in the LASSO regression model using 10-fold cross-validation. The partial likelihood deviance is plotted against log(λ), and the optimal λ is determined at the point with the minimum mean cross-validated error. In the LASSO coefficient profiles, the vertical dashed line represents the optimal log(λ), where variables with non-zero coefficients are selected. Univariate Cox regression analysis was used to identify potential prognostic variables. Variables exhibiting multicollinearity, as indicated by a variance inflation factor (VIF) greater than 4, were excluded from further analysis. The remaining variables were then incorporated into the final multivariate Cox regression model. These models were constructed using the coxph function from the survival package in R. The prognostic variables identified from the training cohort were used to develop a nomogram for predicting the survival probability of patients at 1, 3, and 5 years. Each factor on the nomogram is linked to a specific point on the "Point" scale. The total score is obtained by summing the points for each variable. The probabilities of 1-, 3-, and 5-year OS are indicated by the intersection of the total point score with the corresponding bottom scales. Model calibration was conducted using the training set (70%), the internal validation set (30%), and an external cohort. The Time-Dependent ROC and c-index used to validate the discrimination of model[ 17 ]. Survival rates among different groups were compared using Kaplan-Meier survival curves. To assess the statistical significance of the differences between these groups, the log-rank test or Cox regression analysis was performed. Data analysis and visualization were conducted using R software (Version 4.3.1), with a two-sided P value of < 0.05 considered statistically significant. Results Clinicopathological features and survival outcomes Using the aforementioned inclusion criteria and procedures, a total of 1,735 cases were extracted from the SEER database. Based on the previously defined classifications, 1113 cases were allocated to the large group, while 622 cases were assigned to the non-large group. The large group comprised 550 female and 613 male patients, whereas the non-large group included 251 female and 371 male patients. The median age for the large group was 63 years (IQR, 53–71), compared to 65 years (IQR, 55–73) for the non-large group. The median tumor size in the large group was 250 mm (IQR, 200–310), while in the non-large group it was 100 mm (IQR, 68–130) (Table 1 ). Table 1 The clinical and pathological characteristics of two groups in RLS. Characteristics Large RLS group Non-large RLS group P value n 1113 622 Age, median (IQR) 63 (53, 71) 65 (55, 73) < 0.001 Tumor size, median (IQR) 250 (200, 310) 100 (68, 130) < 0.001 Sex, n (%) 0.065 Female 500 (44.9%) 251 (40.4%) Male 613 (55.1%) 371 (59.6%) Income, n (%) 0.816 High 505 (45.4%) 292 (46.9%) Middle 409 (36.7%) 223 (35.9%) Low 199 (17.9%) 107 (17.2%) City, n (%) 0.343 Metropolitan 997 (89.6%) 566 (91%) Nonmetropolitan 116 (10.4%) 56 (9%) AJCC T stage, n (%) < 0.001 T4 1113 (100%) 0 (0%) T3 0 (0%) 288 (46.3%) T1 0 (0%) 110 (17.7%) T2 0 (0%) 224 (36%) AJCC N stage, n (%) 0.026 N0 1094 (98.3%) 601 (96.6%) N1 19 (1.7%) 21 (3.4%) AJCC M stage, n (%) 0.454 M0 1048 (94.2%) 591 (95%) M1 65 (5.8%) 31 (5%) AJCC TNM stage, n (%) 0.012 III 374 (33.6%) 180 (28.9%) II 68 (6.1%) 62 (10%) I 596 (53.5%) 341 (54.8%) IV 75 (6.7%) 39 (6.3%) Radiotherapy, n (%) 0.101 No/Unknown 845 (75.9%) 450 (72.3%) Yes 268 (24.1%) 172 (27.7%) Chemotherapy, n (%) 0.807 No/Unknown 962 (86.4%) 535 (86%) Yes 151 (13.6%) 87 (14%) Ocurrence pattern, n (%) 0.014 Non_local 596 (53.5%) 295 (47.4%) Local 517 (46.5%) 327 (52.6%) Pathological grade, n (%) 0.009 Undifferentiated 205 (18.4%) 110 (17.7%) Moderately differentiated 75 (6.7%) 37 (5.9%) Poorly differentiated 185 (16.6%) 87 (14%) Well differentiated 491 (44.1%) 259 (41.6%) Unknown 157 (14.1%) 129 (20.7%) Ocurrence sequence, n (%) < 0.001 Recurrence 205 (18.4%) 168 (27%) Primary 908 (81.6%) 454 (73%) Tumors, n (%) 0.133 Single 1077 (96.8%) 593 (95.3%) Mutifocal 36 (3.2%) 29 (4.7%) Histology, n (%) 0.916 DDL 586 (52.7%) 338 (54.3%) WDL 445 (40%) 238 (38.3%) MLS 57 (5.1%) 32 (5.1%) PLS 25 (2.2%) 14 (2.3%) Surgery, n (%) < 0.001 Total surgical 660 (59.3%) 267 (42.9%) partial surgical 385 (34.6%) 291 (46.8%) N0 surgery 68 (6.1%) 64 (10.3%) Abbreviations: TNM, tumor-node-metastasis; WDL, well-differentiated liposarcoma; PLS, pleomorphic liposarcoma; MLS, myxoid liposarcoma; DDL, dedifferentiated liposarcoma; RLS, etroperitoneal liposarcoma. There were significant differences between the two groups in clinical and pathological characteristics, including AJCC TNM stage, occurrence pattern, pathological grade, occurrence sequence, and treatment outcomes (Table 1 , P < 0.05). We found that patients with large tumors have worse survival outcome (Fig. 1 A, P < 0.05, HR = 0.66 [95% CI: 0.53–0.81]). Subgroup analysis of overall survival in the large tumor group revealed that female patients tended to experience longer survival benefits ( Supplementary Fig. 2A ). As age increased, the survival benefit for these patients gradually decreased ( Supplementary Fig. 2B ). The number of tumors and the patients' residing city did not show significant long-term survival differences in this analysis ( Supplementary Fig. 2C-D ). However, these patients exhibited distinct characteristics in terms of occurrence pattern, chemotherapy/radiotherapy, pathological subtypes, TNM stage, and treatment outcomes ( Supplementary Fig. 2E-I, P < 0.001). Therefore, different clinical management strategies and personalized diagnosis and treatment should be given to patients with large RLS. Survival predictive factor screening and nomogram model establishment Building upon the previous study, we further investigated the prognostic factors associated with the large tumor. We randomly divided the cohorts into a training cohort and a validation cohort at a ratio of 7:3. A total of 779 patients were assigned in the training set, and 575 patients were allocated in the internal validation set ( Supplementary Table 1 ). The divided cohorts were comparable in terms of demographic and clinical features (P > 0.05). To identify relevant clinical and pathological predictors of survival outcomes in the training cohort, we performed Lasso regression analysis. Variables with evident multicollinearity, such as T stage, N stage, M stage and pathological grade, were excluded from the analysis. Following this, a total of 12 significant prognostic factors were selected and incorporated into the Lasso regression model for further evaluation (Fig. 1 B and 1 C). In the preliminary Lasso regression model, seven significant factors in the training cohort: age, sex, TNM stage, occurrence pattern, histology, tumor size and surgery were identified based on the value of λ min (Fig. 1 B and 1 C). To enhance the efficiency of prognostic model, we subsequently included the seven identified factors into the Cox regression model for further validation and refinement. The results indicated that sex and tumor size did not demonstrate significant prognostic value and were therefore excluded from the predictive model (Table 2 , P < 0.05). Ultimately, the results of the Cox regression analysis for the five significant factors were consistent with those obtained from the Lasso regression (λ 1−se, Fig. 1 B and 1 C), demonstrating strong coefficients[ 18 ]. Therefore, age, TNM stage, ocurrence pattern, histology and surgery were subsequently incorporated into the final predictive model. Based on the results of the stepwise analysis, we constructed a reliable nomogram model to predict OS in patients with large RLS (Fig. 1 D). Table 2 The univariate and multivariate Cox regression analysis on significant factors in Lasso regression. Characteristics Total(N) Univariate analysis Multivariate Cox analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Age 779 1.041 (1.032–1.050) < 0.001 1.035 (1.026–1.045) < 0.001 Sex 779 Male 417 Reference Reference Female 362 0.736 (0.600–0.902) 0.003 0.852 (0.691–1.051) 0.134 TNM 779 I 422 Reference Reference II 46 1.828 (1.167–2.862) 0.008 1.384 (0.852–2.249) 0.189 III 259 2.568 (2.050–3.218) < 0.001 1.682 (1.233–2.295) 0.001 IV 52 5.536 (3.966–7.728) < 0.001 2.847 (1.929–4.204) < 0.001 Ocurrence pattern 779 Non_local 409 Reference Reference Local 370 0.533 (0.433–0.656) < 0.001 0.729 (0.584–0.911) 0.006 Tumors 779 Single 756 Reference Reference Mutifocal 23 0.600 (0.320–1.126) 0.112 0.791 (0.419–1.494) 0.470 Histology 779 WDL 315 Reference Reference MLS 41 2.187 (1.432–3.341) < 0.001 1.942 (1.259–2.997) 0.003 PLS 20 1.765 (0.952–3.271) 0.071 1.277 (0.659–2.475) 0.468 DDL 403 2.940 (2.344–3.687) < 0.001 1.858 (1.360–2.537) < 0.001 Surgery 779 N0 surgery 50 Reference Reference Partial surgical 269 0.176 (0.123–0.253) < 0.001 0.238 (0.163–0.348) < 0.001 Total surgical 460 0.200 (0.142–0.280) < 0.001 0.210 (0.145–0.303) < 0.001 Abbreviations: TNM, tumor-node-metastasis; WDL, well-differentiated liposarcoma; PLS, pleomorphic liposarcoma; MLS, myxoid liposarcoma; DDL, dedifferentiated liposarcoma; CI, confidence interval. Multidimensional validation of the predictive model's performance The survival prediction model, based on a large dataset, demonstrates robust prognostic value for patients with large tumors using only a few simple clinical indicators (Fig. 1 D). We visualized the risk scores of patients using a heatmap based on the survival risk assessment from the model. The heatmap clearly demonstrates that patients in the low-risk group generally have longer survival times, while those in the high-risk group exhibit shorter survival times, indicating the model's strong risk stratification capability. Furthermore, the heatmap reflects the predictive value of the five prognostic factors in distinguishing survival outcomes between high-risk and low-risk groups (Fig. 1 E). We further utilized time-dependent ROC curves to dynamically evaluate the discriminatory ability and performance of the constructed model, incorporating both survival outcomes and survival time. The results demonstrated that the model exhibited well predictive accuracy for 1-year (AUC = 83.1%), 3-year (AUC = 83.8%) and 5-year (AUC = 81.4%) survival in the training cohort (Fig. 2 A). Additionally, in both the internal (Fig. 2 B) and external validation sets (Fig. 2 C), the AUC values for 1-, 3-, and 5-year survival ranged from 72.9–83.8%, demonstrating robust predictive performance over time. Similarly, the time-dependent concordance index (c-index) was applied to validate the discrimination ability and efficiency of the model. We found that the c-index for 1-, 3-, and 5-year survival approached nearly 80% in the training cohort (Fig. 2 D) and both validation cohorts (Fig. 2 E and 2 F). These results indicated favourable discrimination of the nomogram model. We further assessed the calibration ability of the nomogram by comparing the predicted survival probabilities with the actual survival probabilities using calibration plots. The model demonstrated overall good performance in the training set (Fig. 3 A- 3 C), internal validation set (Fig. 3 D- 3 F) and external validation set (Fig. 3 G- 3 I), with the predicted survival probabilities for 1-year, 3-year, and 5-year survival closely aligning with the actual survival probabilities. This indicates that the nomogram exhibits good calibration. In the external validation set (Fig. 3 G- 3 I), the overall trend remains reasonable, suggesting that the model has a certain degree of generalizability. We also utilized DCA curve to assess the clinical utility of the predictive model. By calculating net benefit across various threshold probabilities, and balancing the trade-off between true positives and false positives, we evaluated the model's practical value. DCA curves were presented for different time points (1-year, 3-year, 5-year) and datasets. In the training set (Fig. 4 A- 4 C), internal validation set (Fig. 4 D- 4 F) and external validation set (Fig. 4 G- 4 I), the black curve illustrates that the model's overall predictive performance surpasses that of individual predictor variables. The nomogram consistently demonstrated superior net benefit at each time point and across all datasets. Notably, in the external validation set (Fig. 4 G- 4 I), the nomogram maintained a higher net benefit, highlighting the model's well generalizability and its ability to preserve predictive accuracy in independent datasets. Its consistent performance and high generalizability further substantiate its potential as a reliable tool for survival prediction and clinical decision-making. Survival analysis based on risk stratification The median OS of patients with large RLS in this study was approximately 89 months (interquartile range [IQR]: 79–97 months) ( Supplementary Fig. 3 ). We used the nomogram model to calculate the total points for each individual and further stratified them based on risk. To validate the effectiveness of the constructed nomogram model, patients with a large RLS were divided into high-risk and low-risk groups. We initially stratified all patients into high-risk and low-risk groups within the training set (Fig. 5 A, HR = 4.12 [3.31–5.12], P < 0.001), internal validation set (Fig. 5 B, HR = 3.34 [2.40–4.46], P < 0.001), and external validation set (Fig. 5 C, HR = 2.73 [1.82–4.10], P < 0.001). The results showed that patients in the high-risk group had significantly shorter survival times, indicating that the predictive model demonstrated strong risk stratification capability. Additionally, we conducted subgroup risk stratification analyses based on key clinicopathological characteristics (Fig. 5 D- 5 K). The results showed that the model consistently exhibited strong predictive performance across all subgroups, underscoring its robust risk stratification capability. These findings highlight the model’s reliability in survival prediction and its potential to serve as a valuable tool for clinical prognosis assessment. Discussion To date, there are only a few case reports of large retroperitoneal liposarcoma available[ 19 , 20 ]. Only a few specialized surgeons have gained enough treatment experience from these patients. Although numerous studies have outlined the characteristics of RLS, this is the first large-scale, comprehensive study to provide a clinical analysis and develop a predictive nomogram model for large RLS. In the present study, we investigated the baseline and clinicopathological characteristics of large RLS and analyzed the prognostic factors of OS. The 5-year overall survival of patients with ordinary RLS usually exceeds 50% in most reports. Noriyuki Masaki et al. reported that the 5-year overall survival rates of patients with WDL and the DDL/myxoid subtype were 100% and 67.4%, respectively[ 8 ]. Alessandro Gronchi et al. reported a series of 144 patients affected by retroperitoneal liposarcoma over a 10-year time span, and the 5-year OS was 61.2%[ 21 ]. In our study, we collected 1,113 cases, making it a relatively large cohort, which allows for a more comprehensive set of findings. The 1-, 3-, 5-, 10-year overall survival rates were about 86%, 71%, 60%, 38%, respectively ( Fig. S3 ). It can be observed that the survival rate for this group of patients after 5 years is relatively low. Compared to non-large RLS, we could see that non-large survival rates were obviously better than those with large tumors (Fig. 1 A). There are many different aspects involved in large RLS, such as the large tumor occupying almost the entire abdominal cavity, close to or even wrapping around large important blood vessels[ 13 , 22 ]. These complicated anatomic characteristics make the surgical procedure very difficult, thereby affecting the survival rate of this group of patients These patients usually experience a complicated surgery, need combined resection, and have a long operation time and a high bleeding volume, which are factors related to serious postoperative complications[ 23 ]. In our previous study, we found that the median intraoperative bleeding volume reached 1500 ml for patients with giant RLS, and the median operation time was 280 minutes[ 13 ]. Many studies have shown that postoperative complications are closely associated with poor prognosis[ 23 – 26 ]. In our previous study, we also found that postoperative complications significantly influenced OS. Specifically, complications graded II-IV (Clavien-Dindo) were associated with a shorter 5-year OS compared to grade I complications[ 13 ]. Actually, this is an indirect reflection of the complicated dilemma in treating large RLS, and serious complications mean that large tumors are difficult for surgeons. In addition, some studies also illustrated that the RLS histologic subtypes have a core effect on prognosis[ 6 , 27 , 28 ]. Noriyuki Masaki et al. reported 40 recurrent retroperitoneal liposarcoma cases and 23 patients with initial WDL, and pathological progression (PP) to DDL was observed in the re-recurrent tumors[ 8 ]. This indicates that RLS has a relapse tendency and that low-grade tumors may progress to high-grade tumors after surgical treatment, and this phenomenon inevitably impacts the outcomes of patients with RLS. However, in patients with large RLS, the increase in tumor size somewhat diminishes the impact of histological subtypes on survival prognosis. In other words, pathological subtypes become less influential in predicting outcomes for these patients, which is a distinctive characteristic of patients with large RLS. Our study investigated the clinicopathological characteristics of the large RLS and explored the significant prognostic factors that are correlated with OS. Lasso and multivariate Cox regression analysis revealed that age, TNM stage, ocurrence pattern, histology and surgery were significant prognostic factors for large RLS. Some results from the cox regression analysis are consistent with those of previous studies[ 29 , 30 ]. In the preceding analysis, we have highlighted the significance of histology in the prognostic assessment of RLS, and explained how surgical complexity and postoperative complications influence prognosis[ 27 ]. Postoperative TNM staging emerges as a critical prognostic factor for patients with large RLS[ 7 ]. By incorporating key variables such as tumor size, lymph node metastasis and distant metastasis, TNM stage offers a more comprehensive evaluation of the essential characteristics and status of patients. For large RLS, the broader surgical resection often leads to the removal of a greater number of lymph nodes, highlighting the importance of conducting a detailed examination of these lymph nodes for accurate staging and prognosis. Study limitations Our study has several limitations. First, as a retrospective analysis primarily relying on medical records from our institution and the SEER database, it is inherently limited by the absence of prospective data. Second, critical therapeutic indicators, such as information on adjuvant therapies, were either missing or unknown for the majority of cases. Third, key pathological data, including tumor necrosis and mitotic count, which are essential for accurate grading, were unavailable for some cases in the external cohort, complicating the pathological assessments. Fourth, given the long study period, we did not address the potential impact of advancements in treatment techniques and other factors on prognosis. Finally, due to the limited number of cases in the external cohorts, 10-year validation data were not available, further limiting the robustness of our findings. Conclusions This study investigated the clinicopathological characteristics and survival outcomes of 1735 patients with large RLS. A prognostic nomogram for predicting OS was developed using lasso and cox regression analyses. The model's predictive accuracy and robustness were validated through time-dependent ROC analysis and C-index, demonstrating excellent discriminatory ability across training and validation cohorts. This rigorously validated nomogram offers a reliable tool for facilitating personalized treatment strategies and enhancing prognosis assessment in patients with large RLS, providing valuable insights for clinical decision-making. Declarations Funding: no funding was received for this study Competing interests : There are no conflicts of interest pertained to this submission. Availability of data and materials : Data analyzed in the study are available upon request pending application and authority approval. Requests to access the datasets should be directed to Yisheng Pan, [email protected] . Ethics approval and consent to participate This study was approved by the Protection of Human Subjects Committee of the Chinese People’s Liberation Army General Hospital. Consent for publication All patients signed publish consent forms for the study. Acknowledgments The authors are grateful to their colleagues for helping collect and analyze data at the Chinese PLA general hospital. 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RADIOLOGY 2016, 281(3):947–957. Yang L, Yang J, Zhou X, Huang L, Zhao W, Wang T, Zhuang J, Tian J. Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. EUR RADIOL. 2019;29(5):2196–206. Herzberg J, Niehaus K, Holl-Ulrich K, Honarpisheh H, Guraya SY, Strate T. Giant retroperitoneal liposarcoma: A case report and literature review. J TAIBAH UNIV MED SC. 2019;14(5):466–71. Salemis NS, Tsiambas E, Karameris A, Tsohataridis E. Giant Retroperitoneal Liposarcoma with Mixed Histological Pattern: A Rare Presentation and Literature Review. J GASTROINTEST CANC. 2009;40(3–4):138–41. Gronchi A, Collini P, Miceli R, Valeri B, Renne SL, Dagrada G, Fiore M, Sanfilippo R, Barisella M, Colombo C, et al. Myogenic differentiation and histologic grading are major prognostic determinants in retroperitoneal liposarcoma. AM J SURG PATHOL. 2015;39(3):383–93. Tseng WW, Madewell JE, Wei W, Somaiah N, Lazar AJ, Ghadimi MP, Hoffman A, Pisters PWT, Lev DC, Pollock RE. Locoregional Disease Patterns in Well-Differentiated and Dedifferentiated Retroperitoneal Liposarcoma: Implications for the Extent of Resection? ANN SURG ONCOL. 2014;21(7):2136–43. Tjeertes EKM, Ultee KHJ, Stolker RJ, Verhagen HJM, Bastos Gonçalves FM, Hoofwijk AGM, Hoeks SE. Perioperative Complications are Associated With Adverse Long-Term Prognosis and Affect the Cause of Death After General Surgery. WORLD J SURG. 2016;40(11):2581–90. Yonezawa N, Murakami H, Demura S, Kato S, Yoshioka K, Shinmura K, Yokogawa N, Shimizu T, Oku N, Kitagawa R, et al. Perioperative Complications and Prognosis of Curative Surgical Resection for Spinal Metastases in Elderly Patients. WORLD NEUROSURG. 2020;137:e144–51. Beck C, Weber K, Brunner M, Agaimy A, Semrau S, Grützmann R, Schellerer V, Merkel S. The influence of postoperative complications on long-term prognosis in patients with colorectal carcinoma. INT J COLORECTAL DIS. 2020;35(6):1055–66. Harimoto N, Shirabe K, Ikegami T, Yoshizumi T, Maeda T, Kajiyama K, Yamanaka T, Maehara Y. Postoperative complications are predictive of poor prognosis in hepatocellular carcinoma. J SURG RES. 2015;199(2):470–7. Bartlett EK, Curtin CE, Seier K, Qin L, Hameed M, Yoon SS, Crago AM, Brennan MF, Singer S. Histologic Subtype Defines the Risk and Kinetics of Recurrence and Death for Primary Extremity/Truncal Liposarcoma. ANN SURG. 2021;273(6):1189–96. Gronchi A, Strauss DC, Miceli R, Bonvalot S, Swallow CJ, Hohenberger P, Van Coevorden F, Rutkowski P, Callegaro D, Hayes AJ, et al. Variability in Patterns of Recurrence After Resection of Primary Retroperitoneal Sarcoma (RPS). ANN SURG. 2016;263(5):1002–9. Zhuang A, Wu Q, Tong H, Zhang Y, Lu W. Development and Validation of a Nomogram for Predicting Recurrence-Free Survival of Surgical Resected Retroperitoneal Liposarcoma. CANCER MANAG RES. 2021;13:6633–9. Nessim C, Raut CP, Callegaro D, Barretta F, Miceli R, Fairweather M, Blay J, Strauss D, Rutkowski P, Ahuja N, et al. Analysis of Differentiation Changes and Outcomes at Time of First Recurrence of Retroperitoneal Liposarcoma by Transatlantic Australasian Retroperitoneal Sarcoma Working Group (TARPSWG). ANN SURG ONCOL. 2021;28(12):7854–63. Additional Declarations No competing interests reported. Supplementary Files Table.S1.docx Fig.S1.jpg Fig.S2.jpg Fig.S3.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5821949","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401913959,"identity":"840ce116-8e96-416d-a5dd-86f324452e0a","order_by":0,"name":"Huan Deng","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Deng","suffix":""},{"id":401913960,"identity":"a4715777-b665-4946-9217-95d89b1ef63a","order_by":1,"name":"Zhenhua Lu","email":"","orcid":"","institution":"Peking University Cancer Hospital \u0026 Institute","correspondingAuthor":false,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Lu","suffix":""},{"id":401913961,"identity":"f792d35a-1af2-480a-8a5b-155b482d85e2","order_by":2,"name":"Bingrui Wang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bingrui","middleName":"","lastName":"Wang","suffix":""},{"id":401913962,"identity":"acb83f3c-7af1-43d6-9f99-32ecc2e32559","order_by":3,"name":"Yajie wang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yajie","middleName":"","lastName":"wang","suffix":""},{"id":401913967,"identity":"d97a904e-3526-40f1-9eb1-15003121b4da","order_by":4,"name":"Lin Xiao","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Xiao","suffix":""},{"id":401913968,"identity":"edb4ea19-f934-4ed7-8bd5-ad166ce79246","order_by":5,"name":"Yisheng Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACe/keMwkQg42BB0TZ8PDzN+DXYjjjDIqWNBnJGQfwazG4kZZsAGGCtRy2MWhIIGRL8sHHBTV3Evv4zwIZv87zGDAcYPzwMQePXyQSGw7POPYssU0iL9l4Zt9tHnPmBmbJmdvw2QLUwsN2GKiFx0yat+c2j2XDATZmXjxaDG6AtPwDauE/Y/6bt+ccj8GBBCK08LYBtTDkmDHz/DhAWIvhjIMNh2f2HTZuk8gxluZtSOaRnHGwGa9f7OUbGw4XfDssO7//jOFnnj929vz8zQc/fMSjBQSYgdixAcRibAOTDfjVQ7XYQ5h/CCoeBaNgFIyCEQgAoI1ZtoH+8RYAAAAASUVORK5CYII=","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yisheng","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2025-01-13 17:53:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5821949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5821949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73899307,"identity":"13bf91ba-83ac-4665-b424-c02dd216dba2","added_by":"auto","created_at":"2025-01-15 17:07:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4639789,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic analysis and nomogram development for patients with large RLS. (A) Kaplan-Meier survival curves comparing OS between two groups (Group 1: Large group; Group 2: Non-group); (B) Selection of the optimal tuning parameter (λ) in the LASSO regression model using 10-fold cross-validation. The partial likelihood deviance is plotted against log(λ), and the optimal λ is determined at the point with the minimum mean cross-validated error; (C) LASSO coefficient profiles of the included variables. The vertical dashed line represents the optimal log(λ), where variables with non-zero coefficients are selected; (D) Nomogram for predicting 1-year, 3-year, and 5-year OS for patients with large RLS; (E) Risk score distribution and stratification of patients into low-risk and high-risk groups. The scatterplot demonstrates risk scores, survival times, and survival status (alive = 0, deceased = 1) along with key patient characteristics, including age, type of surgery, histology, TNM stage, and occurrence pattern (local vs. non-local). The dashed line separates the low-risk group from the high-risk group, as determined by the cutoff value. Abbreviations: OS, overall survival; TNM, tumor-node-metastasis; WDL, well-differentiated liposarcoma; PLS, pleomorphic liposarcoma; MLS, myxoid liposarcoma; DDL, dedifferentiated liposarcoma; RLS, etroperitoneal liposarcoma, LASSO, least absolute shrinkage and selection operator.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5821949/v1/02401f1b4eb04a1312f0bf75.jpg"},{"id":73899314,"identity":"8f339508-584a-4694-ac35-0975d5294c8d","added_by":"auto","created_at":"2025-01-15 17:07:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3548851,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the predictive performance of the prognostic model for large RLS. (A-C) Time-dependent receiver operating characteristic (ROC) curves for 1-year, 3-year, and 5-year OS predictions in the training cohort (A), internal validation cohort (B), and external validation cohort (C). The area under the curve (AUC) values for each time point indicate the model's discriminative ability; (D-F) Calibration of the model using the time-dependent concordance index (C-index) across the training cohort (D), internal validation cohort (E), and external validation cohort (F). The dashed horizontal line represents the reference level for model performance; Abbreviations: ROC, receiver operating characteristic; AUC, area under the curve; OS, overall survival; C-index, concordance index; RLS, etroperitoneal liposarcoma, .\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5821949/v1/71b464dec4858cabdbd388ca.jpg"},{"id":73899318,"identity":"419ebf50-ae7e-4950-96bc-3daad88e0b43","added_by":"auto","created_at":"2025-01-15 17:07:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4984989,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for the nomogram predicting 1-year, 3-year, and 5-year OS in large RLS across different cohorts. (A-C) Calibration curves for the training cohort at 1-year (A), 3-year (B), and 5-year (C) OS; (D-F) Calibration curves for the internal validation cohort at 1-year (D), 3-year (E), and 5-year (F) OS; (G-I) Calibration curves for the external validation cohort at 1-year (G), 3-year (H), and 5-year (I) OS. The x-axis represents the nomogram-predicted survival probability, while the y-axis represents the observed fraction survival probability. The solid diagonal line indicates the ideal prediction, where predicted probabilities perfectly align with observed outcomes. Blue lines show the model's performance, with error bars representing the 95% confidence intervals. The proximity of the calibration curve to the diagonal line demonstrates the accuracy and reliability of the nomogram predictions. Abbreviations: OS, overall survival.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5821949/v1/b5088098375d60a723df89a8.jpg"},{"id":73899313,"identity":"1bad9997-408c-4e3f-ba4d-069ff38da90b","added_by":"auto","created_at":"2025-01-15 17:07:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5685077,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curve for the nomogram predicting 1-year, 3-year, and 5-year OS in large RLS across different cohorts. (A-C) DCA for the training cohort at 1-year (A), 3-year (B), and 5-year (C) OS; (D-F) DCA for the internal validation cohort at 1-year (D), 3-year (E), and 5-year (F) OS; (G-I) DCA for the external validation cohort at 1-year (G), 3-year (H), and 5-year (I) OS. The x-axis represents the threshold probability, while the y-axis represents the net benefit. The nomogram is compared to individual prognostic factors, including TNM stage, occurrence (local vs. non-local), histology (Hist), type of surgery, and age. The \"All positive\" line assumes all patients have the event, while the \"All negative\" line assumes none have the event. Abbreviations: OS, overall survival; TNM, tumor-node-metastasis; DCA, decision curve analysis; RLS, retroperitoneal liposarcoma.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5821949/v1/00e3a85270336e9201599ae3.jpg"},{"id":73899329,"identity":"696b0f59-5794-4764-9154-c3104b781b75","added_by":"auto","created_at":"2025-01-15 17:07:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4546705,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves stratified by risk groups and clinical characteristics for patients with large RLS in the training, internal validation, and external validation cohorts. (A-C) Kaplan-Meier survival curves comparing OS between low-risk and high-risk groups in the training cohort; internal validation cohort (B), and external validation cohort (C); (D-K) Kaplan-Meier survival curves comparing OS between low-risk and high-risk groups stratified by clinical characteristics, including histological subtypes (D-E), occurrence (non-local vs. local; F-G), TNM stages (H-I), and type of surgery (total surgery, partial surgery and no surgery; J-K). The number of patients at risk at each time point is shown below each curve. Abbreviations: OS, overall survival; TNM, tumor-node-metastasis; RLS, retroperitoneal liposarcoma.\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5821949/v1/1152daed0e0a76dbc46621da.jpg"},{"id":73902659,"identity":"75c601ed-594b-4c44-be80-da98619a0310","added_by":"auto","created_at":"2025-01-15 18:02:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12003103,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5821949/v1/358ad62b-d0f4-4494-9c3f-463033ca7f35.pdf"},{"id":73900539,"identity":"e837ecb5-ae60-424a-9069-063cb48743f0","added_by":"auto","created_at":"2025-01-15 17:23:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20628,"visible":true,"origin":"","legend":"","description":"","filename":"Table.S1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5821949/v1/6ddb0cc55e8f1639a1b7d2bc.docx"},{"id":73900305,"identity":"af049f79-4229-4a1f-b449-bf4893f70791","added_by":"auto","created_at":"2025-01-15 17:15:01","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":160535,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5821949/v1/10989c64b6de06b47996a2d1.jpg"},{"id":73899306,"identity":"28ab7d49-4a27-4b18-9ada-5a82b3d1e51e","added_by":"auto","created_at":"2025-01-15 17:07:01","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4584495,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5821949/v1/e1cde4bd9de648975ce5cba4.jpg"},{"id":73900303,"identity":"a9bf83c3-66e8-4f78-9b95-0e94447d4e06","added_by":"auto","created_at":"2025-01-15 17:15:01","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":921426,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5821949/v1/c2cfe22e96f8212e602a7c2f.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a prognostic nomogram for predicting overall survival in patients with large retroperitoneal liposarcoma: a population-based cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRetroperitoneal liposarcoma (RLS) is a rare malignancy that originates in the retroperitoneal space and represents the predominant form of retroperitoneal sarcoma. It accounts for approximately 0.07\u0026ndash;0.2% of all malignancies and 12\u0026ndash;40% of all liposarcomas[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the WHO (World Health Organization) classification, the histology of RLS can be further categorized into four subtypes: well-differentiated liposarcoma (WDL), dedifferentiated liposarcoma (DDL), myxoid liposarcoma (MLS), and pleomorphic liposarcoma (PLS). Among these, WDL and DDL are the predominant subtypes, comprising about 37\u0026ndash;56% of primary retroperitoneal liposarcomas[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The prognosis of RLS is influenced by its histological subtype, with poorly differentiated tumors generally linked to higher rates of local recurrence and distant metastasis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough resection remains the primary treatment for RLS, the tumor exhibits a higher tendency for relapse following surgical intervention[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to the previous literature, the resection margin and histologic subtype are the most important prognostic predictors of RFS and OS for RLS[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A substantial body of research indicates that the combined resection of adjacent organs, such as renal and gastrointestinal tissues, can significantly improve local outcomes by reducing the risk of recurrence and enhancing the effectiveness of treatment[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the complex anatomical features of RLS pose significant challenges, often hindering the surgeon's ability to achieve clear surgical margins, which is frequently associated with an unfavorable prognosis.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Sometimes, complete capsule resection and radical surgical treatment cannot achieve a complete cure in RLS, which is a challenge for surgeons[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor larger RLS tumors, the extent of invasion and treatment challenges differ significantly from those with typical RLS. According to the eighth edition of the AJCC Cancer Staging Manual, tumors larger than 15 cm are classified as the T4 category[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Currently, there is no dedicated research addressing the survival prognosis of patients with T4 stage or the differences in clinical and pathological characteristics compared to those with smaller tumors. In clinical practice, we have observed that tumors with large volumes often invade complex abdominal structures, increasing surgical risks and difficulty[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The short-term and long-term prognosis of these patients differs significantly from that of those with smaller tumors.\u003c/p\u003e \u003cp\u003eIn our study, the term \u0026ldquo;large\u0026rdquo; was specifically defined as a tumor with a maximal diameter exceeding 150 mm (T4 stage)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Compared to other types of RLS, a significant proportion of patients with large tumors have experienced rapid mortality, primarily due to the impacts of local recurrence or distant metastasis. The large tumor volume and its proximity to critical abdominal structures are key factors that distinguish large RLS in this disease, contributing to the increased complexity and difficulty in both diagnosis and treatment.\u003c/p\u003e \u003cp\u003eThe aim of this study was to comprehensively analyze the clinicopathological features and prognostic outcomes of large RLS. We collected retrospective case data from public SEER databases and conducted multidimensional analyses and validations to provide valuable insights for the clinical treatment and prognosis assessment of patients with large RLS. Additionally, survival and prognostic models were assessed to further elucidate the clinical trajectory of this specific RLS. The findings contribute valuable evidence to support the development of personalized clinical management strategies for RLS.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003eThe data utilized in this study were derived from two primary sources. The first source was the public SEER database, accessed through the SEER*Stat software version 8.4.3 provided by the National Cancer Institute (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://seer.cancer.gov/datasoftware/\u003c/span\u003e\u003cspan address=\"https://seer.cancer.gov/datasoftware/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The second source was cases treated at the First Medical Center, CPLAGH from 2000 to 2020. This study was approved by the Protection of Human Subjects Committee of the CPLAGH. The cases of second source served as external validations. The screening of patients with RLS in the SEER database is shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. However, cases with distant organ metastasis were missing from the second source. Finally, a total of 166 patients with T4 stage of RLS were collected from the second source. We extracted key demographic information, clinicopathological characteristics, treatment methods, and vital survival status data from the SEER database. The primary endpoints of this study were OS.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImportant definitions\u003c/h3\u003e\n\u003cp\u003eOverall survival (OS) was defined as the time from the surgery date to the time of the last follow-up or death. Primary RLS was defined as the initial diagnosis of an RLS tumor. Recurrent RLS was defined as a RLS tumour that relapsed at least once since the initial diagnosis. The definition of large RLS was specifically defined as a tumor with a maximal diameter exceeding 150 mm (T4 stage)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. WDL and MLS are histological low-grade tumors, and DDL and PLS are histological high-grade tumors[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The occurrence pattern of local or non-local were defined according to SEER manual (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://seer.cancer.gov/seerstat/variables/seer/lrd-stage/\u003c/span\u003e\u003cspan address=\"https://seer.cancer.gov/seerstat/variables/seer/lrd-stage/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and previous study. Local ocurrence was defined as the tumor's origin site and main growth area are located within an abdominal compartment in this study[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The TNM stages were confirmed according to AJCC Retroperitoneal Soft Tissue Sarcoma Staging System (8th Edition, 2016)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eCategorical data are presented as frequencies (percentages) and compared using either the chi-square test or Fisher's exact test. The normality of continuous variables was assessed using the Shapiro-Wilk test. For variables that did not follow a normal distribution, data are reported as medians with interquartile ranges (IQR, Q1-Q3). Differences between two independent groups were compared using the non-parametric Mann-Whitney U test.\u003c/p\u003e \u003cp\u003eThe least absolute shrinkage and selection operator (LASSO) used to explore optimal predictors for OS. Selection of the optimal tuning parameter (λ) in the LASSO regression model using 10-fold cross-validation. The partial likelihood deviance is plotted against log(λ), and the optimal λ is determined at the point with the minimum mean cross-validated error. In the LASSO coefficient profiles, the vertical dashed line represents the optimal log(λ), where variables with non-zero coefficients are selected. Univariate Cox regression analysis was used to identify potential prognostic variables. Variables exhibiting multicollinearity, as indicated by a variance inflation factor (VIF) greater than 4, were excluded from further analysis. The remaining variables were then incorporated into the final multivariate Cox regression model. These models were constructed using the coxph function from the survival package in R. The prognostic variables identified from the training cohort were used to develop a nomogram for predicting the survival probability of patients at 1, 3, and 5 years. Each factor on the nomogram is linked to a specific point on the \"Point\" scale. The total score is obtained by summing the points for each variable. The probabilities of 1-, 3-, and 5-year OS are indicated by the intersection of the total point score with the corresponding bottom scales. Model calibration was conducted using the training set (70%), the internal validation set (30%), and an external cohort. The Time-Dependent ROC and c-index used to validate the discrimination of model[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSurvival rates among different groups were compared using Kaplan-Meier survival curves. To assess the statistical significance of the differences between these groups, the log-rank test or Cox regression analysis was performed. Data analysis and visualization were conducted using R software (Version 4.3.1), with a two-sided P value of \u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathological features and survival outcomes\u003c/h2\u003e \u003cp\u003eUsing the aforementioned inclusion criteria and procedures, a total of 1,735 cases were extracted from the SEER database. Based on the previously defined classifications, 1113 cases were allocated to the large group, while 622 cases were assigned to the non-large group. The large group comprised 550 female and 613 male patients, whereas the non-large group included 251 female and 371 male patients. The median age for the large group was 63 years (IQR, 53\u0026ndash;71), compared to 65 years (IQR, 55\u0026ndash;73) for the non-large group. The median tumor size in the large group was 250 mm (IQR, 200\u0026ndash;310), while in the non-large group it was 100 mm (IQR, 68\u0026ndash;130) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical and pathological characteristics of two groups in RLS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge RLS\u003c/p\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-large RLS group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (53, 71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (55, 73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250 (200, 310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (68, 130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e613 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e371 (59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e505 (45.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292 (46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e409 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetropolitan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e997 (89.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e566 (91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonmetropolitan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC T stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1113 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288 (46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC N stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1094 (98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e601 (96.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC M stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1048 (94.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e591 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC TNM stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e374 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e596 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e341 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e845 (75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450 (72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e962 (86.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e535 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcurrence pattern, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon_local\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e596 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e517 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological grade, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorly differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e491 (44.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcurrence sequence, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e908 (81.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e454 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumors, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1077 (96.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e593 (95.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutifocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e586 (52.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e445 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal surgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e660 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epartial surgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e291 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0 surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: TNM, tumor-node-metastasis; WDL, well-differentiated liposarcoma; PLS, pleomorphic liposarcoma; MLS, myxoid liposarcoma; DDL, dedifferentiated liposarcoma; RLS, etroperitoneal liposarcoma.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere were significant differences between the two groups in clinical and pathological characteristics, including AJCC TNM stage, occurrence pattern, pathological grade, occurrence sequence, and treatment outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We found that patients with large tumors have worse survival outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, HR\u0026thinsp;=\u0026thinsp;0.66 [95% CI: 0.53\u0026ndash;0.81]). Subgroup analysis of overall survival in the large tumor group revealed that female patients tended to experience longer survival benefits (\u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u003c/b\u003e). As age increased, the survival benefit for these patients gradually decreased (\u003cb\u003eSupplementary Fig.\u0026nbsp;2B\u003c/b\u003e). The number of tumors and the patients' residing city did not show significant long-term survival differences in this analysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;2C-D\u003c/b\u003e). However, these patients exhibited distinct characteristics in terms of occurrence pattern, chemotherapy/radiotherapy, pathological subtypes, TNM stage, and treatment outcomes (\u003cb\u003eSupplementary Fig.\u0026nbsp;2E-I, P\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, different clinical management strategies and personalized diagnosis and treatment should be given to patients with large RLS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSurvival predictive factor screening and nomogram model establishment\u003c/h2\u003e \u003cp\u003eBuilding upon the previous study, we further investigated the prognostic factors associated with the large tumor. We randomly divided the cohorts into a training cohort and a validation cohort at a ratio of 7:3. A total of 779 patients were assigned in the training set, and 575 patients were allocated in the internal validation set (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). The divided cohorts were comparable in terms of demographic and clinical features (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). To identify relevant clinical and pathological predictors of survival outcomes in the training cohort, we performed Lasso regression analysis. Variables with evident multicollinearity, such as T stage, N stage, M stage and pathological grade, were excluded from the analysis. Following this, a total of 12 significant prognostic factors were selected and incorporated into the Lasso regression model for further evaluation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eIn the preliminary Lasso regression model, seven significant factors in the training cohort: age, sex, TNM stage, occurrence pattern, histology, tumor size and surgery were identified based on the value of λ\u003csub\u003emin\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). To enhance the efficiency of prognostic model, we subsequently included the seven identified factors into the Cox regression model for further validation and refinement. The results indicated that sex and tumor size did not demonstrate significant prognostic value and were therefore excluded from the predictive model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Ultimately, the results of the Cox regression analysis for the five significant factors were consistent with those obtained from the Lasso regression (λ\u003csub\u003e1\u0026minus;se,\u003c/sub\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), demonstrating strong coefficients[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, age, TNM stage, ocurrence pattern, histology and surgery were subsequently incorporated into the final predictive model. Based on the results of the stepwise analysis, we constructed a reliable nomogram model to predict OS in patients with large RLS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe univariate and multivariate Cox regression analysis on significant factors in Lasso regression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate Cox analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.041 (1.032\u0026ndash;1.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.035 (1.026\u0026ndash;1.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.736 (0.600\u0026ndash;0.902)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.852 (0.691\u0026ndash;1.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.828 (1.167\u0026ndash;2.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.384 (0.852\u0026ndash;2.249)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.568 (2.050\u0026ndash;3.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.682 (1.233\u0026ndash;2.295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.536 (3.966\u0026ndash;7.728)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.847 (1.929\u0026ndash;4.204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcurrence pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon_local\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.533 (0.433\u0026ndash;0.656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.729 (0.584\u0026ndash;0.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutifocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.600 (0.320\u0026ndash;1.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.791 (0.419\u0026ndash;1.494)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.187 (1.432\u0026ndash;3.341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.942 (1.259\u0026ndash;2.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.765 (0.952\u0026ndash;3.271)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.277 (0.659\u0026ndash;2.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.940 (2.344\u0026ndash;3.687)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.858 (1.360\u0026ndash;2.537)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0 surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial surgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176 (0.123\u0026ndash;0.253)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.238 (0.163\u0026ndash;0.348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal surgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.200 (0.142\u0026ndash;0.280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.210 (0.145\u0026ndash;0.303)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: TNM, tumor-node-metastasis; WDL, well-differentiated liposarcoma; PLS, pleomorphic liposarcoma; MLS, myxoid liposarcoma; DDL, dedifferentiated liposarcoma; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultidimensional validation of the predictive model's performance\u003c/h3\u003e\n\u003cp\u003eThe survival prediction model, based on a large dataset, demonstrates robust prognostic value for patients with large tumors using only a few simple clinical indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). We visualized the risk scores of patients using a heatmap based on the survival risk assessment from the model. The heatmap clearly demonstrates that patients in the low-risk group generally have longer survival times, while those in the high-risk group exhibit shorter survival times, indicating the model's strong risk stratification capability. Furthermore, the heatmap reflects the predictive value of the five prognostic factors in distinguishing survival outcomes between high-risk and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eWe further utilized time-dependent ROC curves to dynamically evaluate the discriminatory ability and performance of the constructed model, incorporating both survival outcomes and survival time. The results demonstrated that the model exhibited well predictive accuracy for 1-year (AUC\u0026thinsp;=\u0026thinsp;83.1%), 3-year (AUC\u0026thinsp;=\u0026thinsp;83.8%) and 5-year (AUC\u0026thinsp;=\u0026thinsp;81.4%) survival in the training cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Additionally, in both the internal (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and external validation sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), the AUC values for 1-, 3-, and 5-year survival ranged from 72.9\u0026ndash;83.8%, demonstrating robust predictive performance over time. Similarly, the time-dependent concordance index (c-index) was applied to validate the discrimination ability and efficiency of the model. We found that the c-index for 1-, 3-, and 5-year survival approached nearly 80% in the training cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) and both validation cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). These results indicated favourable discrimination of the nomogram model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further assessed the calibration ability of the nomogram by comparing the predicted survival probabilities with the actual survival probabilities using calibration plots. The model demonstrated overall good performance in the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), internal validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF) and external validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI), with the predicted survival probabilities for 1-year, 3-year, and 5-year survival closely aligning with the actual survival probabilities. This indicates that the nomogram exhibits good calibration. In the external validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI), the overall trend remains reasonable, suggesting that the model has a certain degree of generalizability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also utilized DCA curve to assess the clinical utility of the predictive model. By calculating net benefit across various threshold probabilities, and balancing the trade-off between true positives and false positives, we evaluated the model's practical value. DCA curves were presented for different time points (1-year, 3-year, 5-year) and datasets. In the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), internal validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF) and external validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI), the black curve illustrates that the model's overall predictive performance surpasses that of individual predictor variables. The nomogram consistently demonstrated superior net benefit at each time point and across all datasets. Notably, in the external validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI), the nomogram maintained a higher net benefit, highlighting the model's well generalizability and its ability to preserve predictive accuracy in independent datasets. Its consistent performance and high generalizability further substantiate its potential as a reliable tool for survival prediction and clinical decision-making.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSurvival analysis based on risk stratification\u003c/h3\u003e\n\u003cp\u003eThe median OS of patients with large RLS in this study was approximately 89 months (interquartile range [IQR]: 79\u0026ndash;97 months) (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). We used the nomogram model to calculate the total points for each individual and further stratified them based on risk. To validate the effectiveness of the constructed nomogram model, patients with a large RLS were divided into high-risk and low-risk groups. We initially stratified all patients into high-risk and low-risk groups within the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, HR\u0026thinsp;=\u0026thinsp;4.12 [3.31\u0026ndash;5.12], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), internal validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, HR\u0026thinsp;=\u0026thinsp;3.34 [2.40\u0026ndash;4.46], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and external validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, HR\u0026thinsp;=\u0026thinsp;2.73 [1.82\u0026ndash;4.10], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results showed that patients in the high-risk group had significantly shorter survival times, indicating that the predictive model demonstrated strong risk stratification capability. Additionally, we conducted subgroup risk stratification analyses based on key clinicopathological characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK). The results showed that the model consistently exhibited strong predictive performance across all subgroups, underscoring its robust risk stratification capability. These findings highlight the model\u0026rsquo;s reliability in survival prediction and its potential to serve as a valuable tool for clinical prognosis assessment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo date, there are only a few case reports of large retroperitoneal liposarcoma available[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Only a few specialized surgeons have gained enough treatment experience from these patients. Although numerous studies have outlined the characteristics of RLS, this is the first large-scale, comprehensive study to provide a clinical analysis and develop a predictive nomogram model for large RLS. In the present study, we investigated the baseline and clinicopathological characteristics of large RLS and analyzed the prognostic factors of OS.\u003c/p\u003e \u003cp\u003eThe 5-year overall survival of patients with ordinary RLS usually exceeds 50% in most reports. Noriyuki Masaki et al. reported that the 5-year overall survival rates of patients with WDL and the DDL/myxoid subtype were 100% and 67.4%, respectively[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Alessandro Gronchi et al. reported a series of 144 patients affected by retroperitoneal liposarcoma over a 10-year time span, and the 5-year OS was 61.2%[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In our study, we collected 1,113 cases, making it a relatively large cohort, which allows for a more comprehensive set of findings. The 1-, 3-, 5-, 10-year overall survival rates were about 86%, 71%, 60%, 38%, respectively (\u003cb\u003eFig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). It can be observed that the survival rate for this group of patients after 5 years is relatively low. Compared to non-large RLS, we could see that non-large survival rates were obviously better than those with large tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). There are many different aspects involved in large RLS, such as the large tumor occupying almost the entire abdominal cavity, close to or even wrapping around large important blood vessels[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These complicated anatomic characteristics make the surgical procedure very difficult, thereby affecting the survival rate of this group of patients\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese patients usually experience a complicated surgery, need combined resection, and have a long operation time and a high bleeding volume, which are factors related to serious postoperative complications[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In our previous study, we found that the median intraoperative bleeding volume reached 1500 ml for patients with giant RLS, and the median operation time was 280 minutes[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Many studies have shown that postoperative complications are closely associated with poor prognosis[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In our previous study, we also found that postoperative complications significantly influenced OS. Specifically, complications graded II-IV (Clavien-Dindo) were associated with a shorter 5-year OS compared to grade I complications[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Actually, this is an indirect reflection of the complicated dilemma in treating large RLS, and serious complications mean that large tumors are difficult for surgeons.\u003c/p\u003e \u003cp\u003eIn addition, some studies also illustrated that the RLS histologic subtypes have a core effect on prognosis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Noriyuki Masaki et al. reported 40 recurrent retroperitoneal liposarcoma cases and 23 patients with initial WDL, and pathological progression (PP) to DDL was observed in the re-recurrent tumors[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This indicates that RLS has a relapse tendency and that low-grade tumors may progress to high-grade tumors after surgical treatment, and this phenomenon inevitably impacts the outcomes of patients with RLS. However, in patients with large RLS, the increase in tumor size somewhat diminishes the impact of histological subtypes on survival prognosis. In other words, pathological subtypes become less influential in predicting outcomes for these patients, which is a distinctive characteristic of patients with large RLS.\u003c/p\u003e \u003cp\u003eOur study investigated the clinicopathological characteristics of the large RLS and explored the significant prognostic factors that are correlated with OS. Lasso and multivariate Cox regression analysis revealed that age, TNM stage, ocurrence pattern, histology and surgery were significant prognostic factors for large RLS. Some results from the cox regression analysis are consistent with those of previous studies[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the preceding analysis, we have highlighted the significance of histology in the prognostic assessment of RLS, and explained how surgical complexity and postoperative complications influence prognosis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Postoperative TNM staging emerges as a critical prognostic factor for patients with large RLS[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. By incorporating key variables such as tumor size, lymph node metastasis and distant metastasis, TNM stage offers a more comprehensive evaluation of the essential characteristics and status of patients. For large RLS, the broader surgical resection often leads to the removal of a greater number of lymph nodes, highlighting the importance of conducting a detailed examination of these lymph nodes for accurate staging and prognosis.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eOur study has several limitations. First, as a retrospective analysis primarily relying on medical records from our institution and the SEER database, it is inherently limited by the absence of prospective data. Second, critical therapeutic indicators, such as information on adjuvant therapies, were either missing or unknown for the majority of cases. Third, key pathological data, including tumor necrosis and mitotic count, which are essential for accurate grading, were unavailable for some cases in the external cohort, complicating the pathological assessments. Fourth, given the long study period, we did not address the potential impact of advancements in treatment techniques and other factors on prognosis. Finally, due to the limited number of cases in the external cohorts, 10-year validation data were not available, further limiting the robustness of our findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study investigated the clinicopathological characteristics and survival outcomes of 1735 patients with large RLS. A prognostic nomogram for predicting OS was developed using lasso and cox regression analyses. The model's predictive accuracy and robustness were validated through time-dependent ROC analysis and C-index, demonstrating excellent discriminatory ability across training and validation cohorts. This rigorously validated nomogram offers a reliable tool for facilitating personalized treatment strategies and enhancing prognosis assessment in patients with large RLS, providing valuable insights for clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eno funding was received for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: There are no conflicts of interest pertained to this submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eData analyzed in the study are available upon request pending application and authority approval. Requests to access the datasets should be\u0026nbsp;directed to Yisheng Pan, [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Protection of Human Subjects Committee of the Chinese People\u0026rsquo;s Liberation Army General Hospital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients signed publish consent forms for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The authors are grateful to their colleagues for helping collect and analyze data at the Chinese PLA general hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuan Deng: conceptualisation, data curation, investigation, project administration, writing-original draft, writing and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhenhua Lu: statistical analysis, investigation, formal analysis.\u003c/p\u003e\n\u003cp\u003eBingrui Wang: built the calibration plots of the dynamic OS nomogram.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYajie Wang: writing results and editing the writing-original draft.\u003c/p\u003e\n\u003cp\u003eLin xiao: data visualization and table editing.\u003c/p\u003e\n\u003cp\u003eYisheng Pan: conceptualisation, Funding, project administration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdarsh V, Lakshmi R. Retroperitoneal liposarcoma: a comprehensive review. Am J Clin Oncol. 2013;12(4):5665\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark JO, Qin L, Prete FP, Antonescu C, Brennan MF, Singer S. Predicting Outcome by Growth Rate of Locally Recurrent Retroperitoneal Liposarcoma. ANN SURG. 2009;250(6):977\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTyler R, Wanigasooriya K, Taniere P, Almond M, Ford S, Desai A, Beggs A. A review of retroperitoneal liposarcoma genomics. CANCER TREAT REV. 2020;86:102013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrans-Atlantic RSWG. Management of metastatic retroperitoneal sarcoma: a consensus approach from the Trans-Atlantic Retroperitoneal Sarcoma Working Group (TARPSWG). ANN ONCOL. 2018;29(4):857.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinger S, Antonescu CR, Riedel E, Brennan MF. Histologic Subtype and Margin of Resection Predict Pattern of Recurrence and Survival for Retroperitoneal Liposarcoma. ANN SURG. 2003;121:52\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan MCB, Brennan MF, Kuk D, Agaram NP, Antonescu CR, Qin L, Moraco N, Crago AM, Singer S. Histology-based Classification Predicts Pattern of Recurrence and Improves Risk Stratification in Primary Retroperitoneal Sarcoma. ANN SURG. 2016;263(3):593\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan P, Tao P, Wang Z, Wang J, Hou Y, Lu W, Ma L, Zhang Y, Tong H. Evaluation of AJCC staging system and proposal of a novel stage grouping system in retroperitoneal liposarcoma: the Fudan Zhongshan experience. FRONT ONCOL. 2024;14:1373762.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasaki N, Onozawa M, Inoue T, Kurobe M, Kawai K, Miyazaki J. Clinical features of multiply recurrent retroperitoneal liposarcoma: A single-center experience. ASIAN J SURG. 2021;44(1):380\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshii K, Yokoyama Y, Nishida Y, Koike H, Yamada S, Kodera Y, Sassa N, Gotoh M, Nagino M. Characteristics of primary and repeated recurrent retroperitoneal liposarcoma: outcomes after aggressive surgeries at a single institution. JPN J CLIN ONCOL. 2020;50(12):1412\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrauss DC, Hayes AJ, Thway K, Moskovic EC, Fisher C, Thomas JM. Surgical management of primary retroperitoneal sarcoma. BRIT J SURG. 2010;97(5):698\u0026ndash;706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SY, Goh BKP, Teo MCC, Chew MH, Chow PKH, Wong WK, Ooi LLPJ, Soo KC. Retroperitoneal liposarcomas: the experience of a tertiary Asian center. WORLD J SURG ONCOL. 2011;9(1):12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng H, Gao J, Xu X, Liu G, Song L, Pan Y, Wei B. Predictors and outcomes of recurrent retroperitoneal liposarcoma: new insights into its recurrence patterns. BMC Cancer. 2023;23(1):1076.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng H, Cao B, Cui H, Chen R, Li H, Zhao R, Chen L, Wei B. Clinical analysis of 5-year survival and recurrence in giant retroperitoneal liposarcoma after surgery. Chin MED J-PEKING. 2023;136(3):373\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBachmann R, Eckert F, Gelfert D, Stroh\u0026auml;ker J, Beltzer C, Ladurner R. Perioperative strategy and outcome in giant retroperitoneal dedifferentiated liposarcoma\u0026mdash;results of a retrospective cohort study. WORLD J SURG ONCOL 2020, 18(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Liu J, Liu J, Yan P, Tang B, Cui Y, Zhao Y, Shi Y, Hao Y, Yu P, et al. A retrospective, single-center cohort study on 65 patients with primary retroperitoneal liposarcoma. ONCOL LETT. 2018;15(2):1799\u0026ndash;810.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuggett BD, Cates J. The Vanderbilt staging system for retroperitoneal sarcoma: a validation study of 6857 patients from the National Cancer Database. Mod PATHOL. 2019;32(4):539\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Liu Z, He L, Chen X, Pan D, Ma Z, Liang C, Tian J, Liang C. Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. \u003cem\u003eRADIOLOGY\u003c/em\u003e 2016, 281(3):947\u0026ndash;957.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Yang J, Zhou X, Huang L, Zhao W, Wang T, Zhuang J, Tian J. Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. EUR RADIOL. 2019;29(5):2196\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerzberg J, Niehaus K, Holl-Ulrich K, Honarpisheh H, Guraya SY, Strate T. Giant retroperitoneal liposarcoma: A case report and literature review. J TAIBAH UNIV MED SC. 2019;14(5):466\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalemis NS, Tsiambas E, Karameris A, Tsohataridis E. Giant Retroperitoneal Liposarcoma with Mixed Histological Pattern: A Rare Presentation and Literature Review. J GASTROINTEST CANC. 2009;40(3\u0026ndash;4):138\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGronchi A, Collini P, Miceli R, Valeri B, Renne SL, Dagrada G, Fiore M, Sanfilippo R, Barisella M, Colombo C, et al. Myogenic differentiation and histologic grading are major prognostic determinants in retroperitoneal liposarcoma. AM J SURG PATHOL. 2015;39(3):383\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTseng WW, Madewell JE, Wei W, Somaiah N, Lazar AJ, Ghadimi MP, Hoffman A, Pisters PWT, Lev DC, Pollock RE. Locoregional Disease Patterns in Well-Differentiated and Dedifferentiated Retroperitoneal Liposarcoma: Implications for the Extent of Resection? ANN SURG ONCOL. 2014;21(7):2136\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTjeertes EKM, Ultee KHJ, Stolker RJ, Verhagen HJM, Bastos Gon\u0026ccedil;alves FM, Hoofwijk AGM, Hoeks SE. Perioperative Complications are Associated With Adverse Long-Term Prognosis and Affect the Cause of Death After General Surgery. WORLD J SURG. 2016;40(11):2581\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYonezawa N, Murakami H, Demura S, Kato S, Yoshioka K, Shinmura K, Yokogawa N, Shimizu T, Oku N, Kitagawa R, et al. Perioperative Complications and Prognosis of Curative Surgical Resection for Spinal Metastases in Elderly Patients. WORLD NEUROSURG. 2020;137:e144\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeck C, Weber K, Brunner M, Agaimy A, Semrau S, Gr\u0026uuml;tzmann R, Schellerer V, Merkel S. The influence of postoperative complications on long-term prognosis in patients with colorectal carcinoma. INT J COLORECTAL DIS. 2020;35(6):1055\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarimoto N, Shirabe K, Ikegami T, Yoshizumi T, Maeda T, Kajiyama K, Yamanaka T, Maehara Y. Postoperative complications are predictive of poor prognosis in hepatocellular carcinoma. J SURG RES. 2015;199(2):470\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartlett EK, Curtin CE, Seier K, Qin L, Hameed M, Yoon SS, Crago AM, Brennan MF, Singer S. Histologic Subtype Defines the Risk and Kinetics of Recurrence and Death for Primary Extremity/Truncal Liposarcoma. ANN SURG. 2021;273(6):1189\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGronchi A, Strauss DC, Miceli R, Bonvalot S, Swallow CJ, Hohenberger P, Van Coevorden F, Rutkowski P, Callegaro D, Hayes AJ, et al. Variability in Patterns of Recurrence After Resection of Primary Retroperitoneal Sarcoma (RPS). ANN SURG. 2016;263(5):1002\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhuang A, Wu Q, Tong H, Zhang Y, Lu W. Development and Validation of a Nomogram for Predicting Recurrence-Free Survival of Surgical Resected Retroperitoneal Liposarcoma. CANCER MANAG RES. 2021;13:6633\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNessim C, Raut CP, Callegaro D, Barretta F, Miceli R, Fairweather M, Blay J, Strauss D, Rutkowski P, Ahuja N, et al. Analysis of Differentiation Changes and Outcomes at Time of First Recurrence of Retroperitoneal Liposarcoma by Transatlantic Australasian Retroperitoneal Sarcoma Working Group (TARPSWG). ANN SURG ONCOL. 2021;28(12):7854\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"retroperitoneal liposarcoma, large, surgery","lastPublishedDoi":"10.21203/rs.3.rs-5821949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5821949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to show the clinicopathological characteristics of large retroperitoneal liposarcoma (RLS) and to developed a customized nomogram model for those patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 1735 patients diagnosed with RLS were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Among them, 1113 patients with a maximum tumor diameter greater than 150 mm were included for further analysis. Nomogram models were developed based on lasso and multivariate cox regression analyses. The 166 patients collected from the same period at First Medical Center, Chinese People Liberation Army General Hospital (CPLAGH), were used for external validations. The model was further validated through multiple dimensions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLarger tumor size in RLS was associated with worse survival outcomes (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;0.66, 95% confidence interval [CI]: 0.53\u0026ndash;0.81, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Lasso and Cox regression analyses consistently identified age, TNM stage, occurrence pattern, histology, and surgery as important prognostic factors for OS. The constructed model demonstrated robust predictive performance, with better time-ROC (Time-Dependent Receiver Operating Characteristic) for 1-year (83.1%), 3-year (83.8%), and 5-year (81.4%) survival in training cohort. The concordance index (C-index) was approximately 0.80 in both the training and validation cohorts, reflecting excellent discriminatory ability of the model. Survival risk stratification analysis revealed significant differences in survival outcomes between the groups (HR\u0026thinsp;=\u0026thinsp;4.12 [3.31\u0026ndash;5.12], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in training cohort). Decision curve analysis (DCA) confirmed that the nomogram provided greater net benefits across a range of threshold probabilities.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study identified important prognostic factors for survival in patients with large RLS and developed a reliable nomogram for predicting OS. The model\u0026rsquo;s strong predictive performance supports its use in personalized treatment strategies, improving prognosis assessment and clinical decision-making for these patients.\u003c/p\u003e","manuscriptTitle":"Development and validation of a prognostic nomogram for predicting overall survival in patients with large retroperitoneal liposarcoma: a population-based cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-15 17:06:56","doi":"10.21203/rs.3.rs-5821949/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fdd5666e-64b8-403b-a09f-0ab323acb8b3","owner":[],"postedDate":"January 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-15T17:53:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-15 17:06:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5821949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5821949","identity":"rs-5821949","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}