Predicting pituitary neuroendocrine tumor risk based on clinical and MRI features nomogram: A multicenter study

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Given the current diagnostic challenges in distinguishing high- and low-risk PitNETs, we investigated whether MRI features combined with clinical biomarkers could improve preoperative risk stratification. Methods This multicenter retrospective study analyzed 548 histopathologically confirmed PitNET cases (training set: n = 319; test set: n = 138 from Center 1; validation set: n = 91 from Center 2). Comprehensive clinical, endocrinological, and MRI parameters were evaluated through logistic regression to construct a predictive model. Diagnostic performance was quantified using area under the ROC curve (AUC), supplemented by calibration plots and decision curve analysis (DCA) to assess clinical utility. Results Significant intergroup differences (all p < 0.05) were observed between high- and low-risk PitNETs: patient age, maximal tumor diameter (p < 0.01), growth hormone (GH), prolactin (PRL), insulin-like growth factor-1 (IGF-1) levels, tumor margin irregularity, optic chiasm compression (p < 0.001), circumferential carotid encasement, and cavernous sinus invasion. Multivariate analysis identified age (OR = 1.04, 95%CI 1.02–1.07), tumor diameter (OR = 1.15, 95%CI 1.08–1.22), PRL (OR = 1.01, 95%CI 1.00-1.02), and IGF-1 (OR = 1.003, 95%CI 1.001–1.005) as independent predictors. The integrated model achieved an AUC of 0.803 (95%CI 0.703–0.903) on external validation set, with excellent calibration and favorable decision curve net benefit. Conclusions Nomogram by integrating clinical and MRI features can be used as a reliable tool to predict risk status in patients with PitNETs. After further external validation, this will help neurosurgeons make critical decisions regarding surgical or alternative treatment strategies for PitNETs. pituitary neuroendocrine tumors magnetic resonance imaging WHO classification risk Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Pituitary adenomas are the most common tumors in the adult saddle region, accounting for approximately 80% of all tumors in the saddle region[ 1 ]. Pituitary adenomas are benign tumors, but their location within the pyriform saddle, coupled with their tendency to invade and compress neurovascular structures, may complicate surgery[ 2 ]. While typically benign, these tumors demonstrate significant biological heterogeneity, with approximately one-third exhibiting aggressive behaviors including local tissue invasion, vascular encasement, and early recurrence despite treatment[ 1 ]. The diagnostic landscape has evolved significantly with the 2021 WHO classification redesignating these tumors as pituitary neuroendocrine tumors (PitNETs) and incorporating transcription factor analysis (PIT1, TPIT, SF1, ERα, GATA3) for molecular subtyping[ 3 – 5 ]. Certain subtypes of PitNETs are known to be aggressive, such as sparse granular somatotroph adenomas, male lactating adenomas, Crooke cell adenomas, silent cortical adenomas, and polyhormonal PIT-1 positive adenomas[ 6 ]. Influenced by the complexity of the anatomical structure of the saddle region and the heterogeneity of tumor subtypes, the response to treatment and prognosis of PitNETs vary, and some high-risk PitNETs often extend to the peri-saddle region and destroy the surrounding tissue structure, showing aggressive growth[ 7 ]. High-risk PitNETs require appropriate therapeutic strategies to control progression and high recurrence rates, especially after incomplete surgical resection. Noninvasive imaging plays a pivotal role in the preoperative assessment and clinical management of PitNETs, providing essential diagnostic, therapeutic, and prognostic information[ 8 ]. Magnetic Resonance Imaging (MRI) serves as the cornerstone imaging modality, offering comprehensive evaluation of both macroscopic tumor characteristics and tissue microstructure that correlate with underlying histopathological heterogeneity. MRI contributes significantly throughout the entire clinical management pathway - from initial diagnosis through treatment planning to post-therapeutic follow-up[ 9 ]. The rich morphological and functional data obtained from MRI, including tumor size, invasion patterns, and enhancement characteristics, are critical for clinical decision-making and monitoring treatment response. While MRI is well-established for preoperative evaluation and postoperative surveillance of PitNETs, the correlation between specific MRI features and tumor risk stratification remains underexplored. This study therefore aims to systematically evaluate the predictive value of preoperative MRI characteristics for determining PitNET risk classification. Materials and Methods Study design and patients This retrospective study was approved by the local Institutional Review Board with a waiver of informed consent. We identified patients with PitNETs who underwent MRI evaluation between June 2021 and June 2024 from the PACS systems of two medical centers. The dataset from the first center was randomly divided into training and test sets at a 7:3 ratio, while data from the second center served as an independent external validation set. All included patients met the following inclusion criteria: (I) no prior biopsy or medical treatment of the lesion before MRI examination; (II) immunohistochemical staining results consistent with the WHO CNS 5 (2021) classification criteria; and (III) completion of comprehensive preoperative hormonal evaluation. Patients were excluded based on the following criteria: (I) absence of preoperative MRI or incomplete MRI sequences; and (II) lack of preoperative pituitary hormone assessment. The final cohort comprised 548 patients (mean age 51.1 years; range 7–82 years), including 319 cases in the training group, 138 in the internal validation group, and 91 in the external validation group. MRI protocol MRI protocol All patients underwent standardized preoperative MRI examinations within one week prior to surgery, with each protocol including essential sequences: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (T1C). The imaging was performed using two clinical MRI systems: a 3.0 Tesla Siemens Verio scanner (Second Hospital of Lanzhou University) and a 1.5 Tesla GE Signa scanner (Affiliated Hospital of Qingdao University). Detailed acquisition parameters for each scanner, including field strength-specific protocols for slice thickness, repetition time (TR), echo time (TE), field of view (FOV), and matrix size, are systematically presented in Table 1 . Table 1 MRI Scanning Instruments and Parameters Hospital Scanner Sequence TR (ms) TE (ms) FOV (mm 2 ) Slice thickness (mm) Matrix A Siemens Verio 3.0 T T1WI 2000 24 230×230 6 320×256 T2WI 4400 102 230×230 6 416×426 T1C 2000 24 230×230 6 320×256 B GE Signa 1.5 T T1WI 2250 24 240×240 5 256×256 T2WI 5100 129.48 256×256 5 255×256 T1C 2250 24 240×240 5 512×512 Histopathological analysis All patients underwent surgical resection of PitNETs within one month following MRI examination and routine laboratory testing. Comprehensive clinicopathological data were collected from preoperative medical records, including patient demographics (age and gender). Tumor subtyping was performed according to the 2021 WHO classification guidelines, incorporating both immunohistochemical profiling and transcription factor analysis[ 10 ]. The diagnostic workup included evaluation of adenohypophyseal hormone expression: growth hormone (GH), prolactin (PRL), adrenocorticotropic hormone (ACTH), thyroid-stimulating hormone (TSH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), and insulin-like growth factor-1 (IGF-1), with hormone immunonegativity defined as < 10% positively staining tumor cells. All hormone-negative cases underwent additional immunohistochemical assessment for transcription factors (SF-1, TPIT, PIT1, ERα, and GATA3), with tumors negative for all markers classified as null cell adenomas[ 3 , 10 ]. Based on established criteria, high-risk PitNET subtypes included: sparsely granulated somatotroph adenomas, male lactotroph adenomas, Crooke cell adenomas, silent corticotroph adenomas, and plurihormonal PIT1-positive adenomas; all other subtypes were categorized as low-risk. Image analysis Two diagnostic neuroimaging physicians with more than 10 years of experience reviewed all images using a double-blind method, and agreement was reached through consultation in case of disagreement. Conventional MRI signs evaluated included maximum tumor diameter, tumor border, necrotic cystic lesions, hemorrhage, encircling carotid artery, cavernous sinus invasion, saddle region enlargement, parasaddle structure invasion, and enhancement modality. Tumor diameter was defined as the maximum diameter of the axial tumor. Cystic change was defined as a tense round-like fluid cystic cavity with cystic fluid consistent with cerebrospinal fluid signal, clear and sharp border, thin cystic wall, and no or mild enhancement; necrosis was defined as a low-density area within the tumor body without tension, irregular morphology, unclear demarcation from the tumor body, and irregular inner wall, and the signal of cystic fluid was slightly higher than the signal of cerebrospinal fluid on T1WI. Tumor boundaries were defined as clear or unclear. Intratumoral stromal hemorrhage was defined as any intrinsic lesion with low signal on T2WI or high signal on T1WI. Encircling carotid artery defined as tumor encircling the carotid artery by > 25%. Committing cavernous sinus pituitary tumor: grades 3 and 4 were identified as invasive pituitary adenomas with reference to Knosp et al[ 11 ] MRI grading criteria. Parasagittal structure invasion was defined as whether the tumor was well demarcated from the parasagittal tissue. Regarding the mode of tumor enhancement, if different degrees of enhancement were observed in 10–90% of the tumor volume, the enhancement was inhomogeneous, otherwise it was homogeneous[ 12 ]. Statistical analysis The kappa test was used for agreement between the two observers for MRI sign observations, and Kappa > 0.6 was considered high agreement. Categorical variables were analyzed using chi-square or Fisher's exact tests, while continuous variables were compared using independent t-tests. Univariate and multivariate logistics regression analyses were performed, and variables with P < 0.05 in the multivariate analysis were used to construct predictive models for PitNETs risk status. A nomogram was developed based on logistic regression results to visualize individualized predictions. The model's performance was evaluated using receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values reported alongside 95% confidence intervals (CI). Calibration curves and decision curve analysis (DCA) were used to assess the nomogram’s predictive consistency and clinical utility. All statistical analyses were performed using SPSS (version 25.0; Chicago, IL) and R software. Results Patient Baseline Characteristics Overall, Interobserver agreement for the MRI features was good to excellent (κ = 0.721–0.915). A total of 548 patients with PitNETs were included in this study, and the 457 internal datasets were randomly divided into a training set (n = 319 cases) and a test set (n = 138 cases) according to a 7:3 ratio. In the training set, there were 154 females and 165 males with a mean age of onset of 50.86 ± 12.54 years; in the test set, there were 59 females and 79 males with a mean age of onset of 50.56 ± 11.53 years. In the external validation set, there were 49 females and 42 males with a mean age of onset of 52.81 ± 20.00 years. Of the included first center PitNETs, 104 were in the high-risk group and 353 were in the low-risk group. Of the 91 patients in the second center, 36 were in the high-risk group and 55 in the low-risk group. Statistical analysis showed that the differences between all clinical and conventional MRI features in the training and test sets were not statistically significant (all p > 0.05) (Table 2 ). Table 2 Comparison of clinical and MRI features of patients with PitNETs Characteristics Training group (n = 319) Test group (n = 138) P-value Gender 0. 308 man 165 79 woman 154 59 Boundary 0.398 clear 198 92 unclear 121 46 Cysts/necrosis changes 0.358 yes 178 70 no 141 68 Hemorrhage 0.894 yes 56 25 no 263 113 Optic cross compression 0.810 yes 246 105 no 73 33 Encircle carotid artery 0.609 yes 178 73 no 141 65 Cavernous sinus invasion 1.000 yes 186 81 no 133 57 Parabrachial structure invasion 0.591 yes 212 88 no 106 50 Sella enlargement 0.776 yes 272 116 no 47 22 Enhancement methods 0.911 Homogeneous 227 97 Inhomogeneous 92 41 Mean age, years 50.86 ± 12.54 50.56 ± 11.53 0.808 Tumor diameter (cm) 24.82 ± 8.62 25.14 ± 11.5 0.736 COR 11.15 ± 6.45 11.21 ± 7.45 0.925 GH 3.85 ± 10.05 3.62 ± 9.36 0.816 ACT 39.58 ± 76.1 42.93 ± 106.72 0.704 T3 1.57 ± 1.61 1.43 ± 0.39 0.321 T4 91.16 ± 34.48 86.92 ± 27.86 0.166 TSH 3.55 ± 11.97 5.03 ± 18.02 0.304 PRL 42.37 ± 98.84 37.93 ± 62.13 0.626 FSH 14.99 ± 18.99 14.38 ± 19.14 0.753 LH 5.76 ± 9.11 4.77 ± 6.75 0.198 IGF-1 177.47 ± 176.65 190.44 ± 180.84 0.475 Univariate and multivariate logistic regression analysis of clinical and routine MRI characteristics Univariate logistic regression analysis showed that Age, Tumor diameter, GH, PRL, IGF-1, Boundary, Optic cross compression, Encircle carotid artery and Cavernous sinus invasion were strongly associated with PitNETs were strongly associated (Table 2 and Fig. 1 ). Multifactorial logistic regression analysis showed that age (OR, 0.97; 95% CI: 0.94, 1.00; P = 0.02), tumor maximum diameter (OR, 1.06; 95% CI: 1.01, 1.11; P = 0.01), PRL (OR, 1.01; 95% CI: 1.00, 1.01; P = 0.01), IGF- 1(OR, 1.00; 95% ci: 1.00, 1.01; P < 0.01) were independent risk factors predicting the risk level of patients with PitNETs (Table 3 ). Based on these results, we generated logistic regression model nomograms using these four independent risk factors to predict the risk degree of patients with PitNETs (Fig. 2 ). Table 3 Univariable and multivariable logistics regression analysis of the features of PitNETs in training set Variables Univariable analysis Multivariable analysis OR 95% CI p value OR 95% CI p value Gender 0.71 0.42 ~ 1.21 0.20 man woman Boundary 2.28 1.33 ~ 3.94 < 0.01 1.79 0.90 ~ 3.55 0.10 clear unclear Cysts/necrosis changes 1.41 0.82 ~ 2.46 0.22 yes no Hemorrhage 1.77 0.91 ~ 3.33 0.08 yes no Optic cross compression 0.42 0.23 ~ 0.75 < 0.01 0.42 0.17 ~ 1.02 0.05 yes no Encircle carotid artery 1.79 1.03 ~ 3.17 0.04 0.70 0.24 ~ 2.06 0.51 yes no Cavernous sinus invasion 2.21 1.25 ~ 4.03 < 0.01 2.79 0.96 ~ 8.50 0.06 yes no Parabrachial structure invasion 1.29 0.72 ~ 2.31 0.39 yes no Sella enlargement 0.77 0.38 ~ 1.59 0.48 yes no Enhancement methods 1.31 0.72 ~ 2.47 0.38 Homogeneous Inhomogeneous Age (years) 0.95 0.93 ~ 0.97 <0.01 0.97 0.94 ~ 1.00 0.02 Tumor diameter (cm) 1.05 1.02 ~ 1.08 <0.01 1.06 1.01 ~ 1.11 0.01 COR (nmol/L) 1.02 0.98 ~ 1.06 0.32 GH (ng/ML) 1.08 1.06 ~ 1.11 <0.01 1.01 0.97 ~ 1.06 0.50 ACT (pg/ML) 1.00 1.00 ~ 1.01 0.27 T3 (nmol/L) 1.01 0.77 ~ 1.17 0.94 T4 (nmol/L) 1.01 1.00 ~ 1.01 0.07 TSH (uIU/ML) 1.00 0.98 ~ 1.02 0.69 PRL (ng/ML) 1.01 1.00 ~ 1.01 <0.01 1.01 1.00 ~ 1.01 0.01 FSH (mIU/ML) 0.99 0.97 ~ 1.00 0.17 LH (mIU/ML) 1.00 0.97 ~ 1.03 0.96 IGF-1 (ng/ML) 1.01 1.00 ~ 1.01 <0.01 1.00 1.00 ~ 1.01 <0.01 Performance evaluation and clinical utility of predictive models The results of ROC curve analysis are shown in Fig. 3 and Table 4 . This study predicted the risk of PitNETs based on logistic regression model. The AUC of the constructed models were 0.835 (95% CI: 0.778–0.891), 0.812 (95% CI: 0.729–0.896), and 0.803 (95% CI: 0.703–0.903) for the training, test, and validation sets, respectively. The calibration curves showed good calibration performance for all models (Fig. 4 ). DCA showed that the nomograms provided good net clinical benefit over most threshold ranges (Fig. 4 ). Table 4 ROC analysis of clinical and MRI features for distinguishing PitNETs risk level Training group Cutoff Sensitivity (%) Specificity (%) AUC(95%CI) 0.549 0.681 0.868 0.835(0.778–0.891) Test group 0.538 0.771 0.767 0.812(0.729–0.896) Validation group 0.586 0.750 0.836 0.803(0.703–0.903) Discussion ur study demonstrated that both clinical and MRI characteristics were significantly associated with PitNETs risk. Multivariate analysis identified four robust independent predictors of PitNETs risk: patient age, maximal tumor diameter, PRL level, and IGF-1 level. The predictive model incorporating these parameters demonstrated excellent discriminative ability for preoperative risk stratification. This tool may facilitate early identification of high-risk cases, potentially prompting more aggressive therapeutic interventions and closer postoperative surveillance. Aggressive PitNETs pose substantial clinical management challenges due to their invasive behavior and refractory progression, often persisting despite multimodal therapy including surgery, pharmacological intervention, and initial radiation treatment[ 13 ], with characteristic relentless growth patterns and metastatic potential frequently causing destructive local morbidity and elevated mortality. This clinical reality underscores the critical need for reliable preoperative risk stratification to optimize therapeutic strategies. While emerging AI) techniques show theoretical promise for enhancing diagnostic accuracy and prognostic prediction in pituitary oncology[ 14 ], current clinical practice remains anchored in conventional MRI assessments where radiologists visually evaluate tumor imaging features to determine anatomical relationships and guide surgical planning[ 15 , 16 ], with demonstrated discriminatory capacity for differentiating pathological subtypes. Although AI implementation faces technical and validation barriers limiting clinical translation[ 17 ], the established utility of MRI biomarkers combined with clinical parameters provides a pragmatic foundation for developing integrated predictive models. Such noninvasive preoperative risk assessment approaches could enhance personalized management while leveraging existing radiologic infrastructure, offering clinically feasible solutions to improve outcomes in this complex patient population through biologically informed treatment stratification. Our study shows a correlation between age and risk of PitNETs. There have been no studies on the difference in age between high-risk and low-risk types of PitNETs. However, in a study on the prognosis of nonfunctional pituitary adenomas (NFPAs), it was shown that the older the age, the longer the PFS and the smaller the maximum diameter of the tumor[ 18 ]. This study likewise showed that SILENT corticotroph adenomas (high-risk type) were more common in younger people. This is in line with the results of this study. A larger sample of PitNETs is still needed to validate the results. The present study showed that PRL expression levels in the high-risk group of patients with PitNETs differed between the two groups. Among the five types of patients in the high-risk group, one type of male urothelial adenoma was included, which may have led to significantly higher PRL levels in the high-risk group than in the low-risk group. IGF-1 is a regulatory factor with a wide range of physiological roles, which promotes cell proliferation, differentiation, migration, and anti-apoptosis[ 19 ]. In this study, there was a differential difference in the expression of IGF-1 in the high-risk and low-risk groups of patients with PitNETs. Studies have confirmed that IGF-1 is highly expressed in intracranial tumors, and the expression level is positively correlated with the degree of tumor malignancy[ 20 ]. The higher tumor aggressiveness in the high-risk group of PitNETs may have contributed to the difference in IGF-1 expression between the high-risk and low-risk groups. This study has some limitations. First, this work is a retrospective study and further multicenter data collection is needed for validation. Second, subtypes of PitNETs were not analyzed in this study, and stratification of different subtypes is needed in the next study. Third, the performance of the model developed in this study was limited, and artificial intelligence methods may be needed for further research in the next step. Conclusion In conclusion, conventional MRI signs combined with clinical features are a particularly practical and useful noninvasive imaging method for preoperative assessment of the risk of PitNETs. Abbreviations PitNETs pituitary neuroendocrine tumors MRI Magnetic Resonance Imaging T1WI T1-weighted imaging T2WI T2-weighted imaging T1C Contrast-enhanced T1-weighted imaging TR Repetition time TE Echo time FOV Field of view GH Growth hormone PRL Prolactin ACTH Adrenocorticotropic hormone TSH Thyroid-stimulating hormone FSH Follicle-stimulating hormone LH Luteinizing hormone IGF-1 Insulin-like growth factor-1 ROC Receiver operating characteristic AUC Area under the curve CI Confidence intervals DCA Decision curve analysis NFPAs Nonfunctional pituitary adenomas Declarations Conflict of interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Consent to participate: Informed consent was obtained from all individual participants included in the study. Clinical Trial Number Not applicable. Human Ethics and Consent to Participate The study protocol was approved by the ethics committee of Qingdao University Affiliated Hospital and Lanzhou University Second Hospital. This retrospective study was conducted in accordance with the principles of the Declaration of Helsinki. The need for informed consent was waived because of the retrospective nature of the study. Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Caiqiang Xue, Wenjie Dong, Chengjian Wang, Ming Liu, Tao Han, Qing Zhou, Peng Zhang, Kai Huang, Junlin Zhou. The first draft of the manuscript was written by Caiqiang Xue and Wenjie Dong, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Consent for publication : All of the authors gave consent for publication of the article. Conflict of interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding: This work was supported by the Cuiying Science and Technology Innovation Program of Lanzhou University Second Hospital (grant number: CY2023-YB-A03) and Qingdao University Hospital Youth Research Fund (grant number: QDFYQN2024218). Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chengjian Wang, Ming Liu, Tao Han, Qing Zhou, Peng Zhang, Kai Huang, Junlin Zhou. The first draft of the manuscript was written by Caiqiang Xue and Qiusui Xiang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgments: Thanks to all the partners who contributed to this research, including Caiqiang Xue, Suixiang Qiu, Chengjian Wang, Ming Liu, Tao Han, Qing Zhou, Peng Zhang, Kai Huang, Junlin Zhou. Data availability: No datasets were generated or analysed during the current study. References Conficoni A, Feraco P, Mazzatenta D, Zoli M, Asioli S, Zenesini C, Fabbri VP, Cellerini M, Bacci A (2020) Biomarkers of pituitary macroadenomas aggressive behaviour: a conventional MRI and DWI 3T study. Br J Radiol 93:20200321 Ahmadi J, North CM, Segall HD, Zee CS, Weiss MH (1986) Cavernous sinus invasion by pituitary adenomas. AJR Am J Roentgenol 146:257–262 Asa SL, Mete O, Perry A, Osamura RY (2022) Overview of the 2022 WHO Classification of Pituitary Tumors. 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Endocr Rev 40:558–574 Sandberg-Nordqvist AC, Stahlbom PA, Reinecke M, Collins VP, von Holst H, Sara V (1993) Characterization of insulin-like growth factor 1 in human primary brain tumors. Cancer Res 53:2475–2478 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9001960","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602580200,"identity":"3b974042-58b5-4c32-bf17-4368b4c48d30","order_by":0,"name":"Caiqiang Xue","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Caiqiang","middleName":"","lastName":"Xue","suffix":""},{"id":602580201,"identity":"8cdf3f19-b574-4004-9ef9-00f46ea415c3","order_by":1,"name":"Suixiang Qiu","email":"","orcid":"","institution":"The First Clinical Medical College of Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Suixiang","middleName":"","lastName":"Qiu","suffix":""},{"id":602580210,"identity":"83f96848-30ef-47b0-8fe0-2b453f31ba17","order_by":2,"name":"Chengjian Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Chengjian","middleName":"","lastName":"Wang","suffix":""},{"id":602580212,"identity":"1988c8eb-56f4-4d7b-8e6c-880aec44958d","order_by":3,"name":"Ming Liu","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Liu","suffix":""},{"id":602580214,"identity":"756d6049-eefe-49eb-8c9c-e4637f699bbf","order_by":4,"name":"Tao Han","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Han","suffix":""},{"id":602580219,"identity":"e4a3c44e-2a45-4580-b5b2-2aa3b34b9a7d","order_by":5,"name":"Qing Zhou","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zhou","suffix":""},{"id":602580221,"identity":"e6b11cff-d569-4a7c-9747-742d70b58969","order_by":6,"name":"Peng Zhang","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhang","suffix":""},{"id":602580223,"identity":"ee4c11e1-d9fd-4adc-bf4b-07ca6aa5a7ea","order_by":7,"name":"Kai Huang","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Huang","suffix":""},{"id":602580226,"identity":"55fbec4a-72e3-469a-93e6-486cf578b317","order_by":8,"name":"Junlin Zhou","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junlin","middleName":"","lastName":"Zhou","suffix":""},{"id":602580229,"identity":"ad34451e-cb3c-4255-a92c-d467cc296354","order_by":9,"name":"Xuejun Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIie3NMWvCQBjG8ecI6BLsekGkX0EIpBX9MHdLXOxUuMnhQMgkZE2/RV1cfeUg05FZsIsUOnVwdGxireIQzdjh/hx373A/XsDl+o8RGInThD28aurfIzgTljUmf7PnNyGdLYF2iZEps9HnSA2lbs+WHNOPWhIUAiSTWL5p+xROirHUfq448q9a0reoyEi+k426L+U6zScRZ9rcI1yuKjKoyON3I1JuQUnYcYt/mwSWaRJFHGaUq2BejMPEj1+fRV5POtZb7w7K9NLMLPlBDXtp2yw2+2k9AZj+fTmVV+t4AHEDXHrQOP13uVwu13U/UhdbXKaR1TIAAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Xuejun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-03-01 14:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9001960/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9001960/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104411904,"identity":"adc86249-4eaa-4195-a04e-3e5d2a3391b3","added_by":"auto","created_at":"2026-03-11 12:58:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13810184,"visible":true,"origin":"","legend":"\u003cp\u003eA-D: Patient is a 26-year-old male with low-risk PitNETs. The pituitary gland is enlarged in the saddle region, with clear, well-defined borders and no compression of the visual crossover, and the enhancement scan reveals marked homogeneous enhancement of the lesion, with no obvious invasion of the cavernous sinus and internal carotid artery on both sides; E-H: Patient is a 43-year-old female with high-risk PitNETs, and the patient's pituitary gland is asymmetric on both sides, with poorly-defined borders, poorly defined morphology, and compression of the visual crossover. compression, its left side is visible lobulation, and protruded into the left cavernous sinus, encircling the left internal carotid artery, enhancement of the lesion is uneven persistent enhancement, adjacent brain parenchyma compression.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9001960/v1/335a7fd766ec3d6da3db6a73.png"},{"id":104413947,"identity":"74f77de2-cbec-4a7b-a76a-91655258999c","added_by":"auto","created_at":"2026-03-11 13:05:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2111858,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram combining age, maximum tumor diameter, PRL, and IGF-1 to predict risk in patients with PitNETs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9001960/v1/9b7f5f4d128c4d713c6784d2.png"},{"id":104411883,"identity":"b918bab0-9fb2-4b87-8f4b-c2f2d810bd73","added_by":"auto","created_at":"2026-03-11 12:57:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2859313,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for the prediction of PitNET hazard for each model in the training set (A), test set (B) and external validation set (C).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9001960/v1/8b658132dde936c8e330e390.png"},{"id":104412010,"identity":"ff3d404c-5787-45fe-ba10-0ed797c5c0a9","added_by":"auto","created_at":"2026-03-11 12:58:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8960991,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves predicted by nomogram in the training group (A), text group(B) and validation group(C). DCA curves predicted by nomogram in the training group(D), text group(E) and validation group(F).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9001960/v1/f6a1dc37b4b7d9137ebc5508.png"},{"id":104782688,"identity":"03ca128c-f50b-43e3-9687-4319f5f0995d","added_by":"auto","created_at":"2026-03-17 07:57:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":34703795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9001960/v1/68812e68-762f-4203-855d-d82ed8dd40ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting pituitary neuroendocrine tumor risk based on clinical and MRI features nomogram: A multicenter study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePituitary adenomas are the most common tumors in the adult saddle region, accounting for approximately 80% of all tumors in the saddle region[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Pituitary adenomas are benign tumors, but their location within the pyriform saddle, coupled with their tendency to invade and compress neurovascular structures, may complicate surgery[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While typically benign, these tumors demonstrate significant biological heterogeneity, with approximately one-third exhibiting aggressive behaviors including local tissue invasion, vascular encasement, and early recurrence despite treatment[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The diagnostic landscape has evolved significantly with the 2021 WHO classification redesignating these tumors as pituitary neuroendocrine tumors (PitNETs) and incorporating transcription factor analysis (PIT1, TPIT, SF1, ERα, GATA3) for molecular subtyping[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Certain subtypes of PitNETs are known to be aggressive, such as sparse granular somatotroph adenomas, male lactating adenomas, Crooke cell adenomas, silent cortical adenomas, and polyhormonal PIT-1 positive adenomas[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Influenced by the complexity of the anatomical structure of the saddle region and the heterogeneity of tumor subtypes, the response to treatment and prognosis of PitNETs vary, and some high-risk PitNETs often extend to the peri-saddle region and destroy the surrounding tissue structure, showing aggressive growth[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. High-risk PitNETs require appropriate therapeutic strategies to control progression and high recurrence rates, especially after incomplete surgical resection.\u003c/p\u003e \u003cp\u003eNoninvasive imaging plays a pivotal role in the preoperative assessment and clinical management of PitNETs, providing essential diagnostic, therapeutic, and prognostic information[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Magnetic Resonance Imaging (MRI) serves as the cornerstone imaging modality, offering comprehensive evaluation of both macroscopic tumor characteristics and tissue microstructure that correlate with underlying histopathological heterogeneity. MRI contributes significantly throughout the entire clinical management pathway - from initial diagnosis through treatment planning to post-therapeutic follow-up[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The rich morphological and functional data obtained from MRI, including tumor size, invasion patterns, and enhancement characteristics, are critical for clinical decision-making and monitoring treatment response. While MRI is well-established for preoperative evaluation and postoperative surveillance of PitNETs, the correlation between specific MRI features and tumor risk stratification remains underexplored. This study therefore aims to systematically evaluate the predictive value of preoperative MRI characteristics for determining PitNET risk classification.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the local Institutional Review Board with a waiver of informed consent. We identified patients with PitNETs who underwent MRI evaluation between June 2021 and June 2024 from the PACS systems of two medical centers. The dataset from the first center was randomly divided into training and test sets at a 7:3 ratio, while data from the second center served as an independent external validation set. All included patients met the following inclusion criteria: (I) no prior biopsy or medical treatment of the lesion before MRI examination; (II) immunohistochemical staining results consistent with the WHO CNS 5 (2021) classification criteria; and (III) completion of comprehensive preoperative hormonal evaluation. Patients were excluded based on the following criteria: (I) absence of preoperative MRI or incomplete MRI sequences; and (II) lack of preoperative pituitary hormone assessment. The final cohort comprised 548 patients (mean age 51.1 years; range 7\u0026ndash;82 years), including 319 cases in the training group, 138 in the internal validation group, and 91 in the external validation group.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMRI protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eMRI protocol\u003c/div\u003e \u003cp\u003eAll patients underwent standardized preoperative MRI examinations within one week prior to surgery, with each protocol including essential sequences: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (T1C). The imaging was performed using two clinical MRI systems: a 3.0 Tesla Siemens Verio scanner (Second Hospital of Lanzhou University) and a 1.5 Tesla GE Signa scanner (Affiliated Hospital of Qingdao University). Detailed acquisition parameters for each scanner, including field strength-specific protocols for slice thickness, repetition time (TR), echo time (TE), field of view (FOV), and matrix size, are systematically presented in 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\u003eMRI Scanning Instruments and Parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScanner\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003cp\u003e(ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTE\u003c/p\u003e \u003cp\u003e(ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFOV\u003c/p\u003e \u003cp\u003e(mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSlice thickness\u003c/p\u003e \u003cp\u003e(mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMatrix\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSiemens Verio\u003c/p\u003e \u003cp\u003e3.0 T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e230\u0026times;230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c8\"\u003e \u003cp\u003e320\u0026times;256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e230\u0026times;230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c8\"\u003e \u003cp\u003e416\u0026times;426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e230\u0026times;230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c8\"\u003e \u003cp\u003e320\u0026times;256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGE Signa\u003c/p\u003e \u003cp\u003e1.5 T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e240\u0026times;240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c8\"\u003e \u003cp\u003e256\u0026times;256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e256\u0026times;256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c8\"\u003e \u003cp\u003e255\u0026times;256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e240\u0026times;240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c8\"\u003e \u003cp\u003e512\u0026times;512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eHistopathological analysis\u003c/h3\u003e\n\u003cp\u003eAll patients underwent surgical resection of PitNETs within one month following MRI examination and routine laboratory testing. Comprehensive clinicopathological data were collected from preoperative medical records, including patient demographics (age and gender). Tumor subtyping was performed according to the 2021 WHO classification guidelines, incorporating both immunohistochemical profiling and transcription factor analysis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The diagnostic workup included evaluation of adenohypophyseal hormone expression: growth hormone (GH), prolactin (PRL), adrenocorticotropic hormone (ACTH), thyroid-stimulating hormone (TSH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), and insulin-like growth factor-1 (IGF-1), with hormone immunonegativity defined as \u0026lt;\u0026thinsp;10% positively staining tumor cells. All hormone-negative cases underwent additional immunohistochemical assessment for transcription factors (SF-1, TPIT, PIT1, ERα, and GATA3), with tumors negative for all markers classified as null cell adenomas[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Based on established criteria, high-risk PitNET subtypes included: sparsely granulated somatotroph adenomas, male lactotroph adenomas, Crooke cell adenomas, silent corticotroph adenomas, and plurihormonal PIT1-positive adenomas; all other subtypes were categorized as low-risk.\u003c/p\u003e\n\u003ch3\u003eImage analysis\u003c/h3\u003e\n\u003cp\u003eTwo diagnostic neuroimaging physicians with more than 10 years of experience reviewed all images using a double-blind method, and agreement was reached through consultation in case of disagreement. Conventional MRI signs evaluated included maximum tumor diameter, tumor border, necrotic cystic lesions, hemorrhage, encircling carotid artery, cavernous sinus invasion, saddle region enlargement, parasaddle structure invasion, and enhancement modality. Tumor diameter was defined as the maximum diameter of the axial tumor. Cystic change was defined as a tense round-like fluid cystic cavity with cystic fluid consistent with cerebrospinal fluid signal, clear and sharp border, thin cystic wall, and no or mild enhancement; necrosis was defined as a low-density area within the tumor body without tension, irregular morphology, unclear demarcation from the tumor body, and irregular inner wall, and the signal of cystic fluid was slightly higher than the signal of cerebrospinal fluid on T1WI. Tumor boundaries were defined as clear or unclear. Intratumoral stromal hemorrhage was defined as any intrinsic lesion with low signal on T2WI or high signal on T1WI. Encircling carotid artery defined as tumor encircling the carotid artery by \u0026gt;\u0026thinsp;25%. Committing cavernous sinus pituitary tumor: grades 3 and 4 were identified as invasive pituitary adenomas with reference to Knosp et al[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] MRI grading criteria. Parasagittal structure invasion was defined as whether the tumor was well demarcated from the parasagittal tissue. Regarding the mode of tumor enhancement, if different degrees of enhancement were observed in 10\u0026ndash;90% of the tumor volume, the enhancement was inhomogeneous, otherwise it was homogeneous[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe kappa test was used for agreement between the two observers for MRI sign observations, and Kappa\u0026thinsp;\u0026gt;\u0026thinsp;0.6 was considered high agreement. Categorical variables were analyzed using chi-square or Fisher's exact tests, while continuous variables were compared using independent t-tests. Univariate and multivariate logistics regression analyses were performed, and variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the multivariate analysis were used to construct predictive models for PitNETs risk status. A nomogram was developed based on logistic regression results to visualize individualized predictions. The model's performance was evaluated using receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values reported alongside 95% confidence intervals (CI). Calibration curves and decision curve analysis (DCA) were used to assess the nomogram\u0026rsquo;s predictive consistency and clinical utility. All statistical analyses were performed using SPSS (version 25.0; Chicago, IL) and R software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient Baseline Characteristics\u003c/h2\u003e \u003cp\u003e Overall, Interobserver agreement for the MRI features was good to excellent (κ\u0026thinsp;=\u0026thinsp;0.721\u0026ndash;0.915). A total of 548 patients with PitNETs were included in this study, and the 457 internal datasets were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;319 cases) and a test set (n\u0026thinsp;=\u0026thinsp;138 cases) according to a 7:3 ratio. In the training set, there were 154 females and 165 males with a mean age of onset of 50.86\u0026thinsp;\u0026plusmn;\u0026thinsp;12.54 years; in the test set, there were 59 females and 79 males with a mean age of onset of 50.56\u0026thinsp;\u0026plusmn;\u0026thinsp;11.53 years. In the external validation set, there were 49 females and 42 males with a mean age of onset of 52.81\u0026thinsp;\u0026plusmn;\u0026thinsp;20.00 years. Of the included first center PitNETs, 104 were in the high-risk group and 353 were in the low-risk group. Of the 91 patients in the second center, 36 were in the high-risk group and 55 in the low-risk group. Statistical analysis showed that the differences between all clinical and conventional MRI features in the training and test sets were not statistically significant (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (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 and MRI features of patients with PitNETs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining group (n\u0026thinsp;=\u0026thinsp;319)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTest group (n\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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\u003e0. 308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewoman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoundary\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\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCysts/necrosis changes\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\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemorrhage\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\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptic cross compression\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\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEncircle carotid artery\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\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCavernous sinus invasion\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\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParabrachial structure invasion\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\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSella enlargement\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\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnhancement methods\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\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomogeneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhomogeneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.86\u0026thinsp;\u0026plusmn;\u0026thinsp;12.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.56\u0026thinsp;\u0026plusmn;\u0026thinsp;11.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.82\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.14\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.15\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.21\u0026thinsp;\u0026plusmn;\u0026thinsp;7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;10.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.58\u0026thinsp;\u0026plusmn;\u0026thinsp;76.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.93\u0026thinsp;\u0026plusmn;\u0026thinsp;106.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.16\u0026thinsp;\u0026plusmn;\u0026thinsp;34.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.92\u0026thinsp;\u0026plusmn;\u0026thinsp;27.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;11.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;18.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.37\u0026thinsp;\u0026plusmn;\u0026thinsp;98.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.93\u0026thinsp;\u0026plusmn;\u0026thinsp;62.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.99\u0026thinsp;\u0026plusmn;\u0026thinsp;18.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.38\u0026thinsp;\u0026plusmn;\u0026thinsp;19.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGF-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177.47\u0026thinsp;\u0026plusmn;\u0026thinsp;176.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190.44\u0026thinsp;\u0026plusmn;\u0026thinsp;180.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.475\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\n\u003ch3\u003eUnivariate and multivariate logistic regression analysis of clinical and routine MRI characteristics\u003c/h3\u003e\n\u003cp\u003eUnivariate logistic regression analysis showed that Age, Tumor diameter, GH, PRL, IGF-1, Boundary, Optic cross compression, Encircle carotid artery and Cavernous sinus invasion were strongly associated with PitNETs were strongly associated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Multifactorial logistic regression analysis showed that age (OR, 0.97; 95% CI: 0.94, 1.00; P\u0026thinsp;=\u0026thinsp;0.02), tumor maximum diameter (OR, 1.06; 95% CI: 1.01, 1.11; P\u0026thinsp;=\u0026thinsp;0.01), PRL (OR, 1.01; 95% CI: 1.00, 1.01; P\u0026thinsp;=\u0026thinsp;0.01), IGF- 1(OR, 1.00; 95% ci: 1.00, 1.01; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were independent risk factors predicting the risk level of patients with PitNETs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on these results, we generated logistic regression model nomograms using these four independent risk factors to predict the risk degree of patients with PitNETs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eUnivariable and multivariable logistics regression analysis of the features of PitNETs in training set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026thinsp;~\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eman\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewoman\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBoundary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33\u0026thinsp;~\u0026thinsp;3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90\u0026thinsp;~\u0026thinsp;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclear\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunclear\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCysts/necrosis changes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026thinsp;~\u0026thinsp;2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemorrhage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;~\u0026thinsp;3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOptic cross compression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026thinsp;~\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u0026thinsp;~\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEncircle carotid artery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026thinsp;~\u0026thinsp;3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u0026thinsp;~\u0026thinsp;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCavernous sinus invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u0026thinsp;~\u0026thinsp;4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u0026thinsp;~\u0026thinsp;8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParabrachial structure invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026thinsp;~\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSella enlargement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026thinsp;~\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnhancement methods\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026thinsp;~\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomogeneous\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhomogeneous\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026thinsp;~\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u0026thinsp;~\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor diameter (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026thinsp;~\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.01\u0026thinsp;~\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOR (nmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026thinsp;~\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGH (ng/ML)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026thinsp;~\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97\u0026thinsp;~\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eACT (pg/ML)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;~\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT3 (nmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026thinsp;~\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT4 (nmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;~\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTSH (uIU/ML)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026thinsp;~\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePRL (ng/ML)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;~\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u0026thinsp;~\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFSH (mIU/ML)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026thinsp;~\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLH (mIU/ML)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026thinsp;~\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIGF-1 (ng/ML)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;~\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u0026thinsp;~\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\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 \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePerformance evaluation and clinical utility of predictive models\u003c/h2\u003e \u003cp\u003eThe results of ROC curve analysis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. This study predicted the risk of PitNETs based on logistic regression model. The AUC of the constructed models were 0.835 (95% CI: 0.778\u0026ndash;0.891), 0.812 (95% CI: 0.729\u0026ndash;0.896), and 0.803 (95% CI: 0.703\u0026ndash;0.903) for the training, test, and validation sets, respectively. The calibration curves showed good calibration performance for all models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). DCA showed that the nomograms provided good net clinical benefit over most threshold ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eROC analysis of clinical and MRI features for distinguishing PitNETs risk level\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTraining group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.835(0.778\u0026ndash;0.891)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.812(0.729\u0026ndash;0.896)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValidation group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.803(0.703\u0026ndash;0.903)\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\u003eur study demonstrated that both clinical and MRI characteristics were significantly associated with PitNETs risk. Multivariate analysis identified four robust independent predictors of PitNETs risk: patient age, maximal tumor diameter, PRL level, and IGF-1 level. The predictive model incorporating these parameters demonstrated excellent discriminative ability for preoperative risk stratification. This tool may facilitate early identification of high-risk cases, potentially prompting more aggressive therapeutic interventions and closer postoperative surveillance.\u003c/p\u003e \u003cp\u003eAggressive PitNETs pose substantial clinical management challenges due to their invasive behavior and refractory progression, often persisting despite multimodal therapy including surgery, pharmacological intervention, and initial radiation treatment[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], with characteristic relentless growth patterns and metastatic potential frequently causing destructive local morbidity and elevated mortality. This clinical reality underscores the critical need for reliable preoperative risk stratification to optimize therapeutic strategies. While emerging AI) techniques show theoretical promise for enhancing diagnostic accuracy and prognostic prediction in pituitary oncology[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], current clinical practice remains anchored in conventional MRI assessments where radiologists visually evaluate tumor imaging features to determine anatomical relationships and guide surgical planning[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], with demonstrated discriminatory capacity for differentiating pathological subtypes. Although AI implementation faces technical and validation barriers limiting clinical translation[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the established utility of MRI biomarkers combined with clinical parameters provides a pragmatic foundation for developing integrated predictive models. Such noninvasive preoperative risk assessment approaches could enhance personalized management while leveraging existing radiologic infrastructure, offering clinically feasible solutions to improve outcomes in this complex patient population through biologically informed treatment stratification.\u003c/p\u003e \u003cp\u003eOur study shows a correlation between age and risk of PitNETs. There have been no studies on the difference in age between high-risk and low-risk types of PitNETs. However, in a study on the prognosis of nonfunctional pituitary adenomas (NFPAs), it was shown that the older the age, the longer the PFS and the smaller the maximum diameter of the tumor[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study likewise showed that SILENT corticotroph adenomas (high-risk type) were more common in younger people. This is in line with the results of this study. A larger sample of PitNETs is still needed to validate the results. The present study showed that PRL expression levels in the high-risk group of patients with PitNETs differed between the two groups. Among the five types of patients in the high-risk group, one type of male urothelial adenoma was included, which may have led to significantly higher PRL levels in the high-risk group than in the low-risk group. IGF-1 is a regulatory factor with a wide range of physiological roles, which promotes cell proliferation, differentiation, migration, and anti-apoptosis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this study, there was a differential difference in the expression of IGF-1 in the high-risk and low-risk groups of patients with PitNETs. Studies have confirmed that IGF-1 is highly expressed in intracranial tumors, and the expression level is positively correlated with the degree of tumor malignancy[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The higher tumor aggressiveness in the high-risk group of PitNETs may have contributed to the difference in IGF-1 expression between the high-risk and low-risk groups.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, this work is a retrospective study and further multicenter data collection is needed for validation. Second, subtypes of PitNETs were not analyzed in this study, and stratification of different subtypes is needed in the next study. Third, the performance of the model developed in this study was limited, and artificial intelligence methods may be needed for further research in the next step.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, conventional MRI signs combined with clinical features are a particularly practical and useful noninvasive imaging method for preoperative assessment of the risk of PitNETs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePitNETs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epituitary neuroendocrine tumors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT1WI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT1-weighted imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2WI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT2-weighted imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT1C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eContrast-enhanced T1-weighted imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRepetition time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEcho time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFOV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eField of view\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrowth hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProlactin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACTH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdrenocorticotropic hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTSH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThyroid-stimulating hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFSH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFollicle-stimulating hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLuteinizing hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIGF-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsulin-like growth factor-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNFPAs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNonfunctional pituitary adenomas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest statement:\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003ch2\u003eClinical Trial Number\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the ethics committee of Qingdao University Affiliated Hospital and Lanzhou University Second Hospital. This retrospective study was conducted in accordance with the principles of the Declaration of Helsinki. The need for informed consent was waived because of the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Caiqiang Xue, Wenjie Dong, Chengjian Wang, Ming Liu, Tao Han, Qing Zhou, Peng Zhang, Kai Huang, Junlin Zhou. The first draft of the manuscript was written by Caiqiang Xue and Wenjie Dong, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the authors gave consent for publication of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Cuiying Science and Technology Innovation Program of Lanzhou University Second Hospital (grant number: CY2023-YB-A03) and Qingdao University Hospital Youth Research Fund (grant number: QDFYQN2024218).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chengjian Wang, Ming Liu, Tao Han, Qing Zhou, Peng Zhang, Kai Huang, Junlin Zhou. The first draft of the manuscript was written by Caiqiang Xue and Qiusui Xiang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\n\u003cp\u003eThanks to all the partners who contributed to this research, including Caiqiang Xue, Suixiang Qiu, Chengjian Wang, Ming Liu, Tao Han, Qing Zhou, Peng Zhang, Kai Huang, Junlin Zhou.\u003c/p\u003e\n\u003ch2\u003eData availability:\u003c/h2\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eConficoni A, Feraco P, Mazzatenta D, Zoli M, Asioli S, Zenesini C, Fabbri VP, Cellerini M, Bacci A (2020) Biomarkers of pituitary macroadenomas aggressive behaviour: a conventional MRI and DWI 3T study. Br J Radiol 93:20200321\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmadi J, North CM, Segall HD, Zee CS, Weiss MH (1986) Cavernous sinus invasion by pituitary adenomas. AJR Am J Roentgenol 146:257\u0026ndash;262\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsa SL, Mete O, Perry A, Osamura RY (2022) Overview of the 2022 WHO Classification of Pituitary Tumors. Endocr Pathol 33:6\u0026ndash;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 23:1231\u0026ndash;1251\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborn AG, Louis DN, Poussaint TY, Linscott LL, Salzman KL (2022) The 2021 World Health Organization Classification of Tumors of the Central Nervous System: What Neuroradiologists Need to Know. AJNR Am J Neuroradiol 43:928\u0026ndash;937\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKontogeorgos G, Thodou E, Osamura RY, Lloyd RV (2022) High-risk pituitary adenomas and strategies for predicting response to treatment. Horm (Athens) 21:1\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Y, Liu Z, Hou B, Li L, Liu X, Liu Z, Wang R, Lin Y, Feng F, Tian J, Feng M (2019) Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma. Eur J Radiol 121:108647\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zheng J, Huang Z, Teng Y, Chen C, Xu J (2023) Predicting visual recovery in pituitary adenoma patients post-endoscopic endonasal transsphenoidal surgery: Harnessing delta-radiomics of the optic chiasm from MRI. Eur Radiol 33:7482\u0026ndash;7493\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan DZ, Hanrahan JG, Baldeweg SE, Dorward NL, Stoyanov D, Marcus HJ (2023) Current and Future Advances in Surgical Therapy for Pituitary Adenoma. Endocr Rev 44:947\u0026ndash;959\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsa SL, Mete O, Riddle ND, Perry A (2023) Multilineage Pituitary Neuroendocrine Tumors (PitNETs) Expressing PIT1 and SF1. Endocr Pathol 34:273\u0026ndash;278\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnosp E, Steiner E, Kitz K, Matula C (1993) Pituitary adenomas with invasion of the cavernous sinus space: a magnetic resonance imaging classification compared with surgical findings, Neurosurgery, 33 610\u0026ndash;617; discussion 617\u0026ndash;618\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng J, Xue C, Liu X, Li S, Zhou J (2023) Differentiating between adult intracranial medulloblastoma and ependymoma using MRI. Clin Radiol 78:e288\u0026ndash;e293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin AL, Rudneva VA, Richards AL, Zhang Y, Woo HJ, Cohen M, Tisnado J, Majd N, Wardlaw SL, Page-Wilson G, Sengupta S, Chow F, Goichot B, Ozer BH, Dietrich J, Nachtigall L, Desai A, Alano T, Ogilive S, Solit DB, Bale TA, Rosenblum M, Donoghue MTA, Geer EB, Tabar V (2024) Genome-wide loss of heterozygosity predicts aggressive, treatment-refractory behavior in pituitary neuroendocrine tumors. Acta Neuropathol 147:85\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBioletto F, Prencipe N, Berton AM, Aversa LS, Cuboni D, Varaldo E, Gasco V, Ghigo E, Grottoli S (2024) Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives. 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Acad Radiol 31:2050\u0026ndash;2060\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShinya Y, Atkinson JLD, Erickson D, Bancos I, Pinheiro Neto CD, Davidge-Pitts CJ, Peris Celda M, Herndon JS, Hong S, Van Gompel JJ (2024) Correlation of older age with better progression-free survival despite less aggressive resection in nonfunctioning pituitary adenomas. J Neurosurg 141:781\u0026ndash;789\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoguszewski CL, Boguszewski M (2019) Growth Hormone's Links to Cancer. Endocr Rev 40:558\u0026ndash;574\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandberg-Nordqvist AC, Stahlbom PA, Reinecke M, Collins VP, von Holst H, Sara V (1993) Characterization of insulin-like growth factor 1 in human primary brain tumors. Cancer Res 53:2475\u0026ndash;2478\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"pituitary neuroendocrine tumors, magnetic resonance imaging, WHO classification, risk","lastPublishedDoi":"10.21203/rs.3.rs-9001960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9001960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe 2021 WHO classification redesignates pituitary adenomas as pituitary neuroendocrine tumors (PitNETs), incorporating transcription factor profiling for subtype stratification. Given the current diagnostic challenges in distinguishing high- and low-risk PitNETs, we investigated whether MRI features combined with clinical biomarkers could improve preoperative risk stratification.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis multicenter retrospective study analyzed 548 histopathologically confirmed PitNET cases (training set: n\u0026thinsp;=\u0026thinsp;319; test set: n\u0026thinsp;=\u0026thinsp;138 from Center 1; validation set: n\u0026thinsp;=\u0026thinsp;91 from Center 2). Comprehensive clinical, endocrinological, and MRI parameters were evaluated through logistic regression to construct a predictive model. Diagnostic performance was quantified using area under the ROC curve (AUC), supplemented by calibration plots and decision curve analysis (DCA) to assess clinical utility.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSignificant intergroup differences (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed between high- and low-risk PitNETs: patient age, maximal tumor diameter (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), growth hormone (GH), prolactin (PRL), insulin-like growth factor-1 (IGF-1) levels, tumor margin irregularity, optic chiasm compression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), circumferential carotid encasement, and cavernous sinus invasion. Multivariate analysis identified age (OR\u0026thinsp;=\u0026thinsp;1.04, 95%CI 1.02\u0026ndash;1.07), tumor diameter (OR\u0026thinsp;=\u0026thinsp;1.15, 95%CI 1.08\u0026ndash;1.22), PRL (OR\u0026thinsp;=\u0026thinsp;1.01, 95%CI 1.00-1.02), and IGF-1 (OR\u0026thinsp;=\u0026thinsp;1.003, 95%CI 1.001\u0026ndash;1.005) as independent predictors. The integrated model achieved an AUC of 0.803 (95%CI 0.703\u0026ndash;0.903) on external validation set, with excellent calibration and favorable decision curve net benefit.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eNomogram by integrating clinical and MRI features can be used as a reliable tool to predict risk status in patients with PitNETs. After further external validation, this will help neurosurgeons make critical decisions regarding surgical or alternative treatment strategies for PitNETs.\u003c/p\u003e","manuscriptTitle":"Predicting pituitary neuroendocrine tumor risk based on clinical and MRI features nomogram: A multicenter study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 11:59:03","doi":"10.21203/rs.3.rs-9001960/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c10f6d93-c939-4933-b9c5-6f932795c2e2","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-15T14:40:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 11:59:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9001960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9001960","identity":"rs-9001960","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}