Clinicopathologic and Molecular Characteristics of Pituitary Neuroendocrine Tumors (PitNETs) Treated with Extra-Pseudocapsule Resection and Their Clinical Implications: A Single-Center Experience with 274 Cases

Clinicopathologic and Molecular Characteristics of Pituitary Neuroendocrine Tumors (PitNETs) Treated with Extra-Pseudocapsule Resection and Their Clinical Implications: A Single-Center Experience with 274 Cases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Clinicopathologic and Molecular Characteristics of Pituitary Neuroendocrine Tumors (PitNETs) Treated with Extra-Pseudocapsule Resection and Their Clinical Implications: A Single-Center Experience with 274 Cases Xing bo Li, Kuo Zeng, Xue yan Wan, Hui yong Liu, Liang Lu, Juan Chen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8561070/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To evaluate associations between transcription factor (TF)–defined molecular lineage and the clinical characteristics of pituitary neuroendocrine tumors (PitNETs). Methods A retrospective cohort analysis was performed in 274 patients undergoing extra-pseudocapsular transsphenoidal resection, tumors were classified by TF-defined lineage and invasiveness (0–3) and Knosp score. Group differences were tested with appropriate parametric/nonparametric and χ²/Fisher’s exact methods. Predictors of GTR were examined using prespecified hierarchical multivariable logistic regression, and model discrimination compared using ROC/AUC with LRT and DeLong testing. Results TF-defined lineage classification improved diagnostic precision. Among tumors previously classified as nonfunctioning adenomas, 74.7% received a definitive lineage assignment. TF-negative and multilineage tumors showed higher invasiveness rates and higher invasiveness grades than single-lineage tumors. The PIT1–GH/PRL subgroup had the highest prevalence and grade of invasiveness; SOX2 positivity was most frequent in this subgroup (41.7%), which also exhibited the lowest GTR rate (58.3%). SOX2-positive tumors were associated with higher preoperative ACTH and GH levels. SOX2 positivity was more common in multilineage tumors (20.5%) and recurrent cases (20.0%). Compared with Trouillas grading alone, incorporation of invasiveness grade and intraoperative features (capsule status and vascularity) improved discrimination for predicting GTR (AUC, 0.866 vs 0.795; ΔAUC = 0.071; DeLong 95% CI, 0.022–0.120; P = 0.0047). Conclusions The 2022 WHO TF-defined lineage system improves diagnostic precision and facilitates interpretation of PitNET differentiation and molecular pathology. Tumors in the PIT1–GH/PRL subgroup demonstrate more aggressive invasive features. SOX2 positivity has the highest proportion in multilineage tumors and in recurrent surgical patients, and close follow-up is needed. Pituitary Neuroendocrine Tumor (PitNET) Extra-Pseudocapsular Resection Tumor Invasiveness SOX2 Figures Figure 1 Figure 2 Figure 3 1. Introduction Since the late 1990s, this center has adopted the diagnostic and therapeutic protocols developed by the Departments of Neurosurgery and Pathology at Erlangen–Nürnberg University (Germany). A pituitary adenoma classification framework based on an immunohistochemical hormone panel (GH, PRL, ACTH, TSH, FSH/LH, and the α-subunit, among others) was implemented to characterize clinical heterogeneity—particularly differences in invasiveness—between functioning and nonfunctioning tumors 1 , 2 . In 2022, the World Health Organization (WHO) updated the Classification of Central Nervous System (CNS) Tumors. This revision renamed pituitary adenoma as pituitary neuroendocrine tumors (PitNETs) and incorporated transcription factors (such as PIT1, TPIT, SF1, GATA3, ERα) into the formal classification of PitNET, promoting pituitary neuroendocrine tumor pathological classification toward a more precise, molecular-based framework 3 , 4 . Notably, the updated classification continues to emphasize tumor invasiveness. Accordingly, our center has maintained a focus on invasiveness and differentiation-related features, which represent key phenotypes of aggressive PitNETs. Since 2011, extra-pseudocapsular gross total resection has been adopted 5 , with subsequent improvement in clinical outcomes 6 . In 2019, SOX2 was introduced as a stemness-associated marker in studies of plurihormonal tumors to support assessment of multilineage differentiation 7 . Since 2023, the WHO lineage-based diagnostic criteria have been implemented in parallel with the pre-existing diagnostic system; however, the associations between TF-defined lineage (and related molecular features) and clinical characteristics under the updated framework have not been systematically summarized. Therefore, the present study retrospectively analyzed PitNET cases treated since 2023 to examine the relationship between molecular pathological features in the updated lineage classification and the clinical characteristics and outcomes, with the aim of informing neurosurgical decision-making in routine practice. 2. Materials and Methods 2.1 Study population Between 2023 and 2025, 305 consecutive transsphenoidal resections for pituitary neuroendocrine tumors (PitNETs) were performed at this center using microscopic or endoscopic extra-pseudocapsular techniques. 294 transsphenoidal resections for PitNETs were performed, including 274 primary surgeries and 20 reoperations for recurrent tumors. The primary-surgery cohort (n = 274) was used for the main analyses. Recurrent cases (n = 20) were analyzed separately as an exploratory cohort. Exclusion criteria for the primary-surgery cohort included incomplete follow-up, postoperative MRI not meeting prespecified assessment standards, and age < 18 years. 2.2 Baseline characteristics Clinical variables included age, sex, maximum tumor diameter (cm), pituitary-axis hormone levels, pseudocapsule integrity, intraoperative dural invasion findings, radiological invasion, tumor texture, intraoperative vascularity, extent of resection, and endocrine outcome (hormonal remission). Pathological variables included immunohistochemical markers specified by the updated classification (PIT1, TPIT, SF1, GATA3, ERα, Ki-67, and SOX2) and histological evidence of dural invasion. 2.3 PitNET classification criteria PitNETs were classified according to the WHO Classification of Tumors of the Central Nervous System (5th edition) based on lineage-defining transcription factor (TF) immunoreactivity. Tumors were assigned to the PIT1, TPIT, or SF1 lineage according to expression of PIT1, TPIT, and SF1, respectively: the PIT1 lineage included GH-, PRL-, and TSH-secreting tumors and mixed PIT1-lineage subtypes; the TPIT lineage corresponded to ACTH-secreting tumors; and the SF1 lineage corresponded to gonadotroph tumors (FSH and/or LH positive). TF-negative tumors were defined by absence of PIT1, TPIT, and SF1 immunoreactivity. Tumors expressing TFs from more than one lineage were categorized as multilineage. Given prior work on plurihormonal PitNETs, SOX2 was evaluated as a stemness-associated marker 7 . SOX2 immunohistochemistry was performed on formalin-fixed, paraffin-embedded sections (4 µm). After deparaffinization and rehydration, heat-induced antigen retrieval was conducted using Tris–EDTA (pH 9.0). Endogenous peroxidase was blocked, and sections were incubated overnight at 4°C with an anti-SOX2 primary antibody (Abcam; rabbit monoclonal; clone EPR3131; ab92494; 1:100), followed by polymer-based HRP detection with DAB chromogen, hematoxylin counterstaining, and mounting. Only nuclear staining in tumor cells was considered positive. The proportion of SOX2-positive tumor nuclei was scored as follows: 0 (0%); 1 (scattered positive cells); 2 (1–5%); 3 (5–10%); 4 (10–30%); 5 (30–50%); 6 (> 50%). A score ≥ 2 (≥ 1%) was defined as SOX2-positive. Scoring was performed independently by two blinded pathologists; discrepancies were resolved by joint review and adjudication by a senior pathologist. 2.4 Invasiveness grading Tumor invasiveness was evaluated using a three-domain framework (radiological, surgical, and histological evidence) according to Lu et al. Radiological invasiveness was assessed on coronal gadolinium-enhanced T1-weighted MRI and was defined by any imaging evidence of extrasellar invasive growth, including (i) cavernous sinus invasion, graded by the Knosp system (Knosp 3–4); (ii) suprasellar invasion beyond the diaphragma sellae; and/or (iii) invasion/extension into the sphenoid sinus. Surgical invasiveness was defined as macroscopic invasive or destructive tumor behavior observed under direct endoscopic/microscopic visualization, including invasion into the cavernous sinus compartment (beyond the medial wall) and/or destructive extension beyond the sella (e.g., invasion into the sphenoid sinus and/or involvement/disruption of the sellar diaphragm). Histological invasiveness was defined as microscopic infiltration of, and/or destructive invasion into, the basal sellar dura (endosteum) by tumor cells on hematoxylin–eosin–stained sections. Based on these three criteria, tumors were assigned an invasiveness grade as follows: grade 0, none of the three criteria present; grade 1, any one criterion present; grade 2, any two criteria present; and grade 3, all three criteria present 8 . 2.5 Extent of resection and hormonal assessment Tumor size was measured on coronal gadolinium-enhanced T1-weighted MRI, using the maximal diameter. Postoperative contrast-enhanced MRI was routinely performed at approximately 3 months after surgery (8–12 weeks) to assess residual tumor and extent of resection. Gross total resection (GTR) was defined as absence of residual enhancing tumor on postoperative MRI. Near-total resection (NTR) was defined as suspicious residual enhancement or residual tumor volume 10% of the initial volume 9 . Pituitary-axis hormone levels were assessed within 7 days preoperatively and on postoperative day 1 to characterize perioperative endocrine status and early biochemical changes. 2.6 Statistical analysis analyses were performed using R (version 4.2.0). Continuous variables are presented as mean ± standard deviation (SD) or median (interquartile range [IQR]), as appropriate. Two-group comparisons used Welch’s t-test or the Wilcoxon rank-sum test. Comparisons across three or more groups used one-way analysis of variance (ANOVA) or the Kruskal–Wallis test. Categorical variables are presented as n (%) and compared using Pearson’s χ² test; Fisher’s exact test was applied to 2×2 tables when expected counts were < 5. For multi-category contingency tables with sparse expected counts, P values were estimated using Monte Carlo χ² simulations (two-sided; 10,000 permutations); additional Monte Carlo permutation tests were performed for selected overall comparisons, as specified in the table notes. Ordinal outcomes (e.g., invasiveness grade and Knosp grade) were compared using the two-sided Mann–Whitney U test. To evaluate predictors of GTR (resection grade 2), four prespecified hierarchical multivariable logistic regression models were fitted. Model 1 included age, sex, maximum tumor diameter, proliferative status (Ki-67 ≥ 3% and/or P53 positivity), and imaging invasiveness. Model 2 replaced imaging invasiveness with invasiveness grade ≥ 2. Model 3 additionally included pseudocapsule integrity and intraoperative vascularity, and Model 4 further included SOX2 status. Results are reported as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Discrimination was assessed using receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC); AUCs were compared using DeLong’s test, and nested models were compared using likelihood ratio tests (LRTs). All tests were two-sided, and P < 0.05 was considered statistically significant. 3. Results 3.1 Clinical characteristics of patients Among 274 PitNETs, 74.7% of tumors previously classified as hormone-immunonegative under the former diagnostic scheme received a definitive TF-defined lineage assignment (Fig. 1 ). Lineage distribution was as follows: SF1-only (n = 95), PIT1-only (n = 60), TPIT-only (n = 53), PIT1 + SF1 (n = 32), TF-negative (n = 22), and rare mixed lineages (SF1 + TPIT, n = 7; PIT1 + TPIT, n = 3; triple-lineage, n = 2). Sex distribution was balanced (female, 136/274; male, 138/274). Age and maximum tumor diameter differed across lineages (both P < 0.001). Patients with PIT1-only tumors were younger than those with SF1-only tumors (q < 0.0001), and PIT1-only tumors were smaller than SF1-only and TF-negative tumors (both q < 0.0001). Clinical manifestations linked to hormone axes demonstrated lineage-related enrichment: acral/facial changes were more frequent in PIT1-related lineages, whereas centripetal obesity was more common in the TPIT-only lineage (P = 0.008) (Table 1 ; Supplementary Table 1). Table 1 Demographic and clinical characteristics stratified by transcription factor lineage. Variable SF1-only (n = 95) PIT1-only (n = 60) TPIT-only (n = 53) PIT1 + SF1 (n = 32) TF-negative (n = 22) SF1 + TPIT (n = 7) PIT1 + TPIT (n = 3) Triple (n = 2) Overall (N = 274) P value (Monte Carlo) Total, n 95 60 53 32 22 7 3 2 274 Female, n (%) 24 (25.3%) 38 (63.3%) 43 (81.1%) 18 (56.2%) 6 (27.3%) 3 (42.9%) 2 (66.7%) 2 (100.0%) 136 (49.6%) < 0.001 Male, n (%) 71 (74.7%) 22 (36.7%) 10 (18.9%) 14 (43.8%) 16 (72.7%) 4 (57.1%) 1 (33.3%) 0 (0.0%) 138 (50.4%) Age (years), mean ± SD 52.8 ± 12.0 40.6 ± 13.7 50.3 ± 11.7 49.4 ± 12.6 49.0 ± 16.1 49.6 ± 10.5 44.3 ± 21.6 30.0 ± 2.8 48.6 ± 13.6 < 0.001 Tumor maximum diameter (cm), mean ± SD 2.6 ± 0.9 1.8 ± 0.9 2.4 ± 1.1 1.9 ± 0.9 3.0 ± 1.1 2.1 ± 1.1 2.2 ± 0.3 1.5 ± 0.7 2.3 ± 1.0 < 0.001 Clinical presentation Headache, n (%) 33 (34.7%) 24 (40.0%) 19 (35.8%) 13 (40.6%) 6 (27.3%) 2 (28.6%) 1 (33.3%) 0 (0.0%) 98 (35.8%) 0.923 Dizziness, n (%) 20 (21.1%) 4 (6.7%) 14 (26.4%) 5 (15.6%) 2 (9.1%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 45 (16.4%) 0.087 Nausea/vomiting, n (%) 7 (7.4%) 0 (0.0%) 0 (0.0%) 1 (3.1%) 2 (9.1%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 10 (3.6%) 0.205 Decreased visual acuity, n (%) 44 (46.3%) 15 (25.0%) 22 (41.5%) 8 (25.0%) 11 (50.0%) 3 (42.9%) 1 (33.3%) 0 (0.0%) 104 (38.0%) 0.078 Blurred vision, n (%) 1 (1.1%) 2 (3.3%) 4 (7.5%) 1 (3.1%) 3 (13.6%) 2 (28.6%) 0 (0.0%) 1 (50.0%) 14 (5.1%) 0.011 Incidental detection on routine examination, n (%) 17 (17.9%) 4 (6.7%) 1 (1.9%) 3 (9.4%) 1 (4.5%) 2 (28.6%) 0 (0.0%) 0 (0.0%) 28 (10.2%) 0.054 Physical findings Visual field defect, n (%) 22 (23.2%) 5 (8.3%) 11 (20.8%) 4 (12.5%) 9 (40.9%) 1 (14.3%) 0 (0.0%) 0 (0.0%) 52 (19.0%) 0.045 Ptosis, n (%) 2 (2.1%) 0 (0.0%) 0 (0.0%) 1 (3.1%) 2 (9.1%) 1 (14.3%) 0 (0.0%) 0 (0.0%) 6 (2.2%) 0.133 Acral enlargement, n (%) 0 (0.0%) 16 (26.7%) 1 (1.9%) 9 (28.1%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 26 (9.5%) < 0.001 Coarsening of facial features, n (%) 0 (0.0%) 18 (30.0%) 1 (1.9%) 10 (31.2%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 29 (10.6%) < 0.001 Centripetal obesity, n (%) 0 (0.0%) 1 (1.7%) 8 (15.1%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 9 (3.3%) 0.008 Menstrual irregularity/amenorrhea, n (%) 5 (5.3%) 19 (31.7%) 6 (11.3%) 6 (18.8%) 2 (9.1%) 0 (0.0%) 2 (66.7%) 2 (100.0%) 42 (15.3%) < 0.001 Galactorrhea, n (%) 0 (0.0%) 4 (6.7%) 2 (3.8%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 6 (2.2%) 0.184 Sexual dysfunction, n (%) 0 (0.0%) 3 (5.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 3 (1.1%) 0.169 Note. Baseline demographics, tumor maximum diameter (cm), clinical symptoms, and physical examination findings are summarized across transcription factor (TF)–defined lineage categories. Continuous variables are presented as mean ± SD and categorical variables as n (%). P values were obtained using Monte Carlo permutation tests (two-sided; 10,000 permutations) for overall comparisons across lineage groups. 3.2. Invasive characteristics of pathological diagnosis in different lineages 3.2.1. PIT1-GH-PRL has stronger invasiveness Tumor invasiveness was assessed using our center’s invasiveness grading system and Knosp grading. The results showed that 95.9% (47/49) of tumors with Knosp ≥ 3 were labeled as invasiveness grade ≥ 2. Among tumors with invasiveness grade ≥ 2, 66.2% (92/139) had Knosp < 3 (Supplementary Table 2). Identification of invasiveness in large tumors was consistent with Knosp, and identification of invasiveness in small tumors was more accurate (Supplementary Table 3). When stratified by TF-defined lineage, TF-negative and multilineage tumors showed higher rates of invasiveness (grade ≥ 2: 54.5% vs 49.5%) and higher invasiveness grades than single-lineage tumors (Table 2 ; Supplementary Table 4). Among the six major hormone-defined subtypes, the GH–PRL group exhibited the highest proportion of Knosp ≥ 3 (29.4%) (Fig. 2 A–B; Supplementary Table 5). Consistently, the PIT1–GH/PRL subgroup showed a higher invasiveness grade compared with other lineages (P = 0.026) (Table 3 ). Within the TPIT + cohort (densely granulated, n = 54; sparsely granulated, n = 7), sparsely granulated tumors demonstrated a right-shift toward higher invasiveness and Knosp grades (invasiveness grade, P = 0.039; Knosp, P = 0.011) (Fig. 2 C–D), whereas no analogous difference was observed in PIT1-lineage tumors (Supplementary Fig. 1A–B). Table 2 Distribution of invasiveness grades across lineage types Lineage type Grade 0 Grade 1 Grade 2 Grade 3 P value (Monte Carlo) SF1-only 9 (9.5%) 34 (35.8%) 34 (35.8%) 18 (18.9%) PIT1-only 17 (28.3%) 14 (23.3%) 20 (33.3%) 9 (15.0%) TPIT-only 12 (22.6%) 19 (35.8%) 15 (28.3%) 7 (13.2%) PIT1 + SF1 3 (9.4%) 11 (34.4%) 10 (31.2%) 8 (25.0%) 0.106 TF-negative 4 (18.2%) 6 (27.3%) 6 (27.3%) 6 (27.3%) SF1 + TPIT 2 (28.6%) 1 (14.3%) 2 (28.6%) 2 (28.6%) PIT1 + TPIT 1 (33.3%) 0 (0.0%) 2 (66.7%) 0 (0.0%) Triple 2 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Note. The distribution of invasiveness grade (0–3) is shown for each TF lineage category. Values are presented as n (row %). P values were calculated using a Monte Carlo χ² test (two-sided; 10,000 permutations). Table 3 Invasiveness (grade ≥ 2) and Knosp status (≥ 3) by lineage subgroup Variable Level Overall SF1-only (n = 95) TPIT-only (n = 53) PIT1 + SF1 (n = 32) TF-negative (n = 22) SF1 + TPIT (n = 7) PIT1 + TPIT (n = 3) Triple (n = 2) PIT1-GHPRL (n = 12) PIT1-only (non–GH-PRL) (n = 48) P value (MC χ²) Invasiveness grade Invasiveness grade < 2 135 (49.3%) 43 (45.3%) 31 (58.5%) 14 (43.8%) 10 (45.5%) 3 (42.9%) 1 (33.3%) 2 (100.0%) 1 (8.3%) 30 (62.5%) 0.026 Invasiveness grade ≥ 2 139 (50.7%) 52 (54.7%) 22 (41.5%) 18 (56.2%) 12 (54.5%) 4 (57.1%) 2 (66.7%) 0 (0.0%) 11 (91.7%) 18 (37.5%) Knosp grade Knosp < 3 225 (82.1%) 80 (84.2%) 45 (84.9%) 25 (78.1%) 14 (63.6%) 6 (85.7%) 3 (100.0%) 2 (100.0%) 8 (66.7%) 42 (87.5%) 0.254 Knosp ≥ 3 49 (17.9%) 15 (15.8%) 8 (15.1%) 7 (21.9%) 8 (36.4%) 1 (14.3%) 0 (0.0%) 0 (0.0%) 4 (33.3%) 6 (12.5%) Note. The proportions of tumors with invasiveness grade ≥ 2 (vs < 2) and with Knosp grade ≥ 3 (vs < 3) are summarized across lineage subgroups. Values are presented as n (% within column). P values were calculated using a Monte Carlo χ² test (two-sided; 10,000 permutations). Figure 1 Sankey diagram linking hormone-defined subtypes with transcription factor (TF)–defined lineages. Nodes on the left represent TF-defined lineages and nodes on the right represent hormone-defined subtypes; numbers adjacent to nodes indicate tumor counts (n). Band width is proportional to the number of tumors within each TF–hormone combination. Percentages shown on bands indicate the proportion of each downstream hormone-defined subtype within a given TF-defined lineage. Bands are color-coded by TF-defined lineage. 3.2.2 Tumor invasiveness is associated with hard texture and rich vascularity Based on established “invasive/high-risk PitNET” features 10 – 12 , variables potentially associated with invasiveness were examined, including markers of proliferative or stemness-related activity (SOX2, Ki-67, P53), integrity of local anatomical barriers (pseudocapsule integrity), and intraoperative characteristics (tumor texture and vascularity) (Supplementary Table 6). In TF-defined lineage groups with n ≥ 10, TF-negative tumors were more frequently hypervascular (P = 0.002) and more often had a firm texture (Table 4 ). In contrast, stratification by hormone-defined subtype showed only borderline differences in texture and vascularity (texture, P = 0.051; vascularity, P = 0.050) (Supplementary Table 7). Table 4 Biomarkers and invasiveness indices across lineage types. Variable Level Overall SF1-only (n = 95) PIT1-only (n = 60) TPIT-only (n = 53) PIT1 + SF1 (n = 32) TF-negative (n = 22) SF1 + TPIT (n = 7) PIT1 + TPIT (n = 3) Triple (n = 2) P value (MC χ²) SOX2 SOX2 negative 232 (84.7%) 81 (85.3%) 52 (86.7%) 44 (83.0%) 26 (81.2%) 20 (90.9%) 5 (71.4%) 3 (100.0%) 1 (50.0%) 0.705 SOX2 positive 42 (15.3%) 14 (14.7%) 8 (13.3%) 9 (17.0%) 6 (18.8%) 2 (9.1%) 2 (28.6%) 0 (0.0%) 1 (50.0%) Capsule No capsule 63 (23.0%) 18 (18.9%) 14 (23.3%) 17 (32.1%) 7 (21.9%) 6 (27.3%) 0 (0.0%) 0 (0.0%) 1 (50.0%) 0.369 Partial/Intact capsule 211 (77.0%) 77 (81.1%) 46 (76.7%) 36 (67.9%) 25 (78.1%) 16 (72.7%) 7 (100.0%) 3 (100.0%) 1 (50.0%) Texture Hard texture 40 (14.6%) 11 (11.6%) 13 (21.7%) 4 (7.5%) 6 (18.8%) 5 (22.7%) 0 (0.0%) 0 (0.0%) 1 (50.0%) 0.153 Soft texture 234 (85.4%) 84 (88.4%) 47 (78.3%) 49 (92.5%) 26 (81.2%) 17 (77.3%) 7 (100.0%) 3 (100.0%) 1 (50.0%) Vascular Poor vascularity 242 (88.3%) 83 (87.4%) 59 (98.3%) 44 (83.0%) 32 (100.0%) 15 (68.2%) 4 (57.1%) 3 (100.0%) 2 (100.0%) 0.002 Rich vascularity 32 (11.7%) 12 (12.6%) 1 (1.7%) 9 (17.0%) 0 (0.0%) 7 (31.8%) 3 (42.9%) 0 (0.0%) 0 (0.0%) Ki-67 Ki-67 < 3 184 (67.2%) 69 (72.6%) 39 (65.0%) 34 (64.2%) 23 (71.9%) 12 (54.5%) 4 (57.1%) 2 (66.7%) 1 (50.0%) 0.789 Ki-67 ≥ 3 90 (32.8%) 26 (27.4%) 21 (35.0%) 19 (35.8%) 9 (28.1%) 10 (45.5%) 3 (42.9%) 1 (33.3%) 1 (50.0%) P53 Mutant (1) 18 (6.6%) 7 (7.4%) 2 (3.3%) 3 (5.7%) 2 (6.2%) 3 (13.6%) 0 (0.0%) 1 (33.3%) 0 (0.0%) 0.404 Wild-type (0) 256 (93.4%) 88 (92.6%) 58 (96.7%) 50 (94.3%) 30 (93.8%) 19 (86.4%) 7 (100.0%) 2 (66.7%) 2 (100.0%) Invasiveness grade Invasiveness grade ≥ 2 139 (50.7%) 52 (54.7%) 29 (48.3%) 22 (41.5%) 18 (56.2%) 12 (54.5%) 4 (57.1%) 2 (66.7%) 0 (0.0%) 0.628 Invasiveness grade < 2 135 (49.3%) 43 (45.3%) 31 (51.7%) 31 (58.5%) 14 (43.8%) 10 (45.5%) 3 (42.9%) 1 (33.3%) 2 (100.0%) Knosp Knosp ≥ 3 49 (17.9%) 15 (15.8%) 10 (16.7%) 8 (15.1%) 7 (21.9%) 8 (36.4%) 1 (14.3%) 0 (0.0%) 0 (0.0%) 0.393 Knosp < 3 225 (82.1%) 80 (84.2%) 50 (83.3%) 45 (84.9%) 25 (78.1%) 14 (63.6%) 6 (85.7%) 3 (100.0%) 2 (100.0%) Note. SOX2 status, pseudocapsule integrity, tumor texture, intraoperative vascularity, Ki-67 category, P53 status, and invasiveness indices (invasiveness grade ≥ 2; Knosp grade ≥ 3) are summarized across TF-defined lineage categories. Values are presented as n (% within column). P values were calculated using a Monte Carlo χ² test (two-sided; 10,000 permutations). Figure 2 Invasiveness across tumor subtypes and lineages. (A–B) Distribution of invasiveness grade (0–3) shown by median (dot) and interquartile range (IQR; horizontal line, 25th–75th percentiles); sample size is shown in parentheses. (A) Stratified by hormone-defined subtype. (B) Stratified by TF-defined lineage. Overall distributions were compared using the Kruskal–Wallis test; P values are shown in panel titles. (C–D) Cumulative distribution curves stratified by granulation pattern within the TPIT-related cohort (TPIT-only, SF1 + TPIT, PIT1 + TPIT, and triple-lineage). (C) Invasiveness grade (0–3). The sparsely granulated group shows higher cumulative proportions at severe-grade thresholds; distributions were compared using a two-sided Mann–Whitney U test (P = 0.039). (D) Knosp grade (0–4); distributions were compared using a two-sided Mann–Whitney U test (P = 0.011). Densely and sparsely granulated tumors are indicated in the legend. 3.3. SOX2-positive expression is higher in multilineage and recurrent patients Overall, SOX2 positivity was observed in 15.3% of tumors. SOX2 positivity was higher in multilineage tumors than in single-lineage tumors (20.5% vs 14.9%) (Supplementary Table 8). Within single-lineage tumors, the PIT1–GH/PRL subgroup showed the highest SOX2 positivity rate, although the between-lineage difference did not reach statistical significance (P = 0.089) (Supplementary Table 8). SOX2-positive tumors were associated with higher preoperative ACTH and GH levels (Supplementary Table 9). Among recurrent cases (n = 20), SOX2 positivity was 20.0% (Supplementary Table 10). 3.4. Risk factors affecting gross total resection In univariable analyses, non-GTR was associated with larger maximum tumor diameter, higher proliferative activity (greater proportion with Ki-67 ≥ 3%), absence of a pseudocapsule, more advanced invasiveness (higher proportions of invasiveness grade ≥ 2 and Knosp ≥ 3), and hypervascularity (all P < 0.001). GTR rates differed across lineages, with the lowest rates observed in the PIT1–GH/PRL subgroup and TF-negative tumors (58.3% and 59.1%, respectively) (Table 5 ). Table 5 Univariate analysis of factors associated with gross total resection (GTR). Variable Level Non-GTR (n = 75) GTR (n = 199) Total (N = 274) P value Test lineage PIT1-GH-PRL 5 (6.7%) 7 (3.5%) 12 (4.4%) 0.442 Monte Carlo χ² PIT1-only 10 (13.3%) 38 (19.1%) 48 (17.5%) PIT1 + SF1 8 (10.7%) 24 (12.1%) 32 (11.7%) PIT1 + TPIT 0 (0.0%) 3 (1.5%) 3 (1.1%) SF1 + TPIT 2 (2.7%) 5 (2.5%) 7 (2.6%) SF1-only 23 (30.7%) 72 (36.2%) 95 (34.7%) TF-negative 9 (12.0%) 13 (6.5%) 22 (8.0%) TPIT-only 18 (24.0%) 35 (17.6%) 53 (19.3%) Triple 0 (0.0%) 2 (1.0%) 2 (0.7%) Hormone type ACTH 5 (6.7%) 22 (11.1%) 27 (9.9%) 0.634 Monte Carlo χ² GH 2 (2.7%) 5 (2.5%) 7 (2.6%) GH-PRL 6 (8.0%) 11 (5.5%) 17 (6.2%) Gonadotroph 13 (17.3%) 51 (25.6%) 64 (23.4%) Non-functioning 28 (37.3%) 59 (29.6%) 87 (31.8%) PRL 8 (10.7%) 22 (11.1%) 30 (10.9%) Plurihormonal-other 11 (14.7%) 27 (13.6%) 38 (13.9%) TSH 2 (2.7%) 2 (1.0%) 4 (1.5%) SOX2_group SOX2 negative 66 (88.0%) 166 (83.4%) 232 (84.7%) 0.348 Pearson χ² SOX2 positive 9 (12.0%) 33 (16.6%) 42 (15.3%) Ki67_group Ki67 < 3 36 (48.0%) 148 (74.4%) 184 (67.2%) < 0.001 Pearson χ² Ki67 ≥ 3 39 (52.0%) 51 (25.6%) 90 (32.8%) Capsule_group No capsule 34 (45.3%) 29 (14.6%) 63 (23.0%) < 0.001 Pearson χ² Partial/Intact capsule 41 (54.7%) 170 (85.4%) 211 (77.0%) Texture_group Soft texture 61 (81.3%) 173 (86.9%) 234 (85.4%) 0.242 Pearson χ² Hard texture 14 (18.7%) 26 (13.1%) 40 (14.6%) Vascular_group Rich vascularity 19 (25.3%) 13 (6.5%) 32 (11.7%) < 0.001 Pearson χ² Poor vascularity 56 (74.7%) 186 (93.5%) 242 (88.3%) P53 Wild-type (0) 70 (93.3%) 186 (93.5%) 256 (93.4%) 1.000 Fisher’s exact Mutant (1) 5 (6.7%) 13 (6.5%) 18 (6.6%) Invasiveness grade < 2 11 (14.7%) 124 (62.3%) 135 (49.3%) < 0.001 Pearson χ² ≥ 2 64 (85.3%) 75 (37.7%) 139 (50.7%) Knosp < 3 43 (57.3%) 182 (91.5%) 225 (82.1%) < 0.001 Pearson χ² ≥ 3 32 (42.7%) 17 (8.5%) 49 (17.9%) Granulation Densely granulated 55 (73.3%) 151 (75.9%) 206 (75.2%) 0.664 Pearson χ² Non-densely granulated (sparsely granulated/negative) 20 (26.7%) 48 (24.1%) 68 (24.8%) Sex Female 34 (45.3%) 102 (51.3%) 136 (49.6%) 0.382 Pearson χ² Male 41 (54.7%) 97 (48.7%) 138 (50.4%) Age mean ± SD 49.6 ± 13.7 48.2 ± 13.5 48.6 ± 13.6 0.445 Welch’s t-test Tumor maximum diameter (cm) mean ± SD 3.0 ± 1.1 2.1 ± 0.9 2.3 ± 1.0 < 0.001 Welch’s t-test Note. Univariable associations between candidate clinical/pathological factors and gross total resection (GTR). Values are shown as n (% within column) for categorical variables and as mean ± SD for continuous variables. P values are two-sided, and the statistical test used for each variable is indicated in the “Test” column (Monte Carlo χ² with 10,000 permutations for multi-category tables; Pearson’s χ² or Fisher’s exact test for 2×2 tables, as appropriate; Welch’s t-test for continuous variables). In multivariable analyses, four prespecified hierarchical logistic regression models were evaluated for prediction of GTR. Referring to the Trouillas stratification concept of “invasiveness + proliferative activity”, the AUC of Model 1 (age, sex, tumor size, proliferation [Ki-67 ≥ 3% or P53 positive] and imaging invasion) was 0.795. After Model 2 replaced the imaging invasion variable with invasiveness grade (≥ 2), discrimination improved to AUC = 0.828. Adding pseudocapsule integrity and vascularity (Model 3) further improved discrimination (AUC = 0.866) and model fit (LRT: LR χ²[df = 2] = 27.773, P < 0.001) (Fig. 3 A). Addition of SOX2 status (Model 4) did not improve discrimination (AUC = 0.866) and did not significantly improve model fit (LRT: LR χ²[df = 1] = 0.031, P = 0.860) (Supplementary Fig. 3A). Figure 3 Receiver operating characteristic (ROC) curves for prespecified multivariable logistic regression models predicting gross total resection (GTR; yes/no). Model 1 included age, sex, maximum tumor diameter, proliferative status (Ki-67 ≥ 3% and/or P53 positivity), and imaging invasiveness. Model 2 replaced imaging invasiveness with invasiveness grade ≥ 2. Model 3 additionally included pseudocapsule integrity (partial/intact vs absent) and intraoperative vascularity (rich vs poor). The diagonal dashed line indicates chance-level discrimination. 4. Discussion With advances in molecular pathology, cell biology, and epigenetics, the World Health Organization (WHO) updated its Classification of Central Nervous System (CNS) Tumors in 2022. This revision not only recommended renaming pituitary adenoma as pituitary neuroendocrine tumors (pituitary neuroendocrine tumors, PitNETs), but also incorporated transcription factors defining pituitary cell lineages (such as PIT1, TPIT, SF1, GATA3, ERα) into the formal classification of PitNET 3 , 4 . Consistent with prior reports, TF-defined lineage assignment improves diagnostic attribution, particularly for tumors previously labeled as nonfunctioning pituitary adenomas. In the present cohort, 74.7% of tumors that were hormone-immunonegative under the former diagnostic scheme received a definitive lineage assignment, aligning with published trends 4 , 13 . Using the invasiveness grading system applied at this center, invasiveness classification showed high concordance with Knosp grading for larger tumors, while identifying additional invasive tumors among smaller lesions. When stratified by hormone-defined subtype, GH–PRL tumors most frequently exhibited high invasiveness grades. Integrating TF-defined lineage further indicated that the PIT1–GH/PRL subgroup tended to show greater invasiveness than other lineages. Moreover, within the TPIT + lineage, the sparsely granulated subtype was associated with higher invasiveness grades and higher Knosp grades. Recent evidence indicates that silent corticotroph adenomas (SCAs) have higher rates of cavernous sinus invasion, lower gross total resection (GTR) rates, and increased recurrence risk 14 . Together, these observations underscore substantial heterogeneity even within the same lineage and support refined risk stratification within TF-defined categories 15 . Multilineage tumors demonstrated higher invasiveness rates than single-lineage tumors, consistent with previous studies 13 ,163, 16 . Importantly, “plurihormonal” and “multilineage” are not interchangeable: under the updated classification, plurihormonal tumors include both multilineage plurihormonal PitNETs and single-lineage plurihormonal PitNETs (e.g., GH/PRL) 3 , 4 . Previously, our center found that 33.3% of plurihormonal adenoma patients were SOX2-positive 7 ; in this study, overall 15.8% of tumors were SOX2-positive, with higher positivity in multilineage (n > 5). Notably, SOX2 positivity was highest in PIT1–GH/PRL tumors (41.7%), which also showed a relatively low GTR rate (58.3%); these tumors were commonly classified as plurihormonal secretory tumors under the former framework. SOX2 positivity was associated with higher preoperative hormone levels, while no independent association between SOX2 status and GTR was identified in multivariable modeling; however, SOX2 positivity appeared enriched among recurrent cases. Given ongoing inconsistency in the literature regarding associations between SOX2, invasiveness, and prognosis, further studies are warranted to clarify the prognostic significance of SOX2—particularly in plurihormonal PitNETs 17 18 . 5. Conclusions The 2022 WHO TF-defined lineage classification improves lineage attribution and facilitates interpretation of PitNET differentiation and molecular pathology. Multilineage PitNETs show a tendency toward greater invasiveness, and the PIT1–GH/PRL subgroup demonstrates more aggressive invasive features together with the highest SOX2 positivity. Although SOX2 was not independently associated with GTR in the current models, its enrichment in plurihormonal and recurrent tumors suggests potential prognostic relevance that warrants validation in larger cohorts with longer follow-up. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was performed in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board (Ethics Committee) of Tongji Hospital, Tongji Medical College (No. TJ-IRB20220325). Data were anonymized prior to analysis to protect patient privacy. Consent to participate The requirement for informed consent to participate was waived by the Institutional Review Board due to the retrospective nature of the cohort study. Consent to publish Not applicable. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 82173136). Author Contribution Conceptualization: X.L., K.Z., and T.L.; Methodology: K.Z.; Software: H.L., L.L.; Validation: H.L., L.L.; Formal analysis: X.L., K.Z.; Investigation: X.L., K.Z.; Resources: T.L., C.K.; Data curation: X.L., K.Z.; Writing—original draft: X.W., J.C., J.W.; Writing—review & editing: X.W., J.W., T.L.; Visualization: X.L.; Supervision: J.W.; Project administration: T.L.; Funding acquisition: T.L. All authors read and approved the final manuscript. Data Availability The data that support the findings of this study are included in the article. Further inquiries are available from the corresponding author upon reasonable request. References Saeger W in Modern Neurosurgery of Meningiomas and Pituitary Adenomas . (ed Fahlbusch R ) 1–3 (Springer Vienna) Saeger W, Wilczak W, Lüdecke DK, Buchfelder M, Fahlbusch R (2003) Hormone markers in pituitary adenomas: changes within last decade resulting from improved method. Endocr Pathol 14:49–54. 10.1385/ep:14:1 Board WHOC, o. TE (2022) Endocrine and Neuroendocrine Tumours, vol 10, 5th edn. International Agency for Research on Cancer Asa SL, Mete O, Perry A, Osamura RY (2022) Overview of the 2022 WHO Classification of Pituitary Tumors. Endocr Pathol 33:6–26. 10.1007/s12022-022-09703-7 Buchfelder M, Schlaffer SM, Zhao Y (2019) The optimal surgical techniques for pituitary tumors. Best Pract Res Clin Endocrinol Metab 33:101299. 10.1016/j.beem.2019.101299 Wan XY et al (2022) Surgical Technique and Efficacy Analysis of Extra-pseudocapsular Transnasal Transsphenoidal Surgery for Pituitary Microprolactinoma. Curr Med Sci 42:1140–1147. 10.1007/s11596-022-2678-1 Shi R et al (2022) Clinicopathological Characteristics of Plurihormonal Pituitary Adenoma. Front Surg 9. 10.3389/fsurg.2022.826720 Lu L et al (2022) Classifying Pituitary Adenoma Invasiveness Based on Radiological, Surgical and Histological Features: A Retrospective Assessment of 903 Cases. J Clin Med 11. 10.3390/jcm11092464 Lee MH et al (2016) Clinical Concerns about Recurrence of Non-Functioning Pituitary Adenoma. Brain tumor Res Treat 4:1–7. 10.14791/btrt.2016.4.1.1 Rutkowski MJ et al (2021) Development and clinical validation of a grading system for pituitary adenoma consistency. J Neurosurg 134:1800–1807. 10.3171/2020.4.Jns193288 Guerra GA et al (2025) Association between pituitary adenoma consistency, resection techniques, and patient outcomes: a single-institution experience. J Neurosurg 142:1674–1681. 10.3171/2024.8.Jns232715 Yang Q, Li X (2019) Molecular Network Basis of Invasive Pituitary Adenoma: A Review. Front Endocrinol (Lausanne) 10:7. 10.3389/fendo.2019.00007 Woo CS et al (2024) A clinicopathological study of non-functioning pituitary neuroendocrine tumours using the World Health Organization 2022 classification. Front Endocrinol 15:1368944. 10.3389/fendo.2024.1368944 He W et al (2025) Treatment Strategies and Long-Term Outcomes in Silent Corticotroph Adenomas: A Single-Center Retrospective Study of 367 Cases. Neurosurgery 96:611–621. 10.1227/neu.0000000000003142 Dottermusch M et al (2024) Pituitary neuroendocrine tumors with PIT1/SF1 co-expression show distinct clinicopathological and molecular features. Acta Neuropathol 147:16. 10.1007/s00401-024-02686-1 Wang X et al (2024) Clinical and Pathological Features of Pit1/SF1 Multilineage Pituitary Neuroendocrine Tumor. Neurosurgery. 10.1227/neu.0000000000002846 Lenders NF et al (2024) Pituitary tumours without distinct lineage differentiation express stem cell marker SOX2. Pituitary 27, 248–258. 10.1007/s11102-024-01385-0 Basaran R et al THE EXPRESSION OF STEM CELL MARKERS (CD133, NESTIN, OCT4, SOX2) IN INVASIVE PITUITARY ADENOMAS. Acta endocrinologica (Bucharest, Romania : (2005)) 16, 303–310, doi:) 16, 303–310. 10.4183/aeb.2020.303 (2020) Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx 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. 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1","display":"","copyAsset":false,"role":"figure","size":278104,"visible":true,"origin":"","legend":"\u003cp\u003eSankey diagram linking hormone-defined subtypes with transcription factor (TF)–defined lineages. Nodes on the left represent TF-defined lineages and nodes on the right represent hormone-defined subtypes; numbers adjacent to nodes indicate tumor counts (n). Band width is proportional to the number of tumors within each TF–hormone combination. Percentages shown on bands indicate the proportion of each downstream hormone-defined subtype within a given TF-defined lineage. Bands are color-coded by TF-defined lineage.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8561070/v1/04b7843dbf84c13b074b7be0.png"},{"id":100414449,"identity":"4e030464-4dca-4a1f-a01c-de96207140e0","added_by":"auto","created_at":"2026-01-16 13:19:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":411537,"visible":true,"origin":"","legend":"\u003cp\u003eInvasiveness across tumor subtypes and lineages. (A–B) Distribution of invasiveness grade (0–3) shown by median (dot) and interquartile range (IQR; horizontal line, 25th–75th percentiles); sample size is shown in parentheses. (A) Stratified by hormone-defined subtype. (B) Stratified by TF-defined lineage. Overall distributions were compared using the Kruskal–Wallis test; P values are shown in panel titles. (C–D) Cumulative distribution curves stratified by granulation pattern within the TPIT-related cohort (TPIT-only, SF1+TPIT, PIT1+TPIT, and triple-lineage). (C) Invasiveness grade (0–3). The sparsely granulated group shows higher cumulative proportions at severe-grade thresholds; distributions were compared using a two-sided Mann–Whitney U test (P=0.039). (D) Knosp grade (0–4); distributions were compared using a two-sided Mann–Whitney U test (P=0.011). Densely and sparsely granulated tumors are indicated in the legend.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8561070/v1/ef48e0880484645187447859.png"},{"id":100414236,"identity":"b00d7d4b-e367-48c1-bb18-944bb5d15fa2","added_by":"auto","created_at":"2026-01-16 13:19:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171115,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for prespecified multivariable logistic regression models predicting gross total resection (GTR; yes/no). Model 1 included age, sex, maximum tumor diameter, proliferative status (Ki-67 ≥3% and/or P53 positivity), and imaging invasiveness. Model 2 replaced imaging invasiveness with invasiveness grade ≥2. Model 3 additionally included pseudocapsule integrity (partial/intact vs absent) and intraoperative vascularity (rich vs poor). The diagonal dashed line indicates chance-level discrimination.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8561070/v1/791c75178d7de826f41f76d0.png"},{"id":101043547,"identity":"8c66bc30-4c2f-4ede-b509-34d0b848daea","added_by":"auto","created_at":"2026-01-24 12:55:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2285846,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8561070/v1/3c00b1f0-9a1a-4819-8e01-293a32dde5a3.pdf"},{"id":100414384,"identity":"ed53018b-f562-4bea-83fd-2d0cf565bb49","added_by":"auto","created_at":"2026-01-16 13:19:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":363539,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8561070/v1/f37b691bde404f0263431960.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinicopathologic and Molecular Characteristics of Pituitary Neuroendocrine Tumors (PitNETs) Treated with Extra-Pseudocapsule Resection and Their Clinical Implications: A Single-Center Experience with 274 Cases","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince the late 1990s, this center has adopted the diagnostic and therapeutic protocols developed by the Departments of Neurosurgery and Pathology at Erlangen\u0026ndash;N\u0026uuml;rnberg University (Germany). A pituitary adenoma classification framework based on an immunohistochemical hormone panel (GH, PRL, ACTH, TSH, FSH/LH, and the α-subunit, among others) was implemented to characterize clinical heterogeneity\u0026mdash;particularly differences in invasiveness\u0026mdash;between functioning and nonfunctioning tumors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In 2022, the World Health Organization (WHO) updated the Classification of Central Nervous System (CNS) Tumors. This revision renamed pituitary adenoma as pituitary neuroendocrine tumors (PitNETs) and incorporated transcription factors (such as PIT1, TPIT, SF1, GATA3, ERα) into the formal classification of PitNET, promoting pituitary neuroendocrine tumor pathological classification toward a more precise, molecular-based framework\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, the updated classification continues to emphasize tumor invasiveness. Accordingly, our center has maintained a focus on invasiveness and differentiation-related features, which represent key phenotypes of aggressive PitNETs. Since 2011, extra-pseudocapsular gross total resection has been adopted\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, with subsequent improvement in clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In 2019, SOX2 was introduced as a stemness-associated marker in studies of plurihormonal tumors to support assessment of multilineage differentiation\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Since 2023, the WHO lineage-based diagnostic criteria have been implemented in parallel with the pre-existing diagnostic system; however, the associations between TF-defined lineage (and related molecular features) and clinical characteristics under the updated framework have not been systematically summarized. Therefore, the present study retrospectively analyzed PitNET cases treated since 2023 to examine the relationship between molecular pathological features in the updated lineage classification and the clinical characteristics and outcomes, with the aim of informing neurosurgical decision-making in routine practice.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eBetween 2023 and 2025, 305 consecutive transsphenoidal resections for pituitary neuroendocrine tumors (PitNETs) were performed at this center using microscopic or endoscopic extra-pseudocapsular techniques. 294 transsphenoidal resections for PitNETs were performed, including 274 primary surgeries and 20 reoperations for recurrent tumors. The primary-surgery cohort (n\u0026thinsp;=\u0026thinsp;274) was used for the main analyses. Recurrent cases (n\u0026thinsp;=\u0026thinsp;20) were analyzed separately as an exploratory cohort. Exclusion criteria for the primary-surgery cohort included incomplete follow-up, postoperative MRI not meeting prespecified assessment standards, and age\u0026thinsp;\u0026lt;\u0026thinsp;18 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Baseline characteristics\u003c/h2\u003e \u003cp\u003eClinical variables included age, sex, maximum tumor diameter (cm), pituitary-axis hormone levels, pseudocapsule integrity, intraoperative dural invasion findings, radiological invasion, tumor texture, intraoperative vascularity, extent of resection, and endocrine outcome (hormonal remission). Pathological variables included immunohistochemical markers specified by the updated classification (PIT1, TPIT, SF1, GATA3, ERα, Ki-67, and SOX2) and histological evidence of dural invasion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 PitNET classification criteria\u003c/h2\u003e \u003cp\u003ePitNETs were classified according to the WHO \u003cem\u003eClassification of Tumors of the Central Nervous System\u003c/em\u003e (5th edition) based on lineage-defining transcription factor (TF) immunoreactivity. Tumors were assigned to the PIT1, TPIT, or SF1 lineage according to expression of PIT1, TPIT, and SF1, respectively: the PIT1 lineage included GH-, PRL-, and TSH-secreting tumors and mixed PIT1-lineage subtypes; the TPIT lineage corresponded to ACTH-secreting tumors; and the SF1 lineage corresponded to gonadotroph tumors (FSH and/or LH positive). TF-negative tumors were defined by absence of PIT1, TPIT, and SF1 immunoreactivity. Tumors expressing TFs from more than one lineage were categorized as multilineage.\u003c/p\u003e \u003cp\u003eGiven prior work on plurihormonal PitNETs, SOX2 was evaluated as a stemness-associated marker\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. SOX2 immunohistochemistry was performed on formalin-fixed, paraffin-embedded sections (4 \u0026micro;m). After deparaffinization and rehydration, heat-induced antigen retrieval was conducted using Tris\u0026ndash;EDTA (pH 9.0). Endogenous peroxidase was blocked, and sections were incubated overnight at 4\u0026deg;C with an anti-SOX2 primary antibody (Abcam; rabbit monoclonal; clone EPR3131; ab92494; 1:100), followed by polymer-based HRP detection with DAB chromogen, hematoxylin counterstaining, and mounting. Only nuclear staining in tumor cells was considered positive. The proportion of SOX2-positive tumor nuclei was scored as follows: 0 (0%); 1 (scattered positive cells); 2 (1\u0026ndash;5%); 3 (5\u0026ndash;10%); 4 (10\u0026ndash;30%); 5 (30\u0026ndash;50%); 6 (\u0026gt;\u0026thinsp;50%). A score\u0026thinsp;\u0026ge;\u0026thinsp;2 (\u0026ge;\u0026thinsp;1%) was defined as SOX2-positive. Scoring was performed independently by two blinded pathologists; discrepancies were resolved by joint review and adjudication by a senior pathologist.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Invasiveness grading\u003c/h2\u003e \u003cp\u003e Tumor invasiveness was evaluated using a three-domain framework (radiological, surgical, and histological evidence) according to Lu et al. Radiological invasiveness was assessed on coronal gadolinium-enhanced T1-weighted MRI and was defined by any imaging evidence of extrasellar invasive growth, including (i) cavernous sinus invasion, graded by the Knosp system (Knosp 3\u0026ndash;4); (ii) suprasellar invasion beyond the diaphragma sellae; and/or (iii) invasion/extension into the sphenoid sinus. Surgical invasiveness was defined as macroscopic invasive or destructive tumor behavior observed under direct endoscopic/microscopic visualization, including invasion into the cavernous sinus compartment (beyond the medial wall) and/or destructive extension beyond the sella (e.g., invasion into the sphenoid sinus and/or involvement/disruption of the sellar diaphragm). Histological invasiveness was defined as microscopic infiltration of, and/or destructive invasion into, the basal sellar dura (endosteum) by tumor cells on hematoxylin\u0026ndash;eosin\u0026ndash;stained sections. Based on these three criteria, tumors were assigned an invasiveness grade as follows: grade 0, none of the three criteria present; grade 1, any one criterion present; grade 2, any two criteria present; and grade 3, all three criteria present\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Extent of resection and hormonal assessment\u003c/h2\u003e \u003cp\u003eTumor size was measured on coronal gadolinium-enhanced T1-weighted MRI, using the maximal diameter. Postoperative contrast-enhanced MRI was routinely performed at approximately 3 months after surgery (8\u0026ndash;12 weeks) to assess residual tumor and extent of resection. Gross total resection (GTR) was defined as absence of residual enhancing tumor on postoperative MRI. Near-total resection (NTR) was defined as suspicious residual enhancement or residual tumor volume\u0026thinsp;\u0026lt;\u0026thinsp;10% of the initial volume. Subtotal resection (STR) was defined as residual tumor volume\u0026thinsp;\u0026gt;\u0026thinsp;10% of the initial volume\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Pituitary-axis hormone levels were assessed within 7 days preoperatively and on postoperative day 1 to characterize perioperative endocrine status and early biochemical changes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eanalyses were performed using R (version 4.2.0). Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range [IQR]), as appropriate. Two-group comparisons used Welch\u0026rsquo;s t-test or the Wilcoxon rank-sum test. Comparisons across three or more groups used one-way analysis of variance (ANOVA) or the Kruskal\u0026ndash;Wallis test. Categorical variables are presented as n (%) and compared using Pearson\u0026rsquo;s χ\u0026sup2; test; Fisher\u0026rsquo;s exact test was applied to 2\u0026times;2 tables when expected counts were \u0026lt;\u0026thinsp;5. For multi-category contingency tables with sparse expected counts, P values were estimated using Monte Carlo χ\u0026sup2; simulations (two-sided; 10,000 permutations); additional Monte Carlo permutation tests were performed for selected overall comparisons, as specified in the table notes. Ordinal outcomes (e.g., invasiveness grade and Knosp grade) were compared using the two-sided Mann\u0026ndash;Whitney U test.\u003c/p\u003e \u003cp\u003eTo evaluate predictors of GTR (resection grade 2), four prespecified hierarchical multivariable logistic regression models were fitted. Model 1 included age, sex, maximum tumor diameter, proliferative status (Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;3% and/or P53 positivity), and imaging invasiveness. Model 2 replaced imaging invasiveness with invasiveness grade\u0026thinsp;\u0026ge;\u0026thinsp;2. Model 3 additionally included pseudocapsule integrity and intraoperative vascularity, and Model 4 further included SOX2 status. Results are reported as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Discrimination was assessed using receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC); AUCs were compared using DeLong\u0026rsquo;s test, and nested models were compared using likelihood ratio tests (LRTs). All tests were two-sided, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Clinical characteristics of patients\u003c/h2\u003e \u003cp\u003eAmong 274 PitNETs, 74.7% of tumors previously classified as hormone-immunonegative under the former diagnostic scheme received a definitive TF-defined lineage assignment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Lineage distribution was as follows: SF1-only (n\u0026thinsp;=\u0026thinsp;95), PIT1-only (n\u0026thinsp;=\u0026thinsp;60), TPIT-only (n\u0026thinsp;=\u0026thinsp;53), PIT1\u0026thinsp;+\u0026thinsp;SF1 (n\u0026thinsp;=\u0026thinsp;32), TF-negative (n\u0026thinsp;=\u0026thinsp;22), and rare mixed lineages (SF1\u0026thinsp;+\u0026thinsp;TPIT, n\u0026thinsp;=\u0026thinsp;7; PIT1\u0026thinsp;+\u0026thinsp;TPIT, n\u0026thinsp;=\u0026thinsp;3; triple-lineage, n\u0026thinsp;=\u0026thinsp;2). Sex distribution was balanced (female, 136/274; male, 138/274). Age and maximum tumor diameter differed across lineages (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with PIT1-only tumors were younger than those with SF1-only tumors (q\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and PIT1-only tumors were smaller than SF1-only and TF-negative tumors (both q\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Clinical manifestations linked to hormone axes demonstrated lineage-related enrichment: acral/facial changes were more frequent in PIT1-related lineages, whereas centripetal obesity was more common in the TPIT-only lineage (P\u0026thinsp;=\u0026thinsp;0.008) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table\u0026nbsp;1).\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\u003eDemographic and clinical characteristics stratified by transcription factor lineage.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSF1-only (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIT1-only (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTPIT-only (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePIT1\u0026thinsp;+\u0026thinsp;SF1 (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTF-negative (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSF1\u0026thinsp;+\u0026thinsp;TPIT (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePIT1\u0026thinsp;+\u0026thinsp;TPIT (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTriple (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;274)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP value (Monte Carlo)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (63.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (81.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (56.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e136 (49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (74.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e138 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.3\u0026thinsp;\u0026plusmn;\u0026thinsp;21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor maximum diameter (cm), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical presentation\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (40.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDizziness, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNausea/vomiting, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecreased visual acuity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e104 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlurred vision, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidental detection on routine examination, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical findings\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisual field defect, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (40.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePtosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcral enlargement, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoarsening of facial features, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentripetal obesity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual irregularity/amenorrhea, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e42 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalactorrhea, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexual dysfunction, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eBaseline demographics, tumor maximum diameter (cm), clinical symptoms, and physical examination findings are summarized across transcription factor (TF)\u0026ndash;defined lineage categories. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and categorical variables as n (%). P values were obtained using Monte Carlo permutation tests (two-sided; 10,000 permutations) for overall comparisons across lineage groups.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Invasive characteristics of pathological diagnosis in different lineages\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. PIT1-GH-PRL has stronger invasiveness\u003c/h2\u003e \u003cp\u003eTumor invasiveness was assessed using our center\u0026rsquo;s invasiveness grading system and Knosp grading. The results showed that 95.9% (47/49) of tumors with Knosp\u0026thinsp;\u0026ge;\u0026thinsp;3 were labeled as invasiveness grade\u0026thinsp;\u0026ge;\u0026thinsp;2. Among tumors with invasiveness grade\u0026thinsp;\u0026ge;\u0026thinsp;2, 66.2% (92/139) had Knosp\u0026thinsp;\u0026lt;\u0026thinsp;3 (Supplementary Table\u0026nbsp;2). Identification of invasiveness in large tumors was consistent with Knosp, and identification of invasiveness in small tumors was more accurate (Supplementary Table\u0026nbsp;3). When stratified by TF-defined lineage, TF-negative and multilineage tumors showed higher rates of invasiveness (grade\u0026thinsp;\u0026ge;\u0026thinsp;2: 54.5% vs 49.5%) and higher invasiveness grades than single-lineage tumors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Table\u0026nbsp;4). Among the six major hormone-defined subtypes, the GH\u0026ndash;PRL group exhibited the highest proportion of Knosp\u0026thinsp;\u0026ge;\u0026thinsp;3 (29.4%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;B; Supplementary Table\u0026nbsp;5). Consistently, the PIT1\u0026ndash;GH/PRL subgroup showed a higher invasiveness grade compared with other lineages (P\u0026thinsp;=\u0026thinsp;0.026) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Within the TPIT\u0026thinsp;+\u0026thinsp;cohort (densely granulated, n\u0026thinsp;=\u0026thinsp;54; sparsely granulated, n\u0026thinsp;=\u0026thinsp;7), sparsely granulated tumors demonstrated a right-shift toward higher invasiveness and Knosp grades (invasiveness grade, P\u0026thinsp;=\u0026thinsp;0.039; Knosp, P\u0026thinsp;=\u0026thinsp;0.011) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u0026ndash;D), whereas no analogous difference was observed in PIT1-lineage tumors (Supplementary Fig.\u0026nbsp;1A\u0026ndash;B).\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\u003eDistribution of invasiveness grades across lineage types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLineage type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrade 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGrade 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value (Monte Carlo)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF1-only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIT1-only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPIT-only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIT1\u0026thinsp;+\u0026thinsp;SF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTF-negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF1\u0026thinsp;+\u0026thinsp;TPIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIT1\u0026thinsp;+\u0026thinsp;TPIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eThe distribution of invasiveness grade (0\u0026ndash;3) is shown for each TF lineage category. Values are presented as n (row %). P values were calculated using a Monte Carlo χ\u0026sup2; test (two-sided; 10,000 permutations).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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\u003eInvasiveness (grade\u0026thinsp;\u0026ge;\u0026thinsp;2) and Knosp status (\u0026ge;\u0026thinsp;3) by lineage subgroup\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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=\"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 \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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSF1-only (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPIT-only (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePIT1\u0026thinsp;+\u0026thinsp;SF1 (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTF-negative (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSF1\u0026thinsp;+\u0026thinsp;TPIT (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePIT1\u0026thinsp;+\u0026thinsp;TPIT (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTriple (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePIT1-GHPRL (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePIT1-only (non\u0026ndash;GH-PRL) (n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP value (MC χ\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasiveness grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvasiveness grade\u0026thinsp;\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31 (58.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e30 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvasiveness grade\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52 (54.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18 (56.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11 (91.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e18 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnosp grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnosp\u0026thinsp;\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e225 (82.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80 (84.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45 (84.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25 (78.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14 (63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e42 (87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnosp\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eThe proportions of tumors with invasiveness grade\u0026thinsp;\u0026ge;\u0026thinsp;2 (vs\u0026thinsp;\u0026lt;\u0026thinsp;2) and with Knosp grade\u0026thinsp;\u0026ge;\u0026thinsp;3 (vs\u0026thinsp;\u0026lt;\u0026thinsp;3) are summarized across lineage subgroups. Values are presented as n (% within column). P values were calculated using a Monte Carlo χ\u0026sup2; test (two-sided; 10,000 permutations).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Sankey diagram linking hormone-defined subtypes with transcription factor (TF)\u0026ndash;defined lineages. Nodes on the left represent TF-defined lineages and nodes on the right represent hormone-defined subtypes; numbers adjacent to nodes indicate tumor counts (n). Band width is proportional to the number of tumors within each TF\u0026ndash;hormone combination. Percentages shown on bands indicate the proportion of each downstream hormone-defined subtype within a given TF-defined lineage. Bands are color-coded by TF-defined lineage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Tumor invasiveness is associated with hard texture and rich vascularity\u003c/h2\u003e \u003cp\u003eBased on established \u0026ldquo;invasive/high-risk PitNET\u0026rdquo; features\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, variables potentially associated with invasiveness were examined, including markers of proliferative or stemness-related activity (SOX2, Ki-67, P53), integrity of local anatomical barriers (pseudocapsule integrity), and intraoperative characteristics (tumor texture and vascularity) (Supplementary Table\u0026nbsp;6). In TF-defined lineage groups with n\u0026thinsp;\u0026ge;\u0026thinsp;10, TF-negative tumors were more frequently hypervascular (P\u0026thinsp;=\u0026thinsp;0.002) and more often had a firm texture (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, stratification by hormone-defined subtype showed only borderline differences in texture and vascularity (texture, P\u0026thinsp;=\u0026thinsp;0.051; vascularity, P\u0026thinsp;=\u0026thinsp;0.050) (Supplementary Table\u0026nbsp;7).\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\u003eBiomarkers and invasiveness indices across lineage types.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"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 \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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSF1-only (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePIT1-only (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTPIT-only (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePIT1\u0026thinsp;+\u0026thinsp;SF1 (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTF-negative (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSF1\u0026thinsp;+\u0026thinsp;TPIT (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePIT1\u0026thinsp;+\u0026thinsp;TPIT (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTriple (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP value (MC χ\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOX2 negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e232 (84.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81 (85.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52 (86.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44 (83.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20 (90.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOX2 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapsule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo capsule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial/Intact capsule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211 (77.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77 (81.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46 (76.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25 (78.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTexture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHard texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoft texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e234 (85.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84 (88.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47 (78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49 (92.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17 (77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor vascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e242 (88.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83 (87.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59 (98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44 (83.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15 (68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRich vascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKi-67\u0026thinsp;\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184 (67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69 (72.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39 (65.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34 (64.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23 (71.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKi-67\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMutant (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWild-type (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256 (93.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88 (92.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50 (94.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30 (93.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19 (86.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasiveness grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvasiveness grade\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52 (54.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18 (56.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvasiveness grade\u0026thinsp;\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31 (58.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnosp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnosp\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnosp\u0026thinsp;\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e225 (82.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80 (84.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45 (84.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25 (78.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14 (63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eNote.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eSOX2 status, pseudocapsule integrity, tumor texture, intraoperative vascularity, Ki-67 category, P53 status, and invasiveness indices (invasiveness grade\u0026thinsp;\u0026ge;\u0026thinsp;2; Knosp grade\u0026thinsp;\u0026ge;\u0026thinsp;3) are summarized across TF-defined lineage categories. Values are presented as n (% within column). P values were calculated using a Monte Carlo χ\u0026sup2; test (two-sided; 10,000 permutations).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Invasiveness across tumor subtypes and lineages. (A\u0026ndash;B) Distribution of invasiveness grade (0\u0026ndash;3) shown by median (dot) and interquartile range (IQR; horizontal line, 25th\u0026ndash;75th percentiles); sample size is shown in parentheses. (A) Stratified by hormone-defined subtype. (B) Stratified by TF-defined lineage. Overall distributions were compared using the Kruskal\u0026ndash;Wallis test; P values are shown in panel titles. (C\u0026ndash;D) Cumulative distribution curves stratified by granulation pattern within the TPIT-related cohort (TPIT-only, SF1\u0026thinsp;+\u0026thinsp;TPIT, PIT1\u0026thinsp;+\u0026thinsp;TPIT, and triple-lineage). (C) Invasiveness grade (0\u0026ndash;3). The sparsely granulated group shows higher cumulative proportions at severe-grade thresholds; distributions were compared using a two-sided Mann\u0026ndash;Whitney U test (P\u0026thinsp;=\u0026thinsp;0.039). (D) Knosp grade (0\u0026ndash;4); distributions were compared using a two-sided Mann\u0026ndash;Whitney U test (P\u0026thinsp;=\u0026thinsp;0.011). Densely and sparsely granulated tumors are indicated in the legend.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. SOX2-positive expression is higher in multilineage and recurrent patients\u003c/h2\u003e \u003cp\u003eOverall, SOX2 positivity was observed in 15.3% of tumors. SOX2 positivity was higher in multilineage tumors than in single-lineage tumors (20.5% vs 14.9%) (Supplementary Table\u0026nbsp;8). Within single-lineage tumors, the PIT1\u0026ndash;GH/PRL subgroup showed the highest SOX2 positivity rate, although the between-lineage difference did not reach statistical significance (P\u0026thinsp;=\u0026thinsp;0.089) (Supplementary Table\u0026nbsp;8). SOX2-positive tumors were associated with higher preoperative ACTH and GH levels (Supplementary Table\u0026nbsp;9). Among recurrent cases (n\u0026thinsp;=\u0026thinsp;20), SOX2 positivity was 20.0% (Supplementary Table\u0026nbsp;10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Risk factors affecting gross total resection\u003c/h2\u003e \u003cp\u003eIn univariable analyses, non-GTR was associated with larger maximum tumor diameter, higher proliferative activity (greater proportion with Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;3%), absence of a pseudocapsule, more advanced invasiveness (higher proportions of invasiveness grade\u0026thinsp;\u0026ge;\u0026thinsp;2 and Knosp\u0026thinsp;\u0026ge;\u0026thinsp;3), and hypervascularity (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). GTR rates differed across lineages, with the lowest rates observed in the PIT1\u0026ndash;GH/PRL subgroup and TF-negative tumors (58.3% and 59.1%, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of factors associated with gross total resection (GTR).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-GTR (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTR (n\u0026thinsp;=\u0026thinsp;199)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;274)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elineage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIT1-GH-PRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMonte Carlo χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIT1-only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIT1\u0026thinsp;+\u0026thinsp;SF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIT1\u0026thinsp;+\u0026thinsp;TPIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSF1\u0026thinsp;+\u0026thinsp;TPIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSF1-only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTF-negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTPIT-only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTriple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHormone type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMonte Carlo χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGH-PRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGonadotroph\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64 (23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-functioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (37.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlurihormonal-other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOX2_group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOX2 negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (88.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166 (83.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e232 (84.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePearson χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOX2 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67_group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKi67\u0026thinsp;\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (48.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e184 (67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePearson χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKi67\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (52.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapsule_group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo capsule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePearson χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial/Intact capsule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (54.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e170 (85.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e211 (77.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTexture_group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoft texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (81.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173 (86.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e234 (85.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePearson χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHard texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular_group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRich vascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePearson χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor vascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56 (74.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e242 (88.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWild-type (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (93.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e256 (93.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFisher\u0026rsquo;s exact\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMutant (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasiveness grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePearson χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64 (85.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e139 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnosp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43 (57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e182 (91.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e225 (82.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePearson χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGranulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDensely granulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55 (73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e151 (75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e206 (75.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePearson χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-densely granulated (sparsely granulated/negative)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e136 (49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePearson χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (54.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWelch\u0026rsquo;s t-test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor maximum diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWelch\u0026rsquo;s t-test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eUnivariable associations between candidate clinical/pathological factors and gross total resection (GTR). Values are shown as n (% within column) for categorical variables and as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables. P values are two-sided, and the statistical test used for each variable is indicated in the \u0026ldquo;Test\u0026rdquo; column (Monte Carlo χ\u0026sup2; with 10,000 permutations for multi-category tables; Pearson\u0026rsquo;s χ\u0026sup2; or Fisher\u0026rsquo;s exact test for 2\u0026times;2 tables, as appropriate; Welch\u0026rsquo;s t-test for continuous variables).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn multivariable analyses, four prespecified hierarchical logistic regression models were evaluated for prediction of GTR. Referring to the Trouillas stratification concept of \u0026ldquo;invasiveness\u0026thinsp;+\u0026thinsp;proliferative activity\u0026rdquo;, the AUC of Model 1 (age, sex, tumor size, proliferation [Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;3% or P53 positive] and imaging invasion) was 0.795. After Model 2 replaced the imaging invasion variable with invasiveness grade (\u0026ge;\u0026thinsp;2), discrimination improved to AUC\u0026thinsp;=\u0026thinsp;0.828. Adding pseudocapsule integrity and vascularity (Model 3) further improved discrimination (AUC\u0026thinsp;=\u0026thinsp;0.866) and model fit (LRT: LR χ\u0026sup2;[df\u0026thinsp;=\u0026thinsp;2]\u0026thinsp;=\u0026thinsp;27.773, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Addition of SOX2 status (Model 4) did not improve discrimination (AUC\u0026thinsp;=\u0026thinsp;0.866) and did not significantly improve model fit (LRT: LR χ\u0026sup2;[df\u0026thinsp;=\u0026thinsp;1]\u0026thinsp;=\u0026thinsp;0.031, P\u0026thinsp;=\u0026thinsp;0.860) (Supplementary Fig.\u0026nbsp;3A).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Receiver operating characteristic (ROC) curves for prespecified multivariable logistic regression models predicting gross total resection (GTR; yes/no). Model 1 included age, sex, maximum tumor diameter, proliferative status (Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;3% and/or P53 positivity), and imaging invasiveness. Model 2 replaced imaging invasiveness with invasiveness grade\u0026thinsp;\u0026ge;\u0026thinsp;2. Model 3 additionally included pseudocapsule integrity (partial/intact vs absent) and intraoperative vascularity (rich vs poor). The diagonal dashed line indicates chance-level discrimination.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith advances in molecular pathology, cell biology, and epigenetics, the World Health Organization (WHO) updated its Classification of Central Nervous System (CNS) Tumors in 2022. This revision not only recommended renaming pituitary adenoma as pituitary neuroendocrine tumors (pituitary neuroendocrine tumors, PitNETs), but also incorporated transcription factors defining pituitary cell lineages (such as PIT1, TPIT, SF1, GATA3, ERα) into the formal classification of PitNET \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Consistent with prior reports, TF-defined lineage assignment improves diagnostic attribution, particularly for tumors previously labeled as nonfunctioning pituitary adenomas. In the present cohort, 74.7% of tumors that were hormone-immunonegative under the former diagnostic scheme received a definitive lineage assignment, aligning with published trends\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing the invasiveness grading system applied at this center, invasiveness classification showed high concordance with Knosp grading for larger tumors, while identifying additional invasive tumors among smaller lesions. When stratified by hormone-defined subtype, GH\u0026ndash;PRL tumors most frequently exhibited high invasiveness grades. Integrating TF-defined lineage further indicated that the PIT1\u0026ndash;GH/PRL subgroup tended to show greater invasiveness than other lineages. Moreover, within the TPIT\u0026thinsp;+\u0026thinsp;lineage, the sparsely granulated subtype was associated with higher invasiveness grades and higher Knosp grades. Recent evidence indicates that silent corticotroph adenomas (SCAs) have higher rates of cavernous sinus invasion, lower gross total resection (GTR) rates, and increased recurrence risk\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Together, these observations underscore substantial heterogeneity even within the same lineage and support refined risk stratification within TF-defined categories\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMultilineage tumors demonstrated higher invasiveness rates than single-lineage tumors, consistent with previous studies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,163,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Importantly, \u0026ldquo;plurihormonal\u0026rdquo; and \u0026ldquo;multilineage\u0026rdquo; are not interchangeable: under the updated classification, plurihormonal tumors include both multilineage plurihormonal PitNETs and single-lineage plurihormonal PitNETs (e.g., GH/PRL) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Previously, our center found that 33.3% of plurihormonal adenoma patients were SOX2-positive\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e; in this study, overall 15.8% of tumors were SOX2-positive, with higher positivity in multilineage (n\u0026thinsp;\u0026gt;\u0026thinsp;5). Notably, SOX2 positivity was highest in PIT1\u0026ndash;GH/PRL tumors (41.7%), which also showed a relatively low GTR rate (58.3%); these tumors were commonly classified as plurihormonal secretory tumors under the former framework. SOX2 positivity was associated with higher preoperative hormone levels, while no independent association between SOX2 status and GTR was identified in multivariable modeling; however, SOX2 positivity appeared enriched among recurrent cases. Given ongoing inconsistency in the literature regarding associations between SOX2, invasiveness, and prognosis, further studies are warranted to clarify the prognostic significance of SOX2\u0026mdash;particularly in plurihormonal PitNETs\u003csup\u003e17 18\u003c/sup\u003e.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe 2022 WHO TF-defined lineage classification improves lineage attribution and facilitates interpretation of PitNET differentiation and molecular pathology. Multilineage PitNETs show a tendency toward greater invasiveness, and the PIT1\u0026ndash;GH/PRL subgroup demonstrates more aggressive invasive features together with the highest SOX2 positivity. Although SOX2 was not independently associated with GTR in the current models, its enrichment in plurihormonal and recurrent tumors suggests potential prognostic relevance that warrants validation in larger cohorts with longer follow-up.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003e This study was performed in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board (Ethics Committee) of Tongji Hospital, Tongji Medical College (No. TJ-IRB20220325). Data were anonymized prior to analysis to protect patient privacy.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e The requirement for informed consent to participate was waived by the Institutional Review Board due to the retrospective nature of the cohort study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 82173136).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: X.L., K.Z., and T.L.; Methodology: K.Z.; Software: H.L., L.L.; Validation: H.L., L.L.; Formal analysis: X.L., K.Z.; Investigation: X.L., K.Z.; Resources: T.L., C.K.; Data curation: X.L., K.Z.; Writing\u0026mdash;original draft: X.W., J.C., J.W.; Writing\u0026mdash;review \u0026amp; editing: X.W., J.W., T.L.; Visualization: X.L.; Supervision: J.W.; Project administration: T.L.; Funding acquisition: T.L. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are included in the article. Further inquiries are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSaeger W in \u003cem\u003eModern Neurosurgery of Meningiomas and Pituitary Adenomas\u003c/em\u003e. (ed Fahlbusch R ) 1\u0026ndash;3 (Springer Vienna)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeger W, Wilczak W, L\u0026uuml;decke DK, Buchfelder M, Fahlbusch R (2003) Hormone markers in pituitary adenomas: changes within last decade resulting from improved method. 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Neurosurgery 96:611\u0026ndash;621. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1227/neu.0000000000003142\u003c/span\u003e\u003cspan address=\"10.1227/neu.0000000000003142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDottermusch M et al (2024) Pituitary neuroendocrine tumors with PIT1/SF1 co-expression show distinct clinicopathological and molecular features. Acta Neuropathol 147:16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00401-024-02686-1\u003c/span\u003e\u003cspan address=\"10.1007/s00401-024-02686-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X et al (2024) Clinical and Pathological Features of Pit1/SF1 Multilineage Pituitary Neuroendocrine Tumor. Neurosurgery. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1227/neu.0000000000002846\u003c/span\u003e\u003cspan address=\"10.1227/neu.0000000000002846\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenders NF et al (2024) Pituitary tumours without distinct lineage differentiation express stem cell marker SOX2. \u003cem\u003ePituitary\u003c/em\u003e 27, 248\u0026ndash;258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11102-024-01385-0\u003c/span\u003e\u003cspan address=\"10.1007/s11102-024-01385-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasaran R et al THE EXPRESSION OF STEM CELL MARKERS (CD133, NESTIN, OCT4, SOX2) IN INVASIVE PITUITARY ADENOMAS. \u003cem\u003eActa endocrinologica (Bucharest, Romania\u003c/em\u003e: (2005)) 16, 303\u0026ndash;310, doi:) 16, 303\u0026ndash;310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4183/aeb.2020.303\u003c/span\u003e\u003cspan address=\"10.4183/aeb.2020.303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020)\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 Tumor (PitNET), Extra-Pseudocapsular Resection, Tumor Invasiveness, SOX2","lastPublishedDoi":"10.21203/rs.3.rs-8561070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8561070/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo evaluate associations between transcription factor (TF)\u0026ndash;defined molecular lineage and the clinical characteristics of pituitary neuroendocrine tumors (PitNETs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort analysis was performed in 274 patients undergoing extra-pseudocapsular transsphenoidal resection, tumors were classified by TF-defined lineage and invasiveness (0\u0026ndash;3) and Knosp score. Group differences were tested with appropriate parametric/nonparametric and χ\u0026sup2;/Fisher\u0026rsquo;s exact methods. Predictors of GTR were examined using prespecified hierarchical multivariable logistic regression, and model discrimination compared using ROC/AUC with LRT and DeLong testing.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTF-defined lineage classification improved diagnostic precision. Among tumors previously classified as nonfunctioning adenomas, 74.7% received a definitive lineage assignment. TF-negative and multilineage tumors showed higher invasiveness rates and higher invasiveness grades than single-lineage tumors. The PIT1\u0026ndash;GH/PRL subgroup had the highest prevalence and grade of invasiveness; SOX2 positivity was most frequent in this subgroup (41.7%), which also exhibited the lowest GTR rate (58.3%). SOX2-positive tumors were associated with higher preoperative ACTH and GH levels. SOX2 positivity was more common in multilineage tumors (20.5%) and recurrent cases (20.0%). Compared with Trouillas grading alone, incorporation of invasiveness grade and intraoperative features (capsule status and vascularity) improved discrimination for predicting GTR (AUC, 0.866 vs 0.795; ΔAUC\u0026thinsp;=\u0026thinsp;0.071; DeLong 95% CI, 0.022\u0026ndash;0.120; P\u0026thinsp;=\u0026thinsp;0.0047).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe 2022 WHO TF-defined lineage system improves diagnostic precision and facilitates interpretation of PitNET differentiation and molecular pathology. Tumors in the PIT1\u0026ndash;GH/PRL subgroup demonstrate more aggressive invasive features. SOX2 positivity has the highest proportion in multilineage tumors and in recurrent surgical patients, and close follow-up is needed.\u003c/p\u003e","manuscriptTitle":"Clinicopathologic and Molecular Characteristics of Pituitary Neuroendocrine Tumors (PitNETs) Treated with Extra-Pseudocapsule Resection and Their Clinical Implications: A Single-Center Experience with 274 Cases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 09:02:25","doi":"10.21203/rs.3.rs-8561070/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":"4645c13b-4956-4fa4-a94e-54e68a3e8ea2","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-24T12:54:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 09:02:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8561070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8561070","identity":"rs-8561070","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}