Predictive efficacy of the advanced lung cancer inflammation index among patients with intrahepatic cholangiocarcinoma

Predictive efficacy of the advanced lung cancer inflammation index among patients with intrahepatic cholangiocarcinoma | 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 Predictive efficacy of the advanced lung cancer inflammation index among patients with intrahepatic cholangiocarcinoma Qizhu Lin, Jun Fu, Tingfeng Huang, Hongzhi Liu, Ruilin Fan, Yongyi Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6452875/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The advanced lung cancer inflammation index (ALI), which combines nutritional and inflammatory indicators, was recently identified as a promising prognostic biomarker. This study evaluates the predictive efficacy of ALI in patients undergoing curative-intent surgery for intrahepatic cholangiocarcinoma (ICC). Methods: Patients who underwent curative-intent surgery for ICC were identified from a large multi-institutional database. Patients were categorized into "low ALI" and "high ALI" groups, and propensity score matching (PSM) was used to minimize intergroup differences. A time-dependent receiver operating characteristic curve (time-dependent ROC) and C-index were calculated to compare the prognostic performance of ALI with other biomarkers. Multivariate Cox regression analysis was conducted to identify independent predictors associated with overall survival (OS). Based on these predictors, a dynamic nomogram was developed to predict OS in ICC patients, and its performance was evaluated using an internal validation dataset. Results: Among 691 patients, 194 (28.08%) were categorized as having low ALI (≤ 27.6. Patients with low ALI had a worse 5-year OS rate (20.2% vs. 45.7%, p < 0.001), and this difference remained significant after PSM (20.2% vs. 42.3%, p < 0.001). Multivariate analysis identified low ALI as an independent predictor of increased mortality both before (hazard ratio [HR] 1.54, 95% CI: 1.25 - 1.90, p < 0.001) and after PSM (HR 1.59, 95% CI: 1.24 - 2.04, p < 0.001). The time-dependent AUC and C-index analyses indicated that ALI (C-index: 0.603) had the best predictive ability for OS in patients with ICC. The dynamic nomogram based on ALI demonstrated excellent predictive accuracy, validated through calibration curves, time-dependent ROC curves, and decision curve analysis. Conclusion: ALI is an independent predictor of OS among patients undergoing curative-intent surgery for ICC. The prognostic ability of the ALI is superior to the other nutrition- /inflammation-related biomarkers. ALI should be incorporated into predictive models to enhance prognostic stratification. advanced lung cancer inflammatory index intrahepatic cholangiocarcinoma inflammation biomarkers prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver cancer following hepatocellular carcinoma (HCC), accounts for about 20% of all primary liver tumors. 1 Although the current incidence of ICC in Asia is significantly higher than that in Western countries, it is important to highlight that the global incidence of ICC has been rising in recent years, with an increase of more than 140% in the United States. 2,3 Radical resection remains the only potential cure for ICC, but only 20%-30% of patients present with resectable disease. 4 Even after curative-intent surgical resection, the 5-year overall survival (OS) is only 25%-40%, with 50%-70% facing tumor recurrence. 5 The most commonly used staging system for ICC is the American Joint Committee on Cancer (AJCC) staging system. 6 Several studies have evaluated its prognostic value, but the results remain controversial and insufficient to support treatment decisions. 7,8 Therefore, there is an urgent need to identify novel prognostic biomarkers that could enable more personalized treatment and surveillance strategies. Recent studies have demonstrated that nutritional status and inflammation play significant roles in tumor initiation and progression. 9-11 The predictive value of several inflammatory- and nutrition-related biomarkers based on preoperative blood indexes has been explored. Specifically, some biomarkers reflect the inflammatory status of patients, such as neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), all of which have been demonstrated to be associated with long-term survival in patients with ICC. 12-14 Additionally, several biomarkers that focus on the nutritional status of patients, such as the Albumin-Bilirubin (ALBI) grade, prognostic nutritional index (PNI), and gamma-glutamyl transpeptidase (GGT)-to-platelet ratio (GPR), all of which have also been shown to be valuable prognostic markers. 15-17 Recently, the advanced lung cancer inflammation index (ALI) has emerged as a promising biomarker of survival outcomes in cancers due to its incorporation of nutritional and inflammatory indicators. ALI was first established by Jafri et al. in 2013 as a prognostic index for non-small cell lung cancer, which is calculated as body mass index (BMI) × albumin / NLR. 18 More recently, ALI has been demonstrated to have prognostic significance as a biomarker in various non-lung cancers including colorectal cancer, gastric tumors, and HCC. 19-21 Although G. Catalano et al. have evaluated the impact of ALI on OS among patients with ICC, they did not further apply ALI to predict long-term survival. 22 Therefore, the predictive value of ALI in ICC patients after surgery requires further investigation. This study aims to investigate the prognostic significance of ALI in ICC patients after surgery. We compared the predictive performance of ALI with 11 other validated inflammatory biomarkers in a cohort of ICC patients. In addition, we developed and validated a nomogram model based on ALI to better stratify patient prognosis and personalize treatment. Materials and methods Study population and selection criteria We retrospectively analyzed the clinical data of 691 patients with ICC who underwent curative-intent surgery were collected from the Primary Liver Cancer Big Data in Fujian province. The patients in this study all provided informed consent prior to surgery, and strict adherence was maintained to the guidelines of the Declaration of Helsinki. Ethical approval was obtained from Ethics Committee of the Mengchao Hepatobiliary Hospital of Fujian Medical University (approval number 2022_077_01). The inclusion criteria were as follows: (1) patients who underwent R0 resection, characterized by complete tumor removal with a negative microscopic margin; (2) postoperative pathological confirmation of ICC; (3) no history of prior anticancer treatment. The exclusion criteria were as follows: (1) preoperative extrahepatic metastases; (2) incomplete clinicopathological data; (3) recurrence or death within 30 days postoperatively, or loss to follow-up shortly after surgery. Variables and outcomes of interest Demographic and clinicopathologic data collected included age, gender, hepatitis B virus (HBV) infection, American Society of Anesthesiologists Physical Status Classification (ASA) score, BMI, and Child-Pugh grade. Hematological parameters included platelet count (PLT), red and white blood cell counts, prothrombin time (PT), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), gamma-glutamyl transpeptidase (GGT), Total Bilirubin, Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), and albumin levels. Tumor number and the size of the largest lesion were assessed preoperatively by CT or MRI. Operative details included the type of hepatectomy, lymph node dissection, margin status, intraoperative blood loss, and transfusion. Additionally, data were collected on histologic tumor grade, presence of macrovascular and microvascular invasion (MVI), surgical margin width, satellite nodules, length of stay (LOS), postoperative 30-day complications, and N stage, according to the 8th edition of the American Joint Committee on Cancer (AJCC) Staging Manual. 6 Major hepatectomy was defined as resection of three or more Couinaud segments according to the consensus of the Brisbane 2000 system. 23 Postoperative complications occurring within 30 days were classified according to the Clavien-dindo classification. 24 Microvascular invasion (MVI) was defined as the presence of intraparenchymal vascular involvement identified on histological examination. 25 Macrovascular invasion was defined as the involvement of primary and secondary branches of the portal vein or hepatic artery, or the invasion of one or more of the three major hepatic veins. 6 The formula for tumor burden score (TBS) computation follows the Pythagorean theorem: TBS 2 = (size of the largest lesion) 2 + (number of tumors) 2 . 26 The optimal cutoff value for TBS (5.00) was determined by X-tile software. The primary outcome of interest was overall survival (OS), defined as the time from the date of curative resection to the date of death or the last follow-up. nutrition- and inflammation-related biomarkers All patients underwent routine preoperative laboratory examinations (routine blood tests, platelet count, neutrophil count, lymphocyte cell count, ALT, AST, tumor serum markers, such as CA19-9 and CEA). By reviewing previous studies, we selected 12 nutrition- and inflammation-related biomarkers for our research, including: NLR, ALI, ALBI, PLR, PNI, GPR, GLR (GGT to lymphocyte ratio), GAR (GGT to albumin ratio), AAPR (Albumin-to-alkaline phosphatase ratio), ANRI (aspartate aminotransferase-to-neutrophil ratio index), APRI (aspartate aminotransferase-to-platelet ratio index), FIB-4 (fibrosis-4 index). The calculation methods for each biomarker combination are shown in Table S1. The cutoff values for ALBI (≤ -2.60 [ALBI grade 1], -2.60 to -1.39 [ALBI grade2], and ≥ -1.39 [ALBI grade 3]) and FIB-4(1.3) are based on previous studies. 15,27 X-tile software was employed to determine the optimal cutoff values for the remaining biomarkers: 2.2 (NLR), 27.6 (ALI), 140.13 (PLR), 46.65 (PNI), 1.51 (GPR), 44.0 (GLR), 5.49 (GAR), 0.6 (AAPR), 4.63 (ANRI), and 0.1 (APRI). Statistical analysis Statistical analyses were performed using R software, version 4.2.2 (http://www.r-project.org). Continuous and categorical variables were reported as medians (interquartile range [IQR]) and frequencies (%), respectively. Categorical variables were compared using either the chi-squared test or Fisher’s exact test, as appropriate, and continuous variables were compared using the Kruskal-Wallis H test. The calculation of OS was performed using the Kaplan-Meier method, and comparisons were made using the log-rank test. Additionally, propensity score matching (PSM) analysis was performed to eliminate intergroup differences in baseline parameters using a 1:1 nearest matching method implemented through the “MatchIt” package. The predictive accuracy of each indicator was assessed using the C-index and time-dependent ROC. The discriminative ability of the model was evaluated using the area under the receiver operating characteristic curve (AUC). Univariate and multivariable Cox regression analyses were carried out to identify independent risk factors for OS. Variables with a significance level of P<0.05 in the univariable analysis were incorporated into the multivariable Cox regression model using forward stepwise variable selection. All reported P-values were two-sided, with values less than 0.05 considered statistically significant. Results Characteristics of the patients A total of 691 patients were included in the study. The patient demographics are shown in Table 1. In the overall cohort, the median age of the participants was 55.0 years, and males were prevalent (n = 472,68.3%). Approximately 48.8% of the patients tested positive for HBsAg. In our population, the median size of the largest tumor was 5.6 cm, and patients with multiple tumors account for only 7.7%. The distribution of TNM stage were as follows: 128 (18.5%) with stage I; 427 (61.8%) with stage II; 136 (19.7%) with stage III. After curative resection, adjuvant therapy was administered to 32.0% of the patients. Overall, a total of 194 patients were identified as having a low ALI (≤ 27.6), and 497 patients were identified as having a high ALI (> 27.6) (Table 2, left panel). We observed that low ALI was significantly associated with higher tumor burden (low vs high ALI, 7.5 [IQR: 5.3-9.6] vs 5.1[IQR: 4.0-7.2], p < 0.001), higher CA 19-9 (low vs high ALI, 57.9 [IQR: 18.3-626.6] vs 33.2[IQR: 14.8-232.9], p = 0.002), and higher TNM stage (low vs high ALI, n = 61 [31.4%] vs n = 97 [19.5%], p = 0.001). Additionally, we found that patients with low ALI exhibited lower BMI (low vs high ALI, 21.7 [IQR: 19.0-24.2] vs 24.5[IQR: 22.2-27.0], p < 0.001), albumin levels(low vs high ALI, 40.4 [IQR: 38.0-43.1] vs 42.9[IQR: 40.8-45.6], p < 0.001), lymphocyte count(low vs high ALI, 1.2 [IQR: 0.9-1.6] vs 1.7[IQR: 1.4-2.2], p < 0.001), but higher neutrophil count(low vs high ALI, 5.6 [IQR: 4.4-7.7] vs 3.6[IQR: 2.9-4.7], p < 0.001), GGT levels(low vs high ALI, 95.0 [IQR: 49.2-207.5] vs 61.0[IQR: 38.0-120.0], p < 0.001), and platelet count(low vs high ALI, 207 [IQR: 151.0-251.8] vs 189.0[IQR: 144.0-230.0], p = 0.011). To mitigate baseline bias, we performed PSM to balance these unequal factors, resulting in 194 patients in each group. Following the matching process, there were no statistically significant differences observed in the clinicopathological parameters between the two groups (Table 2, right panel). Impact of ALI on OS At the end of the last follow-up, 418 patients (60.5%) had died, with the median OS of 24.64 months (95% CI: 21.25 - 27.66). Patients with low ALI exhibited significantly worse 5-year OS compared to those with high ALI (5-year OS: 20.2% vs. 45.7%, p < 0.001, Fig.1A). After PSM, patients in the low ALI group continued to exhibit inferior OS (5-year OS: 20.2% vs. 42.3%, p < 0.001, Fig. 1B). Of note, we further conducted subgroup analyses based on various clinicopathologic features to investigate the prognostic value of ALI in different types of ICC patients. As shown in Fig. 2, our findings demonstrated that ALI maintained its prognostic significance across different subgroups, before and after PSM. In the overall cohort, the multivariate analysis identified low ALI as an independent predictor of increased mortality (low vs. high: hazard ratio [HR] 1.54, 95% CI: 1.25 - 1.90, p < 0.001). Other independent predictors included CA19-9 levels, CEA levels, TNM stage, satellite nodules, TBS, and adjuvant therapy (all P values < 0.05, Table 3). After performing PSM to adjust for potential confounders, we re-evaluated these predictors in the matched cohort. Notably, low ALI remained a significant independent predictor of increased mortality even after adjustment (low vs. high: HR 1.59, 95% CI: 1.24 - 2.04, p < 0.001). Additionally, CA19-9 levels, CEA levels, TNM stage, satellite nodules, TBS, and adjuvant therapy continued to show significant prognostic value in the PSM-adjusted cohort (all p values 0.1 and variance inflation factor (VIF) values < 2, as shown in Table S2). The prognostic ability comparison of the nutrition- and inflammation-related biomarkers A time-dependent ROC curve and C-index were performed to compare the prognostic predictive capacity of 12 nutrition- and inflammation-related biomarkers in the overall cohort. Compared to the other nutrition- and inflammation-related biomarkers, the ALI demonstrated the highest C-index for OS in ICC patients at 1, 3, and 5 years: 0.577 (95% CI, 0.537 - 0.615), 0.607 (95% CI, 0.574 - 0.645), and 0.603 (95% CI, 0.559 - 0.647), respectively (Table 4). Moreover, the ALI exhibited a higher AUC value than the other inflammation/nutrition-based indicators (Fig. 3). Construction and validation of the prognostic nomogram for OS The patients in the overall cohort were randomly divided into a training dataset (n=464) and a validation dataset (n=227) at a 7:3 ratio. No significant differences were observed in the basic clinicopathologic characteristics (Table S3) and survival outcomes (Fig. S1) between the two groups. To improve the predictive accuracy for OS, a nomogram was constructed based on the aforementioned independent variables (Fig. 4). Meanwhile, to facilitate clinical practice, we constructed a web-based dynamic online nomogram, which can be accessed at https://iccnomogramforosbasedonali.shinyapps.io/shiny_nomogram_app/. The time-dependent ROC curves were generated to assess the model’s predictive performance. In the training set, the AUCs for predicting 1-, 3-, and 5-year survival were 0.768, 0.770, and 0.800, respectively (Fig. 5A). Similarly, the AUCs for the 1-, 3-, and 5-year survival predictions in the validation set were 0.692, 0.704, and 0.730, respectively (Fig. 5B). Subsequently, the calibration curves showed high consistency between the predicted and observed OS rates. The standard lines in both the training and validation sets closely aligned with the calibration curves for 1-, 3-, and 5-year OS predictions (Fig. 5C and D). The DCA curves for the nomogram in both the training (Fig. 5E) and validation sets (Fig. 5F) showed higher net benefits than the "All" and "None" strategies for 1-, 3-, and 5-year predictions. These findings highlight the nomogram’s consistent clinical utility and robust prognostic value for ICC patients. Discussion Radical resection is currently the only potentially curative approach for ICC; however, long-term survival following surgery remains unsatisfactory. 28,29 Numerous studies suggest that neoadjuvant and adjuvant therapies offer theoretical benefits in improving post-surgical survival outcomes, including downstaging the primary tumor to facilitate R0 resection and eliminating micrometastatic disease to reduce the risk of recurrence. 30-32 Therefore, performing patient stratification before surgery is crucial for making more precise treatment decisions and improving surgical outcomes. nutrition- and inflammation-related biomarkers have recently been proven to be promising tools for risk stratification in surgical patients. 33-35 In the current study, the impact of the ALI on long-term outcomes was explored and compared with 11 other validated inflammatory biomarkers using a large multi-institutional cohort of patients who underwent curative-intent surgery for ICC. PSM was employed to mitigate the influence of confounding factors. In this study, ALI was identified as an independent predictor of increased mortality and showed superior performance compared to other biomarkers. Furthermore, a dynamic online nomogram based on ALI was developed to facilitate clinical practice, exhibiting excellent AUC values and predictive capabilities. Although the prognostic value of ALI has been validated in various tumors, only one study has explored its impact on postoperative prognosis in ICC patients. 18-21 G. Catalano et al. identified ALI as an independent predictor of OS among ICC patients and compared its predictive accuracy with that of NLR, PLR, and SII. 22 However, this study did not apply ALI to specific prognostic prediction tools, nor did it investigate the predictive efficacy of ali in different subgroups. In this study, we further compared the predictive ability of ALI with that of 11 other biomarkers and validated its predictive capability in different subgroups. we also developed a dynamic nomogram web application based on ALI. In contrast to traditional nomograms that require complex calculations, this web application only requires selecting six data points to predict patient survival probability and generate visual graphs, greatly improving accessibility and convenience. Confounding variables significantly challenge the credibility of retrospective studies. 36 In this study, we ensured the balance of baseline data between the low ALI group and the high ALI group using PSM, and conducted subgroup analyses before and after PSM, which minimized the impact of confounding factors and enhanced the credibility of causal inference. Inflammation plays a crucial role in the development of ICC, which causes cholestasis and resultant cholangiocyte injury. 37,38 Specifically, a proinflammatory cytokine and growth factor-rich environment, along with toxic bile acids, may induce a mitogenic response in normal cholangiocytes, promoting mutation accumulation and uncontrolled cell proliferation. 39 NLR, composed of neutrophils and lymphocytes, can reflect the inflammatory and immune status of patients and is strongly associated with the prognosis of various tumors. 40-42 A retrospective study by Liu J et al. included 1,189 cases of ICC patients, determining that NLR was associated with worse prognosis among ICC patients, especially in patients with HBV infection. 12 Sellers et al. reported the NLR has a stronger impact as a prognostic marker in ICC over the PLR and SII. 34 Studies have reported that neutrophils can promote cacer growth through various mechanisms, including the inhibition of T cell activation, the promotion of genetic instability, and the facilitation of angiogenesis. 43,44 While lymphocytes can inhibit tumor development through the production of tumor-reactive antibodies, cytotoxic effects, promoting phagocytosis by macrophages. 45,46 Therefore, an elevated NLR may indicate impaired innate anti-tumor immunity and greater tumor invasiveness, limiting the survival benefits of simple surgery and suggesting a poorer prognosis. Malnutrition is a common adverse prognostic factor in cancer patients, negatively affecting surgical outcomes, increasing complication rates, and impairing long-term prognosis. 47-49 Malnutrition in cancer patients may result from tumor consumption, metabolic disorders, and anorexia. 50-52 Therefore, assessing nutritional status can not only reflect the progression of the tumor but also evaluate the risks and benefits of surgery for the patient. 53 BMI and albumin are simple and reliable nutritional risk assessment tools in clinical practice, and are significantly associated with the prognosis of cancer patients. Zhou T et al. demonstrated that BMI is an independent risk factor for increased postoperative morbidity in patients who undergo surgical treatment of hilar cholangiocarcinoma. 54 Similarly, Shen J et al. found that lower preoperative serum albumin level is associated with worse long-term survival in ICC patients. 55 Moreover, human serum albumin is synthesized exclusively by liver, making it a crucial indicator for assessing liver reserve function. 56,57 Lower albumin levels suggest reduced liver reserve function, reflecting not only tumor progression but also an increased risk of surgery and a higher incidence of postoperative complications, including liver failure. ALI integrates nutritional and inflammation-related indicators, providing a comprehensive assessment of patients’ nutritional risks and immune status—factors often neglected by traditional stratification tools—and thereby conferring robust predictive capabilities. ALI was initially used to predict the prognosis of patients with advanced non-small cell lung cancer. 18 Subsequently, M. Song et al. compared its predictive efficacy with 15 other biomarkers, demonstrating that ALI outperformed all others in predicting lung cancer prognosis. 58 In recent years, ALI has been applied to tumors beyond lung cancer, particularly gastrointestinal cancers. 20,21 A meta-analysis of 18 studies on seven types of gastrointestinal tumors, including HCC and cholangiocarcinoma, revealed that low ALI is linked to OS, disease-free survival (DFS), and progression-free survival (PFS). 59 Another meta-analysis by Pan et al. included 11 studies with 44,717 participants, concluded that ALI is a valuable predictor of postoperative complications (POCs) and long-term prognosis in gastrointestinal cancer patients. However, the heterogeneity in ALI cutoff values across studies should be considered when interpreting these findings. 60 Consistent with previous studies, our research also demonstrated that ALI exhibited superior predictive performance compared to 11 other biomarkers, and the predictive model based on ALI showed strong predictive accuracy. Therefore, we recommend combining ALI with traditional prognostic risk factors to further optimize prognostic stratification in ICC patients, thereby guiding better treatment decisions. This study has several limitations that should be considered. As with all retrospective studies, selection and information biases may have been introduced. Although the multicenter design enhances generalizability, variations in surgical techniques and perioperative care across institutions may have influenced the results. Although propensity score matching (PSM) based on ALI adjusted for potential confounders, the possibility of hidden confounders and residual bias cannot be ruled out. Furthermore, although the nomogram model showed good predictive performance in both the training and internal validation sets, it requires further validation with external datasets. Therefore, we intend to apply the model to a more diverse patient population in future research for further external validation to improve the accuracy of our findings. This study aims to guide treatment decisions by optimizing patient prognostic stratification; however, due to data limitations, the model’s performance in guiding neoadjuvant therapy, conversion therapy, and other treatments remains unvalidated, necessitating further investigation. Conclusions ALI was an independent predictor of OS in patients undergoing curative-intent surgery for ICC. ALI showed superior predictive performance compared with other biomarkers. The dynamic nomogram based on ALI demonstrated excellent predictive performance and was easily integrated into daily clinical practice, enabling clinicians to develop individualized care strategies and optimize treatment approaches for ICC patients. Abbreviations ALI, advanced lung cancer inflammation index; ICC, intrahepatic cholangiocarcinoma; OS, overall survival; PSM, propensity score matching; ROC, receiver operating characteristic; AUC, area under the curve; HR, hazard ratio; CI, confidence interval; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; ALBI, albumin-bilirubin grade; PNI, prognostic nutritional index; GPR, gamma-glutamyl transpeptidase-to-platelet ratio; GLR, gamma-glutamyl transpeptidase-to-lymphocyte ratio; GAR, gamma-glutamyl transpeptidase-to-albumin ratio; AAPR, albumin-to-alkaline phosphatase ratio; ANRI, aspartate aminotransferase-to-neutrophil ratio index; APRI, aspartate aminotransferase-to-platelet ratio index; FIB-4, fibrosis-4 index; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen 19-9; BMI, body mass index; HBV, hepatitis B virus; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, gamma-glutamyl transpeptidase; ALP, alkaline phosphatase; TBS, tumor burden score; AJCC, American Joint Committee on Cancer; LOS, length of stay; MVI, microvascular invasion; PT, prothrombin time; PLT, platelet count; VIF, variance inflation factor; ULN, upper limit of normal. Declarations Ethics approval and consent to participate The study was reviewed and approved by the Ethics Committee of the Mengchao Hepatobiliary Hospital of Fujian Medical University, and exempts the requirement of written informed consent. All procedures were performed in accordance with World Medical Association Declaration of Helsinki (approval number 2022_077_01). Availability of data and materials The datasets generated or analyzed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author on reasonable request. Consent for publication Not applicable. Funding This study was supported by the National Natural Science Foundation of China (62275050); the National Key Research and Development Program of China (2022YFC2407304); key Clinical Specialty Discipline Construction Program of Fuzhou (20230101); Major Research Projects for Young and Middle-aged Researchers of Fujian Provincial Health Care Commission (2021ZQNZD013); and the Health Science and Technology Innovation Platform Program of Fuzhou (2021-S-wp1). Competing interests The authors declare no competing interests. Authors’ contributions Qizhu Lin and Jun Fu contributed to the study concept, design, and drafted the manuscript. Qizhu Lin and Tingfeng Huang analyzed the data. Hongzhi Liu and Ruilin Fan provided assistance with data collection and verification. Yongyi Zeng contributed to the study concept, design, and manuscript revision. All authors reviewed the manuscript. Acknowledgments We express our gratitude to all the staff contributing to this study. References Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA: a Cancer Journal For Clinicians. 2024;74(1):12-49. Wang Y, Alsaraf Y, Bandaru SS, et al. Epidemiology, survival and new treatment modalities for intrahepatic cholangiocarcinoma. J Gastrointest Oncol. 2024;15(4):1777-1788. Gad MM, Saad AM, Faisaluddin M, et al. Epidemiology of Cholangiocarcinoma; United States Incidence and Mortality Trends. Clin Res Hepatol Gastroenterol. 2020;44(6):885-893. Endo I, Gonen M, Yopp AC, et al. Intrahepatic cholangiocarcinoma: rising frequency, improved survival, and determinants of outcome after resection. Ann Surg. 2008;248(1):84-96. Spolverato G, Vitale A, Cucchetti A, et al. Can hepatic resection provide a long-term cure for patients with intrahepatic cholangiocarcinoma? Cancer. 2015;121(22):3998-4006. Amin MB, Greene FL, Edge SB, et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging. CA: a Cancer Journal For Clinicians. 2017;67(2):93-99. Spolverato G, Bagante F, Weiss M, et al. Comparative performances of the 7th and the 8th editions of the American Joint Committee on Cancer staging systems for intrahepatic cholangiocarcinoma. Journal of Surgical Oncology. 2017;115(6):696-703. Kim Y, Moris DP, Zhang X-F, et al. Evaluation of the 8th edition American Joint Commission on Cancer (AJCC) staging system for patients with intrahepatic cholangiocarcinoma: A surveillance, epidemiology, and end results (SEER) analysis. Journal of Surgical Oncology. 2017;116(6):643-650. Zitvogel L, Pietrocola F, Kroemer G. Nutrition, inflammation and cancer. Nat Immunol. 2017;18(8):843-850. Stumpf F, Keller B, Gressies C, Schuetz P. Inflammation and Nutrition: Friend or Foe? Nutrients. 2023;15(5). Kiely M, Lord B, Ambs S. Immune response and inflammation in cancer health disparities. Trends Cancer. 2022;8(4):316-327. Liu J, Xia Y, Xue F, et al. Elevated serum neutrophil-lymphocyte ratio is associated with worse long-term survival in patients with HBV-related intrahepatic cholangiocarcinoma undergoing resection. Frontiers In Oncology. 2022;12:1012246. Dotto-Vasquez G, Villacorta-Ampuero AK, Ulloque-Badaracco JR, et al. Lymphocyte-to-Monocyte Ratio and Clinical Outcomes in Cholangiocarcinoma: A Systematic Review and Meta-Analysis. Diagnostics (Basel, Switzerland). 2022;12(11). Hu G, Liu Q, Ma J-Y, Liu C-Y. Prognostic Significance of Platelet-to-Lymphocyte Ratio in Cholangiocarcinoma: A Meta-Analysis. Biomed Res Int. 2018;2018:7375169. Johnson PJ, Berhane S, Kagebayashi C, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology. 2015;33(6):550-558. Matsuda T, Umeda Y, Matsuda T, et al. Preoperative prognostic nutritional index predicts postoperative infectious complications and oncological outcomes after hepatectomy in intrahepatic cholangiocarcinoma. BMC Cancer. 2021;21(1):708. Li Q, Zhang R, Fu JL, et al. [Prognostic value of preoperative peripheral blood neutrophil-to-lymphocyte ratio and γ-glutamyl transpeptidase-to-platelet ratio index in patients with hepatitis B virus related intrahepatic cholangiocarcinoma after radical resection]. Zhonghua Yi Xue Za Zhi. 2021;101(38):3134-3140. Jafri SH, Shi R, Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. BMC Cancer. 2013;13:158. Horino T, Tokunaga R, Miyamoto Y, et al. The advanced lung cancer inflammation index is a novel independent prognosticator in colorectal cancer patients after curative resection. Ann Gastroenterol Surg. 2022;6(1):83-91. Pang H, Dai L, Chen L, et al. Prognostic value of the advanced lung cancer inflammation index in patients with gastric cancer after radical gastrectomy: a propensity-score matching cohort study and meta-analysis. BMC Cancer. 2024;24(1):583. Qiu X, Shen S, Lu D, et al. Predictive Efficacy of the Advanced Lung Cancer Inflammation Index in Hepatocellular Carcinoma After Hepatectomy. J Inflamm Res. 2024;17:5197-5210. Catalano G, Alaimo L, Chatzipanagiotou OP, et al. Prognostic value of the advanced lung cancer inflammation index in intrahepatic cholangiocarcinoma. European Journal of Surgical Oncology : the Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology. 2024;50(12):108773. Strasberg SM. Nomenclature of hepatic anatomy and resections: a review of the Brisbane 2000 system. J Hepatobiliary Pancreat Surg. 2005;12(5):351-355. Dindo D, Demartines N, Clavien P-A. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg. 2004;240(2):205-213. [Evidence-based practice guidelines for standardized pathological diagnosis of primary liver cancer in China: 2015]. Zhonghua Gan Zang Bing Za Zhi. 2015;23(5):321-327. Sasaki K, Morioka D, Conci S, et al. The Tumor Burden Score: A New "Metro-ticket" Prognostic Tool For Colorectal Liver Metastases Based on Tumor Size and Number of Tumors. Ann Surg. 2018;267(1):132-141. EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis - 2021 update. Journal of Hepatology. 2021;75(3):659-689. Song Y, Cai M, Li Y, Liu S. The focus clinical research in intrahepatic cholangiocarcinoma. Eur J Med Res. 2022;27(1):116. Sirica AE, Gores GJ, Groopman JD, et al. Intrahepatic Cholangiocarcinoma: Continuing Challenges and Translational Advances. Hepatology. 2019;69(4):1803-1815. Ghio M, Vijay A. Intrahepatic cholangiocarcinoma: Evolving role of neoadjuvant and targeted therapy. Annals of Hepato-Biliary-Pancreatic Surgery. 2023;27(2):123-130. Coffman‐D’Annibale KL, Greten TF. Top advances of the year: Hepatobiliary cancers. Cancer. 2023;129(8):1149-1155. Moris D, Palta M, Kim C, Allen PJ, Morse MA, Lidsky ME. Advances in the treatment of intrahepatic cholangiocarcinoma: An overview of the current and future therapeutic landscape for clinicians. CA Cancer J Clin. 2023;73(2):198-222. Tsilimigras DI, Moris D, Mehta R, et al. The systemic immune-inflammation index predicts prognosis in intrahepatic cholangiocarcinoma: an international multi-institutional analysis. HPB : the Official Journal of the International Hepato Pancreato Biliary Association. 2020;22(12):1667-1674. Sellers CM, Uhlig J, Ludwig JM, Stein SM, Kim HS. Inflammatory markers in intrahepatic cholangiocarcinoma: Effects of advanced liver disease. Cancer Medicine. 2019;8(13):5916-5929. Zhu J, Wang D, Liu C, et al. Development and validation of a new prognostic immune-inflammatory-nutritional score for predicting outcomes after curative resection for intrahepatic cholangiocarcinoma: A multicenter study. Front Immunol. 2023;14:1165510. Jager KJ, Zoccali C, Macleod A, Dekker FW. Confounding: what it is and how to deal with it. Kidney Int. 2008;73(3):256-260. Clements O, Eliahoo J, Kim JU, Taylor-Robinson SD, Khan SA. Risk factors for intrahepatic and extrahepatic cholangiocarcinoma: A systematic review and meta-analysis. Journal of Hepatology. 2020;72(1). Kartikasari AER, Huertas CS, Mitchell A, Plebanski M. Tumor-Induced Inflammatory Cytokines and the Emerging Diagnostic Devices for Cancer Detection and Prognosis. Frontiers In Oncology. 2021;11:692142. Rodrigues PM, Olaizola P, Paiva NA, et al. Pathogenesis of Cholangiocarcinoma. Annu Rev Pathol. 2021;16:433-463. Templeton AJ, McNamara MG, Šeruga B, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst. 2014;106(6):dju124. Cupp MA, Cariolou M, Tzoulaki I, Aune D, Evangelou E, Berlanga-Taylor AJ. Neutrophil to lymphocyte ratio and cancer prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med. 2020;18(1):360. Lin N, Li J, Yao X, et al. Prognostic value of neutrophil-to-lymphocyte ratio in colorectal cancer liver metastasis: A meta-analysis of results from multivariate analysis. Int J Surg. 2022;107:106959. Jaillon S, Ponzetta A, Di Mitri D, Santoni A, Bonecchi R, Mantovani A. Neutrophil diversity and plasticity in tumour progression and therapy. Nat Rev Cancer. 2020;20(9):485-503. Hedrick CC, Malanchi I. Neutrophils in cancer: heterogeneous and multifaceted. Nat Rev Immunol. 2022;22(3):173-187. Chen Y, Tian Z. Innate lymphocytes: pathogenesis and therapeutic targets of liver diseases and cancer. Cell Mol Immunol. 2021;18(1):57-72. Yuen GJ, Demissie E, Pillai S. B lymphocytes and cancer: a love-hate relationship. Trends Cancer. 2016;2(12):747-757. Xu J, Jie Y, Sun Y, Gong D, Fan Y. Association of Global Leadership Initiative on Malnutrition with survival outcomes in patients with cancer: A systematic review and meta-analysis. Clin Nutr. 2022;41(9):1874-1880. Matsui R, Rifu K, Watanabe J, Inaki N, Fukunaga T. Impact of malnutrition as defined by the GLIM criteria on treatment outcomes in patients with cancer: A systematic review and meta-analysis. Clin Nutr. 2023;42(5):615-624. Arends J. Malnutrition in cancer patients: Causes, consequences and treatment options. European Journal of Surgical Oncology : the Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology. 2024;50(5):107074. Na B-G, Han S-S, Cho Y-A, et al. Nutritional Status of Patients with Cancer: A Prospective Cohort Study of 1,588 Hospitalized Patients. Nutr Cancer. 2018;70(8):1228-1236. Laviano A, Meguid MM, Rossi-Fanelli F. Cancer anorexia: clinical implications, pathogenesis, and therapeutic strategies. Lancet Oncol. 2003;4(11):686-694. Antoun S, Raynard B. Muscle protein anabolism in advanced cancer patients: response to protein and amino acids support, and to physical activity. Annals of Oncology : Official Journal of the European Society For Medical Oncology. 2018;29(suppl_2):ii10-ii17. Ravasco P. Nutrition in Cancer Patients. J Clin Med. 2019;8(8):1211. Zhou T, Liu L, Dai H-S, et al. Impact of body mass index on postoperative outcomes in patients undergoing radical resection for hilar cholangiocarcinoma. Journal of Surgical Oncology. 2020;122(7):1418-1425. Shen J, Wen T, Li C, Yan L, Li B, Yang J. The Prognostic Prediction Role of Preoperative Serum Albumin Level in Patients with Intahepatic Cholangiocarcinoma Following Hepatectomy. Dig Dis. 2018;36(4):306-313. Oettl K, Birner-Gruenberger R, Spindelboeck W, et al. Oxidative albumin damage in chronic liver failure: relation to albumin binding capacity, liver dysfunction and survival. Journal of Hepatology. 2013;59(5):978-983. Spinella R, Sawhney R, Jalan R. Albumin in chronic liver disease: structure, functions and therapeutic implications. Hepatol Int. 2016;10(1):124-132. Song M, Zhang Q, Song C, et al. The advanced lung cancer inflammation index is the optimal inflammatory biomarker of overall survival in patients with lung cancer. J Cachexia Sarcopenia Muscle. 2022;13(5):2504-2514. Zhang L, Zhao K, Kuang T, et al. The prognostic value of the advanced lung cancer inflammation index in patients with gastrointestinal malignancy. BMC Cancer. 2023;23(1):101. Pang H-Y, Chen X-F, Yan M-H, et al. Clinical significance of the advanced lung cancer inflammation index in gastrointestinal cancer patients: a systematic review and meta-analysis. Frontiers In Oncology. 2023;13:1021672. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx SupplementaryTablesandfigure.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6452875","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464664184,"identity":"063a0276-5dc4-4f39-9911-a7af23cfd06f","order_by":0,"name":"Qizhu Lin","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qizhu","middleName":"","lastName":"Lin","suffix":""},{"id":464664185,"identity":"88213643-07f4-40c5-a83b-0fbdff06cd6e","order_by":1,"name":"Jun Fu","email":"","orcid":"","institution":"Mengchao Hepatobiliary Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Fu","suffix":""},{"id":464664186,"identity":"38a03e33-369e-452b-8c74-202bf7c1ddac","order_by":2,"name":"Tingfeng Huang","email":"","orcid":"","institution":"Mengchao Hepatobiliary Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingfeng","middleName":"","lastName":"Huang","suffix":""},{"id":464664187,"identity":"80d71bdd-d02d-49a2-b635-d11a81e965a4","order_by":3,"name":"Hongzhi Liu","email":"","orcid":"","institution":"Mengchao Hepatobiliary Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongzhi","middleName":"","lastName":"Liu","suffix":""},{"id":464664188,"identity":"dd2603ec-a5e8-4ff0-b6c2-d597de2c1bf7","order_by":4,"name":"Ruilin Fan","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruilin","middleName":"","lastName":"Fan","suffix":""},{"id":464664189,"identity":"529a420c-ecb5-482e-82ca-481240befee7","order_by":5,"name":"Yongyi Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACPhCRUPHfjo2Z+cCBDxVEaGEDEQ/OMCfzsbMlHpxxhkgtjA/bmBnn8fMYH+ZtIUYL++m0B4ltbMxszDwfDvA2MMjzix0goIUnd7tBwjkePjZm3g0HJHcwGM6cnUDIYbnbJBLKJJjBWgzPMCQY3Cakhf8tUAubAWMbM8+DA4ltxGiRANnSlgDSwnDgIHFa3gL9cuZAMjAADA42nJEg7Bd+/txtD39UHLCT7z/8+POfCht5fmkCWhigsQkDEgSVY2gZBaNgFIyCUYAJALrqQkSJ43vPAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yongyi","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2025-04-15 08:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6452875/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6452875/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83814730,"identity":"e64558e5-eba6-43b3-95f7-462a337dea65","added_by":"auto","created_at":"2025-06-03 07:31:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":243057,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves demonstrating differences in overall survival based on high versus low ALI in the (A) overall and (B) PSM cohort.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6452875/v1/75df547a70bb3a756516daf2.png"},{"id":83815745,"identity":"17f2e352-c2c7-412e-b06b-312afdabbf1e","added_by":"auto","created_at":"2025-06-03 07:39:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1479617,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of ALI for overall survival in the overall (A) and PSM (B) cohorts.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6452875/v1/08b48f5e1da1c340f9889bfa.png"},{"id":83815746,"identity":"95f2d9b9-1ef6-4acb-aba0-bc0a91759e9d","added_by":"auto","created_at":"2025-06-03 07:39:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":233364,"visible":true,"origin":"","legend":"\u003cp\u003eThe time-dependent ROC of inflammation and nutrition-relative indicators for diagnosing overall survival in patients with ICC.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6452875/v1/efedfd11cba6e9e3c4cb1a9a.png"},{"id":83814732,"identity":"78dd829b-f14a-4131-9311-297b5e7ad3f2","added_by":"auto","created_at":"2025-06-03 07:31:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":155046,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for OS Forecast in ICC Patients after resection\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6452875/v1/940ba8397cc8d48fe05c99cf.png"},{"id":83814734,"identity":"184e665e-8018-4575-bece-d81d2368ca3a","added_by":"auto","created_at":"2025-06-03 07:31:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":631010,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram Assessment at 1-, 3-, and 5-Year OS. (A) ROC curves in the training set. (B) ROC curves in the validation set. (C) Calibration curves in the training set. (D) Calibration curves in the validation set. (E) DCA curves in the training set. (F) DCA curves in the validation set.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6452875/v1/73fa4cef7d45cbda2cdca0ac.png"},{"id":88307211,"identity":"127a87bd-d275-4c78-a721-ac14759fe524","added_by":"auto","created_at":"2025-08-05 06:08:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2925623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6452875/v1/bfba60f2-679b-4049-9dab-0f0a55b9c397.pdf"},{"id":83814728,"identity":"d4d41fed-057e-47d9-9066-3bdc2be6ae37","added_by":"auto","created_at":"2025-06-03 07:31:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40425,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6452875/v1/0c6569e1cef3deb797352944.docx"},{"id":83814735,"identity":"8e74391e-0ea0-40d1-91a9-f7d1634cbc98","added_by":"auto","created_at":"2025-06-03 07:31:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":100349,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesandfigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-6452875/v1/d1d67e3ac1ffe90cd1c74a45.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive efficacy of the advanced lung cancer inflammation index among patients with intrahepatic cholangiocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIntrahepatic cholangiocarcinoma (ICC) is the second most common primary liver cancer following hepatocellular carcinoma (HCC), accounts for about 20% of all primary liver tumors.\u003csup\u003e1\u003c/sup\u003e Although the current incidence of ICC in Asia is significantly higher than that in Western countries, it is important to highlight that the global incidence of ICC has been rising in recent years, with an increase of more than 140% in the United States.\u003csup\u003e2,3\u003c/sup\u003e Radical resection remains the only potential cure for ICC, but only 20%-30% of patients present with resectable disease.\u003csup\u003e4\u003c/sup\u003e Even after curative-intent surgical resection, the 5-year overall survival (OS) is only 25%-40%, with 50%-70% facing tumor recurrence.\u003csup\u003e5\u003c/sup\u003e The most commonly used staging system for ICC is the American Joint Committee on Cancer (AJCC) staging system.\u003csup\u003e6\u003c/sup\u003e Several studies have evaluated its prognostic value, but the results remain controversial and insufficient to support treatment decisions.\u003csup\u003e7,8\u003c/sup\u003e Therefore, there is an urgent need to identify novel prognostic biomarkers that could enable more personalized treatment and surveillance strategies.\u003c/p\u003e\n\u003cp\u003eRecent studies have demonstrated that nutritional status and inflammation play significant roles in tumor initiation and progression.\u003csup\u003e9-11\u003c/sup\u003e The predictive value of several inflammatory- and nutrition-related biomarkers based on preoperative blood indexes has been explored. Specifically, some biomarkers reflect the inflammatory status of patients, such as neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), all of which have been demonstrated to be associated with long-term survival in patients with ICC.\u003csup\u003e12-14\u003c/sup\u003e Additionally, several biomarkers that focus on the nutritional status of patients, such as the Albumin-Bilirubin (ALBI) grade, prognostic nutritional index (PNI), and gamma-glutamyl transpeptidase (GGT)-to-platelet ratio (GPR), all of which have also been shown to be valuable prognostic markers.\u003csup\u003e15-17\u003c/sup\u003e Recently, the advanced lung cancer inflammation index (ALI) has emerged as a promising biomarker of survival outcomes in cancers due to its incorporation of nutritional and inflammatory indicators. ALI was first established by Jafri et al. in 2013 as a prognostic index for non-small cell lung cancer, which is calculated as body mass index (BMI) \u0026times; albumin / NLR.\u003csup\u003e18\u003c/sup\u003e More recently, ALI has been demonstrated to have prognostic significance as a biomarker in various non-lung cancers including colorectal cancer, gastric tumors, and HCC.\u003csup\u003e19-21\u003c/sup\u003e Although G. Catalano et al. have evaluated the impact of ALI on OS among patients with ICC, they did not further apply ALI to predict long-term survival.\u003csup\u003e22\u003c/sup\u003e Therefore, the predictive value of ALI in ICC patients after surgery requires further investigation.\u003c/p\u003e\n\u003cp\u003eThis study aims to investigate the prognostic significance of ALI in ICC patients after surgery. We compared the predictive performance of ALI with 11 other validated inflammatory biomarkers in a cohort of ICC patients. In addition, we developed and validated a nomogram model based on ALI to better stratify patient prognosis and personalize treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy population and selection criteria\u003c/p\u003e\n\u003cp\u003eWe retrospectively analyzed the clinical data of 691 patients with ICC who underwent\u0026nbsp;curative-intent surgery were collected from the Primary Liver Cancer Big Data in Fujian province. The patients in this study all provided informed consent prior to surgery, and strict adherence was maintained to the guidelines of the Declaration of Helsinki. Ethical approval was obtained from Ethics Committee of the Mengchao Hepatobiliary Hospital of Fujian Medical University \u0026nbsp;(approval number 2022_077_01).\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria were as follows: (1) patients who underwent R0 resection, characterized by complete tumor removal with a negative microscopic margin; (2) postoperative pathological confirmation of ICC; (3) no history of prior anticancer treatment. The exclusion criteria were as follows: (1) preoperative extrahepatic metastases; (2) incomplete clinicopathological data; (3) recurrence or death within 30 days postoperatively, or loss to follow-up shortly after surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables and outcomes of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic and clinicopathologic data collected included age, gender, hepatitis B virus (HBV) infection, American Society of Anesthesiologists Physical Status Classification (ASA) score, BMI, and Child-Pugh grade. Hematological parameters included platelet count (PLT), red and white blood cell counts, prothrombin time (PT), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), gamma-glutamyl transpeptidase (GGT), Total Bilirubin, Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), and albumin levels. Tumor number and the size of the largest lesion were assessed preoperatively by CT or MRI. Operative details included the type of hepatectomy, lymph node dissection, margin status, intraoperative blood loss, and transfusion. Additionally, data were collected on histologic tumor grade, presence of macrovascular and microvascular invasion (MVI), surgical margin width, satellite nodules, length of stay (LOS), postoperative 30-day complications, and N stage, according to the 8th edition of the American Joint Committee on Cancer (AJCC) Staging Manual.\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eMajor hepatectomy was defined as resection of three or more Couinaud segments according to the consensus of the Brisbane 2000 system.\u003csup\u003e23\u003c/sup\u003e Postoperative complications occurring within 30 days were classified according to the Clavien-dindo classification.\u003csup\u003e24\u003c/sup\u003e Microvascular invasion (MVI) was defined as the presence of intraparenchymal vascular involvement identified on histological examination.\u003csup\u003e25\u003c/sup\u003e Macrovascular invasion was defined as the involvement of primary and secondary branches of the portal vein or hepatic artery, or the invasion of one or more of the three major hepatic veins.\u003csup\u003e6\u003c/sup\u003e The formula for tumor burden score (TBS) computation follows the Pythagorean theorem: TBS\u003csup\u003e2\u003c/sup\u003e = (size of the largest lesion)\u003csup\u003e2\u003c/sup\u003e + (number of tumors)\u003csup\u003e\u0026nbsp;2\u003c/sup\u003e.\u003csup\u003e26\u003c/sup\u003e The optimal cutoff value for TBS (5.00) was determined by X-tile software. The primary outcome of interest was overall survival (OS), defined as the time from the date of curative resection to the date of death or the last follow-up.\u003c/p\u003e\n\u003cp\u003enutrition- and inflammation-related\u0026nbsp;biomarkers\u003c/p\u003e\n\u003cp\u003eAll patients underwent routine preoperative laboratory examinations (routine blood tests, platelet count, neutrophil count, lymphocyte cell count, ALT, AST, tumor serum markers, such as CA19-9 and CEA). By reviewing previous studies, we selected 12 nutrition- and inflammation-related biomarkers for our research, including: NLR, ALI, ALBI, PLR, PNI, GPR, GLR (GGT to lymphocyte ratio), GAR (GGT to albumin ratio), AAPR (Albumin-to-alkaline phosphatase ratio), ANRI (aspartate aminotransferase-to-neutrophil ratio index), APRI (aspartate aminotransferase-to-platelet ratio index), FIB-4 (fibrosis-4 index). The calculation methods for each biomarker combination are shown in Table S1. The cutoff values for ALBI (\u0026le; -2.60 [ALBI grade 1], -2.60 to -1.39 [ALBI grade2], and \u0026ge; -1.39 [ALBI grade 3]) and FIB-4(1.3) are based on previous studies.\u003csup\u003e15,27\u003c/sup\u003e X-tile software was employed to determine the optimal cutoff values for the remaining biomarkers: 2.2 (NLR), 27.6 (ALI), 140.13 (PLR), 46.65 (PNI), 1.51 (GPR), 44.0 (GLR), 5.49 (GAR), 0.6 (AAPR), 4.63 (ANRI), and 0.1 (APRI).\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using R software, version 4.2.2 (http://www.r-project.org). Continuous and categorical variables were reported as medians (interquartile range [IQR]) and frequencies (%), respectively. Categorical variables were compared using either the chi-squared test or Fisher\u0026rsquo;s exact test, as appropriate, and continuous variables were compared using the Kruskal-Wallis H test. The calculation of OS was performed using the Kaplan-Meier method, and comparisons were made using the log-rank test. Additionally, propensity score matching (PSM) analysis was performed to eliminate intergroup differences in baseline parameters using a 1:1 nearest matching method implemented through the \u0026ldquo;MatchIt\u0026rdquo; package. The predictive accuracy of each indicator was assessed using the C-index and time-dependent ROC. The discriminative ability of the model was evaluated using the area under the receiver operating characteristic curve (AUC). Univariate and multivariable Cox regression analyses were carried out to identify independent risk factors for OS. Variables with a significance level of P\u0026lt;0.05 in the univariable analysis were incorporated into the multivariable Cox regression model using forward stepwise variable selection. All reported P-values were two-sided, with values less than 0.05 considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eCharacteristics of the patients\u003c/p\u003e\n\u003cp\u003eA total of 691 patients were included in the study. The patient demographics are shown in Table 1. In the overall cohort, the median age of the participants was 55.0 years, and males were prevalent (n = 472,68.3%). Approximately 48.8% of the patients tested positive for HBsAg. In our population, the median size of the largest tumor was 5.6 cm, and patients with multiple tumors account for only 7.7%. The distribution of TNM stage were as follows: 128 (18.5%) with stage I; 427 (61.8%) with stage II; 136 (19.7%) with stage III. After curative resection, adjuvant therapy was administered to 32.0% of the patients.\u003c/p\u003e\n\u003cp\u003eOverall, a total of 194 patients were identified as having a low ALI (\u0026le; 27.6), and 497 patients were identified as having a high ALI (\u0026gt; 27.6) (Table 2, left panel). We observed that low ALI was significantly associated with higher tumor burden (low vs high ALI, 7.5 [IQR: 5.3-9.6] vs 5.1[IQR: 4.0-7.2],\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001), higher CA 19-9 (low vs high ALI, 57.9 [IQR: 18.3-626.6] vs 33.2[IQR: 14.8-232.9],\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.002), and higher TNM stage (low vs high ALI, n = 61 [31.4%] vs n = 97 [19.5%], \u003cem\u003ep\u003c/em\u003e = 0.001). Additionally, we found that patients with low ALI exhibited lower BMI (low vs high ALI, 21.7 [IQR: 19.0-24.2] vs 24.5[IQR: 22.2-27.0],\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001), albumin levels(low vs high ALI, 40.4 [IQR: 38.0-43.1] vs 42.9[IQR: 40.8-45.6],\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001), lymphocyte count(low vs high ALI, 1.2 [IQR: 0.9-1.6] vs 1.7[IQR: 1.4-2.2],\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001), but higher neutrophil count(low vs high ALI, 5.6 [IQR: 4.4-7.7] vs 3.6[IQR: 2.9-4.7],\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001), GGT levels(low vs high ALI, 95.0 [IQR: 49.2-207.5] vs 61.0[IQR: 38.0-120.0],\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001), and platelet count(low vs high ALI, 207 [IQR: 151.0-251.8] vs 189.0[IQR: 144.0-230.0],\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.011). To mitigate baseline bias, we performed PSM to balance these unequal factors, resulting in 194 patients in each group. Following the matching process, there were no statistically significant differences observed in the clinicopathological parameters between the two groups (Table 2, right panel).\u003c/p\u003e\n\u003cp\u003eImpact of ALI on OS\u003c/p\u003e\n\u003cp\u003eAt the end of the last follow-up, 418 patients (60.5%) had died, with the median OS of 24.64 months (95% CI: 21.25 - 27.66). Patients with low ALI exhibited significantly worse 5-year OS compared to those with high ALI (5-year OS: 20.2% vs. 45.7%, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001, Fig.1A). After PSM, patients in the low ALI group continued to exhibit inferior OS (5-year OS: 20.2% vs. 42.3%, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001, Fig.\u0026nbsp;1B). Of note, we further conducted subgroup analyses based on various clinicopathologic features to investigate the prognostic value of ALI in different types of ICC patients. As shown in Fig.\u0026nbsp;2, our findings demonstrated that ALI maintained its prognostic significance across different subgroups, before and after PSM.\u003c/p\u003e\n\u003cp\u003eIn the overall cohort, the multivariate analysis identified low ALI as an independent predictor of increased mortality (low vs. high: hazard ratio [HR] 1.54, 95% CI: 1.25 - 1.90, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). Other independent predictors included CA19-9 levels, CEA levels, TNM stage, satellite nodules, TBS, and adjuvant therapy (all \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05, Table 3). After performing PSM to adjust for potential confounders, we re-evaluated these predictors in the matched cohort. Notably, low ALI remained a significant independent predictor of increased mortality even after adjustment (low vs. high: HR 1.59, 95% CI: 1.24 - 2.04, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Additionally, CA19-9 levels, CEA levels, TNM stage, satellite nodules, TBS, and adjuvant therapy continued to show significant prognostic value in the PSM-adjusted cohort (all \u003cem\u003ep\u003c/em\u003e values \u0026lt; 0.05, Table 3). Moreover, we further confirm that there is no collinearity among independent variables, either before or after PSM (all candidate variables had tolerance values \u0026gt; 0.1 and variance inflation factor (VIF) values \u0026lt; 2, as shown in Table S2).\u003c/p\u003e\n\u003cp\u003eThe prognostic ability comparison of the nutrition- and inflammation-related biomarkers\u003c/p\u003e\n\u003cp\u003eA time-dependent ROC curve and C-index were performed to compare the prognostic predictive capacity of 12 nutrition- and inflammation-related biomarkers in the overall cohort. Compared to the other nutrition- and inflammation-related biomarkers, the ALI demonstrated the highest C-index for OS in ICC patients at 1, 3, and 5 years: 0.577 (95% CI, 0.537 - 0.615), 0.607 (95% CI, 0.574 - 0.645), and 0.603 (95% CI, 0.559 - 0.647), respectively (Table 4). Moreover, the ALI exhibited a higher AUC value than the other inflammation/nutrition-based indicators (Fig. 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConstruction and validation of the prognostic nomogram for OS\u003c/p\u003e\n\u003cp\u003eThe patients in the overall cohort were randomly divided into a training dataset (n=464) and a validation dataset (n=227) at a 7:3 ratio. No significant differences were observed in the basic clinicopathologic characteristics (Table S3) and survival outcomes (Fig. S1) between the two groups. To improve the predictive accuracy for OS, a nomogram was constructed based on the aforementioned independent variables (Fig.\u0026nbsp;4). Meanwhile, to facilitate clinical practice, we constructed a web-based dynamic online nomogram, which can be accessed at https://iccnomogramforosbasedonali.shinyapps.io/shiny_nomogram_app/.\u003c/p\u003e\n\u003cp\u003eThe time-dependent ROC curves were generated to assess the model\u0026rsquo;s predictive performance. In the training set, the AUCs for predicting 1-, 3-, and 5-year survival were 0.768, 0.770, and 0.800, respectively (Fig. 5A). Similarly, the AUCs for the 1-, 3-, and 5-year survival predictions in the validation set were 0.692, 0.704, and 0.730, respectively (Fig. 5B). Subsequently, the calibration curves showed high consistency between the predicted and observed OS rates. The standard lines in both the training and validation sets closely aligned with the calibration curves for 1-, 3-, and 5-year OS predictions (Fig. 5C and D). The DCA curves for the nomogram in both the training (Fig. 5E) and validation sets (Fig. 5F) showed higher net benefits than the \u0026quot;All\u0026quot; and \u0026quot;None\u0026quot; strategies for 1-, 3-, and 5-year predictions. These findings highlight the nomogram\u0026rsquo;s consistent clinical utility and robust prognostic value for ICC patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRadical resection is currently the only potentially curative approach for ICC; however, long-term survival following surgery remains unsatisfactory.\u003csup\u003e28,29\u003c/sup\u003e Numerous studies suggest that neoadjuvant and adjuvant therapies offer theoretical benefits in improving post-surgical survival outcomes, including downstaging the primary tumor to facilitate R0 resection and eliminating micrometastatic disease to reduce the risk of recurrence.\u003csup\u003e30-32\u003c/sup\u003e Therefore, performing patient stratification before surgery is crucial for making more precise treatment decisions and improving surgical outcomes. nutrition- and inflammation-related biomarkers have recently been proven to be promising tools for risk stratification in surgical patients.\u003csup\u003e33-35\u003c/sup\u003e In the current study, the impact of the ALI on long-term outcomes was explored and compared with 11 other validated inflammatory biomarkers using a large multi-institutional cohort of patients who underwent curative-intent surgery for ICC.\u0026nbsp;PSM was employed to mitigate the influence of confounding factors.\u0026nbsp;In this study, ALI was identified as an independent predictor of increased mortality and showed superior performance compared to other biomarkers. Furthermore, a\u0026nbsp;dynamic online nomogram\u0026nbsp;based on ALI was developed to facilitate clinical practice, exhibiting excellent AUC values and predictive capabilities.\u003c/p\u003e\n\u003cp\u003eAlthough the prognostic value of ALI has been validated in various tumors, only one study has explored its impact on postoperative prognosis in ICC patients.\u003csup\u003e18-21\u003c/sup\u003e G. Catalano et al. identified\u0026nbsp;ALI as an independent predictor of OS among ICC patients and compared its predictive accuracy with that of NLR, PLR, and SII.\u003csup\u003e22\u003c/sup\u003e However, this study did not apply ALI to specific prognostic prediction tools, nor did it investigate the predictive efficacy of ali in different subgroups. In this\u0026nbsp;study, we further compared the predictive ability of ALI with that of 11\u0026nbsp;other biomarkers and validated its predictive capability in different subgroups. we also developed a dynamic nomogram web application based on ALI. In contrast to traditional nomograms that require complex calculations, this web application only requires selecting six data points to predict patient survival probability and generate visual graphs, greatly improving accessibility and convenience. Confounding variables significantly challenge the credibility of retrospective studies.\u003csup\u003e36\u003c/sup\u003e In this study, we ensured the balance of baseline data between the low ALI group and the high ALI group using PSM, and conducted subgroup analyses before and after PSM, which minimized the impact of confounding factors and enhanced the credibility of causal inference.\u003c/p\u003e\n\u003cp\u003eInflammation plays a crucial role in the development of ICC, which causes cholestasis and resultant cholangiocyte injury.\u003csup\u003e37,38\u003c/sup\u003e Specifically, a proinflammatory cytokine and growth factor-rich environment, along with toxic bile acids, may induce a mitogenic response in normal cholangiocytes, promoting mutation accumulation and uncontrolled cell proliferation.\u003csup\u003e39\u003c/sup\u003e NLR, composed of neutrophils and lymphocytes, can reflect the inflammatory and immune status of patients and is strongly associated with the prognosis of various tumors.\u003csup\u003e40-42\u003c/sup\u003e A retrospective study by Liu J et al. included 1,189 cases of ICC patients, determining that NLR was associated with worse prognosis among ICC patients, especially in patients with HBV infection.\u003csup\u003e12\u003c/sup\u003e Sellers et al. reported the NLR has a stronger impact as a prognostic marker in ICC over the PLR and SII.\u003csup\u003e34\u003c/sup\u003e Studies have reported that neutrophils can promote cacer growth through various mechanisms, including the inhibition of T cell activation, the promotion of genetic instability, and the facilitation of angiogenesis.\u003csup\u003e43,44\u003c/sup\u003e While lymphocytes can \u0026nbsp;inhibit tumor development through the production of tumor-reactive antibodies, cytotoxic effects, promoting phagocytosis by macrophages.\u003csup\u003e45,46\u003c/sup\u003e Therefore, an elevated NLR may indicate impaired innate anti-tumor immunity and greater tumor invasiveness, limiting the survival benefits of simple surgery and suggesting a poorer prognosis.\u003c/p\u003e\n\u003cp\u003eMalnutrition is a common adverse prognostic factor in cancer patients, negatively affecting surgical outcomes, increasing complication rates, and impairing long-term prognosis.\u003csup\u003e47-49\u003c/sup\u003e Malnutrition in cancer patients may result from tumor consumption, metabolic disorders, and anorexia.\u003csup\u003e50-52\u003c/sup\u003e Therefore, assessing nutritional status can not only reflect the progression of the tumor but also evaluate the risks and benefits of surgery for the patient.\u003csup\u003e53\u003c/sup\u003e BMI and albumin are simple and reliable nutritional risk assessment tools in clinical practice, and are significantly associated with the prognosis of cancer patients. Zhou T et al. demonstrated that BMI is an independent risk factor for increased postoperative morbidity in patients who undergo surgical treatment of hilar cholangiocarcinoma.\u003csup\u003e54\u003c/sup\u003e Similarly, Shen J et al. found that lower preoperative serum albumin level is associated with worse long-term survival in ICC patients.\u003csup\u003e55\u003c/sup\u003e Moreover, human serum albumin is synthesized exclusively by liver, making it a crucial indicator for assessing liver reserve function.\u003csup\u003e56,57\u003c/sup\u003e Lower albumin levels suggest reduced liver reserve function, reflecting not only tumor progression but also an increased risk of surgery and a higher incidence of postoperative complications, including liver failure.\u003c/p\u003e\n\u003cp\u003eALI integrates nutritional and inflammation-related indicators, providing a comprehensive assessment of patients\u0026rsquo; nutritional risks and immune status\u0026mdash;factors often neglected by traditional stratification tools\u0026mdash;and thereby conferring robust predictive capabilities. ALI was initially used to predict the prognosis of patients with advanced non-small cell lung cancer.\u003csup\u003e18\u003c/sup\u003e Subsequently, M. Song et al. compared its predictive efficacy with 15 other biomarkers, demonstrating that ALI outperformed all others in predicting lung cancer prognosis.\u003csup\u003e58\u003c/sup\u003e In recent years, ALI has been applied to tumors beyond lung cancer, particularly gastrointestinal cancers.\u003csup\u003e20,21\u003c/sup\u003e A meta-analysis of 18 studies on seven types of gastrointestinal tumors, including HCC and cholangiocarcinoma, revealed that low ALI is linked to OS, disease-free survival (DFS), and progression-free survival (PFS).\u003csup\u003e59\u003c/sup\u003e Another meta-analysis by Pan et al. included 11 studies with 44,717 participants, concluded that ALI is a valuable predictor of postoperative complications (POCs) and long-term prognosis in gastrointestinal cancer patients. However, the heterogeneity in ALI cutoff values across studies should be considered when interpreting these findings.\u003csup\u003e60\u003c/sup\u003e Consistent with previous studies, our research also demonstrated that ALI exhibited superior predictive performance compared to 11 other biomarkers, and the predictive model based on ALI showed strong predictive accuracy. Therefore, we recommend combining ALI with traditional prognostic risk factors to further optimize prognostic stratification in ICC patients, thereby guiding better treatment decisions.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations that should be considered. As with all retrospective studies, selection and information biases may have been introduced. Although the multicenter design enhances generalizability, variations in surgical techniques and perioperative care across institutions may have influenced the results. Although propensity score matching (PSM) based on ALI adjusted for potential confounders, the possibility of hidden confounders and residual bias cannot be ruled out. Furthermore, although the nomogram model showed good predictive performance in both the training and internal validation sets, it requires further validation with external datasets. Therefore, we intend to apply the model to a more diverse patient population in future research for further external validation to improve the accuracy of our findings. This study aims to guide treatment decisions by optimizing patient prognostic stratification; however, due to data limitations, the model\u0026rsquo;s performance in guiding neoadjuvant therapy, conversion therapy, and other treatments remains unvalidated, necessitating further investigation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eALI was an independent predictor of OS in patients undergoing curative-intent surgery for ICC. ALI showed superior predictive performance compared with other biomarkers. The dynamic nomogram based on ALI demonstrated excellent predictive performance and was easily integrated into daily clinical practice, enabling clinicians to develop individualized care strategies and optimize treatment approaches for ICC patients.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eALI, advanced lung cancer inflammation index; ICC, intrahepatic cholangiocarcinoma; OS, overall survival; PSM, propensity score matching; ROC, receiver operating characteristic; AUC, area under the curve; HR, hazard ratio; CI, confidence interval; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; ALBI, albumin-bilirubin grade; PNI, prognostic nutritional index; GPR, gamma-glutamyl transpeptidase-to-platelet ratio; GLR, gamma-glutamyl transpeptidase-to-lymphocyte ratio; GAR, gamma-glutamyl transpeptidase-to-albumin ratio; AAPR, albumin-to-alkaline phosphatase ratio; ANRI, aspartate aminotransferase-to-neutrophil ratio index; APRI, aspartate aminotransferase-to-platelet ratio index; FIB-4, fibrosis-4 index; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen 19-9; BMI, body mass index; HBV, hepatitis B virus; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, gamma-glutamyl transpeptidase; ALP, alkaline phosphatase; TBS, tumor burden score; AJCC, American Joint Committee on Cancer; LOS, length of stay; MVI, microvascular invasion; PT, prothrombin time; PLT, platelet count; VIF, variance inflation factor; ULN, upper limit of normal.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and approved by the Ethics Committee of the Mengchao Hepatobiliary Hospital of Fujian Medical University, and exempts the requirement of written informed consent. All procedures were performed in accordance with World Medical Association Declaration of Helsinki (approval number 2022_077_01).\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated or analyzed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (62275050); the National Key Research and Development Program of China (2022YFC2407304); key Clinical Specialty Discipline Construction Program of Fuzhou (20230101); Major Research Projects for Young and Middle-aged Researchers of Fujian Provincial Health Care Commission (2021ZQNZD013); and the Health Science and Technology Innovation Platform Program of Fuzhou (2021-S-wp1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eQizhu Lin and Jun Fu contributed to the study concept, design, and drafted the manuscript. Qizhu Lin and Tingfeng Huang analyzed the data. Hongzhi Liu and Ruilin Fan provided assistance with data collection and verification. Yongyi Zeng contributed to the study concept, design, and manuscript revision. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to all the staff contributing to this study.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. \u003cem\u003eCA: a Cancer Journal For Clinicians. \u003c/em\u003e2024;74(1):12-49.\u003c/li\u003e\n\u003cli\u003eWang Y, Alsaraf Y, Bandaru SS, et al. Epidemiology, survival and new treatment modalities for intrahepatic cholangiocarcinoma. \u003cem\u003eJ Gastrointest Oncol. \u003c/em\u003e2024;15(4):1777-1788.\u003c/li\u003e\n\u003cli\u003eGad MM, Saad AM, Faisaluddin M, et al. Epidemiology of Cholangiocarcinoma; United States Incidence and Mortality Trends. \u003cem\u003eClin Res Hepatol Gastroenterol. \u003c/em\u003e2020;44(6):885-893.\u003c/li\u003e\n\u003cli\u003eEndo I, Gonen M, Yopp AC, et al. Intrahepatic cholangiocarcinoma: rising frequency, improved survival, and determinants of outcome after resection. \u003cem\u003eAnn Surg. \u003c/em\u003e2008;248(1):84-96.\u003c/li\u003e\n\u003cli\u003eSpolverato G, Vitale A, Cucchetti A, et al. Can hepatic resection provide a long-term cure for patients with intrahepatic cholangiocarcinoma? \u003cem\u003eCancer. \u003c/em\u003e2015;121(22):3998-4006.\u003c/li\u003e\n\u003cli\u003eAmin MB, Greene FL, Edge SB, et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more \u0026quot;personalized\u0026quot; approach to cancer staging. \u003cem\u003eCA: a Cancer Journal For Clinicians. \u003c/em\u003e2017;67(2):93-99.\u003c/li\u003e\n\u003cli\u003eSpolverato G, Bagante F, Weiss M, et al. Comparative performances of the 7th and the 8th editions of the American Joint Committee on Cancer staging systems for intrahepatic cholangiocarcinoma. \u003cem\u003eJournal of Surgical Oncology. \u003c/em\u003e2017;115(6):696-703.\u003c/li\u003e\n\u003cli\u003eKim Y, Moris DP, Zhang X-F, et al. Evaluation of the 8th edition American Joint Commission on Cancer (AJCC) staging system for patients with intrahepatic cholangiocarcinoma: A surveillance, epidemiology, and end results (SEER) analysis. \u003cem\u003eJournal of Surgical Oncology. \u003c/em\u003e2017;116(6):643-650.\u003c/li\u003e\n\u003cli\u003eZitvogel L, Pietrocola F, Kroemer G. Nutrition, inflammation and cancer. \u003cem\u003eNat Immunol. \u003c/em\u003e2017;18(8):843-850.\u003c/li\u003e\n\u003cli\u003eStumpf F, Keller B, Gressies C, Schuetz P. Inflammation and Nutrition: Friend or Foe? \u003cem\u003eNutrients. \u003c/em\u003e2023;15(5).\u003c/li\u003e\n\u003cli\u003eKiely M, Lord B, Ambs S. Immune response and inflammation in cancer health disparities. \u003cem\u003eTrends Cancer. \u003c/em\u003e2022;8(4):316-327.\u003c/li\u003e\n\u003cli\u003eLiu J, Xia Y, Xue F, et al. Elevated serum neutrophil-lymphocyte ratio is associated with worse long-term survival in patients with HBV-related intrahepatic cholangiocarcinoma undergoing resection. \u003cem\u003eFrontiers In Oncology. \u003c/em\u003e2022;12:1012246.\u003c/li\u003e\n\u003cli\u003eDotto-Vasquez G, Villacorta-Ampuero AK, Ulloque-Badaracco JR, et al. Lymphocyte-to-Monocyte Ratio and Clinical Outcomes in Cholangiocarcinoma: A Systematic Review and Meta-Analysis. \u003cem\u003eDiagnostics (Basel, Switzerland). \u003c/em\u003e2022;12(11).\u003c/li\u003e\n\u003cli\u003eHu G, Liu Q, Ma J-Y, Liu C-Y. Prognostic Significance of Platelet-to-Lymphocyte Ratio in Cholangiocarcinoma: A Meta-Analysis. \u003cem\u003eBiomed Res Int. \u003c/em\u003e2018;2018:7375169.\u003c/li\u003e\n\u003cli\u003eJohnson PJ, Berhane S, Kagebayashi C, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade. \u003cem\u003eJournal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology. \u003c/em\u003e2015;33(6):550-558.\u003c/li\u003e\n\u003cli\u003eMatsuda T, Umeda Y, Matsuda T, et al. Preoperative prognostic nutritional index predicts postoperative infectious complications and oncological outcomes after hepatectomy in intrahepatic cholangiocarcinoma. \u003cem\u003eBMC Cancer. \u003c/em\u003e2021;21(1):708.\u003c/li\u003e\n\u003cli\u003eLi Q, Zhang R, Fu JL, et al. [Prognostic value of preoperative peripheral blood neutrophil-to-lymphocyte ratio and \u0026gamma;-glutamyl transpeptidase-to-platelet ratio index in patients with hepatitis B virus related intrahepatic cholangiocarcinoma after radical resection]. \u003cem\u003eZhonghua Yi Xue Za Zhi. \u003c/em\u003e2021;101(38):3134-3140.\u003c/li\u003e\n\u003cli\u003eJafri SH, Shi R, Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. \u003cem\u003eBMC Cancer. \u003c/em\u003e2013;13:158.\u003c/li\u003e\n\u003cli\u003eHorino T, Tokunaga R, Miyamoto Y, et al. The advanced lung cancer inflammation index is a novel independent prognosticator in colorectal cancer patients after curative resection. \u003cem\u003eAnn Gastroenterol Surg. \u003c/em\u003e2022;6(1):83-91.\u003c/li\u003e\n\u003cli\u003ePang H, Dai L, Chen L, et al. Prognostic value of the advanced lung cancer inflammation index in patients with gastric cancer after radical gastrectomy: a propensity-score matching cohort study and meta-analysis. \u003cem\u003eBMC Cancer. \u003c/em\u003e2024;24(1):583.\u003c/li\u003e\n\u003cli\u003eQiu X, Shen S, Lu D, et al. Predictive Efficacy of the Advanced Lung Cancer Inflammation Index in Hepatocellular Carcinoma After Hepatectomy. \u003cem\u003eJ Inflamm Res. \u003c/em\u003e2024;17:5197-5210.\u003c/li\u003e\n\u003cli\u003eCatalano G, Alaimo L, Chatzipanagiotou OP, et al. Prognostic value of the advanced lung cancer inflammation index in intrahepatic cholangiocarcinoma. \u003cem\u003eEuropean Journal of Surgical Oncology : the Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology. \u003c/em\u003e2024;50(12):108773.\u003c/li\u003e\n\u003cli\u003eStrasberg SM. Nomenclature of hepatic anatomy and resections: a review of the Brisbane 2000 system. \u003cem\u003eJ Hepatobiliary Pancreat Surg. \u003c/em\u003e2005;12(5):351-355.\u003c/li\u003e\n\u003cli\u003eDindo D, Demartines N, Clavien P-A. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. \u003cem\u003eAnn Surg. \u003c/em\u003e2004;240(2):205-213.\u003c/li\u003e\n\u003cli\u003e[Evidence-based practice guidelines for standardized pathological diagnosis of primary liver cancer in China: 2015]. \u003cem\u003eZhonghua Gan Zang Bing Za Zhi. \u003c/em\u003e2015;23(5):321-327.\u003c/li\u003e\n\u003cli\u003eSasaki K, Morioka D, Conci S, et al. The Tumor Burden Score: A New \u0026quot;Metro-ticket\u0026quot; Prognostic Tool For Colorectal Liver Metastases Based on Tumor Size and Number of Tumors. \u003cem\u003eAnn Surg. \u003c/em\u003e2018;267(1):132-141.\u003c/li\u003e\n\u003cli\u003eEASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis - 2021 update. \u003cem\u003eJournal of Hepatology. \u003c/em\u003e2021;75(3):659-689.\u003c/li\u003e\n\u003cli\u003eSong Y, Cai M, Li Y, Liu S. The focus clinical research in intrahepatic cholangiocarcinoma. \u003cem\u003eEur J Med Res. \u003c/em\u003e2022;27(1):116.\u003c/li\u003e\n\u003cli\u003eSirica AE, Gores GJ, Groopman JD, et al. Intrahepatic Cholangiocarcinoma: Continuing Challenges and Translational Advances. \u003cem\u003eHepatology. \u003c/em\u003e2019;69(4):1803-1815.\u003c/li\u003e\n\u003cli\u003eGhio M, Vijay A. Intrahepatic cholangiocarcinoma: Evolving role of neoadjuvant and targeted therapy. \u003cem\u003eAnnals of Hepato-Biliary-Pancreatic Surgery. \u003c/em\u003e2023;27(2):123-130.\u003c/li\u003e\n\u003cli\u003eCoffman‐D\u0026rsquo;Annibale KL, Greten TF. Top advances of the year: Hepatobiliary cancers. \u003cem\u003eCancer. \u003c/em\u003e2023;129(8):1149-1155.\u003c/li\u003e\n\u003cli\u003eMoris D, Palta M, Kim C, Allen PJ, Morse MA, Lidsky ME. Advances in the treatment of intrahepatic cholangiocarcinoma: An overview of the current and future therapeutic landscape for clinicians. \u003cem\u003eCA Cancer J Clin. \u003c/em\u003e2023;73(2):198-222.\u003c/li\u003e\n\u003cli\u003eTsilimigras DI, Moris D, Mehta R, et al. The systemic immune-inflammation index predicts prognosis in intrahepatic cholangiocarcinoma: an international multi-institutional analysis. \u003cem\u003eHPB : the Official Journal of the International Hepato Pancreato Biliary Association. \u003c/em\u003e2020;22(12):1667-1674.\u003c/li\u003e\n\u003cli\u003eSellers CM, Uhlig J, Ludwig JM, Stein SM, Kim HS. Inflammatory markers in intrahepatic cholangiocarcinoma: Effects of advanced liver disease. \u003cem\u003eCancer Medicine. \u003c/em\u003e2019;8(13):5916-5929.\u003c/li\u003e\n\u003cli\u003eZhu J, Wang D, Liu C, et al. Development and validation of a new prognostic immune-inflammatory-nutritional score for predicting outcomes after curative resection for intrahepatic cholangiocarcinoma: A multicenter study. \u003cem\u003eFront Immunol. \u003c/em\u003e2023;14:1165510.\u003c/li\u003e\n\u003cli\u003eJager KJ, Zoccali C, Macleod A, Dekker FW. Confounding: what it is and how to deal with it. \u003cem\u003eKidney Int. \u003c/em\u003e2008;73(3):256-260.\u003c/li\u003e\n\u003cli\u003eClements O, Eliahoo J, Kim JU, Taylor-Robinson SD, Khan SA. Risk factors for intrahepatic and extrahepatic cholangiocarcinoma: A systematic review and meta-analysis. \u003cem\u003eJournal of Hepatology. \u003c/em\u003e2020;72(1).\u003c/li\u003e\n\u003cli\u003eKartikasari AER, Huertas CS, Mitchell A, Plebanski M. Tumor-Induced Inflammatory Cytokines and the Emerging Diagnostic Devices for Cancer Detection and Prognosis. \u003cem\u003eFrontiers In Oncology. \u003c/em\u003e2021;11:692142.\u003c/li\u003e\n\u003cli\u003eRodrigues PM, Olaizola P, Paiva NA, et al. Pathogenesis of Cholangiocarcinoma. \u003cem\u003eAnnu Rev Pathol. \u003c/em\u003e2021;16:433-463.\u003c/li\u003e\n\u003cli\u003eTempleton AJ, McNamara MG, \u0026Scaron;eruga B, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. \u003cem\u003eJ Natl Cancer Inst. \u003c/em\u003e2014;106(6):dju124.\u003c/li\u003e\n\u003cli\u003eCupp MA, Cariolou M, Tzoulaki I, Aune D, Evangelou E, Berlanga-Taylor AJ. Neutrophil to lymphocyte ratio and cancer prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies. \u003cem\u003eBMC Med. \u003c/em\u003e2020;18(1):360.\u003c/li\u003e\n\u003cli\u003eLin N, Li J, Yao X, et al. Prognostic value of neutrophil-to-lymphocyte ratio in colorectal cancer liver metastasis: A meta-analysis of results from multivariate analysis. \u003cem\u003eInt J Surg. \u003c/em\u003e2022;107:106959.\u003c/li\u003e\n\u003cli\u003eJaillon S, Ponzetta A, Di Mitri D, Santoni A, Bonecchi R, Mantovani A. Neutrophil diversity and plasticity in tumour progression and therapy. \u003cem\u003eNat Rev Cancer. \u003c/em\u003e2020;20(9):485-503.\u003c/li\u003e\n\u003cli\u003eHedrick CC, Malanchi I. Neutrophils in cancer: heterogeneous and multifaceted. \u003cem\u003eNat Rev Immunol. \u003c/em\u003e2022;22(3):173-187.\u003c/li\u003e\n\u003cli\u003eChen Y, Tian Z. Innate lymphocytes: pathogenesis and therapeutic targets of liver diseases and cancer. \u003cem\u003eCell Mol Immunol. \u003c/em\u003e2021;18(1):57-72.\u003c/li\u003e\n\u003cli\u003eYuen GJ, Demissie E, Pillai S. B lymphocytes and cancer: a love-hate relationship. \u003cem\u003eTrends Cancer. \u003c/em\u003e2016;2(12):747-757.\u003c/li\u003e\n\u003cli\u003eXu J, Jie Y, Sun Y, Gong D, Fan Y. Association of Global Leadership Initiative on Malnutrition with survival outcomes in patients with cancer: A systematic review and meta-analysis. \u003cem\u003eClin Nutr. \u003c/em\u003e2022;41(9):1874-1880.\u003c/li\u003e\n\u003cli\u003eMatsui R, Rifu K, Watanabe J, Inaki N, Fukunaga T. Impact of malnutrition as defined by the GLIM criteria on treatment outcomes in patients with cancer: A systematic review and meta-analysis. \u003cem\u003eClin Nutr. \u003c/em\u003e2023;42(5):615-624.\u003c/li\u003e\n\u003cli\u003eArends J. Malnutrition in cancer patients: Causes, consequences and treatment options. \u003cem\u003eEuropean Journal of Surgical Oncology : the Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology. \u003c/em\u003e2024;50(5):107074.\u003c/li\u003e\n\u003cli\u003eNa B-G, Han S-S, Cho Y-A, et al. Nutritional Status of Patients with Cancer: A Prospective Cohort Study of 1,588 Hospitalized Patients. \u003cem\u003eNutr Cancer. \u003c/em\u003e2018;70(8):1228-1236.\u003c/li\u003e\n\u003cli\u003eLaviano A, Meguid MM, Rossi-Fanelli F. Cancer anorexia: clinical implications, pathogenesis, and therapeutic strategies. \u003cem\u003eLancet Oncol. \u003c/em\u003e2003;4(11):686-694.\u003c/li\u003e\n\u003cli\u003eAntoun S, Raynard B. Muscle protein anabolism in advanced cancer patients: response to protein and amino acids support, and to physical activity. \u003cem\u003eAnnals of Oncology : Official Journal of the European Society For Medical Oncology. \u003c/em\u003e2018;29(suppl_2):ii10-ii17.\u003c/li\u003e\n\u003cli\u003eRavasco P. Nutrition in Cancer Patients. \u003cem\u003eJ Clin Med. \u003c/em\u003e2019;8(8):1211.\u003c/li\u003e\n\u003cli\u003eZhou T, Liu L, Dai H-S, et al. Impact of body mass index on postoperative outcomes in patients undergoing radical resection for hilar cholangiocarcinoma. \u003cem\u003eJournal of Surgical Oncology. \u003c/em\u003e2020;122(7):1418-1425.\u003c/li\u003e\n\u003cli\u003eShen J, Wen T, Li C, Yan L, Li B, Yang J. The Prognostic Prediction Role of Preoperative Serum Albumin Level in Patients with Intahepatic Cholangiocarcinoma Following Hepatectomy. \u003cem\u003eDig Dis. \u003c/em\u003e2018;36(4):306-313.\u003c/li\u003e\n\u003cli\u003eOettl K, Birner-Gruenberger R, Spindelboeck W, et al. Oxidative albumin damage in chronic liver failure: relation to albumin binding capacity, liver dysfunction and survival. \u003cem\u003eJournal of Hepatology. \u003c/em\u003e2013;59(5):978-983.\u003c/li\u003e\n\u003cli\u003eSpinella R, Sawhney R, Jalan R. Albumin in chronic liver disease: structure, functions and therapeutic implications. \u003cem\u003eHepatol Int. \u003c/em\u003e2016;10(1):124-132.\u003c/li\u003e\n\u003cli\u003eSong M, Zhang Q, Song C, et al. The advanced lung cancer inflammation index is the optimal inflammatory biomarker of overall survival in patients with lung cancer. \u003cem\u003eJ Cachexia Sarcopenia Muscle. \u003c/em\u003e2022;13(5):2504-2514.\u003c/li\u003e\n\u003cli\u003eZhang L, Zhao K, Kuang T, et al. The prognostic value of the advanced lung cancer inflammation index in patients with gastrointestinal malignancy. \u003cem\u003eBMC Cancer. \u003c/em\u003e2023;23(1):101.\u003c/li\u003e\n\u003cli\u003ePang H-Y, Chen X-F, Yan M-H, et al. Clinical significance of the advanced lung cancer inflammation index in gastrointestinal cancer patients: a systematic review and meta-analysis. \u003cem\u003eFrontiers In Oncology. \u003c/em\u003e2023;13:1021672.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"advanced lung cancer inflammatory index, intrahepatic cholangiocarcinoma, inflammation biomarkers, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-6452875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6452875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eThe advanced lung cancer inflammation index (ALI), which combines nutritional and inflammatory indicators, was recently identified as a promising prognostic biomarker. This study evaluates the predictive efficacy of ALI in patients undergoing curative-intent surgery for intrahepatic cholangiocarcinoma (ICC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003ePatients who underwent curative-intent surgery for ICC were identified from a large multi-institutional database. Patients were categorized into \"low ALI\" and \"high ALI\" groups, and propensity score matching (PSM) was used to minimize intergroup differences. A time-dependent receiver operating characteristic curve (time-dependent ROC) and C-index were calculated to compare the prognostic performance of ALI with other biomarkers. Multivariate Cox regression analysis was conducted to identify independent predictors associated with overall survival (OS). Based on these predictors, a dynamic nomogram was developed to predict OS in ICC patients, and its performance was evaluated using an internal validation dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eAmong 691 patients, 194 (28.08%) were categorized as having low ALI (≤ 27.6. Patients with low ALI had a worse 5-year OS rate (20.2% vs. 45.7%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and this difference remained significant after PSM (20.2% vs. 42.3%, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001). Multivariate analysis identified low ALI as an independent predictor of increased mortality both before (hazard ratio [HR] 1.54, 95% CI: 1.25 - 1.90, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and after PSM (HR 1.59, 95% CI: 1.24 - 2.04, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001). The time-dependent AUC and C-index analyses indicated that ALI (C-index: 0.603) had the best predictive ability for OS in patients with ICC. The dynamic nomogram based on ALI demonstrated excellent predictive accuracy, validated through calibration curves, time-dependent ROC curves, and decision curve analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eALI is an independent predictor of OS among patients undergoing curative-intent surgery for ICC. The prognostic ability of the ALI is superior to the other nutrition- /inflammation-related biomarkers. ALI should be incorporated into predictive models to enhance prognostic stratification.\u003c/p\u003e","manuscriptTitle":"Predictive efficacy of the advanced lung cancer inflammation index among patients with intrahepatic cholangiocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 07:31:01","doi":"10.21203/rs.3.rs-6452875/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3ced774-270a-4c08-ace0-7fde1837ad58","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-05T06:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 07:31:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6452875","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6452875","identity":"rs-6452875","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}