Preoperative platelet immune index as a prognostic marker for colon cancer: A comparative analysis of blood-derived indices

Preoperative platelet immune index as a prognostic marker for colon cancer: A comparative analysis of blood-derived indices | 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 Preoperative platelet immune index as a prognostic marker for colon cancer: A comparative analysis of blood-derived indices Hyo Jun Kim, Ji Won Park, Han-Ki Lim, Min Jung Kim, Rumi Shin, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8307248/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Multiple immune-inflammatory blood indices have been proposed for colon cancer prognostication; however, comparative evaluations of these indices remain limited. We systematically compared these indices and assessed the prognostic performance of a platelet-monocyte-lymphocyte combination. Methods In a discovery cohort of 2,993 patients, we compared multiple immune-inflammatory indices constructed from platelet, neutrophil, monocyte, and lymphocyte counts. The index with the highest relative relevance in a generalized boosted regression model (GBM) was selected. An independent cohort of 1,124 patients was used for validation. The outcomes included overall survival (OS) and progression-free survival (PFS). Results Regarding GBM analysis, the platelet immune index (PII), calculated as (platelet count × monocyte count) / lymphocyte count, showed the highest relative influence for survival prediction among the tested indices. Using a data-derived cutoff of 80.0, high PII score (≥ 80.0) was associated with worse 5-year OS (64.3% vs. 84.8%, P < 0.001) and PFS (52.6% vs. 76.2%, P < 0.001). Using multivariable analysis adjusting for established prognostic factors, high PII values remained independently associated with worse OS (hazard ratio [HR], 1.474; 95% confidence interval [CI], 1.269–1.714) and PFS (HR, 1.422; 95% CI, 1.240–1.630). The findings were validated in the independent cohort. Conclusions Preoperative PII demonstrated independent prognostic value in patients with colon cancer and might aid in risk stratification, particularly within the same TNM stage. High PII score warrants closer postoperative surveillance and consideration in treatment planning. colon cancer immune inflammation platelet immune index prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Each year, approximately 1.15 million patients are newly diagnosed with colon cancer; 577,000 patients die from the disease globally [ 1 ]. Consequently, numerous investigations have been undertaken to identify biomarkers capable of predicting colorectal cancer prognosis. These prognostic biomarkers facilitate identifying high-risk patients, enable personalized postoperative surveillance protocols, and inform appropriate therapeutic interventions during postoperative management. Such biomarkers ultimately contribute to the advancement of precision medicine approaches for managing patients with colon cancer [ 2 ]. Given the established relationship between inflammatory processes, immune responses, and cancer progression, extensive research has investigated prognostic markers derived from inflammation and immune-related blood parameters [ 3 – 6 ]. Regarding most solid malignancies, including colon cancer, host-related factors play crucial roles in tumor development and progression [ 7 ]. Systemic inflammation, for instance, induces various cytokines that potentially influence survival outcomes [ 8 ]. Consequently, immune and inflammation-associated hematological parameters have been incorporated as components of prognostic indices for colon cancer. Among the hematological parameters obtained during complete blood count (CBC) analyses, platelet, neutrophil, monocyte, and lymphocyte counts have been extensively investigated as individual or composite biomarkers. Previous studies have elucidated the specific effects of each cell type on tumor progression and prognosis [ 9 – 14 ]. Neutrophils, platelets, and monocytes demonstrate pro-tumorigenic properties, whereas lymphocytes exhibit tumor-suppressive functions. In the context of dual parameter combinations, the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) have been identified as significant prognostic factors for colon cancer survival across multiple studies [ 3 , 4 , 15 , 16 ]. Extending beyond these binary combinations, limited research has explored multiple parameter combinations. The systemic-immune-inflammation index (SII), which incorporates lymphocyte, neutrophil, and platelet counts, was developed for prognostic stratification and has been established as a predictive factor for colorectal cancer outcomes [ 6 ]. Additionally, the pan-immune-inflammation value (PIV), encompassing four immune-inflammatory blood parameters, has demonstrated efficacy as an independent prognostic factor in metastatic colorectal cancer [ 17 , 18 ]. Recent evidence has highlighted the critical interplay between platelets and monocytes in shaping the tumor microenvironment. Through P-selectin–PSGL-1 binding, platelets form aggregates with monocytes, driving their activation and polarization toward tumor-promoting phenotypes such as M2-type tumor-associated macrophages [ 19 , 20 ]. This interaction is further reinforced by platelet-derived mediators, including P-selectin, tissue factor, PD-L1, and chemokines such as CXCL4 and CCL2, which enhance monocyte recruitment and immune suppression. Platelet-derived microparticles also contribute to transferring cytokines and regulatory microRNAs that amplify tumor-associated inflammation and metastatic potential [ 19 , 21 ]. These processes have been reported to accelerate thrombo-inflammation and correlate with poor outcomes in colorectal and other cancer types [ 20 , 22 ]. Despite developing multiple proposed indices, direct comparative analyses determining optimal prognostic combinations remain limited. This study systematically evaluated various blood parameter combinations to assess their relative prognostic performance, with particular focus on platelet-monocyte-lymphocyte interactions reflected in the platelet immune index (PII). Materials and Methods Patients This retrospective cohort study included patients who underwent surgical resection of primary colon cancer at Seoul National University Hospital between January 2008 and December 2015. The inclusion criteria were: histopathologically confirmed adenocarcinoma, CBC within one week before surgery, and complete clinicopathologic data. Patients were excluded if they had a history of another malignancy, evidence of active infection before surgery, endoscopic resection as the primary treatment, perioperative mortality within 30 days, or incomplete clinical or laboratory records. After applying these criteria, the discovery cohort comprised 2,993 patients. External validation used data of 1,124 patients meeting identical criteria at the National Cancer Center, Korea. Institutional Review Board approval was obtained at both participating institutions; the requirement for informed consent was waived due to the retrospective design of the study. Postoperative surveillance was performed every 3–6 months during the first 5 years and annually thereafter. Each follow-up visit included physical examination, chest radiography, and measurement of serum carcinoembryonic antigen (CEA). Abdominopelvic computed tomography was routinely performed every six months; moreover, colonoscopy was scheduled every one to two years. Recurrence or disease progression was confirmed through radiologic imaging, histopathologic assessment, or both. Definition of immune-inflammation biomarkers Four hematologic variables—platelet, neutrophil, monocyte, and lymphocyte counts—were considered for biomarker construction. Candidate indices were generated by combining these features using multiplication and division. In this framework, platelets, neutrophils, and monocytes were regarded as pro-tumorigenic, whereas lymphocytes were considered anti-tumorigenic and placed in the denominator of composite indices. Considering dual-parameter indices, the NLR was defined as neutrophil count / lymphocyte count; the PLR as platelet count / lymphocyte count; and LMR as lymphocyte count / monocyte count. Based on previous literature, cutoff values were set at 5.0, 150.0, and 3.0 for NLR, PLR, and LMR, respectively. With respect to indices incorporating three parameters, the PII was calculated as (platelet count × monocyte count) / lymphocyte count, each expressed per 1,000/mm³. Optimal cutoff thresholds were determined using maximally selected rank statistics for progression-free survival (PFS), with a value < 80 classified as low PII. The SII was calculated as (neutrophil count × platelet count) / lymphocyte count, with < 730 defining low SII score. The neutrophil–monocyte-to-lymphocyte index was calculated as (neutrophil count × monocyte count) / lymphocyte count, with a cutoff of 510 determined by the same method. In regard to indices incorporating four variables, the PIV was calculated as (neutrophil count × platelet count × monocyte count) / lymphocyte count. The cutoff for PIV was set at 510, again based on maximally selected rank statistics for PFS. Statistical analyses Overall survival (OS) was defined as the interval from surgery to death from any cause, while PFS was defined as the interval from surgery to disease progression or death. To compare the prognostic performance of all indices, we employed a generalized boosted regression model (GBM), a machine learning approach that constructs sequential decision trees to minimize prediction error and quantify the relative influence of each predictor. GBM identified the index with the highest prognostic relevance for OS and PFS. Kaplan–Meier survival curves were used for illustrating differences between the groups; the log-rank test was applied to assess statistical significance. Variables associated with OS or PFS at a threshold of P < 0.05 from univariate analyses were fitted in multivariable Cox proportional hazards models. Statistical analyses were performed using SPSS Statistics for Windows, version 29.0 (IBM Corp., Armonk, NY, USA) and R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). A two-sided P value < 0.05 was considered statistically significant. Results Feature importance of biomarkers using GBM analysis GBM was used for assessing the relative prognostic contribution of different biomarker combinations. When analyzed as continuous variables with OS and PFS as endpoints, the PII demonstrated the strongest influence on both outcomes (Fig. 1 a and 1 b). When dichotomized using respective cutoffs, the PII retained the highest relative importance (Fig. 1 c and 1 d). Clinicopathologic characteristics Preoperative PII values ranged between 5.29 and 712.96, with a median value of 63.44. Based on the optimal cutoff of 80.0 determined using maximally selected rank statistics for PFS, the patients were stratified into low PII (n = 1,951, 65.2%) and high PII values (n = 1,042, 34.8%) groups. The patients in the high PII were significantly older ( P = 0.001), more often male participants ( P = 0.001), had lower body mass index (BMI; P < 0.001), higher American Society of Anesthesiologists (ASA) scores ( P < 0.001), more right-sided tumors ( P < 0.001), elevated CEA levels ( P < 0.001), higher histologic grade ( P < 0.001), advanced TNM stage ( P < 0.001), and increased lymphovascular and perineural invasion (both, P < 0.001). Although the patients in the high PII group received adjuvant chemotherapy more frequently (73.2% vs. 68.9%, P = 0.014), they were possibly selected based on higher-risk features. All study participants were ethnically Asian (Table 1 ). Table 1 Clinicopathologic characteristics according to the Platelet Immune Index Low PII (n = 1951) High PII (n = 1042) P-value Age 62.87 (23–91) 64.28 (17–93) 0.001 Sex 0.001 Male 1093 (56.0%) 652 (62.6%) Female 858 (44.0%) 390 (37.4%) BMI (kg/m2) 25.0 604 (31.0%) 246 (23.7%) ASA score < 0.001 1 774 (39.9%) 325 (31.5%) 2 1087 (56.0%) 626 (60.7%) 3 79 (4.1%) 81 (7.8%) Location < 0.001 Right-sided colon 675 (34.6%) 450 (43.2%) Left-sided colon 1267 (64.9%) 583 (56.0%) Mixed 9 (0.5%) 9 (0.9%) CEA 5 338 (17.3%) 360 (34.5%) Histologic grade < 0.001 Well/moderate 1831 (93.8%) 917 (88.0%) Poor/undifferentiated 120 (6.2%) 125 (12.0%) Tumor stage < 0.001 Ⅰ 352 (18.0%) 78 (7.5%) Ⅱ 649 (33.3%) 341 (32.7%) Ⅲ 715 (36.6%) 315 (30.2%) Ⅳ 235 (12.0%) 308 (29.6%) LVI (present) 616 (31.6%) 471 (45.2%) < 0.001 PNI (present) 578 (29.6%) 415 (39.9%) < 0.001 AC (performed) 1345 (68.9%) 763 (73.2%) 0.014 Abbreviation: PII, platelet immune index; BMI, body mass index; ASA, American Society of Anesthesiologists; CEA, carcinoembryonic antigen; LVI, lymphovascular invasion; PNI, perineural invasion; AC, adjuvant chemotherapy Survival Outcomes The patients with elevated PII scores experienced significantly worse long-term outcomes. The 5-year OS was 64.3% in the high PII versus 84.8% in low PII groups ( P < 0.001; Fig. 2 a). The 5-year PFS was 52.6% versus 76.2%, respectively ( P < 0.001; Fig. 2 b). Stage-stratified analyses showed prognostic discrimination across all TNM stages; however, the absolute differences varied. In patients with stage I disease, the 5-year OS difference was 7.0% (88.2% vs. 95.2%, P = 0.003), while PFS differed by 5.5% (85.3% vs. 90.8%, P = 0.028) (Fig. 3 a and 4 a). Greater absolute differences were observed in stages II-IV (Fig. 3 b–d and 4 b–d). Multivariable Analysis Univariate analysis of OS identified multiple adverse prognostic factors, including advanced age (≥ 60 years, P < 0.001), male sex ( P = 0.003), higher ASA score ( P < 0.001), reduced BMI ( P = 0.001), elevated CEA ( P < 0.001), higher tumor grade ( P < 0.001), advanced disease stage ( P < 0.001), lympho-vascular and perineural invasion (both, P < 0.001), high PII values ( P < 0.001), and receipt of adjuvant chemotherapy ( P = 0.006). Using multivariable analysis, high PII score remained an independent predictor of poor OS (hazard ratio [HR], 1.474; 95% confidence interval [CI], 1.269–1.714; P < 0.001) (Table 2 ). Table 2 Univariate and multivariable Cox’s hazards regression models for overall survival Characteristics Univariate Multivariate Hazard ratio (95% CI) P-value Hazard ratio (95% CI) P-value Age ≤ 60 Reference Reference > 60 1.487 (1.274–1.734) < 0.001 1.465 (1.244–1.724) < 0.001 Sex Male Reference Reference Female 0.803 (0.693–0.930) 0.003 0.857 (0.738–0.996) 0.045 ASA score < 0.001 < 0.001 1 Reference Reference 2 1.373 (1.173–1.608) < 0.001 1.345 (1.142–1.584) < 0.001 3 2.541 (1.945–3.319) < 0.001 1.989 (1.503–2.633) 25 0.763 (0.645–0.901) 0.001 0.859 (0.726–1.018) 0.079 CEA ≤ 5 Reference Reference > 5 3.877 (3.363–4.470) < 0.001 1.634 (1.390–1.922) < 0.001 Histologic grade Low Reference Reference High 2.675 (2.199–3.256) < 0.001 1.744 (1.424–2.137) < 0.001 Stage < 0.001 < 0.001 1 Reference Reference 2 1.461 (1.011–2.109) 0.043 1.767 (1.194–2.614) 0.004 3 2.526 (1.781–3.583) < 0.001 3.340 (2.237–4.985) < 0.001 4 16.908 (12.060-23.706) < 0.001 16.028 (10.726–23.950) < 0.001 LVI Not identified Reference Reference Present 2.968 (2.572–3.426) < 0.001 1.712 (1.454–2.016) < 0.001 PNI Not identified Reference Reference Present 2.439 (2.114–2.815) < 0.001 1.291 (1.096–1.521) 0.002 AC Not performed Reference Reference Performed 1.254 (1.066–1.476) 0.006 0.453 (0.373–0.551) < 0.001 PII Low PII(< 80.0) Reference Reference High PII(≥ 80.0) 2.655 (2.303–3.060) < 0.001 1.474 (1.269–1.714) < 0.001 Abbreviation: ASA, American Society of Anesthesiologists; BMI, body mass index; CEA, carcinoembryonic antigen; CI, confidence interval; LVI, lymphovascular invasion; PNI, perineural invasion; AC, adjuvant chemotherapy; PII, platelet immune index Similarly, univariate analysis of PFS demonstrated that advanced age ( P < 0.001), male sex ( P = 0.012), elevated ASA score ( P < 0.001), lower BMI ( P < 0.001), elevated CEA ( P < 0.001), higher histologic grade ( P < 0.001), advanced TNM stage ( P < 0.001), lympho-vascular and perineural invasion (both, P < 0.001), adjuvant chemotherapy ( P < 0.001), and high PII ( P < 0.001) were associated with inferior outcomes. Multivariate modeling confirmed high PII as an independent adverse prognostic factor for PFS (HR, 1.422; 95% CI, 1.240–1.630; P < 0.001) (Table 3 ). Table 3 Univariate and multivariable Cox’s hazards regression models for progression survival Characteristics Univariate Multivariate Hazard ratio (95% CI) P-value Hazard ratio (95% CI) P-value Age ≤ 60 Reference Reference > 60 1.335 (1.164–1.531) < 0.001 1.295 (1.119–1.499) < 0.001 Sex Male Reference Reference Female 0.843 (0.738–0.964) 0.012 0.865 (0.754–0.992) 0.038 ASA score < 0.001 0.026 1 Reference Reference 2 1.254 (1.089–1.444) 0.002 1.103 (0.953–1.278) 0.188 3 2.125 (1.654–2.729) 25 0.757 (0.651–0.881) 5 3.708 (3.255–4.223) < 0.001 1.872 (1.611–2.174) < 0.001 Histologic grade Low Reference Reference High 2.224 (1.844–2.683) < 0.001 1.510 (1.245–1.831) < 0.001 Stage < 0.001 < 0.001 1 Reference Reference 2 1.727 (1.244–2.399) 0.001 2.047 (1.433–2.924) < 0.001 3 2.836 (2.070–3.886) < 0.001 3.643 (2.521–5.264) < 0.001 4 11.757 (8.638–16.001) < 0.001 9.443 (6.484–13.752) < 0.001 LVI Not identified Reference Reference Present 2.657 (2.334–3.025) < 0.001 1.530 (1.322–1.771) < 0.001 PNI Not identified Reference Reference Present 2.629 (2.308–2.996) < 0.001 1.474 (1.271–1.711) < 0.001 AC Not performed Reference Reference Performed 1.314 (1.128–1.531) < 0.001 0.476 (0.396–0.572) < 0.001 PII Low PII(< 80.0) Reference Reference High PII(≥ 80.0) 2.352 (2.067–2.675) < 0.001 1.422 (1.240–1.630) < 0.001 Abbreviation: ASA, American Society of Anesthesiologists; BMI, body mass index; CEA, carcinoembryonic antigen; CI, confidence interval; LVI, lymphovascular invasion; PNI, perineural invasion; AC, adjuvant chemotherapy; PII, platelet immune index Using subgroup analysis of data of patients with stage IIA (T3N0M0), high PII values independently predicted reduced OS (HR, 1.797; 95% CI, 1.203–2.682; P = 0.004) and PFS (HR, 1.826; 95% CI, 1.279–2.606; P = 0.001). Notably, the conventional biomarker CEA was not a significant predictor of OS from univariate analysis (HR, 1.416; 95% CI, 0.867–2.314; P = 0.164) and of PFS after adjustment for PII (HR, 1.360; 95% CI, 0.894–2.068; P = 0.151) (Additional files 1 and 2). External Validation In the validation cohort, high PII score was associated with worse OS ( P < 0.0001) and PFS ( P = 0.00038) compared with low PII (Additional file 3), confirming prognostic associations in an independent population. Discussion This study systematically compared multiple immune-inflammatory indices and demonstrated that the PII, incorporating platelet, monocyte, and lymphocyte counts, showed strong prognostic associations in patients with colon cancer. By integrating platelet, monocyte, and lymphocyte counts, the PII encapsulates both tumor-promoting and tumor-suppressive mechanisms into a single composite index. Among the array of immune-inflammatory biomarkers analyzed, the PII demonstrated the greatest predictive weight for OS and PFS, as confirmed by GBM. The prognostic value of PII can be explained by the distinct biological functions of its cellular components. Platelets enhance tumor growth and dissemination by supporting angiogenesis, shielding malignant cells from immune recognition, and facilitating metastatic colonization [ 23 – 25 ]. Monocytes recruited to the tumor milieu differentiate into tumor-associated macrophages (TAMs), particularly of the M2 phenotype, which foster tumor invasion, vascularization, and immune evasion. Conversely, lymphocytes—specifically T-cell subsets—constitute the principal mediators of antitumor immunity; a reduced lymphocyte count often signifies systemic immunosuppression and correlates with disease advancement and poor survival [ 26 – 28 ]. Collectively, a high PII value reflects a biologically adverse immune-inflammatory state characterized by augmented tumor-supporting activity and weakened immune defense. Our findings further support the role of platelets and monocytes as key determinants of prognosis in patients with colorectal cancer. Elevated levels of these circulating cells are possibly a reflection of enhanced platelet–monocyte aggregate formation, which fosters a pro-inflammatory and immunosuppressive tumor microenvironment. Molecular changes in activated platelets, such as upregulated P-selectin, CXCL4, and PD-L1 expression, appear to facilitate monocyte recruitment and their differentiation into immunosuppressive TAMs [ 20 , 21 ]. These mechanisms provide a plausible explanation for the association between high platelet and monocyte counts and inferior survival outcomes observed in our study [ 19 , 22 ]. Therapeutically, disrupting platelet–monocyte interactions represent a promising avenue. Preclinical data suggest that antiplatelet agents (e.g., aspirin, ticagrelor) may attenuate aggregate formation and chemokine release, thereby reducing metastatic spread. Moreover, combination strategies integrating antiplatelet therapy with immune checkpoint inhibition could counteract TAM-mediated immune suppression, potentially improving treatment efficacy [ 19 , 29 ]. Future studies should validate these findings in larger clinical cohorts and investigate novel inhibitors targeting P-selectin or CLEC-2 pathways to refine personalized treatment approaches. Circulating lymphocytes, particularly diverse T-cell subsets, play a pivotal role in antitumor immune surveillance and serve as important determinants of prognosis in patients with colorectal cancer [ 30 ]. Multiple studies have consistently shown that individuals with colon carcinoma present with markedly lower absolute lymphocyte counts compared with age- and sex-matched healthy populations. Alterations in the distribution of peripheral lymphocyte subsets reflect a measurable consequence of tumor-driven immunosuppressive mechanisms. Moreover, diminished lymphocyte levels in the circulation have been strongly correlated with adverse clinicopathological characteristics, increased risk of recurrence, and inferior survival outcomes [ 31 , 32 ]. Several hematologic indices, such as the NLR, PLR, LMR, SII, and PIV, have been validated as prognostic markers for colorectal cancer [ 3 – 6 , 15 , 16 ]. However, our findings provide direct comparative evidence that the PII outperforms these established indices in survival prediction. Importantly, its prognostic significance was consistent across all TNM stages, including early-stage disease, suggesting that the PII could serve as a supplementary tool for refining stratification beyond conventional staging systems. Treatment decision-making in patients with colon cancer stage II remains a clinical challenge, particularly regarding the benefit of adjuvant chemotherapy [ 33 ]. Our subgroup analysis demonstrated that the PII was a stronger prognostic factor compared with conventional variables such as CEA levels and histological grade in stage II and T3N0 disease. This implies that the PII could aid in identifying patients who may benefit from additional therapy despite being classified within a relatively favorable stage. Since the PII is derived from routinely obtained preoperative CBC parameters, it offers an inexpensive, easily applicable, and globally accessible biomarker. Patients with elevated PII values may merit intensified postoperative surveillance and consideration of adjuvant strategies that extend beyond current standard recommendations. Several limitations should be acknowledged. The retrospective and single-institution design of the discovery cohort may introduce selection bias; however, external validation at a national cancer center reinforces the robustness of our findings. Approximately one-third of eligible patients were excluded owing to incomplete laboratory data, which might limit the generalizability of the results. Furthermore, the cutoff value for the PII was derived from our cohort and might not be universally applicable; validation in independent populations is required. Although the PII has demonstrated strong prognostic significance, prospective studies and interventional trials are necessary for confirming its role in guiding therapeutic decision-making. Future investigations should also explore integration of the PII with molecular and immunogenomic profiles, thereby enabling its incorporation into multidimensional prognostic frameworks tailored for precision oncology. Conclusion The preoperative PII was a powerful and independent prognostic biomarker for colon cancer. Its ability to stratify prognosis across all disease stages, including early-stage tumors, highlights its potential for clinical application. Given its simplicity, biological rationale, and broad accessibility, the PII warrants further validation in multicenter prospective cohorts and may ultimately become a key component of individualized treatment strategies. Abbreviations ASA American Society of Anesthesiologists BMI body mass index CBC complete blood count CEA carcinoembryonic antigen CI confidence interval GBM generalized boosted regression model HR hazard ratio LMR lymphocyte-to-monocyte ratio NLR neutrophil-to-lymphocyte ratio OS overall survival PII platelet immune index PIV pan-immune-inflammation value PLR platelet-to-lymphocyte ratio PSF progression-free survival SII systemic immune-inflammation index TAMs tumor-associated macrophages Declarations Ethics approval and consent to participate Institutional Review Board approval was obtained at both participating institutions, including approval from the Seoul National University Hospital Institutional Review Board (Seoul National University College of Medicine / Seoul National University Hospital) and the National Cancer Center Institutional Review Board. The requirement for informed consent was waived due to the retrospective design of the study. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and national research committees and with the principles outlined in the Declaration of Helsinki. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This research was supported by a grant from the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (RS-2023-CC140354) and a grant of Korean ARPA-H Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2024-00512405, RS-2460002489). Authors' contributions HJK, JWP: Conceptualization, design, structure, and idea. 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Supplementary Files Supplementaryfigure1OSandPFSinNCC.png Additionalfiles.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Jan, 2026 Reviews received at journal 22 Jan, 2026 Reviews received at journal 15 Jan, 2026 Reviews received at journal 14 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers invited by journal 06 Jan, 2026 Editor assigned by journal 05 Jan, 2026 Editor invited by journal 12 Dec, 2025 Submission checks completed at journal 11 Dec, 2025 First submitted to journal 11 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-8307248","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570625936,"identity":"4b11eb78-9bb3-4966-81a0-3f59caa6b34c","order_by":0,"name":"Hyo Jun Kim","email":"","orcid":"","institution":"Seoul National University Hospital and Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hyo","middleName":"Jun","lastName":"Kim","suffix":""},{"id":570625937,"identity":"a6cdd489-f035-4ab7-8c56-7c1355f565cc","order_by":1,"name":"Ji Won 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1","display":"","copyAsset":false,"role":"figure","size":121478,"visible":true,"origin":"","legend":"\u003cp\u003eGeneralized boosted regression model (GBM) analysis of immune-inflammatory biomarkers for overall survival (OS) and progression-free survival (PFS).\u003c/p\u003e\n\u003cp\u003e(a) Relative influence of each continuous biomarker variable on OS.\u003c/p\u003e\n\u003cp\u003e(b) Relative influence of each continuous biomarker variable on PFS.\u003c/p\u003e\n\u003cp\u003e(c) Relative influence of each biomarker when dichotomized according to its respective cutoff value for OS.\u003c/p\u003e\n\u003cp\u003e(d) Relative influence of each biomarker when dichotomized according to its respective cutoff value for PFS.\u003c/p\u003e\n\u003cp\u003eNote: Relative influence values are unitless and reflect the ranking of variable importance within the GBM model rather than absolute effect size; differences between biomarkers should therefore be interpreted comparatively, not quantitatively.\u003c/p\u003e","description":"","filename":"Figure1GBM.png","url":"https://assets-eu.researchsquare.com/files/rs-8307248/v1/81c506a1661d870bd302708b.png"},{"id":100005575,"identity":"e7189478-efd8-4e6c-a6aa-51f422687d3a","added_by":"auto","created_at":"2026-01-12 05:34:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48455,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival (OS) and progression-free survival (PFS) according to the Platelet Immune Index (PII) group.\u003c/p\u003e","description":"","filename":"Figure2OSandPFSaccordingtoPII.png","url":"https://assets-eu.researchsquare.com/files/rs-8307248/v1/5d41ddcd903dcc87abd1bf1f.png"},{"id":100005582,"identity":"415046b5-b2a5-4be0-b9d9-61237a1f8759","added_by":"auto","created_at":"2026-01-12 05:34:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80260,"visible":true,"origin":"","legend":"\u003cp\u003eStage-specific overall survival (OS) according to the Platelet Immune Index (PII) group.\u003c/p\u003e","description":"","filename":"Figure3OSaccordingtoPIIineachstages.png","url":"https://assets-eu.researchsquare.com/files/rs-8307248/v1/86bf23d2e48b83f4f065f2df.png"},{"id":100005593,"identity":"2a9b10ea-9ca1-43fa-a1a8-1281ec5c17cc","added_by":"auto","created_at":"2026-01-12 05:34:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85476,"visible":true,"origin":"","legend":"\u003cp\u003eStage-specific progression-free survival (PFS) according to the Platelet Immune Index (PII) group.\u003c/p\u003e","description":"","filename":"Figure4PFSaccordingtoPIIineachstages.png","url":"https://assets-eu.researchsquare.com/files/rs-8307248/v1/851e9a9c4bcff4a2b33cb17d.png"},{"id":100380772,"identity":"85a4bdfc-0501-49e4-89f5-78a4d85e7566","added_by":"auto","created_at":"2026-01-16 10:33:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414463,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8307248/v1/042683bb-0cc3-43db-8963-114704906dec.pdf"},{"id":100360227,"identity":"f5326187-7ce5-4381-ad59-5b47d544018d","added_by":"auto","created_at":"2026-01-16 07:38:06","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48645,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1OSandPFSinNCC.png","url":"https://assets-eu.researchsquare.com/files/rs-8307248/v1/ee08c00054314279cf84aeb3.png"},{"id":100360237,"identity":"c5b9c156-fd21-4e2c-82e7-16061ebc4e56","added_by":"auto","created_at":"2026-01-16 07:38:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23500,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-8307248/v1/5ca9e5118d77b9af4e4f97da.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative platelet immune index as a prognostic marker for colon cancer: A comparative analysis of blood-derived indices","fulltext":[{"header":"Background","content":"\u003cp\u003eEach year, approximately 1.15\u0026nbsp;million patients are newly diagnosed with colon cancer; 577,000 patients die from the disease globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Consequently, numerous investigations have been undertaken to identify biomarkers capable of predicting colorectal cancer prognosis. These prognostic biomarkers facilitate identifying high-risk patients, enable personalized postoperative surveillance protocols, and inform appropriate therapeutic interventions during postoperative management. Such biomarkers ultimately contribute to the advancement of precision medicine approaches for managing patients with colon cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the established relationship between inflammatory processes, immune responses, and cancer progression, extensive research has investigated prognostic markers derived from inflammation and immune-related blood parameters [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Regarding most solid malignancies, including colon cancer, host-related factors play crucial roles in tumor development and progression [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Systemic inflammation, for instance, induces various cytokines that potentially influence survival outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, immune and inflammation-associated hematological parameters have been incorporated as components of prognostic indices for colon cancer.\u003c/p\u003e \u003cp\u003eAmong the hematological parameters obtained during complete blood count (CBC) analyses, platelet, neutrophil, monocyte, and lymphocyte counts have been extensively investigated as individual or composite biomarkers. Previous studies have elucidated the specific effects of each cell type on tumor progression and prognosis [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Neutrophils, platelets, and monocytes demonstrate pro-tumorigenic properties, whereas lymphocytes exhibit tumor-suppressive functions. In the context of dual parameter combinations, the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) have been identified as significant prognostic factors for colon cancer survival across multiple studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Extending beyond these binary combinations, limited research has explored multiple parameter combinations. The systemic-immune-inflammation index (SII), which incorporates lymphocyte, neutrophil, and platelet counts, was developed for prognostic stratification and has been established as a predictive factor for colorectal cancer outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, the pan-immune-inflammation value (PIV), encompassing four immune-inflammatory blood parameters, has demonstrated efficacy as an independent prognostic factor in metastatic colorectal cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent evidence has highlighted the critical interplay between platelets and monocytes in shaping the tumor microenvironment. Through P-selectin\u0026ndash;PSGL-1 binding, platelets form aggregates with monocytes, driving their activation and polarization toward tumor-promoting phenotypes such as M2-type tumor-associated macrophages [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This interaction is further reinforced by platelet-derived mediators, including P-selectin, tissue factor, PD-L1, and chemokines such as CXCL4 and CCL2, which enhance monocyte recruitment and immune suppression. Platelet-derived microparticles also contribute to transferring cytokines and regulatory microRNAs that amplify tumor-associated inflammation and metastatic potential [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These processes have been reported to accelerate thrombo-inflammation and correlate with poor outcomes in colorectal and other cancer types [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite developing multiple proposed indices, direct comparative analyses determining optimal prognostic combinations remain limited. This study systematically evaluated various blood parameter combinations to assess their relative prognostic performance, with particular focus on platelet-monocyte-lymphocyte interactions reflected in the platelet immune index (PII).\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included patients who underwent surgical resection of primary colon cancer at Seoul National University Hospital between January 2008 and December 2015. The inclusion criteria were: histopathologically confirmed adenocarcinoma, CBC within one week before surgery, and complete clinicopathologic data. Patients were excluded if they had a history of another malignancy, evidence of active infection before surgery, endoscopic resection as the primary treatment, perioperative mortality within 30 days, or incomplete clinical or laboratory records. After applying these criteria, the discovery cohort comprised 2,993 patients.\u003c/p\u003e \u003cp\u003eExternal validation used data of 1,124 patients meeting identical criteria at the National Cancer Center, Korea. Institutional Review Board approval was obtained at both participating institutions; the requirement for informed consent was waived due to the retrospective design of the study.\u003c/p\u003e \u003cp\u003ePostoperative surveillance was performed every 3\u0026ndash;6 months during the first 5 years and annually thereafter. Each follow-up visit included physical examination, chest radiography, and measurement of serum carcinoembryonic antigen (CEA). Abdominopelvic computed tomography was routinely performed every six months; moreover, colonoscopy was scheduled every one to two years. Recurrence or disease progression was confirmed through radiologic imaging, histopathologic assessment, or both.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of immune-inflammation biomarkers\u003c/h3\u003e\n\u003cp\u003eFour hematologic variables\u0026mdash;platelet, neutrophil, monocyte, and lymphocyte counts\u0026mdash;were considered for biomarker construction. Candidate indices were generated by combining these features using multiplication and division. In this framework, platelets, neutrophils, and monocytes were regarded as pro-tumorigenic, whereas lymphocytes were considered anti-tumorigenic and placed in the denominator of composite indices.\u003c/p\u003e \u003cp\u003eConsidering dual-parameter indices, the NLR was defined as neutrophil count / lymphocyte count; the PLR as platelet count / lymphocyte count; and LMR as lymphocyte count / monocyte count. Based on previous literature, cutoff values were set at 5.0, 150.0, and 3.0 for NLR, PLR, and LMR, respectively.\u003c/p\u003e \u003cp\u003eWith respect to indices incorporating three parameters, the PII was calculated as (platelet count \u0026times; monocyte count) / lymphocyte count, each expressed per 1,000/mm\u0026sup3;. Optimal cutoff thresholds were determined using maximally selected rank statistics for progression-free survival (PFS), with a value\u0026thinsp;\u0026lt;\u0026thinsp;80 classified as low PII. The SII was calculated as (neutrophil count \u0026times; platelet count) / lymphocyte count, with \u0026lt;\u0026thinsp;730 defining low SII score. The neutrophil\u0026ndash;monocyte-to-lymphocyte index was calculated as (neutrophil count \u0026times; monocyte count) / lymphocyte count, with a cutoff of 510 determined by the same method.\u003c/p\u003e \u003cp\u003eIn regard to indices incorporating four variables, the PIV was calculated as (neutrophil count \u0026times; platelet count \u0026times; monocyte count) / lymphocyte count. The cutoff for PIV was set at 510, again based on maximally selected rank statistics for PFS.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eOverall survival (OS) was defined as the interval from surgery to death from any cause, while PFS was defined as the interval from surgery to disease progression or death. To compare the prognostic performance of all indices, we employed a generalized boosted regression model (GBM), a machine learning approach that constructs sequential decision trees to minimize prediction error and quantify the relative influence of each predictor. GBM identified the index with the highest prognostic relevance for OS and PFS.\u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival curves were used for illustrating differences between the groups; the log-rank test was applied to assess statistical significance. Variables associated with OS or PFS at a threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from univariate analyses were fitted in multivariable Cox proportional hazards models. Statistical analyses were performed using SPSS Statistics for Windows, version 29.0 (IBM Corp., Armonk, NY, USA) and R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFeature importance of biomarkers using GBM analysis\u003c/h2\u003e \u003cp\u003eGBM was used for assessing the relative prognostic contribution of different biomarker combinations. When analyzed as continuous variables with OS and PFS as endpoints, the PII demonstrated the strongest influence on both outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). When dichotomized using respective cutoffs, the PII retained the highest relative importance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathologic characteristics\u003c/h2\u003e \u003cp\u003ePreoperative PII values ranged between 5.29 and 712.96, with a median value of 63.44. Based on the optimal cutoff of 80.0 determined using maximally selected rank statistics for PFS, the patients were stratified into low PII (n\u0026thinsp;=\u0026thinsp;1,951, 65.2%) and high PII values (n\u0026thinsp;=\u0026thinsp;1,042, 34.8%) groups. The patients in the high PII were significantly older (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), more often male participants (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), had lower body mass index (BMI; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher American Society of Anesthesiologists (ASA) scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), more right-sided tumors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), elevated CEA levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher histologic grade (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), advanced TNM stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and increased lymphovascular and perineural invasion (both, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although the patients in the high PII group received adjuvant chemotherapy more frequently (73.2% vs. 68.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), they were possibly selected based on higher-risk features. All study participants were ethnically Asian (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathologic characteristics according to the Platelet Immune Index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow PII (n\u0026thinsp;=\u0026thinsp;1951)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh PII (n\u0026thinsp;=\u0026thinsp;1042)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.87 (23\u0026ndash;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.28 (17\u0026ndash;93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1093 (56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e652 (62.6%)\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e858 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e390 (37.4%)\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\u003eBMI (kg/m2)\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\u003e\u0026le;\u0026thinsp;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1346 (69.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e794 (76.3%)\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\u0026gt;\u0026thinsp;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e604 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246 (23.7%)\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\u003eASA score\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e774 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e325 (31.5%)\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1087 (56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e626 (60.7%)\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81 (7.8%)\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\u003eLocation\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\u003eRight-sided colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e675 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e450 (43.2%)\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\u003eLeft-sided colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1267 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e583 (56.0%)\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\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (0.9%)\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1613 (82.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e682 (65.5%)\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\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e338 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360 (34.5%)\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\u003eHistologic grade\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\u003eWell/moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1831 (93.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e917 (88.0%)\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/undifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (12.0%)\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 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\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e352 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78 (7.5%)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e649 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e341 (32.7%)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e715 (36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e315 (30.2%)\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\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e235 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308 (29.6%)\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\u003eLVI (present)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e616 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e471 (45.2%)\u003c/p\u003e \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\u003ePNI (present)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e578 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e415 (39.9%)\u003c/p\u003e \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\u003eAC (performed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1345 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e763 (73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviation: PII, platelet immune index; BMI, body mass index; ASA, American Society of Anesthesiologists; CEA, carcinoembryonic antigen; LVI, lymphovascular invasion; PNI, perineural invasion; AC, adjuvant chemotherapy\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSurvival Outcomes\u003c/h3\u003e\n\u003cp\u003eThe patients with elevated PII scores experienced significantly worse long-term outcomes. The 5-year OS was 64.3% in the high PII versus 84.8% in low PII groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The 5-year PFS was 52.6% versus 76.2%, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStage-stratified analyses showed prognostic discrimination across all TNM stages; however, the absolute differences varied. In patients with stage I disease, the 5-year OS difference was 7.0% (88.2% vs. 95.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), while PFS differed by 5.5% (85.3% vs. 90.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Greater absolute differences were observed in stages II-IV (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb\u0026ndash;d and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u0026ndash;d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMultivariable Analysis\u003c/h3\u003e\n\u003cp\u003eUnivariate analysis of OS identified multiple adverse prognostic factors, including advanced age (\u0026ge;\u0026thinsp;60 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), male sex (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), higher ASA score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reduced BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), elevated CEA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher tumor grade (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), advanced disease stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lympho-vascular and perineural invasion (both, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), high PII values (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and receipt of adjuvant chemotherapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Using multivariable analysis, high PII score remained an independent predictor of poor OS (hazard ratio [HR], 1.474; 95% confidence interval [CI], 1.269\u0026ndash;1.714; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariable Cox\u0026rsquo;s hazards regression models for overall survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.487 (1.274\u0026ndash;1.734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.465 (1.244\u0026ndash;1.724)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eSex\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 \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\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.803 (0.693\u0026ndash;0.930)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.857 (0.738\u0026ndash;0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.373 (1.173\u0026ndash;1.608)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.345 (1.142\u0026ndash;1.584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.541 (1.945\u0026ndash;3.319)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.989 (1.503\u0026ndash;2.633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eBMI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.763 (0.645\u0026ndash;0.901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.859 (0.726\u0026ndash;1.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.877 (3.363\u0026ndash;4.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.634 (1.390\u0026ndash;1.922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHistologic grade\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.675 (2.199\u0026ndash;3.256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.744 (1.424\u0026ndash;2.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.461 (1.011\u0026ndash;2.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.767 (1.194\u0026ndash;2.614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.526 (1.781\u0026ndash;3.583)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.340 (2.237\u0026ndash;4.985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.908 (12.060-23.706)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.028 (10.726\u0026ndash;23.950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eLVI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot identified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.968 (2.572\u0026ndash;3.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.712 (1.454\u0026ndash;2.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePNI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot identified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.439 (2.114\u0026ndash;2.815)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.291 (1.096\u0026ndash;1.521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.254 (1.066\u0026ndash;1.476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.453 (0.373\u0026ndash;0.551)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePII\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow PII(\u0026lt;\u0026thinsp;80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh PII(\u0026ge;\u0026thinsp;80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.655 (2.303\u0026ndash;3.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.474 (1.269\u0026ndash;1.714)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: ASA, American Society of Anesthesiologists; BMI, body mass index; CEA, carcinoembryonic antigen; CI, confidence interval; LVI, lymphovascular invasion; PNI, perineural invasion; AC, adjuvant chemotherapy; PII, platelet immune index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilarly, univariate analysis of PFS demonstrated that advanced age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), male sex (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), elevated ASA score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), elevated CEA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher histologic grade (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), advanced TNM stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lympho-vascular and perineural invasion (both, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), adjuvant chemotherapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and high PII (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with inferior outcomes. Multivariate modeling confirmed high PII as an independent adverse prognostic factor for PFS (HR, 1.422; 95% CI, 1.240\u0026ndash;1.630; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariable Cox\u0026rsquo;s hazards regression models for progression survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.335 (1.164\u0026ndash;1.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.295 (1.119\u0026ndash;1.499)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eSex\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 \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\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.843 (0.738\u0026ndash;0.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.865 (0.754\u0026ndash;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.254 (1.089\u0026ndash;1.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.103 (0.953\u0026ndash;1.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.125 (1.654\u0026ndash;2.729)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.446 (1.113\u0026ndash;1.878)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.757 (0.651\u0026ndash;0.881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.855 (0.733\u0026ndash;0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.708 (3.255\u0026ndash;4.223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.872 (1.611\u0026ndash;2.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHistologic grade\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.224 (1.844\u0026ndash;2.683)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.510 (1.245\u0026ndash;1.831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.727 (1.244\u0026ndash;2.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.047 (1.433\u0026ndash;2.924)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.836 (2.070\u0026ndash;3.886)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.643 (2.521\u0026ndash;5.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.757 (8.638\u0026ndash;16.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.443 (6.484\u0026ndash;13.752)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eLVI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot identified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.657 (2.334\u0026ndash;3.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.530 (1.322\u0026ndash;1.771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePNI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot identified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.629 (2.308\u0026ndash;2.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.474 (1.271\u0026ndash;1.711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eAC\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.314 (1.128\u0026ndash;1.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.476 (0.396\u0026ndash;0.572)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePII\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow PII(\u0026lt;\u0026thinsp;80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh PII(\u0026ge;\u0026thinsp;80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.352 (2.067\u0026ndash;2.675)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.422 (1.240\u0026ndash;1.630)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: ASA, American Society of Anesthesiologists; BMI, body mass index; CEA, carcinoembryonic antigen; CI, confidence interval; LVI, lymphovascular invasion; PNI, perineural invasion; AC, adjuvant chemotherapy; PII, platelet immune index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing subgroup analysis of data of patients with stage IIA (T3N0M0), high PII values independently predicted reduced OS (HR, 1.797; 95% CI, 1.203\u0026ndash;2.682; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and PFS (HR, 1.826; 95% CI, 1.279\u0026ndash;2.606; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Notably, the conventional biomarker CEA was not a significant predictor of OS from univariate analysis (HR, 1.416; 95% CI, 0.867\u0026ndash;2.314; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.164) and of PFS after adjustment for PII (HR, 1.360; 95% CI, 0.894\u0026ndash;2.068; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.151) (Additional files 1 and 2).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExternal Validation\u003c/h2\u003e \u003cp\u003eIn the validation cohort, high PII score was associated with worse OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and PFS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00038) compared with low PII (Additional file 3), confirming prognostic associations in an independent population.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically compared multiple immune-inflammatory indices and demonstrated that the PII, incorporating platelet, monocyte, and lymphocyte counts, showed strong prognostic associations in patients with colon cancer. By integrating platelet, monocyte, and lymphocyte counts, the PII encapsulates both tumor-promoting and tumor-suppressive mechanisms into a single composite index. Among the array of immune-inflammatory biomarkers analyzed, the PII demonstrated the greatest predictive weight for OS and PFS, as confirmed by GBM.\u003c/p\u003e \u003cp\u003eThe prognostic value of PII can be explained by the distinct biological functions of its cellular components. Platelets enhance tumor growth and dissemination by supporting angiogenesis, shielding malignant cells from immune recognition, and facilitating metastatic colonization [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Monocytes recruited to the tumor milieu differentiate into tumor-associated macrophages (TAMs), particularly of the M2 phenotype, which foster tumor invasion, vascularization, and immune evasion. Conversely, lymphocytes\u0026mdash;specifically T-cell subsets\u0026mdash;constitute the principal mediators of antitumor immunity; a reduced lymphocyte count often signifies systemic immunosuppression and correlates with disease advancement and poor survival [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Collectively, a high PII value reflects a biologically adverse immune-inflammatory state characterized by augmented tumor-supporting activity and weakened immune defense.\u003c/p\u003e \u003cp\u003eOur findings further support the role of platelets and monocytes as key determinants of prognosis in patients with colorectal cancer. Elevated levels of these circulating cells are possibly a reflection of enhanced platelet\u0026ndash;monocyte aggregate formation, which fosters a pro-inflammatory and immunosuppressive tumor microenvironment. Molecular changes in activated platelets, such as upregulated P-selectin, CXCL4, and PD-L1 expression, appear to facilitate monocyte recruitment and their differentiation into immunosuppressive TAMs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These mechanisms provide a plausible explanation for the association between high platelet and monocyte counts and inferior survival outcomes observed in our study [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherapeutically, disrupting platelet\u0026ndash;monocyte interactions represent a promising avenue. Preclinical data suggest that antiplatelet agents (e.g., aspirin, ticagrelor) may attenuate aggregate formation and chemokine release, thereby reducing metastatic spread. Moreover, combination strategies integrating antiplatelet therapy with immune checkpoint inhibition could counteract TAM-mediated immune suppression, potentially improving treatment efficacy [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Future studies should validate these findings in larger clinical cohorts and investigate novel inhibitors targeting P-selectin or CLEC-2 pathways to refine personalized treatment approaches.\u003c/p\u003e \u003cp\u003eCirculating lymphocytes, particularly diverse T-cell subsets, play a pivotal role in antitumor immune surveillance and serve as important determinants of prognosis in patients with colorectal cancer [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Multiple studies have consistently shown that individuals with colon carcinoma present with markedly lower absolute lymphocyte counts compared with age- and sex-matched healthy populations. Alterations in the distribution of peripheral lymphocyte subsets reflect a measurable consequence of tumor-driven immunosuppressive mechanisms. Moreover, diminished lymphocyte levels in the circulation have been strongly correlated with adverse clinicopathological characteristics, increased risk of recurrence, and inferior survival outcomes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral hematologic indices, such as the NLR, PLR, LMR, SII, and PIV, have been validated as prognostic markers for colorectal cancer [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, our findings provide direct comparative evidence that the PII outperforms these established indices in survival prediction. Importantly, its prognostic significance was consistent across all TNM stages, including early-stage disease, suggesting that the PII could serve as a supplementary tool for refining stratification beyond conventional staging systems.\u003c/p\u003e \u003cp\u003eTreatment decision-making in patients with colon cancer stage II remains a clinical challenge, particularly regarding the benefit of adjuvant chemotherapy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our subgroup analysis demonstrated that the PII was a stronger prognostic factor compared with conventional variables such as CEA levels and histological grade in stage II and T3N0 disease. This implies that the PII could aid in identifying patients who may benefit from additional therapy despite being classified within a relatively favorable stage. Since the PII is derived from routinely obtained preoperative CBC parameters, it offers an inexpensive, easily applicable, and globally accessible biomarker. Patients with elevated PII values may merit intensified postoperative surveillance and consideration of adjuvant strategies that extend beyond current standard recommendations.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. The retrospective and single-institution design of the discovery cohort may introduce selection bias; however, external validation at a national cancer center reinforces the robustness of our findings. Approximately one-third of eligible patients were excluded owing to incomplete laboratory data, which might limit the generalizability of the results. Furthermore, the cutoff value for the PII was derived from our cohort and might not be universally applicable; validation in independent populations is required. Although the PII has demonstrated strong prognostic significance, prospective studies and interventional trials are necessary for confirming its role in guiding therapeutic decision-making.\u003c/p\u003e \u003cp\u003eFuture investigations should also explore integration of the PII with molecular and immunogenomic profiles, thereby enabling its incorporation into multidimensional prognostic frameworks tailored for precision oncology.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe preoperative PII was a powerful and independent prognostic biomarker for colon cancer. Its ability to stratify prognosis across all disease stages, including early-stage tumors, highlights its potential for clinical application. Given its simplicity, biological rationale, and broad accessibility, the PII warrants further validation in multicenter prospective cohorts and may ultimately become a key component of individualized treatment strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Society of Anesthesiologists\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomplete blood count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecarcinoembryonic antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egeneralized boosted regression model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elymphocyte-to-monocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eplatelet immune index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epan-immune-inflammation value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eplatelet-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystemic immune-inflammation index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor-associated macrophages\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board approval was obtained at both participating institutions, including approval from the Seoul National University Hospital Institutional Review Board (Seoul National University College of Medicine / Seoul National University Hospital) and the National Cancer Center Institutional Review Board. The requirement for informed consent was waived due to the retrospective design of the study. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and national research committees and with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eInterests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by a grant from the National R\u0026amp;D Program for Cancer Control, Ministry of Health \u0026amp; Welfare, Republic of Korea (RS-2023-CC140354) and a grant of Korean ARPA-H Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health \u0026amp; Welfare, Republic of Korea (grant number: RS-2024-00512405, RS-2460002489).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHJK, JWP: Conceptualization, design, structure, and idea. HJK, DWL: Acquisition of data and analysis and interpretation of data. HJK: Writing of the manuscript. JWP, TA: Revising of the manuscript. HJK, JWP, HKL, MJK, RS, DWL, SCP, DAS, JHO, TA, SBR, SYJ, KJP: Review. HJK, JWP, HKL, MJK, RS, DWL, SCP, DAS, JHO, TA, SBR, SYJ, KJP: Final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkugawa Y, Toiyama Y, Yamamoto A, Shigemori T, Ide S, Kitajima T, et al. Lymphocyte-C-reactive protein ratio as promising new marker for predicting surgical and oncological outcomes in colorectal cancer. Ann Surg. 2020;272:342\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalsh SR, Cook EJ, Goulder F, Justin TA, Keeling NJ. Neutrophil-lymphocyte ratio as a prognostic factor in colorectal cancer. J Surg Oncol. 2005;91:181\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalazun KJ, Aldoori A, Malik HZ, Al-Mukhtar A, Prasad KR, Toogood GJ, et al. Elevated preoperative neutrophil to lymphocyte ratio predicts survival following hepatic resection for colorectal liver metastases. Eur J Surg Oncol. 2008;34:55\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkman JL, Shiao SL. Impact of the immune system and immunotherapy in colorectal cancer. J Gastrointest Oncol. 2015;6:208\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen JH, Zhai ET, Yuan YJ, Wu KM, Xu JB, Peng JJ, et al. Systemic immune-inflammation index for predicting prognosis of colorectal cancer. World J Gastroenterol. 2017;23:6261\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001;357:539\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 2014;15:e493\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitamura T, Qian B-Z, Pollard JW. Immune cell promotion of metastasis. Nat Rev Immunol. 2015;15:73\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShibutani M, Maeda K, Nagahara H, Fukuoka T, Nakao S, Matsutani S, et al. The peripheral monocyte count is associated with the density of tumor-associated macrophages in the tumor microenvironment of colorectal cancer: a retrospective study. BMC Cancer. 2017;17:404.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaul ME, Fridlender ZG. Tumour-associated neutrophils in patients with cancer. Nat Rev Clin Oncol. 2019;16:601\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGay LJ, Felding-Habermann B. Contribution of platelets to tumour metastasis. Nat Rev Cancer. 2011;11:123\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaertner F, Massberg S. Patrolling the vascular borders: platelets in immunity to infection and cancer. Nat Rev Immunol. 2019;19:747\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoch M, Beckhove P, Op den Winkel J, Autenrieth D, Wagner P, Nummer D, et al. Tumor infiltrating T lymphocytes in colorectal cancer: tumor-selective activation and cytotoxic activity in situ. Ann Surg. 2006;244:986\u0026ndash;92. discussion 992.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou ZY, Liu HL, Ning N, Li SY, Du XH, Li R. Clinical significance of pre-operative neutrophil lymphocyte ratio and platelet lymphocyte ratio as prognostic factors for patients with colorectal cancer. Oncol Lett. 2016;11:2241\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia LJ, Li W, Zhai JC, Yan CW, Chen JB, Yang H. Significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio and prognostic nutritional index for predicting clinical outcomes in T1-2 rectal cancer. BMC Cancer. 2020;20:208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorti F, Lonardi S, Intini R, Salati M, Fenocchio E, Belli C, et al. The Pan-Immune-Inflammation Value in microsatellite instability-high metastatic colorectal cancer patients treated with immune checkpoint inhibitors. Eur J Cancer. 2021;150:155\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuc\u0026agrave; G, Guarini V, Antoniotti C, Morano F, Moretto R, Corallo S, et al. The Pan-Immune-Inflammation Value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the Valentino and TRIBE first-line trials. Br J Cancer. 2020;123:403\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoiber D, Assinger A. Platelet-leukocyte interplay in cancer development and progression. Cells. 2020;9:855.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Zhou J, Liu Z, Wu T, Li S, Zhang Y, et al. Tumor cell-induced platelet aggregation accelerates hematogenous metastasis of malignant melanoma by triggering macrophage recruitment. J Exp Clin Cancer Res. 2023;42:277.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandel J, Casari M, Stepanyan M, Martyanov A, Deppermann C. Beyond hemostasis: platelet innate immune interactions and thromboinflammation. Int J Mol Sci. 2022;23:3868.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRolling CC, Barrett TJ, Berger JS. Platelet-monocyte aggregates: molecular mediators of thromboinflammation. Front Cardiovasc Med. 2023;10:960398.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGan J, Zhang X, Guo J. The role of platelets in tumor immune evasion and metastasis: mechanisms and therapeutic implications. Cancer Cell Int. 2025;25:258.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWojtukiewicz MZ, Sierko E, Hempel D, Tucker SC, Honn KV. Platelets and cancer angiogenesis nexus. Cancer Metastasis Rev. 2017;36:249\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao XD, Zhang H, Xu ZS, Cheng H, Shen W, Wang XP. Poor prognostic role of the pretreatment platelet counts in colorectal cancer: a meta-analysis. Med (Baltim). 2018;97:e10831.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Tian T, Zhang J. Tumor-associated macrophages (TAMs) in colorectal cancer (CRC): from mechanism to therapy and prognosis. Int J Mol Sci. 2021;22:8470.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu S, Zou Z, Li H, Zou G, Li Z, Xu J, et al. The preoperative peripheral blood monocyte count is associated with liver metastasis and overall survival in colorectal cancer patients. PLoS ONE. 2016;11:e0157486.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInagaki K, Kunisho S, Takigawa H, Yuge R, Oka S, Tanaka S, et al. Role of tumor-associated macrophages at the invasive front in human colorectal cancer progression. Cancer Sci. 2021;112:2692\u0026ndash;704.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlesinger M. Role of platelets and platelet receptors in cancer metastasis. J Hematol Oncol. 2018;11:125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaidhauser J, Nerlinger P, Arndt TT, Schiele S, Sommer F, Wolf S, et al. Alterations of circulating lymphocyte subsets in patients with colorectal carcinoma. Cancer Immunol Immunother. 2022;71:1937\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcMillan DC, Fyffe GD, Wotherspoon HA, Cooke TG, McArdle CS. Prospective study of circulating T-lymphocyte subpopulations and disease progression in colorectal cancer. Dis Colon Rectum. 1997;40:1068\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Huang W, Wu Y, Luo Y, Wu B, Cheng J, et al. Prognostic role of pretreatment blood lymphocyte count in patients with solid tumors: a systematic review and meta-analysis. Cancer Cell Int. 2020;20:15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKannarkatt J, Joseph J, Kurniali PC, Al-Janadi A, Hrinczenko B. Adjuvant chemotherapy for Stage II colon cancer: A clinical dilemma. J Oncol Pract. 2017;13:233\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"colon cancer, immune, inflammation, platelet immune index, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8307248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8307248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMultiple immune-inflammatory blood indices have been proposed for colon cancer prognostication; however, comparative evaluations of these indices remain limited. We systematically compared these indices and assessed the prognostic performance of a platelet-monocyte-lymphocyte combination.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn a discovery cohort of 2,993 patients, we compared multiple immune-inflammatory indices constructed from platelet, neutrophil, monocyte, and lymphocyte counts. The index with the highest relative relevance in a generalized boosted regression model (GBM) was selected. An independent cohort of 1,124 patients was used for validation. The outcomes included overall survival (OS) and progression-free survival (PFS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eRegarding GBM analysis, the platelet immune index (PII), calculated as (platelet count \u0026times; monocyte count) / lymphocyte count, showed the highest relative influence for survival prediction among the tested indices. Using a data-derived cutoff of 80.0, high PII score (\u0026ge;\u0026thinsp;80.0) was associated with worse 5-year OS (64.3% vs. 84.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PFS (52.6% vs. 76.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Using multivariable analysis adjusting for established prognostic factors, high PII values remained independently associated with worse OS (hazard ratio [HR], 1.474; 95% confidence interval [CI], 1.269\u0026ndash;1.714) and PFS (HR, 1.422; 95% CI, 1.240\u0026ndash;1.630). The findings were validated in the independent cohort.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePreoperative PII demonstrated independent prognostic value in patients with colon cancer and might aid in risk stratification, particularly within the same TNM stage. High PII score warrants closer postoperative surveillance and consideration in treatment planning.\u003c/p\u003e","manuscriptTitle":"Preoperative platelet immune index as a prognostic marker for colon cancer: A comparative analysis of blood-derived indices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:34:37","doi":"10.21203/rs.3.rs-8307248/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-24T16:26:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T15:08:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-15T10:24:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T11:15:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274666906945354743698703245126758121597","date":"2026-01-14T11:04:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43991976265749080504738409601422073060","date":"2026-01-12T12:03:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257836894397654247948727405628160921256","date":"2026-01-12T11:21:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220280528026727036845759907189579934536","date":"2026-01-12T07:40:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-06T18:05:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T08:06:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-12T07:13:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-11T08:27:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-12-11T08:19:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78bc8118-140a-4e14-9558-33bda8a3b83c","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T05:34:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 05:34:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8307248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8307248","identity":"rs-8307248","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}