Correlation Between Serum Endocrine Hormone Levels and Malignancy Degree of Prolactinoma and Their Predictive Value for Patient Prognosis

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Methods: A total of 100 prolactinoma patients admitted to the Affiliated Hospital of Xuzhou Medical University from January 2019 to December 2024 were enrolled. Based on tumor invasiveness, patients were divided into benign (n = 74) and malignant (n = 26) groups. Serum endocrine hormone levels were compared between groups. Pearson's test analyzed correlations between hormone levels and tumor malignancy. According to new metastases, recurrence, or death during follow-up, patients were classified into good prognosis (n = 69) and poor prognosis (n = 31) groups. Multivariate logistic regression identified factors influencing poor prognosis. Restricted cubic spline analysis evaluated dose-response relationships between hormone levels and poor prognosis risk. A nomogram model was constructed and its predictive performance evaluated. Results: The malignant group showed significantly higher serum prolactin (PRL) levels but lower free thyroxine (fT4) levels than the benign group (P < 0.001). Serum PRL positively correlated with tumor malignancy (r = 0.460, P < 0.001), while fT4 showed negative correlation (r=-0.453, P < 0.001). Multivariate analysis revealed giant tumor type and elevated PRL as risk factors for poor prognosis, whereas pseudocapsule presence and increased fT4 were protective factors (P < 0.05). Nonlinear relationships existed between poor prognosis risk and PRL/fT4 levels (Pnonlinear < 0.05). In the nomogram model, all four factors had variance inflation factors (VIF) < 5 (1.043–1.091). The model's ROC curve area was 0.888, and Hosmer-Lemeshow test confirmed good accuracy (χ²=12.673, P = 0.124). Conclusion: Serum PRL and fT4 levels significantly correlate with prolactinoma malignancy degree and influence patient prognosis. Biological sciences/Cancer Health sciences/Oncology Health sciences/Risk factors Prolactinoma Endocrine hormones Malignancy degree Prognosis Nomogram model Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Prolactinoma, as a common functional pituitary adenoma, primarily results from abnormal proliferation of pituitary lactotroph cells and excessive prolactin (PRL) secretion, accounting for approximately 50% of all pituitary adenomas with significantly higher prevalence in female patients [ 1 ]. The clinical manifestations of prolactinomas are diverse: female patients often present with menstrual disorders, amenorrhea, galactorrhea, and infertility, whereas male patients may exhibit hypogonadism, erectile dysfunction, and sterility. Additionally, tumor size, location, and mass effects may cause severe complications including headache, visual impairment, and visual field defects, significantly compromising patients' quality of life. Although most prolactinomas demonstrate benign biological behavior, a considerable proportion exhibit malignant clinical features such as invasive growth, drug resistance, or postoperative recurrence, which not only complicate treatment but also markedly affect prognosis [ 2 – 4 ]. Recent advances in endocrinology and imaging have significantly improved diagnostic rates, but accurate assessment of malignancy and prognosis remains clinically challenging. Current WHO classification of pituitary tumors primarily relies on histological features [ 5 ], yet growing evidence indicates that pathological criteria alone are insufficient for comprehensive evaluation of biological behavior and long-term prognosis [ 6 , 7 ]. While imaging parameters like Knosp grading can assess anatomical invasiveness in non-functioning adenomas, their prognostic value remains limited [ 8 ]. Therefore, identifying biomarkers for early and accurate prediction of prolactinoma malignancy and prognosis is crucial for optimizing treatment and improving outcomes. Serum endocrine hormones, as key indicators of endocrine status, are gaining attention for their roles in prolactinoma pathogenesis and prognosis. Studies demonstrate that serum PRL levels correlate significantly with tumor size, invasiveness, and recurrence risk [ 1 , 3 ]. Furthermore, alterations in free thyroxine (fT4) levels may influence tumor behavior [ 6 ]. However, comprehensive investigations into the relationship between serum hormone levels and prolactinoma malignancy/prognosis remain scarce, particularly large-scale clinical validations. This study analyzed clinical data from 100 prolactinoma patients treated at Xuzhou Medical University Affiliated Hospital (January 2019-December 2024) to investigate correlations between serum endocrine hormone levels and tumor malignancy, while evaluating their prognostic value. Our findings provide both theoretical insights and clinical applications for prolactinoma management. Materials and Methods 1.1 Study Population Patients diagnosed with prolactinoma with the following Inclusion criteria were included: (1) Confirmed prolactinoma diagnosis;(2) Radiologically visible sellar mass; (3) Pathological confirmation with immunohistochemical staining positive for PIT-1 and PRL, but negative for growth hormone, TSH, SF-1, and T-PIT; (4) All patients underwent endoscopic transsphenoidal surgery Exclusion criteria were: (1) Concurrent malignancies; (2) Incomplete data or loss to follow-up This study complied with the Declaration of Helsinki, with informed consent obtained. 1.2 Methods 1.2.1 Observational Parameters The following data were obtained: Demographics (Sex, age, BMI, preoperative apoplexy); Imaging (Knosp grade, vascular supply); Pathology (Hardy classification, tumor type, consistency, malignancy, pseudocapsule presence); Biochemical markers (HbA1c analyzed by a TOSOH HLC-723G8 analyzer;·Lipid profiles analyzed by a Beckman Coulter AU5421; Hematological indices analyzed by a Sysmex XE-2100, including NLR, PLR, SII; Endocrine hormones (Fasting serum PRL, fT4, estradiol, and testosterone analyzed by a Beckman Coulter AU5421).This study was in accordance with the provisions of the Medical Ethics Committee of the Affiliated Hospital of Xuzhou Medical University. It has been reviewed by the Ethics Committee and exempted from the informed consent requirement, Ethics No. ( XYFY2022-JS031-01 ). 1.2.2 Malignancy Stratification 26 invasive cases were classified as malignant, while 74 non-invasive as benign. 1.2.3 Follow-up Patients were evaluated at 3/6/12 months post-treatment. Poor prognosis (n = 31 vs. good prognosis n = 69) was defined as new metastases, recurrence, or death. . 1.3 Statistical Analysis Using SPSS 22.0 and R 4.2.3, normally distributed data was expressed as mean ± SD and analyzed by independent t-tests, while categorical data was expressed as % and analyzed by χ²/Fisher's exact tests (%). Pearson correlation (hormones vs. malignancy), multivariate logistic regression (prognostic factors), Restricted cubic spline (dose-response relationships), Nomogram construction with VIF validation (< 5), ROC analysis (discrimination), and Hosmer-Lemeshow test (calibration) were performed as needed. Statistical significance cutoff was set at a two-sided P < 0.05. Results 2.1 Hormonal Profiles by Malignancy Status Malignant group showed significantly higher PRL but lower fT4 than benign group (P 0.05, Table 1 ). Table 1 Comparison of serum endocrine hormone levels in patients with different degrees of malignancy Group n PRL (µg/L) fT4 (pmol/L) Estradiol (pmol/L) Testosterone (nmol/L) Benign Group 74 113.86 ± 20.67 15.39 ± 2.88 87.99 ± 17.96 9.35 ± 2.43 Malignant Group 26 137.16 ± 17.63 12.29 ± 2.13 88.62 ± 19.14 9.82 ± 2.59 t 5.126 5.020 0.151 0.834 P < 0.001 < 0.001 0.880 0.406 2.2 Correlation between Serum endocrine hormone Levels and the malignancy degree of prolactinoma The results of Pearson correlation analysis showed that the serum PRL level was significantly positively correlated with the malignancy degree of prolactinoma (r = 0.460, P < 0.001), and the serum fT4 level was significantly negatively correlated with the malignancy degree of prolactinoma (r=-0.453, P < 0.001), as shown in Fig. 1 . 2.3 Comparison of clinical data between the good prognosis group and the poor prognosis group The tumor types in the poor prognosis group were giant type, malignant tumor, the proportion of tumor without pseudocapsule, and the serum PRL level were significantly higher than those in the good prognosis group, while the serum fT4 level was significantly lower than that in the good prognosis group. The differences were statistically significant (P < 0.05), as shown in Table 2 Table 2 Comparison of clinical data between the good prognosis group and the poor prognosis group ategory Good Prognosis Group (n = 69) Poor Prognosis Group (n = 31) t/χ² P Gender [n (%)] 0.662 0.416 Male 34 (49.28) 18 (58.06) Female 35 (50.72) 13 (41.94) Age (years) 39.06 ± 6.82 39.24 ± 7.11 0.120 0.904 BMI (kg/m²) 25.16 ± 2.14 25.23 ± 2.27 0.148 0.882 Knosp Grade [n (%)] 0.626 0.429 0–2 56 (81.16) 23 (74.19) 3–4 13 (18.84) 8 (25.81) Hardy Grade [n (%)] 0.570 0.450 I-II 39 (56.52) 15 (48.39) III-IV 30 (43.48) 16 (51.61) Preoperative Tumor Apoplexy [n (%)] 0.080 0.777 No 61 (88.41) 28 (90.32) Yes 8 (11.59) 3 (9.68) Tumor Vascularity [n (%)] 0.570 0.450 Rich 30 (43.48) 16 (51.61) Poor 39 (56.52) 15 (48.39) Tumor Type [n (%)] 10.977 0.004 Micro 12 (17.39) 1 (3.23) Macro 39 (56.52) 16 (51.61) Giant 10 (14.49) 14 (45.16) Tumor Consistency [n (%)] 0.050 0.824 Soft 52 (75.36) 24 (77.42) Firm 17 (24.64) 7 (22.58) Tumor Malignancy [n (%)] 8.573 0.003 Benign 57 (82.61) 17 (54.84) Malignant 12 (17.39) 14 (45.16) Pseudocapsule [n (%)] 8.550 0.003 Present 44 (63.77) 10 (32.26) Absent 25 (36.23) 21 (67.74) HbA1c (%) 5.56 ± 1.02 5.71 ± 1.11 0.662 0.510 TC (mmol/L) 4.75 ± 0.86 4.92 ± 0.89 0.904 0.368 TG (mmol/L) 0.98 ± 0.37 1.12 ± 0.45 1.634 0.105 LDL-C (mmol/L) 2.93 ± 0.57 3.10 ± 0.64 1.327 0.187 HDL-C (mmol/L) 1.26 ± 0.34 1.31 ± 0.29 0.710 0.479 NLR 1.75 ± 0.26 1.84 ± 0.30 1.525 0.130 PLR 140.56 ± 18.93 135.84 ± 20.17 1.130 0.261 SII 387.85 ± 27.43 396.61 ± 30.94 1.419 0.159 PRL (µg/L) 112.45 ± 19.09 136.54 ± 20.19 5.733 < 0.001 fT4 (pmol/L) 15.30 ± 2.84 12.98 ± 2.82 3.786 < 0.001 Estradiol (pmol/L) 89.14 ± 18.03 85.96 ± 20.16 0.786 0.434 Testosterone (nmol/L) 9.62 ± 2.51 9.14 ± 2.48 0.888 0.377 2.4 Multivariate Logistic regression analysis was conducted to analyze the factors influencing the prognosis of patients Taking the indicators with statistically significant differences between the poor prognosis group and the good prognosis group as independent variables and the prognosis of patients (good = 0, poor = 1) as dependent variables, the results of multivariate Logistic regression analysis showed that the tumor type was giant type (OR = 20.030, 95%CI: 1.611-249.002, P = 0.020), elevated serum PRL level (OR = 1.064, 95%CI: 1.029–1.101, P < 0.001) are risk factors for poor prognosis in patients with prolactinoma, and the presence of pseudocapsule (OR = 0.298, 95%CI: 0.090–0.991, P = 0.048) and elevated serum fT4 level (OR = 0.729, 95%CI: 0.569–0.934, P = 0.012) were protective factors, as shown in Table 3 . Table 3 Multivariate Logistic regression analysis of the factors influencing the prognosis of patients Variable β SE Wald χ² P OR (95% CI) Tumor Type 6.068 0.048 Micro - - - - 1.000 (Reference) Macro 1.919 1.226 2.448 0.118 6.811 (0.616–75.323) Giant 2.997 1.286 5.433 0.020 20.030 (1.611-249.002) Tumor Malignancy Benign - - - - 1.000 (Reference) Malignant 0.035 0.682 0.003 0.959 1.036 (0.272–3.944) Pseudocapsule Absent - - - - 1.000 (Reference) Present -1.209 0.613 3.896 0.048 0.298 (0.090–0.991) PRL (µg/L) 0.062 0.017 12.992 < 0.001 1.064 (1.029–1.101) fT4 (pmol/L) -0.316 0.126 6.258 0.012 0.729 (0.569–0.934) Constant -5.691 2.614 4.740 0.029 0.003 2.5 Restriction cubic spline analysis of the association between serum endocrine hormone levels and the risk of poor prognosis in patients with prolactinoma The results of restricted cubic spline analysis showed that the risk of poor prognosis in patients with prolactinoma increased with the increase of serum PRL level and decreased with the increase of fT4 level. The risk of poor prognosis in patients has a significant nonlinear relationship with serum PRL (Chi-Square = 26.135, Pnonlinear < 0.001) and fT4 levels (Chi-Square = 10.598, Pnonlinear = 0.014), as shown in Fig. 2 . 2.6 Construction of Nomogram Prediction Model Taking tumor type, pseudocapsule, PRL and fT4 as predictors, a nomogram model for predicting poor prognosis in patients with prolactinoma was constructed. It was verified that the C-index of this nomogram model was 0.821, as shown in Fig. 3 . 2.7 Model Evaluation The results of the multicollinearity test show that the variance inflation factor (VIF) of the four influencing factors in the nomogram model is all < 5 (1.043–1.091), suggesting that there is no multicollinearity problem in the model and it has good stability, as shown in Table 4 . The area under the ROC curve of the model was 0.888 (95%CI: 0.819–0.957), with a sensitivity of 84.19% and a specificity of 75.27%. This model has good discrimination, as shown in Fig. 4 . The Hosmer-Lemeshow test results showed that the nomogram model had good accuracy (χ2 = 12.673, P = 0.124). Table 4 Multicollinearity test of influencing factors for poor prognosis in patients with prolactinoma Influencing Factor Unstandardized Coefficients Standardized Coefficients t-value P-value Collinearity Statistics B SE β Tolerance (Constant) -0.311 0.316 - -0.985 0.327 Tumor Type 0.184 0.063 0.239 2.940 0.004 Pseudocapsule -0.156 0.076 -0.168 -2.061 0.042 PRL (µg/L) 0.008 0.002 0.388 4.675 < 0.001 fT4 (pmol/L) -0.032 0.013 -0.208 -2.521 0.013 Discussion As the most common type of functional pituitary adenoma, prolactinoma exhibits complex pathogenesis and diverse clinical manifestations. A subset of cases demonstrate malignant features such as invasive growth, drug resistance, and postoperative recurrence, posing significant therapeutic challenges [ 9 – 13 ]. Although advances in endocrinology and imaging have markedly improved diagnostic rates [ 14 – 16 ], accurately assessing tumor malignancy and predicting prognosis remain critical unmet needs. Serum endocrine hormones, as key indicators of endocrine status, are increasingly recognized for their roles in prolactinoma development and prognostic evaluation [ 17 , 18 ]. This study analyzed clinical data from 100 prolactinoma patients to investigate correlations between serum hormone levels and tumor malignancy while assessing their prognostic value, aiming to provide novel insights for clinical practice. Hofbauer et al. [ 6 ] reported that during follow-up, prolactinoma patients showed significant reductions in tumor size, volume, and PRL levels but increases in fT4, estradiol, and testosterone compared to baseline. Initial tumor size/volume positively correlated with PRL normalization time (P < 0.05) but negatively with baseline fT4 levels and their changes (P < 0.05), suggesting endocrine hormones associate with pathological features and progression. Our findings further confirmed that the malignant group had significantly higher serum PRL but lower fT4 than the benign group, both showing strong correlations with malignancy. From a molecular perspective, PRL and fT4 may influence tumor behavior through distinct mechanisms.PRL acts not only as a tumor marker but also as a growth factor promoting progression via autocrine/paracrine signaling. Elevated PRL activates PRL receptors (PRLR), stimulating downstream pathways (JAK2/STAT5, PI3K/AKT/mTOR) to enhance proliferation, survival, and invasiveness [ 19 – 21 ]. PRL may also upregulate VEGF expression to facilitate angiogenesis, supporting tumor growth and local infiltration [ 22 ]. Reduced fT4 may diminish thyroid hormone’s tumor-suppressive effects. Thyroid hormones regulate gene expression via nuclear receptors (TRα/TRβ), impacting metabolism, differentiation, and apoptosis [ 23 ]. fT4 deficiency could impair mitochondrial function, forcing tumor cells to rely on glycolysis (Warburg effect) and adapt to hypoxic microenvironments, thereby enhancing malignant phenotypes [ 24 ]. Additionally, fT4 depletion may suppress NK and cytotoxic T-cell activity, weakening immune surveillance and indirectly promoting progression [ 25 ]. Multivariate analysis identified elevated PRL and giant tumor type as risk factors for poor prognosis, whereas pseudocapsule presence and higher fT4 were protective. These align with prior studies. Yogeeta et al. [ 2 ] noted that giant prolactinomas, due to their size and invasiveness, correlate with higher recurrence and worse outcomes, while pseudocapsules may confine tumor spread, improving prognosis. Sosa-Eroza et al. [ 26 ] found that prolactinoma patients achieving normal PRL after > 2 years of cabergoline therapy were more likely to sustain long-term remission, underscoring PRL’s prognostic relevance. Hofbauer et al. [ 6 ] observed that dopamine agonist therapy significantly increased fT4 levels and reduced tumor size/volume, with initial tumor dimensions inversely correlating with fT4 changes, suggesting fT4’s role in clinical remission. Prognostic implications : High PRL reflects hyperactive secretion and proliferative vigor, while giant tumors often invade the sellar region, complicating total resection and increasing recurrence risk..Pseudocapsules demarcate tumor boundaries, limiting infiltration and improving resectability. Higher fT4 may indicate preserved hypothalamic-pituitary-thyroid axis function, implying less pituitary damage, while thyroid hormones’ metabolic/cardiovascular benefits might counteract PRL-induced abnormalities. Our nomogram model integrated these four factors (tumor type, pseudocapsule, PRL, fT4), demonstrating robust stability (VIFs 1.043–1.091) and discrimination (AUC = 0.888), offering a practical tool for prognostic assessment. Limitations : One limitation of this study exists in the sample size. A moderate cohort may affect generalizability, and larger validation studies are needed). External validations might also test the model’s applicability across regions/hospital tiers. In order to elucidate molecular mechanisms,future studies should incorporate genomics/proteomics to analyze hormone-tumor interactions. It might also be interesting to investigate into the effect of brain lesion laterality on the prognosis [ 27 ]. In conclusion, serum PRL and fT4 levels significantly correlate with prolactinoma malignancy and prognosis. Tumor type and pseudocapsule status further modulate outcomes. This study not only advances prognostic tools but also supports mechanistic research. Future work should expand cohorts, integrate multifactorial analyses, and explore molecular pathways to refine therapeutic strategies. Declarations Funding: No Funding Author Contribution YPM, RY mainly wrote the manuscript text,.MPJ prepared the data. FB, YY completed the charts, and all the authors reviewed the manuscript. Data Availability Data is provided within the manuscript or supplementary information files. References Ottenhausen, M., Conrad, J., Wolters, L. M. & Ringel, F. Surgery as first-line treatment for prolactinoma? Discussion of the literature and results of a consecutive series of surgically treated patients. Neurosurg. Rev. 46 (1), 128 (2023). Yogeeta, F. et al. Prolactinoma: Navigating the Dual Challenge of Side Effects and Treatment Strategies - A Comprehensive Review. Ann. Med. Surg. (Lond) . 86 (8), 4613–4623 (2024). Sari, R. et al. Treatment Strategies for Dopamine Agonist-Resistant and Aggressive Prolactinomas: A Comprehensive Analysis of the Literature. Horm. 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Supplementary Files CorrelationBetweenSerumEndocrineHormoneLevelsandMalignancyDegreeofProlactinomaandTheirPredictiveValueforPatientPrognosis.xlsx Cite Share Download PDF Status: Published Journal Publication published 13 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 28 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 07 Nov, 2025 Editor assigned by journal 02 Nov, 2025 Editor invited by journal 14 Oct, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 16 Sep, 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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11:54:53","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100952,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7428081/v1/bab4c55ad2a747ca5b092094.html"},{"id":96284878,"identity":"60f1201f-84dd-4db3-bfa3-e718d6c4c028","added_by":"auto","created_at":"2025-11-19 11:54:53","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3341664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eScatter plot of the relationship between serum endocrine hormone levels and the malignancy degree of prolactinoma\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7428081/v1/4d9f626d2d003dbf2026b785.jpeg"},{"id":96284877,"identity":"f53e8869-f5f6-43c6-ba7a-25fe0a323ca0","added_by":"auto","created_at":"2025-11-19 11:54:53","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3902828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eRestricted cubic spline plot of the association between serum endocrine hormone levels and the risk of poor prognosis in patients with prolactinoma\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7428081/v1/5311d4123023fe25a0a31f34.jpeg"},{"id":96364660,"identity":"5577e098-8c56-4fdf-981d-3a9db2519cae","added_by":"auto","created_at":"2025-11-20 10:09:31","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12827477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNomogram model of poor prognosis in patients with prolactinoma\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7428081/v1/2f1c61f39bb1a6ee25de8fb4.jpeg"},{"id":96284876,"identity":"10712396-1c56-45cf-a21f-c78a4d23f1b1","added_by":"auto","created_at":"2025-11-19 11:54:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22335,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the model.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7428081/v1/843a826818fcbcd69f06c023.png"},{"id":98244904,"identity":"06edcbb6-eac4-48d5-b299-0fc603d26747","added_by":"auto","created_at":"2025-12-15 16:15:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21225704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7428081/v1/825e2d9f-fe3d-4f6b-8524-e203d7e460c0.pdf"},{"id":96364350,"identity":"f573ae3b-cc03-43c4-b42f-1b7df73fd5d7","added_by":"auto","created_at":"2025-11-20 10:09:13","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25583,"visible":true,"origin":"","legend":"","description":"","filename":"CorrelationBetweenSerumEndocrineHormoneLevelsandMalignancyDegreeofProlactinomaandTheirPredictiveValueforPatientPrognosis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7428081/v1/ce1227403667c3dc97d46bc1.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Between Serum Endocrine Hormone Levels and Malignancy Degree of Prolactinoma and Their Predictive Value for Patient Prognosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProlactinoma, as a common functional pituitary adenoma, primarily results from abnormal proliferation of pituitary lactotroph cells and excessive prolactin (PRL) secretion, accounting for approximately 50% of all pituitary adenomas with significantly higher prevalence in female patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The clinical manifestations of prolactinomas are diverse: female patients often present with menstrual disorders, amenorrhea, galactorrhea, and infertility, whereas male patients may exhibit hypogonadism, erectile dysfunction, and sterility. Additionally, tumor size, location, and mass effects may cause severe complications including headache, visual impairment, and visual field defects, significantly compromising patients' quality of life. Although most prolactinomas demonstrate benign biological behavior, a considerable proportion exhibit malignant clinical features such as invasive growth, drug resistance, or postoperative recurrence, which not only complicate treatment but also markedly affect prognosis [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent advances in endocrinology and imaging have significantly improved diagnostic rates, but accurate assessment of malignancy and prognosis remains clinically challenging. Current WHO classification of pituitary tumors primarily relies on histological features [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], yet growing evidence indicates that pathological criteria alone are insufficient for comprehensive evaluation of biological behavior and long-term prognosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While imaging parameters like Knosp grading can assess anatomical invasiveness in non-functioning adenomas, their prognostic value remains limited [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, identifying biomarkers for early and accurate prediction of prolactinoma malignancy and prognosis is crucial for optimizing treatment and improving outcomes.\u003c/p\u003e\u003cp\u003eSerum endocrine hormones, as key indicators of endocrine status, are gaining attention for their roles in prolactinoma pathogenesis and prognosis. Studies demonstrate that serum PRL levels correlate significantly with tumor size, invasiveness, and recurrence risk [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, alterations in free thyroxine (fT4) levels may influence tumor behavior [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, comprehensive investigations into the relationship between serum hormone levels and prolactinoma malignancy/prognosis remain scarce, particularly large-scale clinical validations.\u003c/p\u003e\u003cp\u003eThis study analyzed clinical data from 100 prolactinoma patients treated at Xuzhou Medical University Affiliated Hospital (January 2019-December 2024) to investigate correlations between serum endocrine hormone levels and tumor malignancy, while evaluating their prognostic value. Our findings provide both theoretical insights and clinical applications for prolactinoma management.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Study Population\u003c/h2\u003e\u003cp\u003ePatients diagnosed with prolactinoma with the following Inclusion criteria were included:\u003c/p\u003e\u003cp\u003e(1) Confirmed prolactinoma diagnosis;(2) Radiologically visible sellar mass; (3) Pathological confirmation with immunohistochemical staining positive for PIT-1 and PRL, but negative for growth hormone, TSH, SF-1, and T-PIT; (4) All patients underwent endoscopic transsphenoidal surgery\u003c/p\u003e\u003cp\u003eExclusion criteria were: (1) Concurrent malignancies; (2) Incomplete data or loss to follow-up\u003c/p\u003e\u003cp\u003eThis study complied with the Declaration of Helsinki, with informed consent obtained.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Methods\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e1.2.1 Observational Parameters\u003c/h2\u003e\u003cp\u003eThe following data were obtained: Demographics (Sex, age, BMI, preoperative apoplexy); Imaging (Knosp grade, vascular supply); Pathology (Hardy classification, tumor type, consistency, malignancy, pseudocapsule presence); Biochemical markers (HbA1c analyzed by a TOSOH HLC-723G8 analyzer;\u0026middot;Lipid profiles analyzed by a Beckman Coulter AU5421; Hematological indices analyzed by a Sysmex XE-2100, including NLR, PLR, SII; Endocrine hormones (Fasting serum PRL, fT4, estradiol, and testosterone analyzed by a Beckman Coulter AU5421).This study was in accordance with the provisions of the Medical Ethics Committee of the Affiliated Hospital of Xuzhou Medical University. It has been reviewed by the Ethics Committee and exempted from the informed consent requirement, Ethics No. ( XYFY2022-JS031-01 ).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e1.2.2 Malignancy Stratification\u003c/h2\u003e\u003c/div\u003e\u003c/div\u003e\n\u003cp\u003e26 invasive cases were classified as malignant, while 74 non-invasive as benign.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e1.2.3 Follow-up\u003c/div\u003e\u003cp\u003ePatients were evaluated at 3/6/12 months post-treatment. Poor prognosis (n\u0026thinsp;=\u0026thinsp;31 vs. good prognosis n\u0026thinsp;=\u0026thinsp;69) was defined as new metastases, recurrence, or death. .\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eUsing SPSS 22.0 and R 4.2.3, normally distributed data was expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and analyzed by independent t-tests, while categorical data was expressed as % and analyzed by χ\u0026sup2;/Fisher's exact tests (%). Pearson correlation (hormones vs. malignancy), multivariate logistic regression (prognostic factors), Restricted cubic spline (dose-response relationships), Nomogram construction with VIF validation (\u0026lt;\u0026thinsp;5), ROC analysis (discrimination), and Hosmer-Lemeshow test (calibration) were performed as needed. Statistical significance cutoff was set at a two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Hormonal Profiles by Malignancy Status\u003c/h2\u003e\u003cp\u003eMalignant group showed significantly higher PRL but lower fT4 than benign group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No differences in estradiol/testosterone (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, 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\u003eComparison of serum endocrine hormone levels in patients with different degrees of malignancy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePRL (\u0026micro;g/L)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003efT4 (pmol/L)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEstradiol (pmol/L)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTestosterone (nmol/L)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenign Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113.86\u0026thinsp;\u0026plusmn;\u0026thinsp;20.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87.99\u0026thinsp;\u0026plusmn;\u0026thinsp;17.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137.16\u0026thinsp;\u0026plusmn;\u0026thinsp;17.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.62\u0026thinsp;\u0026plusmn;\u0026thinsp;19.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Correlation between Serum endocrine hormone Levels and the malignancy degree of prolactinoma\u003c/h2\u003e\u003cp\u003eThe results of Pearson correlation analysis showed that the serum PRL level was significantly positively correlated with the malignancy degree of prolactinoma (r\u0026thinsp;=\u0026thinsp;0.460, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the serum fT4 level was significantly negatively correlated with the malignancy degree of prolactinoma (r=-0.453, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Comparison of clinical data between the good prognosis group and the poor prognosis group\u003c/h2\u003e\u003cp\u003eThe tumor types in the poor prognosis group were giant type, malignant tumor, the proportion of tumor without pseudocapsule, and the serum PRL level were significantly higher than those in the good prognosis group, while the serum fT4 level was significantly lower than that in the good prognosis group. The differences were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\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\u003eComparison of clinical data between the good prognosis group and the poor prognosis group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGood Prognosis Group (n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoor Prognosis Group (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et/χ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender [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\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.416\u003c/p\u003e\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\u003e34 (49.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 (58.06)\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\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\u003e35 (50.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (41.94)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.06\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.24\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnosp 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\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56 (81.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23 (74.19)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (18.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (25.81)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHardy 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\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39 (56.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15 (48.39)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII-IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30 (43.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (51.61)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative Tumor Apoplexy [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\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61 (88.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28 (90.32)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (11.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (9.68)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Vascularity [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\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30 (43.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (51.61)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39 (56.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15 (48.39)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Type [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\u003e10.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12 (17.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (3.23)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39 (56.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (51.61)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGiant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (14.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (45.16)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Consistency [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\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52 (75.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24 (77.42)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17 (24.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (22.58)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Malignancy [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\u003e8.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57 (82.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (54.84)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12 (17.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (45.16)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudocapsule [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\u003e8.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44 (63.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (32.26)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (36.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21 (67.74)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e140.56\u0026thinsp;\u0026plusmn;\u0026thinsp;18.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135.84\u0026thinsp;\u0026plusmn;\u0026thinsp;20.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e387.85\u0026thinsp;\u0026plusmn;\u0026thinsp;27.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e396.61\u0026thinsp;\u0026plusmn;\u0026thinsp;30.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRL (\u0026micro;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e112.45\u0026thinsp;\u0026plusmn;\u0026thinsp;19.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136.54\u0026thinsp;\u0026plusmn;\u0026thinsp;20.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efT4 (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstradiol (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.14\u0026thinsp;\u0026plusmn;\u0026thinsp;18.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.96\u0026thinsp;\u0026plusmn;\u0026thinsp;20.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTestosterone (nmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Multivariate Logistic regression analysis was conducted to analyze the factors influencing the prognosis of patients\u003c/h2\u003e\u003cp\u003eTaking the indicators with statistically significant differences between the poor prognosis group and the good prognosis group as independent variables and the prognosis of patients (good\u0026thinsp;=\u0026thinsp;0, poor\u0026thinsp;=\u0026thinsp;1) as dependent variables, the results of multivariate Logistic regression analysis showed that the tumor type was giant type (OR\u0026thinsp;=\u0026thinsp;20.030, 95%CI: 1.611-249.002, P\u0026thinsp;=\u0026thinsp;0.020), elevated serum PRL level (OR\u0026thinsp;=\u0026thinsp;1.064, 95%CI: 1.029\u0026ndash;1.101, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are risk factors for poor prognosis in patients with prolactinoma, and the presence of pseudocapsule (OR\u0026thinsp;=\u0026thinsp;0.298, 95%CI: 0.090\u0026ndash;0.991, P\u0026thinsp;=\u0026thinsp;0.048) and elevated serum fT4 level (OR\u0026thinsp;=\u0026thinsp;0.729, 95%CI: 0.569\u0026ndash;0.934, P\u0026thinsp;=\u0026thinsp;0.012) were protective factors, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Logistic regression analysis of the factors influencing the prognosis of patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Type\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=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMicro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.811 (0.616\u0026ndash;75.323)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGiant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.030 (1.611-249.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Malignancy\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=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.036 (0.272\u0026ndash;3.944)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudocapsule\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=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.298 (0.090\u0026ndash;0.991)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRL (\u0026micro;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.064 (1.029\u0026ndash;1.101)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efT4 (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.729 (0.569\u0026ndash;0.934)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e2.5 Restriction cubic spline analysis of the association between serum endocrine hormone levels and the risk of poor prognosis in patients with prolactinoma\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe results of restricted cubic spline analysis showed that the risk of poor prognosis in patients with prolactinoma increased with the increase of serum PRL level and decreased with the increase of fT4 level. The risk of poor prognosis in patients has a significant nonlinear relationship with serum PRL (Chi-Square\u0026thinsp;=\u0026thinsp;26.135, Pnonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and fT4 levels (Chi-Square\u0026thinsp;=\u0026thinsp;10.598, Pnonlinear\u0026thinsp;=\u0026thinsp;0.014), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Construction of Nomogram Prediction Model\u003c/h2\u003e\u003cp\u003eTaking tumor type, pseudocapsule, PRL and fT4 as predictors, a nomogram model for predicting poor prognosis in patients with prolactinoma was constructed. It was verified that the C-index of this nomogram model was 0.821, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Model Evaluation\u003c/h2\u003e\u003cp\u003eThe results of the multicollinearity test show that the variance inflation factor (VIF) of the four influencing factors in the nomogram model is all \u0026lt;\u0026thinsp;5 (1.043\u0026ndash;1.091), suggesting that there is no multicollinearity problem in the model and it has good stability, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The area under the ROC curve of the model was 0.888 (95%CI: 0.819\u0026ndash;0.957), with a sensitivity of 84.19% and a specificity of 75.27%. This model has good discrimination, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The Hosmer-Lemeshow test results showed that the nomogram model had good accuracy (χ2\u0026thinsp;=\u0026thinsp;12.673, P\u0026thinsp;=\u0026thinsp;0.124).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulticollinearity test of influencing factors for poor prognosis in patients with prolactinoma\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfluencing Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandardized Coefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCollinearity Statistics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTolerance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Constant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudocapsule\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRL (\u0026micro;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efT4 (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs the most common type of functional pituitary adenoma, prolactinoma exhibits complex pathogenesis and diverse clinical manifestations. A subset of cases demonstrate malignant features such as invasive growth, drug resistance, and postoperative recurrence, posing significant therapeutic challenges [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Although advances in endocrinology and imaging have markedly improved diagnostic rates [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], accurately assessing tumor malignancy and predicting prognosis remain critical unmet needs. Serum endocrine hormones, as key indicators of endocrine status, are increasingly recognized for their roles in prolactinoma development and prognostic evaluation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study analyzed clinical data from 100 prolactinoma patients to investigate correlations between serum hormone levels and tumor malignancy while assessing their prognostic value, aiming to provide novel insights for clinical practice.\u003c/p\u003e\u003cp\u003eHofbauer et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] reported that during follow-up, prolactinoma patients showed significant reductions in tumor size, volume, and PRL levels but increases in fT4, estradiol, and testosterone compared to baseline. Initial tumor size/volume positively correlated with PRL normalization time (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but negatively with baseline fT4 levels and their changes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting endocrine hormones associate with pathological features and progression. Our findings further confirmed that the malignant group had significantly higher serum PRL but lower fT4 than the benign group, both showing strong correlations with malignancy.\u003c/p\u003e\u003cp\u003eFrom a molecular perspective, PRL and fT4 may influence tumor behavior through distinct mechanisms.PRL acts not only as a tumor marker but also as a growth factor promoting progression via autocrine/paracrine signaling. Elevated PRL activates PRL receptors (PRLR), stimulating downstream pathways (JAK2/STAT5, PI3K/AKT/mTOR) to enhance proliferation, survival, and invasiveness [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. PRL may also upregulate VEGF expression to facilitate angiogenesis, supporting tumor growth and local infiltration [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Reduced fT4 may diminish thyroid hormone\u0026rsquo;s tumor-suppressive effects. Thyroid hormones regulate gene expression via nuclear receptors (TRα/TRβ), impacting metabolism, differentiation, and apoptosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. fT4 deficiency could impair mitochondrial function, forcing tumor cells to rely on glycolysis (Warburg effect) and adapt to hypoxic microenvironments, thereby enhancing malignant phenotypes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, fT4 depletion may suppress NK and cytotoxic T-cell activity, weakening immune surveillance and indirectly promoting progression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMultivariate analysis identified elevated PRL and giant tumor type as risk factors for poor prognosis, whereas pseudocapsule presence and higher fT4 were protective. These align with prior studies. Yogeeta et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] noted that giant prolactinomas, due to their size and invasiveness, correlate with higher recurrence and worse outcomes, while pseudocapsules may confine tumor spread, improving prognosis. Sosa-Eroza et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] found that prolactinoma patients achieving normal PRL after \u0026gt;\u0026thinsp;2 years of cabergoline therapy were more likely to sustain long-term remission, underscoring PRL\u0026rsquo;s prognostic relevance. Hofbauer et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] observed that dopamine agonist therapy significantly increased fT4 levels and reduced tumor size/volume, with initial tumor dimensions inversely correlating with fT4 changes, suggesting fT4\u0026rsquo;s role in clinical remission.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePrognostic implications\u003c/em\u003e:\u003c/p\u003e\u003cp\u003eHigh PRL reflects hyperactive secretion and proliferative vigor, while giant tumors often invade the sellar region, complicating total resection and increasing recurrence risk..Pseudocapsules demarcate tumor boundaries, limiting infiltration and improving resectability. Higher fT4 may indicate preserved hypothalamic-pituitary-thyroid axis function, implying less pituitary damage, while thyroid hormones\u0026rsquo; metabolic/cardiovascular benefits might counteract PRL-induced abnormalities. Our nomogram model integrated these four factors (tumor type, pseudocapsule, PRL, fT4), demonstrating robust stability (VIFs 1.043\u0026ndash;1.091) and discrimination (AUC\u0026thinsp;=\u0026thinsp;0.888), offering a practical tool for prognostic assessment.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLimitations\u003c/em\u003e:\u003c/p\u003e\u003cp\u003eOne limitation of this study exists in the sample size. A moderate cohort may affect generalizability, and larger validation studies are needed). External validations might also test the model\u0026rsquo;s applicability across regions/hospital tiers. In order to elucidate molecular mechanisms,future studies should incorporate genomics/proteomics to analyze hormone-tumor interactions. It might also be interesting to investigate into the effect of brain lesion laterality on the prognosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn conclusion, serum PRL and fT4 levels significantly correlate with prolactinoma malignancy and prognosis. Tumor type and pseudocapsule status further modulate outcomes. This study not only advances prognostic tools but also supports mechanistic research. Future work should expand cohorts, integrate multifactorial analyses, and explore molecular pathways to refine therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNo Funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYPM, RY mainly wrote the manuscript text,.MPJ prepared the data. FB, YY completed the charts, and all the authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOttenhausen, M., Conrad, J., Wolters, L. M. \u0026amp; Ringel, F. Surgery as first-line treatment for prolactinoma? Discussion of the literature and results of a consecutive series of surgically treated patients. \u003cem\u003eNeurosurg. Rev.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e (1), 128 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYogeeta, F. et al. Prolactinoma: Navigating the Dual Challenge of Side Effects and Treatment Strategies - A Comprehensive Review. \u003cem\u003eAnn. Med. Surg. 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Analysis of Thyroid Hormone Receptor α-Knockout Tadpoles Reveals That the Activation of Cell Cycle Program Is Involved in Thyroid Hormone-Induced Larval Epithelial Cell Death and Adult Intestinal Stem Cell Development During Xenopus tropicalis Metamorphosis. \u003cem\u003eThyroid\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (1), 128\u0026ndash;142 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeligiorgi, M. V. \u0026amp; Trafalis, D. T. The Clinical Relevance of Hypothyroidism in Patients with Solid Non-Thyroid Cancer: A Tantalizing Conundrum. \u003cem\u003eJ. Clin. Med.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (12), 3417 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdamska-Fita, E., Śliwka, P. W., Karbownik-Lewińska, M., Lewiński, A. \u0026amp; Stasiak, M. The Absence of Thyroid-Stimulating Hormone Receptor Expression on Natural Killer T Cells: Implications for the Immune-Endocrine Interaction. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (21), 11434 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSosa-Eroza, E. \u0026amp; Espinosa-C\u0026aacute;rdenas, E. Long-term Discontinuation of Dopamine Agonist Treatment in Patients with Prolactinomas Revisited. \u003cem\u003eArch. Med. Res.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e (8), 102893 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmbwani, G., Shi, Z., Luo, K., Jeong, J. W. \u0026amp; Tan, S. Distinguishing Laterality in Brain Injury in Rabbit Fetal Magnetic Resonance Imaging Using Novel Volume Rendering Techniques. \u003cem\u003eDev. Neurosci.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e (1), 55\u0026ndash;67 (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Prolactinoma, Endocrine hormones, Malignancy degree, Prognosis, Nomogram model","lastPublishedDoi":"10.21203/rs.3.rs-7428081/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7428081/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo investigate the correlation between serum endocrine hormone levels and the malignancy degree of prolactinomas, and analyze their predictive value for patient prognosis. Methods: A total of 100 prolactinoma patients admitted to the Affiliated Hospital of Xuzhou Medical University from January 2019 to December 2024 were enrolled. Based on tumor invasiveness, patients were divided into benign (n\u0026thinsp;=\u0026thinsp;74) and malignant (n\u0026thinsp;=\u0026thinsp;26) groups. Serum endocrine hormone levels were compared between groups. Pearson's test analyzed correlations between hormone levels and tumor malignancy. According to new metastases, recurrence, or death during follow-up, patients were classified into good prognosis (n\u0026thinsp;=\u0026thinsp;69) and poor prognosis (n\u0026thinsp;=\u0026thinsp;31) groups. Multivariate logistic regression identified factors influencing poor prognosis. Restricted cubic spline analysis evaluated dose-response relationships between hormone levels and poor prognosis risk. A nomogram model was constructed and its predictive performance evaluated. Results: The malignant group showed significantly higher serum prolactin (PRL) levels but lower free thyroxine (fT4) levels than the benign group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Serum PRL positively correlated with tumor malignancy (r\u0026thinsp;=\u0026thinsp;0.460, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while fT4 showed negative correlation (r=-0.453, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate analysis revealed giant tumor type and elevated PRL as risk factors for poor prognosis, whereas pseudocapsule presence and increased fT4 were protective factors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Nonlinear relationships existed between poor prognosis risk and PRL/fT4 levels (Pnonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the nomogram model, all four factors had variance inflation factors (VIF)\u0026thinsp;\u0026lt;\u0026thinsp;5 (1.043\u0026ndash;1.091). The model's ROC curve area was 0.888, and Hosmer-Lemeshow test confirmed good accuracy (χ\u0026sup2;=12.673, P\u0026thinsp;=\u0026thinsp;0.124). Conclusion: Serum PRL and fT4 levels significantly correlate with prolactinoma malignancy degree and influence patient prognosis.\u003c/p\u003e","manuscriptTitle":"Correlation Between Serum Endocrine Hormone Levels and Malignancy Degree of Prolactinoma and Their Predictive Value for Patient Prognosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 11:54:48","doi":"10.21203/rs.3.rs-7428081/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-11-28T19:21:32+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"72125739951559138589673856979296544661","date":"2025-11-13T01:27:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-07T10:37:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-02T08:47:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-14T12:20:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T16:24:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-16T16:19:54+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":"0f1dd6da-ac73-44b3-9a6f-30b4654d2c15","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58104206,"name":"Biological sciences/Cancer"},{"id":58104207,"name":"Health sciences/Oncology"},{"id":58104208,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-12-15T16:10:16+00:00","versionOfRecord":{"articleIdentity":"rs-7428081","link":"https://doi.org/10.1038/s41598-025-31090-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-13 15:58:04","publishedOnDateReadable":"December 13th, 2025"},"versionCreatedAt":"2025-11-19 11:54:48","video":"","vorDoi":"10.1038/s41598-025-31090-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-31090-2","workflowStages":[]},"version":"v1","identity":"rs-7428081","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7428081","identity":"rs-7428081","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}