Prediction of postoperative recurrence of hepatocellular carcinoma after radiofrequency ablation combining psychological and sleep quality using a nomogram model based on Cox regression

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The model was further visualized for practical use. Methods A prospective follow-up was conducted at Shengzhou People’s Hospital (the First Affiliated Hospital of Zhejiang University Shengzhou Branch) from January 2013 to December 2021, including HCC patients who underwent RFA. Sleep quality and psychological status were assessed through questionnaires, and relevant baseline and tumor data were collected, including age, gender, Pathology, PT, INR, PLT, Alb, TBIL, AFP, DCP, PHT, ALBI grade, Cirrhosis, ascites, Maximum tumor diameter, and tumor number. Cox proportional hazards models were used to analyze the factors associated with postoperative recurrence, both in univariate and multivariate analysis. A nomogram prediction model was constructed, and its performance was evaluated using ROC curve, AUC, and calibration curve. Results The study included 70 patients with a mean age of 61.07 years (range: 23–87 years). The median time to recurrence was 13 months (range: 2–64 months), and 32 patients (45.70%) experienced recurrence during the follow-up period. Univariate analysis showed significant correlations between AFP, PHT, ALBI grade, DCP, Maximum tumor diameter, tumor number, cirrhosis, SAS, SDS, and postoperative recurrence in HCC patients (P < 0.05). Multivariate analysis confirmed that AFP, ALBI grade, Maximum tumor diameter, cirrhosis, SAS, and SDS were independent risk factors for postoperative recurrence (P < 0.05). The nomogram model based on these factors showed good predictive accuracy with a concordance index of 0.857 (95% CI: 0.798–0.916). The ROC curve analysis demonstrated that the nomogram model had a high predictive accuracy and clinical utility. The calibration curve showed good consistency between the predicted and actual recurrence rates. Additionally, the decision curve analysis indicated that the nomogram model had superior clinical value compared to individual variables. Conclusion AFP, ALBI grade, Maximum tumor diameter, cirrhosis, DCP, SAS, and SDS were identified as independent factors associated with postoperative recurrence in HCC patients undergoing RFA. The nomogram model incorporating these factors can provide better guidance for personalized clinical decision-making. Hepatocellular carcinoma Risk factors Postoperative recurrence Nomogram model Psychological Sleep quality Figures Figure 1 Figure 2 Figure 3 Figure 4 introduction Hepatocellular Carcinoma (HCC) is the sixth most common cancer globally and the third leading cause of cancer-related death[ 1 ]. In 2020, approximately 910,000 new cases and 830,000 deaths were reported worldwide, with China alone accounting for 410,000 new cases, nearly half of the total[ 2 ]. Currently, the main curative treatment strategies for HCC patients include surgical resection, radiofrequency ablation (RFA), and liver transplantation[ 3 ]. Over the past 20 years, local ablative therapies have emerged as safe and effective alternatives, with RFA being considered the most effective option. RFA offers advantages such as minimally invasive nature, short recovery time, and low complications, leading to its increasing popularity among patients[ 4 ]. However, the risk of HCC recurrence within 6 years after RFA is as high as 67.3%[ 5 ], resulting in a relative reduction in overall survival time. Currently, an effective solution to prevent postoperative HCC recurrence has yet to be found, highlighting the critical importance of accurately assessing the risk of recurrence to improve survival rates and reduce mortality for HCC patients. In existing research, many potential factors have been identified as associated with HCC recurrence. For instance, studies by Parissa Tabrizian et al. found that tumor size, alpha-fetoprotein (AFP) levels, BCLC staging of liver cancer, and post-recurrence treatment modalities can predict HCC recurrence[ 6 ]. Similarly, Xiu-Mei Bai et al. identified tumor size, portal hypertension, Child-Pugh score, and AFP as independent predictors of HCC recurrence[ 7 ]. Research by C.M. Conti suggested that psychological stress may increase the risk of cancer recurrence by affecting the immune system[ 8 ], while J.C. Felger’s study indicated that depression may lead to increased inflammatory cytokines, thereby exacerbating the risk of cancer recurrence[ 9 ]. Seohyuk Lee’s research highlighted a significant correlation between both excessively long and short sleep duration and increased postoperative mortality in colorectal cancer[ 10 ]. Hence, psychological status and sleep quality have gained increasing attention in the study of cancer recurrence and survival rates, as these factors may indirectly impact immune function, treatment compliance, and lifestyle factors that influence cancer recurrence. However, the research on the relationship between HCC recurrence and psychological stress and sleep quality remains relatively limited[ 11 ]. Therefore, this study aims to establish a new Cox regression-based Nomogram model that can comprehensively consider psychological status and sleep quality factors to predict the risk of HCC recurrence after surgery. This predictive model assists doctors in making more accurate assessments of each patient’s risk of recurrence, further developing more personalized and effective diagnosis and treatment plans. Study Population and Methods Study Population This study included 70 HCC patients who underwent radiofrequency ablation surgery at Shengzhou People’s Hospital (the First Affiliated Hospital of Zhejiang University Shengzhou Branch) in China from January 2013 to December 2021. The study obtained ethical approval from the Shengzhou People’s Hospital Ethics Committee and met the requirements for exempting patients from signing informed consent. The inclusion criteria were as follows: (1) patients diagnosed with primary HCC through pathology; (2) initial treatment with radiofrequency ablation; (3) liver function classified as Child-Pugh Class A or B; (4) no extrahepatic metastasis or major vessel invasion during radiofrequency ablation treatment. The exclusion criteria were: (1) concomitant tumors in other sites at the time of diagnosis; (2) incomplete clinical data or lack of follow-up information. Based on the above inclusion and exclusion criteria, a total of 70 HCC patients were included in this study. Follow-up was conducted through outpatient visits and telephone interviews. Within the first 2 years after surgery, patients were followed up every 3 months, and after 2 years, they were followed up every 6 months. After 5 years, annual follow-ups were conducted. The follow-up assessments included medical history, physical examinations, chest X-rays, liver and abdominal color Doppler ultrasonography, complete blood count, comprehensive biochemical tests, and tumor markers. CT and MRI scans were performed when necessary. The follow-up period extended until June 30, 2022, with a median follow-up time of (46 ± 1.86) (2 ~ 135) months. Observational and Evaluation Indicators (1) Recurrence outcomes, including the number of recurrences, sites of recurrence, and time to recurrence.(2) Risk factors influencing HCC recurrence: maximum tumor diameter, tumor number, portal hypertension, Child-Pugh score, and alpha-fetoprotein.(3) Construction and evaluation of a Nomogram predictive model combining postoperative HCC recurrence factors with psychological status and sleep quality. Evaluation Criteria: Recurrence refers to the occurrence of malignant tumors related to the primary lesion after HCC surgery, including local recurrence and distant metastasis. Local recurrence is defined as the recurrence in the liver region excluding distant metastasis, confirmed by histopathology or imaging examination. The discriminative ability of the Nomogram predictive model is assessed using a concordance index, which measures its ability to distinguish between patients who experience events and those who do not. A concordance index less than 0.5 indicates poor discriminative ability, 0.65 to 0.75 indicates moderate discriminative ability, and greater than 0.75 indicates good discriminative ability. Zung Self-Rating Anxiety Scale (SAS) and Pittsburgh Sleep Quality Index (PSQI) were used to measure the psychological health status of self-reported participants. The Zung Self-Rating Anxiety Scale, developed by Zung, assesses anxiety symptoms and consists of 20 questions. Questions 1–5 assess emotional symptoms of anxiety, while questions 6–20 assess physical symptoms of anxiety. Responses are rated on a 4-point scale. Statistical Analysis The statistical analysis was performed using R software version 4.2.1. Cox proportional hazards models were used to conduct univariate analysis of factors influencing postoperative HCC recurrence. Variables with a significance level of P < 0.05 in the univariate analysis were included in the multivariate Cox proportional hazards model. The accuracy of patient prognosis was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Kaplan-Meier survival curves were plotted, and the log-rank test was used to compare the outcomes of different risk subgroups. A Nomogram predictive model for postoperative HCC recurrence was constructed using R language. Bootstrap resampling was performed to validate the predictive performance of the Nomogram using the self-modeling dataset. The performance of the predictive model was evaluated using measures such as the concordance index, ROC curve, calibration curve, and decision curve analysis (DCA). Results Among the 70 patients who underwent radiofrequency ablation, 32 experienced recurrence while 38 did not. The average age in the non-recurrence group was 59.92 years, while in the recurrence group, it was 61.69 years. Females accounted for 23.3% (10 individuals) of the non-recurrence group, while females accounted for 27.5% (8 individuals) of the recurrence group. The median overall survival in the non-recurrence group was 11.00 months, while in the recurrence group, it was 6.00 months. The average platelet count in the non-recurrence group was 127.63 × 10^9/L, compared to 136.72 × 10^9/L in the recurrence group. The average albumin level in the non-recurrence group was 42.78 g/L, while in the recurrence group, it was 39.68 g/L. The average total bilirubin level in the non-recurrence group was 16.39 µmol/L, compared to 18.48 µmol/L in the recurrence group. Additionally, the average alpha-fetoprotein (AFP) level in the non-recurrence group was 80.12 ng/mL, while in the recurrence group, it was 817.25 ng/mL. The average des-gamma-carboxy prothrombin (DCP) level in the non-recurrence group was 236.94 mAU/mL, compared to 299.03 mAU/mL in the recurrence group. In terms of Child-Pugh classification, 18.4% (7 cases) in the non-recurrence group were classified as B, while 15.6% (5 cases) in the recurrence group were classified as B. In the non-recurrence group, 28.9% (11 cases) had liver cirrhosis, compared to 43.8% (14 cases) in the recurrence group. Portal hypertension was present in 65.8% (25 cases) of the non-recurrence group, while in the recurrence group, it was present in 56.2% (18 cases). Ascites was present in 78.9% (30 cases) of the non-recurrence group, compared to 81.2% (26 cases) in the recurrence group. The average tumor number in the non-recurrence group was 1.19, while in the recurrence group, it was 1.32. The average maximum tumor diameter in the non-recurrence group was 20.18 mm, compared to 31.22 mm in the recurrence group. The average Zung self-rating anxiety scale (SAS) score in the non-recurrence group was 46.87, while in the recurrence group, it was 60.77. The average self-rating depression scale (SDS) score in the non-recurrence group was 55.13, compared to 64.00 in the recurrence group. The average Pittsburgh sleep quality index (PSQI) score in the non-recurrence group was 13.49, while in the recurrence group, it was 17.20. Table 1 Basic characteristics of the study subjects No Recurrence Recurrence p n 38 32 Age (mean (SD)) 59.92 (8.23) 61.69 (11.45) 0.117 gender female 10 (23.3%) 8 (27.5%) 0.613 male 28(73.6%) 24(72.5%) Overall survival (median [IQR]) 11.00 [6.00, 21.00] 6.00 [3.00, 11.00] < 0.001 PLT (mean (SD)) 127.63 (54.50) 136.72 (89.14) 0.602 Alb (mean (SD)) 42.78 (5.42) 39.68 (5.15) 0.017 TBIL (mean (SD)) 16.39 (10.87) 18.48 (13.14) 0.001 AFP (mean (SD)) 80.12 (149.23) 817.25 (2353.14) 0.018 DCP (mean (SD)) 236.94 (89.55) 299.03 (214.39) < 0.001 Child Pugh A 31 (81.6%) 27 (84.4%) 0.179 B 7 (18.4%) 5 (15.6%) cirrhosis 11 (28.9%) 14 (43.8%) 0.001 PHT 25 (65.8%) 18 (56.2%) 0.108 ascites 30 (78.9%) 26 (81.2%) 0.156 tumor number(mean (SD)) 1.19 (0.47) 1.32 (0.66) 0.005 Maximum tumor diameter (mean (SD)) 20.18 (6.14) 31.22 (5.59) < 0.001 SAS (mean (SD)) 46.87 (4.53) 60.77 (5.66) < 0.001 SDS (mean (SD)) 55.13 (6.93) 64.00 (8.24) < 0.001 PSQI (mean (SD)) 13.49 (2.35) 17.20 (2.30) < 0.001 Survival curves for postoperative recurrence of HCC patients with optimal cutoff values and different clinicopathological characteristics Since the DCP values were outside the normal range, the ROC curve and Youden index were utilized to determine the best cutoff point. This optimal cutoff point is the point on the ROC curve that achieves the best balance between sensitivity and specificity, and it also corresponds to the maximum Youden index. Subsequently, survival curves were plotted based on this optimal cutoff point.The results revealed a significant difference between the two groups based on DCP values (P = 0.018) (Fig. 1 A). Similarly, there were significant differences observed among subgroups based on ALBI grade (P = 0.024) (Fig. 1 B), AFP (P = 0.00034) (Fig. 1 C), maximum tumor diameter (P < 0.0001) (Fig. 1 D), SAS (P = 0.024) (Fig. 1 E), and SDS (P = 0.005) (Fig. 1 F).These findings suggest that different clinical and pathological features are associated with the postoperative recurrence of HCC patients. Analysis of risk factors for postoperative recurrence of HCC patients after RFA Among 70 cases of HCC patients, out of which 32 cases experienced recurrence during the follow-up period, with 25 cases of local recurrence and 13 cases of extrahepatic metastasis. The median time to recurrence was 13 months (2–64 months), and the 5-year cumulative recurrence rate was 70.20%. Univariate Cox proportional hazard model analysis showed that AFP, PHT, ALBI grade, DCP, Maximum tumor diameter, tumor number, cirrhosis, SAS, and SDS were significantly associated with postoperative recurrence in HCC patients (P 0.05). Factors with P < 0.05 from the univariate analysis were included in the multivariate Cox proportional hazard model analysis, and the results showed that AFP, DCP, ALBI grade, Maximum tumor diameter, cirrhosis, SAS, and SDS were all significantly associated with postoperative recurrence in HCC patients (P < 0.05).The establishment of the line graph for postoperative recurrence of HCC patients was done using R software, which incorporated the significant variables obtained from the multivariate Cox proportional hazard model analysis to construct the predictive model for the line graph (Fig. 2 ). By assigning scores to the 7 significant variables in the model and summing them, a total score was obtained, and the corresponding value of the total score represented the patient’s probability of postoperative recurrence. The line graph demonstrated that Maximum tumor diameter had the greatest impact on postoperative recurrence in HCC patients.。 The establishment of the line graph for postoperative recurrence of HCC patients was done using R software, which incorporated the significant variables obtained from the multivariate Cox proportional hazard model analysis to construct the predictive model for the line graph (Fig. 2 ). By assigning scores to the meaningful variables from the model, a total score was obtained by summing these scores, and the corresponding value of the total score represented the patient’s probability of postoperative recurrence. The line graph demonstrated that Maximum tumor diameter had the greatest impact on postoperative recurrence in HCC patients. Internal validation of the line graph for postoperative recurrence in HCC patients Bootstrap resampling with 1000 iterations was used to validate the consistency between the predicted 5-year recurrence rate and the actual recurrence rate. By calculation, a C-index of 0.827 (95% CI: 0.798–0.916) was obtained, which was higher than the C-index of individual independent risk factors. This indicates that the model has good predictive performance. The time-dependent ROC curve showed AUC values of 0.757, 0.863, and 0.873 for 1 year, 3 years, and 5 years, respectively (Fig. 3 A). The 5-year ROC curve displayed that the AUC value of the line graph was higher than that of individual independent risk factors (except for Maximum tumor diameter) (Fig. 3 B). Calibration curve validation The calibration curves of the models for predicting 1-year, 3-year, and 5-year recurrence demonstrated good consistency between the predicted results and the observed results (Fig. 4 ). Discussion In this study, we constructed and validated, for the first time, a nomogram model for predicting postoperative recurrence in HCC patients undergoing radiofrequency ablation (RFA) by combining Cox regression with psychological status. The model revealed a 5-year cumulative recurrence rate of 70.20% in HCC patients after RFA. Independent risk factors affecting postoperative recurrence in HCC were identified through univariate and multivariate Cox regression analysis. Additionally, considering psychological status and sleep quality factors, we found that AFP, DCP, maximum tumor diameter, ALBI grade, cirrhosis, SAS, and SDS significantly influenced post-RFA recurrence in HCC patients. With these findings, we constructed a nomogram model that better predicts the risk of recurrence in HCC patients after RFA and is more in line with real-world scenarios. In clinical practice, the line graph serves as an easy-to-use scoring tool that enables physicians and HCC patients to obtain personalized predictions of recurrence rates. Combining depression and anxiety status allows for a more comprehensive understanding of factors related to tumor metastasis and recurrence, and when considered alongside other key variables, it facilitates better prediction of tumor recurrence. The high incidence of HCC poses a challenge to the long-term survival of patients undergoing RFA [ 12 ]. Early prediction of recurrence and intervention are necessary to prolong survival. Although the prediction of postoperative recurrence has been widely studied, there is still insufficient prediction for HCC recurrence. For example, Wang YY compared the use of Child-Pugh scores to predict the survival of patients with HCC after liver resection and found that ALBI scores proved to be a more accurate and independent prognostic scoring system in certain cases compared to the Child-Pugh score [ 13 ]. Johnson PJ proposed and established evidence-based ALBI scores, providing a new, more concise, and biomarker-based assessment of liver function to predict the survival of HCC patients[ 14 ]. This study also confirmed that ALBI scores can be used to predict post-RFA recurrence. Liver cirrhosis is one of the main risk factors for HCC, and the degree of liver cirrhosis is correlated with the risk of HCC recurrence. Patients with liver cirrhosis have a higher recurrence rate after surgical resection of HCC[ 15 ]. Some predictive variables in this study were consistent with other research results, such as maximum tumor diameter and tumor number, which have been identified as important predictors of HCC recurrence [ 16 ] [ 17 ] [ 18 ]. This study innovatively analyzed the impact of anxiety, depression, and sleep on the risk of HCC recurrence. The study found that anxiety and depression are independent risk factors for HCC recurrence, and assessing and intervening in postoperative anxiety symptoms may help delay cancer recurrence. Early-stage HCC patients with depression have an increased risk of recurrence after percutaneous ablation, suggesting a possible association between depression and HCC recurrence. This highlights the importance of timely identification and treatment of psychological issues [ 19 ]. This study also found that depression is an independent risk factor for recurrence in HCC patients after RFA, and there is a significant association between depressive symptoms and the risk of HCC recurrence. Therefore, assessing and managing mental health after surgery can help improve the recurrence rate in HCC patients after RFA. Depression and anxiety are associated with post-RFA recurrence, and it is speculated that this might be due to the alteration of hormone balance in the body caused by anxiety and depression, such as elevated cortisol levels, which is a stress hormone that may affect the function of the immune system, thus influencing cancer development and recurrence. It may also potentially impair the effectiveness of the immune system and restrict the body’s response to cancer cells. Further research is needed to investigate the mechanisms through which depression and anxiety affect the post-RFA recurrence rate in HCC patients. Our study has several limitations. The nomogram was developed based on data collected from a single institution. Although it demonstrated good performance in the internal validation cohort, external validation in another cohort is necessary to confirm its clinical utility. The model may not be applicable to patients with extremely abnormal laboratory indicators. Declarations Authors ’ contribution WWT and LDZ conceptualized this article and wrote the original draft of the manuscript.WWT, LDZ , LZR, JWY contributed to the study design, data collection, methodology, data analysis, and data interpretation. WWT, LDZ and LZR reviewed and edited the manuscript. LDZ supervised the study group. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. All authors read and approved the final version of the manuscript. Funding The author of this article declares that there is no conflict of interest in any financial or personal relationship related to this article. Availability of data and materials The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Ethics approval and consent to participate The research was conducted in accordance with good clinical practice guidelines and the Helsinki Declaration. The protocol and amendments have been approved by the Ethics Committee of Shengzhou People’s Hospital. Con fl ict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher ’ s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Clinical Trial Number in the manuscript This study is not a clinical trial, no clinical trial number References Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D: Global cancer statistics . CA Cancer J Clin 2011, 61 (2):69-90. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries . CA Cancer J Clin 2021, 71 (3):209-249. Peng ZW, Lin XJ, Zhang YJ, Liang HH, Guo RP, Shi M, Chen MS: Radiofrequency ablation versus hepatic resection for the treatment of hepatocellular carcinomas 2 cm or smaller: a retrospective comparative study . Radiology 2012, 262 (3):1022-1033. 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Tung-Ping Poon R, Fan ST, Wong J: Risk factors, prevention, and management of postoperative recurrence after resection of hepatocellular carcinoma . Ann Surg 2000, 232 (1):10-24. Jwo SC, Chiu JH, Chau GY, Loong CC, Lui WY: Risk factors linked to tumor recurrence of human hepatocellular carcinoma after hepatic resection . Hepatology 1992, 16 (6):1367-1371. Shi-Heng W, Hsu LY, Lin MC, Wu CS: Associations between depression and cancer risk among patients with diabetes mellitus: A population-based cohort study . Cancer Med 2023, 12 (19):19968-19977. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4436481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310994714,"identity":"08f1d994-f475-4f41-b8fa-d3ad2131613d","order_by":0,"name":"Weiwei Tu","email":"","orcid":"","institution":"Shengzhou People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Tu","suffix":""},{"id":310994715,"identity":"a62346d0-e2f0-45a5-84d6-2dcad94a4e9d","order_by":1,"name":"Lizhong Ren","email":"","orcid":"","institution":"Shengzhou People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lizhong","middleName":"","lastName":"Ren","suffix":""},{"id":310994716,"identity":"6214be39-dc69-493f-aba9-01749ed7ca48","order_by":2,"name":"Jinwei Ye","email":"","orcid":"","institution":"Shengzhou People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinwei","middleName":"","lastName":"Ye","suffix":""},{"id":310994717,"identity":"3b274748-2474-4a4b-9486-93a388f1c60c","order_by":3,"name":"Lidan Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYPCCAwwM7I2NDz+QpoXncLOxBGlaJNLbBHiIUWvO3nzsMc+fO/L8kg/bGCQY7OR0Gwhosew5lm7Mw/PMcObsxLYHBQzJxmYHCGgxuJFjJs0jcZhxw+3EdgMJhgOJ2whquf8GqMXgsP2GmwfbJHiI0nKDB6gl4XDihhuMRGqx7ElLk5xz4HDyzJ5EYCAbEOEXc/bDxyTe/Dls289+/OHDDxV2coS9D8RMPChcQgCkhvEHEQpHwSgYBaNgBAMA2IdEh7tkNAgAAAAASUVORK5CYII=","orcid":"","institution":"Shengzhou People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lidan","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-05-17 11:39:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4436481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4436481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58308450,"identity":"82cfc239-9d77-4637-8956-6deb07466bc2","added_by":"auto","created_at":"2024-06-13 18:48:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":929614,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival curves for postoperative recurrence of HCC patients with different clinicopathological characteristics \u003c/strong\u003eA: Survival curve for postoperative recurrence in different DCP groups; B: ALBI grade; C: Survival curve for postoperative recurrence in different AFP groups; D: Survival curve for postoperative recurrence in different MTD groups; E: Survival curve for postoperative recurrence in different SAS groups; F: Survival curve for postoperative recurrence in different SDS groups.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4436481/v1/1afb9a012eff0656b12bba27.png"},{"id":58308447,"identity":"a5fcd608-5c86-4a94-b66d-647b50da1498","added_by":"auto","created_at":"2024-06-13 18:48:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79915,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLine graph for postoperative recurrence of HCC patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4436481/v1/753b0d92dc868fb60910361e.png"},{"id":58308451,"identity":"96bc6566-510f-4e48-8f47-3eb83fc946de","added_by":"auto","created_at":"2024-06-13 18:48:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":481958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInternal validation of the predictive model for postoperative recurrence in HCC patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4436481/v1/b4f9239b8db5221d2b39ee2b.png"},{"id":58308449,"identity":"bbfc8a7d-aa72-4b22-ac69-d6a620c55c34","added_by":"auto","created_at":"2024-06-13 18:48:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":510805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves of the models for predicting 1-year, 3-year, and 5-year recurrence\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4436481/v1/ff66632ce2bc1ebb122a19ba.png"},{"id":75881084,"identity":"8f3f1bf8-cf4e-4aa5-8079-d0856132ccd4","added_by":"auto","created_at":"2025-02-10 08:32:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3773321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4436481/v1/8f97a2e2-30ea-416a-8d06-3300a8e0d0b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of postoperative recurrence of hepatocellular carcinoma after radiofrequency ablation combining psychological and sleep quality using a nomogram model based on Cox regression","fulltext":[{"header":"introduction","content":"\u003cp\u003eHepatocellular Carcinoma (HCC) is the sixth most common cancer globally and the third leading cause of cancer-related death[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2020, approximately 910,000 new cases and 830,000 deaths were reported worldwide, with China alone accounting for 410,000 new cases, nearly half of the total[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently, the main curative treatment strategies for HCC patients include surgical resection, radiofrequency ablation (RFA), and liver transplantation[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Over the past 20 years, local ablative therapies have emerged as safe and effective alternatives, with RFA being considered the most effective option. RFA offers advantages such as minimally invasive nature, short recovery time, and low complications, leading to its increasing popularity among patients[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the risk of HCC recurrence within 6 years after RFA is as high as 67.3%[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], resulting in a relative reduction in overall survival time. Currently, an effective solution to prevent postoperative HCC recurrence has yet to be found, highlighting the critical importance of accurately assessing the risk of recurrence to improve survival rates and reduce mortality for HCC patients.\u003c/p\u003e \u003cp\u003eIn existing research, many potential factors have been identified as associated with HCC recurrence. For instance, studies by Parissa Tabrizian et al. found that tumor size, alpha-fetoprotein (AFP) levels, BCLC staging of liver cancer, and post-recurrence treatment modalities can predict HCC recurrence[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, Xiu-Mei Bai et al. identified tumor size, portal hypertension, Child-Pugh score, and AFP as independent predictors of HCC recurrence[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Research by C.M. Conti suggested that psychological stress may increase the risk of cancer recurrence by affecting the immune system[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], while J.C. Felger\u0026rsquo;s study indicated that depression may lead to increased inflammatory cytokines, thereby exacerbating the risk of cancer recurrence[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Seohyuk Lee\u0026rsquo;s research highlighted a significant correlation between both excessively long and short sleep duration and increased postoperative mortality in colorectal cancer[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Hence, psychological status and sleep quality have gained increasing attention in the study of cancer recurrence and survival rates, as these factors may indirectly impact immune function, treatment compliance, and lifestyle factors that influence cancer recurrence. However, the research on the relationship between HCC recurrence and psychological stress and sleep quality remains relatively limited[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, this study aims to establish a new Cox regression-based Nomogram model that can comprehensively consider psychological status and sleep quality factors to predict the risk of HCC recurrence after surgery. This predictive model assists doctors in making more accurate assessments of each patient\u0026rsquo;s risk of recurrence, further developing more personalized and effective diagnosis and treatment plans.\u003c/p\u003e"},{"header":"Study Population and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis study included 70 HCC patients who underwent radiofrequency ablation surgery at Shengzhou People\u0026rsquo;s Hospital (the First Affiliated Hospital of Zhejiang University Shengzhou Branch) in China from January 2013 to December 2021. The study obtained ethical approval from the Shengzhou People\u0026rsquo;s Hospital Ethics Committee and met the requirements for exempting patients from signing informed consent. The inclusion criteria were as follows: (1) patients diagnosed with primary HCC through pathology; (2) initial treatment with radiofrequency ablation; (3) liver function classified as Child-Pugh Class A or B; (4) no extrahepatic metastasis or major vessel invasion during radiofrequency ablation treatment. The exclusion criteria were: (1) concomitant tumors in other sites at the time of diagnosis; (2) incomplete clinical data or lack of follow-up information.\u003c/p\u003e \u003cp\u003e Based on the above inclusion and exclusion criteria, a total of 70 HCC patients were included in this study. Follow-up was conducted through outpatient visits and telephone interviews. Within the first 2 years after surgery, patients were followed up every 3 months, and after 2 years, they were followed up every 6 months. After 5 years, annual follow-ups were conducted. The follow-up assessments included medical history, physical examinations, chest X-rays, liver and abdominal color Doppler ultrasonography, complete blood count, comprehensive biochemical tests, and tumor markers. CT and MRI scans were performed when necessary. The follow-up period extended until June 30, 2022, with a median follow-up time of (46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86) (2\u0026thinsp;~\u0026thinsp;135) months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eObservational and Evaluation Indicators\u003c/h2\u003e \u003cp\u003e(1) Recurrence outcomes, including the number of recurrences, sites of recurrence, and time to recurrence.(2) Risk factors influencing HCC recurrence: maximum tumor diameter, tumor number, portal hypertension, Child-Pugh score, and alpha-fetoprotein.(3) Construction and evaluation of a Nomogram predictive model combining postoperative HCC recurrence factors with psychological status and sleep quality.\u003c/p\u003e \u003cp\u003eEvaluation Criteria: Recurrence refers to the occurrence of malignant tumors related to the primary lesion after HCC surgery, including local recurrence and distant metastasis. Local recurrence is defined as the recurrence in the liver region excluding distant metastasis, confirmed by histopathology or imaging examination. The discriminative ability of the Nomogram predictive model is assessed using a concordance index, which measures its ability to distinguish between patients who experience events and those who do not. A concordance index less than 0.5 indicates poor discriminative ability, 0.65 to 0.75 indicates moderate discriminative ability, and greater than 0.75 indicates good discriminative ability. Zung Self-Rating Anxiety Scale (SAS) and Pittsburgh Sleep Quality Index (PSQI) were used to measure the psychological health status of self-reported participants. The Zung Self-Rating Anxiety Scale, developed by Zung, assesses anxiety symptoms and consists of 20 questions. Questions 1\u0026ndash;5 assess emotional symptoms of anxiety, while questions 6\u0026ndash;20 assess physical symptoms of anxiety. Responses are rated on a 4-point scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was performed using R software version 4.2.1. Cox proportional hazards models were used to conduct univariate analysis of factors influencing postoperative HCC recurrence. Variables with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were included in the multivariate Cox proportional hazards model. The accuracy of patient prognosis was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Kaplan-Meier survival curves were plotted, and the log-rank test was used to compare the outcomes of different risk subgroups. A Nomogram predictive model for postoperative HCC recurrence was constructed using R language. Bootstrap resampling was performed to validate the predictive performance of the Nomogram using the self-modeling dataset. The performance of the predictive model was evaluated using measures such as the concordance index, ROC curve, calibration curve, and decision curve analysis (DCA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 70 patients who underwent radiofrequency ablation, 32 experienced recurrence while 38 did not. The average age in the non-recurrence group was 59.92 years, while in the recurrence group, it was 61.69 years. Females accounted for 23.3% (10 individuals) of the non-recurrence group, while females accounted for 27.5% (8 individuals) of the recurrence group. The median overall survival in the non-recurrence group was 11.00 months, while in the recurrence group, it was 6.00 months. The average platelet count in the non-recurrence group was 127.63 \u0026times; 10^9/L, compared to 136.72 \u0026times; 10^9/L in the recurrence group. The average albumin level in the non-recurrence group was 42.78 g/L, while in the recurrence group, it was 39.68 g/L. The average total bilirubin level in the non-recurrence group was 16.39 \u0026micro;mol/L, compared to 18.48 \u0026micro;mol/L in the recurrence group. Additionally, the average alpha-fetoprotein (AFP) level in the non-recurrence group was 80.12 ng/mL, while in the recurrence group, it was 817.25 ng/mL. The average des-gamma-carboxy prothrombin (DCP) level in the non-recurrence group was 236.94 mAU/mL, compared to 299.03 mAU/mL in the recurrence group. In terms of Child-Pugh classification, 18.4% (7 cases) in the non-recurrence group were classified as B, while 15.6% (5 cases) in the recurrence group were classified as B. In the non-recurrence group, 28.9% (11 cases) had liver cirrhosis, compared to 43.8% (14 cases) in the recurrence group. Portal hypertension was present in 65.8% (25 cases) of the non-recurrence group, while in the recurrence group, it was present in 56.2% (18 cases). Ascites was present in 78.9% (30 cases) of the non-recurrence group, compared to 81.2% (26 cases) in the recurrence group. The average tumor number in the non-recurrence group was 1.19, while in the recurrence group, it was 1.32. The average maximum tumor diameter in the non-recurrence group was 20.18 mm, compared to 31.22 mm in the recurrence group. The average Zung self-rating anxiety scale (SAS) score in the non-recurrence group was 46.87, while in the recurrence group, it was 60.77. The average self-rating depression scale (SDS) score in the non-recurrence group was 55.13, compared to 64.00 in the recurrence group. The average Pittsburgh sleep quality index (PSQI) score in the non-recurrence group was 13.49, while in the recurrence group, it was 17.20.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of the study subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Recurrence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.92 (8.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.69 (11.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(73.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(72.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall survival (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.00 [6.00, 21.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00 [3.00, 11.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.63 (54.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136.72 (89.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.78 (5.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.68 (5.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.39 (10.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.48 (13.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.12 (149.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e817.25 (2353.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCP (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236.94 (89.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e299.03 (214.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChild Pugh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (81.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (84.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (65.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (56.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eascites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (78.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etumor number(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum tumor diameter (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.18 (6.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.22 (5.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAS (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.87 (4.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.77 (5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.13 (6.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.00 (8.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.49 (2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.20 (2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSurvival curves for postoperative recurrence of HCC patients with optimal cutoff values and different clinicopathological characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSince the DCP values were outside the normal range, the ROC curve and Youden index were utilized to determine the best cutoff point. This optimal cutoff point is the point on the ROC curve that achieves the best balance between sensitivity and specificity, and it also corresponds to the maximum Youden index. Subsequently, survival curves were plotted based on this optimal cutoff point.The results revealed a significant difference between the two groups based on DCP values (P\u0026thinsp;=\u0026thinsp;0.018) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Similarly, there were significant differences observed among subgroups based on ALBI grade (P\u0026thinsp;=\u0026thinsp;0.024) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), AFP (P\u0026thinsp;=\u0026thinsp;0.00034) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), maximum tumor diameter (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), SAS (P\u0026thinsp;=\u0026thinsp;0.024) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), and SDS (P\u0026thinsp;=\u0026thinsp;0.005) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).These findings suggest that different clinical and pathological features are associated with the postoperative recurrence of HCC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of risk factors for postoperative recurrence of HCC patients after RFA\u003c/h2\u003e \u003cp\u003eAmong 70 cases of HCC patients, out of which 32 cases experienced recurrence during the follow-up period, with 25 cases of local recurrence and 13 cases of extrahepatic metastasis. The median time to recurrence was 13 months (2\u0026ndash;64 months), and the 5-year cumulative recurrence rate was 70.20%. Univariate Cox proportional hazard model analysis showed that AFP, PHT, ALBI grade, DCP, Maximum tumor diameter, tumor number, cirrhosis, SAS, and SDS were significantly associated with postoperative recurrence in HCC patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); age, gender, ascites, pathology, PQSI, and Child-Pugh score were not significantly associated with postoperative recurrence in HCC patients (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Factors with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from the univariate analysis were included in the multivariate Cox proportional hazard model analysis, and the results showed that AFP, DCP, ALBI grade, Maximum tumor diameter, cirrhosis, SAS, and SDS were all significantly associated with postoperative recurrence in HCC patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).The establishment of the line graph for postoperative recurrence of HCC patients was done using R software, which incorporated the significant variables obtained from the multivariate Cox proportional hazard model analysis to construct the predictive model for the line graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By assigning scores to the 7 significant variables in the model and summing them, a total score was obtained, and the corresponding value of the total score represented the patient\u0026rsquo;s probability of postoperative recurrence. The line graph demonstrated that Maximum tumor diameter had the greatest impact on postoperative recurrence in HCC patients.。\u003c/p\u003e \u003cp\u003eThe establishment of the line graph for postoperative recurrence of HCC patients was done using R software, which incorporated the significant variables obtained from the multivariate Cox proportional hazard model analysis to construct the predictive model for the line graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By assigning scores to the meaningful variables from the model, a total score was obtained by summing these scores, and the corresponding value of the total score represented the patient\u0026rsquo;s probability of postoperative recurrence. The line graph demonstrated that Maximum tumor diameter had the greatest impact on postoperative recurrence in HCC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInternal validation of the line graph for postoperative recurrence in HCC patients\u003c/h2\u003e \u003cp\u003eBootstrap resampling with 1000 iterations was used to validate the consistency between the predicted 5-year recurrence rate and the actual recurrence rate. By calculation, a C-index of 0.827 (95% CI: 0.798\u0026ndash;0.916) was obtained, which was higher than the C-index of individual independent risk factors. This indicates that the model has good predictive performance. The time-dependent ROC curve showed AUC values of 0.757, 0.863, and 0.873 for 1 year, 3 years, and 5 years, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The 5-year ROC curve displayed that the AUC value of the line graph was higher than that of individual independent risk factors (except for Maximum tumor diameter) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCalibration curve validation\u003c/h2\u003e \u003cp\u003eThe calibration curves of the models for predicting 1-year, 3-year, and 5-year recurrence demonstrated good consistency between the predicted results and the observed results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we constructed and validated, for the first time, a nomogram model for predicting postoperative recurrence in HCC patients undergoing radiofrequency ablation (RFA) by combining Cox regression with psychological status. The model revealed a 5-year cumulative recurrence rate of 70.20% in HCC patients after RFA. Independent risk factors affecting postoperative recurrence in HCC were identified through univariate and multivariate Cox regression analysis. Additionally, considering psychological status and sleep quality factors, we found that AFP, DCP, maximum tumor diameter, ALBI grade, cirrhosis, SAS, and SDS significantly influenced post-RFA recurrence in HCC patients. With these findings, we constructed a nomogram model that better predicts the risk of recurrence in HCC patients after RFA and is more in line with real-world scenarios. In clinical practice, the line graph serves as an easy-to-use scoring tool that enables physicians and HCC patients to obtain personalized predictions of recurrence rates. Combining depression and anxiety status allows for a more comprehensive understanding of factors related to tumor metastasis and recurrence, and when considered alongside other key variables, it facilitates better prediction of tumor recurrence.\u003c/p\u003e \u003cp\u003eThe high incidence of HCC poses a challenge to the long-term survival of patients undergoing RFA [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Early prediction of recurrence and intervention are necessary to prolong survival. Although the prediction of postoperative recurrence has been widely studied, there is still insufficient prediction for HCC recurrence. For example, Wang YY compared the use of Child-Pugh scores to predict the survival of patients with HCC after liver resection and found that ALBI scores proved to be a more accurate and independent prognostic scoring system in certain cases compared to the Child-Pugh score [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Johnson PJ proposed and established evidence-based ALBI scores, providing a new, more concise, and biomarker-based assessment of liver function to predict the survival of HCC patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This study also confirmed that ALBI scores can be used to predict post-RFA recurrence. Liver cirrhosis is one of the main risk factors for HCC, and the degree of liver cirrhosis is correlated with the risk of HCC recurrence. Patients with liver cirrhosis have a higher recurrence rate after surgical resection of HCC[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Some predictive variables in this study were consistent with other research results, such as maximum tumor diameter and tumor number, which have been identified as important predictors of HCC recurrence [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study innovatively analyzed the impact of anxiety, depression, and sleep on the risk of HCC recurrence. The study found that anxiety and depression are independent risk factors for HCC recurrence, and assessing and intervening in postoperative anxiety symptoms may help delay cancer recurrence. Early-stage HCC patients with depression have an increased risk of recurrence after percutaneous ablation, suggesting a possible association between depression and HCC recurrence. This highlights the importance of timely identification and treatment of psychological issues [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This study also found that depression is an independent risk factor for recurrence in HCC patients after RFA, and there is a significant association between depressive symptoms and the risk of HCC recurrence. Therefore, assessing and managing mental health after surgery can help improve the recurrence rate in HCC patients after RFA. Depression and anxiety are associated with post-RFA recurrence, and it is speculated that this might be due to the alteration of hormone balance in the body caused by anxiety and depression, such as elevated cortisol levels, which is a stress hormone that may affect the function of the immune system, thus influencing cancer development and recurrence. It may also potentially impair the effectiveness of the immune system and restrict the body\u0026rsquo;s response to cancer cells. Further research is needed to investigate the mechanisms through which depression and anxiety affect the post-RFA recurrence rate in HCC patients.\u003c/p\u003e \u003cp\u003eOur study has several limitations. The nomogram was developed based on data collected from a single institution. Although it demonstrated good performance in the internal validation cohort, external validation in another cohort is necessary to confirm its clinical utility. The model may not be applicable to patients with extremely abnormal laboratory indicators.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWWT and LDZ conceptualized this article and wrote the original draft of the manuscript.WWT, LDZ , LZR, JWY contributed to the study design, data collection, methodology, data analysis, and data interpretation. WWT, LDZ and LZR reviewed and edited the manuscript. LDZ supervised the study group. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author of this article declares that there is no conflict of interest in any financial or personal relationship related to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was conducted in accordance with good clinical practice guidelines and the Helsinki Declaration. The protocol and amendments have been approved by the Ethics Committee of Shengzhou People\u0026rsquo;s Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCon\u003c/strong\u003e\u003cstrong\u003efl\u003c/strong\u003e\u003cstrong\u003eict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or\u0026nbsp;financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003es note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and \u0026nbsp;do \u0026nbsp;not \u0026nbsp; necessarily \u0026nbsp;represent \u0026nbsp;those \u0026nbsp; of \u0026nbsp;their \u0026nbsp;affiliated organizations, \u0026nbsp;or \u0026nbsp;those \u0026nbsp; of \u0026nbsp;the \u0026nbsp;publisher, \u0026nbsp; the \u0026nbsp;editors \u0026nbsp;and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number in the manuscript\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not a clinical trial, no clinical trial number\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D: \u003cstrong\u003eGlobal cancer statistics\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2011, \u003cstrong\u003e61\u003c/strong\u003e(2):69-90.\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2021, 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\u003cem\u003eCancer Med \u003c/em\u003e2023, \u003cstrong\u003e12\u003c/strong\u003e(19):19968-19977.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, Risk factors, Postoperative recurrence, Nomogram model, Psychological, Sleep quality","lastPublishedDoi":"10.21203/rs.3.rs-4436481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4436481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the risk factors for postoperative recurrence in patients with hepatocellular carcinoma (HCC) and develop a Cox regression-based nomogram model incorporating psychological factors and sleep quality to predict postoperative recurrence after radiofrequency ablation (RFA) for HCC. The model was further visualized for practical use.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective follow-up was conducted at Shengzhou People\u0026rsquo;s Hospital (the First Affiliated Hospital of Zhejiang University Shengzhou Branch) from January 2013 to December 2021, including HCC patients who underwent RFA. Sleep quality and psychological status were assessed through questionnaires, and relevant baseline and tumor data were collected, including age, gender, Pathology, PT, INR, PLT, Alb, TBIL, AFP, DCP, PHT, ALBI grade, Cirrhosis, ascites, Maximum tumor diameter, and tumor number. Cox proportional hazards models were used to analyze the factors associated with postoperative recurrence, both in univariate and multivariate analysis. A nomogram prediction model was constructed, and its performance was evaluated using ROC curve, AUC, and calibration curve.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 70 patients with a mean age of 61.07 years (range: 23\u0026ndash;87 years). The median time to recurrence was 13 months (range: 2\u0026ndash;64 months), and 32 patients (45.70%) experienced recurrence during the follow-up period. Univariate analysis showed significant correlations between AFP, PHT, ALBI grade, DCP, Maximum tumor diameter, tumor number, cirrhosis, SAS, SDS, and postoperative recurrence in HCC patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate analysis confirmed that AFP, ALBI grade, Maximum tumor diameter, cirrhosis, SAS, and SDS were independent risk factors for postoperative recurrence (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The nomogram model based on these factors showed good predictive accuracy with a concordance index of 0.857 (95% CI: 0.798\u0026ndash;0.916). The ROC curve analysis demonstrated that the nomogram model had a high predictive accuracy and clinical utility. The calibration curve showed good consistency between the predicted and actual recurrence rates. Additionally, the decision curve analysis indicated that the nomogram model had superior clinical value compared to individual variables.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAFP, ALBI grade, Maximum tumor diameter, cirrhosis, DCP, SAS, and SDS were identified as independent factors associated with postoperative recurrence in HCC patients undergoing RFA. The nomogram model incorporating these factors can provide better guidance for personalized clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Prediction of postoperative recurrence of hepatocellular carcinoma after radiofrequency ablation combining psychological and sleep quality using a nomogram model based on Cox regression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 18:47:55","doi":"10.21203/rs.3.rs-4436481/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":"13738e13-b10a-4be8-8be9-1762e02ac802","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T08:23:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 18:47:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4436481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4436481","identity":"rs-4436481","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}