Three Decades of Gastrointestinal Stromal Tumor Incidence in the United States: Joinpoint Trend Analysis and ARIMA Forecasting Through 2032

Three Decades of Gastrointestinal Stromal Tumor Incidence in the United States: Joinpoint Trend Analysis and ARIMA Forecasting Through 2032 | 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 Three Decades of Gastrointestinal Stromal Tumor Incidence in the United States: Joinpoint Trend Analysis and ARIMA Forecasting Through 2032 Ali Hemade, Pascale Salameh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7719966/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Gastrointestinal stromal tumors (GIST) are the most common mesenchymal neoplasms of the gastrointestinal tract, with incidence rising globally over the past two decades. The evolution of targeted therapies, particularly imatinib, and recent systemic disruptions such as the COVID-19 pandemic may have influenced incidence patterns at the population level. Methods: We used Surveillance, Epidemiology, and End Results (SEER) data to examine U.S. GIST cases diagnosed between 1992 and 2021. Age-adjusted incidence rates (AAIRs) were calculated per 100,000 persons standardized to the 2000 U.S. population. Temporal trends were analyzed using Joinpoint regression to identify statistically significant inflection points, and Auto-Regressive Integrated Moving Average (ARIMA) models were employed to forecast incidence through 2032. Model performance was assessed via root mean squared error (RMSE), mean absolute percentage error (MAPE), and Theil’s U statistic using a train–test split. Results: The overall mean AAIR for GIST during 1992–2021 was 0.66 per 100,000, with higher rates among Black populations (1.22) compared with White (0.55) and Asian/Pacific Islander groups (0.94). Joinpoint analysis identified two significant breakpoints: 2002, marking a deceleration in incidence growth, and late 2019, where a sharp increase emerged. The ARIMA(0,1,0) with drift model provided the best fit and forecasted AAIR exceeding 2.5 per 100,000 by 2032. Conclusions: GIST incidence in the United States has increased steadily over three decades, with clear demographic disparities and distinct temporal inflection points corresponding to therapeutic and systemic shifts. These projections signal a growing clinical and public-health burden, emphasizing the need for expanded diagnostic capacity, equitable access to molecular testing, and ongoing surveillance to monitor post-pandemic trends. Gastrointestinal stromal tumor (GIST) SEER Epidemiology Age-adjusted incidence rate Joinpoint regression ARIMA forecasting Temporal trends Figures Figure 1 Figure 2 Introduction Gastrointestinal stromal tumors (GIST) represent the most prevalent mesenchymal neoplasms of the digestive tract, arising chiefly from the interstitial cells of Cajal that orchestrate peristaltic activity throughout the gastrointestinal (GI) tract [1]. Although infrequent compared with epithelial malignancies, GISTs command disproportionate clinical attention because of their unpredictable biological behavior, ranging from indolent nodules to fulminant sarcomas capable of widespread metastasis [2]. Early pathological descriptions in the pre-molecular era often misclassified GISTs as leiomyomas or leiomyosarcomas, but advances in immunohistochemistry—particularly the discovery of ubiquitous KIT (CD117) and DOG-1 expression—have refined diagnostic accuracy and underscored their distinct nosological identity [3]. Contemporary management therefore hinges on a nuanced understanding of tumor biology rather than purely histologic appearance, aligning therapeutic strategies with the molecular circuitry that drives oncogenesis [4]. Against this backdrop, rigorous epidemiologic surveillance has become critical for planning resource allocation, designing targeted screening initiatives, and benchmarking advances in precision therapy [5]. Yet, despite two decades of therapeutic progress, population-level trends in GIST incidence remain incompletely characterized on temporal, racial, and geographic axes, warranting renewed investigation [6]. Pathogenetically, the archetypal driver lesions in GIST involve gain-of-function mutations in the receptor tyrosine kinases KIT or PDGFRA , which constitutively activate downstream MAPK and PI3K–Akt signaling, thereby fostering unchecked cellular proliferation and survival [7]. Approximately 75–80 % of tumors harbor exon 11 or exon 9 KIT mutations, whereas 5–10 % display exon 18 PDGFRA substitutions—most classically Asp842Val—that confer primary resistance to first-line tyrosine-kinase inhibition [8]. Less common oncogenic culprits include succinate dehydrogenase deficiency, BRAF V600E alterations, NF1 loss, or NTRK gene fusions, each imparting unique therapeutic vulnerabilities and prognostic signatures [9]. Molecular subclassification has therefore transcended academic taxonomy to become the backbone of personalized treatment, dictating adjuvant protocols, surveillance intensity, and clinical trial eligibility. [10] The paradigm-shifting approval of imatinib mesylate by the U.S. Food and Drug Administration (FDA) in 2002 for unresectable or metastatic GIST epitomizes the triumph of rational, mutation-directed therapy, transforming a once-intractable sarcoma into a chronic, controllable disease for many patients [11]. Subsequent trials have extended imatinib into the adjuvant realm, demonstrating durable improvements in recurrence-free and overall survival for high-risk cohorts when administered for three years or longer [12]. Despite these advances, comprehensive, population-based surveillance remains indispensable for quantifying the real-world impact of novel therapies, capturing secular changes in incidence, and identifying emergent disparities [5]. Age-adjusted incidence rates (AAIR) in the United States have historically hovered between 0.55 and 0.78 per 100,000 persons, yet more recent SEER analyses suggest a steady upward trajectory over the past two decades [5]. A 2024 SEER cohort study encompassing >12,000 cases corroborated this trend, documenting a significant annual increase across most demographic strata and projecting further escalation through at least 2030 [2]. International data echo these findings; for instance, an Italian registry covering >4 million inhabitants reported a mean incidence of 1.1 per 100,000 between 2010 and 2020, reinforcing the global nature of the rise [13]. Systematic reviews likewise estimate worldwide incidence at 10–15 per million, albeit with pronounced regional heterogeneity, underscoring methodological differences and possible environmental or genetic modifiers [14]. Nonetheless, whether these observed increases reflect genuine disease proliferation, enhanced diagnostic scrutiny, or coding artifacts remains the subject of vigorous debate [4]. Disparities in GIST burden by race, sex, and socioeconomic context further complicate epidemiologic appraisal [15]. African American patients consistently exhibit nearly double the incidence of their White counterparts and disproportionately worse survival outcomes, implicating both biological and systemic factors in disease course [6]. Multi-ethnic analyses reveal comparatively favorable survival among Asian and Pacific Islander populations, hinting at ancestry-linked genomic patterns or differential access to specialized care [16]. Sex-based variation remains less pronounced but detectable, with males experiencing marginally higher incidence and mortality, possibly attributable to divergent exposure profiles or hormonal influences on tumor biology [5]. Geographic gradients also emerge: urban, high-resource regions report elevated incidence, likely mirroring superior diagnostic infrastructure, whereas rural catchment areas may under-capture cases, thereby masking true prevalence [5]. These inequities underscore the necessity for nuanced, disaggregated surveillance capable of informing tailored public-health interventions [15]. Temporal interpretation is equally fraught because the therapeutic landscape has evolved dramatically since the late 1990s [11]. The introduction of imatinib not only improved survival but plausibly altered diagnostic practices by incentivizing earlier detection of smaller, potentially resectable lesions that might formerly have escaped notice [12]. Conversely, the COVID-19 pandemic provoked widespread disruptions in elective endoscopy, imaging, and outpatient oncology services, leading to cascading delays in tumor identification and staging across malignancies, including sarcomas [17]. An emerging literature suggests that pandemic-related diagnostic latency may transiently inflate incident presentations once routine services resume, thereby distorting short-term epidemiologic curves [17]. Parsing the relative contributions of therapeutic innovation versus system-level perturbation therefore demands sophisticated time-series methodologies capable of detecting inflection points and forecasting future trajectories with quantifiable uncertainty [2]. Joinpoint regression excels at revealing statistically significant shifts in longitudinal trends, whereas Auto-Regressive Integrated Moving Average (ARIMA) models extrapolate beyond observed data to anticipate future incidence, both indispensable for rigorous public-health planning [2]. Notwithstanding prior SEER studies, many analyses truncate follow-up at 2011 or 2015, omit pandemic-era data, or lack robust forecasting frameworks, leaving critical gaps in our understanding of contemporary GIST epidemiology [5]. Accordingly, a comprehensive appraisal spanning 1992–2021 is essential to contextualize two decades of molecular diagnostics, targeted therapeutics, and unprecedented healthcare disruptions within a single, coherent narrative [2]. Leveraging the expansive SEER repository affords unparalleled statistical power and demographic breadth, while modern analytic techniques enable fine-grained detection of subtle trend inflections that might otherwise remain obscured [14]. Such an approach promises actionable insights into the timing and magnitude of incidence shifts, thereby guiding allocation of endoscopic resources, informing genetic-counseling protocols, and optimizing survivorship services as the GIST population grows [6]. Moreover, accurate projections to 2032 will aid policymakers in forecasting pharmaceutical demand for TKIs, surgical case-load planning, and equitable distribution of sarcoma-specialist referrals [10]. Ultimately, bridging epidemiologic surveillance with molecular oncology stands to accelerate progress toward precision-public health paradigms that deliver personalized care at scale [16]. Against this backdrop, the present study pursues four interrelated objectives. First, we quantify temporal trends in AAIR for GIST across the United States from 1992 to 2021, stratifying by age, sex, race, and treatment modality to delineate nuanced demographic patterns. Second, we apply Joinpoint regression to identify statistically significant inflection points—“break years”—that coincide with major therapeutic or systemic events, thereby contextualizing epidemiologic shifts within broader healthcare milestones. Third, we construct and validate ARIMA models to forecast AAIR through 2032, providing a data-driven basis for forward-looking resource allocation and survivorship planning. Finally, we interpret these findings in light of existing literature on molecular pathogenesis, therapeutic breakthroughs, and health-system disruptions to furnish a holistic narrative that links bench, bedside, and population health. By integrating robust statistical methodology with the granularity of modern cancer registries, our analysis aspires to refine the collective understanding of how scientific innovation and societal perturbations jointly sculpt the epidemiology of this prototypical precision-oncology malignancy. Methods Statistical Analysis This study utilized data from the Surveillance, Epidemiology, and End Results (SEER) Program, a national population-based cancer registry that provides detailed information on cancer incidence, treatment, and survival in the United States. Data on gastrointestinal stromal tumors (GIST) were extracted for the period 1992 to 2021, with the primary outcome being the age-adjusted incidence rate (AAIR) per 100,000 persons, adjusted to the 2000 U.S. standard population. The dataset was stratified by race, sex, and treatment type, including surgical intervention, chemotherapy, and radiotherapy. Descriptive statistics were computed to summarize the distribution of GIST incidence across different demographic groups. Mean, median, and interquartile ranges were calculated for each stratification, as shown in Table 1. To evaluate temporal trends in GIST incidence, Joinpoint Regression Analysis was performed to detect statistically significant changes in incidence trends. This model estimates breakpoints, known as joinpoints, where the trend significantly shifts and calculates the Annual Percentage Change (APC) before and after each breakpoint. The statistical significance of each joinpoint was determined using Monte Carlo permutation tests, with a significance threshold of p < 0.05. To forecast future GIST incidence trends, Auto-Regressive Integrated Moving Average (ARIMA) models were employed. Multiple ARIMA models were tested, and the final model was selected based on the Akaike Information Criterion (AIC) to ensure optimal fit. The forecasting model was validated using a train-test split approach, where data prior to 2015 were used for model training, while data from 2015 to 2021 were reserved for testing. The predictive performance of the ARIMA models was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Theil’s U statistic, which compares the model’s predictive power against a naïve forecasting approach. To ensure robustness, both an auto-selected ARIMA model and a manually specified ARIMA(1,1,1) model were tested, and their accuracy was compared, as shown in Table 3. The final model was then used to project GIST incidence rates from 2022 to 2032. All statistical analyses were conducted in R (version 4.2.2), and figures were generated using ggplot2. Results The overall mean age-adjusted incidence rate (AAIR) for GIST between 1992 and 2021 was 0.66 per 100,000 persons, with values ranging from 0.00 to 2.20. Incidence rates varied significantly by race and sex, with Black populations exhibiting the highest mean incidence rate (1.22 per 100,000 persons), followed by Asian/Pacific Islanders (0.94 per 100,000) and White individuals (0.55 per 100,000). Sex-based differences were also observed, with males experiencing higher incidence rates than females (0.74 vs. 0.59 per 100,000 persons, respectively). Treatment data revealed that 50.3% of patients underwent surgery, while 24.6% received chemotherapy, and 60% received radiotherapy, further summarized in Table 1. Table 1. Summary Statistics of GIST Incidence (1992–2021) Variable Mean Median Min Max Overall AAIR 0.66 0.70 0.00 2.20 White 0.55 0.60 0.00 1.90 Black 1.22 1.25 0.10 4.10 Asian/Pacific Islander 0.94 1.05 0.00 2.40 Male 0.74 0.80 0.00 2.10 Female 0.59 0.60 0.00 2.30 Joinpoint regression analysis identified two significant breakpoints in the incidence trend, as detailed in Table 2. The first breakpoint occurred in 2002 (±0.87 years), marking a deceleration in incidence growth. This shift coincides with the FDA approval of Imatinib (Gleevec), the first targeted therapy for GIST, which likely contributed to earlier detection and reduced late-stage diagnoses. The second breakpoint was detected in 2019.7 (±0.12 years), where a sharp increase in incidence was observed, potentially due to healthcare disruptions during the COVID-19 pandemic that delayed routine screening and cancer detection. The joinpoint regression model demonstrated a strong fit, with an adjusted R² of 0.9544, indicating that the model explained over 95% of the variance in incidence trends. Table 2. Joinpoint Regression Breakpoints and Trends Break Year Annual Percentage Change (APC) Trend Before 2002 +0.0885% Increasing 2002 - 2019 -0.0774% Slowed growth After 2019 +0.9748% Sharp increase The ARIMA model comparison demonstrated that the ARIMA(0,1,0) with drift model was the best fit for forecasting GIST incidence trends, as summarized in Table 3. Table 3. ARIMA Model Performance Evaluation Metric Training Set Test Set RMSE 0.1025 0.5412 MAPE 13.66% 16.39% Theil’s U NA 1.165 Forecasting projections indicate a continued increase in GIST incidence rates over the next decade, with the rate expected to exceed 2.5 per 100,000 persons by 2032, as detailed in Table 4. Table 4. Forecasted GIST Incidence Rates (2022–2032) Year Projected Incidence Rate (per 100,000 persons) 2022 1.70 2025 1.95 2030 2.30 2032 2.50 The model suggested a steady linear increase in incidence, likely influenced by improved detection, changing population risk factors, and increased utilization of screening methods. However, confidence intervals widened over time, indicating increasing uncertainty in long-term projections. Discussion Summary of Key Findings Our population‐based analysis estimated a sustained rise in the age-adjusted incidence of gastrointestinal stromal tumors (GIST), with the overall mean AAIR higher than historical registry estimates and showing clear heterogeneity by demographic strata, consistent with contemporary SEER-based reports documenting increasing GIST incidence across multiple digestive sites and population groups [2]. Race- and sex-stratified summaries revealed materially higher rates in certain racial groups and a modest male predominance, echoing prior registry analyses and meta-epidemiologic observations of disparity in GIST burden and outcomes [15]. All incidence estimates were age-standardized to the year-2000 U.S. standard population to enable valid temporal comparisons, in alignment with established national vital statistics methodology for age adjustment [18]. Use of SEER registry–compatible definitions and denominators supports generalizability and reproducibility of our estimates within a widely used national cancer surveillance framework [19]. We identified two statistically significant joinpoints—an inflection around calendar year 2002 indicating deceleration of growth and a second inflection at ~2019.7 indicating a sharp contemporaneous increase—derived via established permutation-based Joinpoint regression techniques optimized for cancer trend surveillance [20]. The joinpoint in the early 2000s coincides with the clinical adoption of imatinib for advanced GIST, which fundamentally altered disease natural history and likely shifted the observed case mix through earlier treatment and improved survival [21]. The later joinpoint at 2019.7 temporally overlaps the emergence of COVID-19–related disruptions in oncology pathways and endoscopic services, which multiple studies have shown to depress diagnostic activity acutely and generate downstream backlogs [22]. For forward projection, the best-fit time-series specification was an ARIMA(0,1,0) with drift, a parsimonious random-walk model that outperformed higher-order alternatives and aligns with prior cancer-trend forecasting experience using integrated autoregressive models [23]. Under this specification, forecasts indicated continued growth with point estimates surpassing 2.5 per 100,000 by 2032, consistent with comparative evaluations showing that simple stochastic trend models can yield robust medium-term incidence trajectories in oncology surveillance [24]. Collectively, these findings are concordant with the broader literature describing rising GIST incidence in population data and reinforce the need to interrogate diagnostic, therapeutic, and systems-level drivers of these trends [4]. External comparisons with European population-based registries, including nationwide analyses from the Netherlands, similarly document increasing GIST incidence, suggesting shared transnational determinants in detection and care delivery [25]. Parallel reports have highlighted the growing identification of incidentally detected small gastric GISTs, a pattern that can elevate observed incidence through stage migration effects without necessarily reflecting a true etiologic surge [26]. Registry-linkage studies using pathology archives have also demonstrated rising GIST case ascertainment over two decades, further supporting that secular improvements in recognition contribute to the observed trend [27]. Against this backdrop, the 2002 joinpoint plausibly reflects early therapeutic epoch changes surrounding tyrosine kinase inhibition, when mutation-specific sensitivity to imatinib began to influence diagnostic vigilance and clinical pathways [28]. The net effect observed here—initial growth with post-2002 deceleration, followed by a pronounced 2019.7 inflection—fit with a narrative of therapeutic maturation followed by pandemic-related perturbation, consistent with outcomes-oriented trend analyses in GIST [29]. The late-2019 acceleration aligns temporally with large documented contractions in endoscopic and cancer diagnostic throughput and consequent rebound phenomena as services recovered, which would be expected to transiently inflate incidence as deferred patients present [30]. Interpretation of Trends Early-2000s deceleration and the imatinib era The deceleration of GIST incidence growth around 2002 is biologically and clinically plausible in light of the transformative impact of imatinib, which from its landmark trial onward reshaped the trajectory of advanced disease and catalyzed earlier molecularly informed management [21]. Early translational work established that genotype strongly modulates imatinib responsiveness, with KIT exon-specific differences informing dosing and expectations of response, which likely spurred more targeted diagnostic confirmation rather than late symptomatic discovery [28]. As second-line and third-line kinase inhibitors were introduced for imatinib-refractory disease, clinical pathways incentivized earlier referral and systematic mutation testing, gradually stabilizing case ascertainment while improving survival [21]. Regulatory approval and dissemination of sunitinib in 2006, with documented progression-free survival benefits after imatinib failure, further normalized long-term therapeutic stewardship that can reduce late emergency presentations [31]. Subsequent demonstration of regorafenib efficacy after failure of prior TKIs extended disease control horizons and entrenched a chronic disease model for metastatic GIST, altering patterns of surveillance and timing of diagnosis [32]. Longitudinal analyses of sunitinib-treated cohorts corroborated durable benefit after imatinib failure, which may translate at the population level into more planned evaluations and fewer incidental late-stage detections [33]. Mechanistic studies clarified that secondary resistance often emerges through KIT or PDGFRA re-mutations yet remains kinase-dependent, supporting continued inhibition strategies that encourage structured follow-up rather than episodic crisis-driven care [28]. In the adjuvant setting, randomized evidence showed that extending imatinib to three years improves both recurrence-free and overall survival compared with one year, which plausibly decreased the share of late-stage recurrences entering incidence streams as advanced metastatic conversions [34]. A decade follow-up of the same cohort confirmed sustained overall survival benefit from longer adjuvant therapy, reinforcing therapeutic shifts that can reshape the clinical spectrum at diagnosis over time [12]. Contemporary practice guidelines from expert societies reflected these data, enshrining molecular testing and risk-adapted adjuvant therapy as standards, thereby promoting earlier risk stratification and structured surveillance [35]. In parallel, improvements in diagnostic immunohistochemistry—most notably the adoption of DOG1 staining alongside KIT—enhanced the sensitivity of GIST identification, reducing misclassification but also promoting consistent attribution of small subclinical lesions that would previously have been missed [36]. Subsequent studies confirmed DOG1’s utility across mutation subtypes and even in KIT-negative tumors, standardizing diagnostic workflows and potentially smoothing secular increases into a more stable post-adoption incidence slope [37]. Taken together, the imatinib era introduced durable survival gains and systematic, genotype-informed care pathways that can slow apparent growth in incidence by shifting detection toward earlier, planned contexts and away from late symptomatic discovery, matching the deceleration we observed after 2002 [37]. Post-2019 increase and COVID-19–related diagnostic disruption The sharp post-2019 acceleration in incidence is temporally synchronous with the onset of the COVID-19 pandemic, which triggered rapid, unprecedented reductions in endoscopic capacity and oncology diagnostic activity across multiple health systems [30]. Modeling and national operational data from England documented a large accumulated backlog in gastrointestinal endoscopy, with forecasts indicating prolonged recovery periods even under aggressive mitigation, a pattern expected to drive a catch-up surge in detected lesions once services resumed [38]. Oncology delivery data from integrated U.S. systems showed immediate declines in new patient encounters and cancer-related care in early 2020, consistent with widespread care deferral among symptomatic and screen-eligible populations [39]. Multiple observational studies reported reduced diagnostic throughput for gastrointestinal malignancies during pandemic peaks, followed by partial rebounds that still lagged pre-pandemic volumes, implying a rolling backlog entering subsequent years [40]. Reviews of endoscopy and cancer screening services synthesized these findings and emphasized that the backlog would persist without deliberate rebalancing of resources, mirroring the step-up effect in our joinpoint around 2019 [41]. Guidance and safety frameworks for endoscopy during COVID-19 recommended procedural triage, enhanced infection control, and deferment of nonurgent indications, which collectively constrained diagnostic access for indolent or nonspecific presentations typical of many GISTs [42]. Surveys of endoscopy units likewise documented profound capacity reductions and workflow changes that directly translate into delayed diagnosis and a subsequent surge of deferred cases when normal operations resumed [43]. Beyond the procedural bottleneck, pandemic-era health-seeking behavior shifted due to infection concerns and logistical barriers, lowering timely referral rates and compounding the diagnostic queue that was later cleared in bursts [22]. International series described stage migration and reduced incident cancer detections during the first pandemic waves, with later periods showing compensatory increases as programs recovered, a dynamic consistent with the abrupt incidence inflection we detected [44]. Although GIST is not a screening-program malignancy, many tumors are discovered incidentally during upper endoscopy or cross-sectional imaging for nonspecific symptoms, so any broad reduction in GI diagnostics can delay discovery and then yield clustered catch-up diagnoses [45]. Complementary U.K. database analyses confirmed national-level contractions to fractions of baseline endoscopic activity at the pandemic peak, further supporting that backlog mechanics materially affected gastrointestinal tumor detection patterns [30]. These service disruptions and their phased recovery likely inflated near-term incidence as deferred, symptomatic, or previously surveilled patients re-entered care, producing an apparent step-change rather than a gradual slope increase [38]. Importantly, contemporaneous literature also indicates that pandemic-era diagnostic deferrals were socially patterned, with the largest care gaps among populations already experiencing cancer disparities, which could accentuate race- and sex-specific incidence heterogeneity [2]. The aggregate picture therefore supports a causal chain in which procedural contractions, referral delays, and patient hesitancy produced a transient deficit in incident detections followed by a concentrated surplus, manifesting as a joinpoint in late 2019 with a sharp positive slope [40]. From a methodological perspective, ARIMA models applied to epidemiologic time series are well suited to capture such nonstationary shocks via level shifts and drift, and prior oncology forecasting has shown that low-order integrated processes can accommodate pandemic-era perturbations with acceptable calibration [23]. Comparative work in cancer surveillance has further demonstrated that parsimonious stochastic models often outperform more complex specifications when the signal contains policy or systems shocks, reinforcing confidence in the ARIMA(0,1,0)+drift choice here [24]. While our projections indicate continued growth to beyond 2.5 per 100,000 by 2032, it is critical to interpret this trajectory in the context of lingering diagnostic backlogs and evolving endoscopy capacity, factors that can sustain elevated detection for several years [38]. Simultaneously, improvements in guideline-concordant imaging and molecular work-up may continue to increase ascertainment of small, asymptomatic GISTs, which would raise observed incidence even if true etiologic risk remains stable [35]. Epidemiologic heterogeneity—by anatomic site, age, and mutation profile—adds another layer, as stomach-predominant, KIT-mutant tumors may be more sensitive to detection trends tied to upper endoscopy than small-bowel tumors, potentially shifting site-specific incidence patterns during recovery [46]. Finally, persistent inequities in access and referral may differentially modulate the pace of backlog clearance across communities, necessitating focused surveillance of race- and sex-specific trends to prevent widening disparities as systems normalize [15]. Integrating biology, care pathways, and surveillance Foundational discoveries of oncogenic KIT mutations and their therapeutic tractability transformed GIST from a historically lethal sarcoma to a model of precision oncology, anchoring contemporary interpretations of incidence dynamics in the interplay between biology and care systems [47]. Early prognostic series linked specific KIT alterations to outcomes, accelerating the push for molecular testing that, over time, improved classification accuracy and may have moderated unmeasured misclassification in registry trends [48]. Conceptual models positioning GIST as an interstitial cell of Cajal–derived neoplasm clarified disease ontogeny and aligned clinical detection with site-specific symptomatology and incidental discovery pathways [49]. Comprehensive reviews of GIST biology and diagnosis emphasized the increasing role of immunohistochemistry and mutation profiling in routine practice, supporting more consistent case ascertainment and staging [50]. Seminal overviews underscored the rapid evolution from pathologic curiosity to a defined kinase-driven entity, an arc that naturally impacts incidence through both real changes in survival and apparent changes in diagnostic coding and recognition [46]. Recent clinical reviews similarly note that improved detection and risk stratification, along with targeted therapy, have increased reported incidence in population registries, reinforcing the multi-factorial backdrop for our findings [45]. Pathology-focused syntheses highlight that KIT/DOG1 immunophenotyping and attention to site-dependent morphology improved GIST case capture over time, helping to standardize reporting across centers and periods [51]. Mutation-specific treatment nuances—such as limited imatinib activity in PDGFRA D842V tumors compared with other PDGFRA variants—have shaped surveillance intensity and referral timing, further intertwining biology with observed incidence [52]. Genotype-phenotype analyses also informed second-line selection, as sunitinib activity varies by primary and secondary kinase genotype, which can influence longitudinal follow-up structures and transitions in care [53]. Against this biologic-therapeutic canvas, broad pandemics and system shocks act as exogenous modifiers of detection, triage, and backlog clearance, producing pattern breaks detectable by joinpoint methods when the shocks are large and temporally concentrated [20]. Methodological refinements in joinpoint analysis—including robust permutation testing and clustering approaches—improve the specificity of change-point detection and support our inference that the 2002 and 2019.7 inflections represent true shifts rather than statistical noise [54]. Comparisons across forecasting families in cancer surveillance show that, for data streams affected by secular improvements and episodic shocks, ARIMA-class models with drift can balance bias and variance while providing transparent, policy-relevant projections [24]. This synthesis suggests that the early-2000s deceleration reflects maturation of targeted therapy and standardized diagnostics, whereas the post-2019 surge reflects backlog-driven catch-up following pandemic disruptions, together yielding the biphasic pattern observed in our analysis [41]. Comparison with Existing Literature Our findings align with recent SEER-based cohort analyses showing that GIST incidence has risen across major digestive organ sites over the last two decades, reinforcing that the upward trajectory is a reproducible population-level phenomenon rather than an artifact of a single registry or time window [2]. This pattern is concordant with broader global syntheses estimating a worldwide incidence of roughly 10–15 per million, while emphasizing substantial regional heterogeneity that likely reflects differences in diagnostic access, coding practices, and true biological variability [4]. Parallel European registry work, including detailed regional studies from Spain’s Region of Murcia, similarly documents increasing case ascertainment and supports the interpretation that improved recognition and recording contribute to secular rises in reported incidence [55]. A contemporary U.S. population-based analysis of 23,001 patients further substantiates that age-adjusted incidence rates for common digestive GISTs increased between 2000 and 2019, suggesting shared determinants across settings and analytic approaches [2]. Narrative and systematic reviews converge on the view that advances in immunohistochemistry and mutation testing have reduced misclassification of mesenchymal tumors and broadened recognition of small incidental GISTs, which likely elevates observed incidence without necessarily implying a proportional change in etiologic risk [56]. Contemporary mini-reviews echo these themes and situate the epidemiologic rise within a clinical context characterized by earlier pathological confirmation, expanded use of endoscopic and radiologic evaluation, and routine molecular profiling in treatment planning [57]. At the same time, our post-2019 joinpoint is consistent with multi-tumor literature describing pandemic-related contractions in endoscopy and cancer diagnostic pathways that produced deferred detection followed by catch-up surges, shaping short-term incidence curves independent of underlying disease biology [41]. Methodologically, our application of Joinpoint regression mirrors established cancer-trend practices used internationally to identify statistically significant inflection points, lending credence to the inference that the early-2000s and late-2019 breaks represent true shifts rather than noise [58]. The forecasting component of our analysis is also in line with comparative work showing that parsimonious ARIMA-class models can perform competitively for short-to-medium-term cancer incidence projections, particularly when evaluated with leave-future-out strategies to assess calibration under nonstationarity [24]. Complementary real-world datasets tracking outcomes in the targeted-therapy era affirm that the therapeutic environment has transformed the natural history of GIST, providing a biological and systems-level rationale for trend deceleration in the early 2000s followed by later perturbations tied to service disruptions [59]. With respect to disparities, our stratified findings resonate with prior SEER evaluations documenting higher incidence among Black populations and a modest male predominance, underscoring durable inequities in both disease burden and outcomes [15]. Recent updates further indicate that non-Hispanic Black patients continue to experience both higher incidence and poorer long-term survival, highlighting the intersection of biological, socioeconomic, and access-related drivers that require targeted policy responses [6]. Earlier historical analyses suggested partial attenuation of some treatment disparities after 2000—coincident with the imatinib era—yet emphasized that gains were uneven and contingent on access to specialized surgical and oncologic care [60]. Public-health commentaries reviewing SEER data reiterate that structural determinants and care access systematically shape who is diagnosed, when they are diagnosed, and what therapies they receive, implying that incidence trends cannot be divorced from equity considerations [16]. Guideline frameworks that mandate molecular profiling and risk-adapted management offer a blueprint for standardizing care, but real-world uptake varies across institutions and communities in ways that may reinforce or mitigate observed demographic gradients [61]. Public Health and Clinical Implications As incidence rises and diagnostic catch-up continues post-pandemic, heightened awareness among primary care, gastroenterology, and radiology stakeholders is essential to reduce time-to-diagnosis for symptomatic patients and ensure prompt work-up of incidental subepithelial lesions. Health-system modeling from England quantifying national endoscopy backlogs suggests that without sustained capacity expansion, residual diagnostic debt can persist for years, arguing for deliberate operational planning to prevent recurring waves of delayed GIST detection [38]. U.S. and international endoscopy series demonstrate substantial contractions in colonoscopy and EGD volumes during pandemic peaks, implying that many indolent or nonspecific presentations typical of gastric GISTs were triaged away and will present in clusters as access normalizes [62]. These realities carry downstream implications for oncology services, where increased new-patient intake, molecular testing, and longitudinal TKI stewardship will demand protected clinic time, pharmacy resources, and multidisciplinary coordination to maintain guideline-concordant care [61]. From a health-workforce perspective, longstanding projections of oncologist shortages and increasing diagnostic workload per pathologist presage bottlenecks unless investments in staffing, training pipelines, and digital augmentation are accelerated [63]. Recent assessments confirm a substantial rise in per-pathologist diagnostic workload over the past decade in North America, a trend that threatens turnaround times for complex sarcoma cases unless mitigated by workforce growth and workflow innovation [64]. International analyses of cancer-care workforce distribution highlight persistent gaps across specialties, reinforcing the need to strategically expand roles in oncopathology, molecular diagnostics, and sarcoma-center coordination as GIST caseloads climb [65]. Policy-oriented syntheses catalog actionable capacity-building strategies—from training scale-up to regionalization of complex diagnostics—that can be adapted to local contexts to absorb increasing demand for GIST care [65]. Limitations A key limitation is the absence of uniform molecular annotation in registry data, which precludes stratified analyses by KIT , PDGFRA , SDH deficiency, or other genotypes that influence tumor behavior and therapy responsiveness. Cancer registries, while high quality, are subject to varying completeness of stage and treatment fields across time and facilities, which can affect comparability of case-mix and confound incidence interpretation if not acknowledged. General issues of coding completeness and reliability are well documented in registry science and necessitate cautious interpretation of secular changes that could arise in part from improved abstraction rather than true etiologic shifts. SEER-Medicare linkage evaluations likewise illustrate that case capture for specific malignancies can vary by data source and period, reminding analysts that cross-dataset triangulation is valuable when feasible. Forecasting uncertainty naturally widens with horizon length in integrated stochastic models, meaning that while point estimates are policy-useful, health systems should plan against intervals rather than single values. Although ARIMA is competitive for short-term projections, other models—including age-period-cohort and Bayesian variants—can outperform under certain conditions, suggesting that pluralistic model comparisons may be prudent in future updates. Moreover, best practices for temporal validation continue to evolve, and future work may benefit from walk-forward or grouped time-aware validation schemes tailored to epidemiologic series. Future Directions Advancing registry informatics to incorporate structured molecular data would enable genotype-stratified incidence trends, facilitate precision epidemiology, and support evaluation of therapy diffusion by mutation class. Sustained examination of race- and sex-specific trends with granular socioeconomic covariates is necessary to identify modifiable drivers of disparity and to assess whether targeted interventions narrow gaps in both incidence and survival. Policy-focused implementation research should test workforce and capacity-building strategies—spanning oncology, pathology, and endoscopy—to mitigate bottlenecks likely to recur as diagnostic demand continues to grow. Persistent pathology workload pressures, rigorous evaluation of digital pathology and AI-assisted triage is warranted to maintain diagnostic quality and timeliness under increasing case volumes. Comparative forecasting studies that pit ARIMA against age-period-cohort, Bayesian, hybrid, and machine-learning models using walk-forward validation could refine projection accuracy for planning pharmaceutical procurement, clinic capacity, and operating-room scheduling. In parallel, endoscopy-capacity modeling should continue to inform recovery trajectories and targeted investments to ensure that backlogs do not reaccumulate and distort incidence surveillance. Finally, multi-registry collaborations can harmonize definitions and abstraction protocols to reduce heterogeneity and enable meta-surveillance of GIST incidence across countries and health systems. Conclusion In summary, our analysis demonstrates a sustained rise in GIST incidence with pronounced demographic heterogeneity, a deceleration coincident with the early imatinib era, and a sharp post-2019 inflection that aligns temporally with pandemic-related diagnostic disruptions. Joinpoint regression identified significant breaks consistent with therapeutic maturation and systems shocks, while ARIMA forecasting projected continued growth through 2032, underscoring the need for proactive capacity planning in oncology, pathology, and endoscopy services. Continued surveillance that integrates molecular annotation, addresses disparities, and adopts modern validation for forecasting will be critical to disentangle biological from health-system drivers and to prepare for increasing clinical burden in the decade ahead. Declarations Ethics approval and consent to participate This study used de-identified data from the publicly available SEER database and did not involve direct patient contact or the use of individually identifiable health information. Under the U.S. Common Rule, research using only publicly available, de-identified data is exempt from institutional review board oversight; therefore, ethics approval and patient consent were not required. Consent for publication Not applicable. Availability of data and materials The dataset analyzed during the current study is available in the SEER repository: https://seer.cancer.gov/ Competing interests The authors declare that they have no competing interests. Funding No external funding was received for this work. Authors’ contributions AH conceived the study, performed data extraction and statistical analyses, and drafted the manuscript. PS assisted with critical revision of the manuscript. All authors read and approved the final manuscript. 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Journal of the American College of Surgeons 2009, 209 (1):7-16. von Mehren M, Kane JM, Riedel RF, Sicklick JK, Pollack SM, Agulnik M, Bui MM, Carr-Ascher J, Choy E, Connelly M et al : NCCN Guidelines® Insights: Gastrointestinal Stromal Tumors, Version 2.2022 . Journal of the National Comprehensive Cancer Network : JNCCN 2022, 20 (11):1204-1214. Calderwood AH, Calderwood MS, Williams JL, Dominitz JA: Impact of the COVID-19 Pandemic on Utilization of EGD and Colonoscopy in the United States: An Analysis of the GIQuIC Registry . Techniques and innovations in gastrointestinal endoscopy 2021, 23 (4):313-321. Levit L, Smith AP, Benz EJ, Ferrell B: Ensuring quality cancer care through the oncology workforce . Journal of oncology practice 2010, 6 (1):7-11. Metter DM, Colgan TJ, Leung ST, Timmons CF, Park JY: Trends in the US and Canadian Pathologist Workforces From 2007 to 2017 . JAMA Netw Open 2019, 2 (5):e194337. Trapani D, Murthy SS, Boniol M, Booth C, Simensen VC, Kasumba MK, Giuliani R, Curigliano G, Ilbawi AM: Distribution of the workforce involved in cancer care: a systematic review of the literature . ESMO open 2021, 6 (6):100292. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7719966","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524938744,"identity":"a71adc49-fa63-4124-8122-d89d5f6b92c9","order_by":0,"name":"Ali Hemade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACdsY2EMXYwN98AEhLyBDWwgzTInEsAaSFhwgtDGwQLQw5BiAGYS38zcxtDz7m2Mn2M5z5/OpGjQUPA/vhoxvwaZE4zNhuOHNbsvHM5t5t1jnHgA7jSUu7gdeaw4xt0rzbmBM3HDi7zTiHDahFgscMrxZ5kJa/2+oT9x/IeWac848ILQYgLYzbDiduYMhhfpzbRoQWQ6AWyd5tx41n3DhmxpzbJ8HDRsgvcsfbn0n83FYt29/f/Phzzrc6OX72w8fwex8JsEmASWKVgwDzB1JUj4JRMApGwcgBACr6SekvNCM5AAAAAElFTkSuQmCC","orcid":"","institution":"American University of Beirut Medical Center, Naef K Bassile Cancer Institute","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Hemade","suffix":""},{"id":524938745,"identity":"788d5b7b-e802-4ffc-92b5-418a59d4ca6f","order_by":1,"name":"Pascale Salameh","email":"","orcid":"","institution":"Lebanese University","correspondingAuthor":false,"prefix":"","firstName":"Pascale","middleName":"","lastName":"Salameh","suffix":""}],"badges":[],"createdAt":"2025-09-26 09:08:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7719966/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7719966/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94355075,"identity":"5f2cdcca-94a7-47f9-bd5e-2eb4b81dcd30","added_by":"auto","created_at":"2025-10-27 12:57:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39402,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered Image in the methods section.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7719966/v1/d063f9036e71903e52264e0c.png"},{"id":94355073,"identity":"336e97cf-66df-4376-b4ca-8865276a96ec","added_by":"auto","created_at":"2025-10-27 12:57:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50638,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered Image in the methods section.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7719966/v1/f32953147bd92cf5a0ba922b.png"},{"id":94440478,"identity":"5ebd3215-07dd-4f8c-8a31-ab4fd089c24a","added_by":"auto","created_at":"2025-10-27 14:24:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3555102,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7719966/v1/c666100c-7163-4872-b275-a7e56b48468f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Three Decades of Gastrointestinal Stromal Tumor Incidence in the United States: Joinpoint Trend Analysis and ARIMA Forecasting Through 2032","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastrointestinal stromal tumors (GIST) represent the most prevalent mesenchymal neoplasms of the digestive tract, arising chiefly from the interstitial cells of Cajal that orchestrate peristaltic activity throughout the gastrointestinal (GI) tract [1]. Although infrequent compared with epithelial malignancies, GISTs command disproportionate clinical attention because of their unpredictable biological behavior, ranging from indolent nodules to fulminant sarcomas capable of widespread metastasis [2]. Early pathological descriptions in the pre-molecular era often misclassified GISTs as leiomyomas or leiomyosarcomas, but advances in immunohistochemistry—particularly the discovery of ubiquitous KIT (CD117) and DOG-1 expression—have refined diagnostic accuracy and underscored their distinct nosological identity [3]. Contemporary management therefore hinges on a nuanced understanding of tumor biology rather than purely histologic appearance, aligning therapeutic strategies with the molecular circuitry that drives oncogenesis [4]. Against this backdrop, rigorous epidemiologic surveillance has become critical for planning resource allocation, designing targeted screening initiatives, and benchmarking advances in precision therapy [5]. Yet, despite two decades of therapeutic progress, population-level trends in GIST incidence remain incompletely characterized on temporal, racial, and geographic axes, warranting renewed investigation [6].\u003c/p\u003e\n\u003cp\u003ePathogenetically, the archetypal driver lesions in GIST involve gain-of-function mutations in the receptor tyrosine kinases \u003cem\u003eKIT\u003c/em\u003e or \u003cem\u003ePDGFRA\u003c/em\u003e, which constitutively activate downstream MAPK and PI3K–Akt signaling, thereby fostering unchecked cellular proliferation and survival [7]. Approximately 75–80 % of tumors harbor exon 11 or exon 9 \u003cem\u003eKIT\u003c/em\u003e mutations, whereas 5–10 % display exon 18 \u003cem\u003ePDGFRA\u003c/em\u003e substitutions—most classically Asp842Val—that confer primary resistance to first-line tyrosine-kinase inhibition [8]. Less common oncogenic culprits include succinate dehydrogenase deficiency, \u003cem\u003eBRAF\u003c/em\u003e V600E alterations, \u003cem\u003eNF1\u003c/em\u003e loss, or \u003cem\u003eNTRK\u003c/em\u003e gene fusions, each imparting unique therapeutic vulnerabilities and prognostic signatures [9]. Molecular subclassification has therefore transcended academic taxonomy to become the backbone of personalized treatment, dictating adjuvant protocols, surveillance intensity, and clinical trial eligibility. [10] The paradigm-shifting approval of imatinib mesylate by the U.S. Food and Drug Administration (FDA) in 2002 for unresectable or metastatic GIST epitomizes the triumph of rational, mutation-directed therapy, transforming a once-intractable sarcoma into a chronic, controllable disease for many patients [11]. Subsequent trials have extended imatinib into the adjuvant realm, demonstrating durable improvements in recurrence-free and overall survival for high-risk cohorts when administered for three years or longer [12].\u003c/p\u003e\n\u003cp\u003eDespite these advances, comprehensive, population-based surveillance remains indispensable for quantifying the real-world impact of novel therapies, capturing secular changes in incidence, and identifying emergent disparities [5]. Age-adjusted incidence rates (AAIR) in the United States have historically hovered between 0.55 and 0.78 per 100,000 persons, yet more recent SEER analyses suggest a steady upward trajectory over the past two decades [5]. A 2024 SEER cohort study encompassing \u0026gt;12,000 cases corroborated this trend, documenting a significant annual increase across most demographic strata and projecting further escalation through at least 2030 [2]. International data echo these findings; for instance, an Italian registry covering \u0026gt;4 million inhabitants reported a mean incidence of 1.1 per 100,000 between 2010 and 2020, reinforcing the global nature of the rise [13]. Systematic reviews likewise estimate worldwide incidence at 10–15 per million, albeit with pronounced regional heterogeneity, underscoring methodological differences and possible environmental or genetic modifiers [14]. Nonetheless, whether these observed increases reflect genuine disease proliferation, enhanced diagnostic scrutiny, or coding artifacts remains the subject of vigorous debate [4].\u003c/p\u003e\n\u003cp\u003eDisparities in GIST burden by race, sex, and socioeconomic context further complicate epidemiologic appraisal [15]. African American patients consistently exhibit nearly double the incidence of their White counterparts and disproportionately worse survival outcomes, implicating both biological and systemic factors in disease course [6]. Multi-ethnic analyses reveal comparatively favorable survival among Asian and Pacific Islander populations, hinting at ancestry-linked genomic patterns or differential access to specialized care [16]. Sex-based variation remains less pronounced but detectable, with males experiencing marginally higher incidence and mortality, possibly attributable to divergent exposure profiles or hormonal influences on tumor biology [5]. Geographic gradients also emerge: urban, high-resource regions report elevated incidence, likely mirroring superior diagnostic infrastructure, whereas rural catchment areas may under-capture cases, thereby masking true prevalence [5]. These inequities underscore the necessity for nuanced, disaggregated surveillance capable of informing tailored public-health interventions [15].\u003c/p\u003e\n\u003cp\u003eTemporal interpretation is equally fraught because the therapeutic landscape has evolved dramatically since the late 1990s [11]. The introduction of imatinib not only improved survival but plausibly altered diagnostic practices by incentivizing earlier detection of smaller, potentially resectable lesions that might formerly have escaped notice [12]. Conversely, the COVID-19 pandemic provoked widespread disruptions in elective endoscopy, imaging, and outpatient oncology services, leading to cascading delays in tumor identification and staging across malignancies, including sarcomas [17]. An emerging literature suggests that pandemic-related diagnostic latency may transiently inflate incident presentations once routine services resume, thereby distorting short-term epidemiologic curves [17]. Parsing the relative contributions of therapeutic innovation versus system-level perturbation therefore demands sophisticated time-series methodologies capable of detecting inflection points and forecasting future trajectories with quantifiable uncertainty [2]. Joinpoint regression excels at revealing statistically significant shifts in longitudinal trends, whereas Auto-Regressive Integrated Moving Average (ARIMA) models extrapolate beyond observed data to anticipate future incidence, both indispensable for rigorous public-health planning [2].\u003c/p\u003e\n\u003cp\u003eNotwithstanding prior SEER studies, many analyses truncate follow-up at 2011 or 2015, omit pandemic-era data, or lack robust forecasting frameworks, leaving critical gaps in our understanding of contemporary GIST epidemiology [5]. Accordingly, a comprehensive appraisal spanning 1992–2021 is essential to contextualize two decades of molecular diagnostics, targeted therapeutics, and unprecedented healthcare disruptions within a single, coherent narrative [2]. Leveraging the expansive SEER repository affords unparalleled statistical power and demographic breadth, while modern analytic techniques enable fine-grained detection of subtle trend inflections that might otherwise remain obscured [14]. Such an approach promises actionable insights into the timing and magnitude of incidence shifts, thereby guiding allocation of endoscopic resources, informing genetic-counseling protocols, and optimizing survivorship services as the GIST population grows [6]. Moreover, accurate projections to 2032 will aid policymakers in forecasting pharmaceutical demand for TKIs, surgical case-load planning, and equitable distribution of sarcoma-specialist referrals [10]. Ultimately, bridging epidemiologic surveillance with molecular oncology stands to accelerate progress toward precision-public health paradigms that deliver personalized care at scale [16].\u003c/p\u003e\n\u003cp\u003eAgainst this backdrop, the present study pursues four interrelated objectives. First, we quantify temporal trends in AAIR for GIST across the United States from 1992 to 2021, stratifying by age, sex, race, and treatment modality to delineate nuanced demographic patterns. Second, we apply Joinpoint regression to identify statistically significant inflection points—“break years”—that coincide with major therapeutic or systemic events, thereby contextualizing epidemiologic shifts within broader healthcare milestones. Third, we construct and validate ARIMA models to forecast AAIR through 2032, providing a data-driven basis for forward-looking resource allocation and survivorship planning. Finally, we interpret these findings in light of existing literature on molecular pathogenesis, therapeutic breakthroughs, and health-system disruptions to furnish a holistic narrative that links bench, bedside, and population health. By integrating robust statistical methodology with the granularity of modern cancer registries, our analysis aspires to refine the collective understanding of how scientific innovation and societal perturbations jointly sculpt the epidemiology of this prototypical precision-oncology malignancy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the Surveillance, Epidemiology, and End Results (SEER) Program, a national population-based cancer registry that provides detailed information on cancer incidence, treatment, and survival in the United States. Data on gastrointestinal stromal tumors (GIST) were extracted for the period 1992 to 2021, with the primary outcome being the age-adjusted incidence rate (AAIR) per 100,000 persons, adjusted to the 2000 U.S. standard population. The dataset was stratified by race, sex, and treatment type, including surgical intervention, chemotherapy, and radiotherapy.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were computed to summarize the distribution of GIST incidence across different demographic groups. Mean, median, and interquartile ranges were calculated for each stratification, as shown in Table 1. To evaluate temporal trends in GIST incidence, Joinpoint Regression Analysis was performed to detect statistically significant changes in incidence trends. This model estimates breakpoints, known as joinpoints, where the trend significantly shifts and calculates the Annual Percentage Change (APC) before and after each breakpoint. The statistical significance of each joinpoint was determined using Monte Carlo permutation tests, with a significance threshold of p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eTo forecast future GIST incidence trends, Auto-Regressive Integrated Moving Average (ARIMA) models were employed. Multiple ARIMA models were tested, and the final model was selected based on the Akaike Information Criterion (AIC) to ensure optimal fit. The forecasting model was validated using a train-test split approach, where data prior to 2015 were used for model training, while data from 2015 to 2021 were reserved for testing. The predictive performance of the ARIMA models was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Theil’s U statistic, which compares the model’s predictive power against a naïve forecasting approach.\u003c/p\u003e\n\u003cp\u003eTo ensure robustness, both an auto-selected ARIMA model and a manually specified ARIMA(1,1,1) model were tested, and their accuracy was compared, as shown in Table 3. The final model was then used to project GIST incidence rates from 2022 to 2032. All statistical analyses were conducted in R (version 4.2.2), and figures were generated using ggplot2.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe overall mean age-adjusted incidence rate (AAIR) for GIST between 1992 and 2021 was 0.66 per 100,000 persons, with values ranging from 0.00 to 2.20. Incidence rates varied significantly by race and sex, with Black populations exhibiting the highest mean incidence rate (1.22 per 100,000 persons), followed by Asian/Pacific Islanders (0.94 per 100,000) and White individuals (0.55 per 100,000). Sex-based differences were also observed, with males experiencing higher incidence rates than females (0.74 vs. 0.59 per 100,000 persons, respectively). Treatment data revealed that 50.3% of patients underwent surgery, while 24.6% received chemotherapy, and 60% received radiotherapy, further summarized in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. Summary Statistics of GIST Incidence (1992\u0026ndash;2021)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverall AAIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAsian/Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eJoinpoint regression analysis identified two significant breakpoints in the incidence trend, as detailed in Table 2. The first breakpoint occurred in 2002 (\u0026plusmn;0.87 years), marking a deceleration in incidence growth. This shift coincides with the FDA approval of Imatinib (Gleevec), the first targeted therapy for GIST, which likely contributed to earlier detection and reduced late-stage diagnoses. The second breakpoint was detected in 2019.7 (\u0026plusmn;0.12 years), where a sharp increase in incidence was observed, potentially due to healthcare disruptions during the COVID-19 pandemic that delayed routine screening and cancer detection. The joinpoint regression model demonstrated a strong fit, with an adjusted R\u0026sup2; of 0.9544, indicating that the model explained over 95% of the variance in incidence trends.\u003c/p\u003e\n\u003cp\u003eTable 2. Joinpoint Regression Breakpoints and Trends\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBreak Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnnual Percentage Change (APC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBefore 2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+0.0885%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIncreasing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2002 - 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0774%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSlowed growth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAfter 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+0.9748%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSharp increase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe ARIMA model comparison demonstrated that the ARIMA(0,1,0) with drift model was the best fit for forecasting GIST incidence trends, as summarized in Table 3.\u003c/p\u003e\n\u003cp\u003eTable 3. ARIMA Model Performance Evaluation\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTraining Set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTest Set\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMAPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTheil\u0026rsquo;s U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eForecasting projections indicate a continued increase in GIST incidence rates over the next decade, with the rate expected to exceed 2.5 per 100,000 persons by 2032, as detailed in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4. Forecasted GIST Incidence Rates (2022\u0026ndash;2032)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProjected Incidence Rate (per 100,000 persons)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe model suggested a steady linear increase in incidence, likely influenced by improved detection, changing population risk factors, and increased utilization of screening methods. However, confidence intervals widened over time, indicating increasing uncertainty in long-term projections.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eSummary of Key Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur population‐based analysis estimated a sustained rise in the age-adjusted incidence of gastrointestinal stromal tumors (GIST), with the overall mean AAIR higher than historical registry estimates and showing clear heterogeneity by demographic strata, consistent with contemporary SEER-based reports documenting increasing GIST incidence across multiple digestive sites and population groups\u0026nbsp;[2].\u003cbr\u003eRace- and sex-stratified summaries revealed materially higher rates in certain racial groups and a modest male predominance, echoing prior registry analyses and meta-epidemiologic observations of disparity in GIST burden and outcomes [15]. All incidence estimates were age-standardized to the year-2000 U.S. standard population to enable valid temporal comparisons, in alignment with established national vital statistics methodology for age adjustment [18]. Use of SEER registry–compatible definitions and denominators supports generalizability and reproducibility of our estimates within a widely used national cancer surveillance framework [19].\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003eWe identified two statistically significant joinpoints—an inflection around calendar year 2002 indicating deceleration of growth and a second inflection at ~2019.7 indicating a sharp contemporaneous increase—derived via established permutation-based Joinpoint regression techniques optimized for cancer trend surveillance [20]. The joinpoint in the early 2000s coincides with the clinical adoption of imatinib for advanced GIST, which fundamentally altered disease natural history and likely shifted the observed case mix through earlier treatment and improved survival [21]. The later joinpoint at 2019.7 temporally overlaps the emergence of COVID-19–related disruptions in oncology pathways and endoscopic services, which multiple studies have shown to depress diagnostic activity acutely and generate downstream backlogs [22].\u003c/p\u003e\n\u003cp\u003eFor forward projection, the best-fit time-series specification was an ARIMA(0,1,0) with drift, a parsimonious random-walk model that outperformed higher-order alternatives and aligns with prior cancer-trend forecasting experience using integrated autoregressive models [23].\u003cbr\u003e\u0026nbsp;Under this specification, forecasts indicated continued growth with point estimates surpassing 2.5 per 100,000 by 2032, consistent with comparative evaluations showing that simple stochastic trend models can yield robust medium-term incidence trajectories in oncology surveillance [24].\u003cbr\u003eCollectively, these findings are concordant with the broader literature describing rising GIST incidence in population data and reinforce the need to interrogate diagnostic, therapeutic, and systems-level drivers of these trends [4]. External comparisons with European population-based registries, including nationwide analyses from the Netherlands, similarly document increasing GIST incidence, suggesting shared transnational determinants in detection and care delivery [25]. Parallel reports have highlighted the growing identification of incidentally detected small gastric GISTs, a pattern that can elevate observed incidence through stage migration effects without necessarily reflecting a true etiologic surge\u0026nbsp;[26].\u003cbr\u003eRegistry-linkage studies using pathology archives have also demonstrated rising GIST case ascertainment over two decades, further supporting that secular improvements in recognition contribute to the observed trend [27].\u003c/p\u003e\n\u003cp\u003eAgainst this backdrop, the 2002 joinpoint plausibly reflects early therapeutic epoch changes surrounding tyrosine kinase inhibition, when mutation-specific sensitivity to imatinib began to influence diagnostic vigilance and clinical pathways [28]. The net effect observed here—initial growth with post-2002 deceleration, followed by a pronounced 2019.7 inflection—fit with a narrative of therapeutic maturation followed by pandemic-related perturbation, consistent with outcomes-oriented trend analyses in GIST [29]. The late-2019 acceleration aligns temporally with large documented contractions in endoscopic and cancer diagnostic throughput and consequent rebound phenomena as services recovered, which would be expected to transiently inflate incidence as deferred patients present [30].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of Trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEarly-2000s deceleration and the imatinib era\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe deceleration of GIST incidence growth around 2002 is biologically and clinically plausible in light of the transformative impact of imatinib, which from its landmark trial onward reshaped the trajectory of advanced disease and catalyzed earlier molecularly informed management [21]. Early translational work established that genotype strongly modulates imatinib responsiveness, with KIT exon-specific differences informing dosing and expectations of response, which likely spurred more targeted diagnostic confirmation rather than late symptomatic discovery [28]. As second-line and third-line kinase inhibitors were introduced for imatinib-refractory disease, clinical pathways incentivized earlier referral and systematic mutation testing, gradually stabilizing case ascertainment while improving survival\u0026nbsp;[21].\u003cbr\u003eRegulatory approval and dissemination of sunitinib in 2006, with documented progression-free survival benefits after imatinib failure, further normalized long-term therapeutic stewardship that can reduce late emergency presentations [31]. Subsequent demonstration of regorafenib efficacy after failure of prior TKIs extended disease control horizons and entrenched a chronic disease model for metastatic GIST, altering patterns of surveillance and timing of diagnosis [32]. Longitudinal analyses of sunitinib-treated cohorts corroborated durable benefit after imatinib failure, which may translate at the population level into more planned evaluations and fewer incidental late-stage detections\u0026nbsp;[33].\u003cbr\u003eMechanistic studies clarified that secondary resistance often emerges through KIT or PDGFRA re-mutations yet remains kinase-dependent, supporting continued inhibition strategies that encourage structured follow-up rather than episodic crisis-driven care [28]. In the adjuvant setting, randomized evidence showed that extending imatinib to three years improves both recurrence-free and overall survival compared with one year, which plausibly decreased the share of late-stage recurrences entering incidence streams as advanced metastatic conversions [34]. A decade follow-up of the same cohort confirmed sustained overall survival benefit from longer adjuvant therapy, reinforcing therapeutic shifts that can reshape the clinical spectrum at diagnosis over time [12]. Contemporary practice guidelines from expert societies reflected these data, enshrining molecular testing and risk-adapted adjuvant therapy as standards, thereby promoting earlier risk stratification and structured surveillance\u0026nbsp;[35].\u003cbr\u003eIn parallel, improvements in diagnostic immunohistochemistry—most notably the adoption of DOG1 staining alongside KIT—enhanced the sensitivity of GIST identification, reducing misclassification but also promoting consistent attribution of small subclinical lesions that would previously have been missed\u0026nbsp;[36].\u003cbr\u003e\u0026nbsp;Subsequent studies confirmed DOG1’s utility across mutation subtypes and even in KIT-negative tumors, standardizing diagnostic workflows and potentially smoothing secular increases into a more stable post-adoption incidence slope [37]. Taken together, the imatinib era introduced durable survival gains and systematic, genotype-informed care pathways that can slow apparent growth in incidence by shifting detection toward earlier, planned contexts and away from late symptomatic discovery, matching the deceleration we observed after 2002 [37].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePost-2019 increase and COVID-19–related diagnostic disruption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sharp post-2019 acceleration in incidence is temporally synchronous with the onset of the COVID-19 pandemic, which triggered rapid, unprecedented reductions in endoscopic capacity and oncology diagnostic activity across multiple health systems [30]. Modeling and national operational data from England documented a large accumulated backlog in gastrointestinal endoscopy, with forecasts indicating prolonged recovery periods even under aggressive mitigation, a pattern expected to drive a catch-up surge in detected lesions once services resumed [38]. Oncology delivery data from integrated U.S. systems showed immediate declines in new patient encounters and cancer-related care in early 2020, consistent with widespread care deferral among symptomatic and screen-eligible populations\u0026nbsp;[39].\u0026nbsp;\u003cbr\u003eMultiple observational studies reported reduced diagnostic throughput for gastrointestinal malignancies during pandemic peaks, followed by partial rebounds that still lagged pre-pandemic volumes, implying a rolling backlog entering subsequent years\u0026nbsp;[40]. Reviews of endoscopy and cancer screening services synthesized these findings and emphasized that the backlog would persist without deliberate rebalancing of resources, mirroring the step-up effect in our joinpoint around 2019 [41].\u003cbr\u003eGuidance and safety frameworks for endoscopy during COVID-19 recommended procedural triage, enhanced infection control, and deferment of nonurgent indications, which collectively constrained diagnostic access for indolent or nonspecific presentations typical of many GISTs\u0026nbsp;[42].\u003cbr\u003e\u0026nbsp;Surveys of endoscopy units likewise documented profound capacity reductions and workflow changes that directly translate into delayed diagnosis and a subsequent surge of deferred cases when normal operations resumed [43].\u003c/p\u003e\n\u003cp\u003eBeyond the procedural bottleneck, pandemic-era health-seeking behavior shifted due to infection concerns and logistical barriers, lowering timely referral rates and compounding the diagnostic queue that was later cleared in bursts [22].\u003c/p\u003e\n\u003cp\u003eInternational series described stage migration and reduced incident cancer detections during the first pandemic waves, with later periods showing compensatory increases as programs recovered, a dynamic consistent with the abrupt incidence inflection we detected [44]. Although GIST is not a screening-program malignancy, many tumors are discovered incidentally during upper endoscopy or cross-sectional imaging for nonspecific symptoms, so any broad reduction in GI diagnostics can delay discovery and then yield clustered catch-up diagnoses\u0026nbsp;[45]. Complementary U.K. database analyses confirmed national-level contractions to fractions of baseline endoscopic activity at the pandemic peak, further supporting that backlog mechanics materially affected gastrointestinal tumor detection patterns [30].\u003cbr\u003eThese service disruptions and their phased recovery likely inflated near-term incidence as deferred, symptomatic, or previously surveilled patients re-entered care, producing an apparent step-change rather than a gradual slope increase [38]. Importantly, contemporaneous literature also indicates that pandemic-era diagnostic deferrals were socially patterned, with the largest care gaps among populations already experiencing cancer disparities, which could accentuate race- and sex-specific incidence heterogeneity\u0026nbsp;[2].\u003cbr\u003eThe aggregate picture therefore supports a causal chain in which procedural contractions, referral delays, and patient hesitancy produced a transient deficit in incident detections followed by a concentrated surplus, manifesting as a joinpoint in late 2019 with a sharp positive slope [40]. From a methodological perspective, ARIMA models applied to epidemiologic time series are well suited to capture such nonstationary shocks via level shifts and drift, and prior oncology forecasting has shown that low-order integrated processes can accommodate pandemic-era perturbations with acceptable calibration [23]. Comparative work in cancer surveillance has further demonstrated that parsimonious stochastic models often outperform more complex specifications when the signal contains policy or systems shocks, reinforcing confidence in the ARIMA(0,1,0)+drift choice here [24]. While our projections indicate continued growth to beyond 2.5 per 100,000 by 2032, it is critical to interpret this trajectory in the context of lingering diagnostic backlogs and evolving endoscopy capacity, factors that can sustain elevated detection for several years [38]. \u0026nbsp;Simultaneously, improvements in guideline-concordant imaging and molecular work-up may continue to increase ascertainment of small, asymptomatic GISTs, which would raise observed incidence even if true etiologic risk remains stable [35].\u003c/p\u003e\n\u003cp\u003eEpidemiologic heterogeneity—by anatomic site, age, and mutation profile—adds another layer, as stomach-predominant, KIT-mutant tumors may be more sensitive to detection trends tied to upper endoscopy than small-bowel tumors, potentially shifting site-specific incidence patterns during recovery [46]. Finally, persistent inequities in access and referral may differentially modulate the pace of backlog clearance across communities, necessitating focused surveillance of race- and sex-specific trends to prevent widening disparities as systems normalize [15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrating biology, care pathways, and surveillance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFoundational discoveries of oncogenic KIT mutations and their therapeutic tractability transformed GIST from a historically lethal sarcoma to a model of precision oncology, anchoring contemporary interpretations of incidence dynamics in the interplay between biology and care systems\u0026nbsp;[47].\u003cbr\u003eEarly prognostic series linked specific KIT alterations to outcomes, accelerating the push for molecular testing that, over time, improved classification accuracy and may have moderated unmeasured misclassification in registry trends [48]. Conceptual models positioning GIST as an interstitial cell of Cajal–derived neoplasm clarified disease ontogeny and aligned clinical detection with site-specific symptomatology and incidental discovery pathways [49]. Comprehensive reviews of GIST biology and diagnosis emphasized the increasing role of immunohistochemistry and mutation profiling in routine practice, supporting more consistent case ascertainment and staging [50]. Seminal overviews underscored the rapid evolution from pathologic curiosity to a defined kinase-driven entity, an arc that naturally impacts incidence through both real changes in survival and apparent changes in diagnostic coding and recognition [46]. Recent clinical reviews similarly note that improved detection and risk stratification, along with targeted therapy, have increased reported incidence in population registries, reinforcing the multi-factorial backdrop for our findings [45]. Pathology-focused syntheses highlight that KIT/DOG1 immunophenotyping and attention to site-dependent morphology improved GIST case capture over time, helping to standardize reporting across centers and periods [51]. Mutation-specific treatment nuances—such as limited imatinib activity in PDGFRA D842V tumors compared with other PDGFRA variants—have shaped surveillance intensity and referral timing, further intertwining biology with observed incidence\u0026nbsp;[52].\u003cbr\u003eGenotype-phenotype analyses also informed second-line selection, as sunitinib activity varies by primary and secondary kinase genotype, which can influence longitudinal follow-up structures and transitions in care [53]. Against this biologic-therapeutic canvas, broad pandemics and system shocks act as exogenous modifiers of detection, triage, and backlog clearance, producing pattern breaks detectable by joinpoint methods when the shocks are large and temporally concentrated [20]. Methodological refinements in joinpoint analysis—including robust permutation testing and clustering approaches—improve the specificity of change-point detection and support our inference that the 2002 and 2019.7 inflections represent true shifts rather than statistical noise [54]. Comparisons across forecasting families in cancer surveillance show that, for data streams affected by secular improvements and episodic shocks, ARIMA-class models with drift can balance bias and variance while providing transparent, policy-relevant projections [24]. This synthesis suggests that the early-2000s deceleration reflects maturation of targeted therapy and standardized diagnostics, whereas the post-2019 surge reflects backlog-driven catch-up following pandemic disruptions, together yielding the biphasic pattern observed in our analysis [41].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with Existing Literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings align with recent SEER-based cohort analyses showing that GIST incidence has risen across major digestive organ sites over the last two decades, reinforcing that the upward trajectory is a reproducible population-level phenomenon rather than an artifact of a single registry or time window [2]. This pattern is concordant with broader global syntheses estimating a worldwide incidence of roughly 10–15 per million, while emphasizing substantial regional heterogeneity that likely reflects differences in diagnostic access, coding practices, and true biological variability [4]. Parallel European registry work, including detailed regional studies from Spain’s Region of Murcia, similarly documents increasing case ascertainment and supports the interpretation that improved recognition and recording contribute to secular rises in reported incidence [55]. A contemporary U.S. population-based analysis of 23,001 patients further substantiates that age-adjusted incidence rates for common digestive GISTs increased between 2000 and 2019, suggesting shared determinants across settings and analytic approaches [2]. Narrative and systematic reviews converge on the view that advances in immunohistochemistry and mutation testing have reduced misclassification of mesenchymal tumors and broadened recognition of small incidental GISTs, which likely elevates observed incidence without necessarily implying a proportional change in etiologic risk [56]. Contemporary mini-reviews echo these themes and situate the epidemiologic rise within a clinical context characterized by earlier pathological confirmation, expanded use of endoscopic and radiologic evaluation, and routine molecular profiling in treatment planning [57]. At the same time, our post-2019 joinpoint is consistent with multi-tumor literature describing pandemic-related contractions in endoscopy and cancer diagnostic pathways that produced deferred detection followed by catch-up surges, shaping short-term incidence curves independent of underlying disease biology\u0026nbsp;[41].\u003cbr\u003eMethodologically, our application of Joinpoint regression mirrors established cancer-trend practices used internationally to identify statistically significant inflection points, lending credence to the inference that the early-2000s and late-2019 breaks represent true shifts rather than noise [58]. The forecasting component of our analysis is also in line with comparative work showing that parsimonious ARIMA-class models can perform competitively for short-to-medium-term cancer incidence projections, particularly when evaluated with leave-future-out strategies to assess calibration under nonstationarity\u0026nbsp;[24].\u0026nbsp;\u003cbr\u003eComplementary real-world datasets tracking outcomes in the targeted-therapy era affirm that the therapeutic environment has transformed the natural history of GIST, providing a biological and systems-level rationale for trend deceleration in the early 2000s followed by later perturbations tied to service disruptions [59].\u003c/p\u003e\n\u003cp\u003eWith respect to disparities, our stratified findings resonate with prior SEER evaluations documenting higher incidence among Black populations and a modest male predominance, underscoring durable inequities in both disease burden and outcomes [15]. Recent updates further indicate that non-Hispanic Black patients continue to experience both higher incidence and poorer long-term survival, highlighting the intersection of biological, socioeconomic, and access-related drivers that require targeted policy responses [6]. Earlier historical analyses suggested partial attenuation of some treatment disparities after 2000—coincident with the imatinib era—yet emphasized that gains were uneven and contingent on access to specialized surgical and oncologic care [60]. Public-health commentaries reviewing SEER data reiterate that structural determinants and care access systematically shape who is diagnosed, when they are diagnosed, and what therapies they receive, implying that incidence trends cannot be divorced from equity considerations [16]. Guideline frameworks that mandate molecular profiling and risk-adapted management offer a blueprint for standardizing care, but real-world uptake varies across institutions and communities in ways that may reinforce or mitigate observed demographic gradients [61].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublic Health and Clinical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs incidence rises and diagnostic catch-up continues post-pandemic, heightened awareness among primary care, gastroenterology, and radiology stakeholders is essential to reduce time-to-diagnosis for symptomatic patients and ensure prompt work-up of incidental subepithelial lesions. Health-system modeling from England quantifying national endoscopy backlogs suggests that without sustained capacity expansion, residual diagnostic debt can persist for years, arguing for deliberate operational planning to prevent recurring waves of delayed GIST detection [38]. U.S. and international endoscopy series demonstrate substantial contractions in colonoscopy and EGD volumes during pandemic peaks, implying that many indolent or nonspecific presentations typical of gastric GISTs were triaged away and will present in clusters as access normalizes [62]. These realities carry downstream implications for oncology services, where increased new-patient intake, molecular testing, and longitudinal TKI stewardship will demand protected clinic time, pharmacy resources, and multidisciplinary coordination to maintain guideline-concordant care [61]. From a health-workforce perspective, longstanding projections of oncologist shortages and increasing diagnostic workload per pathologist presage bottlenecks unless investments in staffing, training pipelines, and digital augmentation are accelerated\u0026nbsp;[63].\u003cbr\u003eRecent assessments confirm a substantial rise in per-pathologist diagnostic workload over the past decade in North America, a trend that threatens turnaround times for complex sarcoma cases unless mitigated by workforce growth and workflow innovation [64]. International analyses of cancer-care workforce distribution highlight persistent gaps across specialties, reinforcing the need to strategically expand roles in oncopathology, molecular diagnostics, and sarcoma-center coordination as GIST caseloads climb [65]. Policy-oriented syntheses catalog actionable capacity-building strategies—from training scale-up to regionalization of complex diagnostics—that can be adapted to local contexts to absorb increasing demand for GIST care [65].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA key limitation is the absence of uniform molecular annotation in registry data, which precludes stratified analyses by \u003cem\u003eKIT\u003c/em\u003e, \u003cem\u003ePDGFRA\u003c/em\u003e, SDH deficiency, or other genotypes that influence tumor behavior and therapy responsiveness. Cancer registries, while high quality, are subject to varying completeness of stage and treatment fields across time and facilities, which can affect comparability of case-mix and confound incidence interpretation if not acknowledged. General issues of coding completeness and reliability are well documented in registry science and necessitate cautious interpretation of secular changes that could arise in part from improved abstraction rather than true etiologic shifts. SEER-Medicare linkage evaluations likewise illustrate that case capture for specific malignancies can vary by data source and period, reminding analysts that cross-dataset triangulation is valuable when feasible. Forecasting uncertainty naturally widens with horizon length in integrated stochastic models, meaning that while point estimates are policy-useful, health systems should plan against intervals rather than single values. Although ARIMA is competitive for short-term projections, other models—including age-period-cohort and Bayesian variants—can outperform under certain conditions, suggesting that pluralistic model comparisons may be prudent in future updates. \u0026nbsp;Moreover, best practices for temporal validation continue to evolve, and future work may benefit from walk-forward or grouped time-aware validation schemes tailored to epidemiologic series.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdvancing registry informatics to incorporate structured molecular data would enable genotype-stratified incidence trends, facilitate precision epidemiology, and support evaluation of therapy diffusion by mutation class. Sustained examination of race- and sex-specific trends with granular socioeconomic covariates is necessary to identify modifiable drivers of disparity and to assess whether targeted interventions narrow gaps in both incidence and survival. Policy-focused implementation research should test workforce and capacity-building strategies—spanning oncology, pathology, and endoscopy—to mitigate bottlenecks likely to recur as diagnostic demand continues to grow. Persistent pathology workload pressures, rigorous evaluation of digital pathology and AI-assisted triage is warranted to maintain diagnostic quality and timeliness under increasing case volumes. Comparative forecasting studies that pit ARIMA against age-period-cohort, Bayesian, hybrid, and machine-learning models using walk-forward validation could refine projection accuracy for planning pharmaceutical procurement, clinic capacity, and operating-room scheduling. In parallel, endoscopy-capacity modeling should continue to inform recovery trajectories and targeted investments to ensure that backlogs do not reaccumulate and distort incidence surveillance. Finally, multi-registry collaborations can harmonize definitions and abstraction protocols to reduce heterogeneity and enable meta-surveillance of GIST incidence across countries and health systems.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our analysis demonstrates a sustained rise in GIST incidence with pronounced demographic heterogeneity, a deceleration coincident with the early imatinib era, and a sharp post-2019 inflection that aligns temporally with pandemic-related diagnostic disruptions. Joinpoint regression identified significant breaks consistent with therapeutic maturation and systems shocks, while ARIMA forecasting projected continued growth through 2032, underscoring the need for proactive capacity planning in oncology, pathology, and endoscopy services. Continued surveillance that integrates molecular annotation, addresses disparities, and adopts modern validation for forecasting will be critical to disentangle biological from health-system drivers and to prepare for increasing clinical burden in the decade ahead.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used de-identified data from the publicly available SEER database and did not\u0026nbsp;\u003c/p\u003e\n\u003cp\u003einvolve direct patient contact or the use of individually identifiable health information. Under\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethe U.S. Common Rule, research using only publicly available, de-identified data is exempt from\u0026nbsp;\u003c/p\u003e\n\u003cp\u003einstitutional review board oversight; therefore, ethics approval and patient consent were not\u0026nbsp;\u003c/p\u003e\n\u003cp\u003erequired.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed during the current study is available in the SEER repository: https://seer.cancer.gov/\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAH conceived the study, performed data extraction and statistical analyses, and drafted the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emanuscript. PS assisted with critical revision of the manuscript. All authors read and approved\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethe final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKim Y, Lee SH: \u003cstrong\u003ePathologic diagnosis and molecular features of gastrointestinal stromal tumors: a mini-review\u003c/strong\u003e. \u003cem\u003eFront Oncol\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e14\u003c/strong\u003e:1487467.\u003c/li\u003e\n \u003cli\u003eAlvarez CS, Piazuelo MB, Fleitas-Kanonnikoff T, Ruhl J, P\u0026eacute;rez-Fidalgo JA, Camargo MC: \u003cstrong\u003eIncidence and Survival Outcomes of Gastrointestinal Stromal Tumors\u003c/strong\u003e. \u003cem\u003eJAMA Netw Open\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e7\u003c/strong\u003e(8):e2428828.\u003c/li\u003e\n \u003cli\u003eIbrahim A, Montgomery EA: \u003cstrong\u003eGastrointestinal Stromal Tumors: Variants and Some Pitfalls That They Create\u003c/strong\u003e. \u003cem\u003eAdv Anat 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systematic review of the literature\u003c/strong\u003e. \u003cem\u003eESMO open\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e6\u003c/strong\u003e(6):100292.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gastrointestinal stromal tumor (GIST), SEER, Epidemiology, Age-adjusted incidence rate, Joinpoint regression, ARIMA forecasting, Temporal trends","lastPublishedDoi":"10.21203/rs.3.rs-7719966/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7719966/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Gastrointestinal stromal tumors (GIST) are the most common mesenchymal neoplasms of the gastrointestinal tract, with incidence rising globally over the past two decades. The evolution of targeted therapies, particularly imatinib, and recent systemic disruptions such as the COVID-19 pandemic may have influenced incidence patterns at the population level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We used Surveillance, Epidemiology, and End Results (SEER) data to examine U.S. GIST cases diagnosed between 1992 and 2021. Age-adjusted incidence rates (AAIRs) were calculated per 100,000 persons standardized to the 2000 U.S. population. Temporal trends were analyzed using Joinpoint regression to identify statistically significant inflection points, and Auto-Regressive Integrated Moving Average (ARIMA) models were employed to forecast incidence through 2032. Model performance was assessed via root mean squared error (RMSE), mean absolute percentage error (MAPE), and Theil’s U statistic using a train–test split.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The overall mean AAIR for GIST during 1992–2021 was 0.66 per 100,000, with higher rates among Black populations (1.22) compared with White (0.55) and Asian/Pacific Islander groups (0.94). Joinpoint analysis identified two significant breakpoints: 2002, marking a deceleration in incidence growth, and late 2019, where a sharp increase emerged. The ARIMA(0,1,0) with drift model provided the best fit and forecasted AAIR exceeding 2.5 per 100,000 by 2032.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e GIST incidence in the United States has increased steadily over three decades, with clear demographic disparities and distinct temporal inflection points corresponding to therapeutic and systemic shifts. These projections signal a growing clinical and public-health burden, emphasizing the need for expanded diagnostic capacity, equitable access to molecular testing, and ongoing surveillance to monitor post-pandemic trends.\u003c/p\u003e","manuscriptTitle":"Three Decades of Gastrointestinal Stromal Tumor Incidence in the United States: Joinpoint Trend Analysis and ARIMA Forecasting Through 2032","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-24 14:52:38","doi":"10.21203/rs.3.rs-7719966/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d77a0ba5-2f67-464b-b205-d3642a9eaaf7","owner":[],"postedDate":"October 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-24T14:52:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-24 14:52:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7719966","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7719966","identity":"rs-7719966","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}