Prognostic Significance of Standard Uptake Value (SUVmax) and Primary Tumor Size Predicting Patient Survival in Vulvar Tumors

Prognostic Significance of Standard Uptake Value (SUVmax) and Primary Tumor Size Predicting Patient Survival in Vulvar Tumors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Significance of Standard Uptake Value (SUV max ) and Primary Tumor Size Predicting Patient Survival in Vulvar Tumors Mayur Virarkar, Sonia Rao, AjayKumar C Morani, Sanaz Javadi, Sai Swarupa Vulasala, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4838363/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Oct, 2024 Read the published version in Abdominal Radiology → Version 1 posted 4 You are reading this latest preprint version Abstract Aim: This study analyzed the associations between various clinical and imaging parameters with overall survival (OS) and recurrence-free survival (RFS) in vulvar cancer. Materials and Methods: A total of 45 patients diagnosed with vulva tumors were retrospectively analyzed. Data were extracted from medical records, including age, tumor size, ADC, SUVmax, and metastases identified through MRI and PET. Survival outcomes were estimated using Kaplan-Meier methods, while associations between variables and survival were assessed using Cox regression. Optimal cut-points for continuous variables were determined using maximally selected rank statistics. Results: The median OS was 9.97 years, with age, tumor size, and SUVmax measurements significantly influencing OS. Optimal cut-points at 4-year survival were established for age 65.9 years, the largest axial dimension of 5.50 cm, craniocaudal dimension of 4.7 cm, SUVmax of 22.0, and ADC value of 1.026 x10-3 mm2/s. Patients with measurements above these cut points typically had worse survival outcomes. Conclusion: Age, Size, and SUVmax predict survival in patients with vulvar cancer. Figures Figure 1 Figure 2 Introduction Primary vulvar malignancy is a rare gynecological neoplasm, constituting 5–8% of cases [ 1 ]. It is the fourth most common gynecological malignancy that usually affects post-menopausal women with a median age of 68 years [ 2 , 3 ]. Squamous cell carcinoma (SCC) constitutes greater than 90% of vulvar cancer cases. Around 59% of vulvar cancers demonstrate localized disease, whereas 30% and 6% metastasize to regional lymph [ 2 , 4 ]. The 5-year rate is 86%, 53%, and 19% for localized disease, regional spread, and distant spread, respectively [ 2 , 4 ]. To predict survival rates and patient outcomes, researchers have explored various clinical and imaging parameters that could predict survival in patients with vulvar cancer. Among these parameters are the Apparent Diffusion Coefficient (ADC), the Standard Uptake Value (SUV max ), and the primary tumor size [ 5 – 7 ]. ADC is a measure derived from diffusion-weighted MRI that reflects the degree of water molecule diffusion within a tissue. Changes in ADC values are a valuable predictor of response, disease progression, and survival in patients with cervical cancer [ 7 ]. On the other hand, SUV max measures the maximum standardized uptake value for F-18 fluorodeoxyglucose (FDG) of the tumor, which can be obtained from positron emission tomography (PET) scans. High SUV max values have been associated with a higher risk of metastases and worse survival outcomes [ 5 , 6 ]. This article aims to explore deeper into these parameters' prognostic significance in predicting survival in vulvar tumors. The study analyzed data from patients diagnosed with vulvar tumors, examining the associations between age, tumor size, ADC, SUV max , and metastases with overall survival. The findings of this study could potentially contribute to the development of more effective, personalized treatment strategies for patients with vulvar cancer. Materials and methods This retrospective study was performed at our institution after the approval of the institutional review board, which granted a waiver of informed consent. Patient population After obtaining approval from the institutional review board, we accessed the institutional clinical database to identify patients who had undergone both a pelvic MRI and an 18F-FDG-PET/CT scan from June 2021 to December 2021. We retrieved 85 patients with squamous cell carcinoma of the vulva who had baseline imaging at our institution before therapy. The inclusion criteria specified that patients must have received both imaging studies within four weeks of each other before treatment. From the initial pool, 45 patients were deemed eligible and were included in the analysis. Reference standard The reference standard comprised histological confirmation of the primary vulvar cancer. The patient's records were then mined for the patient's age, the date of diagnosis, the date of death, and the date of last follow-up. Imaging MRI Protocol All patients were imaged with a 3.0-T MRI scanner (Optima MR450w, Discovery MR750w, and Discovery MR750; GE Healthcare) using an eight-channel abdominal array coil and an endorectal coil (MR Innerva; MEDRAD). Specifications advanced over the study period but typically included a small field of view; axial, sagittal, and coronal fast spin-echo T2-weighted imaging; axial and sagittal diffusion-weighted imaging with b values of 50 sec/mm2 and 800 sec/mm2 with apparent diffusion coefficient (ADC) reconstruction; and dynamic contrast-enhanced imaging. Dynamic contrast-enhanced MRI was performed after the intravenous injection of gadopentetate dimeglumine (Magnevist, Bayer Healthcare Pharmaceuticals) at 0.1 mmol/kg of body weight at a rate of 3 mL/sec via a power injector; the examination consisted of 29–33 consecutive acquisitions over approximately 3.5 minutes. There were few outside imaging facilities with limited sequences. 18F-FDG PET/CT The institution's 18F-FDG PET/CT protocol complies with the American College of Radiology- American College of Nuclear Medicine guidelines. In brief, except for water, patients fasted for at least 4 hours before injecting approximately 370 MBq (10 mCi) of 18F-Fluciclovine. At 5 minutes following the injection, PET/CT imaging was performed from the skull to the mid-thigh to the vertex of the head. All PET/CT was performed on integrated PET/CT scanners, either on a GE 64-slice Discovery 710 PET/CT scanner or GE Discovery MI 64slice PETCT (GE Healthcare, Waukesha, Wisconsin, USA) or a Siemens 64-slice Biograph mCT PET/CT scanner (Siemens Medical Systems, Erlangen, Germany) using an institutional standard protocol. Low-dose CT was performed with tube-current modulation with both intravenous and oral contrast. Reader The MRI and PET/CT were read by a board-certified abdominal radiologist (PB and SJ) with more than 15 years and 20 years of experience. The research assistant (SR) documented the size, location of the tumor, the ADC, and SUV max values of vulvar cancer and the site of metastases during the reading sessions. Statistical Methods Patient age, tumor size, ADC, and SUV max were summarized using means, standard deviations (SD), medians, and ranges. Sites of metastases were summarized using frequencies and percentages. Overall survival (OS) was estimated using the Kaplan-Meier method. Cox regression was used to associate the candidate variables with OS and RFS. Maximally selected rank statistics from the ‘maxstat’ R package were used to determine the optimal cut-points for continuous variables. Tumor size discrepancies between axial T2, axial post-contrast, and PET were compared using paired t-tests. All tests were two-sided, and p-values of 0.05 or less were considered statistically significant. Statistical analysis was done using R (version 4.3.1, R Development Core Team). Result Descriptive analysis patients. A total of 45 patients were included in the study. All patients have squamous cell cancer of the vulva. Eighteen patients underwent surgical resection. All patients had radiation vs radiation and chemotherapy during the disease. Ten patients had Stage I, six had Stage II, fourteen had Stage III, and ten had Stage IV disease. The table shows each variable's number of patients (N), mean, standard deviation (SD), median, and range. Patients had a mean age of 63.4 years (SD: 14.35). The size of the primary vulvar tumor measured on Axial T2, sagittal T2, axial contrast, and sagittal contrast had means of 4.8 cm., 3.5 cm., 4.8 cm., and 3.4 cm., respectively, with standard deviations of 2.7, 2.0, 3.0, and 1.5. The mean ADC on the axial image was 0.99 x10 − 3 mm 2 /s (SD: 0.03), and for sagittal ADC was 1.0 x10 − 3 mm 2 /s (SD: 0.01). The tumor's mean SUV max was 16.0 (SD: 9.2) (Table 1 ). Table 1 Summary of patient age, MRI size, ADC, and SUV max . The data in the question summarizes patient age, MRI size, ADC, and SUV max for a study involving 45 patients diagnosed with vulvar tumors. The table below presents the summary statistics: Parameter N Mean (SD) Median (Range). Age (years) 45 63.4 (14.3) 64.3 (34.0, 89.6) Tumor size on axial T2 (cm.) 45 4.8 (2.7) 4.0 (1.6, 14.4) Tumor size sagittal T2 (cm.) 44 3.5 (2.0) 3.6 (1.0, 10.2) Tumor size Axial contrast (cm.) 41 4.8 (3.0) 4.1 (1.2, 15.4) Tumor ADC value on axial images (x10 − 3 mm 2 /s) 32 0.9 (0.3) 0.9 (0.4, 1.7) Tumor ADC value on sagittal images (x10 − 3 mm 2 /s) 13 1.0 (0.1) 0.9 (0.6, 1.3) SUV max measurements of the tumor 45 16.0 (9.2) 14.1 (4.5, 47.9) The table provides a comprehensive overview of each parameter's mean, median, and range, which can be used to understand the distribution of these parameters in the patient population. The standard deviation (SD) indicates the variability or dispersion of the data points around the mean for each parameter. The range provides the minimum and maximum values observed for each parameter. The 'N' value represents the number of data points available for each parameter. Table 2 summarizes the distribution of metastatic sites in 45 patients with cancer. The most common site for metastasis is the left inguinal node, affecting nine patients, which is 20% of the total. The subcutaneous perineal region is the second most common site, with five patients (11.11%). Metastases to the right inguinal node, retroperitoneal disease, left external iliac node, and bilateral common iliac node were seen in 3 patients, representing 6.67% for each site. The left internal iliac node is involved in 2 patients (4.44%). Metastases to the right supraclavicular node, mediastinal, abdominal, right lung, mesorectal node, inferior pubis ramus, and vaginal are the least common, each found in 1 patient, accounting for 2.22% per site. Table 2 Metastases detected on imaging. Metastasis Location Patients Percentage (%) Left inguinal node 9 20 Subcutaneous perineal 5 11.11 Right inguinal node 3 6.67 Retroperitoneal disease 3 6.67 Left external iliac node 3 6.67 Bilateral common iliac node 3 6.67 Left internal iliac node 2 4.44 Right supraclavicular node 1 2.22 Mediastinal node 1 2.22 Abdominal node 1 2.22 Right lung 1 2.22 Mesorectal node 1 2.22 Inferior pubis ramus 1 2.22 The table details metastasis locations in 45 cancer patients, highlighting the left inguinal node as the most frequent site (20%). Subcutaneous perineal and several lymph nodes (inguinal, retroperitoneal, external iliac, and common iliac) represent the next most common sites (6.67–11.11%). Less common sites include the right supraclavicular node, mediastinal node, abdominal node, right lung, mesorectal node, inferior pubis ramus, and vaginal, each with one case (2.22%). Overall survival. A total of 45 patients were followed from the diagnosis of primary tumor; 14 (31.1%) of them died. Median survival was 9.97 years. Survival rates at 2, 4, and 8 years were 76.6%, 66.0%, and 50.9%, respectively (Table 3 ). Table 3 Overall survival. Time Period Survival Rate 95% Confidence Interval 2 years 76.6% 64.1–91.6% 4 years 66.0% 50.2–86.8% 8 years 50.9% 32.2–80.4% This table presents the survival rates at 2, 4, and 8 years following the diagnosis of primary vulvar tumors in a cohort of 45 patients. The 95% confidence interval for each survival rate is also provided, which indicates the precision of the survival rate estimate. The wider the confidence interval, the less precise the estimate. The median survival was 9.97 years, and 14 out of the 45 patients (31.1%) died during the follow-up period. A total of 21 (46.7%) had recurrence or died. Median survival was 4.24 years. The survival rate at two years was 50.8%. Univariate Cox Regression analysis correlating candidate variables with overall survival (OS) in patients with vulvar tumors. There was a statistically significant increase in mortality with an age increase of 1 year HR of 1.049, a p-value of 0.041. An increase in the size of 1 cm of the tumor showed an increase in mortality. For example, the craniocaudal dimension increase measured on Sagittal T2 and Sagittal dynamic imaging had an R of 1.345, a p-value of 0.020 and HR of 1.646, and a p-value of 0.053, respectively. There was an increase in SUVmax measurements, but one unit had an HR of 1.070 and a p-value of 0.008. (Table 4 ). Table 4 Summary of the Univariate Cox Regression analysis correlating candidate variables with overall survival (OS) in patients with vulvar tumors. Parameters Estimate Std error HR CI lower HR CI upper HR P value Age 0.04 0.02 1.0 1.0 1.0 0.04 Axial measurement 0.17 0.08 1.1 1.0 1.4 0.04 Craniocaudal measurement 0.29 0.12 1.3 1.0 1.7 0.02 Axial contrast measurement 0.16 0.08 1.1 0.9 1.3 0.06 Sagittal contrast measurement 0.49 0.2 1.6 0.9 2.7 0.054 SUV max measurements of the tumor 0.06 0.02 1.0 1.0 1.1 0.008 The table contains parameters: estimate : The regression coefficient for each variable. Std. error : The standard error of the estimate, which measures the accuracy of the estimate. HR (Hazard Ratio) : The exponentiated estimate, indicating the multiplicative effect on the hazard for a one-unit increase in the variable. CI lower HR and CI upper HR : The lower and upper bounds of the confidence interval for the hazard ratio, respectively. P value : The p-value associated with the hypothesis test for the variable (testing the null hypothesis that the estimate is zero). Concordance: A measure of the predictive accuracy of the model. The statistical results show that age, axial T2 measurement, sagittal T2 measurement, and SUV max tumor measurements significantly impact hazard. Each year of age increases hazard by 4.9%, axial T2 by 18.6%, sagittal T2 by 34.5%, and SUV max measurements by 7%, with respective p-values suggesting strong evidence against the null hypothesis. Sagittal contrast measurement, with a 64.6% hazard increase per unit, is marginally significant and warrants cautious interpretation. Other variables, although affecting hazard, are not statistically significant. Univariate Cox Regression for RFS. Interval increase in the age of one year decreased the recurrence-free survival HR 1.046, p-value = 0.016. Survival Analysis Results The survival analysis results for the age and size of the primary tumor on the axial dimension, craniocaudal dimension, SUV max , and axial ADC were obtained (Table 5 , Fig. 2 ). Patients with a age > 65.8 years, axial dimension > 5.5 cm, sagittal dimension > 4.7 cm, SUVmax > 22 and ADC value < 1.026 x10 − 3 mm 2 /s had poorer survival p = 0.0035, p = 0.0083, p = 0.0072, p = 0.0015, p = 0.0062. Table 5 shows the optimal cutpoints for each variable, which were determined using maximally selected rank statistics. These cutpoints can classify patients into "high" and "low" groups based on their variable values. Patients with measurements above the cutpoints are considered to have worse survival outcomes. Factor Level Total N Number of Events Median Survival (95%CI) Rate at 2 Years (95%CI) Rate at 4 Years (95%CI) P-value 45 14 9.9 (4.3, NA) 0.7 (0.6, 0.9) 0.6 (0.5, 0.8) Age High 19 9 3.1 (1.1, NA) 0.6 (0.4, 0.9) 0.4 (0.2, 0.9) 0.00 Low 26 5 9.9 (9.9, NA) 0.8 (0.7, 1) 0.7 (0.5, 1) Axial measurement High 11 7 1.9 (1.0, NA) 0.4 (0.2, 0.9) 0.4 (0.2, 0.9) 0.00 Low 34 7 9.9 (9.9, NA) 0.8 (0.7, 1) 0.7 (0.5, 0.9) Craniocaudal measurement High 11 7 3.1 (1.0, NA) 0.5 (0.3, 1) 0.4 (0.2, 0.9) 0.00 Low 33 6 9.9 (9.9, NA) 0.8 (0.7, 1) 0.7 (0.6, 1) SUV max high 9 7 1.9 (1.1, NA) 0.4 (0.1, 0.9) 0.2 (0.0, 0.8) 0.00 low 36 7 9.9 (6.0, NA) 0.8 (0.7, 1) 0.8 (0.6, 1) Axial ADC high 12 6 1.9 (1.3, NA) 0.4 (0.2, 0.9) 0.4 (0.2, 0.9) 0.00 low 20 2 NA (NA, NA) 0.9 (0.8, 1) 0.8 (0.5, 1) The table summarizes survival analysis results from a medical study, possibly cancer related. The table is organized by different factors (age, Axial T2, Sagittal T2, SUV max , Axial ADC), and for each factor, the data is split into two levels: high and low. Size Discrepancies Between Different Imaging Methods The size discrepancies between different imaging methods were not statistically significant (Table 6 ). The comparisons were made between Axial Post Contrast vs. Axial T2, Axial T2 vs. PET Size, and Axial Post Contrast vs. PET Size. The p-values for these comparisons were 0.9, 0.2, and 0.4, respectively, indicating no statistically significant difference in sizes between the methods. Table 6 presents size discrepancies between different imaging methods for tumor detection. The data is tabulated as follows: Comparison p-value N Mean (SD) cm. Median (Range) cm. Difference (Mean, SD) Axial Contrast vs. Axial T2 0.9 41 Axial Contrast: 4.8 (3.0), Axial T2: 4.8 (2.7) Axial Contrast: 4.1 (1.2, 15.4), Axial T2: 4.0 (1.6, 14.4) -0.0 (1.0) Axial T2 vs. PET Size 0.2 28 Axial Measurement (PET): 6.0 (3.5), Axial T2: 4.8 (2.7) Axial Measurement (PET): 5.2 (1.3, 15.9), Axial T2: 4.0 (1.6, 14.4) -0.5 (2.5) Axial Contrast vs. PET Size 0.4 27 Axial Contrast: 4.8 (3.0), Axial Measurement (PET): 6.0 (3.5) Axial Contrast: 4.1 (1.2, 15.4), Axial Measurement (PET): 5.2 (1.3, 15.9) 0.4 (2.4) The p-values indicate no statistically significant size difference between the different imaging methods. The mean, standard deviation (SD), and median values provide an overview of the size measurements obtained from each method. The difference column shows the average difference in size measurements and the standard deviation of the differences. Discussion Our study shows that age, the largest size ADC values of the primary tumor on MRI and SUV max from PET scans are significant prognostic indicators for survival in vulvar cancer patients. Specifically, increased values in these parameters correlated with reduced survival outcomes, except for the ADC values. This study's findings enrich our comprehension of how such parameters can be harnessed to predict survival outcomes more accurately, offering a more nuanced approach to patient care. Age is a recurring theme in oncological research as a determinant of survival outcomes [ 8 ]. Our study reaffirms this relationship, with age emerging as a potent predictor for overall survival in patients with vulvar cancer. This observation aligns with the broader understanding of oncology, where older age often corresponds to a decreased survival rate [ 8 , 9 ]. The underlying reasons could be manifold. For instance, older patients might have a reduced physiological ability to combat the disease [ 10 ], a waning immune response [ 11 ], and a higher likelihood of concurrent medical conditions that might complicate treatment or recovery [ 9 , 12 ]. The significant role of Axial T2 and Sagittal T2 MRI measurements in predicting survival provides valuable insights into the disease's progression. These metrics indicate the primary tumor's size and directly correlate with survival outcomes. As revealed by these measurements, larger tumors might denote a more aggressive disease variant or a prolonged disease course without intervention [ 13 – 17 ]. In a study involving 416 women diagnosed with cervical cancer, MRI tumor size variables yielded high areas under the time-dependent receiver operating characteristics (tdROC) curves for predicting survival five years after diagnosis [ 13 ]. Similarly, in lower-grade gliomas, MRI features such as the longest axis length of the tumor were significantly associated with poor survival [ 17 ]. It's pertinent to understand the implications of these findings. Tumor size has always been a cornerstone in oncological assessments, acting as a surrogate marker for disease stage, potential metastasis, and overall aggressiveness. Our findings underscore its pivotal role, especially in vulvar cancer, where precise measurements can significantly influence therapeutic decisions. The SUV max values, derived from PET scans, can be used as prognostic indicators. This metric is a mirror of the tumor's metabolic activity. A higher SUV max is often a sign of a tumor with heightened metabolic activity, suggesting a more aggressive phenotype [ 6 , 18 – 24 ]. In the context of vulvar cancer, a study demonstrated the prognostic value of SUV max , with PET/CT imaging significantly impacting treatment decision-making [ 6 ]. This aligns with prior studies across various malignancies where an increased SUV max has been associated with aggressive tumor behavior and, consequently, reduced survival. Our study not only reiterates the prognostic importance of SUV max in vulvar cancer but also refines its clinical utility by identifying an optimal cutpoint. Such a threshold can be instrumental in stratifying patients into risk categories, thereby guiding clinicians in tailoring therapies more effectively. The Apparent Diffusion Coefficient (ADC) values, derived from MRI, have been shown to correlate with tumor cellularity and aggressiveness in various types of cancer [ 25 – 27 ]. The ADC values represent the diffusion of water molecules within tissues, and a lower ADC value typically indicates higher cellularity and potentially more aggressive tumors [ 8 , 25 , 28 ]. This correlation has been observed in cancers such as colon cancer, where the ADC value of the primary tumor has been used as a biomarker to predict metastasis [ 26 ]. In our study, tumors with ADC value 0.05). Our study showed that metastases to the lungs and bone were rare. Only one patient had bone metastasis. None of the patients had liver metastases or peritoneal disease [ 29 ]. Only three patients had retroperitoneal disease. Another noteworthy aspect of our study was the exploration of size measurements across different imaging modalities. Consistent measures, irrespective of the imaging technique, provide a robust foundation for clinical decision-making [ 30 , 31 ]. Such uniformity ensures that therapeutic choices often hinge on tumor size remain standardized, irrespective of the imaging modality employed [ 32 , 33 ]. This is particularly relevant in multi-center trials or settings where patients might undergo imaging at different facilities with varied equipment. Limitations While the study provides a comprehensive analysis of the clinical and imaging parameters of vulvar cancer prognosis, there are inherent limitations. The study is based on historical data, which inherently carries potential biases. The data might not account for all possible confounding variables or changes in clinical practices over time. With data from only 45 patients and from a single institution, the findings must be more generalizable to a larger, more diverse population. While the study focused on clinical and imaging parameters, it did not incorporate potential molecular or genetic markers that could have prognostic significance. Though no significant discrepancies were found between imaging techniques, equipment, and protocol variations, especially outside imaging facilities, could introduce subtle biases. Given the specific inclusion criteria, there's potential for selection bias, which could impact the generalizability of the findings. Depending on the duration of the follow-up period, some long-term outcomes or late recurrences might need to be captured. As with any retrospective study, unmeasured confounders could not be accounted for in the analysis. The study relied on imaging (MRI and PET) for metastasis detection, which, while accurate, might not capture micrometastases as effectively as pathological examinations. Conclusion This retrospective study on vulvar cancer provides invaluable insights into the predictive value of various clinical and imaging parameters on patient survival outcomes. Age and tumor size measured on MRI and SUV max (from PET scans) emerged as significant prognostic indicators. Specifically, an increase in these parameters was associated with a reduced survival rate. However, a decrease in ADC suggested poorer survival. They suggest that both modalities provide prognostic information in these patients. It also suggests that chest CTs may not be indicated in this cancer as lung metastases are rare and may not occur in the absence of other metastatic sites. In summary, for each measurement, patients with values above the optimal cutpoint (high) at 4-year survival generally had shorter median survival than those below the cutpoint (low). The p-values indicate that these differences in survival are statistically significant. These cutpoints can be crucial in clinical decision-making as they provide thresholds for risk stratification. Declarations Author Contribution M.V., S.R., A.M., S.Z., and P.B. led the conceptualization and design of the research. M.V., S.V. and S.J. gathered and analyzed the data ensuring accuracy and reliability. M.V., A.M., S.V., S.Z., and P.B. drafted the manuscript. M.V., A.M., and P.B. revised the manuscript for intellectual content and approved the final version. References Shetty, A.S. and C.O. Menias, MR Imaging of Vulvar and Vaginal Cancer . 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Prieske, K., et al., Patterns of distant metastases in vulvar cancer . Gynecol Oncol, 2016. 142(3): p. 427–34. Sullivan, D.C., L.H. Schwartz, and B. Zhao, The imaging viewpoint: how imaging affects determination of progression-free survival . Clin Cancer Res, 2013. 19(10): p. 2621–8. Virarkar, M., et al., Vulvar Cancer: 2021 Revised FIGO Staging System and the Role of Imaging . Cancers (Basel), 2022. 14(9). Ellingson, B.M., et al., Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials . Neuro Oncol, 2015. 17(9): p. 1188–98. García-Figueiras, R., et al., How clinical imaging can assess cancer biology . Insights Imaging, 2019. 10(1): p. 28. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Oct, 2024 Read the published version in Abdominal Radiology → Version 1 posted Editorial decision: Revision requested 10 Aug, 2024 Editor assigned by journal 10 Aug, 2024 Submission checks completed at journal 10 Aug, 2024 First submitted to journal 31 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4838363","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338514346,"identity":"04627ee9-f147-4095-8c90-42852c1027b9","order_by":0,"name":"Mayur Virarkar","email":"","orcid":"","institution":"University of Florida College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mayur","middleName":"","lastName":"Virarkar","suffix":""},{"id":338514347,"identity":"f7625345-0e15-44d5-becb-925e894b2646","order_by":1,"name":"Sonia Rao","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"","lastName":"Rao","suffix":""},{"id":338514348,"identity":"b57bf6ee-18bd-4a2b-9e13-dcbf0734e1c3","order_by":2,"name":"AjayKumar C Morani","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"AjayKumar","middleName":"C","lastName":"Morani","suffix":""},{"id":338514349,"identity":"120d8551-7097-4462-8e05-0eae6fd67de0","order_by":3,"name":"Sanaz Javadi","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Sanaz","middleName":"","lastName":"Javadi","suffix":""},{"id":338514350,"identity":"bd647279-67a2-4e2f-8c3c-21d2fbe6563b","order_by":4,"name":"Sai Swarupa Vulasala","email":"data:image/png;base64,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","orcid":"","institution":"University of Florida College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Sai","middleName":"Swarupa","lastName":"Vulasala","suffix":""},{"id":338514351,"identity":"7341a90c-ad0e-4aed-a22c-88ec63675b63","order_by":5,"name":"Sun Jia","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Jia","suffix":""},{"id":338514352,"identity":"d64193d9-2352-4fae-99aa-c4c2e3c279fd","order_by":6,"name":"Priya Bhosale","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Priya","middleName":"","lastName":"Bhosale","suffix":""}],"badges":[],"createdAt":"2024-08-01 00:24:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4838363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4838363/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00261-024-04645-6","type":"published","date":"2024-10-26T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66084961,"identity":"3d6a5ac1-0b3d-4013-9c45-f5e73a44f544","added_by":"auto","created_at":"2024-10-07 14:23:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":390507,"visible":true,"origin":"","legend":"\u003cp\u003eA 46-year-old female with squamous cell carcinoma of the vulva. (A) Sagittal T2WI, (B) axial fat-saturated T2WI, (C) sagittal post-contrast T1WI, (D) axial post-contrast T1WI, (E) Axial diffusion-weighted image, (F) axial ADC map of MRI images and (G) axial FDG PET/CT image show FDG avid enhancing mass of the right vulva with restricted diffusion. The red arrow demonstrates the measurement of the tumor. The red circle in the ADC map images reflects the region of interest (ROI).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4838363/v1/c1a714a24fe5ef21059a687b.png"},{"id":66081930,"identity":"cc77a6f1-e410-4d75-933a-a41f486cf046","added_by":"auto","created_at":"2024-10-07 14:07:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":219654,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Analysis.\u003c/p\u003e\n\u003cp\u003eA. \u003cstrong\u003eAge:\u003c/strong\u003e 65.89 Axial T2 Measurement: 5.50cm Sagittal T2 Measurement: 4.70cm, SUVmax tumor measurement: 22.0 Axial ADC: 1.026 x10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s. Age: Patients with age \u0026gt; 65.8 years (high) had a poorer median survival of 3.1 years, with 2-year and 4-year survival rates of 0.6 and 0.4, respectively. This is significantly different (p = 0.004) from those with age \u0026lt;= 65.8 years (low).\u003c/p\u003e\n\u003cp\u003eB. Size of the primary tumor on \u003cstrong\u003eaxial T2 sequence\u003c/strong\u003e Patients with an Axial T2 measurement \u0026gt; 5.5 cm. (high) had a median survival of 1.9 years. This is significantly different (p = 0.008) from those with measurements \u0026lt;= 5.5 cm. (low).\u003c/p\u003e\n\u003cp\u003eC. Craniocaudal tumor size on \u003cstrong\u003esagittal T2 Measurement:\u003c/strong\u003e Patients with a Sagittal T2 measurement \u0026gt; 4.7 cm. (high) had a median survival of 3.1 years. This is significantly different (p = 0.007) from those with measurements \u0026lt;= 4.7 cm. (low).\u003c/p\u003e\n\u003cp\u003eD. \u003cstrong\u003eSUV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003emax\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e measurements of the tumor:\u003c/strong\u003e Patients with SUV\u003csub\u003emax\u003c/sub\u003e measurements \u0026gt; 22.0 (high) had a median survival of 1.92 years. This is significantly different (p = 0.002) from those with measurements \u0026lt;= 22.0 (low).\u003c/p\u003e\n\u003cp\u003eE. \u003cstrong\u003eAxial ADC:\u003c/strong\u003e Patients with an Axial ADC measurement \u0026gt; 1.026 x10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s (high) had a median survival of 1.9 years. This is significantly different (p = 0.006) from those with measurements \u0026lt;= 1.026 x10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s (low).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4838363/v1/c4a647ebd4c393a69c29856c.png"},{"id":67681850,"identity":"08241107-f566-4f8b-b313-9346de205031","added_by":"auto","created_at":"2024-10-28 16:10:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1465942,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4838363/v1/0b39a647-fda0-476f-9160-72c5836ccdca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrognostic Significance of Standard Uptake Value (SUV\u003csub\u003emax\u003c/sub\u003e) and Primary Tumor Size Predicting Patient Survival in Vulvar Tumors\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary vulvar malignancy is a rare gynecological neoplasm, constituting 5\u0026ndash;8% of cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is the fourth most common gynecological malignancy that usually affects post-menopausal women with a median age of 68 years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Squamous cell carcinoma (SCC) constitutes greater than 90% of vulvar cancer cases. Around 59% of vulvar cancers demonstrate localized disease, whereas 30% and 6% metastasize to regional lymph [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The 5-year rate is 86%, 53%, and 19% for localized disease, regional spread, and distant spread, respectively [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo predict survival rates and patient outcomes, researchers have explored various clinical and imaging parameters that could predict survival in patients with vulvar cancer. Among these parameters are the Apparent Diffusion Coefficient (ADC), the Standard Uptake Value (SUV\u003csub\u003emax\u003c/sub\u003e), and the primary tumor size [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. ADC is a measure derived from diffusion-weighted MRI that reflects the degree of water molecule diffusion within a tissue. Changes in ADC values are a valuable predictor of response, disease progression, and survival in patients with cervical cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. On the other hand, SUV\u003csub\u003emax\u003c/sub\u003e measures the maximum standardized uptake value for F-18 fluorodeoxyglucose (FDG) of the tumor, which can be obtained from positron emission tomography (PET) scans. High SUV\u003csub\u003emax\u003c/sub\u003e values have been associated with a higher risk of metastases and worse survival outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis article aims to explore deeper into these parameters' prognostic significance in predicting survival in vulvar tumors. The study analyzed data from patients diagnosed with vulvar tumors, examining the associations between age, tumor size, ADC, SUV\u003csub\u003emax\u003c/sub\u003e, and metastases with overall survival. The findings of this study could potentially contribute to the development of more effective, personalized treatment strategies for patients with vulvar cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e This retrospective study was performed at our institution after the approval of the institutional review board, which granted a waiver of informed consent.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient population\u003c/h2\u003e \u003cp\u003eAfter obtaining approval from the institutional review board, we accessed the institutional clinical database to identify patients who had undergone both a pelvic MRI and an 18F-FDG-PET/CT scan from June 2021 to December 2021. We retrieved 85 patients with squamous cell carcinoma of the vulva who had baseline imaging at our institution before therapy. The inclusion criteria specified that patients must have received both imaging studies within four weeks of each other before treatment. From the initial pool, 45 patients were deemed eligible and were included in the analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReference standard\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe reference standard comprised histological confirmation of the primary vulvar cancer. The patient's records were then mined for the patient's age, the date of diagnosis, the date of death, and the date of last follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eImaging\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eMRI Protocol\u003c/h2\u003e \u003cp\u003eAll patients were imaged with a 3.0-T MRI scanner (Optima MR450w, Discovery MR750w, and Discovery MR750; GE Healthcare) using an eight-channel abdominal array coil and an endorectal coil (MR Innerva; MEDRAD). Specifications advanced over the study period but typically included a small field of view; axial, sagittal, and coronal fast spin-echo T2-weighted imaging; axial and sagittal diffusion-weighted imaging with b values of 50 sec/mm2 and 800 sec/mm2 with apparent diffusion coefficient (ADC) reconstruction; and dynamic contrast-enhanced imaging. Dynamic contrast-enhanced MRI was performed after the intravenous injection of gadopentetate dimeglumine (Magnevist, Bayer Healthcare Pharmaceuticals) at 0.1 mmol/kg of body weight at a rate of 3 mL/sec via a power injector; the examination consisted of 29\u0026ndash;33 consecutive acquisitions over approximately 3.5 minutes. There were few outside imaging facilities with limited sequences.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e18F-FDG PET/CT\u003c/h2\u003e \u003cp\u003e The institution's 18F-FDG PET/CT protocol complies with the American College of Radiology- American College of Nuclear Medicine guidelines. In brief, except for water, patients fasted for at least 4 hours before injecting approximately 370 MBq (10 mCi) of 18F-Fluciclovine. At 5 minutes following the injection, PET/CT imaging was performed from the skull to the mid-thigh to the vertex of the head. All PET/CT was performed on integrated PET/CT scanners, either on a GE 64-slice Discovery 710 PET/CT scanner or GE Discovery MI 64slice PETCT (GE Healthcare, Waukesha, Wisconsin, USA) or a Siemens 64-slice Biograph mCT PET/CT scanner (Siemens Medical Systems, Erlangen, Germany) using an institutional standard protocol. Low-dose CT was performed with tube-current modulation with both intravenous and oral contrast.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eReader\u003c/h2\u003e \u003cp\u003eThe MRI and PET/CT were read by a board-certified abdominal radiologist (PB and SJ) with more than 15 years and 20 years of experience. The research assistant (SR) documented the size, location of the tumor, the ADC, and SUV max values of vulvar cancer and the site of metastases during the reading sessions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003ePatient age, tumor size, ADC, and SUV\u003csub\u003emax\u003c/sub\u003e were summarized using means, standard deviations (SD), medians, and ranges. Sites of metastases were summarized using frequencies and percentages. Overall survival (OS) was estimated using the Kaplan-Meier method. Cox regression was used to associate the candidate variables with OS and RFS. Maximally selected rank statistics from the \u0026lsquo;maxstat\u0026rsquo; R package were used to determine the optimal cut-points for continuous variables. Tumor size discrepancies between axial T2, axial post-contrast, and PET were compared using paired t-tests. All tests were two-sided, and p-values of 0.05 or less were considered statistically significant. Statistical analysis was done using R (version 4.3.1, R Development Core Team).\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analysis\u003c/h2\u003e \u003cp\u003epatients. A total of 45 patients were included in the study. All patients have squamous cell cancer of the vulva. Eighteen patients underwent surgical resection. All patients had radiation vs radiation and chemotherapy during the disease. Ten patients had Stage I, six had Stage II, fourteen had Stage III, and ten had Stage IV disease. The table shows each variable's number of patients (N), mean, standard deviation (SD), median, and range. Patients had a mean age of 63.4 years (SD: 14.35). The size of the primary vulvar tumor measured on Axial T2, sagittal T2, axial contrast, and sagittal contrast had means of 4.8 cm., 3.5 cm., 4.8 cm., and 3.4 cm., respectively, with standard deviations of 2.7, 2.0, 3.0, and 1.5. The mean ADC on the axial image was 0.99 x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s (SD: 0.03), and for sagittal ADC was 1.0 x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s (SD: 0.01). The tumor's mean SUV\u003csub\u003emax\u003c/sub\u003e was 16.0 (SD: 9.2) (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\u003eSummary of patient age, MRI size, ADC, and SUV\u003csub\u003emax\u003c/sub\u003e. The data in the question summarizes patient age, MRI size, ADC, and SUV\u003csub\u003emax\u003c/sub\u003e for a study involving 45 patients diagnosed with vulvar tumors. The table below presents the summary statistics:\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (Range).\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.4 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.3 (34.0, 89.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size on axial T2 (cm.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.0 (1.6, 14.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size sagittal T2 (cm.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.6 (1.0, 10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size Axial contrast (cm.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.1 (1.2, 15.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor ADC value on axial images (x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.4, 1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor ADC value on sagittal images (x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.6, 1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e measurements of the tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.0 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.1 (4.5, 47.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe table provides a comprehensive overview of each parameter's mean, median, and range, which can be used to understand the distribution of these parameters in the patient population. The standard deviation (SD) indicates the variability or dispersion of the data points around the mean for each parameter. The range provides the minimum and maximum values observed for each parameter. The 'N' value represents the number of data points available for each parameter.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the distribution of metastatic sites in 45 patients with cancer. The most common site for metastasis is the left inguinal node, affecting nine patients, which is 20% of the total. The subcutaneous perineal region is the second most common site, with five patients (11.11%). Metastases to the right inguinal node, retroperitoneal disease, left external iliac node, and bilateral common iliac node were seen in 3 patients, representing 6.67% for each site. The left internal iliac node is involved in 2 patients (4.44%). Metastases to the right supraclavicular node, mediastinal, abdominal, right lung, mesorectal node, inferior pubis ramus, and vaginal are the least common, each found in 1 patient, accounting for 2.22% per site.\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\u003eMetastases detected on imaging.\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=\"char\" char=\".\" 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\u003eMetastasis Location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft inguinal node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous perineal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight inguinal node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetroperitoneal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft external iliac node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral common iliac node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft internal iliac node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight supraclavicular node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediastinal node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesorectal node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior pubis ramus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eThe table details metastasis locations in 45 cancer patients, highlighting the left inguinal node as the most frequent site (20%). Subcutaneous perineal and several lymph nodes (inguinal, retroperitoneal, external iliac, and common iliac) represent the next most common sites (6.67\u0026ndash;11.11%). Less common sites include the right supraclavicular node, mediastinal node, abdominal node, right lung, mesorectal node, inferior pubis ramus, and vaginal, each with one case (2.22%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOverall survival.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 45 patients were followed from the diagnosis of primary tumor; 14 (31.1%) of them died. Median survival was 9.97 years. Survival rates at 2, 4, and 8 years were 76.6%, 66.0%, and 50.9%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eOverall 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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime Period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurvival Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.1\u0026ndash;91.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.2\u0026ndash;86.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.2\u0026ndash;80.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eThis table presents the survival rates at 2, 4, and 8 years following the diagnosis of primary vulvar tumors in a cohort of 45 patients. The 95% confidence interval for each survival rate is also provided, which indicates the precision of the survival rate estimate. The wider the confidence interval, the less precise the estimate. The median survival was 9.97 years, and 14 out of the 45 patients (31.1%) died during the follow-up period.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of 21 (46.7%) had recurrence or died. Median survival was 4.24 years. The survival rate at two years was 50.8%.\u003c/p\u003e \u003cp\u003e \u003cb\u003eUnivariate Cox Regression analysis correlating candidate variables with overall survival (OS) in patients with vulvar tumors.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere was a statistically significant increase in mortality with an age increase of 1 year HR of 1.049, a p-value of 0.041. An increase in the size of 1 cm of the tumor showed an increase in mortality. For example, the craniocaudal dimension increase measured on Sagittal T2 and Sagittal dynamic imaging had an R of 1.345, a p-value of 0.020 and HR of 1.646, and a p-value of 0.053, respectively. There was an increase in SUVmax measurements, but one unit had an HR of 1.070 and a p-value of 0.008. (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 the Univariate Cox Regression analysis correlating candidate variables with overall survival (OS) in patients with vulvar tumors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCI lower HR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI upper HR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAxial measurement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCraniocaudal measurement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAxial contrast measurement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSagittal contrast measurement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSUV\u003c/b\u003e\u003csub\u003emax\u003c/sub\u003e \u003cb\u003emeasurements of the tumor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe table contains \u003cb\u003eparameters: estimate\u003c/b\u003e: The regression coefficient for each variable. \u003cb\u003eStd. error\u003c/b\u003e: The standard error of the estimate, which measures the accuracy of the estimate. \u003cb\u003eHR (Hazard Ratio)\u003c/b\u003e: The exponentiated estimate, indicating the multiplicative effect on the hazard for a one-unit increase in the variable. \u003cb\u003eCI lower HR and CI upper HR\u003c/b\u003e: The lower and upper bounds of the confidence interval for the hazard ratio, respectively. \u003cb\u003eP value\u003c/b\u003e: The p-value associated with the hypothesis test for the variable (testing the null hypothesis that the estimate is zero). Concordance: A measure of the predictive accuracy of the model. The statistical results show that age, axial T2 measurement, sagittal T2 measurement, and SUV\u003csub\u003emax\u003c/sub\u003e tumor measurements significantly impact hazard. Each year of age increases hazard by 4.9%, axial T2 by 18.6%, sagittal T2 by 34.5%, and SUV\u003csub\u003emax\u003c/sub\u003e measurements by 7%, with respective p-values suggesting strong evidence against the null hypothesis. Sagittal contrast measurement, with a 64.6% hazard increase per unit, is marginally significant and warrants cautious interpretation. Other variables, although affecting hazard, are not statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eUnivariate Cox Regression for RFS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInterval increase in the age of one year decreased the recurrence-free survival HR 1.046, p-value\u0026thinsp;=\u0026thinsp;0.016.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis Results\u003c/h2\u003e \u003cp\u003eThe survival analysis results for the age and size of the primary tumor on the axial dimension, craniocaudal dimension, SUV\u003csub\u003emax\u003c/sub\u003e, and axial ADC were obtained (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients with a age\u0026thinsp;\u0026gt;\u0026thinsp;65.8 years, axial dimension\u0026thinsp;\u0026gt;\u0026thinsp;5.5 cm, sagittal dimension\u0026thinsp;\u0026gt;\u0026thinsp;4.7 cm, SUVmax\u0026thinsp;\u0026gt;\u0026thinsp;22 and ADC value\u0026thinsp;\u0026lt;\u0026thinsp;1.026 x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s had poorer survival p\u0026thinsp;=\u0026thinsp;0.0035, p\u0026thinsp;=\u0026thinsp;0.0083, p\u0026thinsp;=\u0026thinsp;0.0072, p\u0026thinsp;=\u0026thinsp;0.0015, p\u0026thinsp;=\u0026thinsp;0.0062.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eshows the optimal cutpoints for each variable, which were determined using maximally selected rank statistics. These cutpoints can classify patients into \"high\" and \"low\" groups based on their variable values. Patients with measurements above the cutpoints are considered to have worse survival outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Events\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian Survival (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRate at 2 Years (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRate at 4 Years (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9 (4.3, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7 (0.6, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6 (0.5, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1 (1.1, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6 (0.4, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4 (0.2, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9 (9.9, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8 (0.7, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7 (0.5, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAxial measurement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9 (1.0, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4 (0.2, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4 (0.2, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9 (9.9, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8 (0.7, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7 (0.5, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCraniocaudal measurement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1 (1.0, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5 (0.3, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4 (0.2, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9 (9.9, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8 (0.7, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7 (0.6, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSUV\u003c/b\u003e\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9 (1.1, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4 (0.1, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2 (0.0, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9 (6.0, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8 (0.7, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8 (0.6, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAxial ADC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9 (1.3, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4 (0.2, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4 (0.2, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA (NA, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9 (0.8, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8 (0.5, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eThe table summarizes survival analysis results from a medical study, possibly cancer related. The table is organized by different factors (age, Axial T2, Sagittal T2, SUV\u003csub\u003emax\u003c/sub\u003e, Axial ADC), and for each factor, the data is split into two levels: high and low.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSize Discrepancies Between Different Imaging Methods\u003c/h2\u003e \u003cp\u003eThe size discrepancies between different imaging methods were not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The comparisons were made between Axial Post Contrast vs. Axial T2, Axial T2 vs. PET Size, and Axial Post Contrast vs. PET Size. The p-values for these comparisons were 0.9, 0.2, and 0.4, respectively, indicating no statistically significant difference in sizes between the methods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003epresents size discrepancies between different imaging methods for tumor detection. The data is tabulated as follows:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (SD) cm.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (Range) cm.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDifference (Mean, SD)\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\u003eAxial Contrast vs. Axial T2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAxial Contrast: 4.8 (3.0), Axial T2: 4.8 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAxial Contrast: 4.1 (1.2, 15.4), Axial T2: 4.0 (1.6, 14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAxial T2 vs. PET Size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAxial Measurement (PET): 6.0 (3.5), Axial T2: 4.8 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAxial Measurement (PET): 5.2 (1.3, 15.9), Axial T2: 4.0 (1.6, 14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.5 (2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAxial Contrast vs. PET Size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAxial Contrast: 4.8 (3.0), Axial Measurement (PET): 6.0 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAxial Contrast: 4.1 (1.2, 15.4), Axial Measurement (PET): 5.2 (1.3, 15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eThe p-values indicate no statistically significant size difference between the different imaging methods. The mean, standard deviation (SD), and median values provide an overview of the size measurements obtained from each method. The difference column shows the average difference in size measurements and the standard deviation of the differences.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study shows that age, the largest size ADC values of the primary tumor on MRI and SUV\u003csub\u003emax\u003c/sub\u003e from PET scans are significant prognostic indicators for survival in vulvar cancer patients. Specifically, increased values in these parameters correlated with reduced survival outcomes, except for the ADC values. This study's findings enrich our comprehension of how such parameters can be harnessed to predict survival outcomes more accurately, offering a more nuanced approach to patient care.\u003c/p\u003e \u003cp\u003eAge is a recurring theme in oncological research as a determinant of survival outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Our study reaffirms this relationship, with age emerging as a potent predictor for overall survival in patients with vulvar cancer. This observation aligns with the broader understanding of oncology, where older age often corresponds to a decreased survival rate [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The underlying reasons could be manifold. For instance, older patients might have a reduced physiological ability to combat the disease [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], a waning immune response [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and a higher likelihood of concurrent medical conditions that might complicate treatment or recovery [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe significant role of Axial T2 and Sagittal T2 MRI measurements in predicting survival provides valuable insights into the disease's progression. These metrics indicate the primary tumor's size and directly correlate with survival outcomes. As revealed by these measurements, larger tumors might denote a more aggressive disease variant or a prolonged disease course without intervention [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In a study involving 416 women diagnosed with cervical cancer, MRI tumor size variables yielded high areas under the time-dependent receiver operating characteristics (tdROC) curves for predicting survival five years after diagnosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similarly, in lower-grade gliomas, MRI features such as the longest axis length of the tumor were significantly associated with poor survival [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It's pertinent to understand the implications of these findings. Tumor size has always been a cornerstone in oncological assessments, acting as a surrogate marker for disease stage, potential metastasis, and overall aggressiveness. Our findings underscore its pivotal role, especially in vulvar cancer, where precise measurements can significantly influence therapeutic decisions.\u003c/p\u003e \u003cp\u003eThe SUV\u003csub\u003emax\u003c/sub\u003e values, derived from PET scans, can be used as prognostic indicators. This metric is a mirror of the tumor's metabolic activity. A higher SUV\u003csub\u003emax\u003c/sub\u003e is often a sign of a tumor with heightened metabolic activity, suggesting a more aggressive phenotype [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In the context of vulvar cancer, a study demonstrated the prognostic value of SUV\u003csub\u003emax\u003c/sub\u003e, with PET/CT imaging significantly impacting treatment decision-making [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This aligns with prior studies across various malignancies where an increased SUV\u003csub\u003emax\u003c/sub\u003e has been associated with aggressive tumor behavior and, consequently, reduced survival. Our study not only reiterates the prognostic importance of SUV\u003csub\u003emax\u003c/sub\u003e in vulvar cancer but also refines its clinical utility by identifying an optimal cutpoint. Such a threshold can be instrumental in stratifying patients into risk categories, thereby guiding clinicians in tailoring therapies more effectively.\u003c/p\u003e \u003cp\u003eThe Apparent Diffusion Coefficient (ADC) values, derived from MRI, have been shown to correlate with tumor cellularity and aggressiveness in various types of cancer [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The ADC values represent the diffusion of water molecules within tissues, and a lower ADC value typically indicates higher cellularity and potentially more aggressive tumors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This correlation has been observed in cancers such as colon cancer, where the ADC value of the primary tumor has been used as a biomarker to predict metastasis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In our study, tumors with ADC value\u0026thinsp;\u0026lt;\u0026thinsp;1.026 x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s had poorer survival, p\u0026thinsp;=\u0026thinsp;0.0083 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eOur study showed that metastases to the lungs and bone were rare. Only one patient had bone metastasis. None of the patients had liver metastases or peritoneal disease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Only three patients had retroperitoneal disease.\u003c/p\u003e \u003cp\u003eAnother noteworthy aspect of our study was the exploration of size measurements across different imaging modalities. Consistent measures, irrespective of the imaging technique, provide a robust foundation for clinical decision-making [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Such uniformity ensures that therapeutic choices often hinge on tumor size remain standardized, irrespective of the imaging modality employed [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This is particularly relevant in multi-center trials or settings where patients might undergo imaging at different facilities with varied equipment.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile the study provides a comprehensive analysis of the clinical and imaging parameters of vulvar cancer prognosis, there are inherent limitations. The study is based on historical data, which inherently carries potential biases. The data might not account for all possible confounding variables or changes in clinical practices over time. With data from only 45 patients and from a single institution, the findings must be more generalizable to a larger, more diverse population. While the study focused on clinical and imaging parameters, it did not incorporate potential molecular or genetic markers that could have prognostic significance. Though no significant discrepancies were found between imaging techniques, equipment, and protocol variations, especially outside imaging facilities, could introduce subtle biases. Given the specific inclusion criteria, there's potential for selection bias, which could impact the generalizability of the findings. Depending on the duration of the follow-up period, some long-term outcomes or late recurrences might need to be captured. As with any retrospective study, unmeasured confounders could not be accounted for in the analysis. The study relied on imaging (MRI and PET) for metastasis detection, which, while accurate, might not capture micrometastases as effectively as pathological examinations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis retrospective study on vulvar cancer provides invaluable insights into the predictive value of various clinical and imaging parameters on patient survival outcomes. Age and tumor size measured on MRI and SUV\u003csub\u003emax\u003c/sub\u003e (from PET scans) emerged as significant prognostic indicators. Specifically, an increase in these parameters was associated with a reduced survival rate. However, a decrease in ADC suggested poorer survival. They suggest that both modalities provide prognostic information in these patients. It also suggests that chest CTs may not be indicated in this cancer as lung metastases are rare and may not occur in the absence of other metastatic sites.\u003c/p\u003e\n\u003cp\u003eIn summary, for each measurement, patients with values above the optimal cutpoint (high) at 4-year survival generally had shorter median survival than those below the cutpoint (low). The p-values indicate that these differences in survival are statistically significant. These cutpoints can be crucial in clinical decision-making as they provide thresholds for risk stratification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.V., S.R., A.M., S.Z., and P.B. led the conceptualization and design of the research. M.V., S.V. and S.J. gathered and analyzed the data ensuring accuracy and reliability. M.V., A.M., S.V., S.Z., and P.B. drafted the manuscript. M.V., A.M., and P.B. revised the manuscript for intellectual content and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShetty, A.S. and C.O. Menias, \u003cem\u003eMR Imaging of Vulvar and Vaginal Cancer\u003c/em\u003e. Magn Reson Imaging Clin N Am, 2017. 25(3): p. 481\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChow, L., et al., \u003cem\u003eGynecologic tumor board: a radiologist's guide to vulvar and vaginal malignancies\u003c/em\u003e. Abdom Radiol (NY), 2021. 46(12): p. 5669\u0026ndash;5686.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerlo, S., \u003cem\u003eModern treatment of vulvar cancer\u003c/em\u003e. Radiol Oncol, 2020. 54(4): p. 371\u0026ndash;376.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerrado, M.A., M. Horta, and T.M. Cunha, \u003cem\u003eState of the art in vulvar cancer imaging\u003c/em\u003e. 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Neuro Oncol, 2017. 19(6): p. 862\u0026ndash;870.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBae, S.U., et al., \u003cem\u003eAccuracy of F-18 FDG PET/CT with optimal cut-offs of maximum standardized uptake value according to size for diagnosis of regional lymph node metastasis in patients with rectal cancer\u003c/em\u003e. Cancer Imaging, 2018. 18(1): p. 32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitajima, K., et al., \u003cem\u003ePrognostic significance of SUVmax (maximum standardized uptake value) measured by [\u0026sup1;⁸F]FDG PET/CT in endometrial cancer\u003c/em\u003e. Eur J Nucl Med Mol Imaging, 2012. 39(5): p. 840\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoksal, D., et al., \u003cem\u003eThe correlation of SUVmax with pathological characteristics of primary tumor and the value of Tumor/ Lymph node SUVmax ratio for predicting metastasis to lymph nodes in resected NSCLC patients\u003c/em\u003e. J Cardiothorac Surg, 2013. 8: p. 63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoksal, D., et al., \u003cem\u003eThe correlation of SUVmax with pathological characteristics of primary tumor and the value of Tumor/ Lymph node SUVmax ratio for predicting metastasis to lymph nodes in resected NSCLC patients\u003c/em\u003e. Journal of Cardiothoracic Surgery, 2013. 8(1): p. 63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinfath, M., et al., \u003cem\u003eSimple changes of individual studies can improve the reproducibility of the biomedical scientific process as a whole\u003c/em\u003e. PLoS One, 2018. 13(9): p. e0202762.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, S., et al., \u003cem\u003eCorrelation of 18F-FDG PET/CT SUVmax with clinical features, D-dimer and LDH in patients with primary intestinal lymphoma\u003c/em\u003e. J Int Med Res, 2021. 49(7): p. 3000605211029809.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, W., et al., \u003cem\u003ePrognostic significance of SUV on PET/CT in patients with localised oesophagogastric junction cancer receiving neoadjuvant chemotherapy/chemoradiation:a systematic review and meta-analysis\u003c/em\u003e. Br J Radiol, 2012. 85(1017): p. e694-701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, L., et al., \u003cem\u003eThe correlation between apparent diffusion coefficient and tumor cellularity in patients: a meta-analysis\u003c/em\u003e. PLoS One, 2013. 8(11): p. e79008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNerad, E., et al., \u003cem\u003eThe Apparent Diffusion Coefficient (ADC) is a useful biomarker in predicting metastatic colon cancer using the ADC-value of the primary tumor\u003c/em\u003e. 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Zhao, \u003cem\u003eThe imaging viewpoint: how imaging affects determination of progression-free survival\u003c/em\u003e. Clin Cancer Res, 2013. 19(10): p. 2621\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVirarkar, M., et al., \u003cem\u003eVulvar Cancer: 2021 Revised FIGO Staging System and the Role of Imaging\u003c/em\u003e. Cancers (Basel), 2022. 14(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllingson, B.M., et al., \u003cem\u003eConsensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials\u003c/em\u003e. Neuro Oncol, 2015. 17(9): p. 1188\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Figueiras, R., et al., \u003cem\u003eHow clinical imaging can assess cancer biology\u003c/em\u003e. Insights Imaging, 2019. 10(1): p. 28.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4838363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4838363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim:\u003c/strong\u003e This study analyzed the associations between various clinical and imaging parameters with overall survival (OS) and recurrence-free survival (RFS) in vulvar cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods:\u003c/strong\u003e A total of 45 patients diagnosed with vulva tumors were retrospectively analyzed. Data were extracted from medical records, including age, tumor size, ADC, SUVmax, and metastases identified through MRI and PET. Survival outcomes were estimated using Kaplan-Meier methods, while associations between variables and survival were assessed using Cox regression. Optimal cut-points for continuous variables were determined using maximally selected rank statistics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe median OS was 9.97 years, with age, tumor size, and SUVmax measurements significantly influencing OS. Optimal cut-points at 4-year survival were established for age 65.9 years, the largest axial dimension of 5.50 cm, craniocaudal dimension of 4.7 cm, SUVmax of 22.0, and ADC value of 1.026 x10-3 mm2/s. Patients with measurements above these cut points typically had worse survival outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Age, Size, and SUVmax predict survival in patients with vulvar cancer.\u003c/p\u003e","manuscriptTitle":"Prognostic Significance of Standard Uptake Value (SUVmax) and Primary Tumor Size Predicting Patient Survival in Vulvar Tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-07 14:06:55","doi":"10.21203/rs.3.rs-4838363/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-10T14:13:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-10T10:07:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-10T10:06:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2024-08-01T00:22:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3d5fdfb3-408e-4000-9603-edcb1939dd24","owner":[],"postedDate":"October 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T16:00:42+00:00","versionOfRecord":{"articleIdentity":"rs-4838363","link":"https://doi.org/10.1007/s00261-024-04645-6","journal":{"identity":"abdominal-radiology","isVorOnly":false,"title":"Abdominal Radiology"},"publishedOn":"2024-10-26 15:57:19","publishedOnDateReadable":"October 26th, 2024"},"versionCreatedAt":"2024-10-07 14:06:55","video":"","vorDoi":"10.1007/s00261-024-04645-6","vorDoiUrl":"https://doi.org/10.1007/s00261-024-04645-6","workflowStages":[]},"version":"v1","identity":"rs-4838363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4838363","identity":"rs-4838363","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}