Quantitative Imaging Biomarkers as Prognostic Indicators in Squamous Cell Cervical Carcinoma: A Retrospective Cohort Analysis

Quantitative Imaging Biomarkers as Prognostic Indicators in Squamous Cell Cervical Carcinoma: A Retrospective Cohort Analysis | 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 Quantitative Imaging Biomarkers as Prognostic Indicators in Squamous Cell Cervical Carcinoma: A Retrospective Cohort Analysis MAYUR VIRARKAR, SANAZ JAVADI, AATTIQAH AZIZ, RITU SHAH, SUN JIA, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8006282/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Dec, 2025 Read the published version in Abdominal Radiology → Version 1 posted 11 You are reading this latest preprint version Abstract Objective To evaluate quantitative imaging biomarkers, including MRI tumor size, apparent diffusion coefficient (ADC), arterial-phase enhancement, and PET maximum standardized uptake value (SUVmax), as prognostic indicators of overall survival (OS), recurrence-free survival (RFS), and treatment response in cervical squamous cell carcinoma (SCC). Materials and Methods Fifty patients with biopsy-proven SCC who underwent pre- and post-treatment MRI and FDG-PET/CT were retrospectively analyzed. Tumor dimensions (axial, sagittal), ADC, SUVmax, and arterial enhancement were assessed. Survival was estimated by Kaplan–Meier, and associations with OS and RFS were tested using Cox regression. Logistic regression was used to determine predictors of treatment response according to RECIST and PERCIST criteria. Results Tumor size, SUVmax, and ADC changed significantly post-treatment ( p < 0.001). Persisting arterial enhancement was seen in 35% of tumors. Higher post-treatment SUVmax predicted worse OS (HR = 1.078, p = 0.008) and RFS (HR = 1.049, p = 0.046). Larger residual sagittal tumor size showed borderline associations with inferior OS (HR = 1.38, p = 0.089) and RFS (HR = 1.31, p = 0.057). Increases in tumor size significantly correlated with persistent arterial enhancement (axial OR = 1.58, p = 0.025; sagittal OR = 1.93, p = 0.002). Persistent enhancement trended toward worse RFS (HR ≈ 2.24, p = 0.107). In multivariable analysis, post-treatment pelvic lymph node positivity remained independently associated with poorer RFS. Baseline ADC predicted metabolic response: a higher pre-treatment ADC was associated with a failure to achieve a partial metabolic response per PERCIST (OR = 1.007, p = 0.015). Conclusion Post-treatment SUVmax, residual tumor size, arterial enhancement, and pelvic lymph node involvement are key prognostic indicators in cervical SCC. Persistent enhancement may reflect treatment-resistant vascularity. Integrating these biomarkers into clinical workflows may improve risk stratification and guide personalized management. SUVmax size enhancement cervix. ADC Figures Figure 1 Figure 2 Figure 3 Introduction Cervical cancer remains a major global health challenge, representing the fourth most common cancer among women worldwide [ 1 ]. Despite advances in screening and vaccination programs, a substantial number of patients continue to present with locally advanced disease requiring definitive chemoradiotherapy. Prognosis is typically assessed using clinical staging systems such as FIGO; however, these rely on morphologic findings and provide limited insight into tumor biology and treatment response [ 2 ]. Consequently, there is increasing interest in identifying noninvasive imaging biomarkers that can refine prognostication, guide therapeutic decision-making, and detect early signs of treatment resistance. Magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) are routinely employed in the evaluation of cervical cancer [ 2 ]. MRI offers excellent soft-tissue resolution, enabling the assessment of tumor dimensions, local invasion, and functional parameters, such as the apparent diffusion coefficient (ADC), through diffusion-weighted imaging [ 3 ]. PET/CT provides complementary metabolic information by measuring the maximum standardized uptake value (SUVmax), which reflects tumor glycolytic activity [ 4 ]. Dynamic contrast-enhanced MRI further enables the evaluation of tumor vascularity, with arterial-phase enhancement serving as a potential indicator of persistent angiogenesis [ 5 ] (Fig. 1 ). Collectively, these modalities capture different aspects of tumor phenotype, yet their integrated role in predicting outcomes in cervical squamous cell carcinoma (SCC) remains underexplored. Previous studies have suggested associations between post-treatment imaging features and survival; however, small sample sizes, heterogeneous histologies, or incomplete follow-up have limited the findings of most. In particular, the prognostic value of arterial enhancement and lymph node involvement has not been systematically examined with quantitative biomarkers such as ADC and SUVmax. Moreover, the predictive utility of these markers in defining radiologic (RECIST) and metabolic (PERCIST) treatment responses has not been fully characterized. This study investigated the prognostic significance of quantitative imaging biomarkers, including tumor size, ADC, SUVmax, and arterial enhancement, on overall survival (OS), recurrence-free survival (RFS), and treatment response in patients with cervical SCC. Material and Methods Study Design and Population This retrospective cohort study included patients with pathologically confirmed squamous cell carcinoma (SCC) of the cervix who underwent both pelvic MRI and 18F-FDG PET/CT before and after definitive treatment between January 2010 and December 2020 at our institution. Institutional review board approval was obtained, and informed consent was waived due to the retrospective design. Patients were included if they had a histologically confirmed diagnosis of cervical squamous cell carcinoma (SCC) and had completed both baseline pelvic MRI and 18F-FDG PET/CT within six weeks before starting chemoradiotherapy. Post-treatment MRI and PET/CT were required within twelve weeks of completing therapy, along with at least twelve months of clinical follow-up or documentation of recurrence or death within that period. Only studies with adequate image quality, allowing for quantitative analysis of tumor size, apparent diffusion coefficient (ADC), SUVmax, and arterial enhancement, were included. Patients were excluded if they had non-squamous histology (such as adenocarcinoma, adenosquamous, or small-cell types), prior hysterectomy, pelvic radiation, or systemic therapy before baseline imaging. Exclusion also applied to cases lacking either pre- or post-treatment MRI or PET/CT, incomplete or poor-quality imaging data that prevented quantitative assessment, evidence of distant metastasis beyond the pelvic or para-aortic nodes that precluded curative-intent treatment, or insufficient follow-up of less than twelve months without documentation of recurrence or death. Of the 1,000 patients identified in Montage, only 50 patients were included in the study, as they met the criteria. Imaging Analysis All patients underwent pre- and post-treatment pelvic MRI and 18F-FDG PET/CT on clinically approved scanners under standardized protocols. For PET/CT, patients fasted for at least 6 hours before 18F-FDG administration, and serum glucose was confirmed to be < 120 mg/dL before tracer injection. An intravenous dose of 185–370 MBq (5–10 mCi) of 18F-FDG was administered, followed by a 60-minute uptake period in a quiet room. Patients were instructed to void their bladder immediately before imaging to minimize pelvic urinary artifact. PET/CT was performed on integrated GE Discovery systems (GE Healthcare). Non-contrast CT was acquired helically from the skull base to the mid-thigh using 120 kVp, 300 mAs, 3.75-mm slice thickness, and a 0.5-second rotation. PET data were reconstructed using ordered subset expectation maximization (OSEM) algorithms, yielding attenuation-and non-attenuation-corrected images that were reviewed in axial, sagittal, and coronal planes, with fused PET/CT images for interpretation. Pelvic MRI was performed on a 1.5 T or 3.0 T system (GE Healthcare) using a phased-array torso coil. Sequences included axial and sagittal T2-weighted fast spin-echo (FSE), axial T1-weighted pre- and post-contrast sequences, diffusion-weighted imaging (DWI; b values 0, 500, and 1000 s/mm²) with corresponding ADC maps, and dynamic contrast-enhanced (DCE) MRI using gadobutrol (0.1 mmol/kg). Arterial-phase enhancement was assessed on early post-contrast sequences. Tumor size was measured as the maximum diameter in axial and sagittal planes on T2-weighted MRI. ADC values were obtained by manually placing regions of interest (ROIs) within the solid portion of the tumor, avoiding necrotic or hemorrhagic areas. PET/CT SUVmax was measured using 3D spherical volumes of interest over the most metabolically active tumor region. Pelvic lymph nodes were considered positive on MRI if they demonstrated a short-axis diameter ≥ 1.0 cm, central necrosis, or irregular margins, and on PET/CT if they exhibited focal FDG uptake above background activity. Imaging analyses were performed by two radiologists (with 15- and 24-year experience in oncologic imaging) specializing in gynecologic imaging, with consensus used to resolve discrepancies. Statistical Analysis All statistical analyses were conducted using R software (version 4.3.1, R Development Core Team). Continuous variables, including tumor size, ADC, and SUVmax, were summarized with means, standard deviations, medians, and ranges. In contrast, categorical variables, such as arterial enhancement and lymph node status, were summarized with frequencies and percentages. Paired comparisons of pre- and post-treatment imaging biomarkers were performed using the Wilcoxon signed-rank test for continuous measures and the McNemar test for categorical features. Overall survival (OS) and recurrence-free survival (RFS) were estimated using the Kaplan–Meier method, with survival probabilities reported at 2, 5, and 10 years. Associations between imaging biomarkers and survival outcomes were assessed using univariate Cox proportional hazards models; hazard ratios (HRs) with 95% confidence intervals (CIs) were reported. Variables with p < 0.10 in univariate analysis were considered for multivariable Cox regression. Logistic regression was used to evaluate predictors of treatment response, as defined by the RECIST 1.1 and PERCIST criteria. Results Patient Characteristics Fifty patients with histologically confirmed squamous cell carcinoma (SCC) of the cervix met the inclusion criteria and were included in the final analysis. The mean age was 46.4 years (SD, 14.1; range, 23–79 years). Median clinical follow-up was 46.5 months. Treatment Outcomes and Survival At the last follow-up, 10 patients (20%) had died, and 16 (35%) had experienced recurrence or death (Table 1 ). Median overall survival (OS) was 13.5 years (95% CI: 9.4). Estimated OS rates were 91.4% at 2 years, 82.7% at 5 years, 77.5% at 8 years, and 64.6% at 10 years. Recurrence-free survival (RFS) rates were 72.6% at 2 years and 57.7% at 5 years (Table 2 ). Table 1 Overall Survival (OS) – Kaplan-Meier analysis. Statistic Value N 50 Events (Deaths) 10 (20%) Median Survival 13.49 years (95% CI: 9.38 – NA) 2-year Survival 91.4% (95% CI: 83.7–99.8%) 5-year Survival 82.7% (95% CI: 71.5–95.5%) 8-year Survival 77.5% (95% CI: 64–93.9%) 10-year Survival 64.6% (95% CI: 43–96.9%) This table presents overall survival data for 50 patients who were followed from the time of diagnosis. Median survival was 13.49 years with 10 events (20%). Survival probabilities at 2, 5, 8, and 10 years are reported with 95% confidence intervals (CI). Table 2 Recurrence-Free Survival (RFS) – Kaplan-Meier analysis. Statistic Value N 46 Events (Recurrence or Death) 16 (35%) Median Survival Not reached (95% CI: 3.66 – NA) 2-year RFS 72.6% (95% CI: 60.4–87.1%) 5-year RFS 57.7% (95% CI: 42.9–77.5%) This table presents recurrence-free survival data for 46 patients after treatment, with 16 recurrence or death events (35%). Median RFS was not reached. Two- and five-year RFS probabilities with 95% CI are reported. Imaging Biomarkers: Pre- vs. Post-Treatment All imaging biomarkers showed significant post-treatment changes (Table 3 ). Tumor size : Median axial diameter decreased from 4.7 cm to 1.2 cm ( p < 0.001); sagittal diameter decreased from 3.6 cm to 1.1 cm ( p < 0.001). ADC values : Median ADC increased from 0.82 ×10 –3 mm 2 /s to 1.35 ×10 –3 mm 2 /s ( p < 0.001). SUVmax : Median SUVmax decreased from 17.8 to 4.8 ( p < 0.001). Arterial enhancement : Present in 98% of tumors pre-treatment and persisted in 35% post-treatment. Pelvic lymph nodes : Involvement decreased from 72% on MRI and 52% on PET at baseline to 20% and 8% after treatment. Table 3 Tumor Size, ADC, and SUV Changes (Wilcoxon test). Measure Pre-treatment (Mean ± SD) Post-treatment (Mean ± SD) Difference (Mean ± SD) Median (Range) Change p-value Tumor Size Axial (cm) 4.5 ± 1.7 1.6 ± 1.7 -2.9 ± 1.9 -2.8 (-7.8, 1.6) < 0.001 Tumor Size Sagittal (cm) 3.8 ± 1.6 1.4 ± 1.6 -2.4 ± 2.1 -2.3 (-6.3, 2.0) < 0.001 ADC (×10⁻³ mm²/s) 847.6 ± 151.1 1367.6 ± 374.3 + 512.4 ± 409.4 510 (-316, 1736) < 0.001 SUV Max 19.4 ± 9.0 7.1 ± 6.5 -12.3 ± 9.6 -10.9 (-40.9, 10.4) < 0.001 This table summarizes pre- and post-treatment measurements for tumor size (axial and sagittal), apparent diffusion coefficient (ADC), and maximum standardized uptake value (SUVmax). Mean, standard deviation, and median with range are reported. Differences were assessed using the Wilcoxon signed-rank test, with statistically significant improvements observed in all parameters (p < 0.001). Prognostic Associations On univariate Cox regression, higher post-treatment SUVmax was associated with worse OS (HR = 1.078, 95% CI: 1.02–1.14, p = 0.008) (Table 4 ) and RFS (HR = 1.049, 95% CI: 1.00–1.10, p = 0.046) (Table 5 ). Larger post-treatment sagittal tumor size showed borderline associations with inferior OS (HR = 1.38, p = 0.089) and RFS (HR = 1.31, p = 0.057) (Fig. 2 ). Table 4 Cox Regression – Predictors of Overall Survival (OS). Variable HR (95% CI) p-value Conclusion Age 1.02 (0.98–1.06) 0.422 Not significant Size Sagittal 1.38 (0.95–1.99) 0.089 Marginal – worse OS Size Axial 1.22 (0.81–1.82) 0.340 Not significant ADC 0.63 (0.08–5.17) 0.669 Not significant SUV 1.04 (0.95–1.13) 0.432 Not significant This table summarizes the univariate Cox regression analyses correlating age, changes in tumor size (axial and sagittal), changes in ADC, and changes in SUV with overall survival. Increase in sagittal tumor size was marginally associated with worse OS (HR = 1.38, p = 0.089). Table 5 Cox Regression – Predictors of Recurrence-Free Survival (RFS). Variable HR (95% CI) p-value Conclusion Age 1.02 (0.99–1.05) 0.266 Not significant Size Sagittal 1.30 (0.99–1.71) 0.057 Marginal – worse RFS Size Axial 1.21 (0.92–1.59) 0.173 Not significant ADC 0.76 (0.17–3.43) 0.719 Not significant SUV 1.01 (0.95–1.08) 0.699 Not significant This table presents univariate Cox regression analyses correlating clinical and imaging variables with RFS. Increased sagittal tumor size was marginally associated with worse RFS (HR = 1.30, p = 0.057). Logistic regression analysis revealed that a post-treatment increase in axial and sagittal tumor size was correlated with the persistence of arterial enhancement (axial OR = 1.58, p = 0.025; sagittal OR = 1.93, p = 0.002). Persistent arterial enhancement trended toward worse RFS (HR ≈ 2.24, p = 0.107) (Tables 6 and 7 ). In multivariable analysis, only post-treatment pelvic lymph node positivity on MRI remained independently associated with inferior RFS ( p < 0.05). Table 6 Logistic Regression – Predictors of Post-treatment Arterial Enhancement. Variable OR (95% CI) p-value Conclusion Age 1.00 (0.96–1.04) 0.940 Not significant Size Axial 1.58 (1.10–2.47) 0.025 Significant Size Sagittal 1.93 (1.33–3.13) 0.002 Significant ADC 0.21 (0.03–1.10) 0.085 Trend, not significant SUV 1.04 (0.97–1.11) 0.300 Not significant This table shows univariate logistic regression analyses of age, changes in tumor size, ADC, and SUV in predicting post-treatment arterial enhancement on imaging. Increases in axial and sagittal tumor size were significantly associated with arterial enhancement (p = 0.025 and p = 0.002, respectively). Table 7 Post-treatment Predictors of OS and RFS. Predictor HR (95% CI) p-value Outcome SUV.Max.post 1.08 (1.02–1.14) 0.008 Worse OS SUV.Max.post 1.05 (1.00–1.10) 0.046 Worse RFS ADC Pre/Post NS – Not predictive Arterial Enhancement Post HR = 2.24 (0.84–5.97) 0.107 Not significant This table provides univariate Cox regression results for post-treatment SUV, ADC, and arterial enhancement in relation to OS and RFS. Higher post-treatment SUV was significantly associated with worse OS (p = 0.008) and RFS (p = 0.046). Predictors of Treatment Response Baseline ADC values were predictive of metabolic response. Higher pre-treatment ADC was correlated with a failure to achieve a partial metabolic response per PERCIST (OR = 1.007, 95% CI: 1.002–1.014, p = 0.015) (Table 8 ) (Fig. 3 ). Baseline tumor size showed a nonsignificant trend toward predicting RECIST response. Table 8 Predictive of Treatment Response (RECIST and PERCIST). Predictor OR (95% CI) p-value Conclusion ADC.pre (PERCIST) 1.007 (1.002–1.014) 0.015 Higher baseline ADC → worse response SUV.Max.pre NS 0.347 Not predictive Tumor Size Sagittal.pre OR = 0.60 (0.31–1.03) 0.091 Trend Tumor Size Axial.pre OR = 0.59 (0.32–0.99) 0.059 Borderline This table summarizes the results of logistic regression analyses examining the predictive value of pretreatment tumor size, ADC, and SUV for treatment response, using both RECIST and PERCIST criteria. A higher pretreatment ADC was significantly correlated with a poor metabolic response by PERCIST (OR = 1.007, p = 0.015). Discussion SUVmax and PET Metabolic Parameters Our study demonstrates that post-treatment SUVmax serves as the most significant predictor of survival outcomes in cervical squamous cell carcinoma, with higher values being significantly associated with worse overall survival (HR = 1.078, p = 0.008) and recurrence-free survival (HR = 1.049, p = 0.046). This finding aligns with the substantial body of evidence supporting PET metabolic parameters as robust prognostic indicators in cervical cancer. A comprehensive meta-analysis of 1313 patients across 14 studies confirmed that patients with high primary tumor SUVmax have significantly shorter overall survival compared to those with low SUVmax (HR 2.582, 95% CI 1.936–3.443, p < 0.001), with similarly adverse effects on event-free survival (HR 1.938, 95% CI 1.203–3.054, p = 0.004) [ 6 ]. The superiority of post-treatment over pre-treatment PET parameters has been consistently validated, with an extensive Swedish cohort study of 133 patients demonstrating that post-treatment metabolic parameters exhibit superior prognostic value compared to baseline measurements [ 7 ]. A Turkish multi-center study of 194 patients with a 12.5-year follow-up further corroborated these findings, showing that SUVmax independently predicted local recurrence (HR = 1.15, p = 0.001). In contrast, metabolic tumor volume (MTV) and SUVmean were predictive of distant metastasis [ 8 ]. Apparent Diffusion Coefficient (ADC) This study also identifies pre-treatment ADC values as significant predictors of treatment response, with higher baseline ADC values associated with a failure to achieve a partial metabolic response (OR = 1.007, p = 0.015). This finding contributes to the growing evidence supporting ADC as a valuable biomarker for treatment prediction and prognosis in cervical cancer. A comprehensive meta-analysis of 2,314 patients across 22 studies demonstrated that ADC values achieve excellent diagnostic performance for detecting lymph node metastasis, with a pooled sensitivity of 86% and a specificity of 85%, significantly outperforming conventional imaging methods [ 9 ]. The prognostic utility of ADC extends beyond lymph node assessment, with multiple studies establishing its role as an independent predictor of disease outcomes. An extensive Norwegian study of 179 patients revealed that a low tumor ADC mean predicted reduced disease-specific survival (HR = 0.96, p = 0.001), with normalized ADC ratios providing even stronger prognostic information [ 9 ]. Another study of 219 patients demonstrated that the mean absolute deviation (MAD) from ADC maps, representing ADC distribution uniformity, served as an independent protective factor against early recurrence (HR = 0.978, p = 0.001) [ 10 ]. This suggests tumor heterogeneity assessed through ADC analysis may provide additional prognostic information beyond simple mean values. The superior performance of normalized ADC ratios, particularly myometrium ADC/tumor ADC ratios, highlights the importance of accounting for MRI acquisition-related variations and suggests that relative measurements may be more robust and clinically applicable than absolute ADC values. The ability of baseline ADC to predict treatment response early in therapy makes it particularly valuable for adaptive treatment strategies, potentially allowing modification of treatment approaches based on predicted response patterns. Tumor Size Parameters In addition, our study findings regarding tumor size parameters align with established evidence demonstrating the prognostic significance of baseline tumor dimensions and volumetric changes during treatment. Post-treatment sagittal tumor size showed borderline associations with inferior survival outcomes (HR = 1.38 for overall survival, p = 0.089), reflecting the established principle that larger residual disease portends worse prognosis. A Chinese study of 217 patients provided compelling evidence for the prognostic value of the tumor volume reduction rate (TVRR), identifying it as an independent predictor of prognosis. Patients achieving a TVRR greater than 82.19% demonstrated significantly better outcomes [ 11 ]. Mid-treatment tumor volume greater than 11.38 cm³ emerged as an independent predictor of poor survival (HR = 3.192, p = 0.034), emphasizing the importance of assessing early treatment response through volumetric analysis. The relationship between tumor size parameters and treatment outcomes reflects fundamental principles of tumor biology and the response to treatment. Larger tumors typically harbor greater cellular heterogeneity, increased hypoxic regions, and enhanced resistance mechanisms, reducing treatment sensitivity. The Korean multi-center study demonstrated that a baseline tumor volume greater than 61.6 cm³ was associated with adverse outcomes (HR = 0.417, p = 0.032). In comparison, the threshold of 11.38 cm³ for mid-treatment volume provided optimal discrimination for survival prediction [ 12 ]. The prognostic superiority of TVRR over static size measurements suggests that dynamic assessment of treatment response provides more meaningful information than baseline tumor dimensions alone. Patients with poor volume reduction (< 82%) during external beam radiotherapy represent a high-risk population that may benefit from treatment intensification or modified approaches. Limitations This study has several limitations. First, its retrospective design introduces the possibility of selection bias and limits control over imaging acquisition timing and treatment heterogeneity. Second, the relatively modest sample size (n = 50) may reduce the statistical power to detect associations, particularly in subgroup analyses such as nodal involvement or arterial enhancement persistence. Third, imaging was performed on multiple MRI and PET/CT platforms over a decade, which may have introduced technical variability in SUV measurements, ADC quantification, and contrast-enhanced sequences. Fourth, inter-observer variability in tumor measurements, ADC placement, and assessment of arterial enhancement was not formally evaluated; thus, the reproducibility of these imaging biomarkers remains to be validated. Fifth, although RECIST and PERCIST criteria were used to define treatment response, histopathologic confirmation of residual disease was not consistently available. Sixth, some patients had limited follow-up, which may have potentially underestimated late recurrences or survival outcomes. Finally, the study focused exclusively on squamous histology, and findings may not generalize to other cervical cancer subtypes such as adenocarcinoma or adenosquamous carcinoma. Clinical Implications Given these prognostic associations, patients demonstrating elevated post-treatment SUVmax, larger residual tumor dimensions, or persistent arterial enhancement warrant surveillance protocols. Close monitoring with serial MRI examinations allows assessment of residual tumor size and perfusion characteristics, while PET imaging provides complementary metabolic information to detect early recurrence or metastatic progression. Additionally, regular cervical cytology, including Pap smears, remains essential for identifying local residual disease at the primary site. This integrated multimodality surveillance approach enables early detection of disease progression, potentially facilitating timely therapeutic intervention and improving long-term outcomes in high-risk patients. Implications for Future Research The findings from this study highlight the potential of quantitative imaging biomarkers as noninvasive tools for risk stratification in cervical SCC. Future research should focus on validating these prognostic markers in larger, prospective, and multicenter cohorts to improve generalizability and reproducibility. Standardizing imaging acquisition protocols and quantitative measurement techniques, particularly for ADC and SUV, will be critical to reducing inter-institutional variability. Conclusion Quantitative imaging biomarkers from MRI and PET provide important prognostic information in cervical squamous cell carcinoma. Higher post-treatment SUVmax, residual tumor size, and persistent arterial enhancement were associated with worse survival outcomes, while pelvic lymph node positivity after therapy emerged as an independent predictor of recurrence. Pre-treatment ADC values correlated with metabolic response, suggesting a role in predicting treatment efficacy. Declarations Author Contribution Conceptualization, M.V.; P.B. Formal Analysis, S.J. Investigation, A.A.; Data Curation, A.A.; Writing– Original Draft Preparation, M.V., P.B., S.J. Writing– Review & Editing, M.V.; P.B.; S.J.; R.I.; D.G. References Siegel, R.L., et al., Cancer statistics, 2025 . CA Cancer J Clin, 2025. 75(1): p. 10–45. Saleh, M., et al., Cervical cancer: 2018 revised international federation of gynecology and obstetrics staging system and the role of imaging . American Journal of Roentgenology, 2020. 214(5): p. 1182–1195. Gennarini, M., et al., Multi-model quantitative MRI of uterine cancers in precision medicine’s era—a narrative review . Insights into Imaging, 2025. 16(1): p. 113. Yadav, D., et al., Unraveling the Role of PET in Cervical Cancer: Review of Current Applications and Future Horizons . Journal of Imaging, 2025. 11(2): p. 63. Avesani, G., et al., The Utility of Contrast-Enhanced Magnetic Resonance Imaging in Uterine Cervical Cancer: A Systematic Review . Life (Basel), 2023. 13(6). Han, L., et al., A systematic review and meta-analysis of the prognostic impact of pretreatment fluorodeoxyglucose positron emission tomography/computed tomography parameters in patients with locally advanced cervical cancer treated with concomitant chemoradiotherapy . Diagnostics, 2021. 11(7): p. 1258. Markus, M., et al., Metabolic parameters of [18F] FDG PET-CT before and after radiotherapy may predict survival and recurrence in cervical cancer . Acta Oncologica, 2023. 62(2): p. 180–188. Onal, C., et al., Long-term assessment of clinical parameters and positron emission tomography parameters in predicting recurrence in uterine cervical cancer patients receiving definitive chemoradiotherapy . Nuclear Medicine Communications, 2024. 45(3): p. 203–210. Ren, J., et al., Diagnostic performance of ADC values and MRI-based radiomics analysis for detecting lymph node metastasis in patients with cervical cancer: a systematic review and meta-analysis . European Journal of Radiology, 2022. 156: p. 110504. Zhang, X., et al., The value of whole-tumor texture analysis of ADC in predicting the early recurrence of locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy . Frontiers in Oncology, 2022. 12: p. 852308. Sun, C., et al., The prognostic value of tumor size, volume and tumor volume reduction rate during concurrent chemoradiotherapy in patients with cervical cancer . Frontiers in oncology, 2022. 12: p. 934110. Lee, K.C., et al., The predictive value of tumor size, volume, and markers during radiation therapy in patients with cervical cancer . International Journal of Gynecological Cancer, 2017. 27(1): p. 123–130. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Dec, 2025 Read the published version in Abdominal Radiology → Version 1 posted Editorial decision: Revision requested 04 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviews received at journal 30 Nov, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviews received at journal 19 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers invited by journal 09 Nov, 2025 Editor assigned by journal 02 Nov, 2025 Submission checks completed at journal 02 Nov, 2025 First submitted to journal 01 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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-8006282","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542236195,"identity":"592acbab-f91c-42b8-b53a-1da747960a28","order_by":0,"name":"MAYUR VIRARKAR","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie2RMUvDQBTHnzxoHY5mvdDQfgLhSiBVCPWrpBzEpUPBJZsHDl38APot6u7wQoYup10jXQQhUwdXwaGXWCiFnnUUvB+8Ox68H/93HIDD8YdhptBUjE2bTc1Bv1PSb0WL4wpslQKOKmczHXUzKILh7PldfD4t29590uIkoNcpk4NKpCeRr6Fggb4Kx3fVCrmmRgl9m0JGUZAybp5BjFYIC9XqfggYz23Kcr1VvArzL3rBfgFNyo1VKZuUmHGeomREKExKrSTCqlTXF0rUSoVhQBIHOr89J8EHD/rNsph8fFUZv+Reiv6aRrJXyrykLO53FodTAE7NF4hdKwFOlLm5ZbymvR8/+mHU4XA4/ikbawVZfj1ki/sAAAAASUVORK5CYII=","orcid":"","institution":"University of Florida","correspondingAuthor":true,"prefix":"","firstName":"MAYUR","middleName":"","lastName":"VIRARKAR","suffix":""},{"id":542236196,"identity":"34e74eac-c12f-42a9-bcd4-cc2be94900a0","order_by":1,"name":"SANAZ JAVADI","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"SANAZ","middleName":"","lastName":"JAVADI","suffix":""},{"id":542236197,"identity":"fa4b4591-c3d4-47e9-835f-989c8cad16f0","order_by":2,"name":"AATTIQAH AZIZ","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"AATTIQAH","middleName":"","lastName":"AZIZ","suffix":""},{"id":542236198,"identity":"2dd1545e-4c4f-4651-a7cc-c37b43185d61","order_by":3,"name":"RITU SHAH","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"RITU","middleName":"","lastName":"SHAH","suffix":""},{"id":542236199,"identity":"b9dbf198-ca94-4ac8-a5d1-fd17fd9006e0","order_by":4,"name":"SUN JIA","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"SUN","middleName":"","lastName":"JIA","suffix":""},{"id":542236201,"identity":"69e4c274-193d-46f8-817e-05b35e4504cd","order_by":5,"name":"AJAYKUMAR. C. MORANI","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"AJAYKUMAR.","middleName":"C.","lastName":"MORANI","suffix":""},{"id":542236203,"identity":"79566f4a-1373-43ee-bdcf-8eb424e78211","order_by":6,"name":"PRIYA BHOSALE","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"PRIYA","middleName":"","lastName":"BHOSALE","suffix":""}],"badges":[],"createdAt":"2025-11-01 14:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8006282/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8006282/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00261-025-05351-7","type":"published","date":"2025-12-27T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96205460,"identity":"71be510d-af55-45af-9450-a8ad40907df9","added_by":"auto","created_at":"2025-11-18 17:10:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3275843,"visible":true,"origin":"","legend":"","description":"","filename":"CervixBlind.docx","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/24910d4a2f25919ca6f5c3d2.docx"},{"id":96205456,"identity":"ef0d7d70-3772-488e-bab0-bb20b731b6d3","added_by":"auto","created_at":"2025-11-18 17:10:00","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8609,"visible":true,"origin":"","legend":"","description":"","filename":"d9b13c1dda264afdadde9d649de86983.json","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/a439422adf03393845cd966c.json"},{"id":96252575,"identity":"4075b0a2-36aa-4b6b-809e-75d8dc758846","added_by":"auto","created_at":"2025-11-19 07:41:13","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69314,"visible":true,"origin":"","legend":"","description":"","filename":"d9b13c1dda264afdadde9d649de869831enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/b7ae5d6662a2ae32cb581aba.xml"},{"id":96252359,"identity":"6554aad0-186a-4bb9-b283-63e841d8ab8c","added_by":"auto","created_at":"2025-11-19 07:40:51","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1501948,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/7e73ab3f880a96d58f1f7665.png"},{"id":96205458,"identity":"792cc586-5af1-474f-8fd1-f18972a64337","added_by":"auto","created_at":"2025-11-18 17:10:00","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":595081,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/44a1ee11ed844c5d89bc88cd.jpeg"},{"id":96205464,"identity":"43691b84-c007-4611-afe7-a2e381fdb40e","added_by":"auto","created_at":"2025-11-18 17:10:01","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":572156,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/e58b011f0e44fffa4879f4dd.jpeg"},{"id":96252487,"identity":"00d496dc-9317-4858-b772-717b03af2e58","added_by":"auto","created_at":"2025-11-19 07:41:02","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185370,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/061f114d4051adc9f7940da7.png"},{"id":96205463,"identity":"b8f14051-894e-452b-b538-86c1d1504909","added_by":"auto","created_at":"2025-11-18 17:10:01","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85329,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/dbe36fc3fd28afb4d6980212.png"},{"id":96205468,"identity":"1b7deb23-e733-4452-afe9-7580c21aac98","added_by":"auto","created_at":"2025-11-18 17:10:01","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62864,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/6e30ef22bc51fe857b0df70e.png"},{"id":96205461,"identity":"43e66f2d-1a4d-4b02-abfa-93ade102c09f","added_by":"auto","created_at":"2025-11-18 17:10:00","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67171,"visible":true,"origin":"","legend":"","description":"","filename":"d9b13c1dda264afdadde9d649de869831structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/557f6973b3e07d7c76a98ad0.xml"},{"id":96205467,"identity":"b9c2d1f1-9ed8-4fbc-8138-1b5045dae799","added_by":"auto","created_at":"2025-11-18 17:10:01","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72878,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/cca21219e4e9f806170102b2.html"},{"id":96205455,"identity":"5aa30880-ab79-4b56-bcb8-ef49dbe2516c","added_by":"auto","created_at":"2025-11-18 17:10:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":359338,"visible":true,"origin":"","legend":"\u003cp\u003e42-year-old female with cervical carcinoma, 3 months post-radiation. (A) Sagittal T2WI, (B) Axial T2WI, (C) Axial diffusion-weighted image, (D) post-contrast T1WI, (E) Axial FDG PET/CT images demonstrate a residual tumor (arrows) with restricted diffusion, intermediate T2 hyperintensity, and FDG avidity. The dotted linear lines in images A and B measure the sagittal, transverse, and axial dimensions, ADC in image C, and SUVmax in image E. There is a persistent enhancement in the post-contrast sagittal image D.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/92d5ab2d07117d747bedf2eb.png"},{"id":96205459,"identity":"46bc048b-8a3a-4cde-bfd0-3acfc050ccec","added_by":"auto","created_at":"2025-11-18 17:10:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":631307,"visible":true,"origin":"","legend":"\u003cp\u003eAnnotated scatter plot of pSUV versus Tumor.Size.Sagittal.pre. The solid blue line represents the linear regression trend with a shaded 95% confidence interval. The dashed red line at –30% delineates PERCIST metabolic response categories. Labels mark the response and non-response zones, highlighting the inverse correlation between tumor size and metabolic change.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/aa3d8a450c971e011c755134.png"},{"id":96205466,"identity":"9d12b5d3-0d03-417a-96fd-499bff98df7e","added_by":"auto","created_at":"2025-11-18 17:10:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":209662,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of pSUV vs. ADC.pre with the PERCIST threshold at –30%. The lower region indicates metabolic responders, and the upper region represents stable or progressive disease. The circled cluster highlights outliers with high ADCpre and poor response, reflecting the inverse association between baseline ADC and metabolic improvement.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/40650fe24fba75dac3f13335.png"},{"id":99172405,"identity":"b5e3dc02-4604-4ff2-8a3d-078f4fa9acec","added_by":"auto","created_at":"2025-12-29 16:08:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2008618,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8006282/v1/4573cc35-bad8-4c9c-98f9-1f89d81b1b5e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative Imaging Biomarkers as Prognostic Indicators in Squamous Cell Cervical Carcinoma: A Retrospective Cohort Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer remains a major global health challenge, representing the fourth most common cancer among women worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advances in screening and vaccination programs, a substantial number of patients continue to present with locally advanced disease requiring definitive chemoradiotherapy. Prognosis is typically assessed using clinical staging systems such as FIGO; however, these rely on morphologic findings and provide limited insight into tumor biology and treatment response [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consequently, there is increasing interest in identifying noninvasive imaging biomarkers that can refine prognostication, guide therapeutic decision-making, and detect early signs of treatment resistance.\u003c/p\u003e\u003cp\u003eMagnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) are routinely employed in the evaluation of cervical cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MRI offers excellent soft-tissue resolution, enabling the assessment of tumor dimensions, local invasion, and functional parameters, such as the apparent diffusion coefficient (ADC), through diffusion-weighted imaging [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. PET/CT provides complementary metabolic information by measuring the maximum standardized uptake value (SUVmax), which reflects tumor glycolytic activity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Dynamic contrast-enhanced MRI further enables the evaluation of tumor vascularity, with arterial-phase enhancement serving as a potential indicator of persistent angiogenesis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Collectively, these modalities capture different aspects of tumor phenotype, yet their integrated role in predicting outcomes in cervical squamous cell carcinoma (SCC) remains underexplored.\u003c/p\u003e\u003cp\u003ePrevious studies have suggested associations between post-treatment imaging features and survival; however, small sample sizes, heterogeneous histologies, or incomplete follow-up have limited the findings of most. In particular, the prognostic value of arterial enhancement and lymph node involvement has not been systematically examined with quantitative biomarkers such as ADC and SUVmax. Moreover, the predictive utility of these markers in defining radiologic (RECIST) and metabolic (PERCIST) treatment responses has not been fully characterized. This study investigated the prognostic significance of quantitative imaging biomarkers, including tumor size, ADC, SUVmax, and arterial enhancement, on overall survival (OS), recurrence-free survival (RFS), and treatment response in patients with cervical SCC.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Population\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study included patients with pathologically confirmed squamous cell carcinoma (SCC) of the cervix who underwent both pelvic MRI and 18F-FDG PET/CT before and after definitive treatment between January 2010 and December 2020 at our institution. Institutional review board approval was obtained, and informed consent was waived due to the retrospective design.\u003c/p\u003e\u003cp\u003ePatients were included if they had a histologically confirmed diagnosis of cervical squamous cell carcinoma (SCC) and had completed both baseline pelvic MRI and 18F-FDG PET/CT within six weeks before starting chemoradiotherapy. Post-treatment MRI and PET/CT were required within twelve weeks of completing therapy, along with at least twelve months of clinical follow-up or documentation of recurrence or death within that period. Only studies with adequate image quality, allowing for quantitative analysis of tumor size, apparent diffusion coefficient (ADC), SUVmax, and arterial enhancement, were included. Patients were excluded if they had non-squamous histology (such as adenocarcinoma, adenosquamous, or small-cell types), prior hysterectomy, pelvic radiation, or systemic therapy before baseline imaging. Exclusion also applied to cases lacking either pre- or post-treatment MRI or PET/CT, incomplete or poor-quality imaging data that prevented quantitative assessment, evidence of distant metastasis beyond the pelvic or para-aortic nodes that precluded curative-intent treatment, or insufficient follow-up of less than twelve months without documentation of recurrence or death. Of the 1,000 patients identified in Montage, only 50 patients were included in the study, as they met the criteria.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImaging Analysis\u003c/h3\u003e\n\u003cp\u003eAll patients underwent pre- and post-treatment pelvic MRI and 18F-FDG PET/CT on clinically approved scanners under standardized protocols. For PET/CT, patients fasted for at least 6 hours before 18F-FDG administration, and serum glucose was confirmed to be \u0026lt;\u0026thinsp;120 mg/dL before tracer injection. An intravenous dose of 185\u0026ndash;370 MBq (5\u0026ndash;10 mCi) of 18F-FDG was administered, followed by a 60-minute uptake period in a quiet room. Patients were instructed to void their bladder immediately before imaging to minimize pelvic urinary artifact. PET/CT was performed on integrated GE Discovery systems (GE Healthcare). Non-contrast CT was acquired helically from the skull base to the mid-thigh using 120 kVp, 300 mAs, 3.75-mm slice thickness, and a 0.5-second rotation. PET data were reconstructed using ordered subset expectation maximization (OSEM) algorithms, yielding attenuation-and non-attenuation-corrected images that were reviewed in axial, sagittal, and coronal planes, with fused PET/CT images for interpretation.\u003c/p\u003e\u003cp\u003ePelvic MRI was performed on a 1.5 T or 3.0 T system (GE Healthcare) using a phased-array torso coil. Sequences included axial and sagittal T2-weighted fast spin-echo (FSE), axial T1-weighted pre- and post-contrast sequences, diffusion-weighted imaging (DWI; b values 0, 500, and 1000 s/mm\u0026sup2;) with corresponding ADC maps, and dynamic contrast-enhanced (DCE) MRI using gadobutrol (0.1 mmol/kg). Arterial-phase enhancement was assessed on early post-contrast sequences. Tumor size was measured as the maximum diameter in axial and sagittal planes on T2-weighted MRI. ADC values were obtained by manually placing regions of interest (ROIs) within the solid portion of the tumor, avoiding necrotic or hemorrhagic areas. PET/CT SUVmax was measured using 3D spherical volumes of interest over the most metabolically active tumor region.\u003c/p\u003e\u003cp\u003ePelvic lymph nodes were considered positive on MRI if they demonstrated a short-axis diameter\u0026thinsp;\u0026ge;\u0026thinsp;1.0 cm, central necrosis, or irregular margins, and on PET/CT if they exhibited focal FDG uptake above background activity. Imaging analyses were performed by two radiologists (with 15- and 24-year experience in oncologic imaging) specializing in gynecologic imaging, with consensus used to resolve discrepancies.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using R software (version 4.3.1, R Development Core Team). Continuous variables, including tumor size, ADC, and SUVmax, were summarized with means, standard deviations, medians, and ranges. In contrast, categorical variables, such as arterial enhancement and lymph node status, were summarized with frequencies and percentages. Paired comparisons of pre- and post-treatment imaging biomarkers were performed using the Wilcoxon signed-rank test for continuous measures and the McNemar test for categorical features.\u003c/p\u003e\u003cp\u003eOverall survival (OS) and recurrence-free survival (RFS) were estimated using the Kaplan\u0026ndash;Meier method, with survival probabilities reported at 2, 5, and 10 years. Associations between imaging biomarkers and survival outcomes were assessed using univariate Cox proportional hazards models; hazard ratios (HRs) with 95% confidence intervals (CIs) were reported. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analysis were considered for multivariable Cox regression. Logistic regression was used to evaluate predictors of treatment response, as defined by the RECIST 1.1 and PERCIST criteria.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003ePatient Characteristics\u003c/h2\u003e\n \u003cp\u003eFifty patients with histologically confirmed squamous cell carcinoma (SCC) of the cervix met the inclusion criteria and were included in the final analysis. The mean age was 46.4 years (SD, 14.1; range, 23\u0026ndash;79 years). Median clinical follow-up was 46.5 months.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eTreatment Outcomes and Survival\u003c/h2\u003e\n \u003cp\u003eAt the last follow-up, 10 patients (20%) had died, and 16 (35%) had experienced recurrence or death (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Median overall survival (OS) was 13.5 years (95% CI: 9.4). Estimated OS rates were 91.4% at 2 years, 82.7% at 5 years, 77.5% at 8 years, and 64.6% at 10 years. Recurrence-free survival (RFS) rates were 72.6% at 2 years and 57.7% at 5 years (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverall Survival (OS) \u0026ndash; Kaplan-Meier analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvents (Deaths)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.49 years (95% CI: 9.38 \u0026ndash; NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-year Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.4% (95% CI: 83.7\u0026ndash;99.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5-year Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.7% (95% CI: 71.5\u0026ndash;95.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8-year Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.5% (95% CI: 64\u0026ndash;93.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10-year Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.6% (95% CI: 43\u0026ndash;96.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThis table presents overall survival data for 50 patients who were followed from the time of diagnosis. Median survival was 13.49 years with 10 events (20%). Survival probabilities at 2, 5, 8, and 10 years are reported with 95% confidence intervals (CI).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRecurrence-Free Survival (RFS) \u0026ndash; Kaplan-Meier analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvents (Recurrence or Death)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reached (95% CI: 3.66 \u0026ndash; NA)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-year RFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.6% (95% CI: 60.4\u0026ndash;87.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5-year RFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.7% (95% CI: 42.9\u0026ndash;77.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThis table presents recurrence-free survival data for 46 patients after treatment, with 16 recurrence or death events (35%). Median RFS was not reached. Two- and five-year RFS probabilities with 95% CI are reported.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eImaging Biomarkers: Pre- vs. Post-Treatment\u003c/h3\u003e\n\u003cp\u003eAll imaging biomarkers showed significant post-treatment changes (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cstrong\u003eTumor size\u003c/strong\u003e: Median axial diameter decreased from 4.7 cm to 1.2 cm (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); sagittal diameter decreased from 3.6 cm to 1.1 cm (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cstrong\u003eADC values\u003c/strong\u003e: Median ADC increased from 0.82 \u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s to 1.35 \u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cstrong\u003eSUVmax\u003c/strong\u003e: Median SUVmax decreased from 17.8 to 4.8 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cstrong\u003eArterial enhancement\u003c/strong\u003e: Present in 98% of tumors pre-treatment and persisted in 35% post-treatment. \u003cstrong\u003ePelvic lymph nodes\u003c/strong\u003e: Involvement decreased from 72% on MRI and 52% on PET at baseline to 20% and 8% after treatment.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTumor Size, ADC, and SUV Changes (Wilcoxon test).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-treatment (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost-treatment (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian (Range) Change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor Size Axial (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.8 (-7.8, 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor Size Sagittal (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.3 (-6.3, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADC (\u0026times;10⁻\u0026sup3; mm\u0026sup2;/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e847.6\u0026thinsp;\u0026plusmn;\u0026thinsp;151.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1367.6\u0026thinsp;\u0026plusmn;\u0026thinsp;374.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;512.4\u0026thinsp;\u0026plusmn;\u0026thinsp;409.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e510 (-316, 1736)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUV Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.9 (-40.9, 10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis table summarizes pre- and post-treatment measurements for tumor size (axial and sagittal), apparent diffusion coefficient (ADC), and maximum standardized uptake value (SUVmax). Mean, standard deviation, and median with range are reported. Differences were assessed using the Wilcoxon signed-rank test, with statistically significant improvements observed in all parameters (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003ch3\u003ePrognostic Associations\u003c/h3\u003e\n\u003cp\u003eOn univariate Cox regression, higher post-treatment SUVmax was associated with worse OS (HR\u0026thinsp;=\u0026thinsp;1.078, 95% CI: 1.02\u0026ndash;1.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) and RFS (HR\u0026thinsp;=\u0026thinsp;1.049, 95% CI: 1.00\u0026ndash;1.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Larger post-treatment sagittal tumor size showed borderline associations with inferior OS (HR\u0026thinsp;=\u0026thinsp;1.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.089) and RFS (HR\u0026thinsp;=\u0026thinsp;1.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCox Regression \u0026ndash; Predictors of Overall Survival (OS).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConclusion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (0.98\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSize Sagittal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38 (0.95\u0026ndash;1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarginal \u0026ndash; worse OS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSize Axial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.22 (0.81\u0026ndash;1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63 (0.08\u0026ndash;5.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04 (0.95\u0026ndash;1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis table summarizes the univariate Cox regression analyses correlating age, changes in tumor size (axial and sagittal), changes in ADC, and changes in SUV with overall survival. Increase in sagittal tumor size was marginally associated with worse OS (HR\u0026thinsp;=\u0026thinsp;1.38, p\u0026thinsp;=\u0026thinsp;0.089).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCox Regression \u0026ndash; Predictors of Recurrence-Free Survival (RFS).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConclusion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (0.99\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSize Sagittal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.30 (0.99\u0026ndash;1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarginal \u0026ndash; worse RFS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSize Axial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21 (0.92\u0026ndash;1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76 (0.17\u0026ndash;3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01 (0.95\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis table presents univariate Cox regression analyses correlating clinical and imaging variables with RFS. Increased sagittal tumor size was marginally associated with worse RFS (HR\u0026thinsp;=\u0026thinsp;1.30, p\u0026thinsp;=\u0026thinsp;0.057).\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis revealed that a post-treatment increase in axial and sagittal tumor size was correlated with the persistence of arterial enhancement (axial OR\u0026thinsp;=\u0026thinsp;1.58, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.025; sagittal OR\u0026thinsp;=\u0026thinsp;1.93, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.002). Persistent arterial enhancement trended toward worse RFS (HR\u0026thinsp;\u0026asymp;\u0026thinsp;2.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.107) (Tables \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). In multivariable analysis, only post-treatment pelvic lymph node positivity on MRI remained independently associated with inferior RFS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic Regression \u0026ndash; Predictors of Post-treatment Arterial Enhancement.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConclusion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00 (0.96\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSize Axial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.58 (1.10\u0026ndash;2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSize Sagittal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.93 (1.33\u0026ndash;3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21 (0.03\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrend, not significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04 (0.97\u0026ndash;1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis table shows univariate logistic regression analyses of age, changes in tumor size, ADC, and SUV in predicting post-treatment arterial enhancement on imaging. Increases in axial and sagittal tumor size were significantly associated with arterial enhancement (p\u0026thinsp;=\u0026thinsp;0.025 and p\u0026thinsp;=\u0026thinsp;0.002, respectively).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePost-treatment Predictors of OS and RFS.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUV.Max.post\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (1.02\u0026ndash;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorse OS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUV.Max.post\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (1.00\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorse RFS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADC Pre/Post\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot predictive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArterial Enhancement Post\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR\u0026thinsp;=\u0026thinsp;2.24 (0.84\u0026ndash;5.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis table provides univariate Cox regression results for post-treatment SUV, ADC, and arterial enhancement in relation to OS and RFS. Higher post-treatment SUV was significantly associated with worse OS (p\u0026thinsp;=\u0026thinsp;0.008) and RFS (p\u0026thinsp;=\u0026thinsp;0.046).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictors of Treatment Response\u003c/h2\u003e\n \u003cp\u003eBaseline ADC values were predictive of metabolic response. Higher pre-treatment ADC was correlated with a failure to achieve a partial metabolic response per PERCIST (OR\u0026thinsp;=\u0026thinsp;1.007, 95% CI: 1.002\u0026ndash;1.014, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.015) (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Baseline tumor size showed a nonsignificant trend toward predicting RECIST response.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePredictive of Treatment Response (RECIST and PERCIST).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 23.7479%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.5623%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConclusion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADC.pre (PERCIST)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.7479%;\"\u003e\n \u003cp\u003e1.007 (1.002\u0026ndash;1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.5623%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher baseline ADC \u0026rarr; worse response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUV.Max.pre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.7479%;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.5623%;\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot predictive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor Size Sagittal.pre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.7479%;\"\u003e\n \u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.60 (0.31\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.5623%;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor Size Axial.pre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 23.7479%;\"\u003e\n \u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.59 (0.32\u0026ndash;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.5623%;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorderline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThis table summarizes the results of logistic regression analyses examining the predictive value of pretreatment tumor size, ADC, and SUV for treatment response, using both RECIST and PERCIST criteria. A higher pretreatment ADC was significantly correlated with a poor metabolic response by PERCIST (OR\u0026thinsp;=\u0026thinsp;1.007, p\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSUVmax and PET Metabolic Parameters\u003c/h2\u003e\u003cp\u003eOur study demonstrates that post-treatment SUVmax serves as the most significant predictor of survival outcomes in cervical squamous cell carcinoma, with higher values being significantly associated with worse overall survival (HR\u0026thinsp;=\u0026thinsp;1.078, p\u0026thinsp;=\u0026thinsp;0.008) and recurrence-free survival (HR\u0026thinsp;=\u0026thinsp;1.049, p\u0026thinsp;=\u0026thinsp;0.046). This finding aligns with the substantial body of evidence supporting PET metabolic parameters as robust prognostic indicators in cervical cancer.\u003c/p\u003e\u003cp\u003eA comprehensive meta-analysis of 1313 patients across 14 studies confirmed that patients with high primary tumor SUVmax have significantly shorter overall survival compared to those with low SUVmax (HR 2.582, 95% CI 1.936\u0026ndash;3.443, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with similarly adverse effects on event-free survival (HR 1.938, 95% CI 1.203\u0026ndash;3.054, p\u0026thinsp;=\u0026thinsp;0.004) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The superiority of post-treatment over pre-treatment PET parameters has been consistently validated, with an extensive Swedish cohort study of 133 patients demonstrating that post-treatment metabolic parameters exhibit superior prognostic value compared to baseline measurements [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A Turkish multi-center study of 194 patients with a 12.5-year follow-up further corroborated these findings, showing that SUVmax independently predicted local recurrence (HR\u0026thinsp;=\u0026thinsp;1.15, p\u0026thinsp;=\u0026thinsp;0.001). In contrast, metabolic tumor volume (MTV) and SUVmean were predictive of distant metastasis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eApparent Diffusion Coefficient (ADC)\u003c/h2\u003e\u003cp\u003eThis study also identifies pre-treatment ADC values as significant predictors of treatment response, with higher baseline ADC values associated with a failure to achieve a partial metabolic response (OR\u0026thinsp;=\u0026thinsp;1.007, p\u0026thinsp;=\u0026thinsp;0.015). This finding contributes to the growing evidence supporting ADC as a valuable biomarker for treatment prediction and prognosis in cervical cancer.\u003c/p\u003e\u003cp\u003eA comprehensive meta-analysis of 2,314 patients across 22 studies demonstrated that ADC values achieve excellent diagnostic performance for detecting lymph node metastasis, with a pooled sensitivity of 86% and a specificity of 85%, significantly outperforming conventional imaging methods [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The prognostic utility of ADC extends beyond lymph node assessment, with multiple studies establishing its role as an independent predictor of disease outcomes. An extensive Norwegian study of 179 patients revealed that a low tumor ADC mean predicted reduced disease-specific survival (HR\u0026thinsp;=\u0026thinsp;0.96, p\u0026thinsp;=\u0026thinsp;0.001), with normalized ADC ratios providing even stronger prognostic information [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Another study of 219 patients demonstrated that the mean absolute deviation (MAD) from ADC maps, representing ADC distribution uniformity, served as an independent protective factor against early recurrence (HR\u0026thinsp;=\u0026thinsp;0.978, p\u0026thinsp;=\u0026thinsp;0.001) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This suggests tumor heterogeneity assessed through ADC analysis may provide additional prognostic information beyond simple mean values. The superior performance of normalized ADC ratios, particularly myometrium ADC/tumor ADC ratios, highlights the importance of accounting for MRI acquisition-related variations and suggests that relative measurements may be more robust and clinically applicable than absolute ADC values.\u003c/p\u003e\u003cp\u003eThe ability of baseline ADC to predict treatment response early in therapy makes it particularly valuable for adaptive treatment strategies, potentially allowing modification of treatment approaches based on predicted response patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eTumor Size Parameters\u003c/h2\u003e\u003cp\u003eIn addition, our study findings regarding tumor size parameters align with established evidence demonstrating the prognostic significance of baseline tumor dimensions and volumetric changes during treatment. Post-treatment sagittal tumor size showed borderline associations with inferior survival outcomes (HR\u0026thinsp;=\u0026thinsp;1.38 for overall survival, p\u0026thinsp;=\u0026thinsp;0.089), reflecting the established principle that larger residual disease portends worse prognosis. A Chinese study of 217 patients provided compelling evidence for the prognostic value of the tumor volume reduction rate (TVRR), identifying it as an independent predictor of prognosis. Patients achieving a TVRR greater than 82.19% demonstrated significantly better outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Mid-treatment tumor volume greater than 11.38 cm\u0026sup3; emerged as an independent predictor of poor survival (HR\u0026thinsp;=\u0026thinsp;3.192, p\u0026thinsp;=\u0026thinsp;0.034), emphasizing the importance of assessing early treatment response through volumetric analysis.\u003c/p\u003e\u003cp\u003eThe relationship between tumor size parameters and treatment outcomes reflects fundamental principles of tumor biology and the response to treatment. Larger tumors typically harbor greater cellular heterogeneity, increased hypoxic regions, and enhanced resistance mechanisms, reducing treatment sensitivity. The Korean multi-center study demonstrated that a baseline tumor volume greater than 61.6 cm\u0026sup3; was associated with adverse outcomes (HR\u0026thinsp;=\u0026thinsp;0.417, p\u0026thinsp;=\u0026thinsp;0.032). In comparison, the threshold of 11.38 cm\u0026sup3; for mid-treatment volume provided optimal discrimination for survival prediction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The prognostic superiority of TVRR over static size measurements suggests that dynamic assessment of treatment response provides more meaningful information than baseline tumor dimensions alone. Patients with poor volume reduction (\u0026lt;\u0026thinsp;82%) during external beam radiotherapy represent a high-risk population that may benefit from treatment intensification or modified approaches.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, its retrospective design introduces the possibility of selection bias and limits control over imaging acquisition timing and treatment heterogeneity. Second, the relatively modest sample size (n\u0026thinsp;=\u0026thinsp;50) may reduce the statistical power to detect associations, particularly in subgroup analyses such as nodal involvement or arterial enhancement persistence. Third, imaging was performed on multiple MRI and PET/CT platforms over a decade, which may have introduced technical variability in SUV measurements, ADC quantification, and contrast-enhanced sequences. Fourth, inter-observer variability in tumor measurements, ADC placement, and assessment of arterial enhancement was not formally evaluated; thus, the reproducibility of these imaging biomarkers remains to be validated. Fifth, although RECIST and PERCIST criteria were used to define treatment response, histopathologic confirmation of residual disease was not consistently available. Sixth, some patients had limited follow-up, which may have potentially underestimated late recurrences or survival outcomes. Finally, the study focused exclusively on squamous histology, and findings may not generalize to other cervical cancer subtypes such as adenocarcinoma or adenosquamous carcinoma.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications\u003c/h2\u003e\u003cp\u003eGiven these prognostic associations, patients demonstrating elevated post-treatment SUVmax, larger residual tumor dimensions, or persistent arterial enhancement warrant surveillance protocols. Close monitoring with serial MRI examinations allows assessment of residual tumor size and perfusion characteristics, while PET imaging provides complementary metabolic information to detect early recurrence or metastatic progression. Additionally, regular cervical cytology, including Pap smears, remains essential for identifying local residual disease at the primary site. This integrated multimodality surveillance approach enables early detection of disease progression, potentially facilitating timely therapeutic intervention and improving long-term outcomes in high-risk patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eImplications for Future Research\u003c/h2\u003e\u003cp\u003eThe findings from this study highlight the potential of quantitative imaging biomarkers as noninvasive tools for risk stratification in cervical SCC. Future research should focus on validating these prognostic markers in larger, prospective, and multicenter cohorts to improve generalizability and reproducibility. Standardizing imaging acquisition protocols and quantitative measurement techniques, particularly for ADC and SUV, will be critical to reducing inter-institutional variability.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eQuantitative imaging biomarkers from MRI and PET provide important prognostic information in cervical squamous cell carcinoma. Higher post-treatment SUVmax, residual tumor size, and persistent arterial enhancement were associated with worse survival outcomes, while pelvic lymph node positivity after therapy emerged as an independent predictor of recurrence. Pre-treatment ADC values correlated with metabolic response, suggesting a role in predicting treatment efficacy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, M.V.; P.B. Formal Analysis, S.J. Investigation, A.A.; Data Curation, A.A.; Writing\u0026ndash; Original Draft Preparation, M.V., P.B., S.J. Writing\u0026ndash; Review \u0026amp; Editing, M.V.; P.B.; S.J.; R.I.; D.G.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R.L., et al., \u003cem\u003eCancer statistics, 2025\u003c/em\u003e. CA Cancer J Clin, 2025. 75(1): p. 10\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaleh, M., et al., \u003cem\u003eCervical cancer: 2018 revised international federation of gynecology and obstetrics staging system and the role of imaging\u003c/em\u003e. American Journal of Roentgenology, 2020. 214(5): p. 1182\u0026ndash;1195.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGennarini, M., et al., \u003cem\u003eMulti-model quantitative MRI of uterine cancers in precision medicine\u0026rsquo;s era\u0026mdash;a narrative review\u003c/em\u003e. Insights into Imaging, 2025. 16(1): p. 113.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYadav, D., et al., \u003cem\u003eUnraveling the Role of PET in Cervical Cancer: Review of Current Applications and Future Horizons\u003c/em\u003e. Journal of Imaging, 2025. 11(2): p. 63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAvesani, G., et al., \u003cem\u003eThe Utility of Contrast-Enhanced Magnetic Resonance Imaging in Uterine Cervical Cancer: A Systematic Review\u003c/em\u003e. Life (Basel), 2023. 13(6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan, L., et al., \u003cem\u003eA systematic review and meta-analysis of the prognostic impact of pretreatment fluorodeoxyglucose positron emission tomography/computed tomography parameters in patients with locally advanced cervical cancer treated with concomitant chemoradiotherapy\u003c/em\u003e. Diagnostics, 2021. 11(7): p. 1258.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarkus, M., et al., \u003cem\u003eMetabolic parameters of [18F] FDG PET-CT before and after radiotherapy may predict survival and recurrence in cervical cancer\u003c/em\u003e. Acta Oncologica, 2023. 62(2): p. 180\u0026ndash;188.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOnal, C., et al., \u003cem\u003eLong-term assessment of clinical parameters and positron emission tomography parameters in predicting recurrence in uterine cervical cancer patients receiving definitive chemoradiotherapy\u003c/em\u003e. Nuclear Medicine Communications, 2024. 45(3): p. 203\u0026ndash;210.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen, J., et al., \u003cem\u003eDiagnostic performance of ADC values and MRI-based radiomics analysis for detecting lymph node metastasis in patients with cervical cancer: a systematic review and meta-analysis\u003c/em\u003e. European Journal of Radiology, 2022. 156: p. 110504.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, X., et al., \u003cem\u003eThe value of whole-tumor texture analysis of ADC in predicting the early recurrence of locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy\u003c/em\u003e. Frontiers in Oncology, 2022. 12: p. 852308.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun, C., et al., \u003cem\u003eThe prognostic value of tumor size, volume and tumor volume reduction rate during concurrent chemoradiotherapy in patients with cervical cancer\u003c/em\u003e. Frontiers in oncology, 2022. 12: p. 934110.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee, K.C., et al., \u003cem\u003eThe predictive value of tumor size, volume, and markers during radiation therapy in patients with cervical cancer\u003c/em\u003e. International Journal of Gynecological Cancer, 2017. 27(1): p. 123\u0026ndash;130.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"SUVmax, size, enhancement, cervix. ADC","lastPublishedDoi":"10.21203/rs.3.rs-8006282/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8006282/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo evaluate quantitative imaging biomarkers, including MRI tumor size, apparent diffusion coefficient (ADC), arterial-phase enhancement, and PET maximum standardized uptake value (SUVmax), as prognostic indicators of overall survival (OS), recurrence-free survival (RFS), and treatment response in cervical squamous cell carcinoma (SCC).\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e\u003cp\u003eFifty patients with biopsy-proven SCC who underwent pre- and post-treatment MRI and FDG-PET/CT were retrospectively analyzed. Tumor dimensions (axial, sagittal), ADC, SUVmax, and arterial enhancement were assessed. Survival was estimated by Kaplan\u0026ndash;Meier, and associations with OS and RFS were tested using Cox regression. Logistic regression was used to determine predictors of treatment response according to RECIST and PERCIST criteria.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eTumor size, SUVmax, and ADC changed significantly post-treatment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Persisting arterial enhancement was seen in 35% of tumors. Higher post-treatment SUVmax predicted worse OS (HR\u0026thinsp;=\u0026thinsp;1.078, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) and RFS (HR\u0026thinsp;=\u0026thinsp;1.049, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046). Larger residual sagittal tumor size showed borderline associations with inferior OS (HR\u0026thinsp;=\u0026thinsp;1.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.089) and RFS (HR\u0026thinsp;=\u0026thinsp;1.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057). Increases in tumor size significantly correlated with persistent arterial enhancement (axial OR\u0026thinsp;=\u0026thinsp;1.58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025; sagittal OR\u0026thinsp;=\u0026thinsp;1.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Persistent enhancement trended toward worse RFS (HR\u0026thinsp;\u0026asymp;\u0026thinsp;2.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.107). In multivariable analysis, post-treatment pelvic lymph node positivity remained independently associated with poorer RFS. Baseline ADC predicted metabolic response: a higher pre-treatment ADC was associated with a failure to achieve a partial metabolic response per PERCIST (OR\u0026thinsp;=\u0026thinsp;1.007, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.015).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePost-treatment SUVmax, residual tumor size, arterial enhancement, and pelvic lymph node involvement are key prognostic indicators in cervical SCC. Persistent enhancement may reflect treatment-resistant vascularity. Integrating these biomarkers into clinical workflows may improve risk stratification and guide personalized management.\u003c/p\u003e","manuscriptTitle":"Quantitative Imaging Biomarkers as Prognostic Indicators in Squamous Cell Cervical Carcinoma: A Retrospective Cohort Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 17:09:54","doi":"10.21203/rs.3.rs-8006282/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-04T22:42:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T11:30:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T14:55:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218393280259715265265845749048293626","date":"2025-11-22T00:36:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19067430313528096356908591658723199545","date":"2025-11-20T07:56:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T22:36:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129509803683072107355975055526755636451","date":"2025-11-19T18:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-09T16:35:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T01:07:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T01:06:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2025-11-01T14:11:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e05520d9-1f69-4f44-b4c4-8650c69ecd79","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:03:57+00:00","versionOfRecord":{"articleIdentity":"rs-8006282","link":"https://doi.org/10.1007/s00261-025-05351-7","journal":{"identity":"abdominal-radiology","isVorOnly":false,"title":"Abdominal Radiology"},"publishedOn":"2025-12-27 15:57:30","publishedOnDateReadable":"December 27th, 2025"},"versionCreatedAt":"2025-11-18 17:09:54","video":"","vorDoi":"10.1007/s00261-025-05351-7","vorDoiUrl":"https://doi.org/10.1007/s00261-025-05351-7","workflowStages":[]},"version":"v1","identity":"rs-8006282","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8006282","identity":"rs-8006282","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}