How hot is too hot? Use of PET to evaluate response to radiation therapy for patients with cervical cancer

How hot is too hot? 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Use of PET to evaluate response to radiation therapy for patients with cervical cancer Claudia Bale, Janina Pearce, Xiaoyan Deng, Dipankar Bandyopathy, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6560042/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study sought to determine the relationship between cervical cancer recurrence and post-treatment change in standardized uptake value (SUV) of 18 F-2-fluoro-2-deoxy-D-glucose-positron emission tomography (FDG-PET) in the cervix and lymph nodes. Methods A retrospective study of patients who received curative intent radiation therapy for biopsy-proven stage I-IVA locally advanced cervical cancer from 2009–2021 was performed. Percent differences in SUVs at the cervix and the most avid and distant lymph nodes were calculated from pre- and post-treatment scans and used as independent variables for analyses. The primary outcome was recurrence rate, and secondary outcomes were overall and progression-free survival. Results 55 patients met eligibility criteria. Recurrence rate was 27% (15/55); of these, 33% had local recurrence (5/55) and 67% had distant recurrence (10/55). Median percent decrease of cervical SUV after treatment in those with and without recurrence was similar (71.4 vs 68.8, p = 0.89); this remained consistent when analyzing those with local recurrence only (70.5, p = 0.95). When the percent decrease in cervical SUV was examined in intervals ( 75%), this was also not predictive of local (p = 0.91) or overall (p = 0.75) recurrence. Median percent decrease at the most avid and distant lymph node in those with and without recurrence was not significantly different (p > 0.05). Neither change in cervical nor lymph node SUV were associated with overall or progression-free survival. Conclusion Changes in SUV after treatment are likely not a reliable stand-alone marker for predicting recurrence or survival in locally advanced cervical cancer after treatment with radiation therapy. SUV change cancer recurrence lymph node avidity metabolic imaging standardized uptake value Figures Figure 1 Introduction Cervical cancer is the 4th most frequently diagnosed cancer and cause of cancer death in women worldwide[ 1 , 2 ]. While cervical cancer was historically clinically staged, in 2018, the International Federation of Gynecology and Obstetrics (FIGO) updated its guidelines, highlighting the importance of imaging as part of clinical staging and planning optimal treatment for patients with cervical cancer [ 3 ]. Positron Emission Tomography with radiotracer uptake of 18 F-2-fluoro-2-deoxy-D-glucose (FDG-PET) is a tool for staging primary and recurrent cervical cancer that detects physiologic changes based on degree of uptake and metabolism of glucose, a marker for tumors [ 4 ]. Of patients who present with cervical cancer, an estimated median 37% present with locally advanced disease requiring primary radiation therapy with or without chemotherapy [ 5 ] for curative intent treatment. Patients now routinely obtain FDG-PET pre- and post-treatment to assess response; however, there are inconsistent data on how to interpret residual avidity in the post-treatment period, leaving providers with a clinical conundrum and patients at risk for false positive or false negative interpretations [ 6 ]. Post-treatment FDG-PET avidity in the cervix has been found to be a significant independent predictor of survival for patients with cervical cancer [ 7 – 9 ] with prognostic value in assessing patterns of treatment failure and for eligibility for salvage therapy [ 9 ]. Optimal timing of post-treatment imaging is still undefined and has varied in other studies with combined FDG-PET/Computed Tomography (CT) scanning [ 10 – 13 ]. A retrospective study found that post-treatment FDG-PET avidity alone was the most significant prognostic factor for developing metastatic disease and survival outcomes but with large variation in time from treatment to follow up (range 1 to 12 months, mean 3 months) [ 7 ]. In a prospective study, Schwarz et al demonstrated that 3-month post-treatment FDG-PET avidity was a significant predictor of survival [ 14 ]. While these studies highlight the importance of the presence or lack of avidity in post-treatment scans, only one study [ 15 ] quantified change in maximum SUV of FDG in the cervix as percentage of residual activity and found no significant different between a cohort of 20 patients with locally advanced cervical cancer (8 of whom developed recurrence). No studies have attempted to quantify changes in SUVs in the lymph nodes in the context of recurrence or survival outcomes. Furthermore, no association between the percent change in avidity and recurrence and survival has been published to date. The objective of this study was to determine the relationship between cancer recurrence and the post-treatment percent change in SUV of FDG-PET in both the cervix and the lymph nodes. Methods Patient selection Following public registration of the investigation (researchregistry10267) and institutional review board approval (IRB HM20024055), a retrospective cohort study was performed at a single-site urban, safety net, tertiary referral institution with National Cancer Institute (NCI) designation. Patients at least 18 years of age with biopsy proven stage I-IVA locally advanced cervical cancer between 2009–2021 who received curative intent radiation therapy at our tertiary care academic institution were eligible for inclusion. Additional inclusion criteria required patients to have both a pre-treatment scan (before starting radiation therapy) and at least one post-treatment FDG-PET scan completed within 5 months of their radiation therapy end date. Cervical cancer was classified according to the 2018 FIGO staging system. Patients with metastatic disease at diagnosis, history of hysterectomy, or who received palliative intent treatment were excluded. All patients included in the final analysis (55/55, 100%) completed the prescribed radiation therapy regimen and underwent both pre-treatment and post-treatment FDG-PET imaging as required by the study protocol. There was no loss to follow-up for the primary outcome assessment of post-treatment imaging changes. For survival analysis, clinical follow-up duration varied based on treatment dates within our study period (2009–2021), with complete follow-up data available for all patients through their last clinical visit or date of death. Clinical data Clinical data collected included age and body mass index (BMI) at diagnosis, race, ethnicity, marital status, employment status, urban (population > 2500) vs rural (< 2500) home address, medical comorbidities (diabetes, HTN, HIV, sarcoidosis, tobacco use), receipt of chemotherapy and/or immunotherapy, tumor size and histology, and cancer stage [ 16 – 18 ]. Overall survival and progression-free survival were measured from the date of radiation therapy initiation to the date of last clinical follow up or date of death for overall survival, or date of recurrence for progression free survival. Recurrence was confirmed histologically and/or radiologically. The same PET scanner from 2013 to the present; however, prior to 2013 a different scanner was in use. However, all scans were performed at the home institution in a standardized manner. All patients were treated with definitive intent with at least 45Gy with external beam radiotherapy. Patients either received 3D or intensity-modulated radiation therapy (IMRT) and either intracavitary or hybrid intracavitary/interstitial brachytherapy throughout the time course of the study. Approximately 60 minutes (accepted range 55–75 min). minutes after intravenous administration of 10–15 mCi of F-18 fluorodeoxyglucose, three-dimensional emission acquisition of the body extending from the skull to base to mid-thighs was obtained. Blood glucose was measured prior to tracer injection. Noncontrast, low-dose CT acquisition was obtained for attenuation correction of the emission acquisition and anatomical localization of tracer distribution. A maximum intensity projection reconstruction of the PET acquisition was made for display as a rotating whole body volumetric image. Tomographic images were computer generated and displayed from transaxial, coronal, and sagittal planes. PET and CT images were fused to aid in determining anatomical location of F-18 FDG tracer distribution. To quantitate abnormal FDG uptake, F-18 was measured in the region of abnormality, normalized to the administered dose and the patient’s body weight, and expressed as a SUV. The SUVs of avid lymph nodes were reviewed in the radiology imaging reports with the most avid lymph node selected based on the highest reported SUV, and the most distant lymph node was selected based on the most anatomically distant lymph node from the cervix. Pre- and post-treatment FDG-PET scans were assessed by radiology providers and percent differences in the SUV at the cervix, the most avid lymph node, and the most distant lymph node were calculated and used as the independent variables for analyses. Statistical analysis The PROCESS guidelines for reporting retrospective studies were followed [ 19 , 20 ]. Due to a non-normal distribution, the Wilcoxon non-parametric test was implemented for comparison of SUV distribution-related disease recurrence. Patient demographics and tumor characteristics between those with and without recurrence were compared using either non-parametric median two-sample tests, Cochran-Mantel-Haenszel tests, Fischer’s Exact tests, or Chi-Squared tests as appropriate. To determine the effect of percent decrease of cervical SUV in combination with other covariates on recurrence risk, logistic regressions were performed, for the outcomes of overall recurrence as well as local recurrence only. The main independent variable was the percent decrease of cervical SUV and the seventeen aforementioned covariates were considered. A stepwise selection method was used in the multivariate logistic regression to identify the important covariates alongside with the main factor (percent decrease in max SUV at the cervix) that had significant effect on the outcome of generalized recurrence and the local recurrence. The same analysis was conducted with percent decrease in SUV of the most distant node, and again for the most avid node. Percent change in cervical SUV was additionally analyzed in discrete intervals ( 75%) using the Cochran-Mantel-Haenszel test method. The survival outcomes were estimated using the Kaplan-Meier method. The Cox Proportional Hazards Model was conducted separately for the overall survival and the progression free survival analyses. Multivariate analyses of the covariate factors were performed with results expressed as hazard ratios (HR) with 95% confidence intervals (CI) with a two-sided significant level of 5% ( p < 0.05). All analysis were conducted using SAS (Statistical Analysis System, version 9.4) software. Results Patient characteristics Median patient age at time of treatment was 46 years (range 22–83) and median BMI was 29.3 kg/m 2 (range 17.7–50.8). Histology included 87% squamous cell carcinoma (48/55), 11% adenocarcinoma (6/55), and 2% adenosquamous carcinoma (1/55). Post-treatment FDG-PET scans were performed between 2–5 months (mean 3.5 months) after treatment completion for all patients. As this was a retrospective imaging analysis study, no study-specific interventions beyond standard clinical care were performed. Therefore, no study-related adverse events occurred. Standard treatment-related toxicities from radiation therapy and concurrent chemotherapy were managed according to institutional protocols but were not specifically analyzed as part of this imaging-focused investigation. No complications were reported during the FDG-PET imaging procedures themselves for any of the 55 patients included in the analysis. Overall recurrence rate was 27% (15/55); of these, 33% had local recurrence (5/55) and 67% had distant recurrence (10/55). All but 2 patients received chemotherapy, and 12 patients (7 disease free, 5 recurrence) received immunotherapy with radiation therapy and chemotherapy on clinical trial. All characteristics are presented in Table 1 . Table 1 Patient Demographics and Tumor Characteristics Patient characteristics Patients (n, %) Total (n = 55) Without Disease Recurrence (n = 40) With Disease Recurrence (n = 15) P-value Age at Diagnosis, years 0.40 Median 46 47 42 Range 22–83 22–83 27–76 Race 0.59 Caucasian 27 18 (66.67%) 9 (33.33%) African American 22 17 (77.27%) 5 (22.73%) Other 6 5 (83.33%) 1 (16.67%) Ethnicity 1.00 Hispanic 1 1 (100%) 0 (0%) Non-Hispanic 54 39 (72.22%) 15 (27.78%) Clinical Stage 0.012 I 7 7 (100%) 0 (0%) II 28 22 (78.57%) 6 (21.43%) III 15 6 (40%) 9 (60%) IVA 1 1 (100%) 0 (0%) Unknown 4 4 (100%) 0 (0%) Histology 0.79 Squamous 48 35 (72.92%) 13 (27.08%) Adenosquamous 6 4 (66.67%) 2 (33.33%) Adenocarcinoma 1 1 (100%) 0 (0%) Tumor Grade 0.47 1 5 4 (80%) 1 (20%) 2 15 9 (60%) 6 (40%) 3 14 12 (85.71%) 2 (14.29%) Unknown 21 15 (71.43%) 6 (28.57%) Tumor size, mm 0.74 Median 58 57 58 Range 20–100 20–100 40–90 Pre-treatment Lymph Node Involvement 0.36 Yes 35 24 (68.57%) 11 (31.43%) No 21 16 (80%) 4 (20%) Chemotherapy Regimen 0.47 Cisplatin 53 39 (73.58%) 14 (26.42%) None 2 1 (50%) 1 (50%) Pre- and Post-Treatment FDG uptake and recurrence The comparison of the percent changes in pre- and post- treatment cervical FDG uptake, most distant, and most avid lymph node as predictors of recurrence are shown in Table 2 . Table 2 Percent changes in the cervical, most distant, and most avid lymph node max SUV in patients with and without disease recurrence. Max SUV changes Without Disease Recurrence (n = 40) With Disease Recurrence (n = 15) P-value Cervix Continuous Interval 0.282 0.753 N for analysis 40 15 Mean, % change 65.86 54.66 Median, % change 71.36 68.82 Standard deviation 29.95 67.14 Most distant lymph node 0.667 N for analysis 23 9 Mean, % change 82.44 80.00 Median, % change 100 100 Standard deviation 35.48 31.28 Most avid lymph node 0.245 N for analysis 23 11 Mean, % change 79.69 70.65 Median, % change 100 63.68 Standard deviation 33.83 30.81 Median percent decrease of cervical SUV after treatment in those with and without recurrence was similar (71.4 vs 68.8, p = 0.887); this remained consistent when analyzing those with local recurrence only (70.5 vs 68.8, p = 0.953). The multivariate logistic regressions for both overall recurrence and local recurrence demonstrates that the percent decrease in cervical SUV was not a significant predictor of overall (OR: 0.989, p = 0.282) or local (OR: 0.997, p = 0.755) disease recurrence. With the stepwise selection method, none of these covariates were selected at the 0.05 significant level, indicating that these covariates had no significant effect on overall or local disease recurrence. When the percent decrease in cervical SUV was examined in intervals ( 75%) the Cochran-Mantel-Haenszel test showed that the overall recurrence rate was not significantly different among the intervals (p = 0.753); this remained consistent when analyzing those with local recurrence only (p = 0.91). The multivariate logistic regression results showed that the interval percent decrease in cervical SUV was not a significant indicator for both overall and local recurrence (p = 0.7357 and 0.9852, respectively). No covariates were significant predictors of recurrence (p > 0.05 for all). For the lymph nodes, the median percent decrease of SUV at the most avid and most distant lymph node from pre- to post-treatment PET in those with and without recurrence was not significantly different (p > 0.05 for both) for the 34/55 patients with nodal involvement. Percent change of SUV in the most distant node was not predictive of local (OR 1.257, p = 0.0097) or overall (OR 0.995, p = 0.9996) disease recurrence. Percent change of SUV of most avid lymph node were similar in that it was also not predictive of local (OR 1.314, p = 0.898) or overall (OR 0.993, p = 0.5815) disease recurrence. No covariates were significant predictors of recurrence. Pre- and Post-Treatment FDG uptake and survival Patients with and without recurrence differed in overall survival (p < 0.0001) and progression free survival (p < 0.0001) outcomes as expected. However, the interval percent decrease in cervical SUV was not significantly associated with overall survival (Fig. 1 A) or progression free survival (Fig. 1 B). Additionally, percent changes in pre- and post- treatment cervical FDG uptake, most distant, and most avid lymph node as continuous variables were not associated with overall survival or progression free survival (Table 3 , with visual representation in Fig. 1 C-F). Table 3 Percent changes in the cervical, most distant, and most avid lymph node max standardized uptake and survival outcomes Max SUV Changes Overall Survival Progression Free Survival Overall survival hazard ratio HR (95% CI) P value Progression free survival hazard ratio HR (95% CI) P value Cervix 0.994 (0.987, 1.001) 0.1199 0.997 (0.988, 1.006) 0.5361 Most distant lymph node 0.997 (0.983, 1.012) 0.7288 0.999 (0.981, 1.018) 0.9319 Most avid lymph node 0.994 (.980, 1.008) 0.3868 0.994 (0.978, 1.010) 0.4572 In multivariate analysis of percent change of cervical SUV, receipt of chemotherapy was the only covariate that was a significant predictor of longer overall survival (HR 0.17, p = 0.031), while higher BMI was the only covariate that was a significant predictor of longer progression free survival (HR 0.89, p = 0.02). Receipt of immunotherapy was associated with shorter progression-free survival but was not statistically significant (HR 3.348, p = 0.053). Examining percent change in cervical SUV in intervals, no covariates were significant for overall survival, while again a higher BMI was significantly associated with longer progression-free survival (HR 0.90, p = 0.034) and receipt of immunotherapy was significantly associated with shorter progression-free survival (HR 3.70, p = 0.042) (Table 4 ). Table 4 Summary of covariates per analysis that were significant predictors of overall or progression-free survival Overall Survival Overall survival hazard ratio HR (95% Confidence Interval) P value Cervix, Continuous Chemotherapy 0.172 (0.035, 0.853) 0.0312 Most Distant Lymph node Chemotherapy 0.165 (0.029, 0.939) 0.042 Most avid lymph node Chemotherapy 0.139 (0.023, 0.834) 0.031 Progression-Free Survival Progression free survival hazard ratio HR (95% Confidence Interval) P value Cervix, Continuous BMI 0.891 (0.808, 0.982) 0.0198 Cervix, Interval Immunotherapy BMI 3.689 (1.051, 12.945) 0.901 (0.818, 0.992) 0.0416 0.0337 In multivariate analysis of percent change of lymph node SUV, of the covariate data collected, chemotherapy was a significant predictor of longer overall survival for the percent change of SUV for most distant node (HR 0.17, p = 0.042) and most avid node (HR 0.14, p = 0.031) (Table 4 ). No other covariates were significant predictors for overall survival nor progression-free survival. Discussion Summary of Main Results Percent change of SUV in the cervix, the most avid lymph node, and the most distant node were not significant predictors of overall or local recurrence compared to those without disease recurrence, nor were they individual predictors of overall or progression free survival in this cohort. In multivariate analysis examining percent change of cervical max SUV, receipt of chemotherapy and higher BMI were significant predictors of longer overall survival and longer progression free survival, respectively. When examining interval percent change of cervical max SUV, higher BMI was again a significant predictor or progression-free survival, and receipt of immunotherapy was a significant predictor of shorter progression-free survival. For percent change in SUV for the most distant and most avid lymph node SUV, receipt of chemotherapy was a significant predictor for longer overall survival. Results in the Context of Published Literature Curative radiation results in regression of tumor size as evidenced on pelvic examination; however, lymph node response and/or general progression cannot be fully assessed by physical exam alone. FDG-PET has become a tool to detect cervical cancer recurrence by assessing SUV to monitor metabolic response to irradiated areas and assess for new or progressive disease [ 7 ]. A 2013 meta-analysis by Zhao and colleagues [ 21 ] concluded that SUV has predictive value in survival outcomes for guiding treatments; however, individual studies were small with heterogenous samples that yielded conflicting results. Twelve of these studies examined max SUV of pre-treatment scans, while two studies used max SUV of post-treatment scans as prognostic factors of disease recurrence and survival outcomes. Only one study [ 15 ] collected SUVs from both pre and post-treatment scans and found no significant differences between the percent residual activity of cervical max SUV in those with or without recurrence in a cohort of 20 patients. This study was the closest to our study design with similar results. In another meta-analysis by Sarkeret al [ 22 ] (with six of 16 studies overlapping with Zhao’s meta-analysis [ 21 ]), cervical max SUV was not a significant independent prognostic factor in the multivariate analyses conducted, except in 2 of 14 studies. They concluded that high SUV (≥ 13.4) should be considered at increased risk for recurrence; however, there is no overall clinical consensus within the field to determine what change in max cervical SUV confirms persistent or recurrent disease or to what degree of lymph node positive for FDG uptake is clinically significant. We chose to investigate the percent change of cervical max SUV between pre-treatment and post-treatment scans as a continuous variable and in discrete intervals of percentage change rather than focusing on a singular cut off value on pre- or post-treatment scans. We also expanded our analysis further by examining the percent change in the most avid and the most distant lymph nodes for lymph node positive patients who had FDG avidity reported for their pre and post-treatment scans. Within our cohort, neither numerical value of SUV of these locations nor the percent change of SUV between pre- and post-treatment scan were reliable predictors of recurrence alone. Recent interest has been given to other markers of disease recurrence on FDG-PET scans. In a retrospective analysis by Pedraza and colleagues [ 23 ], they found that cervical max SUV was not an independent prognostic factor in multivariable analysis, while metabolic tumor volume and total lesion glycolysis in combination with texture analysis were significant prognostic factors. Other retrospective and prospective studies have similarly demonstrated metabolic tumor volume and/or total lesion glycolysis [ 24 , 25 ] as well as intratumoral heterogeneity [ 26 , 27 ] are significant prognostic factors in predicting treatment outcome and survival in locally advanced cervical cancer patients. However, these parameters are not routinely collected in many institutions including ours, limiting the clinical application of these findings. Strengths and Weaknesses: Strengths of this study include complete data for each patient and pre and post treatment SUV. Additionally, all post-treatment scans were completed within a similar 3–5 month post-treatment timeframe, decreasing the post-treatment time variable that could have otherwise been a confounding factor. Use of FDG-PET with noncontrast CT has wide applicability across sites and institutions versus a scan with a dedicated diagnostic CT scan that may not be available at all institutions. Limitations of this investigation include the retrospective nature, single-center, and small sample size, all of which may lead to inherent selection bias. However, our institution is a tertiary referral and NCI center, which we believe helps to mitigate this risk. Use of date of last clinical follow-up likely underestimated actual overall survival. Progression free survival data was limited due to the variable start dates of radiation therapy. This led to more longitudinal data in patient who began therapy at the beginning of our designated timeframe (2009) compared to those completing therapy and post-treatment scan just prior to the end of our data-collection timeframe (2021). Additionally, we recognize that FIGO scoring changed in 2018 which was in the later portion of our time period for data collection; however, we mitigated the effects of this change in staging by using the 2018 staging for all patients for consistency. We also used FDG avidity of the lymph nodes as dictated by the radiology reports and so we could not determine if this avidity was specific for metastasis vs reactivity. Implications for Practice and Future Research Further work investigating SUV changes at the primary tumor site and lymph nodes in combination with individual patient characteristics as well as individual tumor profiling data will be necessary to help develop accurate, individualized recurrence risk assessments. Based on our data, changes in SUV at the primary tumor site and the lymph nodes after treatment are likely not a reliable stand-alone marker for predicting recurrence or overall or progression free survival. It will likely be necessary to combine SUV changes with other patient characteristics and tumor data to predict disease recurrence and guide clinical decision-making. Declarations Funding Services in support of this research project were generated by the VCU Massey Cancer Center {Massey Cancer Information Core (CIC)] Shared Resource, supported, in part, with funding from NIH-NCI Cancer Center Support Grant P30 CA016059. Competing Interests The authors declare that they have no known competing financial or personal conflicts of interest. Ethics Approval This retrospective study was performed at a single-site urban, safety net, tertiary referral institution with National Cancer Institute (NCI) designation following public registration of the investigation (researchregistry10267) and institutional review board approval (IRB HM20024055). Consent to participate This study was conducted as a retrospective review of existing medical records following institutional review board approval (IRB HM20024055). As a retrospective chart review study that involved no direct patient contact and minimal risk to subjects, a waiver of informed consent was granted by the IRB in accordance with institutional policies. All patient data were de-identified during analysis and reporting to protect patient privacy, with no identifying information included in the manuscript. No images of patients were used in this publication. Author Contribution All authors contributed to the study conception and design. Protocol/project development; data analysis; manuscript writing/editing was performed by CB, JP, and SS. Protocol and project development was additionally supported by EF, DM and LR. XD and DB performed majority of the data analysis and data interpretation. NY and CS contributed to manuscript writing and editing. The first draft of the manuscript was written by CB and all the authors commented on previous versions of the manuscript. All authors have read and approved the final manuscript. Acknowledgement Services in support of this research project were generated by the VCU Massey Cancer Center [Massey Cancer Informatics Core (CIC)] Shared Resource, supported, in part, with funding from NIH-NCI Cancer Center Support Grant P30 CA016059. References Brisson, M and Drolet, M. Global elimination of cervical cancer as a public health problem. Lancet Oncol (2019). 20(3): p. 319–321. doi: 10.1016/S1470-2045(19)30072-5. Fowler, JR; Maani, EV; Dunton, CJ; Gasalberti, DP; Jack, BW. 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Prognostic Implications of the SUVmax of Primary Tumors and Metastatic Lymph Node Measured by 18F-FDG PET in Patients With Uterine Cervical Cancer: A Meta-analysis. Clinical Nuclear Medicine (2016). 41(1): p. 34–40. doi: 10.1097/RLU.0000000000001049 . Pedraza S, Seiffert AP, Sarandeses P, Muñoz-Lopez B, Gómez EJ, Sánchez-González P, Pérez-Regadera JF. The value of metabolic parameters and textural analysis in predicting prognosis in locally advanced cervical cancer treated with chemoradiotherapy. Strahlenther Onkol (2022). 198(9): p. 792–801. doi: 10.1007/s00066-022-01900-x . Han S, Kim H, Kim YJ, Suh CH, Woo S. Prognostic Value of Volume-Based Metabolic Parameters of (18)F-FDG PET/CT in Uterine Cervical Cancer: A Systematic Review and Meta-Analysis. AJR Am J Roentgenol (2018). (5): p. 1112–1121. doi: 10.2214/AJR.18.19734.211 Cegla P, Hofheinz F, Cholewiński W, Czepczyński R, Kubiak A, van den Hoff J, Boś-Liedke A, Roszak A, Burchardt E. Prognostic Value of Pretherapeutic Primary Tumor MTV from [(18)F]FDG PET in Radically Treated Cervical Cancer Patients. Metabolites (2021). 11(12). doi: 10.3390/metabo11120809 . Pinho DF, King B, Xi Y, Albuquerque K, Lea J, Subramaniam RM. Value of Intratumoral Metabolic Heterogeneity and Quantitative (18)F-FDG PET/CT Parameters in Predicting Prognosis for Patients With Cervical Cancer. AJR Am J Roentgenol (2020). 214(4): p. 908–916. doi: 10.2214/AJR.19.21604 . Kidd, EA and Grigsby, PW. Intratumoral metabolic heterogeneity of cervical cancer. Clin Cancer Res (2008). 14(16): p. 5236–41. doi: 10.1158/1078-0432.CCR-07-5252 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6560042","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458904030,"identity":"4d50b065-d7a7-4191-9769-5ddb79b3bc4d","order_by":0,"name":"Claudia Bale","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYBACAwbmBiBlA+HxEKeFEaQljXQth0nQYs5+sPHBj5rzif0zEhgfvG0jQotlT2KzYc+x24kzbiQwG84lRovBgcQ2aQa228YGEgls0rxEaTn/sP03w79zIC3sv4nTciOxjZmx7YAcyBZmorRYznjYLNnblywncQbImHOOCC3m/MkHP/z4ZsfD3w5kvCkjQgsSAEfQKBgFo2AUjAKqAACdmDV8IR84nAAAAABJRU5ErkJggg==","orcid":"","institution":"Virginia Commonwealth University Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Bale","suffix":""},{"id":458904031,"identity":"4bc1ca7a-7c8f-4f70-9bff-722870c1fe3f","order_by":1,"name":"Janina Pearce","email":"","orcid":"","institution":"Virginia Commonwealth University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Janina","middleName":"","lastName":"Pearce","suffix":""},{"id":458904034,"identity":"fcab7d74-fe91-463a-8f9c-9545f81ca9eb","order_by":2,"name":"Xiaoyan Deng","email":"","orcid":"","institution":"Virginia Commonwealth University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Deng","suffix":""},{"id":458904036,"identity":"60a28710-6e2b-40ab-9bbc-eb56d7f5ad5a","order_by":3,"name":"Dipankar Bandyopathy","email":"","orcid":"","institution":"Virginia Commonwealth University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Dipankar","middleName":"","lastName":"Bandyopathy","suffix":""},{"id":458904037,"identity":"4e0b6c37-12e6-419d-87cb-1c05886d1d60","order_by":4,"name":"Nophar Yarden","email":"","orcid":"","institution":"Virginia Commonwealth University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Nophar","middleName":"","lastName":"Yarden","suffix":""},{"id":458904038,"identity":"837b5d81-06b7-451a-8d44-863664c54ce0","order_by":5,"name":"Catherine Sport","email":"","orcid":"","institution":"Virginia Commonwealth University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Sport","suffix":""},{"id":458904039,"identity":"49822c59-69e6-4d0e-a501-93a04c40dbd7","order_by":6,"name":"Devin Miller","email":"","orcid":"","institution":"Virginia Commonwealth University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Devin","middleName":"","lastName":"Miller","suffix":""},{"id":458904040,"identity":"65c77235-0da9-4037-a3e0-91bb36428522","order_by":7,"name":"Leslie Randall","email":"","orcid":"","institution":"Virginia Commonwealth University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Leslie","middleName":"","lastName":"Randall","suffix":""},{"id":458904041,"identity":"aacb2c66-0cb2-442b-9a2e-c49417a7f570","order_by":8,"name":"Emma Fields","email":"","orcid":"","institution":"Virginia Commonwealth University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Fields","suffix":""},{"id":458904042,"identity":"1e1696cf-8b14-478b-b122-5abec1715bf0","order_by":9,"name":"Stephanie Sullivan","email":"","orcid":"","institution":"Virginia Commonwealth University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"","lastName":"Sullivan","suffix":""}],"badges":[],"createdAt":"2025-04-30 01:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6560042/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6560042/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83294077,"identity":"8748041c-4b8a-48f0-b64e-2be641520fcb","added_by":"auto","created_at":"2025-05-22 13:28:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":264827,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier curves based on categorization of percent change of cervical uptake values for A: overall survival, B: progression-free survival. Forrest plots demonstrating hazard ratios for covariates related to C: cervical uptake changes and overall survival, D: cervical uptake changes and progression free survival, E: distant lymph node uptake changes and overall survival, F: most avid lymph node uptake changes and overall survival.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6560042/v1/3d8231caeaa0cab99c9d1eec.png"},{"id":101665891,"identity":"ebfc66ad-621f-476e-b7d2-e98134028926","added_by":"auto","created_at":"2026-02-02 11:42:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1398782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6560042/v1/bb7bdd97-737e-404f-a1a0-cb0c7f05ca0e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How hot is too hot? Use of PET to evaluate response to radiation therapy for patients with cervical cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer is the 4th most frequently diagnosed cancer and cause of cancer death in women worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While cervical cancer was historically clinically staged, in 2018, the International Federation of Gynecology and Obstetrics (FIGO) updated its guidelines, highlighting the importance of imaging as part of clinical staging and planning optimal treatment for patients with cervical cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Positron Emission Tomography with radiotracer uptake of \u003csup\u003e18\u003c/sup\u003eF-2-fluoro-2-deoxy-D-glucose (FDG-PET) is a tool for staging primary and recurrent cervical cancer that detects physiologic changes based on degree of uptake and metabolism of glucose, a marker for tumors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOf patients who present with cervical cancer, an estimated median 37% present with locally advanced disease requiring primary radiation therapy with or without chemotherapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] for curative intent treatment. Patients now routinely obtain FDG-PET pre- and post-treatment to assess response; however, there are inconsistent data on how to interpret residual avidity in the post-treatment period, leaving providers with a clinical conundrum and patients at risk for false positive or false negative interpretations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePost-treatment FDG-PET avidity in the cervix has been found to be a significant independent predictor of survival for patients with cervical cancer [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] with prognostic value in assessing patterns of treatment failure and for eligibility for salvage therapy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Optimal timing of post-treatment imaging is still undefined and has varied in other studies with combined FDG-PET/Computed Tomography (CT) scanning [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A retrospective study found that post-treatment FDG-PET avidity alone was the most significant prognostic factor for developing metastatic disease and survival outcomes but with large variation in time from treatment to follow up (range 1 to 12 months, mean 3 months) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In a prospective study, Schwarz et al demonstrated that 3-month post-treatment FDG-PET avidity was a significant predictor of survival [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While these studies highlight the importance of the presence or lack of avidity in post-treatment scans, only one study [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] quantified change in maximum SUV of FDG in the cervix as percentage of residual activity and found no significant different between a cohort of 20 patients with locally advanced cervical cancer (8 of whom developed recurrence). No studies have attempted to quantify changes in SUVs in the lymph nodes in the context of recurrence or survival outcomes. Furthermore, no association between the percent change in avidity and recurrence and survival has been published to date.\u003c/p\u003e \u003cp\u003eThe objective of this study was to determine the relationship between cancer recurrence and the post-treatment percent change in SUV of FDG-PET in both the cervix and the lymph nodes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003ePatient selection\u003c/p\u003e \u003cp\u003e Following public registration of the investigation (researchregistry10267) and institutional review board approval (IRB HM20024055), a retrospective cohort study was performed at a single-site urban, safety net, tertiary referral institution with National Cancer Institute (NCI) designation. Patients at least 18 years of age with biopsy proven stage I-IVA locally advanced cervical cancer between 2009\u0026ndash;2021 who received curative intent radiation therapy at our tertiary care academic institution were eligible for inclusion. Additional inclusion criteria required patients to have both a pre-treatment scan (before starting radiation therapy) and at least one post-treatment FDG-PET scan completed within 5 months of their radiation therapy end date. Cervical cancer was classified according to the 2018 FIGO staging system. Patients with metastatic disease at diagnosis, history of hysterectomy, or who received palliative intent treatment were excluded.\u003c/p\u003e \u003cp\u003eAll patients included in the final analysis (55/55, 100%) completed the prescribed radiation therapy regimen and underwent both pre-treatment and post-treatment FDG-PET imaging as required by the study protocol. There was no loss to follow-up for the primary outcome assessment of post-treatment imaging changes. For survival analysis, clinical follow-up duration varied based on treatment dates within our study period (2009\u0026ndash;2021), with complete follow-up data available for all patients through their last clinical visit or date of death.\u003c/p\u003e \u003cp\u003eClinical data\u003c/p\u003e \u003cp\u003eClinical data collected included age and body mass index (BMI) at diagnosis, race, ethnicity, marital status, employment status, urban (population\u0026thinsp;\u0026gt;\u0026thinsp;2500) vs rural (\u0026lt;\u0026thinsp;2500) home address, medical comorbidities (diabetes, HTN, HIV, sarcoidosis, tobacco use), receipt of chemotherapy and/or immunotherapy, tumor size and histology, and cancer stage [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall survival and progression-free survival were measured from the date of radiation therapy initiation to the date of last clinical follow up or date of death for overall survival, or date of recurrence for progression free survival. Recurrence was confirmed histologically and/or radiologically.\u003c/p\u003e \u003cp\u003eThe same PET scanner from 2013 to the present; however, prior to 2013 a different scanner was in use. However, all scans were performed at the home institution in a standardized manner. All patients were treated with definitive intent with at least 45Gy with external beam radiotherapy. Patients either received 3D or intensity-modulated radiation therapy (IMRT) and either intracavitary or hybrid intracavitary/interstitial brachytherapy throughout the time course of the study. Approximately 60 minutes (accepted range 55\u0026ndash;75 min). minutes after intravenous administration of 10\u0026ndash;15 mCi of F-18 fluorodeoxyglucose, three-dimensional emission acquisition of the body extending from the skull to base to mid-thighs was obtained. Blood glucose was measured prior to tracer injection. Noncontrast, low-dose CT acquisition was obtained for attenuation correction of the emission acquisition and anatomical localization of tracer distribution. A maximum intensity projection reconstruction of the PET acquisition was made for display as a rotating whole body volumetric image. Tomographic images were computer generated and displayed from transaxial, coronal, and sagittal planes. PET and CT images were fused to aid in determining anatomical location of F-18 FDG tracer distribution. To quantitate abnormal FDG uptake, F-18 was measured in the region of abnormality, normalized to the administered dose and the patient\u0026rsquo;s body weight, and expressed as a SUV.\u003c/p\u003e \u003cp\u003eThe SUVs of avid lymph nodes were reviewed in the radiology imaging reports with the most avid lymph node selected based on the highest reported SUV, and the most distant lymph node was selected based on the most anatomically distant lymph node from the cervix. Pre- and post-treatment FDG-PET scans were assessed by radiology providers and percent differences in the SUV at the cervix, the most avid lymph node, and the most distant lymph node were calculated and used as the independent variables for analyses.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe PROCESS guidelines for reporting retrospective studies were followed [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Due to a non-normal distribution, the Wilcoxon non-parametric test was implemented for comparison of SUV distribution-related disease recurrence. Patient demographics and tumor characteristics between those with and without recurrence were compared using either non-parametric median two-sample tests, Cochran-Mantel-Haenszel tests, Fischer\u0026rsquo;s Exact tests, or Chi-Squared tests as appropriate.\u003c/p\u003e \u003cp\u003eTo determine the effect of percent decrease of cervical SUV in combination with other covariates on recurrence risk, logistic regressions were performed, for the outcomes of overall recurrence as well as local recurrence only. The main independent variable was the percent decrease of cervical SUV and the seventeen aforementioned covariates were considered. A stepwise selection method was used in the multivariate logistic regression to identify the important covariates alongside with the main factor (percent decrease in max SUV at the cervix) that had significant effect on the outcome of generalized recurrence and the local recurrence. The same analysis was conducted with percent decrease in SUV of the most distant node, and again for the most avid node. Percent change in cervical SUV was additionally analyzed in discrete intervals (\u0026lt;\u0026thinsp;25%, 25\u0026ndash;50%, 50\u0026ndash;75%, \u0026gt;\u0026thinsp;75%) using the Cochran-Mantel-Haenszel test method.\u003c/p\u003e \u003cp\u003eThe survival outcomes were estimated using the Kaplan-Meier method. The Cox Proportional Hazards Model was conducted separately for the overall survival and the progression free survival analyses. Multivariate analyses of the covariate factors were performed with results expressed as hazard ratios (HR) with 95% confidence intervals (CI) with a two-sided significant level of 5% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All analysis were conducted using SAS (Statistical Analysis System, version 9.4) software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePatient characteristics\u003c/p\u003e \u003cp\u003eMedian patient age at time of treatment was 46 years (range 22\u0026ndash;83) and median BMI was 29.3 kg/m\u003csup\u003e2\u003c/sup\u003e (range 17.7\u0026ndash;50.8). Histology included 87% squamous cell carcinoma (48/55), 11% adenocarcinoma (6/55), and 2% adenosquamous carcinoma (1/55). Post-treatment FDG-PET scans were performed between 2\u0026ndash;5 months (mean 3.5 months) after treatment completion for all patients. As this was a retrospective imaging analysis study, no study-specific interventions beyond standard clinical care were performed. Therefore, no study-related adverse events occurred. Standard treatment-related toxicities from radiation therapy and concurrent chemotherapy were managed according to institutional protocols but were not specifically analyzed as part of this imaging-focused investigation. No complications were reported during the FDG-PET imaging procedures themselves for any of the 55 patients included in the analysis. Overall recurrence rate was 27% (15/55); of these, 33% had local recurrence (5/55) and 67% had distant recurrence (10/55). All but 2 patients received chemotherapy, and 12 patients (7 disease free, 5 recurrence) received immunotherapy with radiation therapy and chemotherapy on clinical trial. All characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Demographics and Tumor Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePatient\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003echaracteristics\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003ePatients (n, %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;55)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWithout Disease Recurrence (n\u0026thinsp;=\u0026thinsp;40)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eWith Disease Recurrence (n\u0026thinsp;=\u0026thinsp;15)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at Diagnosis, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u0026ndash;83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u0026ndash;83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u0026ndash;76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaucasian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (66.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAfrican American\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (77.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (22.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (83.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (16.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHispanic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Hispanic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (72.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (27.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (78.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (21.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIVA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSquamous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (72.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (27.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdenosquamous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (66.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdenocarcinoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor Grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (85.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (71.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (28.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size, mm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026ndash;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-treatment Lymph Node Involvement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (68.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (31.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy Regimen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCisplatin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (73.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (26.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePre- and Post-Treatment FDG uptake and recurrence\u003c/p\u003e \u003cp\u003eThe comparison of the percent changes in pre- and post- treatment cervical FDG uptake, most distant, and most avid lymph node as predictors of recurrence are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercent changes in the cervical, most distant, and most avid lymph node max SUV in patients with and without disease recurrence.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMax SUV changes\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout Disease Recurrence (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWith Disease Recurrence (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCervix\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eContinuous\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eInterval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN for analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean, % change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian, % change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard deviation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMost distant lymph node\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN for analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean, % change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian, % change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard deviation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMost avid lymph node\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN for analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean, % change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian, % change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard deviation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMedian percent decrease of cervical SUV after treatment in those with and without recurrence was similar (71.4 vs 68.8, p\u0026thinsp;=\u0026thinsp;0.887); this remained consistent when analyzing those with local recurrence only (70.5 vs 68.8, p\u0026thinsp;=\u0026thinsp;0.953). The multivariate logistic regressions for both overall recurrence and local recurrence demonstrates that the percent decrease in cervical SUV was not a significant predictor of overall (OR: 0.989, p\u0026thinsp;=\u0026thinsp;0.282) or local (OR: 0.997, p\u0026thinsp;=\u0026thinsp;0.755) disease recurrence. With the stepwise selection method, none of these covariates were selected at the 0.05 significant level, indicating that these covariates had no significant effect on overall or local disease recurrence.\u003c/p\u003e \u003cp\u003eWhen the percent decrease in cervical SUV was examined in intervals (\u0026lt;\u0026thinsp;25%, 25\u0026ndash;50%, 50\u0026ndash;75%, \u0026gt;\u0026thinsp;75%) the Cochran-Mantel-Haenszel test showed that the overall recurrence rate was not significantly different among the intervals (p\u0026thinsp;=\u0026thinsp;0.753); this remained consistent when analyzing those with local recurrence only (p\u0026thinsp;=\u0026thinsp;0.91). The multivariate logistic regression results showed that the interval percent decrease in cervical SUV was not a significant indicator for both overall and local recurrence (p\u0026thinsp;=\u0026thinsp;0.7357 and 0.9852, respectively). No covariates were significant predictors of recurrence (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all).\u003c/p\u003e \u003cp\u003eFor the lymph nodes, the median percent decrease of SUV at the most avid and most distant lymph node from pre- to post-treatment PET in those with and without recurrence was not significantly different (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for both) for the 34/55 patients with nodal involvement. Percent change of SUV in the most distant node was not predictive of local (OR 1.257, p\u0026thinsp;=\u0026thinsp;0.0097) or overall (OR 0.995, p\u0026thinsp;=\u0026thinsp;0.9996) disease recurrence. Percent change of SUV of most avid lymph node were similar in that it was also not predictive of local (OR 1.314, p\u0026thinsp;=\u0026thinsp;0.898) or overall (OR 0.993, p\u0026thinsp;=\u0026thinsp;0.5815) disease recurrence. No covariates were significant predictors of recurrence.\u003c/p\u003e \u003cp\u003ePre- and Post-Treatment FDG uptake and survival\u003c/p\u003e \u003cp\u003ePatients with and without recurrence differed in overall survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and progression free survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) outcomes as expected. However, the interval percent decrease in cervical SUV was not significantly associated with overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) or progression free survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Additionally, percent changes in pre- and post- treatment cervical FDG uptake, most distant, and most avid lymph node as continuous variables were not associated with overall survival or progression free survival (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, with visual representation in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercent changes in the cervical, most distant, and most avid lymph node max standardized uptake and survival outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMax SUV Changes\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOverall Survival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eProgression Free Survival\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOverall survival hazard ratio HR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eProgression free survival hazard ratio HR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.994 (0.987, 1.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997 (0.988, 1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost distant lymph node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.997 (0.983, 1.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999 (0.981, 1.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost avid lymph node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.994 (.980, 1.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994 (0.978, 1.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn multivariate analysis of percent change of cervical SUV, receipt of chemotherapy was the only covariate that was a significant predictor of longer overall survival (HR 0.17, p\u0026thinsp;=\u0026thinsp;0.031), while higher BMI was the only covariate that was a significant predictor of longer progression free survival (HR 0.89, p\u0026thinsp;=\u0026thinsp;0.02). Receipt of immunotherapy was associated with shorter progression-free survival but was not statistically significant (HR 3.348, p\u0026thinsp;=\u0026thinsp;0.053). Examining percent change in cervical SUV in intervals, no covariates were significant for overall survival, while again a higher BMI was significantly associated with longer progression-free survival (HR 0.90, p\u0026thinsp;=\u0026thinsp;0.034) and receipt of immunotherapy was significantly associated with shorter progression-free survival (HR 3.70, p\u0026thinsp;=\u0026thinsp;0.042) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of covariates per analysis that were significant predictors of overall or progression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOverall Survival\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall survival hazard ratio HR\u003c/p\u003e \u003cp\u003e(95% Confidence Interval)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCervix, Continuous\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.172 (0.035, 0.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMost Distant Lymph node\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.165 (0.029, 0.939)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMost avid lymph node\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.139 (0.023, 0.834)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProgression-Free Survival\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eProgression free survival hazard ratio HR (95% Confidence Interval)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCervix, Continuous\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.891 (0.808, 0.982)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCervix, Interval\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eImmunotherapy\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.689 (1.051, 12.945)\u003c/p\u003e \u003cp\u003e0.901 (0.818, 0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0416\u003c/p\u003e \u003cp\u003e0.0337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn multivariate analysis of percent change of lymph node SUV, of the covariate data collected, chemotherapy was a significant predictor of longer overall survival for the percent change of SUV for most distant node (HR 0.17, p\u0026thinsp;=\u0026thinsp;0.042) and most avid node (HR 0.14, p\u0026thinsp;=\u0026thinsp;0.031) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). No other covariates were significant predictors for overall survival nor progression-free survival.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSummary of Main Results\u003c/p\u003e \u003cp\u003ePercent change of SUV in the cervix, the most avid lymph node, and the most distant node were not significant predictors of overall or local recurrence compared to those without disease recurrence, nor were they individual predictors of overall or progression free survival in this cohort. In multivariate analysis examining percent change of cervical max SUV, receipt of chemotherapy and higher BMI were significant predictors of longer overall survival and longer progression free survival, respectively. When examining interval percent change of cervical max SUV, higher BMI was again a significant predictor or progression-free survival, and receipt of immunotherapy was a significant predictor of shorter progression-free survival. For percent change in SUV for the most distant and most avid lymph node SUV, receipt of chemotherapy was a significant predictor for longer overall survival.\u003c/p\u003e \u003cp\u003eResults in the Context of Published Literature\u003c/p\u003e \u003cp\u003eCurative radiation results in regression of tumor size as evidenced on pelvic examination; however, lymph node response and/or general progression cannot be fully assessed by physical exam alone. FDG-PET has become a tool to detect cervical cancer recurrence by assessing SUV to monitor metabolic response to irradiated areas and assess for new or progressive disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA 2013 meta-analysis by Zhao and colleagues [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] concluded that SUV has predictive value in survival outcomes for guiding treatments; however, individual studies were small with heterogenous samples that yielded conflicting results. Twelve of these studies examined max SUV of pre-treatment scans, while two studies used max SUV of post-treatment scans as prognostic factors of disease recurrence and survival outcomes. Only one study [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] collected SUVs from both pre and post-treatment scans and found no significant differences between the percent residual activity of cervical max SUV in those with or without recurrence in a cohort of 20 patients. This study was the closest to our study design with similar results. In another meta-analysis by Sarkeret al [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (with six of 16 studies overlapping with Zhao\u0026rsquo;s meta-analysis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]), cervical max SUV was not a significant independent prognostic factor in the multivariate analyses conducted, except in 2 of 14 studies. They concluded that high SUV (\u0026ge;\u0026thinsp;13.4) should be considered at increased risk for recurrence; however, there is no overall clinical consensus within the field to determine what change in max cervical SUV confirms persistent or recurrent disease or to what degree of lymph node positive for FDG uptake is clinically significant.\u003c/p\u003e \u003cp\u003eWe chose to investigate the percent change of cervical max SUV between pre-treatment and post-treatment scans as a continuous variable and in discrete intervals of percentage change rather than focusing on a singular cut off value on pre- or post-treatment scans. We also expanded our analysis further by examining the percent change in the most avid and the most distant lymph nodes for lymph node positive patients who had FDG avidity reported for their pre and post-treatment scans. Within our cohort, neither numerical value of SUV of these locations nor the percent change of SUV between pre- and post-treatment scan were reliable predictors of recurrence alone.\u003c/p\u003e \u003cp\u003eRecent interest has been given to other markers of disease recurrence on FDG-PET scans. In a retrospective analysis by Pedraza and colleagues [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], they found that cervical max SUV was not an independent prognostic factor in multivariable analysis, while metabolic tumor volume and total lesion glycolysis in combination with texture analysis were significant prognostic factors. Other retrospective and prospective studies have similarly demonstrated metabolic tumor volume and/or total lesion glycolysis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] as well as intratumoral heterogeneity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] are significant prognostic factors in predicting treatment outcome and survival in locally advanced cervical cancer patients. However, these parameters are not routinely collected in many institutions including ours, limiting the clinical application of these findings.\u003c/p\u003e \u003cp\u003eStrengths and Weaknesses:\u003c/p\u003e \u003cp\u003eStrengths of this study include complete data for each patient and pre and post treatment SUV. Additionally, all post-treatment scans were completed within a similar 3\u0026ndash;5 month post-treatment timeframe, decreasing the post-treatment time variable that could have otherwise been a confounding factor. Use of FDG-PET with noncontrast CT has wide applicability across sites and institutions versus a scan with a dedicated diagnostic CT scan that may not be available at all institutions. Limitations of this investigation include the retrospective nature, single-center, and small sample size, all of which may lead to inherent selection bias. However, our institution is a tertiary referral and NCI center, which we believe helps to mitigate this risk. Use of date of last clinical follow-up likely underestimated actual overall survival. Progression free survival data was limited due to the variable start dates of radiation therapy. This led to more longitudinal data in patient who began therapy at the beginning of our designated timeframe (2009) compared to those completing therapy and post-treatment scan just prior to the end of our data-collection timeframe (2021). Additionally, we recognize that FIGO scoring changed in 2018 which was in the later portion of our time period for data collection; however, we mitigated the effects of this change in staging by using the 2018 staging for all patients for consistency. We also used FDG avidity of the lymph nodes as dictated by the radiology reports and so we could not determine if this avidity was specific for metastasis vs reactivity.\u003c/p\u003e \u003cp\u003eImplications for Practice and Future Research\u003c/p\u003e \u003cp\u003eFurther work investigating SUV changes at the primary tumor site and lymph nodes in combination with individual patient characteristics as well as individual tumor profiling data will be necessary to help develop accurate, individualized recurrence risk assessments.\u003c/p\u003e \u003cp\u003eBased on our data, changes in SUV at the primary tumor site and the lymph nodes after treatment are likely not a reliable stand-alone marker for predicting recurrence or overall or progression free survival. It will likely be necessary to combine SUV changes with other patient characteristics and tumor data to predict disease recurrence and guide clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eServices in support of this research project were generated by the VCU Massey Cancer Center {Massey Cancer Information Core (CIC)] Shared Resource, supported, in part, with funding from NIH-NCI Cancer Center Support Grant P30 CA016059.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial or personal conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was performed at a single-site urban, safety net, tertiary referral institution with National Cancer Institute (NCI) designation following public registration of the investigation (researchregistry10267) and institutional review board approval (IRB HM20024055).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as a retrospective review of existing medical records following institutional review board approval (IRB HM20024055). As a retrospective chart review study that involved no direct patient contact and minimal risk to subjects, a waiver of informed consent was granted by the IRB in accordance with institutional policies. All patient data were de-identified during analysis and reporting to protect patient privacy, with no identifying information included in the manuscript. No images of patients were used in this publication.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Protocol/project development; data analysis; manuscript writing/editing was performed by CB, JP, and SS. Protocol and project development was additionally supported by EF, DM and LR. XD and DB performed majority of the data analysis and data interpretation. NY and CS contributed to manuscript writing and editing. The first draft of the manuscript was written by CB and all the authors commented on previous versions of the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eServices in support of this research project were generated by the VCU Massey Cancer Center [Massey Cancer Informatics Core (CIC)] Shared Resource, supported, in part, with funding from NIH-NCI Cancer Center Support Grant P30 CA016059.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrisson, M and Drolet, M. Global elimination of cervical cancer as a public health problem. Lancet Oncol (2019). 20(3): p. 319\u0026ndash;321. doi: 10.1016/S1470-2045(19)30072-5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFowler, JR; Maani, EV; Dunton, CJ; Gasalberti, DP; Jack, BW. Cervical Cancer, in StatPearls (2022). 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Clin Cancer Res (2008). 14(16): p. 5236\u0026ndash;41. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-07-5252\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-07-5252\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SUV change, cancer recurrence, lymph node avidity, metabolic imaging, standardized uptake value","lastPublishedDoi":"10.21203/rs.3.rs-6560042/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6560042/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study sought to determine the relationship between cervical cancer recurrence and post-treatment change in standardized uptake value (SUV) of \u003csup\u003e18\u003c/sup\u003eF-2-fluoro-2-deoxy-D-glucose-positron emission tomography (FDG-PET) in the cervix and lymph nodes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A retrospective study of patients who received curative intent radiation therapy for biopsy-proven stage I-IVA locally advanced cervical cancer from 2009\u0026ndash;2021 was performed. Percent differences in SUVs at the cervix and the most avid and distant lymph nodes were calculated from pre- and post-treatment scans and used as independent variables for analyses. The primary outcome was recurrence rate, and secondary outcomes were overall and progression-free survival.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e55 patients met eligibility criteria. Recurrence rate was 27% (15/55); of these, 33% had local recurrence (5/55) and 67% had distant recurrence (10/55). Median percent decrease of cervical SUV after treatment in those with and without recurrence was similar (71.4 vs 68.8, p\u0026thinsp;=\u0026thinsp;0.89); this remained consistent when analyzing those with local recurrence only (70.5, p\u0026thinsp;=\u0026thinsp;0.95). When the percent decrease in cervical SUV was examined in intervals (\u0026lt;\u0026thinsp;25%, 25\u0026ndash;50%, 50\u0026ndash;75%, \u0026gt;\u0026thinsp;75%), this was also not predictive of local (p\u0026thinsp;=\u0026thinsp;0.91) or overall (p\u0026thinsp;=\u0026thinsp;0.75) recurrence. Median percent decrease at the most avid and distant lymph node in those with and without recurrence was not significantly different (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Neither change in cervical nor lymph node SUV were associated with overall or progression-free survival.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eChanges in SUV after treatment are likely not a reliable stand-alone marker for predicting recurrence or survival in locally advanced cervical cancer after treatment with radiation therapy.\u003c/p\u003e","manuscriptTitle":"How hot is too hot? Use of PET to evaluate response to radiation therapy for patients with cervical cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 13:28:54","doi":"10.21203/rs.3.rs-6560042/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62b09dde-3c02-46c8-b7bc-5e8f884d4fed","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T11:41:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-22 13:28:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6560042","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6560042","identity":"rs-6560042","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}