Developing a Diagnostic Model to Differentiate the Well-differentiated Lipomatous Tumors Based on Clinicopathological Characteristics

Developing a Diagnostic Model to Differentiate the Well-differentiated Lipomatous Tumors Based on Clinicopathological Characteristics | 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 Article Developing a Diagnostic Model to Differentiate the Well-differentiated Lipomatous Tumors Based on Clinicopathological Characteristics Jingjing Wu, Zhenzhen Zhang, Fangling Song, Xiangna Chen, Shanshan Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7254061/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This study evaluated the diagnostic value of clinicopathological features and immunohistochemical markers for distinguishing lipomas from atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLTs). An integrated diagnostic model for ALTs/WDLTs was developed to provide guidance for diagnosis, treatment planning, and prognosis. A retrospective analysis was conducted on 216 lipomatous tumor cases diagnosed between February 2018 and December 2024, including lipomas (n = 149), spindle cell lipomas (n = 3), ALTs/WDLTs (n = 62), and early de-differentiated liposarcomas (n = 2). Immunohistochemical data for MDM2, CDK4, and p16 were available for 131 patients. MDM2 amplification was significantly more frequent in patients ≥ 55 years and in tumors of the lower limbs (especially thigh) and retroperitoneum (p = 0.000). Larger tumor size and multiplicity were also associated with MDM2 amplification (p < 0.05). Immunohistochemistry sensitivities for ALTs/WDLTs vs. lipomas: 65% (MDM2), 100% (CDK4), 80.39% (p16); combined, specificity was 100% and sensitivity 85.58%. The diagnostic model achieved 93.33% sensitivity and 72.22% specificity. Scores 0.6521 indicated a higher likelihood of liposarcoma. Age ≥ 55, lower extremity/retroperitoneal location, tumor diameter ≥ 9.9 cm, and positive markers were independent risk factors. This model provides an effective tool for ALT/WDLT identification. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Diseases Health sciences/Medical research Health sciences/Oncology clinicopathological factors diagnostic prediction model double minute homologue 2 (MDM2) fluorescence in situ hybridization (FISH) Well-differentiated lipomatous tumors Figures Figure 1 Figure 2 Figure 3 Introduction Lipomatous tumors are common soft tissue tumors encountered in clinical practice. Well-differentiated lipomatous tumors (WDLTs) mainly consist of mature adipocytes and range from benign types (e.g., lipoma) to locally aggressive intermediate tumors (e.g., atypical lipomatous tumor/well-differentiated liposarcoma [ALT/WDLPS]). Although they share similar morphological features, their biological behaviors differ significantly. ALT/WDLPS may recur after surgery and progress to de-differentiated liposarcoma (DDLPS). Distinguishing ALT/WDLPS without obvious cytological atypia from lipomas is often challenging [ 1 , 2 ]. Accurate diagnosis and differentiation of WDLPS are essential for appropriate surgical planning and prognostic management. Advances in imaging and molecular pathology have improved the differential diagnosis of WDLTs. Clinical studies indicate that patient age (> 60 years), tumor location (deep lower extremities), tumor size (> 10 cm), and the presence of histologic lipoblasts are important features for differentiating ALT/WDLPS from lipoma [ 3 – 5 ]. Among these, tumor size and deep location (e.g., retroperitoneum) consistently correlate with malignancy risk, although the exact size cut-offs vary. Multiparametric magnetic resonance imaging (MRI) findings, such as thickened fibrous septa (> 2 mm), nodular enhancement (> 1 cm), and adipose tissue content < 75%, are independent predictors of ALT. However, current diagnostic models have limitations, especially for tumors lacking typical MRI features or atypical lipoblasts and when MRI is not available. Additionally, MRI is more costly than ultrasonography or computed tomography (CT). This highlights the need for alternative diagnostic methods that balance accuracy and cost-effectiveness, such as combining ultrasonography with immunohistochemical (IHC) markers, to build a more comprehensive diagnostic model. Previous research has shown that amplification of the 12q13-15 chromosomal region drives MDM2 overexpression and also affects p16 regulation through the CDK4-RB1 pathway [ 6 ]. However, current IHC methods relying on single markers are limited in fully capturing this molecular complexity, reducing their ability to distinguish benign from malignant lipomatous tumors. Meanwhile, MDM2 fluorescence in situ hybridization (FISH), considered the diagnostic gold standard for WDLTs, faces barriers in routine clinical use due to its high cost and need for specialized molecular pathology equipment [ 7 ]. This study aimed to integrate clinicopathological features with cost-effective IHC markers (MDM2, CDK4, and p16) to develop a practical diagnostic model that does not require molecular testing. The goal is to overcome diagnostic challenges in resource-limited settings and offer evidence-based support for clinical decision-making, treatment planning, and prognosis assessment. Results Clinical Characteristics of WDLTs This study included 216 patients: 106 male (49.1%) and 110 female (50.9%) individuals, with a male-to-female ratio of 1:1.04. The mean patient age was 52.98 (range: 8–84) years, and the mean tumor diameter was 9.86 cm. The tumor size distribution was as follows: 35 cases (16.2%) <5 cm, 95 cases (43.9%) between 5 and 10 cm, and 86 cases (39.8%) ≥10 cm. Tumors were primarily located in the trunk (98 cases, 45.37%), lower limbs (52 cases, 24.07%), head and neck (32 cases, 14.81%), upper limbs (15 cases, 6.94%), retroperitoneum (eight cases, 3.70%), and other sites, including the axilla, spinal canal, scrotum, inguinal region, testis, and perineum (11 cases, 5.09%). Among these cases, 181 (83.8%) were solitary tumors and 35 (16.2%) were multifocal tumors. Histologically, all tumors consisted of well-differentiated adipocytes arranged in variably sized lobules separated by fibrous septa. No mitotic activity or necrosis was observed (Figure 1). Correlation between MDM2 Gene Status and WDLTs Of the 216 cases, 64 (29.63%) demonstrated MDM2 gene amplification, comprising 62 cases of ALT/WDLTs and two early DDLPS cases. The remaining 152 cases (70.37%) were MDM2-negative, including 149 lipomas and three spindle cell lipomas. MDM2 amplification was significantly associated with patient age ≥55 years, tumors located in the lower limbs (especially the thighs), and the retroperitoneal region. A statistically significant correlation was observed between MDM2 amplification and larger tumor size (p<0.05), whereas no significant association was found with sex (Table 1). ROC curve analysis identified a tumor diameter of 9.9 cm as the optimal threshold for predicting MDM2 amplification, yielding a sensitivity of 0.825, specificity of 0.778, Youden index of 0.603, and an AUC of 0.855 (Figure 2). Sensitivity and Specificity of MDM2/CDK4/p16 IHC in Diagnosing WDLTs Complete IHC data for MDM2, CDK4, and p16 were available for 131 patients, including 42 cases of WDLTs and 89 cases of lipoma. The sensitivity of MDM2 immunostaining for diagnosing ALT/WDLTs was 65.0% (39/60), that of CDK4 was 100% (29/29), and that of p16 was 80.39% (41/51). Pairwise combinations of markers did not significantly improve diagnostic sensitivity or specificity; however, the combined use of all three markers significantly enhanced diagnostic performance, achieving a sensitivity of 100% and specificity of 85.58% (Table 2). Development of an Integrated Diagnostic Model for ALT/WDLTs We compiled the clinical and pathological variables (age, sex, tumor location, unifocal vs. multifocal presentation, maximum tumor diameter, and IHC expression of MDM2, CDK4, and p16) into a comprehensive dataset. Using the odds ratios derived from the multivariable logistic regression, we constructed a simplified diagnostic model (Figure 3A) to differentiate lipomas from ALT/WDLTs. Risk scores generated from the model were subjected to K-means clustering analysis, and the midpoints between cluster centers were selected as cut-off values to stratify patients into low-, intermediate-, and high-risk groups. Thresholds of 0.2191 and 0.6521 were established for risk stratification and validated in the test set (Figure 3B–C). ROC curve analysis showed excellent diagnostic performance, with a sensitivity of 0.933, specificity of 0.722, and AUC of 0.928 (Figure 3D), indicating the robustness of the model in distinguishing ALT/WDLTs from lipomas. Calibration curves (Supplementary Figure 1A, B) demonstrated high concordance between the predicted probabilities and observed outcomes. Furthermore, when applied to 39 biopsy specimens of WDLTs, the model achieved an accuracy of 92.31%, a sensitivity of 96.15%, and a specificity of 84.62%, thereby supporting its utility in preoperative diagnostic settings. Discussion ALT/WDLPS accounts for 40–45% of all liposarcomas, representing the most common subtype. It is estimated that approximately 10.64% of WDLTs without atypia in the deep soft tissues of the extremities or trunk walls are ultimately diagnosed as ALT [ 4 ]. Compared to other soft tissue tumors, the differential diagnosis between deep-seated lipomas and ALT/WDLPS is particularly crucial for preoperative planning. Previous studies have identified multiple risk factors for diagnosing ALT, including advanced age, deep anatomical location, tumor diameter > 10 cm, occurrence in the extremities (particularly the lower limbs), thickened fibrous septa (> 2 mm), nodular enhancement (> 1 cm), adipose tissue content < 75%, and presence of atypical lipoblasts [ 3 , 4 ]. However, some scholars note that although the diagnosis of ALT/WDLPS demonstrates high sensitivity, its specificity remains relatively low, making definitive diagnosis based solely on clinical parameters and MRI features challenging [ 8 ]. Moreover, given the relatively high cost of MRI, there is a lack of reported diagnostic models for WDLTs that exclude MRI assessment or those lacking characteristic MRI findings or atypical lipoblasts. This study specifically enrolled patients with WDLTs without prominent fibrous septa or atypical lipoblasts. Based on previous research, we selected five clinical parameters (age, sex, anatomical location, tumor diameter, and tumor multiplicity) and three immunohistochemical markers (MDM2, p16, and CDK4) to construct a diagnostic system for distinguishing ALT/WDLPS from lipoma. The combined clinical-immunohistochemical scoring model demonstrated excellent discriminatory performance (sensitivity, 93.33%; specificity, 72.22%). In comparable studies, Cheng et al. [ 4 ] reported a clinical-MRI-based diagnostic system with 90% sensitivity and 92.5% specificity, while Asano et al. [ 3 ] developed a system incorporating clinical, MRI, and histological features, achieving 87.6% sensitivity and 91.1% specificity. Notably, Cheng’s study focused exclusively on deep-seated adipocytic tumors, whereas Asano’s cohort included cases with histological features, such as atypical lipoblasts. These differences in case composition are likely to contribute to variations in the diagnostic accuracy across studies. Brisson et al. [ 8 ] emphasized that histopathological examination remains essential for distinguishing ALT/WDLPS from lipoma; hence, preoperative needle biopsy is routinely performed for lipomatous tumors. Ultrasound-guided biopsy has particularly improved the diagnostic accuracy for deep, irregularly shaped ALT/WDLPS. Importantly, our diagnostic model also demonstrated applicability to ultrasound-guided biopsy specimens of lipomatous tumors. ALT/WDLTs may closely mimic normal adipose tissue and lipomas. In ALT/WDLTs, atypical lipoblasts are rare; therefore, histological diagnosis can be challenging, and IHC and molecular analyses are often necessary as diagnostic adjuncts. Amplification of the 12q13–15 region, which includes the MDM2 and CDK4 genes, is commonly observed in ALT/WDLTs [ 1 ]. This amplification affects the expression of both MDM2 and CDK4. The current literature shows variable sensitivity and specificity for MDM2 and CDK4 in different ALT studies. The reported specificities ranged from 82.8–100% for MDM2 (average, 94.7%) and 69.0–100% for CDK4 (average, 88.8%) [ 9 , 10 ]. These differences may be attributed to the varying case numbers and tumor types across studies. Additionally, some researchers have proposed that p16 may help distinguish between lipomas, WDLTs, and DDLPS. CDKN2A encodesp16Ink4A (p16), which inhibits cell cycle progression via CDK- and RB1-dependent mechanisms. Loss-of-function mutations and deletions in p16 have been observed in various malignancies. In contrast, other studies have reported that p16 is overexpressed less frequently [ 2 ]. Aslam et al. detected the p16 protein by IHC in 36 cases (including WDLTs and DDLPS), finding a sensitivity of 60.9% and specificity of 53.8%. When MDM2, CDK4, and p16 were used in combination to differentiate WDLTs/DDLPS, the sensitivity and specificity decreased to 43.47% and 15.38%, respectively [ 11 ]. These findings suggest that the diagnostic utility of IHC markers alone is limited, while combined use of MDM2, CDK4, and p16 offers some auxiliary value. The results of this study support this conclusion. Notably, ALT/WDLPS cannot be ruled out in cases with negative MDM2 expression. Among our 42 WDLT cases, three were negative for MDM2 protein expression (but positive for CDK4 and p16); however, FISH analysis revealed MDM2 amplification. In such cases, IHC detection of CDK4 and/or p16 may provide a useful screening tool. This finding aligns with the results reported by Machado [ 12 ]. Importantly, atypical lipoblasts are not exclusive to ALT/WDLTs and may also appear in other lipogenic tumors, such as dysplastic lipoma and spindle cell/pleomorphic lipoma. Dysplastic lipomas typically arise in the subcutaneous tissue of the upper back, shoulders, and posterior neck in middle-aged and elderly individuals. These tumors display mild-to-moderate nuclear atypia, and binucleated or multinucleated lipoblasts may be observed focally [ 13 ]. Spindle cell/pleomorphic lipomas commonly occur in the extremities of older adults. Histologically, they contain varying proportions of atypical spindle cells, adipocytes, and/or pleomorphic lipoblasts set within a myxoid or collagenous stroma with an infiltrative growth pattern. Most cases express CD34, and approximately 50% show loss of Rb expression due to RB1 gene deletion [ 14 ]. In our cohort, two cases of atypical spindle cell lipomas were identified. Histologically, they featured atypical lipoblasts without a clear spindle cell component and had a fibrotic collagenous stroma. Molecular testing revealed an Rb gene deletion without MDM2 amplification. These findings suggest that dysplastic and atypical spindle cell lipomas are not associated with MDM2 amplification, a distinction that provides a key molecular pathological basis for differentiating these entities from ALT/WDLTs. MDM2 amplification is an important molecular marker for ALT/WDLPS. Therefore, FISH detection of MDM2 amplification can be used as the gold standard for diagnosing ALT/WDLPS [ 15 , 16 ]. Ware et al. found that the amplification rate of MDM2 in peripheral WDLPS located in the extremities was significantly lower than that in central WDLPS located in the retroperitoneum or intra-abdominal cavity. Compared to central WDLPS, peripheral WDLPS typically follows a more indolent clinical course and is associated with a higher overall survival rate. The lower amplification rate in peripheral tumors may suggest that WDLPS with minimal MDM2 amplification has a reduced likelihood of recurrence and dedifferentiation. In contrast, the higher amplification rate observed in retroperitoneal tumors may be related to their longer duration and the challenge of early clinical detection. Furthermore, the study showed that in DDLPS, the MDM2 amplification pattern is not only diagnostic but also prognostic. Tumors with clustered MDM2 amplifications tend to have a higher risk of recurrence and progression [ 17 – 19 ]. Currently, there is no literature evaluating the prognostic or clinical significance of MDM2 gene amplification in adipocytic tumors without typical cytological atypia. Thus, conducting long-term follow-up studies on a large number of mature lipoma-like tumors with MDM2 amplification but lacking cytological atypia is essential to clarify their clinical course and biological behavior. Although MDM2 FISH testing is a valuable diagnostic tool, its implementation requires specialized equipment and reagents that are not universally available across all medical institutions. Furthermore, it is not always practical to perform a biopsy and subsequent FISH analysis for every patient presenting with a deep-seated lipomatous tumor. This limitation prompts a critical clinical question: under what circumstances should MDM2 FISH testing be pursued? Our diagnostic model addresses this challenge through a risk-stratification framework. For patients with a calculated risk score below 0.2191, suggesting a high likelihood of a benign lipoma, clinical surveillance or marginal excision is recommended, and FISH testing may be safely omitted. Conversely, for patients with a risk score exceeding 0.6521, indicative of a high probability of liposarcoma, an initial biopsy followed by confirmatory MDM2 FISH testing is advised. This approach offers a practical and cost-efficient diagnostic strategy. This study had several limitations. First, the cohort was restricted to patients with WDLTs. Potential ultrasound features were not included in the model for deep-seated tumors requiring ultrasound-guided biopsy. Future work should focus on extracting and integrating these imaging characteristics to improve diagnostic accuracy. Second, the retrospective design may have introduced confounding factors that could not be fully controlled. Larger, prospective studies are needed to further validate these findings and refine the model. Conclusion In summary, our study identified age ≥ 55 years, tumor location in the lower extremities or retroperitoneum, a maximum diameter ≥ 9.9 cm, and positive immunohistochemical staining for MDM2, CDK4, and p16 as independent risk factors for distinguishing ALT/WDLPS from lipoma (p < 0.05). A diagnostic model incorporating these clinical and immunohistochemical features, developed through multivariate logistic regression, demonstrated excellent discriminative ability, with a sensitivity of 93.33% and specificity of 72.22%. Based on the calculated risk scores, we propose the following clinical management strategy. For patients with a score below 0.2191, which indicates a high probability of lipoma, clinical surveillance or marginal excision is appropriate. For scores ranging from 0.2191 to 0.6521, surgical resection combined with immunohistochemical testing for MDM2, CDK4, and p16 is recommended. For scores above 0.6521, which suggest a high likelihood of liposarcoma, diagnostic biopsy followed by confirmation using MDM2 FISH is warranted. These stratified recommendations provide clear evidence-based guidance for treatment decisions and follow-up planning, thereby supporting optimal patient care. Materials and Methods Clinical Data We retrospectively reviewed 216 cases of WDLTs diagnosed at the Department of Pathology, Fujian Medical University Affiliated First Hospital, between February 2018 and December 2024. Clinical records and surgical histopathological specimens were analyzed, including 39 cases with available preoperative biopsy results. Microscopically, all cases exhibited WDLT features without evident atypical lipoblasts or pleomorphic cells in the fibrous septa. Diagnoses were made in accordance with the 2020 WHO classification of soft tissue tumors and independently reviewed by two senior pathologists. The histological subtypes included lipoma (n = 149), spindle cell lipoma (n = 3), atypical lipomatous tumor/well-differentiated liposarcoma (ALT/WDLPS; n = 62), and early de-differentiated liposarcoma (DDLPS; n = 2). All cases were confirmed using MDM2 FISH. IHC data for MDM2, CDK4, and p16 were available for 131 patients. The study was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University. (Approval No. 2015084-2), and written informed consent was obtained from all patients. All procedures followed the Declaration of Helsinki. IHC Staining Tissue specimens were fixed in 10% neutral buffered formalin, followed by routine dehydration, paraffin embedding, and sectioning to a thickness of 4 µm. IHC was performed using the two-step EnVision method. Appropriate positive and negative controls were used for each antibody. Antibodies against MDM2, CDK4, and p16 (Fuzhou Maixin Biotechnology Development Co., Ltd., Fuzhou, China) were applied using an automated IHC system according to the manufacturer’s instructions. Only clear nuclear staining was considered positive, and cytoplasmic or perinuclear staining was excluded. Five consecutive high-power fields (400× magnification) were examined for semiquantitative assessment. Scoring was based on the proportion of positive cells (0: 0%; 1: 50%) and staining intensity (0: negative; 1: weak; 2: moderate; 3: strong). A total score of ≥ 4 points was defined as positive expression of MDM2, CDK4, or p16. FISH Detection Paraffin-embedded sections (4 µm thick) were dewaxed, rehydrated, and enzymatically digested. MDM2-specific probes (Abipin Biotechnology Co., Ltd., Guangzhou, China) were applied, covered with coverslips, and hybridized using an automated FISH system (Leica S500, Wetzlar, Germany) for 12–20 h. Following post-hybridization washing, counterstaining with DAPI (4′,6-diamidino-2-phenylindole) and mounting were performed. Fluorescence signals were visualized using an Olympus BX53F2C fluorescence microscope (Tokyo, Japan). For each case, 100 nuclei were evaluated, and only those exhibiting concurrent MDM2 and CEP12 signals were included in the analysis. A case was considered FISH-positive if more than 20% of the nuclei exhibited an MDM2/CEP12 signal ratio > 2 [ 20 ]. All FISH slides were independently evaluated by two blinded reviewers, a certified FISH technologist and a pathologist, both unaware of the original diagnoses. Statistical Analysis Statistical analyses were performed using SPSS version 21.0 (IBM Corp., Armonk, NY, USA). Data are presented as mean or frequency, as appropriate. Group comparisons between ALT/WDLT and lipoma cases were made using Student’s t-test for continuous variables and the chi-square test or Fisher’s exact test for categorical variables. Data processing and further analyses were conducted using R software (Vienna, Austria). The dataset was randomly divided into training and internal validation sets in a 7:3 ratio. Univariate logistic regression was used to identify potential predictors in the training set, followed by multivariate logistic regression to construct a diagnostic nomogram. Risk scores from the model were subjected to K-means clustering, and the midpoints between cluster centers were used as cut-off values for risk stratification. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Model calibration and consistency were assessed using bootstrap resampling and calibration curves. All statistical tests were two-sided, and a p-value < 0.05 was considered statistically significant. Abbreviations AUC, area under the curve; ALT, atypical lipomatous tumor; CT, computed tomography; DDLPS, dedifferentiated liposarcoma; FISH, fluorescence in situ hybridization; IHC, immunohistochemical; MRI, magnetic resonance imaging; ROC, receiver operating characteristic; WDLTs, well-differentiated lipomatous tumors Declarations Acknowledgements N/A. Author Contributions Conceptualization, J.W.; Methodology, F.S and S.H.; Investigation, S.H. and X.C.; Data curation, F.S; Formal analysis, J.W and X.C; Writing – original draft, J.W.; Writing – review & editing, J.W and Z.Z.; Visualization, Z.Z. All authors have read and agreed to the published version of the manuscript. Declaration of competing interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Data Availability Statement All data generated or analyzed during this study are included in this published article. Funding This study was supported by Talent Recruitment Program of the First Affiliated Hospital of Fujian Medical University (YJRC3500). The funders had no role in the study design, collection, analysis and interpretation of data, writing of the report, or decision to submit the article for publication. Ethics approval The study was approved by the institutional ethics committee (Approval No. 2015084-2) Consent to participate/consent to publish Written informed consent was obtained from all patients. References Sbaraglia M, Bellan E, Dei Tos AP. The 2020 WHO Classification of Soft Tissue Tumours: News and perspectives. Pathologica . 2021;113(2):70–84. https://doi.org/10.32074/1591-951X-213. Anju MS, Chandramohan K, Bhargavan RV, Somanathan T, Subhadradevi L. An overview on liposarcoma subtypes: Genetic alterations and recent advances in therapeutic strategies. 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Am J Clin Pathol . 2014;141(3):334–341. https://doi.org/10.1309/AJCPLYU89XHSNHQO Tables Table 1. Results of univariate statistical analysis Variable MDM2 non-amplified (N=152) MDM2 amplified (N=64) Significance Age, mean (years) 0.037 <55 years 86 (56.58%) 26 (40.63%) ≥55 years 66 (43.42%) 38 (59.38%) Sex, % 0.300 Male 71 (46.71%) 35 (54.69%) Female 81 (53.29%) 29 (45.31%) Tumor size, mean (mm) 7.64 15.23 0.000 Tumor site 0.000 Upper limb 14 (9.21%) 1 (1.56%) Lower limb 18 (11.84%) 34 (53.13%) Trunk 79 (51.97%) 19 (29.69%) Head/Neck 32 (21.05%) 0 (0%) Retroperitoneal 0(0%) 8 (12.5%) Others 9 (5.92%) 2 (3.13%) Number of tumors 0.002 Signal 135 (88.82%) 46 (71.88%) Multiple 17 (11.18%) 18 (28.13%) 1. MDM2 Non-Amplified Group The "other" locations in this group included the groin (one case), spinal canal (two cases), axilla (five cases), and perineum (one case). 2. MDM2 Amplified Group The "other" locations in this group included the scrotum (one case) and testis (one case). Table 2. Sensitivity and specificity of markers or a combination of markers in IHC Sensitivity (%) Specificity (%) MDM2+ 65 (39/60) 96.77 (68/71) CDK4+ 100 (29/29) 87.25 (89/102) P16+ 80.39 (41/51) 98.75 (79/80) MDM2+CDK4+p16- / / MDM2+p16+CDK4- 73.33 (11/15) 73.28 (85/116) MDM2-CDK4+p16+ 100 (2/2) 68.99 (89/129) MDM2+CDK4+p16+ 100 (27/27) 85.58 (89/104) Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.docx SupplementaryFigure1.tif Cite Share Download PDF Status: Published Journal Publication published 05 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 09 Aug, 2025 Reviewers invited by journal 09 Aug, 2025 Editor assigned by journal 09 Aug, 2025 Editor invited by journal 06 Aug, 2025 Submission checks completed at journal 04 Aug, 2025 First submitted to journal 04 Aug, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7254061","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":498110244,"identity":"77adf27f-a99e-4e6a-823a-e03d8785626f","order_by":0,"name":"Jingjing Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYBACxobDBx//qbCRI14Lc+OxZAOeM2nGxGthbz6jJsHbdiixgWgtvG1nmA0k2A6k9x1PYPzwMYcILZI9Zw8+MOC5kzvzzANmyZnbiNBiOONcskGCxLPcDTcS2Jh5idFif/+NmcQBg8PpBkRrYWw4YybZkHA4gRQtx5KNGQ6kGc4887CZOL+Ao5Lxn4083/Hkgx8+EqMFAQ6QEDUwLQmk6hgFo2AUjIKRAgBRWkELBfRbVwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital, Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Wu","suffix":""},{"id":498110245,"identity":"5bc4da83-95b8-4f88-9c6a-28ebbf37aec0","order_by":1,"name":"Zhenzhen Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenzhen","middleName":"","lastName":"Zhang","suffix":""},{"id":498110246,"identity":"b35b0b54-38ec-4bfe-a174-6d67d29b2f47","order_by":2,"name":"Fangling Song","email":"","orcid":"","institution":"The First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fangling","middleName":"","lastName":"Song","suffix":""},{"id":498110247,"identity":"527fa1f5-24c9-4fb5-ba39-f4d9cec9b2cc","order_by":3,"name":"Xiangna Chen","email":"","orcid":"","institution":"The First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiangna","middleName":"","lastName":"Chen","suffix":""},{"id":498110248,"identity":"66f3f0f9-8a1e-4973-9077-a7949b3119c6","order_by":4,"name":"Shanshan Huang","email":"","orcid":"","institution":"The First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-07-30 14:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7254061/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7254061/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-22547-5","type":"published","date":"2025-11-05T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89232262,"identity":"13d6394b-6d9f-4d18-8127-415ec1bce145","added_by":"auto","created_at":"2025-08-17 14:24:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4064622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicroscopic Characteristics of Well-Differentiated Lipogenic Tumors\u003c/strong\u003e\u003cbr\u003e\n \u003cstrong\u003eLipoma:\u003c/strong\u003e\u003cbr\u003e\n(A) Composed of well-differentiated adipocytes with tightly arranged tumor cells.\u003cbr\u003e\n(B) Tumor lobulated by fibrous septa into variably sized adipose lobules.\u003cbr\u003e\n(C) No \u003cem\u003eMDM2\u003c/em\u003e gene amplification observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAtypical Lipomatous Tumors/Well-Differentiated Liposarcomas (ALT/WDLPS):\u003c/strong\u003e\u003cbr\u003e\n(D) Adipocytesappear round or polygonal, with large lipid droplets displacing the cytoplasm and nucleitoward the periphery. Nuclei are flattened and oval.\u003cbr\u003e\n(E)Mature adipocytes wereobserved withoutmitotic activity or necrosis.\u003cbr\u003e\n(F)Cluster-type \u003cem\u003eMDM2\u003c/em\u003e gene amplification present.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7254061/v1/b8ad8deecfeacd0def9116d1.png"},{"id":89232263,"identity":"fa18ee86-5cf7-4b8a-965c-24ca0da81ee6","added_by":"auto","created_at":"2025-08-17 14:24:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1711391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve Analysis of Tumor Maximum Diameter and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMDM2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eGene Amplification in Well-Differentiated Lipogenic Tumors\u003c/strong\u003e\u003cbr\u003e\nThe ROC curve analysis demonstratedthat a maximum tumor diameter of 9.9 cm served as the optimal cut-off value for predicting MDM2 amplification, yielding a sensitivity of 0.825, specificity of 0.778, Youden index of 0.603, and an area under the curve (AUC) of 0.855.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7254061/v1/9c355ed18aa2ffbac66c11cd.png"},{"id":89232265,"identity":"7cf862f5-4278-4e57-b8c1-b99ad121eb2a","added_by":"auto","created_at":"2025-08-17 14:24:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1318879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the Integrated Diagnostic Model for Atypical Lipomatous Tumors/Well-Differentiated Liposarcomas (ALT/WDLPS)\u003c/strong\u003e\u003cbr\u003e\n(A) Themodel incorporated eight predictive factors, each assigned a weighted score on a linear scale based on its association with malignancy risk. The total score was used to estimate the probability of ALT/WDLPS.\u003cbr\u003e\n(B–C) Risk scores derived from the model were subjected to K-means clustering in the training set (B). Two cut-off values (0.2191 and 0.6521) were identified from the midpoints between adjacent cluster centers, stratifying patients into low-,intermediate-, and high-risk groups. This stratification was validated in the test set (C).\u003cbr\u003e\n(D) ROC curve analysis showed excellent diagnostic performance, with a sensitivity of 0.933, specificity of 0.722, and an AUC of 0.928.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7254061/v1/b51c4c73b770bb869a8a8a6c.png"},{"id":95563872,"identity":"5725d594-8b9b-4b60-b4b3-5b89e98624dd","added_by":"auto","created_at":"2025-11-10 15:59:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6542517,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7254061/v1/edfe12ba-819f-4c67-96c3-27cc2337df9d.pdf"},{"id":89233251,"identity":"250cc11d-7d53-483d-a8a2-ea069dccc147","added_by":"auto","created_at":"2025-08-17 14:32:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12309,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7254061/v1/7f80cd6413994004465c086c.docx"},{"id":89232268,"identity":"70b2a4d5-3795-446a-bd6a-47fa5a3c6344","added_by":"auto","created_at":"2025-08-17 14:24:15","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8448436,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7254061/v1/ba4176d4168003cbec735f85.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developing a Diagnostic Model to Differentiate the Well-differentiated Lipomatous Tumors Based on Clinicopathological Characteristics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLipomatous tumors are common soft tissue tumors encountered in clinical practice. Well-differentiated lipomatous tumors (WDLTs) mainly consist of mature adipocytes and range from benign types (e.g., lipoma) to locally aggressive intermediate tumors (e.g., atypical lipomatous tumor/well-differentiated liposarcoma [ALT/WDLPS]). Although they share similar morphological features, their biological behaviors differ significantly. ALT/WDLPS may recur after surgery and progress to de-differentiated liposarcoma (DDLPS). Distinguishing ALT/WDLPS without obvious cytological atypia from lipomas is often challenging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Accurate diagnosis and differentiation of WDLPS are essential for appropriate surgical planning and prognostic management.\u003c/p\u003e\u003cp\u003eAdvances in imaging and molecular pathology have improved the differential diagnosis of WDLTs. Clinical studies indicate that patient age (\u0026gt;\u0026thinsp;60 years), tumor location (deep lower extremities), tumor size (\u0026gt;\u0026thinsp;10 cm), and the presence of histologic lipoblasts are important features for differentiating ALT/WDLPS from lipoma [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among these, tumor size and deep location (e.g., retroperitoneum) consistently correlate with malignancy risk, although the exact size cut-offs vary. Multiparametric magnetic resonance imaging (MRI) findings, such as thickened fibrous septa (\u0026gt;\u0026thinsp;2 mm), nodular enhancement (\u0026gt;\u0026thinsp;1 cm), and adipose tissue content\u0026thinsp;\u0026lt;\u0026thinsp;75%, are independent predictors of ALT. However, current diagnostic models have limitations, especially for tumors lacking typical MRI features or atypical lipoblasts and when MRI is not available. Additionally, MRI is more costly than ultrasonography or computed tomography (CT). This highlights the need for alternative diagnostic methods that balance accuracy and cost-effectiveness, such as combining ultrasonography with immunohistochemical (IHC) markers, to build a more comprehensive diagnostic model.\u003c/p\u003e\u003cp\u003ePrevious research has shown that amplification of the 12q13-15 chromosomal region drives MDM2 overexpression and also affects p16 regulation through the CDK4-RB1 pathway [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, current IHC methods relying on single markers are limited in fully capturing this molecular complexity, reducing their ability to distinguish benign from malignant lipomatous tumors. Meanwhile, MDM2 fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization (FISH), considered the diagnostic gold standard for WDLTs, faces barriers in routine clinical use due to its high cost and need for specialized molecular pathology equipment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aimed to integrate clinicopathological features with cost-effective IHC markers (MDM2, CDK4, and p16) to develop a practical diagnostic model that does not require molecular testing. The goal is to overcome diagnostic challenges in resource-limited settings and offer evidence-based support for clinical decision-making, treatment planning, and prognosis assessment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical Characteristics of WDLTs\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 216 patients: 106 male (49.1%) and 110 female (50.9%) individuals, with a male-to-female ratio of 1:1.04. The mean patient age was 52.98 (range: 8\u0026ndash;84) years, and the mean tumor diameter was 9.86 cm. The tumor size distribution was as follows: 35 cases (16.2%) \u0026lt;5 cm, 95 cases (43.9%) between 5 and 10 cm, and 86 cases (39.8%) \u0026ge;10 cm. Tumors were primarily located in the trunk (98 cases, 45.37%), lower limbs (52 cases, 24.07%), head and neck (32 cases, 14.81%), upper limbs (15 cases, 6.94%), retroperitoneum (eight cases, 3.70%), and other sites, including the axilla, spinal canal, scrotum, inguinal region, testis, and perineum (11 cases, 5.09%).\u003cbr\u003e\u0026nbsp;Among these cases, 181 (83.8%) were solitary tumors and 35 (16.2%) were multifocal tumors. Histologically, all tumors consisted of well-differentiated adipocytes arranged in variably sized lobules separated by fibrous septa. No mitotic activity or necrosis was observed (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelation between MDM2 Gene Status and WDLTs\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 216 cases, 64 (29.63%) demonstrated \u003cem\u003eMDM2\u003c/em\u003e gene amplification, comprising 62 cases of ALT/WDLTs and two early DDLPS cases. The remaining 152 cases (70.37%) were MDM2-negative, including 149 lipomas and three spindle cell lipomas.\u0026nbsp;\u003cem\u003eMDM2\u003c/em\u003e amplification was significantly associated with patient age \u0026ge;55 years, tumors located in the lower limbs (especially the thighs), and the retroperitoneal region. A statistically significant correlation was observed between MDM2 amplification and larger tumor size (p\u0026lt;0.05), whereas no significant association was found with sex (Table 1).\u003cbr\u003e\u0026nbsp;ROC curve analysis identified a tumor diameter of 9.9 cm as the optimal threshold for predicting MDM2 amplification, yielding a sensitivity of 0.825, specificity of 0.778, Youden index of 0.603, and an AUC of 0.855 (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSensitivity and Specificity of MDM2/CDK4/p16 IHC in Diagnosing WDLTs\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComplete IHC data for MDM2, CDK4, and p16 were available for 131 patients, including 42 cases of WDLTs and 89 cases of lipoma. The sensitivity of MDM2 immunostaining for diagnosing ALT/WDLTs was 65.0% (39/60), that of CDK4 was 100% (29/29), and that of p16 was 80.39% (41/51). Pairwise combinations of markers did not significantly improve diagnostic sensitivity or specificity; however, the combined use of all three markers significantly enhanced diagnostic performance, achieving a sensitivity of 100% and specificity of 85.58% (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDevelopment of an Integrated Diagnostic Model for ALT/WDLTs\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe compiled the clinical and pathological variables (age, sex, tumor location, unifocal vs. multifocal presentation, maximum tumor diameter, and IHC expression of MDM2, CDK4, and p16) into a comprehensive dataset. Using the odds ratios derived from the multivariable logistic regression, we constructed a simplified diagnostic model (Figure 3A) to differentiate lipomas from ALT/WDLTs.\u003cbr\u003eRisk scores generated from the model were subjected to\u0026nbsp;\u003cem\u003eK-means\u003c/em\u003e clustering analysis, and the midpoints between cluster centers were selected as cut-off values to stratify patients into low-, intermediate-, and high-risk groups. Thresholds of 0.2191 and 0.6521 were established for risk stratification and validated in the test set (Figure 3B\u0026ndash;C).\u003cbr\u003e\u0026nbsp;ROC curve analysis showed excellent diagnostic performance, with a sensitivity of 0.933, specificity of 0.722, and AUC of 0.928 (Figure 3D), indicating the robustness of the model in distinguishing ALT/WDLTs from lipomas. Calibration curves (Supplementary Figure 1A, B) demonstrated high concordance between the predicted probabilities and observed outcomes.\u003cbr\u003e\u0026nbsp;Furthermore, when applied to 39 biopsy specimens of WDLTs, the model achieved an accuracy of 92.31%, a sensitivity of 96.15%, and a specificity of 84.62%, thereby supporting its utility in preoperative diagnostic settings.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eALT/WDLPS accounts for 40\u0026ndash;45% of all liposarcomas, representing the most common subtype. It is estimated that approximately 10.64% of WDLTs without atypia in the deep soft tissues of the extremities or trunk walls are ultimately diagnosed as ALT [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Compared to other soft tissue tumors, the differential diagnosis between deep-seated lipomas and ALT/WDLPS is particularly crucial for preoperative planning. Previous studies have identified multiple risk factors for diagnosing ALT, including advanced age, deep anatomical location, tumor diameter\u0026thinsp;\u0026gt;\u0026thinsp;10 cm, occurrence in the extremities (particularly the lower limbs), thickened fibrous septa (\u0026gt;\u0026thinsp;2 mm), nodular enhancement (\u0026gt;\u0026thinsp;1 cm), adipose tissue content\u0026thinsp;\u0026lt;\u0026thinsp;75%, and presence of atypical lipoblasts [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, some scholars note that although the diagnosis of ALT/WDLPS demonstrates high sensitivity, its specificity remains relatively low, making definitive diagnosis based solely on clinical parameters and MRI features challenging [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, given the relatively high cost of MRI, there is a lack of reported diagnostic models for WDLTs that exclude MRI assessment or those lacking characteristic MRI findings or atypical lipoblasts. This study specifically enrolled patients with WDLTs without prominent fibrous septa or atypical lipoblasts. Based on previous research, we selected five clinical parameters (age, sex, anatomical location, tumor diameter, and tumor multiplicity) and three immunohistochemical markers (MDM2, p16, and CDK4) to construct a diagnostic system for distinguishing ALT/WDLPS from lipoma. The combined clinical-immunohistochemical scoring model demonstrated excellent discriminatory performance (sensitivity, 93.33%; specificity, 72.22%).\u003c/p\u003e\u003cp\u003eIn comparable studies, Cheng et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] reported a clinical-MRI-based diagnostic system with 90% sensitivity and 92.5% specificity, while Asano et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] developed a system incorporating clinical, MRI, and histological features, achieving 87.6% sensitivity and 91.1% specificity. Notably, Cheng\u0026rsquo;s study focused exclusively on deep-seated adipocytic tumors, whereas Asano\u0026rsquo;s cohort included cases with histological features, such as atypical lipoblasts. These differences in case composition are likely to contribute to variations in the diagnostic accuracy across studies. Brisson et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] emphasized that histopathological examination remains essential for distinguishing ALT/WDLPS from lipoma; hence, preoperative needle biopsy is routinely performed for lipomatous tumors. Ultrasound-guided biopsy has particularly improved the diagnostic accuracy for deep, irregularly shaped ALT/WDLPS. Importantly, our diagnostic model also demonstrated applicability to ultrasound-guided biopsy specimens of lipomatous tumors.\u003c/p\u003e\u003cp\u003eALT/WDLTs may closely mimic normal adipose tissue and lipomas. In ALT/WDLTs, atypical lipoblasts are rare; therefore, histological diagnosis can be challenging, and IHC and molecular analyses are often necessary as diagnostic adjuncts. Amplification of the 12q13\u0026ndash;15 region, which includes the \u003cem\u003eMDM2\u003c/em\u003e and \u003cem\u003eCDK4\u003c/em\u003e genes, is commonly observed in ALT/WDLTs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This amplification affects the expression of both MDM2 and CDK4. The current literature shows variable sensitivity and specificity for MDM2 and CDK4 in different ALT studies. The reported specificities ranged from 82.8\u0026ndash;100% for MDM2 (average, 94.7%) and 69.0\u0026ndash;100% for CDK4 (average, 88.8%) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These differences may be attributed to the varying case numbers and tumor types across studies.\u003c/p\u003e\u003cp\u003eAdditionally, some researchers have proposed that p16 may help distinguish between lipomas, WDLTs, and DDLPS. CDKN2A encodesp16Ink4A (p16), which inhibits cell cycle progression via CDK- and RB1-dependent mechanisms. Loss-of-function mutations and deletions in p16 have been observed in various malignancies. In contrast, other studies have reported that p16 is overexpressed less frequently [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Aslam et al. detected the p16 protein by IHC in 36 cases (including WDLTs and DDLPS), finding a sensitivity of 60.9% and specificity of 53.8%. When MDM2, CDK4, and p16 were used in combination to differentiate WDLTs/DDLPS, the sensitivity and specificity decreased to 43.47% and 15.38%, respectively [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings suggest that the diagnostic utility of IHC markers alone is limited, while combined use of MDM2, CDK4, and p16 offers some auxiliary value. The results of this study support this conclusion.\u003c/p\u003e\u003cp\u003eNotably, ALT/WDLPS cannot be ruled out in cases with negative MDM2 expression. Among our 42 WDLT cases, three were negative for MDM2 protein expression (but positive for CDK4 and p16); however, FISH analysis revealed MDM2 amplification. In such cases, IHC detection of CDK4 and/or p16 may provide a useful screening tool. This finding aligns with the results reported by Machado [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eImportantly, atypical lipoblasts are not exclusive to ALT/WDLTs and may also appear in other lipogenic tumors, such as dysplastic lipoma and spindle cell/pleomorphic lipoma. Dysplastic lipomas typically arise in the subcutaneous tissue of the upper back, shoulders, and posterior neck in middle-aged and elderly individuals. These tumors display mild-to-moderate nuclear atypia, and binucleated or multinucleated lipoblasts may be observed focally [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Spindle cell/pleomorphic lipomas commonly occur in the extremities of older adults. Histologically, they contain varying proportions of atypical spindle cells, adipocytes, and/or pleomorphic lipoblasts set within a myxoid or collagenous stroma with an infiltrative growth pattern. Most cases express CD34, and approximately 50% show loss of Rb expression due to \u003cem\u003eRB1\u003c/em\u003e gene deletion [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our cohort, two cases of atypical spindle cell lipomas were identified. Histologically, they featured atypical lipoblasts without a clear spindle cell component and had a fibrotic collagenous stroma. Molecular testing revealed an \u003cem\u003eRb\u003c/em\u003e gene deletion without MDM2 amplification. These findings suggest that dysplastic and atypical spindle cell lipomas are not associated with MDM2 amplification, a distinction that provides a key molecular pathological basis for differentiating these entities from ALT/WDLTs.\u003c/p\u003e\u003cp\u003eMDM2 amplification is an important molecular marker for ALT/WDLPS. Therefore, FISH detection of \u003cem\u003eMDM2\u003c/em\u003e amplification can be used as the gold standard for diagnosing ALT/WDLPS [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Ware et al. found that the amplification rate of MDM2 in peripheral WDLPS located in the extremities was significantly lower than that in central WDLPS located in the retroperitoneum or intra-abdominal cavity. Compared to central WDLPS, peripheral WDLPS typically follows a more indolent clinical course and is associated with a higher overall survival rate. The lower amplification rate in peripheral tumors may suggest that WDLPS with minimal MDM2 amplification has a reduced likelihood of recurrence and dedifferentiation. In contrast, the higher amplification rate observed in retroperitoneal tumors may be related to their longer duration and the challenge of early clinical detection.\u003c/p\u003e\u003cp\u003eFurthermore, the study showed that in DDLPS, the MDM2 amplification pattern is not only diagnostic but also prognostic. Tumors with clustered MDM2 amplifications tend to have a higher risk of recurrence and progression [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Currently, there is no literature evaluating the prognostic or clinical significance of \u003cem\u003eMDM2\u003c/em\u003e gene amplification in adipocytic tumors without typical cytological atypia. Thus, conducting long-term follow-up studies on a large number of mature lipoma-like tumors with MDM2 amplification but lacking cytological atypia is essential to clarify their clinical course and biological behavior.\u003c/p\u003e\u003cp\u003eAlthough MDM2 FISH testing is a valuable diagnostic tool, its implementation requires specialized equipment and reagents that are not universally available across all medical institutions. Furthermore, it is not always practical to perform a biopsy and subsequent FISH analysis for every patient presenting with a deep-seated lipomatous tumor. This limitation prompts a critical clinical question: under what circumstances should MDM2 FISH testing be pursued?\u003c/p\u003e\u003cp\u003eOur diagnostic model addresses this challenge through a risk-stratification framework. For patients with a calculated risk score below 0.2191, suggesting a high likelihood of a benign lipoma, clinical surveillance or marginal excision is recommended, and FISH testing may be safely omitted. Conversely, for patients with a risk score exceeding 0.6521, indicative of a high probability of liposarcoma, an initial biopsy followed by confirmatory MDM2 FISH testing is advised. This approach offers a practical and cost-efficient diagnostic strategy.\u003c/p\u003e\u003cp\u003eThis study had several limitations. First, the cohort was restricted to patients with WDLTs. Potential ultrasound features were not included in the model for deep-seated tumors requiring ultrasound-guided biopsy. Future work should focus on extracting and integrating these imaging characteristics to improve diagnostic accuracy. Second, the retrospective design may have introduced confounding factors that could not be fully controlled. Larger, prospective studies are needed to further validate these findings and refine the model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study identified age\u0026thinsp;\u0026ge;\u0026thinsp;55 years, tumor location in the lower extremities or retroperitoneum, a maximum diameter\u0026thinsp;\u0026ge;\u0026thinsp;9.9 cm, and positive immunohistochemical staining for MDM2, CDK4, and p16 as independent risk factors for distinguishing ALT/WDLPS from lipoma (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A diagnostic model incorporating these clinical and immunohistochemical features, developed through multivariate logistic regression, demonstrated excellent discriminative ability, with a sensitivity of 93.33% and specificity of 72.22%.\u003c/p\u003e\u003cp\u003eBased on the calculated risk scores, we propose the following clinical management strategy. For patients with a score below 0.2191, which indicates a high probability of lipoma, clinical surveillance or marginal excision is appropriate. For scores ranging from 0.2191 to 0.6521, surgical resection combined with immunohistochemical testing for MDM2, CDK4, and p16 is recommended. For scores above 0.6521, which suggest a high likelihood of liposarcoma, diagnostic biopsy followed by confirmation using MDM2 FISH is warranted. These stratified recommendations provide clear evidence-based guidance for treatment decisions and follow-up planning, thereby supporting optimal patient care.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eClinical Data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe retrospectively reviewed 216 cases of WDLTs diagnosed at the Department of Pathology, Fujian Medical University Affiliated First Hospital, between February 2018 and December 2024. Clinical records and surgical histopathological specimens were analyzed, including 39 cases with available preoperative biopsy results. Microscopically, all cases exhibited WDLT features without evident atypical lipoblasts or pleomorphic cells in the fibrous septa. Diagnoses were made in accordance with the 2020 WHO classification of soft tissue tumors and independently reviewed by two senior pathologists. The histological subtypes included lipoma (n\u0026thinsp;=\u0026thinsp;149), spindle cell lipoma (n\u0026thinsp;=\u0026thinsp;3), atypical lipomatous tumor/well-differentiated liposarcoma (ALT/WDLPS; n\u0026thinsp;=\u0026thinsp;62), and early de-differentiated liposarcoma (DDLPS; n\u0026thinsp;=\u0026thinsp;2). All cases were confirmed using MDM2 FISH. IHC data for MDM2, CDK4, and p16 were available for 131 patients. The study was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University. (Approval No. 2015084-2), and written informed consent was obtained from all patients. All procedures followed the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIHC Staining\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTissue specimens were fixed in 10% neutral buffered formalin, followed by routine dehydration, paraffin embedding, and sectioning to a thickness of 4 \u0026micro;m. IHC was performed using the two-step EnVision method. Appropriate positive and negative controls were used for each antibody. Antibodies against MDM2, CDK4, and p16 (Fuzhou Maixin Biotechnology Development Co., Ltd., Fuzhou, China) were applied using an automated IHC system according to the manufacturer\u0026rsquo;s instructions. Only clear nuclear staining was considered positive, and cytoplasmic or perinuclear staining was excluded. Five consecutive high-power fields (400\u0026times; magnification) were examined for semiquantitative assessment. Scoring was based on the proportion of positive cells (0: 0%; 1: \u0026lt;10%; 2: 10\u0026ndash;50%; 3: \u0026gt;50%) and staining intensity (0: negative; 1: weak; 2: moderate; 3: strong). A total score of \u0026ge;\u0026thinsp;4 points was defined as positive expression of MDM2, CDK4, or p16.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFISH Detection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParaffin-embedded sections (4 \u0026micro;m thick) were dewaxed, rehydrated, and enzymatically digested. MDM2-specific probes (Abipin Biotechnology Co., Ltd., Guangzhou, China) were applied, covered with coverslips, and hybridized using an automated FISH system (Leica S500, Wetzlar, Germany) for 12\u0026ndash;20 h. Following post-hybridization washing, counterstaining with DAPI (4\u0026prime;,6-diamidino-2-phenylindole) and mounting were performed. Fluorescence signals were visualized using an Olympus BX53F2C fluorescence microscope (Tokyo, Japan). For each case, 100 nuclei were evaluated, and only those exhibiting concurrent MDM2 and CEP12 signals were included in the analysis. A case was considered FISH-positive if more than 20% of the nuclei exhibited an MDM2/CEP12 signal ratio\u0026thinsp;\u0026gt;\u0026thinsp;2 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. All FISH slides were independently evaluated by two blinded reviewers, a certified FISH technologist and a pathologist, both unaware of the original diagnoses.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using SPSS version 21.0 (IBM Corp., Armonk, NY, USA). Data are presented as mean or frequency, as appropriate. Group comparisons between ALT/WDLT and lipoma cases were made using Student\u0026rsquo;s t-test for continuous variables and the chi-square test or Fisher\u0026rsquo;s exact test for categorical variables.\u003c/p\u003e\u003cp\u003eData processing and further analyses were conducted using R software (Vienna, Austria). The dataset was randomly divided into training and internal validation sets in a 7:3 ratio. Univariate logistic regression was used to identify potential predictors in the training set, followed by multivariate logistic regression to construct a diagnostic nomogram. Risk scores from the model were subjected to K-means clustering, and the midpoints between cluster centers were used as cut-off values for risk stratification. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Model calibration and consistency were assessed using bootstrap resampling and calibration curves. All statistical tests were two-sided, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC, area under the curve; ALT, atypical lipomatous tumor; CT, computed tomography; DDLPS, dedifferentiated liposarcoma; FISH, fluorescence in situ hybridization; IHC, immunohistochemical; MRI, magnetic resonance imaging; ROC, receiver operating characteristic; WDLTs, well-differentiated lipomatous tumors\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, J.W.; Methodology, F.S and S.H.; Investigation, S.H. and X.C.; Data curation, F.S; Formal analysis, J.W and X.C; Writing \u0026ndash; original draft, J.W.; Writing \u0026ndash; review \u0026amp; editing, J.W and Z.Z.; Visualization, Z.Z. All authors have read and agreed to the published version of the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Talent Recruitment Program of the First Affiliated Hospital of Fujian Medical University (YJRC3500). The funders had no role in the study design, collection, analysis and interpretation of data, writing of the report, or decision to submit the article for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the institutional ethics committee (Approval No. 2015084-2)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate/consent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all patients.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSbaraglia M, Bellan E, Dei Tos AP. The 2020 WHO Classification of Soft Tissue Tumours: News and perspectives. \u003cem\u003ePathologica\u003c/em\u003e. 2021;113(2):70\u0026ndash;84. https://doi.org/10.32074/1591-951X-213.\u003c/li\u003e\n\u003cli\u003eAnju MS, Chandramohan K, Bhargavan RV, Somanathan T, Subhadradevi L. An overview on liposarcoma subtypes: Genetic alterations and recent advances in therapeutic strategies. \u003cem\u003eJ Mol Histol\u003c/em\u003e. 2024;55(3):227\u0026ndash;240. https://doi.org/10.1007/s10735-024-10195-4 \u003c/li\u003e\n\u003cli\u003eAsano Y, Miwa S, Yamamoto N, Hayashi K, Takeuchi A, Igarashi K, Yonezawa H, Araki Y, Morinaga S, Nojima T, Ikeda H, Tsuchiya H. A scoring system combining clinical, radiological, and histopathological examinations for differential diagnosis between lipoma and atypical lipomatous tumor/well-differentiated liposarcoma. \u003cem\u003eSci Rep\u003c/em\u003e. 2022;12(1):237. https://doi.org/10.1038/s41598-021-04004-1 \u003c/li\u003e\n\u003cli\u003eCheng Y, Ko AT, Huang JH, Lee BC, Yang RS, Liang CW, Tai HC, Cheng NC. Developing a clinical scoring system to differentiate deep-seated atypical lipomatous tumor from lipoma of soft tissue. \u003cem\u003eAsian J Surg\u003c/em\u003e. 2019;42(8):832\u0026ndash;838. https://doi.org/10.1016/j.asjsur.2018.12.012 \u003c/li\u003e\n\u003cli\u003eBird JE, Morse LJ, Feng L, Wang WL, Lin PP, Moon BS, Lazar AJ, Satcher RL, Madewell JE, Lewis VO. Non-radiographic risk factors differentiating atypical lipomatous tumors from lipomas. \u003cem\u003eFront Oncol\u003c/em\u003e. 2016;6:197. https://doi.org/10.3389/fonc.2016.00197 \u003c/li\u003e\n\u003cli\u003eNomura K, Tomita M, Kuroda K, Souda M, Chiba K, Yonekura A, Osaki M. Overdiagnosis of atypical lipomatous tumors/well-differentiated liposarcomas by morphological diagnosis using only HE stained specimens: a case\u0026ndash;control study with MDM2/CDK4 immunostaining and MDM2/CDK4 fluorescence in situ hybridization. \u003cem\u003eBMC Cancer\u003c/em\u003e. 2024;24(1):1437. https://doi.org/10.1186/s12885-024-13215-4 \u003c/li\u003e\n\u003cli\u003eClay MR, Martinez AP, Weiss SW, Edgar MA. MDM2 amplification in problematic lipomatous tumors: analysis of FISH testing criteria. \u003cem\u003eAm J Surg Pathol\u003c/em\u003e. 2015;39(10):1433\u0026ndash;1439. https://doi.org/10.1097/PAS.0000000000000468 \u003c/li\u003e\n\u003cli\u003eBrisson M, Kashima T, Del Grande F, Khan A, Schweitzer M, Winalski CS. MRI characteristics of lipoma and atypical lipomatous tumor/well-differentiated liposarcoma: retrospective comparison with histology and MDM2 gene amplification. \u003cem\u003eSkeletal Radiol\u003c/em\u003e. 2013;42:635\u0026ndash;647. https://doi.org/10.1007/s00256-012-1517-z \u003c/li\u003e\n\u003cli\u003eThway K, Flora R, Shah C, Olmos D, Fisher C. Diagnostic utility of p16, CDK4, and MDM2 as an immunohistochemical panel in distinguishing well-differentiated and de-differentiated liposarcomas from other adipocytic tumors. \u003cem\u003eAm J Surg Pathol\u003c/em\u003e. 2012;36:462\u0026ndash;469. https://doi.org/10.1097/PAS.0b013e3182417330 \u003c/li\u003e\n\u003cli\u003eClay MR, Martinez AP, Weiss SW, Edgar MA. MDM2 and CDK4 immunohistochemistry: should it be used in problematic differentiated lipomatous tumors? A new perspective. \u003cem\u003eAm J Surg Pathol\u003c/em\u003e. 2016;40:1647\u0026ndash;1652. https://doi.org/10.1097/PAS.0000000000000713 \u003c/li\u003e\n\u003cli\u003eAslam A, Din HU, Qadir A, Aslam U, Humayoun S, Ahmed W. Diagnostic accuracy of immunohistochemical expression of p16, MDM2, and CDK4 in well-differentiated and de-differentiated liposarcoma in MDM2 fluorescent in situ hybridisation confirmed cases. \u003cem\u003eJ Coll Physicians Surg Pak\u003c/em\u003e. 2024;34(9):1051\u0026ndash;1055. https://doi.org/10.29271/jcpsp.2024.09.1051 \u003c/li\u003e\n\u003cli\u003eMachado I, Vargas AC, Maclean F, Llombart-Bosch A. Negative MDM2/CDK4 immunoreactivity does not fully exclude MDM2/CDK4 amplification in a subset of atypical lipomatous tumor/well differentiated liposarcoma. \u003cem\u003ePathol Res Pract\u003c/em\u003e. 2022;232:153839. https://doi.org/10.1016/j.prp.2022.153839 \u003c/li\u003e\n\u003cli\u003eMichal M, Agaimy A, Lui\u0026ntilde;a Contreras A, Svajdler M, Kazakov DV, Steiner P, Grossmann P, Martinek P, Hadravsky L, Michalova K, Svajdler P, Szep Z, Michal M, Fetsch JF. Dysplastic lipoma: a distinctive atypical lipomatous neoplasm with anisocytosis, focal nuclear atypia, p53 overexpression, and a lack of MDM2 gene amplification by FISH; a report of 66 cases demonstrating occasional multifocality and a rare association with retinoblastoma. \u003cem\u003eAm J Surg Pathol\u003c/em\u003e. 2018;42(11):1530\u0026ndash;1540. https://doi.org/10.1097/PAS.0000000000001129 \u003c/li\u003e\n\u003cli\u003eAnderson WJ, Fletcher CDM, Jo VY. Atypical pleomorphic lipomatous tumor: expanding our current understanding in a clinicopathologic analysis of 64 cases. \u003cem\u003eAm J Surg Pathol\u003c/em\u003e. 2021;45(9):1282\u0026ndash;1292. https://doi.org/10.1097/PAS.0000000000001706 \u003c/li\u003e\n\u003cli\u003eGambella A, Bertero L, Rond\u0026oacute;n-Lagos M, Verdun Di Cantogno L, Rangel N, Pitino C, Ricci AA, Mangherini L, Castellano I, Cassoni P. FISH diagnostic assessment of MDM2 amplification in liposarcoma: potential pitfalls and troubleshooting recommendations. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2023;24(2):1342. https://doi.org/10.3390/ijms24021342 \u003c/li\u003e\n\u003cli\u003eAslam A, Din HU, Qadir A, Aslam U, Humayoun S, Ahmed W. Diagnostic accuracy of immunohistochemical expression of p16, MDM2, and CDK4 in well-differentiated and de-differentiated liposarcoma in MDM2 fluorescent in situ hybridisation confirmed cases. \u003cem\u003eJ Coll Physicians Surg Pak\u003c/em\u003e. 2024;34(9):1051\u0026ndash;1055. https://doi.org/10.29271/jcpsp.2024.09.1051 \u003c/li\u003e\n\u003cli\u003eWare PL, Snow AN, Gvalani M, Pettenati MJ, Qasem SA. MDM2 copy numbers in well-differentiated and de-differentiated liposarcoma: characterizing progression to high-grade tumors. \u003cem\u003eAm J Clin Pathol\u003c/em\u003e. 2014;141(3):334\u0026ndash;341. https://doi.org/10.1309/AJCPLYU89XHSNHQO \u003c/li\u003e\n\u003cli\u003eBill KLJ, Seligson ND, Hays JL, Awasthi A, Demoret B, Stets CW, Duggan MC, Bupathi M, Brock GN, Millis SZ, Shakya R, Timmers CD, Wakely PE Jr, Pollock RE, Chen JL. Degree of MDM2 amplification affects clinical outcomes in de-differentiated liposarcoma. \u003cem\u003eOncologist\u003c/em\u003e. 2019;24(7):989\u0026ndash;996. https://doi.org/10.1634/theoncologist.2019-0047 \u003c/li\u003e\n\u003cli\u003eClay MR, Martinez AP, Weiss SW, Edgar MA. MDM2 amplification in problematic lipomatous tumors: analysis of FISH testing criteria. \u003cem\u003eAm J Surg Pathol\u003c/em\u003e. 2015;39(10):1433\u0026ndash;1439. https://doi.org/10.1097/PAS.0000000000000468 \u003c/li\u003e\n\u003cli\u003eWare PL, Snow AN, Gvalani M, Pettenati MJ, Qasem SA. MDM2 copy numbers in well-differentiated and de-differentiated liposarcoma: characterizing progression to high-grade tumors. \u003cem\u003eAm J Clin Pathol\u003c/em\u003e. 2014;141(3):334\u0026ndash;341. https://doi.org/10.1309/AJCPLYU89XHSNHQO \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Results of univariate statistical analysis\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMDM2 non-amplified (N=152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMDM2 amplified (N=64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, mean (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86 (56.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (40.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge;55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66 (43.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (59.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71 (46.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (54.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81 (53.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29 (45.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor size, mean (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Upper limb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (9.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (1.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Lower limb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (11.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34 (53.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Trunk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79 (51.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (29.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Head/Neck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (21.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Retroperitoneal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (5.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (3.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Signal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e135 (88.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (71.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Multiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (11.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (28.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1. MDM2 Non-Amplified Group\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;other\u0026quot; locations in this group included the groin (one case), spinal canal (two cases), axilla (five cases), and perineum (one case).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. MDM2 Amplified Group\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;other\u0026quot; locations in this group included the scrotum (one case) and testis (one case).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Sensitivity and specificity of markers or a combination of markers in IHC\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;Sensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eSpecificity\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMDM2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e65 (39/60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e96.77 (68/71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eCDK4+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e100 (29/29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e87.25 (89/102)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eP16+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e80.39 (41/51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e98.75 (79/80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMDM2+CDK4+p16-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMDM2+p16+CDK4-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e73.33 (11/15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e73.28 (85/116)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMDM2-CDK4+p16+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e100 (2/2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e68.99 (89/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMDM2+CDK4+p16+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e100 (27/27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e85.58 (89/104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"clinicopathological factors, diagnostic prediction model, double minute homologue 2 (MDM2), fluorescence in situ hybridization (FISH), Well-differentiated lipomatous tumors","lastPublishedDoi":"10.21203/rs.3.rs-7254061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7254061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluated the diagnostic value of clinicopathological features and immunohistochemical markers for distinguishing lipomas from atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLTs). An integrated diagnostic model for ALTs/WDLTs was developed to provide guidance for diagnosis, treatment planning, and prognosis. A retrospective analysis was conducted on 216 lipomatous tumor cases diagnosed between February 2018 and December 2024, including lipomas (n\u0026thinsp;=\u0026thinsp;149), spindle cell lipomas (n\u0026thinsp;=\u0026thinsp;3), ALTs/WDLTs (n\u0026thinsp;=\u0026thinsp;62), and early de-differentiated liposarcomas (n\u0026thinsp;=\u0026thinsp;2). Immunohistochemical data for MDM2, CDK4, and p16 were available for 131 patients. MDM2 amplification was significantly more frequent in patients\u0026thinsp;\u0026ge;\u0026thinsp;55 years and in tumors of the lower limbs (especially thigh) and retroperitoneum (p\u0026thinsp;=\u0026thinsp;0.000). Larger tumor size and multiplicity were also associated with MDM2 amplification (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Immunohistochemistry sensitivities for ALTs/WDLTs vs. lipomas: 65% (MDM2), 100% (CDK4), 80.39% (p16); combined, specificity was 100% and sensitivity 85.58%. The diagnostic model achieved 93.33% sensitivity and 72.22% specificity. Scores\u0026thinsp;\u0026lt;\u0026thinsp;0.2191 indicated a higher likelihood of lipoma, while scores\u0026thinsp;\u0026gt;\u0026thinsp;0.6521 indicated a higher likelihood of liposarcoma. Age\u0026thinsp;\u0026ge;\u0026thinsp;55, lower extremity/retroperitoneal location, tumor diameter\u0026thinsp;\u0026ge;\u0026thinsp;9.9 cm, and positive markers were independent risk factors. This model provides an effective tool for ALT/WDLT identification.\u003c/p\u003e","manuscriptTitle":"Developing a Diagnostic Model to Differentiate the Well-differentiated Lipomatous Tumors Based on Clinicopathological Characteristics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-17 14:24:10","doi":"10.21203/rs.3.rs-7254061/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-10T12:00:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T02:39:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269799514910857045007049078598089642161","date":"2025-08-16T10:07:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T15:21:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197477739173994009017847865747200611334","date":"2025-08-09T15:06:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-09T08:23:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-09T08:21:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-06T06:30:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-04T15:00:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-04T14:57:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a0a0b1c-dbeb-4a7f-8e65-74017d557b4d","owner":[],"postedDate":"August 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52914103,"name":"Health sciences/Biomarkers"},{"id":52914104,"name":"Biological sciences/Cancer"},{"id":52914105,"name":"Health sciences/Diseases"},{"id":52914106,"name":"Health sciences/Medical research"},{"id":52914107,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-11-10T15:58:26+00:00","versionOfRecord":{"articleIdentity":"rs-7254061","link":"https://doi.org/10.1038/s41598-025-22547-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-05 15:56:51","publishedOnDateReadable":"November 5th, 2025"},"versionCreatedAt":"2025-08-17 14:24:10","video":"","vorDoi":"10.1038/s41598-025-22547-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-22547-5","workflowStages":[]},"version":"v1","identity":"rs-7254061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7254061","identity":"rs-7254061","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}