{"paper_id":"27c4e47d-6b64-4825-84ea-c9aba5830ae1","body_text":"Ex Vivo Drug Sensitivity in Patient-derived 3D Cultures in Acromegaly and its Association with Clinical Predictors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Ex Vivo Drug Sensitivity in Patient-derived 3D Cultures in Acromegaly and its Association with Clinical Predictors Yasutaka Tsujimoto¹, Atsushi Ishida², Frederico Gaia Costa Silva, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9461574/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 8 You are reading this latest preprint version Abstract Purpose: Personalized therapy in acromegaly is limited by interindividual variability in drug responses and the lack of robust markers predicting tumor shrinkage, rather than biochemical control alone. To test whether ex vivo drug-induced viability changes in patient-derived 3D cultures (Pd3D) of GH–secreting pituitary adenomas reflect tumor cell-intrinsic pharmacological sensitivity and align with established clinical predictors. Methods: Spheroid-based Pd3D cultures were established from 27 patients with acromegaly. Cultures were exposed to octreotide, cabergoline, pasireotide, or vehicle control. We assessed cell viability changes; sample-level responder status (viability reduction vs vehicle, p < 0.05); and associations between responder status and known predictive markers, including clinical characteristics, MRI findings, dynamic drug tests, and pathological features. In 6 cases, AI-based digital image analysis quantified pre- and post-treatment SSTR2 expression in liquid-based cytology (LBC). Results: All agents modestly reduced median cell viability (84–86%, p < 0.05), with responder rates of 33–40%. Concordance with established predictors was observed: octreotide responders correlated with T2 hypointensity (88% vs 44%, p = 0.04); cabergoline with positive bromocriptine tests (100% vs 45%, p = 0.03); and pasireotide with sparsely granulated patterns (64% vs 19%, p = 0.04). AI-based dynamic analysis demonstrated that ex vivo responders showed relatively stable SSTR2 expression after treatment, whereas nonresponders exhibited marked depletion. Conclusion: Spheroid-based Pd3D ex vivo viability assays revealed modest but significant cohort-level effects and sample-level concordance with clinical predictors, supporting the platform's validity. Additionally, AI-based quantification of SSTR2 dynamics captured functional receptor shifts. Acromegaly Patient-derived 3D cultures Somatostatin receptor Digital pathology Precision medicine Figures Figure 1 Figure 2 Introduction Acromegaly imposes a significant disease burden through excess systemic growth hormone (GH)/insulin-like growth factor-1 (IGF-1) and local tumor mass effects [ 1 ]. Although biochemical normalization is essential for improving survival, 40–60% of patients require adjuvant medical therapy post-surgery [ 2 ]. Notably, biochemical control and tumor remission are not always achieved concurrently in clinical practice, and marked interindividual variability in drug response complicates therapeutic decision-making [ 3 ]. This trial-and-error approach places a considerable burden on patients and underscores the need to optimize therapeutic pathways [ 4 – 7 ]. While predictors of biochemical control have been extensively investigated, robust markers that directly forecast tumor shrinkage remain limited [ 8 – 10 ]. Across oncology, three-dimensional (3D) tumor culture systems have gained attention as tools to predict drug sensitivity because they mimic tumor architecture, cell–cell and cell–matrix interactions, and physiologically relevant nutrient and oxygen gradients, thereby providing more representative response readouts [ 11 – 15 ]. In contrast, traditional two-dimensional (2D) primary cultures often suffer from loss of native architecture and functional receptor expression [ 16 ]. Among 3D systems, organoids and spheroids represent two widely used but conceptually distinct models. Organoids typically self-organize and retain stem or progenitor compartments, whereas spheroids generally arise from the aggregation of primary cells or cell lines and lack self-organization. Although organoid models are valued for their higher intra-and inter-assay reproducibility and their ability to support long-term maintenance of lineage programs, their widespread application is constrained by higher cost and longer setup and expansion times. In pituitary adenomas, clinical validation remains limited, as high costs and technically demanding workflows restrict accessibility [ 16 – 19 ]. To address these challenges, we previously established a rapid and cost-conscious spheroid-based patient-derived 3D (Pd3D) cultures system from pituitary adenomas to perform ex vivo drug testing; in a small number of cases, ex vivo readouts from this platform aligned with clinical outcomes, including hormonal response and volumetric tumor shrinkage, although further research is required [ 20 – 22 ]. Although this approach may not fulfill all the rigorous criteria of organoids, it represents a robust tumor spheroid model that preserves tumor-intrinsic drug responsiveness and is well suited for clinical oriented drug screening. In the present study, we applied this Pd3D workflow to a consecutive series of patients with acromegaly. Using Pd3D cultures, we quantified drug-induced changes in cell viability and compared these readouts with established predictors of biochemical response to medical therapy. In addition, we examined the relationship between ex vivo drug responsiveness and somatostatin receptor (SSTR) expression levels assessed in liquid-based cytology (LBC) preparations, which provide a robust platform for immunostaining and ancillary molecular analyses in rare endocrine tumor samples [ 23 ]. To ensure objective quantification and reproducibility, we employed an Artificial Intelligence (AI)-based digital image analysis system [ 24 ]. This integrated approach allowed us to explore whether ex vivo functional assays could offer complementary predictive value beyond conventional hormonal markers. An overview of the study workflow is shown in Fig. 1 a. Materials and Methods Participants and Data Acquisition Between November 2023 and November 2024, 27 consecutive patients with acromegaly who underwent transsphenoidal surgery at Moriyama Memorial Hospital were enrolled, and tumor specimens were collected intraoperatively. Acromegaly was diagnosed according to international guidelines [ 2 ]. Clinical variables were retrospectively extracted from electronic medical records with a focus on previously proposed predictors of pharmacological responses. Extracted data included age, sex, serum GH levels, and age- and sex-adjusted IGF-1 standard-deviation scores (SDS). Additional parameters included basal and nadir GH levels during a 75-g oral glucose tolerance test (OGTT), the timing of the GH nadir, and results of octreotide and bromocriptine tests. The octreotide test was considered positive when GH decreased by ≥ 75% after a subcutaneous 100 µg [ 25 ]. The bromocriptine test was considered positive when GH decreased by ≥ 50% after oral administration of 2.5 mg [ 26 ]. Pituitary MRI variables included maximum tumor diameter, Knosp grade, and qualitative T2-weighted imaging (T2WI) signal intensity. MRI assessments were performed by endocrinologists, neurosurgeons, or radiologists. Histopathology variables, evaluated by an experienced pathologist, included GH immunohistochemistry, Ki-67 labeling index, semi-quantitative immunohistochemistry (IHC) expression scores for somatostatin receptor subtype 2 (SSTR2) and subtype 5 (SSTR5), and granulation pattern (densely or sparsely granulated) [ 27 ]. Owing to the retrospective design, not all clinical, radiological, and histopathological variables were available for every case. Patient-derived 3D (Pd3D) Cultures Spheroid-based Pd3D cultures were established as previously described [ 20 – 22 ]. Freshly resected tumor tissue was immediately placed on ice in phosphate-buffered saline (PBS; Gibco, Waltham, MA, USA) and enzymatically dissociated in Dulbecco’s Modified Eagle Medium (DMEM; Gibco, Waltham, MA, USA) containing 0.3% bovine serum albumin (Sigma-Aldrich, St. Louis, MO, USA), 0.35% collagenase (FUJIFILM Wako, Osaka, Japan), 0.15% hyaluronidase (FUJIFILM Wako, Osaka, Japan), and 0.1% Y-27632 (FUJIFILM Wako, Osaka, Japan). Cells were embedded as domes in growth factor–reduced phenol red–free Matrigel (Corning, NY, USA) in white flat-bottom 96-well plates (Thermo Fisher Scientific, Waltham, MA, USA) at a density of 10,000 cells per well. Matrigel domes were polymerized with the plates inverted at 37°C for 10 min to promote gravitational settling and cell aggregation at the apex of each dome, thereby forming spheroids. Each well then received 50 µL of DMEM supplemented with 10% fetal bovine serum (FBS; Gibco, Waltham, MA, USA) and penicillin–streptomycin (Gibco, Waltham, MA, USA). Plates were incubated at 37°C in 5% CO₂. Drug Exposure and Cell Viability Readout After 24 h of initial culture, the medium was replaced with fresh medium containing either vehicle (DMSO; Nacalai Tesque, Kyoto, Japan; final DMSO ≤ 0.1% [v/v]) or one of the following drugs at 10 nM: octreotide (Selleck Chemicals, Houston, TX, USA), cabergoline (Tocris, Bristol, UK), or pasireotide (ChemScene, Monmouth Junction, NJ, USA). Each condition was tested in three to five replicate wells per sample, depending on cell yield. A single medium refresh was performed 72 h after drug addition, and cultures were maintained for 7 d from treatment initiation. Representative images at this endpoint are shown in Online Resource 1. Cell viability was quantified with the RealTime-Glo™ MT Cell Viability Assay (Promega, Madison, WI, USA) according to the manufacturer’s instructions. Luminescence was recorded immediately before drug exposure (baseline) and at the end of treatment using an EnSpire Multimode Plate Reader (PerkinElmer, Waltham, MA, USA). For each well, endpoint luminescence was divided by its corresponding baseline value and multiplied by 100 to yield relative viability (%). For each treatment arm within a patient, the median relative viability (%) across replicate wells was used for analysis. At the patient-sample-level, a drug was classified as yielding an ex vivo response (“responder”) if the median relative viability (%) in the treatment arm was significantly lower than that in the contemporaneous vehicle control at the endpoint (two-sided p < 0.05); otherwise, the sample was considered a “nonresponder.” Immunocytochemistry of Dissociated Cells from Pd3D Using the BD SurePath™ Basal cell suspensions were obtained immediately after enzymatic dissociation. Following vehicle or octreotide treatment, cells were harvested from Matrigel domes using Cell Recovery Solution (Corning, NY, USA) according to the manufacturer’s instructions to generate cell suspensions. All specimens were processed under identical conditions using the BD SurePath™ liquid-based cytology system (BD Biosciences, Franklin Lakes, NJ, USA). Freshly collected cell suspensions were transferred into BD CytoRich™ Red preservative solution (BD Biosciences, Franklin Lakes, NJ, USA) and fixed at room temperature for at least 30 min in accordance with the manufacturer’s protocol. Fixed samples were centrifuged at 2,000 rpm for 3 min, the supernatant was discarded, and the resulting cell pellet was gently resuspended. An appropriate volume of the suspension was loaded into a BD SurePath settling chamber mounted on a BD SurePath-precoated glass slide (BD Biosciences, Franklin Lakes, NJ, USA). After allowing the cells to settle for 3 min, the chamber was removed and the slide was immediately transferred to 95% ethanol for additional fixation. Immunocytochemistry was performed using a BOND-III automated immunostainer (Leica Biosystems, Nussloch, Germany) according to the manufacturer’s default immunohistochemistry program. Heat-induced antigen retrieval was performed using Epitope Retrieval Solution 1 (ER1; pH 6.0; Leica Biosystems, Nussloch, Germany) for 10 min. Because BD SurePath cytology slides were used rather than paraffin-embedded sections, deparaffinization steps were omitted. Primary antibodies for somatostatin receptor included rabbit monoclonal anti-SSTR2 (clone UMB1, Abcam, Cat# ab134152, RRID:AB_2737601, Cambridge, UK; 1:1,500) and rabbit monoclonal anti-SSTR5 (clone UMB4, Abcam, Cat# ab109495, RRID:AB_10859946, Cambridge, UK; 1:20). Antibodies were applied according to the manufacturer’s datasheets, and a polymer-based horseradish peroxidase detection system supplied by the instrument’s manufacturer was used. Identical antibody panel and staining conditions were applied to basal, vehicle-treated, and octreotide-treated samples. Digital Image Analysis for SSTR Expression Quantification In a pilot subgroup, cytological whole-slide images (WSIs) derived from six tumor samples (Samples A–F) were analyzed. For each case, treated specimens were prepared under three conditions: immediately after dissociation (basal), and after 7-day exposure to vehicle or octreotide (10 nM). Digital image analysis was performed using the open-source software QuPath (version 0.5.1) [ 24 ]. To quantify the immunostaining intensity, color deconvolution was applied as a preprocessing step using standard optical density vectors: hematoxylin (nuclear stain) at (0.651, 0.701, 0.290), DAB (0.269, 0.568, 0.778), and residual (0.633, -0.713, 0.302). Tumor cells were identified using a cell detection algorithm with the following parameters: pixel size, 0.5 µm; background radius, 8 µm; median filter radius, 1.5 µm; sigma, 1.8 µm; and threshold, 0.15. The maximum background intensity was set at 2. Detection was restricted to cell areas between 15 µm² and 150 µm², to exclude debris and non-specific aggregates. Automated detections were manually reviewed to remove dust particles, inflammatory cells, and doublets. Regions of interest (ROIs) were adjusted according to cellularity: the central area was analyzed for specimens with high cell density, whereas the entire slide area was evaluated for samples with low cell counts. Cells were classified into four intensity categories based on mean DAB optical density across the entire cell area. In the absence of internal calibration controls for LBC specimens, standard intensity thresholds were utilized, referencing the established criteria for SSTR2 digital analysis [ 28 ]: negative (< 0.2, blue), 1+ (weak; 0.2–0.4, yellow), 2+ (moderate; 0.4–0.6, orange), and 3+ (strong; > 0.6, red) (Online Resource 2). Finally, the H-score (range: 0–300) was calculated for each sample using the standard formula: H-score = (1 × % of 1 + cells) + (2 × % of 2 + cells) + (3 × % of 3 + cells) [ 29 ]. For comparison, SSTR2 expression in corresponding formalin-fixed paraffin-embedded (FFPE) tissue sections was evaluated using the Volante score system (score 0–3), as previously described [ 30 ]. Statistical Analysis Continuous or ordinal variables were compared using the Mann–Whitney U test, and categorical variables were compared using Fisher’s exact test. Statistical significance was defined as p < 0.05. Data are presented as median with interquartile ranges (IQRs; 25th–75th percentiles). Analyses were performed using JMP Pro 18 software (SAS Institute Inc., Cary, NC, USA). Results Patient Characteristics Twenty-seven consecutive patients with acromegaly were included in this study (Table 1). The median age at surgery was 46.0 years [36.5–56.0], and 13/27 (48.1%) were female. At baseline, the median serum IGF-1 SDS was + 6.2 [+ 5.4 to + 7.4], and the median serum GH levels were 14.7 ng/mL [5.7–35.6]. An octreotide test was performed in 19 patients, of whom 15/19 (78.9%) met the predefined positivity criterion (≥ 75% GH reduction), with a median suppression of 82.6% [63.9–90.7]. A bromocriptine test was conducted in 18 patients, 12/18 (66.7%) were classified as positive, with a median suppression rate of 69.6% [39.5–88.1]. Table 1 Baseline Clinical Characteristics of the Study Cohort Total number available cases (cases) 27 Baseline characteristics Age at the time of surgery (years) 46 [36.5–56] 27 Sex, female (cases, %) 13 (48%) 27 Basal GH (ng/mL) 14.7 [5.7–35.6] 27 Nadir GH on 75gOGTT (ng/mL) 16 [6.5–33.2] 26 IGF-1 SDS + 6.2 [+ 5.4-+7.4] 27 PRL (ng/mL) 11.9 [7.4–19.1] 25 Dynamic endocrinological test Octreotide suppression test, positive (cases, %) 15 (78%) 19 Octreotide suppression test, suppression rate (%) 82.6 [63.9–90.7] Octreotide suppression test, nadir time (hours) 4 [2–5] Bromocriptine suppression test, positive (cases, %) 12 (66%) 18 Bromocriptine suppression test, suppression rate (%) 69.6 [39.5–88.1] Bromocriptine suppression test, nadir time (hours) 6 [4–6] Pituitary MRI findings 27 Macroadenoma > 10mm (cases, %) 26 (96%) Maximum tumor diameter (mm) 19.5 [15.2–27.7] Knosp grade, invasion (cases, %) 7 (25%) Knosp grade [0/1/2/3/4] (cases) [5/11/4/4/3] MRI T2WI hypointensity (cases, %) 16 (59%) Histological findings 27 Granulated pattern, densely (cases, %) 17 (62%) SSTR2 [0/1/2/3] (cases) [1/1/8/17] SSTR5 [0/1/2/3] (cases) [4/4/15/4] Ki-67 labeling index (%) 0.5 [0.3–1.2] Data are presented as numbers (%) for categorical variables and as medians [25th–75th percentiles] for continuous variables. The numbers of patients with available data for each parameter are listed in the rightmost column. Abbreviations: GH, growth hormone; OGTT, oral glucose tolerance test; IGF-1, insulin-like growth factor-1; SDS, standard deviation score; PRL, prolactin; MRI, magnetic resonance imaging; SSTR, somatostatin receptor. On pituitary MRI, 26/27 (96.3%) patients had macroadenomas (> 10 mm). The median maximum tumor diameter was 19.5 mm [15.2–27.7]. Cavernous sinus invasion (Knosp grade 3–4) was observed in 7/27 (25.9%) patients, and MRI T2WI hypointensity was present in 16/27 (59.3%) patients. Histopathology analysis showed a densely granulated pattern in 17/27 (63.0%). The median Ki-67 labeling index was 0.5% [0.3–1.2]. Semi-quantitative immunohistochemical expression of SSTR2 tended to be higher than that of SSTR5 in the majority of evaluable samples. Data completeness varied across variables owing to the retrospective study design. Overall, the characteristics of this cohort were broadly consistent with those reported in previous studies on acromegaly [31]. Association between known predictive markers and Pd3D cultures responsiveness. Across all 27 samples, all control cultures maintained robust viability from baseline to the endpoint (Online Resource 3). Each tumor sample was classified as an ex vivo responder or nonresponder in the Pd3D culture assay according to the prespecified within-sample criteria described in the Methods section. That is, responders exhibited a significant decrease in cell viability upon drug exposure, whereas nonresponders maintained robust viability. Relative cell viability (%) was consistently lower in responders than in nonresponders for all three agents: octreotide (84% [75–88] vs 100% [93–105], p < 0.01), cabergoline (84% [79–92] vs 94% [86–106], p = 0.04); and pasireotide (86% [85–89] vs 95% [88–100], p = 0.01) (Fig. 1b). The proportion of responders was 9/27 (33%) for octreotide, 9/27 (33%) for cabergoline, and 11/27 (40%) for pasireotide. Octreotide Baseline characteristics, including age, sex, baseline GH level, and IGF-1 SDS, did not differ between the responder and nonresponder. Octreotide test results were also comparable between the two groups in the proportion positive rate, GH suppression magnitude, and time to nadir. On MRI, macroadenomas were present in 8/9 (88%) responders and in all 18/18 (100%) nonresponders. Maximum tumor diameter was 13 mm [13–27 mm] vs 21 mm [17–27] ( p = 0.16), and cavernous sinus invasion (Knosp 3–4) was observed in 3/9 (33%) vs 4/18 (22%) ( p = 0.65). In contrast, MRI T2WI hypointensity, a previously reported marker of responsiveness to first-generation somatostatin receptor ligands (SRLs), was significantly more frequent among responders than among nonresponders (88% vs 44%, p = 0.04). Histopathological features, including granulation pattern and semi-quantitative SSTR2/SSTR5 expression scores, did not differ significantly between the two groups ( p > 0.99, p = 0.11, respectively; Table 2a). Cabergoline Baseline clinical characteristics were similar between cabergoline responders and nonresponders. In contrast, the bromocriptine test was positive in all responders but in less than half of nonresponders (100% vs 45%, p = 0.03), a difference that reached statistical significance. Neither the GH suppression rate nor the time to GH nadir during the bromocriptine test differed between the groups. MRI-derived variables, including tumor size, Knosp grade, and T2WI signal intensity, were comparable between responders and nonresponders, as were granulation patterns (Table 2b). Notably, semi-quantitative SSTR5 expression was significantly higher in responders than in nonresponders ( p = 0.03), whereas SSTR2 expression did not differ between groups ( p = 0.34). Pasireotide Baseline characteristics and MRI findings did not differ between the groups. Tumors with sparsely granulated patterns were more common in responders than in nonresponders (64% vs 19%, p = 0.04), whereas SSTR2/SSTR5 expression scores did not differ between the groups (Table 2c). AI-Assessed Temporal Dynamics of SSTR2 Expression in LBC Specimens An additional six patients with acromegaly were included for whom both ex vivo drug sensitivity assays and AI-based immunocytochemical analysis were performed. The basal SSTR2 H-scores generally corresponded to the diagnostic Volante scores obtained from the corresponding FFPE tissues (Table 3). Following octreotide treatment, distinct quantitative changes in SSTR2 expression were observed between responders and nonresponders. Ex vivo responders (Samples A, B, and C) showed relatively stable SSTR2 expression, in contrast to the marked depletion observed in nonresponders (Fig. 2a). Samples A and C exhibited increased H-scores (+ 30.8% and + 45.9%, respectively), accompanied by a shift in intensity fractions, in which negative (0) cells decreased, whereas 1+ (weak) and 2+ (moderate) positive cells increased (Fig. 2b). Sample B, which had the highest baseline expression, maintained a high proportion of 3+ (strong) cells (approximately 60%) despite a slight increase in negative (0) cells, distinguishing it from the depletion pattern observed in nonresponders (Fig. 2b, Table 3). In contrast, nonresponders (Samples D, E and F) exhibited a marked reduction in H-scores compared to vehicle-treated controls (Fig. 2a). Sample D showed a 60.8% decrease (145.9 to 57.1), characterized by a marked increase in negative 0 cells from 3.2% to 63.5% and a depletion of 3+ (strong) cells (Fig. 4B). Sample E showed a 30.3% reduction in H-score (167.0 to 116.4), with 3+ (strong) cells decreasing from 18.5% to 7.7%. Similarly, Sample F demonstrated a 44.7% reduction in H-score (131.7 to 72.8), with 3+ (strong) cells decreasing from 36.7% to 14.2% (Fig. 2b, Table 3). Table 3 Clinical Characteristics and AI-Based SSTR2 Dynamics in the Pilot Subgroup (n = 6) Sample A B C D E F Status responder responder responder nonresponder nonresponder nonresponder Age (yo) 25 51 45 51 47 51 Sex M M F M F M GH (ng/mL) 13.0 48.1 81.2 3.3 49.8 11.6 IGF-1 (ng/mL) 616 775 896 367 316 797 IGF-1 SDS + 5.5 + 8.1 + 9.8 + 3.9 + 3.7 8.2 MRI Size (mm) 13 21 47 6 13 18 Knosp grade 1 1 2 0 3 3 T2WI intensity Low Low Low Low High High FFPE Volante SSTR2 score 3+ 3+ 2+ 2+ 3+ 2+ LBC H-score Basal 102.8 60.3 32.4 106 150.3 74.8 Vehicle 104.6 245.8 43.6 167 145.9 131.7 Octreotide 136.9 233 63.7 116.4 57.1 72.8 delta H-score (%) 30.8 -5.2 45.9 -30.3 -60.8 -44.7 Status refers to the classification of responders or nonresponders based on ex vivo assays. Sex was denoted as F (female) or M (male). The FFPE SSTR2 score indicated the conventional immunohistochemical grade (Volante score 0–3+) assessed using formalin-fixed paraffin-embedded surgical tissue. H-scores (range, 0–300) were quantified using AI-based digital image analysis of the liquid-based cytology (LBC) specimens. The delta H-score (%) was calculated as the percentage change in the H-score after octreotide treatment relative to that of the vehicle control. GH, growth hormone; IGF-1, insulin-like growth factor-1; SDS, standard deviation score; MRI, magnetic resonance imaging;(T2WI); LBC, liquid-based cytology. Although statistical significance could not be determined owing to the limited sample size, these findings suggest an association between the quantitative temporal dynamics of SSTR2 expression and drug responsiveness. Discussion Using our previously reported simple ex vivo spheroid-based Pd3D culture assay, we evaluated whether tumor-specific cell viability responses to octreotide, cabergoline, and pasireotide reflect clinically meaningful drug responsiveness in a consecutive cohort of patients with acromegaly derived from resected tumors. Although the overall reduction in viability was modest, substantial intertumoral variability was observed, and these responses showed concordance with established clinical response indices. This modest reduction is consistent with the primary pharmacological mechanisms of SRLs and dopamine agonists, which involve inhibition of hormone secretion, cell cycle arrest, and reduction in cell volume, rather than acute massive cytotoxicity. These findings provide initial evidence supporting the validity of this assay and suggest that it captures relative pharmacological susceptibility. Specifically, the significantly lower relative viability in the responder group compared to the nonresponder group confirms that this assay accurately distinguishes tumor-intrinsic drug sensitivity. Pd3D culture systems have been shown to capture patient-specific drug responses, and are increasingly recognized as translational functional assays [ 11 – 15 ]. In particular, cell-viability–based readouts from various patient-derived 3D tumor models have been reported to predict radiographic tumor responses, although discordant results have also been described [ 32 , 33 ]. In pituitary adenomas, adoption and clinical validation of such models remain at an early stage, partly because high costs and technically demanding workflows limit their broad implementation [ 16 – 18 ]. In the present study, we applied a rapid and cost-effective Pd3D platform to a consecutive series of 27 pituitary adenoma specimens. This approach enables parallel within-sample testing of multiple agents with short turnaround time using a simple luminescence-based viability readout. These characteristics support the use of normalized luminescence as a pragmatic pharmacodynamic surrogate, reducing between-sample variability and allowing patient-level classification. The present findings support the rationale for future prospective studies examining whether ex vivo effects correlate with subsequent tumor shrinkage and biochemical remission in the same patients. In acromegaly, pharmacological therapy is essential for residual or recurrent disease and is sometimes selected as primary treatment. Available agents for pituitary tumor reduction include first-generation SRLs (octreotide or lanreotide), dopamine agonists (cabergoline or bromocriptine), and pasireotide [ 1 ]. Responsiveness varied among the agents, highlighting the need for reliable predictive markers. Among them, responses to first-generation SRLs have been the most extensively studied [ 34 , 35 ]. The reported predictors include age, sex, baseline GH and IGF-1 levels [ 36 – 38 ], octreotide test results [ 39 , 40 ], and MRI T2WI signal intensity [ 41 , 42 ]. Notably, T2WI hypointensity often corresponds to a densely granulated pattern and strong SSTR2 expression, and has been associated with favorable response [ 43 ]. For dopamine agonists, low baseline GH or IGF-1 levels and positive bromocriptine test results have been suggested as predictors [ 1 , 26 , 44 ]. Pasireotide has been reported to be effective in patients resistant to first-generation SRLs and may be more effective in tumors with high T2WI signal intensity and, in some studies, a sparsely granulated pattern [ 9 , 45 ]. However, most studies have focused on biochemical outcomes, and predictors of tumor shrinkage remain limited. In clinical practice, biochemical control often, but not invariably, coincides with radiographic response [ 46 ], underscoring the need for approaches that can anticipate both outcomes [ 3 , 9 , 10 ]. In our study, an ex vivo Pd3D culture assay generated sample-level viability readouts for each drug and demonstrated partial concordance with established clinical predictors, including octreotide responsiveness with T2WI hypointensity, cabergoline responsiveness with bromocriptine testing, and pasireotide with a sparsely granulated pattern. At the same time, discordant cases were also observed. This concordance suggests that Pd3D cultures preserve key tumor features relevant to drug responsiveness, supporting the face validity and feasibility of the assay. These features likely included preservation of SSTR2/5 and dopamine receptor (D2R) expression, the histopathological granulation pattern that contributes to the tumor’s T2WI MRI signal, downstream signaling pathways, and the cell—matrix context. Together, these features may underline clinical indicators such as responsiveness in dynamic drug tests. Although the clinical use of these dynamic suppression tests remains controversial, we utilized them as an immediate in vivo biological reference to validate whether our culture system successfully preserved the native functional receptor phenotypes. In addition, the 3D architecture facilitates the re-establishment of native cell—cell and cell—matrix interactions and microenvironmental gradients that influence receptor trafficking and signaling fidelity, features emphasized in contemporary pituitary 3D model work [ 16 ], and may stabilize SSTR/D2R localization and Gi/o-coupled signaling, improving the capture of patient-intrinsic drug sensitivity in our assay. Complementing these viability assays, quantitative analysis of LBC specimens captured pharmacological dynamics of SSTR2 in Pd3D models. This methodology has previously been applied to hormone receptor assessment in breast cancer and immunocytochemical characterization of neuroendocrine carcinomas, supporting its utility as a robust foundation for molecular analysis in endocrine oncology [ 47 – 49 ]. In the present study, the concordance between baseline LBC-derived H-scores and FFPE-based Volante scores supports the validity of this approach as a surrogate for conventional pathology. Furthermore, digital image analysis of SSTR2 expression in FFPE tissues has been shown to correlate strongly with manual pathological scoring, providing an objective basis for receptor quantification [ 28 ]. Our findings in LBC specimens extend this concept to cytological samples. The observed dynamic IHC approach revealed functional shifts—specifically the upregulation or maintenance of high intensity fractions in responders and the depletion of receptor-positive cells in nonresponders—offering a more functional prediction of therapeutic response compared to static evaluation. To our knowledge, this is the first study to demonstrate the feasibility of AI-based quantitative cytometry in capturing drug-induced receptor dynamics in patient-derived pituitary models. By addressing the challenge of low cellular yield inherent in ex vivo assays, this dynamic LBC approach serves as a preliminary and promising exploratory proof-of-concept for integrated functional and pathological assessments, though further large-scale validation is warranted to confirm its predictive value. An additional observation in this dataset was the association between cabergoline responsiveness and higher SSTR5 expression. This finding suggests receptor-level crosstalk as a potential mechanistic link. Hetero-oligomerization between D2R and SSTR5 can potentiate Gi/o signaling and alter desensitization/internalization, offering a rationale for enhanced dopamine agonist efficacy in SSTR5-high tumors [ 50 ]. Thus, this ex vivo assay is a promising translational tool for identifying previously unrecognized predictors and mechanisms of drug sensitivity. Conversely, we observed a partial discordance between ex vivo responses to SRLs and established predictive markers. Limitations of this study are as follows. First, the model-intrinsic features of patient-derived spheroids can introduce selection and context effects; unlike organoids which maintain complex tissue architecture, our spheroid culture mainly enriches sphere-forming subpopulations and depletes stromal, vascular, and immune compartments. Extracellular matrix composition/mechanics and serum-borne cues may shift differentiation state and receptor trafficking such that in-culture SSTR/D2R abundance or granulation patterns may not perfectly reflect bulk-tumor readouts [ 16 ]. Second, interobserver variability in radiologic and pathologic classifications and spatial intratumoral heterogeneity can decouple imaging surrogates and receptor/secretory phenotypes from cell-intrinsic drug sensitivity. Third, given our sample size, chance variation may have contributed to the observed discordance with established predictive markers. This limitation also extended to the AI-based LBC analysis, where a small number of cases and the absence of a predefined cutoff value precluded formal statistical verification, underscoring the exploratory nature of this pilot sub-analysis and necessitating further validation of the observed trends in larger cohorts. Fourth, although our responder definition lacked a predefined biological threshold, the tightly converged effect sizes across all three agents (median relative viabilities of 84–86%; Fig. 1 b) strongly suggest that our statistical approach effectively captured a biologically distinct phenotype rather than random technical noise. Building on these results, establishing an optimal cutoff value to redefine responsiveness will be a crucial next step to further refine this assay and enhance its clinical utility. Finally, in this cohort, many patients achieved oncologic remission by surgery, and additional follow-up was required before medical therapy was indicated for residual or recurrent disease, which precludes the correlation within the present study of ex vivo effect sizes with subsequent tumor shrinkage or biochemical remission. While this pilot study does not immediately alter current clinical guidelines, applying AI-based analysis to the entire cohort and integrating multiomics analyses would provide deeper molecular insights and establish a critical foundation for future precision medicine, which remains a vital next step. In summary, using a spheroid-based Pd3D culture platform previously described in case reports, we demonstrated that ex vivo cell viability responses show concordance with established clinical predictors of drug response in acromegaly. These findings support the validity of this assay and suggest that it may serve as a complementary predictive tool alongside conventional clinicopathological markers for optimizing drug selection to achieve tumor control. Declarations Acknowledgement The authors sincerely thank Professor Junya Fukuoka for his extensive support and expertise in AI-based quantitative immunocytochemical assessment of LBC specimens. We also gratefully acknowledge Ms. Mari Motoyoshi, Ms. Ikue Saita, Ms. Marina Saito, and Ms. Mayuko Nikabu for their substantial support with specimen transportation, handling, and management, which was essential for the successful completion of this study. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yasutaka Tsujimoto, Atsushi Ishida, and Hidenori Fukuoka. The first draft of the manuscript was written by Yasutaka Tsujimoto and Hidenori Fukuoka, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding This work was partially supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (KAKENHI; grant numbers 19K09003 (HF), 22K08654 (HF), 25K02700 (HF), and 23K15412 (HS)); the Program for Forming Japan's Peak Research Universities (J-PEAKS) from the Japan Society for the Promotion of Science (JSPS) (TA); and by AMED under Grant Number JP256f0137011 (TA). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval All procedures complied with the protocol approved by the Research Ethics Committee of Kobe University Hospital and Moriyama Memorial Hospital (IRB #1363 and B240223, respectively) and adhered to the Declaration of Helsinki. Consent to participate Written informed consent was obtained from all participants prior to surgery and sample collection. Data Availability Restrictions apply to the availability of some or all data generated or analyzed during this study to preserve patient confidentiality or because they were used under license. The corresponding author will on request detail the restrictions and any conditions under which access to some data may be provided. References Fleseriu M, Langlois F, Lim DST et al (2022) Acromegaly: pathogenesis, diagnosis, and management. Lancet Diabetes Endocrinol 10:804–826. https://doi.org/10.1016/S2213-8587(22)00244-3 Katznelson L, Laws ER Jr, Melmed S et al (2014) Acromegaly: an endocrine society clinical practice guideline. J Clin Endocrinol Metab 99:3933–3951. https://doi.org/10.1210/jc.2014-2700 Gadelha MR, Wildemberg LE, Marques NV, Kasuki L (2025) Medical treatment of acromegaly: Navigating the present, shaping the future. 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Additional Declarations No competing interests reported. Supplementary Files Table2.docx OnlineResources.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 May, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 19 Apr, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9461574\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":629666526,\"identity\":\"a7d6e21d-ca77-4af4-8b22-2d9a624e86d0\",\"order_by\":0,\"name\":\"Yasutaka Tsujimoto¹\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Kobe University Graduate School of 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Hospital\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Hidenori\",\"middleName\":\"\",\"lastName\":\"Fukuoka\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-19 11:23:17\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9461574/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9461574/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":108383295,\"identity\":\"c2b2fb5e-67d8-4ce0-a133-501bcbce98dd\",\"added_by\":\"auto\",\"created_at\":\"2026-05-04 05:45:21\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":401092,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy overview and Pd3D assay workflow and Comparison of “responder” and “nonresponder” groups to each drug in patient-derived 3D cultures\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ea. Consecutive patients with acromegaly underwent resection, and fresh tissue was processed into Pd3D cultures exposed to vehicle, octreotide, cabergoline, or pasireotide, and luminescence was recorded at baseline and endpoint for relative viability (%). AI-based digital image analysis was performed on the LBC specimens. The readouts were compared with established clinical, MRI, and pathological predictors. Abbreviations: Pd3D, patient-derived 3D; LBC, liquid-based cytology; MRI, magnetic resonance imaging; T2WI; SSTR, somatostatin receptor. (Parts of this figure were created using elements from Servier Medical Art, licensed under CC BY 4.0.)\\u003c/p\\u003e\\n\\u003cp\\u003eb. Relative viability (%) at endpoint for “responder” and “nonresponder” groups for each treatment: octreotide, cabergoline, and pasireotide. Bars indicate medians (IQR 25th–75th percentiles). The number of samples in each group was as follows: octreotide (responder, n = 9; nonresponder, n = 18), cabergoline (responder, n = 9; nonresponder, n = 18), and pasireotide (responder, n = 11; nonresponder, n = 16). * \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05, Mann–Whitney U test.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9461574/v1/8ce41f05b541e77882c36ada.png\"},{\"id\":108383297,\"identity\":\"2685a316-c63a-47bb-969a-b691411dfbdb\",\"added_by\":\"auto\",\"created_at\":\"2026-05-04 05:45:21\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":185371,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eQuantitative dynamics of SSTR2 expression in response to octreotide treatment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ea. Trajectories of SSTR2 H-scores. Individual lines represent the shift in H-score from vehicle-treated controls to octreotide-treated conditions for each case (Samples A–F). Green lines indicate ex vivo responders, and blue lines indicate nonresponders. The H-score (range 0–300) reflects the total intensity and quantity of receptor expression.\\u003c/p\\u003e\\n\\u003cp\\u003eb. Shifts in SSTR2 staining intensity distribution. 100% stacked bar charts illustrate the percentage of cells in each intensity category (negative: 0; weak: 1+; moderate: 2+; and strong: 3+) for each sample. Paired bars (Veh and Oct) show changes before and after treatment. Colors indicate receptor expression levels: gray (negative, 0), yellow (weak, 1+), orange (moderate, 2+), and red (strong, 3+).\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: Veh, vehicle; Oct, octreotide.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9461574/v1/c650f8fc8b89d72b86e7b2c8.png\"},{\"id\":109067454,\"identity\":\"2043605a-afd0-4db6-b38f-884bb99740ba\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 09:52:48\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":975989,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9461574/v1/794c4fa1-0af9-443f-9522-ef1169b18e3c.pdf\"},{\"id\":108383298,\"identity\":\"557973fa-61fb-46aa-a66f-591619618d5c\",\"added_by\":\"auto\",\"created_at\":\"2026-05-04 05:45:21\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":25853,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Table2.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9461574/v1/b39a71bb599ce4ba1e66357c.docx\"},{\"id\":108492726,\"identity\":\"c1448e01-f259-4e52-9c10-4268fd9569ec\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 09:58:27\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":4716921,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"OnlineResources.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9461574/v1/3182938ba0de8737e7f0649f.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Ex Vivo Drug Sensitivity in Patient-derived 3D Cultures in Acromegaly and its Association with Clinical Predictors\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eAcromegaly imposes a significant disease burden through excess systemic growth hormone (GH)/insulin-like growth factor-1 (IGF-1) and local tumor mass effects [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Although biochemical normalization is essential for improving survival, 40\\u0026ndash;60% of patients require adjuvant medical therapy post-surgery [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Notably, biochemical control and tumor remission are not always achieved concurrently in clinical practice, and marked interindividual variability in drug response complicates therapeutic decision-making [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. This trial-and-error approach places a considerable burden on patients and underscores the need to optimize therapeutic pathways [\\u003cspan additionalcitationids=\\\"CR5 CR6\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. While predictors of biochemical control have been extensively investigated, robust markers that directly forecast tumor shrinkage remain limited [\\u003cspan additionalcitationids=\\\"CR9\\\" citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAcross oncology, three-dimensional (3D) tumor culture systems have gained attention as tools to predict drug sensitivity because they mimic tumor architecture, cell\\u0026ndash;cell and cell\\u0026ndash;matrix interactions, and physiologically relevant nutrient and oxygen gradients, thereby providing more representative response readouts [\\u003cspan additionalcitationids=\\\"CR12 CR13 CR14\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. In contrast, traditional two-dimensional (2D) primary cultures often suffer from loss of native architecture and functional receptor expression [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Among 3D systems, organoids and spheroids represent two widely used but conceptually distinct models. Organoids typically self-organize and retain stem or progenitor compartments, whereas spheroids generally arise from the aggregation of primary cells or cell lines and lack self-organization. Although organoid models are valued for their higher intra-and inter-assay reproducibility and their ability to support long-term maintenance of lineage programs, their widespread application is constrained by higher cost and longer setup and expansion times. In pituitary adenomas, clinical validation remains limited, as high costs and technically demanding workflows restrict accessibility [\\u003cspan additionalcitationids=\\\"CR17 CR18\\\" citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTo address these challenges, we previously established a rapid and cost-conscious spheroid-based patient-derived 3D (Pd3D) cultures system from pituitary adenomas to perform \\u003cem\\u003eex vivo\\u003c/em\\u003e drug testing; in a small number of cases, \\u003cem\\u003eex vivo\\u003c/em\\u003e readouts from this platform aligned with clinical outcomes, including hormonal response and volumetric tumor shrinkage, although further research is required [\\u003cspan additionalcitationids=\\\"CR21\\\" citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Although this approach may not fulfill all the rigorous criteria of organoids, it represents a robust tumor spheroid model that preserves tumor-intrinsic drug responsiveness and is well suited for clinical oriented drug screening.\\u003c/p\\u003e \\u003cp\\u003eIn the present study, we applied this Pd3D workflow to a consecutive series of patients with acromegaly. Using Pd3D cultures, we quantified drug-induced changes in cell viability and compared these readouts with established predictors of biochemical response to medical therapy. In addition, we examined the relationship between \\u003cem\\u003eex vivo\\u003c/em\\u003e drug responsiveness and somatostatin receptor (SSTR) expression levels assessed in liquid-based cytology (LBC) preparations, which provide a robust platform for immunostaining and ancillary molecular analyses in rare endocrine tumor samples [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. To ensure objective quantification and reproducibility, we employed an Artificial Intelligence (AI)-based digital image analysis system [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. This integrated approach allowed us to explore whether \\u003cem\\u003eex vivo\\u003c/em\\u003e functional assays could offer complementary predictive value beyond conventional hormonal markers. An overview of the study workflow is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eParticipants and Data Acquisition\\u003c/h2\\u003e \\u003cp\\u003eBetween November 2023 and November 2024, 27 consecutive patients with acromegaly who underwent transsphenoidal surgery at Moriyama Memorial Hospital were enrolled, and tumor specimens were collected intraoperatively. Acromegaly was diagnosed according to international guidelines [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Clinical variables were retrospectively extracted from electronic medical records with a focus on previously proposed predictors of pharmacological responses. Extracted data included age, sex, serum GH levels, and age- and sex-adjusted IGF-1 standard-deviation scores (SDS). Additional parameters included basal and nadir GH levels during a 75-g oral glucose tolerance test (OGTT), the timing of the GH nadir, and results of octreotide and bromocriptine tests. The octreotide test was considered positive when GH decreased by \\u0026ge;\\u0026thinsp;75% after a subcutaneous 100 \\u0026micro;g [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. The bromocriptine test was considered positive when GH decreased by \\u0026ge;\\u0026thinsp;50% after oral administration of 2.5 mg [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Pituitary MRI variables included maximum tumor diameter, Knosp grade, and qualitative T2-weighted imaging (T2WI) signal intensity. MRI assessments were performed by endocrinologists, neurosurgeons, or radiologists. Histopathology variables, evaluated by an experienced pathologist, included GH immunohistochemistry, Ki-67 labeling index, semi-quantitative immunohistochemistry (IHC) expression scores for somatostatin receptor subtype 2 (SSTR2) and subtype 5 (SSTR5), and granulation pattern (densely or sparsely granulated) [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Owing to the retrospective design, not all clinical, radiological, and histopathological variables were available for every case.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003ePatient-derived 3D (Pd3D) Cultures\\u003c/h3\\u003e\\n\\u003cp\\u003eSpheroid-based Pd3D cultures were established as previously described [\\u003cspan additionalcitationids=\\\"CR21\\\" citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Freshly resected tumor tissue was immediately placed on ice in phosphate-buffered saline (PBS; Gibco, Waltham, MA, USA) and enzymatically dissociated in Dulbecco\\u0026rsquo;s Modified Eagle Medium (DMEM; Gibco, Waltham, MA, USA) containing 0.3% bovine serum albumin (Sigma-Aldrich, St. Louis, MO, USA), 0.35% collagenase (FUJIFILM Wako, Osaka, Japan), 0.15% hyaluronidase (FUJIFILM Wako, Osaka, Japan), and 0.1% Y-27632 (FUJIFILM Wako, Osaka, Japan). Cells were embedded as domes in growth factor\\u0026ndash;reduced phenol red\\u0026ndash;free Matrigel (Corning, NY, USA) in white flat-bottom 96-well plates (Thermo Fisher Scientific, Waltham, MA, USA) at a density of 10,000 cells per well. Matrigel domes were polymerized with the plates inverted at 37\\u0026deg;C for 10 min to promote gravitational settling and cell aggregation at the apex of each dome, thereby forming spheroids. Each well then received 50 \\u0026micro;L of DMEM supplemented with 10% fetal bovine serum (FBS; Gibco, Waltham, MA, USA) and penicillin\\u0026ndash;streptomycin (Gibco, Waltham, MA, USA). Plates were incubated at 37\\u0026deg;C in 5% CO₂.\\u003c/p\\u003e\\n\\u003ch3\\u003eDrug Exposure and Cell Viability Readout\\u003c/h3\\u003e\\n\\u003cp\\u003eAfter 24 h of initial culture, the medium was replaced with fresh medium containing either vehicle (DMSO; Nacalai Tesque, Kyoto, Japan; final DMSO\\u0026thinsp;\\u0026le;\\u0026thinsp;0.1% [v/v]) or one of the following drugs at 10 nM: octreotide (Selleck Chemicals, Houston, TX, USA), cabergoline (Tocris, Bristol, UK), or pasireotide (ChemScene, Monmouth Junction, NJ, USA). Each condition was tested in three to five replicate wells per sample, depending on cell yield. A single medium refresh was performed 72 h after drug addition, and cultures were maintained for 7 d from treatment initiation. Representative images at this endpoint are shown in Online Resource 1.\\u003c/p\\u003e \\u003cp\\u003eCell viability was quantified with the RealTime-Glo\\u0026trade; MT Cell Viability Assay (Promega, Madison, WI, USA) according to the manufacturer\\u0026rsquo;s instructions. Luminescence was recorded immediately before drug exposure (baseline) and at the end of treatment using an EnSpire Multimode Plate Reader (PerkinElmer, Waltham, MA, USA). For each well, endpoint luminescence was divided by its corresponding baseline value and multiplied by 100 to yield relative viability (%). For each treatment arm within a patient, the median relative viability (%) across replicate wells was used for analysis.\\u003c/p\\u003e \\u003cp\\u003eAt the patient-sample-level, a drug was classified as yielding an \\u003cem\\u003eex vivo\\u003c/em\\u003e response (\\u0026ldquo;responder\\u0026rdquo;) if the median relative viability (%) in the treatment arm was significantly lower than that in the contemporaneous vehicle control at the endpoint (two-sided \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05); otherwise, the sample was considered a \\u0026ldquo;nonresponder.\\u0026rdquo;\\u003c/p\\u003e\\n\\u003ch3\\u003eImmunocytochemistry of Dissociated Cells from Pd3D Using the BD SurePath™\\u003c/h3\\u003e\\n\\u003cp\\u003eBasal cell suspensions were obtained immediately after enzymatic dissociation. Following vehicle or octreotide treatment, cells were harvested from Matrigel domes using Cell Recovery Solution (Corning, NY, USA) according to the manufacturer\\u0026rsquo;s instructions to generate cell suspensions. All specimens were processed under identical conditions using the BD SurePath\\u0026trade; liquid-based cytology system (BD Biosciences, Franklin Lakes, NJ, USA).\\u003c/p\\u003e \\u003cp\\u003eFreshly collected cell suspensions were transferred into BD CytoRich\\u0026trade; Red preservative solution (BD Biosciences, Franklin Lakes, NJ, USA) and fixed at room temperature for at least 30 min in accordance with the manufacturer\\u0026rsquo;s protocol. Fixed samples were centrifuged at 2,000 rpm for 3 min, the supernatant was discarded, and the resulting cell pellet was gently resuspended. An appropriate volume of the suspension was loaded into a BD SurePath settling chamber mounted on a BD SurePath-precoated glass slide (BD Biosciences, Franklin Lakes, NJ, USA). After allowing the cells to settle for 3 min, the chamber was removed and the slide was immediately transferred to 95% ethanol for additional fixation.\\u003c/p\\u003e \\u003cp\\u003eImmunocytochemistry was performed using a BOND-III automated immunostainer (Leica Biosystems, Nussloch, Germany) according to the manufacturer\\u0026rsquo;s default immunohistochemistry program. Heat-induced antigen retrieval was performed using Epitope Retrieval Solution 1 (ER1; pH 6.0; Leica Biosystems, Nussloch, Germany) for 10 min. Because BD SurePath cytology slides were used rather than paraffin-embedded sections, deparaffinization steps were omitted.\\u003c/p\\u003e \\u003cp\\u003ePrimary antibodies for somatostatin receptor included rabbit monoclonal anti-SSTR2 (clone UMB1, Abcam, Cat# ab134152, RRID:AB_2737601, Cambridge, UK; 1:1,500) and rabbit monoclonal anti-SSTR5 (clone UMB4, Abcam, Cat# ab109495, RRID:AB_10859946, Cambridge, UK; 1:20). Antibodies were applied according to the manufacturer\\u0026rsquo;s datasheets, and a polymer-based horseradish peroxidase detection system supplied by the instrument\\u0026rsquo;s manufacturer was used. Identical antibody panel and staining conditions were applied to basal, vehicle-treated, and octreotide-treated samples.\\u003c/p\\u003e\\n\\u003ch3\\u003eDigital Image Analysis for SSTR Expression Quantification\\u003c/h3\\u003e\\n\\u003cp\\u003eIn a pilot subgroup, cytological whole-slide images (WSIs) derived from six tumor samples (Samples A\\u0026ndash;F) were analyzed. For each case, treated specimens were prepared under three conditions: immediately after dissociation (basal), and after 7-day exposure to vehicle or octreotide (10 nM). Digital image analysis was performed using the open-source software QuPath (version 0.5.1) [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. To quantify the immunostaining intensity, color deconvolution was applied as a preprocessing step using standard optical density vectors: hematoxylin (nuclear stain) at (0.651, 0.701, 0.290), DAB (0.269, 0.568, 0.778), and residual (0.633, -0.713, 0.302).\\u003c/p\\u003e \\u003cp\\u003eTumor cells were identified using a cell detection algorithm with the following parameters: pixel size, 0.5 \\u0026micro;m; background radius, 8 \\u0026micro;m; median filter radius, 1.5 \\u0026micro;m; sigma, 1.8 \\u0026micro;m; and threshold, 0.15. The maximum background intensity was set at 2. Detection was restricted to cell areas between 15 \\u0026micro;m\\u0026sup2; and 150 \\u0026micro;m\\u0026sup2;, to exclude debris and non-specific aggregates. Automated detections were manually reviewed to remove dust particles, inflammatory cells, and doublets. Regions of interest (ROIs) were adjusted according to cellularity: the central area was analyzed for specimens with high cell density, whereas the entire slide area was evaluated for samples with low cell counts.\\u003c/p\\u003e \\u003cp\\u003eCells were classified into four intensity categories based on mean DAB optical density across the entire cell area. In the absence of internal calibration controls for LBC specimens, standard intensity thresholds were utilized, referencing the established criteria for SSTR2 digital analysis [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]: negative (\\u0026lt;\\u0026thinsp;0.2, blue), 1+ (weak; 0.2\\u0026ndash;0.4, yellow), 2+ (moderate; 0.4\\u0026ndash;0.6, orange), and 3+ (strong; \\u0026gt; 0.6, red) (Online Resource 2). Finally, the H-score (range: 0\\u0026ndash;300) was calculated for each sample using the standard formula: H-score = (1 \\u0026times; % of 1\\u0026thinsp;+\\u0026thinsp;cells) + (2 \\u0026times; % of 2\\u0026thinsp;+\\u0026thinsp;cells) + (3 \\u0026times; % of 3\\u0026thinsp;+\\u0026thinsp;cells) [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. For comparison, SSTR2 expression in corresponding formalin-fixed paraffin-embedded (FFPE) tissue sections was evaluated using the Volante score system (score 0\\u0026ndash;3), as previously described [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eContinuous or ordinal variables were compared using the Mann\\u0026ndash;Whitney \\u003cem\\u003eU\\u003c/em\\u003e test, and categorical variables were compared using Fisher\\u0026rsquo;s exact test. Statistical significance was defined as \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. Data are presented as median with interquartile ranges (IQRs; 25th\\u0026ndash;75th percentiles). Analyses were performed using JMP Pro 18 software (SAS Institute Inc., Cary, NC, USA).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\"\\u003e\\n \\u003ch2\\u003ePatient Characteristics\\u003c/h2\\u003e\\n \\u003cp\\u003eTwenty-seven consecutive patients with acromegaly were included in this study (Table 1). The median age at surgery was 46.0 years [36.5–56.0], and 13/27 (48.1%) were female. At baseline, the median serum IGF-1 SDS was + 6.2 [+ 5.4 to + 7.4], and the median serum GH levels were 14.7 ng/mL [5.7–35.6]. An octreotide test was performed in 19 patients, of whom 15/19 (78.9%) met the predefined positivity criterion (≥ 75% GH reduction), with a median suppression of 82.6% [63.9–90.7]. A bromocriptine test was conducted in 18 patients, 12/18 (66.7%) were classified as positive, with a median suppression rate of 69.6% [39.5–88.1].\\u0026nbsp;\\u003c/p\\u003e\\n \\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eBaseline Clinical Characteristics of the Study Cohort\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTotal number\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003eavailable cases\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e27\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eBaseline characteristics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eAge at the time of surgery\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(years)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[36.5–56]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSex, female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases, %)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e13 (48%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eBasal GH\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(ng/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e14.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[5.7–35.6]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNadir GH on 75gOGTT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(ng/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[6.5–33.2]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eIGF-1 SDS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e+ 6.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[+ 5.4-+7.4]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003ePRL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(ng/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e11.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[7.4–19.1]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eDynamic endocrinological test\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOctreotide suppression test, positive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases, %)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e15 (78%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOctreotide suppression test, suppression rate\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e82.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[63.9–90.7]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOctreotide suppression test, nadir time\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(hours)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[2–5]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eBromocriptine suppression test, positive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases, %)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e12 (66%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eBromocriptine suppression test, suppression rate\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e69.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[39.5–88.1]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eBromocriptine suppression test, nadir time\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(hours)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[4–6]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003ePituitary MRI findings\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMacroadenoma \\u0026gt; 10mm\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases, %)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e26 (96%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMaximum tumor diameter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(mm)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e19.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[15.2–27.7]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eKnosp grade, invasion\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases, %)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e7 (25%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eKnosp grade [0/1/2/3/4]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e[5/11/4/4/3]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMRI T2WI hypointensity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases, %)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e16 (59%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eHistological findings\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eGranulated pattern, densely\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases, %)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e17 (62%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSSTR2 [0/1/2/3]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e[1/1/8/17]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSSTR5 [0/1/2/3]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(cases)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e[4/4/15/4]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eKi-67 labeling index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e[0.3–1.2]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003eData are presented as numbers (%) for categorical variables and as medians [25th–75th percentiles] for continuous variables. The numbers of patients with available data for each parameter are listed in the rightmost column.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003eAbbreviations: GH, growth hormone; OGTT, oral glucose tolerance test; IGF-1, insulin-like growth factor-1; SDS, standard deviation score; PRL, prolactin; MRI, magnetic resonance imaging; SSTR, somatostatin receptor.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003eOn pituitary MRI, 26/27 (96.3%) patients had macroadenomas (\\u0026gt; 10 mm). The median maximum tumor diameter was 19.5 mm [15.2–27.7]. Cavernous sinus invasion (Knosp grade 3–4) was observed in 7/27 (25.9%) patients, and MRI T2WI hypointensity was present in 16/27 (59.3%) patients. Histopathology analysis showed a densely granulated pattern in 17/27 (63.0%). The median Ki-67 labeling index was 0.5% [0.3–1.2]. Semi-quantitative immunohistochemical expression of SSTR2 tended to be higher than that of SSTR5 in the majority of evaluable samples. Data completeness varied across variables owing to the retrospective study design.\\u003c/p\\u003e\\n \\u003cp\\u003eOverall, the characteristics of this cohort were broadly consistent with those reported in previous studies on acromegaly [31].\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAssociation between known predictive markers and Pd3D cultures responsiveness.\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eAcross all 27 samples, all control cultures maintained robust viability from baseline to the endpoint (Online Resource 3). Each tumor sample was classified as an \\u003cem\\u003eex vivo\\u003c/em\\u003e responder or nonresponder in the Pd3D culture assay according to the prespecified within-sample criteria described in the Methods section. That is, responders exhibited a significant decrease in cell viability upon drug exposure, whereas nonresponders maintained robust viability. Relative cell viability (%) was consistently lower in responders than in nonresponders for all three agents: octreotide (84% [75–88] \\u003cem\\u003evs\\u003c/em\\u003e 100% [93–105], \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.01), cabergoline (84% [79–92] \\u003cem\\u003evs\\u003c/em\\u003e 94% [86–106], \\u003cem\\u003ep\\u003c/em\\u003e = 0.04); and pasireotide (86% [85–89] \\u003cem\\u003evs\\u003c/em\\u003e 95% [88–100], \\u003cem\\u003ep\\u003c/em\\u003e = 0.01) (Fig.\\u0026nbsp;1b). The proportion of responders was 9/27 (33%) for octreotide, 9/27 (33%) for cabergoline, and 11/27 (40%) for pasireotide.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec11\\\"\\u003e\\n \\u003ch2\\u003eOctreotide\\u003c/h2\\u003e\\n \\u003cp\\u003eBaseline characteristics, including age, sex, baseline GH level, and IGF-1 SDS, did not differ between the responder and nonresponder. Octreotide test results were also comparable between the two groups in the proportion positive rate, GH suppression magnitude, and time to nadir. On MRI, macroadenomas were present in 8/9 (88%) responders and in all 18/18 (100%) nonresponders. Maximum tumor diameter was 13 mm [13–27 mm] \\u003cem\\u003evs\\u003c/em\\u003e 21 mm [17–27] (\\u003cem\\u003ep\\u003c/em\\u003e = 0.16), and cavernous sinus invasion (Knosp 3–4) was observed in 3/9 (33%) \\u003cem\\u003evs\\u003c/em\\u003e 4/18 (22%) (\\u003cem\\u003ep\\u003c/em\\u003e = 0.65).\\u003c/p\\u003e\\n \\u003cp\\u003eIn contrast, MRI T2WI hypointensity, a previously reported marker of responsiveness to first-generation somatostatin receptor ligands (SRLs), was significantly more frequent among responders than among nonresponders (88% \\u003cem\\u003evs\\u003c/em\\u003e 44%, \\u003cem\\u003ep\\u003c/em\\u003e = 0.04). Histopathological features, including granulation pattern and semi-quantitative SSTR2/SSTR5 expression scores, did not differ significantly between the two groups (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; 0.99, \\u003cem\\u003ep\\u003c/em\\u003e = 0.11, respectively; Table 2a).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\"\\u003e\\n \\u003ch2\\u003eCabergoline\\u003c/h2\\u003e\\n \\u003cp\\u003eBaseline clinical characteristics were similar between cabergoline responders and nonresponders. In contrast, the bromocriptine test was positive in all responders but in less than half of nonresponders (100% \\u003cem\\u003evs\\u003c/em\\u003e 45%, \\u003cem\\u003ep\\u003c/em\\u003e = 0.03), a difference that reached statistical significance. Neither the GH suppression rate nor the time to GH nadir during the bromocriptine test differed between the groups.\\u003c/p\\u003e\\n \\u003cp\\u003eMRI-derived variables, including tumor size, Knosp grade, and T2WI signal intensity, were comparable between responders and nonresponders, as were granulation patterns (Table\\u0026nbsp;2b). Notably, semi-quantitative SSTR5 expression was significantly higher in responders than in nonresponders (\\u003cem\\u003ep\\u003c/em\\u003e = 0.03), whereas SSTR2 expression did not differ between groups (\\u003cem\\u003ep\\u003c/em\\u003e = 0.34).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\"\\u003e\\n \\u003ch2\\u003ePasireotide\\u003c/h2\\u003e\\n \\u003cp\\u003eBaseline characteristics and MRI findings did not differ between the groups. Tumors with sparsely granulated patterns were more common in responders than in nonresponders (64% \\u003cem\\u003evs\\u003c/em\\u003e 19%, \\u003cem\\u003ep\\u003c/em\\u003e = 0.04), whereas SSTR2/SSTR5 expression scores did not differ between the groups (Table\\u0026nbsp;2c).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\"\\u003e\\n \\u003ch2\\u003eAI-Assessed Temporal Dynamics of SSTR2 Expression in LBC Specimens\\u003c/h2\\u003e\\n \\u003cp\\u003eAn additional six patients with acromegaly were included for whom both \\u003cem\\u003eex vivo\\u003c/em\\u003e drug sensitivity assays and AI-based immunocytochemical analysis were performed. The basal SSTR2 H-scores generally corresponded to the diagnostic Volante scores obtained from the corresponding FFPE tissues (Table 3). Following octreotide treatment, distinct quantitative changes in SSTR2 expression were observed between responders and nonresponders. \\u003cem\\u003eEx vivo\\u003c/em\\u003e responders (Samples A, B, and C) showed relatively stable SSTR2 expression, in contrast to the marked depletion observed in nonresponders (Fig. 2a). Samples A and C exhibited increased H-scores (+ 30.8% and + 45.9%, respectively), accompanied by a shift in intensity fractions, in which negative (0) cells decreased, whereas 1+ (weak) and 2+ (moderate) positive cells increased (Fig. 2b). Sample B, which had the highest baseline expression, maintained a high proportion of 3+ (strong) cells (approximately 60%) despite a slight increase in negative (0) cells, distinguishing it from the depletion pattern observed in nonresponders (Fig. 2b, Table 3). In contrast, nonresponders (Samples D, E and F) exhibited a marked reduction in H-scores compared to vehicle-treated controls (Fig. 2a). Sample D showed a 60.8% decrease (145.9 to 57.1), characterized by a marked increase in negative 0 cells from 3.2% to 63.5% and a depletion of 3+ (strong) cells (Fig. 4B). Sample E showed a 30.3% reduction in H-score (167.0 to 116.4), with 3+ (strong) cells decreasing from 18.5% to 7.7%. Similarly, Sample F demonstrated a 44.7% reduction in H-score (131.7 to 72.8), with 3+ (strong) cells decreasing from 36.7% to 14.2% (Fig. 2b, Table 3).\\u0026nbsp;\\u003c/p\\u003e\\n \\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eClinical Characteristics and AI-Based SSTR2 Dynamics in the Pilot Subgroup (n = 6)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSample\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eA\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003eB\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003eC\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003eD\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003eE\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eStatus\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eresponder\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003eresponder\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003eresponder\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003enonresponder\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003enonresponder\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003enonresponder\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e(yo)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSex\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eGH\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e(ng/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e13.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e48.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e81.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e3.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e49.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e11.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eIGF-1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e(ng/mL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e616\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e775\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e896\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e367\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e316\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e797\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eIGF-1 SDS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e+ 5.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e+ 8.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e+ 9.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e+ 3.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e+ 3.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e8.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eMRI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eSize\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e(mm)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eKnosp grade\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eT2WI intensity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eLow\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003eLow\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003eLow\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003eLow\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003eHigh\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003eHigh\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eFFPE\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eVolante SSTR2 score\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e3+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e3+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e2+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e2+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e3+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e2+\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003eLBC H-score\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eBasal\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e102.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e60.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e32.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e106\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e150.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e74.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eVehicle\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e104.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e245.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e43.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e167\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e145.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e131.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eOctreotide\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e136.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e233\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e63.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e116.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e57.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e72.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003edelta H-score\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e30.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e-5.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e45.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e-30.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\n \\u003cp\\u003e-60.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\n \\u003cp\\u003e-44.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"9\\\"\\u003eStatus refers to the classification of responders or nonresponders based on \\u003cem\\u003eex vivo\\u003c/em\\u003e assays. Sex was denoted as F (female) or M (male). The FFPE SSTR2 score indicated the conventional immunohistochemical grade (Volante score 0–3+) assessed using formalin-fixed paraffin-embedded surgical tissue. H-scores (range, 0–300) were quantified using AI-based digital image analysis of the liquid-based cytology (LBC) specimens. The delta H-score (%) was calculated as the percentage change in the H-score after octreotide treatment relative to that of the vehicle control. GH, growth hormone; IGF-1, insulin-like growth factor-1; SDS, standard deviation score; MRI, magnetic resonance imaging;(T2WI); LBC, liquid-based cytology.\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003eAlthough statistical significance could not be determined owing to the limited sample size, these findings suggest an association between the quantitative temporal dynamics of SSTR2 expression and drug responsiveness.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eUsing our previously reported simple \\u003cem\\u003eex vivo\\u003c/em\\u003e spheroid-based Pd3D culture assay, we evaluated whether tumor-specific cell viability responses to octreotide, cabergoline, and pasireotide reflect clinically meaningful drug responsiveness in a consecutive cohort of patients with acromegaly derived from resected tumors. Although the overall reduction in viability was modest, substantial intertumoral variability was observed, and these responses showed concordance with established clinical response indices. This modest reduction is consistent with the primary pharmacological mechanisms of SRLs and dopamine agonists, which involve inhibition of hormone secretion, cell cycle arrest, and reduction in cell volume, rather than acute massive cytotoxicity. These findings provide initial evidence supporting the validity of this assay and suggest that it captures relative pharmacological susceptibility. Specifically, the significantly lower relative viability in the responder group compared to the nonresponder group confirms that this assay accurately distinguishes tumor-intrinsic drug sensitivity.\\u003c/p\\u003e \\u003cp\\u003ePd3D culture systems have been shown to capture patient-specific drug responses, and are increasingly recognized as translational functional assays [\\u003cspan additionalcitationids=\\\"CR12 CR13 CR14\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. In particular, cell-viability\\u0026ndash;based readouts from various patient-derived 3D tumor models have been reported to predict radiographic tumor responses, although discordant results have also been described [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. In pituitary adenomas, adoption and clinical validation of such models remain at an early stage, partly because high costs and technically demanding workflows limit their broad implementation [\\u003cspan additionalcitationids=\\\"CR17\\\" citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. In the present study, we applied a rapid and cost-effective Pd3D platform to a consecutive series of 27 pituitary adenoma specimens. This approach enables parallel within-sample testing of multiple agents with short turnaround time using a simple luminescence-based viability readout. These characteristics support the use of normalized luminescence as a pragmatic pharmacodynamic surrogate, reducing between-sample variability and allowing patient-level classification. The present findings support the rationale for future prospective studies examining whether \\u003cem\\u003eex vivo\\u003c/em\\u003e effects correlate with subsequent tumor shrinkage and biochemical remission in the same patients.\\u003c/p\\u003e \\u003cp\\u003eIn acromegaly, pharmacological therapy is essential for residual or recurrent disease and is sometimes selected as primary treatment. Available agents for pituitary tumor reduction include first-generation SRLs (octreotide or lanreotide), dopamine agonists (cabergoline or bromocriptine), and pasireotide [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eResponsiveness varied among the agents, highlighting the need for reliable predictive markers. Among them, responses to first-generation SRLs have been the most extensively studied [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. The reported predictors include age, sex, baseline GH and IGF-1 levels [\\u003cspan additionalcitationids=\\\"CR37\\\" citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e], octreotide test results [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e], and MRI T2WI signal intensity [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. Notably, T2WI hypointensity often corresponds to a densely granulated pattern and strong SSTR2 expression, and has been associated with favorable response [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. For dopamine agonists, low baseline GH or IGF-1 levels and positive bromocriptine test results have been suggested as predictors [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. Pasireotide has been reported to be effective in patients resistant to first-generation SRLs and may be more effective in tumors with high T2WI signal intensity and, in some studies, a sparsely granulated pattern [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. However, most studies have focused on biochemical outcomes, and predictors of tumor shrinkage remain limited. In clinical practice, biochemical control often, but not invariably, coincides with radiographic response [\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e], underscoring the need for approaches that can anticipate both outcomes [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn our study, an \\u003cem\\u003eex vivo\\u003c/em\\u003e Pd3D culture assay generated sample-level viability readouts for each drug and demonstrated partial concordance with established clinical predictors, including octreotide responsiveness with T2WI hypointensity, cabergoline responsiveness with bromocriptine testing, and pasireotide with a sparsely granulated pattern. At the same time, discordant cases were also observed. This concordance suggests that Pd3D cultures preserve key tumor features relevant to drug responsiveness, supporting the face validity and feasibility of the assay. These features likely included preservation of SSTR2/5 and dopamine receptor (D2R) expression, the histopathological granulation pattern that contributes to the tumor\\u0026rsquo;s T2WI MRI signal, downstream signaling pathways, and the cell\\u0026mdash;matrix context. Together, these features may underline clinical indicators such as responsiveness in dynamic drug tests. Although the clinical use of these dynamic suppression tests remains controversial, we utilized them as an immediate \\u003cem\\u003ein vivo\\u003c/em\\u003e biological reference to validate whether our culture system successfully preserved the native functional receptor phenotypes. In addition, the 3D architecture facilitates the re-establishment of native cell\\u0026mdash;cell and cell\\u0026mdash;matrix interactions and microenvironmental gradients that influence receptor trafficking and signaling fidelity, features emphasized in contemporary pituitary 3D model work [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e], and may stabilize SSTR/D2R localization and Gi/o-coupled signaling, improving the capture of patient-intrinsic drug sensitivity in our assay.\\u003c/p\\u003e \\u003cp\\u003eComplementing these viability assays, quantitative analysis of LBC specimens captured pharmacological dynamics of SSTR2 in Pd3D models. This methodology has previously been applied to hormone receptor assessment in breast cancer and immunocytochemical characterization of neuroendocrine carcinomas, supporting its utility as a robust foundation for molecular analysis in endocrine oncology [\\u003cspan additionalcitationids=\\\"CR48\\\" citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. In the present study, the concordance between baseline LBC-derived H-scores and FFPE-based Volante scores supports the validity of this approach as a surrogate for conventional pathology. Furthermore, digital image analysis of SSTR2 expression in FFPE tissues has been shown to correlate strongly with manual pathological scoring, providing an objective basis for receptor quantification [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Our findings in LBC specimens extend this concept to cytological samples. The observed dynamic IHC approach revealed functional shifts\\u0026mdash;specifically the upregulation or maintenance of high intensity fractions in responders and the depletion of receptor-positive cells in nonresponders\\u0026mdash;offering a more functional prediction of therapeutic response compared to static evaluation. To our knowledge, this is the first study to demonstrate the feasibility of AI-based quantitative cytometry in capturing drug-induced receptor dynamics in patient-derived pituitary models. By addressing the challenge of low cellular yield inherent in \\u003cem\\u003eex vivo\\u003c/em\\u003e assays, this dynamic LBC approach serves as a preliminary and promising exploratory proof-of-concept for integrated functional and pathological assessments, though further large-scale validation is warranted to confirm its predictive value.\\u003c/p\\u003e \\u003cp\\u003eAn additional observation in this dataset was the association between cabergoline responsiveness and higher SSTR5 expression. This finding suggests receptor-level crosstalk as a potential mechanistic link. Hetero-oligomerization between D2R and SSTR5 can potentiate Gi/o signaling and alter desensitization/internalization, offering a rationale for enhanced dopamine agonist efficacy in SSTR5-high tumors [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. Thus, this \\u003cem\\u003eex vivo\\u003c/em\\u003e assay is a promising translational tool for identifying previously unrecognized predictors and mechanisms of drug sensitivity.\\u003c/p\\u003e \\u003cp\\u003eConversely, we observed a partial discordance between \\u003cem\\u003eex vivo\\u003c/em\\u003e responses to SRLs and established predictive markers. Limitations of this study are as follows. First, the model-intrinsic features of patient-derived spheroids can introduce selection and context effects; unlike organoids which maintain complex tissue architecture, our spheroid culture mainly enriches sphere-forming subpopulations and depletes stromal, vascular, and immune compartments. Extracellular matrix composition/mechanics and serum-borne cues may shift differentiation state and receptor trafficking such that in-culture SSTR/D2R abundance or granulation patterns may not perfectly reflect bulk-tumor readouts [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Second, interobserver variability in radiologic and pathologic classifications and spatial intratumoral heterogeneity can decouple imaging surrogates and receptor/secretory phenotypes from cell-intrinsic drug sensitivity. Third, given our sample size, chance variation may have contributed to the observed discordance with established predictive markers. This limitation also extended to the AI-based LBC analysis, where a small number of cases and the absence of a predefined cutoff value precluded formal statistical verification, underscoring the exploratory nature of this pilot sub-analysis and necessitating further validation of the observed trends in larger cohorts. Fourth, although our responder definition lacked a predefined biological threshold, the tightly converged effect sizes across all three agents (median relative viabilities of 84\\u0026ndash;86%; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb) strongly suggest that our statistical approach effectively captured a biologically distinct phenotype rather than random technical noise. Building on these results, establishing an optimal cutoff value to redefine responsiveness will be a crucial next step to further refine this assay and enhance its clinical utility. Finally, in this cohort, many patients achieved oncologic remission by surgery, and additional follow-up was required before medical therapy was indicated for residual or recurrent disease, which precludes the correlation within the present study of \\u003cem\\u003eex vivo\\u003c/em\\u003e effect sizes with subsequent tumor shrinkage or biochemical remission. While this pilot study does not immediately alter current clinical guidelines, applying AI-based analysis to the entire cohort and integrating multiomics analyses would provide deeper molecular insights and establish a critical foundation for future precision medicine, which remains a vital next step.\\u003c/p\\u003e \\u003cp\\u003eIn summary, using a spheroid-based Pd3D culture platform previously described in case reports, we demonstrated that \\u003cem\\u003eex vivo\\u003c/em\\u003e cell viability responses show concordance with established clinical predictors of drug response in acromegaly. These findings support the validity of this assay and suggest that it may serve as a complementary predictive tool alongside conventional clinicopathological markers for optimizing drug selection to achieve tumor control.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors sincerely thank Professor Junya Fukuoka for his extensive support and expertise in AI-based quantitative immunocytochemical assessment of LBC specimens. We also gratefully acknowledge Ms. Mari Motoyoshi, Ms. Ikue Saita, Ms. Marina Saito, and Ms. Mayuko Nikabu for their substantial support with specimen transportation, handling, and management, which was essential for the successful completion of this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yasutaka Tsujimoto, Atsushi Ishida, and Hidenori Fukuoka. The first draft of the manuscript was written by Yasutaka Tsujimoto and Hidenori Fukuoka, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\\u003c/p\\u003e\\u003cp\\u003eFunding This work was partially supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (KAKENHI; grant numbers 19K09003 (HF), 22K08654 (HF), 25K02700 (HF), and 23K15412 (HS)); the Program for Forming Japan\\u0026apos;s Peak Research Universities (J-PEAKS) from the Japan Society for the Promotion of Science (JSPS) (TA); and by AMED under Grant Number JP256f0137011 (TA).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e The authors have no relevant financial or non-financial interests to disclose.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval\\u003c/strong\\u003e All procedures complied with the protocol approved by the Research Ethics Committee of Kobe University Hospital and Moriyama Memorial Hospital (IRB #1363 and B240223, respectively) and adhered to the Declaration of Helsinki.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to participate\\u003c/strong\\u003e Written informed consent was obtained from all participants prior to surgery and sample collection.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e Restrictions apply to the availability of some or all data generated or analyzed during this study to preserve patient confidentiality or because they were used under license. 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Pituitary 23:171\\u0026ndash;181. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s11102-019-01020-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s11102-019-01020-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCoopmans EC, Korevaar TIM, van Meyel SWF et al (2020) Multivariable Prediction Model for Biochemical Response to First-Generation Somatostatin Receptor Ligands in Acromegaly. 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Pituitary 27:33\\u0026ndash;43. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s11102-023-01362-z\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s11102-023-01362-z\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHeck A, Ringstad G, Fougner SL et al (2012) Intensity of pituitary adenoma on T2-weighted magnetic resonance imaging predicts the response to octreotide treatment in newly diagnosed acromegaly: Intensity of somatotroph adenoma on T2-weighted MRI. 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Endocr Relat Cancer 23:871\\u0026ndash;881. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1530/ERC-16-0356\\u003c/span\\u003e\\u003cspan address=\\\"10.1530/ERC-16-0356\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBrzana J, Yedinak CG, Gultekin SH et al (2013) Growth hormone granulation pattern and somatostatin receptor subtype 2A correlate with postoperative somatostatin receptor ligand response in acromegaly: a large single center experience. Pituitary 16:490\\u0026ndash;498. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s11102-012-0445-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s11102-012-0445-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOshino S, Saitoh Y, Kasayama S et al (2006) Short-term preoperative octreotide treatment of GH-secreting pituitary adenoma: predictors of tumor shrinkage. 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Eur J Endocrinol 182:595\\u0026ndash;605. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1530/EJE-19-0840\\u003c/span\\u003e\\u003cspan address=\\\"10.1530/EJE-19-0840\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCozzi R, Attanasio R, Montini M et al (2003) Four-year treatment with octreotide-long-acting repeatable in 110 acromegalic patients: predictive value of short-term results? 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Cytopathology 32:813\\u0026ndash;818. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/cyt.13030\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/cyt.13030\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRocheville M, Lange DC, Kumar U et al (2000) Receptors for dopamine and somatostatin: formation of hetero-oligomers with enhanced functional activity. Science 288:154\\u0026ndash;157. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1126/science.288.5463.154\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/science.288.5463.154\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"},{\"header\":\"Table 2\",\"content\":\"\\u003cp\\u003eTable 2 is available in the Supplementary Files section.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"pituitary\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pitu\",\"sideBox\":\"Learn more about [Pituitary]()\",\"snPcode\":\"11102\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11102/3\",\"title\":\"Pituitary\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Acromegaly, Patient-derived 3D cultures, Somatostatin receptor, Digital pathology, Precision medicine\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9461574/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9461574/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003ePurpose:\\u003c/h2\\u003e \\u003cp\\u003ePersonalized therapy in acromegaly is limited by interindividual variability in drug responses and the lack of robust markers predicting tumor shrinkage, rather than biochemical control alone. To test whether \\u003cem\\u003eex vivo\\u003c/em\\u003e drug-induced viability changes in patient-derived 3D cultures (Pd3D) of GH\\u0026ndash;secreting pituitary adenomas reflect tumor cell-intrinsic pharmacological sensitivity and align with established clinical predictors.\\u003c/p\\u003e\\u003ch2\\u003eMethods:\\u003c/h2\\u003e \\u003cp\\u003eSpheroid-based Pd3D cultures were established from 27 patients with acromegaly. Cultures were exposed to octreotide, cabergoline, pasireotide, or vehicle control. We assessed cell viability changes; sample-level responder status (viability reduction \\u003cem\\u003evs\\u003c/em\\u003e vehicle, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05); and associations between responder status and known predictive markers, including clinical characteristics, MRI findings, dynamic drug tests, and pathological features. In 6 cases, AI-based digital image analysis quantified pre- and post-treatment SSTR2 expression in liquid-based cytology (LBC).\\u003c/p\\u003e\\u003ch2\\u003eResults:\\u003c/h2\\u003e \\u003cp\\u003eAll agents modestly reduced median cell viability (84\\u0026ndash;86%, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), with responder rates of 33\\u0026ndash;40%. Concordance with established predictors was observed: octreotide responders correlated with T2 hypointensity (88% \\u003cem\\u003evs\\u003c/em\\u003e 44%, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.04); cabergoline with positive bromocriptine tests (100% \\u003cem\\u003evs\\u003c/em\\u003e 45%, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.03); and pasireotide with sparsely granulated patterns (64% \\u003cem\\u003evs\\u003c/em\\u003e 19%, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.04). AI-based dynamic analysis demonstrated that \\u003cem\\u003eex vivo\\u003c/em\\u003e responders showed relatively stable SSTR2 expression after treatment, whereas nonresponders exhibited marked depletion.\\u003c/p\\u003e\\u003ch2\\u003eConclusion:\\u003c/h2\\u003e \\u003cp\\u003eSpheroid-based Pd3D \\u003cem\\u003eex vivo\\u003c/em\\u003e viability assays revealed modest but significant cohort-level effects and sample-level concordance with clinical predictors, supporting the platform's validity. Additionally, AI-based quantification of SSTR2 dynamics captured functional receptor shifts.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Ex Vivo Drug Sensitivity in Patient-derived 3D Cultures in Acromegaly and its Association with Clinical Predictors\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-04 05:45:16\",\"doi\":\"10.21203/rs.3.rs-9461574/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-05-17T06:45:10+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"163653142685290968015322203317053358654\",\"date\":\"2026-04-26T07:23:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-24T14:07:47+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"76037374816962840944049856990904647084\",\"date\":\"2026-04-23T15:53:35+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-04-20T15:32:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-04-20T07:45:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-04-20T07:44:56+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Pituitary\",\"date\":\"2026-04-19T11:19:12+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"pituitary\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pitu\",\"sideBox\":\"Learn more about [Pituitary]()\",\"snPcode\":\"11102\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11102/3\",\"title\":\"Pituitary\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"b63fc7ed-ea1c-426f-996e-4dc244de9900\",\"owner\":[],\"postedDate\":\"May 4th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-05-17T06:45:10+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"in-revision\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-17T06:54:16+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-04 05:45:16\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9461574\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9461574\",\"identity\":\"rs-9461574\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}