Nomogram for Predicting Post-progression-free Survival in Patients with Recurrent Pancreatic Ductal Adenocarcinoma after Radical Surgery

Nomogram for Predicting Post-progression-free Survival in Patients with Recurrent Pancreatic Ductal Adenocarcinoma after Radical Surgery | 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 Nomogram for Predicting Post-progression-free Survival in Patients with Recurrent Pancreatic Ductal Adenocarcinoma after Radical Surgery Dailei Qin, Pu Xi, Kewei Huang, Lingmin Jiang, Zeihui Yao, Ran Wei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4380896/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Radical resection is the only curative method for patients with pancreatic adenocarcinoma (PDAC). However, nearly 85% of PDAC patients suffer from local or distant recurrence within five years after curative resection. Furthermore, the progression of recurrent lesions accelerated the death of PDAC patients. However, the influence of clinicopathological factors on post-progression-free survival (PPFS), defined as the period from tumor recurrence to the timing of the progression of recurrent lesions, has rarely been discussed. The present study aimed to explore the independent prognostic factors for PPFS and construct a nomogram for PPFS prediction. Methods: The 200 recurrent PDAC patients were randomly divided into training and validation groups, from which the clinicopathological characteristics were compared through a chi-square test. Consequently, these factors were enrolled in the multivariate COX regression to screen the independent prognostic factors of PPFS. Then, the Kaplan-Meier survival analysis based on the independent prognostic factors was performed. At last, we constructed a nomogram model for PPFS prediction, followed by an effectiveness examination. Results: PDAC patients who received multi-agent chemotherapy after surgery showed a better PPFS than the single-agent chemotherapy group. PDAC patients who received multi-agent chemotherapy after recurrence showed a similar PPFS compared to the single-agent chemotherapy group. Local recurrence with distant metastases, early recurrence, lympho-vascular invasion, higher T stage, and higher N stage predicted worse PPFS in recurrent PDAC patients. Finally, a nomogram to indicate the progression of recurrent lesions was constructed based on the independent prognostic factors. Conclusion: Chemotherapy after surgery, chemotherapy after recurrence, lymph vascular invasion, T stage, N stage, recurrence patterns, and time to recurrence were independent prognostic factors for PPFS. The nomogram model provided a new way for PPFS prediction in recurrent PDAC patients. Nomogram recurrence chemotherapy pancreatic adenocarcinoma post-progression-free survival. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction PDAC is the major component of pancreatic cancer 1,2 . As reported in previous research, the 5-year overall survival rate of PDAC is less than 10% 3 . Moreover, PDAC was expected to become the second leading cause of cancer-related death by 2030 4 . Radical resection was the only curative method for PDAC 5 . Unfortunately, 85% of resected cases may eventually suffer from tumor recurrence 6,7 . For instance, the advancement of recurrent lesions reflected the weakness of the anti-tumor immune system and the production of circulating tumor cells, predicting poor prognosis 8–10 . Meanwhile, NCCN guidelines have demonstrated that the progression of recurrent lesions is crucial for clinicians to consider alternative chemotherapy regimens 11 . Therefore, exploring independent prognostic factors of PPFS and further creating an analysis tool to predict the risk of PPFS is crucial for developing suitable adjuvant chemotherapy regimens for recurrent PDAC patients. In the previous research, post-progression survival has been thoroughly discussed in different tumor cohorts, such as pancreatic, liver, lung cancer, and cholangiocarcinoma 12–15 . However, the PPFS is also noteworthy but rarely investigated. In the present study, we retrospectively explored the independent prognostic factors for PPFS in the cohort of recurrent PDAC patients and constructed a nomogram for PPFS prediction. Methods Patients’ enrollment and grouping Patients who underwent radical resection for PDAC at Sun Yat-sen University Cancer Center (SYSUCC) from January 2008 to December 2019 were enrolled in the present study (Fig. 1 ). Detailly, tumoral resectability was investigated by a professional multidisciplinary team for PDAC based on imaging findings from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography-CT (PET-CT). Moreover, three chief physicians skilled in pancreaticoduodenectomy and distal pancreatectomy performed all of the surgeries included in this study. Meanwhile, the attending clinician and resident physician were also required to participate in the surgical procedure. Furthermore, open, laparoscopic, and robotic surgery was chosen according to the clinical tumor state with consent obtained from patients. The inclusion criteria concluded as follows: ( 1 ) histopathological examination reveals a definite diagnosis of PDAC; ( 2 ) diagnosed with tumor recurrence postoperatively according to the results of CT, tumor markers, and biopsy pathology; ( 3 ) the medical records were available. On the contrary, the exclusion criteria were represented as follows: ( 1 ) patients with a second tumor before surgery, ( 2 ) patients who received neoadjuvant chemotherapy, ( 3 ) patients without R0 resection (the margin for R0 resection was described as 1.5-2mm in the previous study) 16 , ( 4 ) lost follow-up, ( 5 ) the number of dissected lymph nodes was no more than 15. Then, the patients enrolled in the present study were divided into training and validation cohorts according to the leave-one-out method. Consent to use the medical records was obtained from all patients in the present study. The Ethics Committee of Sun Yat-sen University Cancer Center approved the retrospective study (No. B2024-202-01). This study was registered with https://www.chictr.org.cn/index.html . This work has been reported according to the STROCSS (Strengthening the Reporting of Cohort Studies in Surgery) criteria. Collection of clinicopathological characteristics The pathological diagnosis was acquired from experienced pathologists, including the tumor size, tumor differentiation, lymph node metastasis, microvascular invasion, lymph vascular invasion, and adjacent organ invasion. Moreover, several inflammation indices, such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), were involved in this study. Besides, this study also registered clinical factors such as age, gender, serum levels of carbohydrate antigen 19 − 9 (CA19-9), and carcinoembryonic antigen (CEA) after confirmation of tumor recurrence. Moreover, the chemotherapy regimens mentioned in the present study were applied after radical surgery and tumor recurrence, respectively, according to the recommendation from NCCN (2021 Ver2.0) guidelines for PDAC 11 . Importantly, patients' will and general condition were considered before developing the chemotherapy schema. In detail, the chemotherapy regimens were divided into three levels through the variety of drug utilization. The patients who never received chemotherapy after recurrence were classified as “untreated,” the patients who got only gemcitabine, tegafur, or capecitabine therapies after tumor recurrence were defined as “single-agent chemotherapy,” the patients who underwent FOLFIRINOX (oxaliplatin, Irinotecan, calcium folinate, fluorouracil), AG (nab-paclitaxel, gemcitabine), FOLFOX (oxaliplatin, calcium folinate, fluorouracil), FOLFIRI (Irinotecan, calcium folinate, fluorouracil), GS (gemcitabine, tegafur), GP (gemcitabine, cisplatin) chemotherapy schema after tumor relapse were categorized as “multi-agent chemotherapy.” Moreover, the key features of tumor recurrence were also registered in this research, including time to recurrence (the cut-off value to define early and late recurrence was one year after surgery) and recurrence patterns (the definition of different relapse patterns referred from the research by Groot) 7,17 . Follow-up and outcome adjudication The follow-up began at the time of tumor recurrence after radical surgery. The recurrent PDAC patients were suggested for outpatient review every three months. Meanwhile, abdominal and chest CT, CA19-9, and CEA were performed regularly after surgery. If the outpatient review were unavailable for some patients, telephone contact would be the alternative method. The endpoint of the present study was progression in recurrent lesions, which is defined as follows: (A) ≥ 20% increase in maximum diameter of the primary recurrent lesions, (B) or detection of any new recurrent lesions in the distant tissue. The outcome adjudication was made after the imaging examination or pathology diagnosis during follow-up. Statistical analysis The comparison of clinicopathological characteristics between the early and late recurrent groups was conducted using the chi-square test. The relationship between clinicopathological factors and PPFS was investigated using Kaplan–Meier methods. In detail, the log-rank test was utilized when the survival curve was not crossed, while the landmark analysis was applied when the survival curve was crossed. Multivariate Cox regression analysis was used to detect the independent prognostic factors for PPFS after completing the study of univariate Cox regression. The concordance indexes (C-indexes), calibration plots, and decision curve analyses (DCA) were utilized to compare the predictive ability between the nomogram and TNM-stage prediction models. A two-tailed P < 0.05 was considered statistically significant in the present study. All statistical analyses were conducted using SPSS software version 22 and R software version 4.2.2 (R Development Core Team; http://www.r-project.org ). Moreover, the R packages “getsummary, tidyverse, survival, plyr, broom, forestmodel, ggplot2, rms, survminer, and ggDCA” were used in this research. Results Patients’ clinicopathologic characteristics A total of 394 PDAC patients received radical surgery between January 2008 and December 2019 at SYSUCC, while 212 cases of them were eventually diagnosed with tumor recurrence. Meanwhile, 12 recurrent PDAC patients were eliminated from the research according to the exclusion criteria as follows: patients with a second tumor before surgery (2 cases), patients who received neo-adjuvant chemotherapy (1 case), patients without R0 resection (1 case), lost follow-up (6 cases), the number of dissected lymph nodes was less than 15 (2 cases). At last, 200 recurrent PDAC patients were enrolled in the present study. Subsequently, the 200 patients were divided into a training cohort (132 cases) and a validation cohort (68 cases). For the training cohort, the median PPFS was 5.25 months. For the validation cohort, the median PPFS was 5.25 months. The clinicopathological characteristics of the two groups were established in Table 1 . Based on the results of the chi-square test, T stage, TNM stage, tumor differentiation, chemotherapy after recurrence, and recurrence patterns showed a significant difference between the training and validation cohorts. Table 1 Clinicopathological characteristics of patients with PDAC in the training and validation cohort. Characteristic Training, N = 132 1 validation, N = 68 1 p-value 2 Gender 0.7 Female 80 (61%) 43 (63%) Male 52 (39%) 25 (37%) Age 0.3 0.9 Head 103 (78%) 53 (78%) Body and Tail 29 (22%) 15 (22%) T stage < 0.001 T1 7 (5.3%) 14 (21%) T2 54 (41%) 14 (21%) T3 71 (54%) 40 (59%) N stage 0.064 N0 28 (21%) 19 (28%) N1 51 (39%) 33 (49%) N2 53 (40%) 16 (24%) TNM stage 0.033 I 15 (11%) 12 (18%) II 64 (48%) 41 (60%) III 53 (40%) 15 (22%) Tumor differentiation 0.005 Well-Moderate 34 (26%) 31 (46%) Poor 98 (74%) 37 (54%) Adjacent organ invasion 0.2 Absence 52 (39%) 21 (31%) Presence 80 (61%) 47 (69%) Microvascular invasion 0.4 Absence 67 (51%) 39 (57%) Presence 65 (49%) 29 (43%) Lymph vascular invasion 0.12 Absence 85 (64%) 36 (53%) Presence 47 (36%) 32 (47%) Perineural invasion 0.5 Absence 27 (20%) 17 (25%) Presence 105 (80%) 51 (75%) Table 1 Continued. Characteristic Training, N = 132 1 validation, N = 68 1 p-value 2 Chemotherapy after surgery > 0.9 Untreated 52 (39%) 25 (37%) Single-agent therapy 57 (43%) 31 (46%) Multi-agent therapy 23 (17%) 12 (18%) Chemotherapy after recurrence < 0.001 Untreated 47 (36%) 15 (22%) Single-agent therapy 29 (22%) 37 (54%) Multi-agent therapy 56 (42%) 16 (24%) Recurrence patterns 0.006 Local 13 (9.8%) 18 (26%) Lung only 29 (22%) 9 (13%) Liver only 35 (27%) 22 (32%) Local and distant 55 (42%) 19 (28%) Time to recurrence 0.9 Early recurrence 89 (67%) 45 (66%) Late recurrence 43 (33%) 23 (34%) CA199 0.5 <35 31 (23%) 19 (28%) ≥35 101 (77%) 49 (72%) CEA 0.8 <5 61 (46%) 33 (49%) ≥5 71 (54%) 35 (51%) NLR 0.078 <224.65 38 (29%) 28 (41%) ≥224.65 94 (71%) 40 (59%) PLR 0.3 <2.28 88 (67%) 50 (74%) ≥2.28 44 (33%) 18 (26%) Prognostic factors for PPFS As shown in Table 2 , 18 factors were enrolled into univariate Cox regression in the training cohort. As the results showed, 11 factors were described to be correlated with PPFS: tumor differentiation (Well-Moderate VS Poor, HR = 1.54, 95% CI 1.05–2.25 p = 0.026), T stage (T3 VS T1, HR = 3.47, 95% CI 1.47–8.17 p = 0.005. T2 VS T1, HR = 2.85, CI 1.19–6.81 p = 0.019), N stage (N2 VS N0, HR = 2.19, 95% CI 1.34–3.57 p = 0.002. N1 VS N0, HR = 1.51, CI 0.91–2.51 p = 0.108), time to recurrence (Late recurrence VS Early recurrence, HR = 0.61, 95% CI 0.41–0.92 p = 0.02), CA19-9 (< 35 VS ≥ 35, HR = 1.64, 95% CI 1.04–2.59 p = 0.033), NLR (< 224.65 VS ≥ 224.65, HR = 1.57, 95% CI 1.03–2.38 p = 0.036), adjacent organ invasion (Presence VS Absence, HR = 1.47, 95% CI 1.01–2.04 p = 0.044), lymph vascular invasion (Presence VS Absence, HR = 1.54, 95% CI 1.05–2.25 p = 0.026), recurrence pattern (Local and distant VS Local, HR = 3.31, 95% CI 1.32–8.31 p = 0.011. Liver only VS Local, HR = 3.64, 95% CI 1.41–9.39 p = 0.007. Lung only VS Local, HR = 3.42, 95% CI 1.32–8.86 p = 0.011), chemotherapy after surgery (Multi-agent therapy VS Untreated, HR = 0.45 95% CI 0.26–0.78 p = 0.005. Single-agent therapy VS Untreated, HR = 0.65 95% CI 0.44–0.97 p = 0.035), chemotherapy after recurrence (Multi-agent therapy VS Untreated, HR = 0.54 95% CI 0.36–0.82 p = 0.005. Single-agent therapy VS Untreated, HR = 0.63 95% CI 0.38–1.06 p = 0.081). Then, these factors were enrolled into the multivariate Cox regression, and the results were established in Fig. 2 . The Lymph vascular invasion (Presence VS Absence, HR = 4.21, 95% CI 2.42–7.33 p < 0.001), T stage (T3 VS T1, HR = 3.86, 95% CI 1.39–10.71 p = 0.010. T2 VS T1, HR = 3.23, CI 1.24–8.42 p = 0.017), N stage (N2 VS N0, HR = 2.59, 95% CI 1.43–4.70 p = 0.002. N1 VS N0, HR = 2.43, CI 1.28–4.62 p = 0.007), recurrence pattern (Local and distant VS Local, HR = 5.11, 95% CI 1.84–14.16 p = 0.002. Liver only VS Local, HR = 5.02, 95% CI 1.79–14.06 p = 0.002. Lung only VS Local, HR = 4.77, 95% CI 1.65–13.82 p = 0.004), time to recurrence (Late recurrence VS Early recurrence, HR = 0.54, 95% CI 0.30–0.96 p = 0.035), chemotherapy after surgery (Multi-agent therapy VS Untreated, HR = 0.48 95% CI 0.30–0.77 p = 0.002. Single-agent therapy VS Untreated, HR = 0.57 95% CI 0.32–1.02 p = 0.058), chemotherapy after recurrence (Multi-agent therapy VS Untreated, HR = 0.36 95% CI 0.18–0.70 p = 0.003. Single-agent therapy VS Untreated, HR = 0.64 95% CI 0.40–1.02 p = 0.058) were considered as independent prognostic factors for PPFS in recurrent PDAC patients. Table 2 Univariate Cox regression in the training cohort. Characteristics HR p CI Characteristics HR p CI Gender Perineural invasion Male Ref Absence Ref Female 0.86 0.412 0.59–1.24 Presence 1.18 0.473 0.75–1.83 Age Microvascular invasion <60 Ref Absence Ref ≥60 1.01 0.956 0.68–1.5 Presence 1.1 0.603 0.77–1.58 Tumor site Adjacent organ invasion Head Ref Absence Ref Body and Tail 1.28 0.264 0.83–1.97 Presence 1.47 0.044 1.01–2.14 Tumor differentiation Lymph vascular invasion Well-Moderate Ref Absence Ref Poor 1.68 0.019 1.09–2.59 Presence 1.54 0.026 1.05–2.25 T stage Recurrence patterns T1 Ref Local Ref T2 2.85 0.019 1.19–6.81 Lung only 3.42 0.011 1.32–8.86 T3 3.47 0.005 1.47–8.17 Liver only 3.64 0.007 1.41–9.39 N stage Local and distant 3.31 0.011 1.32–8.31 N0 Ref Chemotherapy after surgery N1 1.51 0.108 0.91–2.51 Untreated Ref N2 2.19 0.002 1.34–3.57 Single-agent therapy 0.65 0.035 0.44–0.97 Time to recurrence Multi-agent therapy 0.45 0.005 0.26–0.78 Early recurrence Ref Chemotherapy after recurrence Late recurrence 0.61 0.02 0.41–0.92 Untreated Ref CA199 Single-agent therapy 0.63 0.081 0.38–1.06 <35 Ref Multi-agent therapy 0.54 0.003 0.36–0.82 ≥35 1.64 0.033 1.04–2.59 CEA <5 Ref ≥5 1.27 0.214 0.87–1.85 PLR <2.28 Ref ≥2.28 0.91 0.629 0.61–1.35 NLR <224.65 Ref ≥224.65 1.57 0.036 1.03–2.38 Survival analysis for independent prognostic factors The patients who received multi-agent therapy showed better PPFS than patients treated with single-agent therapy. ( p = 0.0089) (Fig. 3 A). Meanwhile, the patients treated with multi-agent or single-agent chemotherapy regiments after tumor relapse were estimated to have a similar prognosis ( p = 0.011) (Fig. 3 B). The PDAC patients in the T1 stage had a finer prognosis than patients in the T2 or T3 stage ( p = 0.011) (Fig. 3 C). A worse outcome will be found in patients with relatively higher N stages ( p = 0.0051) (Fig. 3 D). The patients with local recurrence had the best prognosis among the four relapse patterns mentioned in this study ( p = 0.038) (Fig. 3 E). PDAC patients with late recurrence had a relatively better prognosis than early recurrent patients ( p = 0.019) (Fig. 3 F). Finally, positive lymph vascular invasion in PDAC patients predicted worse outcomes when compared with negative lymph vascular invasion ( p = 0.026) (Fig. 3 G). Construction and validation of a nomogram for PPFS prediction As shown in Fig. 4 , a specific nomogram for PPFS prediction in patients with PDAC was built on independent prognostic factors. The recurrence patterns are the factor that displayed the most prominent effect in this model, followed by the lymph vascular invasion, T-stage, N-stage, chemotherapy after recurrence, chemotherapy after surgery, and time to recurrence. Furthermore, to detect the accuracy of the nomogram model, the calibration plots were produced, demonstrating a high conformity between the actual and predictive PPFS in both training and validation groups (Fig. 5 ). Meanwhile, to assess the discriminatory ability between the nomogram and TNM stage system, the C-index was calculated based on the training cohort and validation cohort; as shown in Table 3 , the C-index of the nomogram model was significantly higher than that in the TNM stage in both training and validation cohort. Finally, the decision curve analysis was fabricated to compare the clinical benefits of the nomogram and the TNM stage system (Fig. 6 ). It means that the nomogram built in the present study could be more beneficial for clinical prediction than the TNM stage system. Table 3 Comparison of the C-index between nomogram and TNM stage. Cohort Model C-index Validation cohort Nomogram 0.739 TNM stage 0.726 Training cohort Nomogram 0.609 TNM stage 0.596 Discussion Previous research explained lymph node metastasis as an essential predictor for tumor progression 22–25 . Meanwhile, the consensus in the Japanese Pancreatic Society also cited that confirmation of N9 and N16 lymph node metastasis was intimately linked with tumor relapse and distant metastasis 12 . Similarly, the higher N stage and positive lymph vascular invasion portended early advancement of recurrent lesions in the present research. The T stage represented the tumor diameters measured by the pathologists, indicating the tumor burden. Moreover, it has also been estimated as the reference standard for chemotherapeutic efficacy 11,26,27 . In this study, we found that the higher T stage was one of the independent prognostic factors for PPFS, portending a shorter PPFS. Different recurrence patterns of PDAC patients are usually linked with diverse post-progression survival 7,17,28 . In the earlier study, the local and distant recurrence pattern heralded the poorest post-progression survival among the four abovementioned recurrence patterns 7 . The present study also estimated that the liver-only, local, and distant recurrence patterns predicted poorer PPFS compared to the local recurrence patterns. Because of the larger volumes and sufficient blood supply, the liver was the common departure point of distant tumor metastases among the PDAC 29 . Accordingly, the PDAC patients with liver metastases were more likely to be detected with new relapse lesions in distant organs compared to those patients with local recurrence. Furthermore, there was a higher possibility for patients with local and distant recurrence to be detected with relapse lesions advancement because of the late tumor stage 7,30 . Since the high invasive capacity of PDAC, micro-metastatic lesions and residual tumor foci commonly co-existed in the same patient 18,31,32 , adjuvant chemotherapy was imminent after radical resection 33–36 . The preceding research declared that chemotherapy inhibited tumor progression and metastasis 37–40 . Moreover, the overall survival of PDAC patients who received AG and Folfirinox schema was 6.8 and 11 months, respectively. In comparison, the progression-free survival of AG and Folfirinox regiments was 3.3 and 6.4 months, respectively. 34,41,42 . However, the tumor-inhibiting effect of chemotherapy on the relapse lesions had been rarely discussed before. The present study found that PDAC patients receiving multi-agent chemotherapy after surgery showed better PPFS than single-agent therapy patients. Meanwhile, we also found that multi-agent chemotherapy and single-agent chemotherapy had similar efficacy in limiting the progression of relapse foci when compared with untreated recurrent PDAC patients. It indicated that the resistance to chemotherapy was significantly strengthened in the recurrent lesion compared with the primary tumor. Meanwhile, the progression of recurrent lesions may be too fast to be restricted by the chemotherapeutic agent. Therefore, the superiority of the multi-agent regimen was masked when compared with the single-agent schema in recurrent PDAC patients 43–47 . Based on the results of the present study, we argue that multi-agent chemotherapy brings more survival benefits to PDAC patients after radical surgery than a single-agent scheme. However, single-agent chemotherapy regimens should also be recommended for recurrent PDAC patients with poor chemotherapy tolerance to reduce the toxic side effects of chemotherapeutic agents 48,49 . Last but not least, we investigated the relationship between the time to recurrence and PPFS. The results declared that PDAC patients with late relapse had exclusively longer PPFS compared with the early relapse patients. In the previous research, early recurrence reflected the malignant phenotype of PDAC 12,50,51 . It means the patients with early relapse were more likely to combine with late tumor stage, poorer tumor differentiation, and stronger drug-resistant effect, causing a more rapid progression of recurrent lesions. The development of recurrent lesions was crucial for replacing chemotherapy schemes according to the NCCN guidelines 11 . Furthermore, individualized intervention for PDAC patients should be managed based on the risk of relapse lesions advancement. Therefore, precise prediction for PPFS is essential for clinical decision-making. After statistical analysis, our research selected several independent prognostic factors from high-dimensional radiological and pathological variables. Furthermore, we also constructed a nomogram for PPFS prediction based on those independent prognostic factors. The results of contrast analysis (the calibration curve, DCA, and C-index) between the training and validation cohort demonstrated this nomogram system's strong predictive and discriminative power. Thus, clinicians can precisely assess the risk of progression in relapse lesions for PDAC patients by using the nomogram system fabricated in this study. There are still some limitations to the present study. First, some variables, such as serum total bilirubin levels, C-reactive protein, and albumin, had not been included in this research. At the same time, some of the characteristics were only vaguely described during the information collection. For example, the specific chemotherapy regimens and the lymph node metastasis group had not been displayed in detail. The involvement of the characteristics mentioned above could further refine the predictive effect of the nomogram. Second, more precise results would be established in future exploration when a larger sample size was available. Third, some modified described methods for lymph node metastasis, such as LNR and LODDS, have already been proposed in some research 52 . However, we still referred to the description of lymph node metastasis from the AJCC guidelines in the present study. More persuasive results would be available if future research could apply the modified methods to lymph node metastasis discrimination. Conclusion Chemotherapy after surgery, chemotherapy after recurrence, lymph vascular invasion, T stage, N stage, recurrence patterns, and time to recurrence were independent prognostic factors for PPFS. The nomogram model provided a new way for PPFS prediction in recurrent PDAC patients. Declarations Author contribution D.Q. authored the manuscript; P.X. analyzed and visualized the results; K.H. collected research data; L.J. and Z.Y. completed patient recruitment; R.W. and S.L. reviewed and edited the manuscript. Funding National Natural Science Funds (Nos. 81972299 and 81672390) National Key Research and Development Plan (No. 2017YFC0910002). Availability of data and materials The data is unavailable. Declarations Ethics approval and consent to participate The Institute Research Ethics Committee of the Sun Yat-Sen University Cancer Center, Guangzhou, China, approved this study (No. B2024-202-01). Writ- ten informed consents were exempted because of retrospective analysis. Consent for publication All authors declare the final manuscript and submit it to this journal. Competing interests. There are no competing interests. References Siegel R, Miller K, Jemal A. Cancer statistics, 2020. CA: a cancer journal for clinicians . 2020;70(1):7-30. doi:10.3322/caac.21590 Rawla P, Sunkara T, Gaduputi V. Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World journal of oncology . 2019;10(1):10-27. doi:10.14740/wjon1166 Ellison L, Wilkins K. An update on cancer survival. Health reports . 2010;21(3):55-60. Rahib L, Smith B, Aizenberg R, Rosenzweig A, Fleshman J, Matrisian L. 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Recurrence after neoadjuvant therapy and resection of borderline resectable and locally advanced pancreatic cancer. European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology . 2019;45(9):1674-1683. doi:10.1016/j.ejso.2019.04.007 Akhtar M, Haider A, Rashid S, Al-Nabet A. Paget's "Seed and Soil" Theory of Cancer Metastasis: An Idea Whose Time has Come. Advances in anatomic pathology . 2019;26(1):69-74. doi:10.1097/pap.0000000000000219 Psaila B, Lyden D. The metastatic niche: adapting the foreign soil. Nature reviews Cancer . 2009;9(4):285-93. doi:10.1038/nrc2621 Herrmann R, Bodoky G, Ruhstaller T, et al. Gemcitabine plus capecitabine compared with gemcitabine alone in advanced pancreatic cancer: a randomized, multicenter, phase III trial of the Swiss Group for Clinical Cancer Research and the Central European Cooperative Oncology Group. Journal of clinical oncology : official journal of the American Society of Clinical Oncology . 2007;25(16):2212-7. doi:10.1200/jco.2006.09.0886 Conroy T, Desseigne F, Ychou M, et al. FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. The New England journal of medicine . 2011;364(19):1817-25. doi:10.1056/NEJMoa1011923 Mavros M, Moris D, Karanicolas P, Katz M, O'Reilly E, Pawlik T. Clinical Trials of Systemic Chemotherapy for Resectable Pancreatic Cancer: A Review. JAMA surgery . 2021;156(7):663-672. doi:10.1001/jamasurg.2021.0149 Von Hoff D, Ervin T, Arena F, et al. Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. The New England journal of medicine . 2013;369(18):1691-703. doi:10.1056/NEJMoa1304369 Sohal D, Duong M, Ahmad S, et al. Efficacy of Perioperative Chemotherapy for Resectable Pancreatic Adenocarcinoma: A Phase 2 Randomized Clinical Trial. JAMA oncology . 2021;7(3):421-427. doi:10.1001/jamaoncol.2020.7328 Xiong X, Mao Q, Yang J, Chen S, Li X. Clinical effectiveness of fluorouracil and cisplatin intraperitoneal perfusion combined with intravenous chemotherapy for peritoneal metastasis in gastric cancer. European review for medical and pharmacological sciences . 2023;27(18):8716-8731. doi:10.26355/eurrev_202309_33794 Inworn N, Senavat P, Aleenajitpong N, et al. Predictive Factors for the Survival Outcomes of Preoperative Chemotherapy in Patients with Resectable and Borderline Resectable Colorectal Cancer with Liver Metastasis. Asian Pacific journal of cancer prevention : APJCP . 2023;24(9):3037-3047. doi:10.31557/apjcp.2023.24.9.3037 Zeng Y, Zhang S, Li S, et al. Normalizing Tumor Blood Vessels to Improve Chemotherapy and Inhibit Breast Cancer Metastasis by Multifunctional Nanoparticles. Molecular pharmaceutics . 2023;20(10):5078-5089. doi:10.1021/acs.molpharmaceut.3c00381 Huang J, Guo W, Liu Z. Discussion on gemcitabine combined with targeted drugs in the treatment of pancreatic cancer. World journal of gastroenterology . 2023;29(3):579-581. doi:10.3748/wjg.v29.i3.579 Perri G, Prakash L, Qiao W, et al. Response and Survival Associated With First-line FOLFIRINOX vs Gemcitabine and nab-Paclitaxel Chemotherapy for Localized Pancreatic Ductal Adenocarcinoma. JAMA surgery . 2020;155(9):832-839. doi:10.1001/jamasurg.2020.2286 Yan W, Si L, Ding Y, Qiu S, Zhang Q, Liu L. Neoadjuvant chemotherapy does not improve the prognosis and lymph node metastasis rate of locally advanced cervical squamous cell carcinoma: A retrospective cohort study in China. Medicine . 2019;98(39):e17234. doi:10.1097/md.0000000000017234 Inoue Y, Fujii K, Tashiro K, et al. Preoperative Chemotherapy May Not Influence the Remnant Liver Regenerations and Outcomes After Hepatectomy for Colorectal Liver Metastasis. World journal of surgery . 2018;42(10):3316-3330. doi:10.1007/s00268-018-4590-1 Hoyle M, Crathorne L, Peters J, et al. The clinical effectiveness and cost-effectiveness of cetuximab (mono- or combination chemotherapy), bevacizumab (combination with non-oxaliplatin chemotherapy) and panitumumab (monotherapy) for the treatment of metastatic colorectal cancer after first-line chemotherapy (review of technology appraisal No.150 and part review of technology appraisal No. 118): a systematic review and economic model. Health technology assessment (Winchester, England) . 2013;17(14):1-237. doi:10.3310/hta17140 Ranson M, Hersey P, Thompson D, et al. Randomized trial of the combination of lomeguatrib and temozolomide compared with temozolomide alone in chemotherapy naive patients with metastatic cutaneous melanoma. Journal of clinical oncology : official journal of the American Society of Clinical Oncology . 2007;25(18):2540-5. doi:10.1200/jco.2007.10.8217 Rosenberg S, Yang J, Schwartzentruber D, et al. Prospective randomized trial of the treatment of patients with metastatic melanoma using chemotherapy with cisplatin, dacarbazine, and tamoxifen alone or in combination with interleukin-2 and interferon alfa-2b. Journal of clinical oncology : official journal of the American Society of Clinical Oncology . 1999;17(3):968-75. doi:10.1200/jco.1999.17.3.968 Varol U, Dirican A, Yildiz I, et al. First-line mono-chemotherapy in frail elderly patients with metastatic colorectal cancer. Asian Pacific journal of cancer prevention : APJCP . 2014;15(7):3157-61. doi:10.7314/apjcp.2014.15.7.3157 Yhim H, Lee J, Kim K, et al. Increased risk of venous and arterial thromboembolism in patients with colorectal cancer receiving cetuximab-based combination chemotherapy: A population-based study in Korea. Thrombosis research . 2023;231:50-57. doi:10.1016/j.thromres.2023.10.005 Zhao J, Zhang W, Zhang J, et al. Independent Risk Factors of Early Recurrence After Curative Resection for Perihilar Cholangiocarcinoma: Adjuvant Chemotherapy May Be Beneficial in Early Recurrence Subgroup. Cancer management and research . 2020;12:13111-13123. doi:10.2147/cmar.S289094 He C, Cai Z, Zhang Y, Lin X. Comparative Recurrence Analysis of Pancreatic Adenocarcinoma after Resection. Journal of oncology . 2021;2021:3809095. doi:10.1155/2021/3809095 Prassas D, Safi S, Stylianidi M, et al. N, LNR or LODDS: Which Is the Most Appropriate Lymph Node Classification Scheme for Patients with Radically Resected Pancreatic Cancer? Cancers . 2022;14(7)doi:10.3390/cancers14071834 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4380896","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":302730773,"identity":"d031ebc1-038a-4fc0-9865-35545be12312","order_by":0,"name":"Dailei Qin","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Dailei","middleName":"","lastName":"Qin","suffix":""},{"id":302730774,"identity":"3fccdbab-4b75-4736-bdb3-4328d1496ea8","order_by":1,"name":"Pu Xi","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Pu","middleName":"","lastName":"Xi","suffix":""},{"id":302730775,"identity":"52854319-75f1-4d29-99a8-6a9d1b846c13","order_by":2,"name":"Kewei Huang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Kewei","middleName":"","lastName":"Huang","suffix":""},{"id":302730776,"identity":"3e639a12-e83b-451d-aba9-2be08ee7c3c0","order_by":3,"name":"Lingmin Jiang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Lingmin","middleName":"","lastName":"Jiang","suffix":""},{"id":302730777,"identity":"fa13ca99-b3b4-461b-9b5f-dc49687813be","order_by":4,"name":"Zeihui Yao","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zeihui","middleName":"","lastName":"Yao","suffix":""},{"id":302730778,"identity":"06d91967-9a07-4fab-b59c-d5c7d1809ff8","order_by":5,"name":"Ran Wei","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Wei","suffix":""},{"id":302730779,"identity":"d340bdb5-a006-4f3c-826a-bdef0ec58446","order_by":6,"name":"Shengping Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBACPmYGNjCDH4gPPIAJ8+DRwgbTItkA1JJAlBYGqBaDA0CCOC3s7M8efNxRm2d87fBDoC11ifNnJDA+eNvGIG+O02E85oYzzxwvNrudZgDUcjhxw40EZsO5bQyGOxtwamGT5m07lrjtdgJIy4HEDRIJIBGGBLBTsWphfyb9F6hl8+z0DzCHsf/Gr4XBTJqxrSZxg3QOyBbmxIYbCWzM+LXwmEn2th0olridU3AgweCw8YYzD5sl55yTMNyAQws///FnEj/b6vL4Z6dv/vChok52fnvywQ9vymzkcdkCBYcTILQBg2MDA2MDkCWBVz0Q1CXAWPaElI6CUTAKRsHIAwAUiFxRRY+bEQAAAABJRU5ErkJggg==","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Shengping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-05-07 07:02:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4380896/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4380896/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57298314,"identity":"94747222-33b5-4ed9-bef8-c4505245410c","added_by":"auto","created_at":"2024-05-28 20:34:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chat of patients’ enrollment and study design.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4380896/v1/8fd68affd1b19cc1ff650f4d.png"},{"id":57298125,"identity":"f3c065fb-347d-435a-bda7-5cecd646a609","added_by":"auto","created_at":"2024-05-28 20:26:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":242891,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot of Multivariate Cox regression for independent prognostic factors in the training cohort.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4380896/v1/531c0dea93826b7a0d5d3265.png"},{"id":57298127,"identity":"4d434e77-295c-446e-801e-154188427851","added_by":"auto","created_at":"2024-05-28 20:26:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129494,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the 1-year PPFS rates of PDAC patients after radical resection in the training cohort.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4380896/v1/9891fc63ed42bfd41031e109.png"},{"id":57298128,"identity":"e1f14d89-0dca-457e-aa10-b36d233fb314","added_by":"auto","created_at":"2024-05-28 20:26:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":559868,"visible":true,"origin":"","legend":"\u003cp\u003eThe survival analysis is based on different independent prognostic factors in the training cohort. (A) survival analysis based on chemotherapy after surgery; (B) survival analysis based on chemotherapy after recurrence; (C) survival analysis based on T stage; (D) survival analysis based on N stage. (E) survival analysis based on recurrence patterns; (F) survival analysis based on time to recurrence; (G) survival analysis based on lymph vascular invasion.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4380896/v1/8e77d4fa6175fbb5daddad3e.png"},{"id":57298131,"identity":"49cff4fe-47aa-48a2-8094-be1887ab2ac9","added_by":"auto","created_at":"2024-05-28 20:26:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":293297,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration plot for predicting PPFS rates in PDAC. (A) The calibration plot for predicting the 1-year PPFS rates in the training cohort; (B) The calibration plot for predicting the 1-year PPFS rates in the validation cohort.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4380896/v1/63a90cda271504f86617884d.png"},{"id":57298315,"identity":"2731779d-bb14-4977-9b5b-76b00ad6d838","added_by":"auto","created_at":"2024-05-28 20:34:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":345599,"visible":true,"origin":"","legend":"\u003cp\u003eThe decision curve analysis for the nomogram and TNM stage (training VS validation cohort). A decision curve analysis for the nomogram and TNM stage in the training cohort; B decision curve analysis for the nomogram and TNM stage in the validation cohort.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4380896/v1/6bcdb37f3f2430a2a41fb5cb.png"},{"id":76950635,"identity":"e0cedb3e-6517-4fec-9d41-7330df1b064e","added_by":"auto","created_at":"2025-02-23 07:31:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2371306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4380896/v1/7355adc4-36e6-4479-ab27-f9d6f0af07a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram for Predicting Post-progression-free Survival in Patients with Recurrent Pancreatic Ductal Adenocarcinoma after Radical Surgery","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePDAC is the major component of pancreatic cancer\u003csup\u003e1,2\u003c/sup\u003e. As reported in previous research, the 5-year overall survival rate of PDAC is less than 10%\u003csup\u003e3\u003c/sup\u003e. Moreover, PDAC was expected to become the second leading cause of cancer-related death by 2030 \u003csup\u003e4\u003c/sup\u003e. Radical resection was the only curative method for PDAC\u003csup\u003e5\u003c/sup\u003e. Unfortunately, 85% of resected cases may eventually suffer from tumor recurrence\u003csup\u003e6,7\u003c/sup\u003e. For instance, the advancement of recurrent lesions reflected the weakness of the anti-tumor immune system and the production of circulating tumor cells, predicting poor prognosis\u003csup\u003e8\u0026ndash;10\u003c/sup\u003e. Meanwhile, NCCN guidelines have demonstrated that the progression of recurrent lesions is crucial for clinicians to consider alternative chemotherapy regimens \u003csup\u003e11\u003c/sup\u003e. Therefore, exploring independent prognostic factors of PPFS and further creating an analysis tool to predict the risk of PPFS is crucial for developing suitable adjuvant chemotherapy regimens for recurrent PDAC patients.\u003c/p\u003e \u003cp\u003eIn the previous research, post-progression survival has been thoroughly discussed in different tumor cohorts, such as pancreatic, liver, lung cancer, and cholangiocarcinoma\u003csup\u003e12\u0026ndash;15\u003c/sup\u003e. However, the PPFS is also noteworthy but rarely investigated. In the present study, we retrospectively explored the independent prognostic factors for PPFS in the cohort of recurrent PDAC patients and constructed a nomogram for PPFS prediction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u0026rsquo; enrollment and grouping\u003c/h2\u003e \u003cp\u003ePatients who underwent radical resection for PDAC at Sun Yat-sen University Cancer Center (SYSUCC) from January 2008 to December 2019 were enrolled in the present study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Detailly, tumoral resectability was investigated by a professional multidisciplinary team for PDAC based on imaging findings from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography-CT (PET-CT). Moreover, three chief physicians skilled in pancreaticoduodenectomy and distal pancreatectomy performed all of the surgeries included in this study. Meanwhile, the attending clinician and resident physician were also required to participate in the surgical procedure. Furthermore, open, laparoscopic, and robotic surgery was chosen according to the clinical tumor state with consent obtained from patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe inclusion criteria concluded as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) histopathological examination reveals a definite diagnosis of PDAC; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) diagnosed with tumor recurrence postoperatively according to the results of CT, tumor markers, and biopsy pathology; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the medical records were available. On the contrary, the exclusion criteria were represented as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients with a second tumor before surgery, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) patients who received neoadjuvant chemotherapy, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) patients without R0 resection (the margin for R0 resection was described as 1.5-2mm in the previous study)\u003csup\u003e16\u003c/sup\u003e, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) lost follow-up, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) the number of dissected lymph nodes was no more than 15. Then, the patients enrolled in the present study were divided into training and validation cohorts according to the leave-one-out method.\u003c/p\u003e \u003cp\u003eConsent to use the medical records was obtained from all patients in the present study. The Ethics Committee of Sun Yat-sen University Cancer Center approved the retrospective study (No. B2024-202-01). This study was registered with \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.chictr.org.cn/index.html\u003c/span\u003e\u003cspan address=\"https://www.chictr.org.cn/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This work has been reported according to the STROCSS (Strengthening the Reporting of Cohort Studies in Surgery) criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCollection of clinicopathological characteristics\u003c/h2\u003e \u003cp\u003eThe pathological diagnosis was acquired from experienced pathologists, including the tumor size, tumor differentiation, lymph node metastasis, microvascular invasion, lymph vascular invasion, and adjacent organ invasion. Moreover, several inflammation indices, such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), were involved in this study. Besides, this study also registered clinical factors such as age, gender, serum levels of carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9), and carcinoembryonic antigen (CEA) after confirmation of tumor recurrence. Moreover, the chemotherapy regimens mentioned in the present study were applied after radical surgery and tumor recurrence, respectively, according to the recommendation from NCCN (2021 Ver2.0) guidelines for PDAC\u003csup\u003e11\u003c/sup\u003e. Importantly, patients' will and general condition were considered before developing the chemotherapy schema. In detail, the chemotherapy regimens were divided into three levels through the variety of drug utilization. The patients who never received chemotherapy after recurrence were classified as \u0026ldquo;untreated,\u0026rdquo; the patients who got only gemcitabine, tegafur, or capecitabine therapies after tumor recurrence were defined as \u0026ldquo;single-agent chemotherapy,\u0026rdquo; the patients who underwent FOLFIRINOX (oxaliplatin, Irinotecan, calcium folinate, fluorouracil), AG (nab-paclitaxel, gemcitabine), FOLFOX (oxaliplatin, calcium folinate, fluorouracil), FOLFIRI (Irinotecan, calcium folinate, fluorouracil), GS (gemcitabine, tegafur), GP (gemcitabine, cisplatin) chemotherapy schema after tumor relapse were categorized as \u0026ldquo;multi-agent chemotherapy.\u0026rdquo; Moreover, the key features of tumor recurrence were also registered in this research, including time to recurrence (the cut-off value to define early and late recurrence was one year after surgery) and recurrence patterns (the definition of different relapse patterns referred from the research by Groot)\u003csup\u003e7,17\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up and outcome adjudication\u003c/h2\u003e \u003cp\u003eThe follow-up began at the time of tumor recurrence after radical surgery. The recurrent PDAC patients were suggested for outpatient review every three months. Meanwhile, abdominal and chest CT, CA19-9, and CEA were performed regularly after surgery. If the outpatient review were unavailable for some patients, telephone contact would be the alternative method. The endpoint of the present study was progression in recurrent lesions, which is defined as follows: (A)\u0026thinsp;\u0026ge;\u0026thinsp;20% increase in maximum diameter of the primary recurrent lesions, (B) or detection of any new recurrent lesions in the distant tissue. The outcome adjudication was made after the imaging examination or pathology diagnosis during follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe comparison of clinicopathological characteristics between the early and late recurrent groups was conducted using the chi-square test. The relationship between clinicopathological factors and PPFS was investigated using Kaplan\u0026ndash;Meier methods. In detail, the log-rank test was utilized when the survival curve was not crossed, while the landmark analysis was applied when the survival curve was crossed. Multivariate Cox regression analysis was used to detect the independent prognostic factors for PPFS after completing the study of univariate Cox regression. The concordance indexes (C-indexes), calibration plots, and decision curve analyses (DCA) were utilized to compare the predictive ability between the nomogram and TNM-stage prediction models. A two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant in the present study. All statistical analyses were conducted using SPSS software version 22 and R software version 4.2.2 (R Development Core Team; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Moreover, the R packages \u0026ldquo;getsummary, tidyverse, survival, plyr, broom, forestmodel, ggplot2, rms, survminer, and ggDCA\u0026rdquo; were used in this research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u0026rsquo; clinicopathologic characteristics\u003c/h2\u003e \u003cp\u003eA total of 394 PDAC patients received radical surgery between January 2008 and December 2019 at SYSUCC, while 212 cases of them were eventually diagnosed with tumor recurrence. Meanwhile, 12 recurrent PDAC patients were eliminated from the research according to the exclusion criteria as follows: patients with a second tumor before surgery (2 cases), patients who received neo-adjuvant chemotherapy (1 case), patients without R0 resection (1 case), lost follow-up (6 cases), the number of dissected lymph nodes was less than 15 (2 cases). At last, 200 recurrent PDAC patients were enrolled in the present study. Subsequently, the 200 patients were divided into a training cohort (132 cases) and a validation cohort (68 cases). For the training cohort, the median PPFS was 5.25 months. For the validation cohort, the median PPFS was 5.25 months. The clinicopathological characteristics of the two groups were established in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Based on the results of the chi-square test, T stage, TNM stage, tumor differentiation, chemotherapy after recurrence, and recurrence patterns showed a significant difference between the training and validation cohorts.\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\u003eClinicopathological characteristics of patients with PDAC in the training and validation cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining, N\u0026thinsp;=\u0026thinsp;132\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003evalidation, N\u0026thinsp;=\u0026thinsp;68\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody and Tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-Moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjacent organ invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicrovascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph vascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerineural invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContinued.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining, N\u0026thinsp;=\u0026thinsp;132\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003evalidation, N\u0026thinsp;=\u0026thinsp;68\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy after surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUntreated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle-agent therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulti-agent therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy after recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUntreated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle-agent therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulti-agent therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrence patterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal and distant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime to recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;224.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;224.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic factors for PPFS\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 18 factors were enrolled into univariate Cox regression in the training cohort. As the results showed, 11 factors were described to be correlated with PPFS: tumor differentiation (Well-Moderate VS Poor, HR\u0026thinsp;=\u0026thinsp;1.54, 95% CI 1.05\u0026ndash;2.25 p\u0026thinsp;=\u0026thinsp;0.026), T stage (T3 VS T1, HR\u0026thinsp;=\u0026thinsp;3.47, 95% CI 1.47\u0026ndash;8.17 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005. T2 VS T1, HR\u0026thinsp;=\u0026thinsp;2.85, CI 1.19\u0026ndash;6.81 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), N stage (N2 VS N0, HR\u0026thinsp;=\u0026thinsp;2.19, 95% CI 1.34\u0026ndash;3.57 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002. N1 VS N0, HR\u0026thinsp;=\u0026thinsp;1.51, CI 0.91\u0026ndash;2.51 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.108), time to recurrence (Late recurrence VS Early recurrence, HR\u0026thinsp;=\u0026thinsp;0.61, 95% CI 0.41\u0026ndash;0.92 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), CA19-9 (\u0026lt;\u0026thinsp;35 VS\u0026thinsp;\u0026ge;\u0026thinsp;35, HR\u0026thinsp;=\u0026thinsp;1.64, 95% CI 1.04\u0026ndash;2.59 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), NLR (\u0026lt;\u0026thinsp;224.65 VS\u0026thinsp;\u0026ge;\u0026thinsp;224.65, HR\u0026thinsp;=\u0026thinsp;1.57, 95% CI 1.03\u0026ndash;2.38 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), adjacent organ invasion (Presence VS Absence, HR\u0026thinsp;=\u0026thinsp;1.47, 95% CI 1.01\u0026ndash;2.04 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), lymph vascular invasion (Presence VS Absence, HR\u0026thinsp;=\u0026thinsp;1.54, 95% CI 1.05\u0026ndash;2.25 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), recurrence pattern (Local and distant VS Local, HR\u0026thinsp;=\u0026thinsp;3.31, 95% CI 1.32\u0026ndash;8.31 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011. Liver only VS Local, HR\u0026thinsp;=\u0026thinsp;3.64, 95% CI 1.41\u0026ndash;9.39 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007. Lung only VS Local, HR\u0026thinsp;=\u0026thinsp;3.42, 95% CI 1.32\u0026ndash;8.86 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), chemotherapy after surgery (Multi-agent therapy VS Untreated, HR\u0026thinsp;=\u0026thinsp;0.45 95% CI 0.26\u0026ndash;0.78 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005. Single-agent therapy VS Untreated, HR\u0026thinsp;=\u0026thinsp;0.65 95% CI 0.44\u0026ndash;0.97 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), chemotherapy after recurrence (Multi-agent therapy VS Untreated, HR\u0026thinsp;=\u0026thinsp;0.54 95% CI 0.36\u0026ndash;0.82 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005. Single-agent therapy VS Untreated, HR\u0026thinsp;=\u0026thinsp;0.63 95% CI 0.38\u0026ndash;1.06 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.081). Then, these factors were enrolled into the multivariate Cox regression, and the results were established in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The Lymph vascular invasion (Presence VS Absence, HR\u0026thinsp;=\u0026thinsp;4.21, 95% CI 2.42\u0026ndash;7.33 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), T stage (T3 VS T1, HR\u0026thinsp;=\u0026thinsp;3.86, 95% CI 1.39\u0026ndash;10.71 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010. T2 VS T1, HR\u0026thinsp;=\u0026thinsp;3.23, CI 1.24\u0026ndash;8.42 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), N stage (N2 VS N0, HR\u0026thinsp;=\u0026thinsp;2.59, 95% CI 1.43\u0026ndash;4.70 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002. N1 VS N0, HR\u0026thinsp;=\u0026thinsp;2.43, CI 1.28\u0026ndash;4.62 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), recurrence pattern (Local and distant VS Local, HR\u0026thinsp;=\u0026thinsp;5.11, 95% CI 1.84\u0026ndash;14.16 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002. Liver only VS Local, HR\u0026thinsp;=\u0026thinsp;5.02, 95% CI 1.79\u0026ndash;14.06 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002. Lung only VS Local, HR\u0026thinsp;=\u0026thinsp;4.77, 95% CI 1.65\u0026ndash;13.82 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), time to recurrence (Late recurrence VS Early recurrence, HR\u0026thinsp;=\u0026thinsp;0.54, 95% CI 0.30\u0026ndash;0.96 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), chemotherapy after surgery (Multi-agent therapy VS Untreated, HR\u0026thinsp;=\u0026thinsp;0.48 95% CI 0.30\u0026ndash;0.77 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002. Single-agent therapy VS Untreated, HR\u0026thinsp;=\u0026thinsp;0.57 95% CI 0.32\u0026ndash;1.02 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.058), chemotherapy after recurrence (Multi-agent therapy VS Untreated, HR\u0026thinsp;=\u0026thinsp;0.36 95% CI 0.18\u0026ndash;0.70 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003. Single-agent therapy VS Untreated, HR\u0026thinsp;=\u0026thinsp;0.64 95% CI 0.40\u0026ndash;1.02 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.058) were considered as independent prognostic factors for PPFS in recurrent PDAC patients.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Cox regression in the training cohort.\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=\"left\" 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\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePerineural invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u0026ndash;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.75\u0026ndash;1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMicrovascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026ge;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.77\u0026ndash;1.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjacent organ invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eHead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eBody and Tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u0026ndash;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.01\u0026ndash;2.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLymph vascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eWell-Moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09\u0026ndash;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.05\u0026ndash;2.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecurrence patterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.19\u0026ndash;6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLung only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.32\u0026ndash;8.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47\u0026ndash;8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLiver only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.41\u0026ndash;9.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocal and distant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.32\u0026ndash;8.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemotherapy after surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u0026ndash;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUntreated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34\u0026ndash;3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSingle-agent therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.44\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime to recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMulti-agent therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.26\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemotherapy after recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eLate recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\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\u003e0.41\u0026ndash;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUntreated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eCA199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSingle-agent therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.38\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMulti-agent therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.36\u0026ndash;0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04\u0026ndash;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026lt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026ge;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u0026ndash;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026lt;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026ge;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u0026ndash;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026lt;224.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026ge;224.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u0026ndash;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis for independent prognostic factors\u003c/h2\u003e \u003cp\u003eThe patients who received multi-agent therapy showed better PPFS than patients treated with single-agent therapy. (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0089) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Meanwhile, the patients treated with multi-agent or single-agent chemotherapy regiments after tumor relapse were estimated to have a similar prognosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The PDAC patients in the T1 stage had a finer prognosis than patients in the T2 or T3 stage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). A worse outcome will be found in patients with relatively higher N stages (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0051) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The patients with local recurrence had the best prognosis among the four relapse patterns mentioned in this study (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). PDAC patients with late recurrence had a relatively better prognosis than early recurrent patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Finally, positive lymph vascular invasion in PDAC patients predicted worse outcomes when compared with negative lymph vascular invasion (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of a nomogram for PPFS prediction\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a specific nomogram for PPFS prediction in patients with PDAC was built on independent prognostic factors. The recurrence patterns are the factor that displayed the most prominent effect in this model, followed by the lymph vascular invasion, T-stage, N-stage, chemotherapy after recurrence, chemotherapy after surgery, and time to recurrence. Furthermore, to detect the accuracy of the nomogram model, the calibration plots were produced, demonstrating a high conformity between the actual and predictive PPFS in both training and validation groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Meanwhile, to assess the discriminatory ability between the nomogram and TNM stage system, the C-index was calculated based on the training cohort and validation cohort; as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the C-index of the nomogram model was significantly higher than that in the TNM stage in both training and validation cohort. Finally, the decision curve analysis was fabricated to compare the clinical benefits of the nomogram and the TNM stage system (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). It means that the nomogram built in the present study could be more beneficial for clinical prediction than the TNM stage system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the C-index between nomogram and TNM stage.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC-index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNomogram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.739\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\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNomogram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.609\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\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious research explained lymph node metastasis as an essential predictor for tumor progression\u003csup\u003e22\u0026ndash;25\u003c/sup\u003e. Meanwhile, the consensus in the Japanese Pancreatic Society also cited that confirmation of N9 and N16 lymph node metastasis was intimately linked with tumor relapse and distant metastasis\u003csup\u003e12\u003c/sup\u003e. Similarly, the higher N stage and positive lymph vascular invasion portended early advancement of recurrent lesions in the present research.\u003c/p\u003e \u003cp\u003eThe T stage represented the tumor diameters measured by the pathologists, indicating the tumor burden. Moreover, it has also been estimated as the reference standard for chemotherapeutic efficacy\u003csup\u003e11,26,27\u003c/sup\u003e. In this study, we found that the higher T stage was one of the independent prognostic factors for PPFS, portending a shorter PPFS.\u003c/p\u003e \u003cp\u003eDifferent recurrence patterns of PDAC patients are usually linked with diverse post-progression survival\u003csup\u003e7,17,28\u003c/sup\u003e. In the earlier study, the local and distant recurrence pattern heralded the poorest post-progression survival among the four abovementioned recurrence patterns \u003csup\u003e7\u003c/sup\u003e. The present study also estimated that the liver-only, local, and distant recurrence patterns predicted poorer PPFS compared to the local recurrence patterns. Because of the larger volumes and sufficient blood supply, the liver was the common departure point of distant tumor metastases among the PDAC\u003csup\u003e29\u003c/sup\u003e. Accordingly, the PDAC patients with liver metastases were more likely to be detected with new relapse lesions in distant organs compared to those patients with local recurrence. Furthermore, there was a higher possibility for patients with local and distant recurrence to be detected with relapse lesions advancement because of the late tumor stage\u003csup\u003e7,30\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince the high invasive capacity of PDAC, micro-metastatic lesions and residual tumor foci commonly co-existed in the same patient\u003csup\u003e18,31,32\u003c/sup\u003e, adjuvant chemotherapy was imminent after radical resection\u003csup\u003e33\u0026ndash;36\u003c/sup\u003e. The preceding research declared that chemotherapy inhibited tumor progression and metastasis\u003csup\u003e37\u0026ndash;40\u003c/sup\u003e. Moreover, the overall survival of PDAC patients who received AG and Folfirinox schema was 6.8 and 11 months, respectively. In comparison, the progression-free survival of AG and Folfirinox regiments was 3.3 and 6.4 months, respectively. \u003csup\u003e34,41,42\u003c/sup\u003e. However, the tumor-inhibiting effect of chemotherapy on the relapse lesions had been rarely discussed before. The present study found that PDAC patients receiving multi-agent chemotherapy after surgery showed better PPFS than single-agent therapy patients. Meanwhile, we also found that multi-agent chemotherapy and single-agent chemotherapy had similar efficacy in limiting the progression of relapse foci when compared with untreated recurrent PDAC patients. It indicated that the resistance to chemotherapy was significantly strengthened in the recurrent lesion compared with the primary tumor. Meanwhile, the progression of recurrent lesions may be too fast to be restricted by the chemotherapeutic agent. Therefore, the superiority of the multi-agent regimen was masked when compared with the single-agent schema in recurrent PDAC patients \u003csup\u003e43\u0026ndash;47\u003c/sup\u003e. Based on the results of the present study, we argue that multi-agent chemotherapy brings more survival benefits to PDAC patients after radical surgery than a single-agent scheme. However, single-agent chemotherapy regimens should also be recommended for recurrent PDAC patients with poor chemotherapy tolerance to reduce the toxic side effects of chemotherapeutic agents\u003csup\u003e48,49\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLast but not least, we investigated the relationship between the time to recurrence and PPFS. The results declared that PDAC patients with late relapse had exclusively longer PPFS compared with the early relapse patients. In the previous research, early recurrence reflected the malignant phenotype of PDAC\u003csup\u003e12,50,51\u003c/sup\u003e. It means the patients with early relapse were more likely to combine with late tumor stage, poorer tumor differentiation, and stronger drug-resistant effect, causing a more rapid progression of recurrent lesions.\u003c/p\u003e \u003cp\u003eThe development of recurrent lesions was crucial for replacing chemotherapy schemes according to the NCCN guidelines \u003csup\u003e11\u003c/sup\u003e. Furthermore, individualized intervention for PDAC patients should be managed based on the risk of relapse lesions advancement. Therefore, precise prediction for PPFS is essential for clinical decision-making. After statistical analysis, our research selected several independent prognostic factors from high-dimensional radiological and pathological variables. Furthermore, we also constructed a nomogram for PPFS prediction based on those independent prognostic factors. The results of contrast analysis (the calibration curve, DCA, and C-index) between the training and validation cohort demonstrated this nomogram system's strong predictive and discriminative power. Thus, clinicians can precisely assess the risk of progression in relapse lesions for PDAC patients by using the nomogram system fabricated in this study.\u003c/p\u003e \u003cp\u003eThere are still some limitations to the present study. First, some variables, such as serum total bilirubin levels, C-reactive protein, and albumin, had not been included in this research. At the same time, some of the characteristics were only vaguely described during the information collection. For example, the specific chemotherapy regimens and the lymph node metastasis group had not been displayed in detail. The involvement of the characteristics mentioned above could further refine the predictive effect of the nomogram. Second, more precise results would be established in future exploration when a larger sample size was available. Third, some modified described methods for lymph node metastasis, such as LNR and LODDS, have already been proposed in some research \u003csup\u003e52\u003c/sup\u003e. However, we still referred to the description of lymph node metastasis from the AJCC guidelines in the present study. More persuasive results would be available if future research could apply the modified methods to lymph node metastasis discrimination.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eChemotherapy after surgery, chemotherapy after recurrence, lymph vascular invasion, T stage, N stage, recurrence patterns, and time to recurrence were independent prognostic factors for PPFS. The nomogram model provided a new way for PPFS prediction in recurrent PDAC patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.Q. authored the manuscript; P.X. analyzed and visualized the results; K.H. collected research data; L.J. and Z.Y. completed patient recruitment; R.W. and S.L. reviewed and edited the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Funds (Nos. 81972299 and 81672390)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNational Key Research and Development Plan (No. 2017YFC0910002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data is unavailable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institute Research Ethics Committee of the Sun Yat-Sen University Cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCenter, Guangzhou, China, approved this study (No. B2024-202-01). Writ-\u003c/p\u003e\n\u003cp\u003eten informed consents were exempted because of retrospective analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare the final manuscript and submit it to this journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSiegel R, Miller K, Jemal A. Cancer statistics, 2020. \u003cem\u003eCA: a cancer journal for clinicians\u003c/em\u003e. 2020;70(1):7-30. doi:10.3322/caac.21590\u003c/li\u003e\n \u003cli\u003eRawla P, Sunkara T, Gaduputi V. Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. \u003cem\u003eWorld journal of oncology\u003c/em\u003e. 2019;10(1):10-27. doi:10.14740/wjon1166\u003c/li\u003e\n \u003cli\u003eEllison L, Wilkins K. 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N, LNR or LODDS: Which Is the Most Appropriate Lymph Node Classification Scheme for Patients with Radically Resected Pancreatic Cancer? \u003cem\u003eCancers\u003c/em\u003e. 2022;14(7)doi:10.3390/cancers14071834\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nomogram, recurrence, chemotherapy, pancreatic adenocarcinoma, post-progression-free survival.","lastPublishedDoi":"10.21203/rs.3.rs-4380896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4380896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eRadical resection is the only curative method for patients with pancreatic adenocarcinoma (PDAC). However, nearly 85% of PDAC patients suffer from local or distant recurrence within five years after curative resection. Furthermore, the progression of recurrent lesions accelerated the death of PDAC patients. However, the influence of clinicopathological factors on post-progression-free survival (PPFS), defined as the period from tumor recurrence to the timing of the progression of recurrent lesions, has rarely been discussed. The present study aimed to explore the independent prognostic factors for PPFS and construct a nomogram for PPFS prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe 200 recurrent PDAC patients were randomly divided into training and validation groups, from which the clinicopathological characteristics were compared through a chi-square test. Consequently, these factors were enrolled in the multivariate COX regression to screen the independent prognostic factors of PPFS. Then, the Kaplan-Meier survival analysis based on the independent prognostic factors was performed. At last, we constructed a nomogram model for PPFS prediction, followed by an effectiveness examination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e PDAC patients who received multi-agent chemotherapy after surgery showed a better PPFS than the single-agent chemotherapy group. PDAC patients who received multi-agent chemotherapy after recurrence showed a similar PPFS compared to the single-agent chemotherapy group. Local recurrence with distant metastases, early recurrence, lympho-vascular invasion, higher T stage, and higher N stage predicted worse PPFS in recurrent PDAC patients. Finally, a nomogram to indicate the progression of recurrent lesions was constructed based on the independent prognostic factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eChemotherapy after surgery, chemotherapy after recurrence,\u003cstrong\u003e \u003c/strong\u003elymph vascular invasion, T stage, N stage, recurrence patterns, and time to recurrence were independent prognostic factors for PPFS. The nomogram model provided a new way for PPFS prediction in recurrent PDAC patients.\u003c/p\u003e","manuscriptTitle":"Nomogram for Predicting Post-progression-free Survival in Patients with Recurrent Pancreatic Ductal Adenocarcinoma after Radical Surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-28 20:25:57","doi":"10.21203/rs.3.rs-4380896/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"516b2085-4c94-49c3-8921-6cb8dd935396","owner":[],"postedDate":"May 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-23T07:23:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-28 20:25:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4380896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4380896","identity":"rs-4380896","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}