A model for predicting risk of early recurrence in patients undergoing radiofrequency ablation for early-stage hepatocellular carcinoma

A model for predicting risk of early recurrence in patients undergoing radiofrequency ablation for early-stage hepatocellular carcinoma | 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 Article A model for predicting risk of early recurrence in patients undergoing radiofrequency ablation for early-stage hepatocellular carcinoma Yi-Hao Yen, Kwong-Ming Kee, Chao-Hung Hung, Chien-Hung Chen, Tsung-Hui Hu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3901300/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 Few studies have reported models predicting early recurrence in patients undergoing radiofrequency ablation (RFA) for early-stage hepatocellular carcinoma (HCC). We aim to present such a model. We enrolled 791 patients with newly diagnosed early-stage HCC (i.e., within Milan criteria) and Child–Pugh class A liver disease undergoing percutaneous RFA. Survival analysis was performed using the Kaplan − Meier method with the log-rank test. Cox proportional hazards analysis was used to identify prognostic factors associated with early recurrence (i.e., recurrence within two years after RFA). Internal validation was performed with a bootstrapping method. Early recurrence was identified in 270 (34.1%) patients. Multivariate analysis showed that multiple tumors (HR = 1.450; 95% CI = 1.098–1.914; p = 0.009), alpha-fetoprotein (AFP) ≥ 20 ng/ml (HR = 1.614; 95% CI = 1.268–2.054; p < 0.001), and Model for End-Stage Liver Disease (MELD) score (HR = 1.026; 95% CI = 1.003–1.049; p = 0.025) were associated with early recurrence. We constructed a predictive model with these variables. This model provided three risk strata for recurrence-free survival (RFS): low risk, intermediate risk, and high risk, with two-year RFS of 63%, 57%, and 40%, respectively (p < 0.001). Calibration plots showed overall high agreement between the predictions made by the model and observed outcomes. In conclusion, we developed a risk prediction model to predict early recurrence in patients undergoing RFA for early-stage HCC. radiofrequency ablation hepatocellular carcinoma early recurrence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Radiofrequency ablation (RFA) is widely used to treat hepatocellular carcinoma (HCC) and is recommended as one of the curative treatments for early-stage disease [ 1 , 2 ]. However, the tumor recurrence rate of patients with HCC is high after RFA [ 3 – 6 ]. IMbrave050 is a randomized, open-label, multicenter, phase 3 trial that enrolled patients with HCC undergoing liver resection or RFA with high risk of tumor recurrence. The results showed that the adjuvant atezolizumab plus bevacizumab was associated with significantly improved recurrence-free survival (RFS) (median: not evaluable [NE; 95% confidence interval (CI): 22.1–NE]) compared to active surveillance (median: NE; 95% CI: 21.4–NE; hazard ratio [HR]: 0.72 [adjusted 95% CI: 0.53–0.98; p = 0·012]) [ 7 ]. IMbrave050 is the first phase 3 study of adjuvant treatment for HCC to report positive results [ 7 ]. However, this trial mainly enrolled patients undergoing liver resection. Only 41 (12%) patients out of 334 in the atezolizumab plus bevacizumab arm and only 42 (13%) patients out of 334 in the active surveillance arm underwent RFA [ 7 ]. Therefore, we could not confirm whether patients with HCC undergoing RFA in this trial would benefit from adjuvant treatment. HCC recurrence is generally classified into early or late using two years after resection or ablation as the cutoff time point based on the assumed mechanisms [ 8 ], i.e., early recurrence is assumed to result from pre-existing intrahepatic metastasis, whereas late recurrence is regarded as de novo tumor growth due to underlying liver disease [ 9 ]. Therefore, the ideal candidates for adjuvant treatment would be those with a high risk of early recurrence. Only a few studies have reported risk factors or have modeled early recurrence in patients undergoing RFA for early-stage HCC [ 10 – 12 ]. Therefore, we aimed to develop such a model in this retrospective study. Materials and methods The requirement of informed consent was waived by the Institutional Review Board of Chang Gung Memorial Hospital–Kaohsiung Branch (reference number: 202201189B0) due to the retrospective nature of this study and anonymous analysis of data. All methods were performed in accordance with the relevant guidelines and regulations. Research have been performed in accordance with the Declaration of Helsinki. The experimental protocols were approved by the Institutional Review Board of Chang Gung Memorial Hospital–Kaohsiung Branch (reference number: 202201189B0). All experiments were performed in accordance with relevant guidelines and regulations. Data were extracted from the Kaohsiung Chang Gung Memorial Hospital HCC registry database. Our HCC registry has adopted the original Barcelona Clinic Liver Cancer (BCLC) staging [ 13 ]. BCLC stage 0 is defined as a single tumor of ≤ 2.0 cm without macrovascular invasion or extrahepatic metastasis, with an Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 0 and Child–Pugh class A liver disease [ 14 ]. BCLC stage A is defined as a single tumor of 2–5 cm or < 3 tumor nodules of ≤ 3 cm without macrovascular invasion or extrahepatic metastasis, with an ECOG-PS of 0 and Child–Pugh class A or B liver disease. The criterion for inclusion in this study was patients with newly diagnosed early-stage HCC (i.e., within Milan criteria) and Child–Pugh class A liver disease undergoing percutaneous RFA. The exclusion criterion was patients with incomplete ablation. The diagnosis of HCC was according to guideline recommendations. 1, 2 The flowchart of patient enrollment is shown in Fig. 1 . Workup before RFA Contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) of the abdomen and chest X-ray were performed for tumor staging of all patients. Chest CT or bone scan was performed if lung or bone metastasis was suspected. Serum alpha-fetoprotein (AFP), hepatitis B virus surface antigen (HBsAg), and anti-hepatitis C virus (HCV) antibody levels, blood biochemistry, complete blood cell count, and prothrombin time were obtained within one week of RFA. The formula for Model for End-Stage Liver Disease (MELD) was 9.57 × log e (creatinine) + 3.78 × log e (total bilirubin) + 11.2 × log e (international normalized ratio [INR]) + 6.43 [ 15 ]. RFA procedure All patients underwent general anesthesia or sedation. Anesthesia or sedation was described in detail in our previous study [ 16 ]. All patients underwent ultrasound-guided RFA. Patients with a tumor size of ≤ 3.0 cm were ablated with a single electrode device and patients with a tumor size of 3.0–5.0 cm were ablated with a multiple-electrode switching system (Medtronic, Minneapolis, MN, USA; RF Medical Co., Seoul, Korea; STARmed, Seoul, South Korea). Contrast-enhanced CT or MRI scans were performed to assess treatment effects one month after RFA. Complete ablation was considered if no enhanced area was demonstrated at the site of the index tumor. After RFA, all patients were followed up with serum AFP plus ultrasound every three months and contrast-enhanced CT every 6–12 months. The primary endpoint was RFS, which was measured from the date of RFA treatment to the date of first recurrence or the last follow-up date. Overall survival (OS) was calculated as the time from the date of RFA treatment until death or the last follow-up date. Recurrence was defined according to guideline recommendations [ 1 , 2 ]. Statistical analyses Data are presented as number (percentage) or median (interquartile range [IQR]). OS and RFS were compared between groups using the Kaplan–Meier estimator and log-rank test. To identify prognostic factors, a multivariate regression analysis was performed using univariate Cox proportional hazards analysis, with p < 0.05 as the significance level. The risk score was derived using the beta coefficient estimated from Cox regression analysis. We then divided the study cohort into three risk groups using the 50th and 85th percentiles of the estimated risk score based on early recurrence [ 17 ]: low-risk (minimum risk) group of patients with their risk scores within the 50th percentile; intermediate-risk (medium risk) group with their risk scores between the 51th and 85th percentiles; and high-risk (maximum risk) group with their risk scores above the 85th percentile. Internal validation was performed by using bootstrapping with 200 resamplings. The model was calibrated using calibration plots and by comparing predicted and observed survival curves. All p-values were two-tailed, and a p < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS Statistics software (version 25; IBM Corp., Armonk, NY, USA). Results Characteristics of patients with early-stage HCC undergoing RFA In the cohort of patients in this study, 389 (49.2%) were aged > 65 years; 495 (62.6%) were male; 162 (20.5%) were with multiple tumors; 459 (58.0%) were with tumors > 20 mm in size; 289 (36.5%) were BCLC stage 0; 502 (63.5%) were BCLC stage A; 289 (36.5%) had an AFP level of ≥ 20 ng/ml; 330 (41.7%) were HBsAg positive; 376 (47.5%) were anti-HCV positive; 378 (47.8%) were with HCCs diagnosed by pathology; the median (IQR) body mass index was 25.2 (22.8–27.9); and the median (IQR) MELD score was 8.2 (6.8–10.1). Five-year OS and RFS in this cohort The five-year OS was 60% (Fig. 2 ) and the five-year RFS was 34% (Fig. 3 ). The median (IQR) follow-up period was 2.3 (0.9–4.9) years. Univariate and multivariate Cox proportional hazards analyses of two-year RFS Univariate analyses showed that multiple tumors (HR = 1.387; 95% CI = 1.052–1.828; p = 0.020), AFP ≥ 20 ng/ml (HR = 1.574; 95% CI = 1.239–2.000; p < 0.001), MELD score per one increase (HR = 1.025; 95% CI = 1.002–1.048; p = 0.030), and HBsAg positivity (HR = 0.768; 95% CI = 0.600–0.983; p = 0.036) were associated with inferior two-year RFS (Table 1 ). Multivariate analyses showed that multiple tumors (HR = 1.450; 95% CI = 1.098–1.914; p = 0.009), AFP ≥ 20 ng/ml (HR = 1.614; 95% CI = 1.268–2.054; p < 0.001), and MELD score per one increase (HR = 1.026; 95% CI = 1.003–1.049; p = 0.025) were associated with inferior two-year RFS (Table 2 ). Table 1 Univariate Cox proportional hazards analysis of two-year recurrence-free survival. HR (95% CI) p Age > 65 years 0.980 (0.772–1.244) 0.869 Men 0.868 (0.681–1.106) 0.253 Multiple tumors 1.387 (1.052–1.828) 0.020 Tumor size > 20 mm 1.275 (0.997–1.630) 0.053 AFP ≥ 20 ng/ml 1.574 (1.239–2.000) < 0.001 MELD, per one increase 1.025 (1.002–1.048) 0.030 HBsAg positive 0.768 (0.600–0.983) 0.036 Anti-HCV positive 1.241 (0.978–1.575) 0.075 AFP, alpha-fetoprotein; HBsAg, hepatitis B virus surface antigen, HCV, hepatitis C virus; MELD, Model for End-Stage Liver Disease; HR, hazard ratio; CI, confidence interval Table 2 Multivariate Cox proportional hazards analysis of two-year recurrence-free survival. beta coefficient HR 95% CI p Multiple tumors 0.371 1.450 1.098–1.914 0.009 AFP ≥ 20 ng/ml 0.479 1.614 1.268–2.054 < 0.001 MELD score 0.026 1.026 1.003–1.049 0.025 AFP, alpha-fetoprotein; MELD, Model for End-Stage Liver Disease; HR, hazard ratio; CI, confidence interval Construction of the model for predicting early recurrence We used the beta coefficients of the multivariate Cox regression analyses to predict early recurrence (Table 2 ). Three predictive factors—AFP level, tumor number, and MELD score—were included in the final model. The formula for the risk score was as follows: risk score = 0.371 × tumor number (multiple vs single) + 0.479 × AFP level (≥ 20 vs < 20 ng/ml) + 0.026 × MELD score. A value of 1 was assigned to patients with multiple tumors and an AFP level of ≥ 20 ng/mL. A value of 0 was assigned to patients with a single tumor and an AFP level of < 20 ng/mL. For example, for a patient with HCC and a single tumor, AFP level of ≥ 20 ng/ml and MELD score of 9, the risk score = 0.371 × 0 + 0.479 × 1 + 0.026 × 9 = 0.713. After excluding four patients with missing MELD scores and one patient with missing AFP level, data for the remaining 786 patients were applied to the risk score to predict early recurrence. The patient-specific linear prediction was calculated and cutoff values were applied to classify patients into three prognostic groups. Patients with scores ≤ 0.5382 were classified as low risk, those with scores 0.5382–0.7624 as medium risk, and those with scores > 0.7624 as high risk; 400 patients (50.9%) were at low risk, 269 (34.2%) at medium risk, and 117 (14.9%) at high risk. During the follow-up period, 114 (28.5%) patients showed early recurrence in the low-risk group, 96 (35.7%) in the intermediate-risk group, and 60 (51.3%) in the high-risk group. Their two-year RFS was 63%, 57%, and 40% (p < 0.001), respectively (Fig. 4 ). Two-year RFS was significantly different between the low- and medium-risk groups (p = 0.039), between the low- and high-risk groups (p < 0.001), and between the medium and high-risk groups (p = 0.001; Fig. 4 ). During the follow-up period, 75 (18.8%) patients died within five years after RFA in the low-risk group, 84 (31.2%) in the intermediate-risk group, and 45 (38.5%) in the high-risk group. This model also provided three risk strata for five-year OS: low risk, with five-year OS of 70%; intermediate risk, with five-year OS of 55%; and high risk, with five-year OS of 40% (p < 0.001). Five-year OS was significantly different between the low- and medium-risk groups (p = 0.001), between the low- and high-risk groups (p < 0.001), and between the medium and high-risk groups (p = 0.045; Fig. 5 ). Calibration The calibration plots showed overall high agreement between the predictions made by the model and observed outcomes (Fig. 6 ). Discussion We developed a model with three variables—AFP level, tumor number, and MELD score—to predict early recurrence. This model could stratify patients into three groups based on early recurrence. Internal validation with bootstrapping showed overall high agreement between the predictions made by the model and observed outcomes. In addition, it could also stratify patients into three groups based on five-year OS. Elevated AFP levels and multiple tumors are well-known prognostic factors of patients with HCC [ 1 , 2 ]. Our study showed that an AFP level of ≥ 20 ng/mL was associated with early recurrence. This cutoff was set according to the American Association for the Study of Liver Diseases guidelines, which recommend that patients at risk of and undergoing surveillance for HCC with an AFP level of ≥ 20 ng/mL should be examined using contrast-enhanced CT or MRI [ 2 ]. Late recurrence is regarded as de novo tumor growth due to underlying liver disease [ 9 ]. Therefore, poor liver function reserve is assumed to be associated with late recurrence. However, a higher MELD score was associated with early recurrence in the present study. A similar finding was noted in previous studies, which also showed that poor liver function reserve (i.e., a lower albumin level and higher albumin–bilirubin grade) was associated with early recurrence [ 10 , 18 ]. Tumor size is a well-known prognostic factor of patients with HCC [ 1 , 2 ]. We used 20 mm as the cutoff tumor size in the univariate analysis for two-year RFS due to multiple studies showing that a tumor size of > 20 mm is a significant predictive factor of local tumor progression after RFA [ 19 – 23 ]. However, this variable was not statistically significant in the univariate analysis of two-year RFS (p = 0.053). The model in this study also provided three risk strata for OS—low risk, with five-year OS of 70%; intermediate risk, with five-year OS of 55%; and high risk, with five-year OS of 40% (p < 0.001). We believe that high-risk patients may be ideal candidates for adjuvant treatment due to poor five-year OS; low-risk patients may not benefit from adjuvant treatment due to satisfactory five-year OS, whereas the benefit for the intermediate group is debatable. Therefore, our model could be applied to clinical practice. The IMbrave050 trial enrolled patients undergoing ablation with a single tumor of > 2 cm but ≤ 5 cm or multiple tumors (up to four), all ≤ 5 cm. This trial excluded patients with BCLC stage 0 [ 7 ]. However, according to our predictive model, a patient with BCLC stage 0 presenting with an AFP level of ≥ 20 ng/ml and a MELD score of ≥ 11 is at high risk of early recurrence. Therefore, we believe that BCLC stage 0 patients with AFP elevation and poor liver function reserve may benefit from adjuvant therapy. Although multiple studies have reported predictive factors of RFS of patients with early-stage HCC undergoing RFA [ 10 – 12 , 24 – 27 ],few have reported predictive factors of early recurrence. Xin et al. used the systemic inflammation response index (defined as neutrophil count × monocyte count/lymphocyte count), AFP, and tumor number and size to predict two-year RFS. 11 Yang et al. used multiple tumors and AFP, gamma-glutamyltransferase (γ-GT), and serum albumin levels to predict early recurrence [ 10 ]. Cha et al. predicted early recurrence using variables including age, albumin–bilirubin grade, AFP, protein induced by vitamin K absence or antagonist-II, and variables from image studies, such as non-rim hyperenhancement, enhancing capsule, low signal intensity of the lesion on hepatobiliary phase (HBP) on gadoxetic acid-enhanced MRI, etc. Although Cha et al.’s model is complicated, the addition of image features that suggest risk of microvascular invasion, i.e., peritumoral parenchymal enhancement and peritumoral hypointensity on HBP, may increase the predictive accuracy of early recurrence [ 12 ]. The strength of the present study is its large sample size. However, it also has limitations: being a retrospective single-center study and the model lacking external validation. Conclusion We have developed a risk prediction model to forecast early recurrence in patients with early-stage HCC undergoing RFA. This model can be applied to ascertain patients with HCC who might benefit from adjuvant treatment. However, external validation of our model is necessary. Declarations Conflict of interest: The authors have no conflicts of interest to disclose for all authors. Data availability: all data is available Ethics approval: The requirement of informed consent was waived by the Institutional Review Board of Chang Gung Memorial Hospital–Kaohsiung Branch (reference number: 202201189B0) due to the retrospective nature of this study and anonymous analysis of data. Consent to participate. Written consents were waived by the IRB due to the retrospective nature of this study and anonymous analysis of data. Consent for publication. All authors agree to publication if the paper is accepted Acknowledgement The authors thank Cancer Center, Kaohsiung Chang Gung Memorial Hospital for the provision of HCC registry data. The authors thank Chih-Yun Lin and Nien-Tzu Hsu and the Biostatistics Center, Kaohsiung Chang Gung Memorial Hospital for statistics work. This study was supported by Grant CMRPG8N1131 from the Chang Gung Memorial Hospital-Kaohsiung Medical Center, Taiwan. No conflict of interests. Authors’ Contributions Study conception and design: YHY Acquisition of data: all authors Analysis and interpretation of data: CYL Drafting of manuscript: YHY Critical revision of manuscript: all authors Approval of manuscript: all authors Data Availability Statement Raw data for the cohort involved in this study is available via the following digital object identifier: https: https://www.dropbox.com/scl/fi/unezavq59ethkc6sprtu9/raw-data.xlsx?rlkey=cwu1zq6f9bguxiq0ghqqfvvh3&dl=0 Funding information This study was supported by Grant CMRPG8N1131 from the Chang Gung Memorial Hospital-Kaohsiung Medical Center, Taiwan. No conflict of interests. References European Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol 2018 ;69:182-236. doi: 10.1016/j.jhep.2018.03.019. Marrero JA, Kulik LM, Sirlin CB, et al. 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Liver Int. 2020 May;40(5):1189-1200. doi: 10.1111/liv.14406. Kim CG, Lee HW, Choi HJ, et al. Development and validation of a prognostic model for patients with hepatocellular carcinoma undergoing radiofrequency ablation. Cancer Med. 2019;8:5023-5032. doi: 10.1002/cam4.2417. 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. 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-3901300","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":273113869,"identity":"e310ce11-dd1e-4d09-b1b2-c8e01d03ef10","order_by":0,"name":"Yi-Hao 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kwong-Ming","middleName":"","lastName":"Kee","suffix":""},{"id":273113871,"identity":"d28c6c01-3f13-4516-a1a3-96895c654712","order_by":2,"name":"Chao-Hung Hung","email":"","orcid":"","institution":"Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chao-Hung","middleName":"","lastName":"Hung","suffix":""},{"id":273113872,"identity":"8817b739-9a77-4429-90df-15f940aa236a","order_by":3,"name":"Chien-Hung Chen","email":"","orcid":"","institution":"Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chien-Hung","middleName":"","lastName":"Chen","suffix":""},{"id":273113873,"identity":"2d587294-b3c9-41d7-8212-a0108715cdcd","order_by":4,"name":"Tsung-Hui Hu","email":"","orcid":"","institution":"Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tsung-Hui","middleName":"","lastName":"Hu","suffix":""},{"id":273113874,"identity":"34735cd0-3ab9-48e6-ab3d-833515c02626","order_by":5,"name":"Jing-Houng Wang","email":"","orcid":"","institution":"Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing-Houng","middleName":"","lastName":"Wang","suffix":""},{"id":273113875,"identity":"8a5e4aa8-2ab3-4aeb-8fb8-1f79dc0e7247","order_by":6,"name":"Chih-Yun Lin","email":"","orcid":"","institution":"Kaohsiung Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chih-Yun","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-01-26 23:29:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3901300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3901300/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51238280,"identity":"6c5d6c80-1f28-4ba4-ac9b-ac0ece67b6c5","added_by":"auto","created_at":"2024-02-16 16:48:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51873,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient enrollment.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3901300/v1/65b663d1c1f28e06fc2d096b.png"},{"id":51238313,"identity":"1ef4dd3c-1ea8-4363-8cda-0119e718a16f","added_by":"auto","created_at":"2024-02-16 16:48:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72470,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves for overall survival.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3901300/v1/11cc34ea88ddec656bf21071.png"},{"id":51238281,"identity":"f4f090d7-89fd-4dd9-9a07-60241d804e5c","added_by":"auto","created_at":"2024-02-16 16:48:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77400,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves for recurrence-free survival.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3901300/v1/d9497dd00448b8b7b60bcd4b.png"},{"id":51238914,"identity":"ed2355ef-3948-4a1d-ad94-3cdafbe77c72","added_by":"auto","created_at":"2024-02-16 16:56:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104221,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves for two-year recurrence-free survival stratified by risk groups.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3901300/v1/ea4c3bdf2e4139306935fb85.png"},{"id":51238316,"identity":"7c0f98dd-1957-4aa1-aa37-328c3300a0d4","added_by":"auto","created_at":"2024-02-16 16:48:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101789,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves for five-year overall survival stratified by risk groups.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3901300/v1/00a245fc51e2cda219ac4d07.png"},{"id":51238314,"identity":"125ee374-02af-47e6-aaf6-db50de1de6db","added_by":"auto","created_at":"2024-02-16 16:48:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":119680,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots for our model for predicting two-year recurrence-free survival. Blue dashed line: observed; solid red line: optimism-corrected.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-3901300/v1/b5e56859d3494d7fbe2e49f4.png"},{"id":57186699,"identity":"7daf6658-3169-4f38-90ec-0f1cec7cbbed","added_by":"auto","created_at":"2024-05-27 06:05:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":975793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3901300/v1/ec7fb8ee-6225-490c-8251-7423b143cd98.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A model for predicting risk of early recurrence in patients undergoing radiofrequency ablation for early-stage hepatocellular carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRadiofrequency ablation (RFA) is widely used to treat hepatocellular carcinoma (HCC) and is recommended as one of the curative treatments for early-stage disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the tumor recurrence rate of patients with HCC is high after RFA [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. IMbrave050 is a randomized, open-label, multicenter, phase 3 trial that enrolled patients with HCC undergoing liver resection or RFA with high risk of tumor recurrence. The results showed that the adjuvant atezolizumab plus bevacizumab was associated with significantly improved recurrence-free survival (RFS) (median: not evaluable [NE; 95% confidence interval (CI): 22.1\u0026ndash;NE]) compared to active surveillance (median: NE; 95% CI: 21.4\u0026ndash;NE; hazard ratio [HR]: 0.72 [adjusted 95% CI: 0.53\u0026ndash;0.98; p\u0026thinsp;=\u0026thinsp;0\u0026middot;012]) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIMbrave050 is the first phase 3 study of adjuvant treatment for HCC to report positive results [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, this trial mainly enrolled patients undergoing liver resection. Only 41 (12%) patients out of 334 in the atezolizumab plus bevacizumab arm and only 42 (13%) patients out of 334 in the active surveillance arm underwent RFA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, we could not confirm whether patients with HCC undergoing RFA in this trial would benefit from adjuvant treatment.\u003c/p\u003e \u003cp\u003eHCC recurrence is generally classified into early or late using two years after resection or ablation as the cutoff time point based on the assumed mechanisms [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], i.e., early recurrence is assumed to result from pre-existing intrahepatic metastasis, whereas late recurrence is regarded as de novo tumor growth due to underlying liver disease [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, the ideal candidates for adjuvant treatment would be those with a high risk of early recurrence. Only a few studies have reported risk factors or have modeled early recurrence in patients undergoing RFA for early-stage HCC [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, we aimed to develop such a model in this retrospective study.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe requirement of informed consent was waived by the Institutional Review Board of Chang Gung Memorial Hospital\u0026ndash;Kaohsiung Branch (reference number: 202201189B0) due to the retrospective nature of this study and anonymous analysis of data. All methods were performed in accordance with the relevant guidelines and regulations. Research have been performed in accordance with the Declaration of Helsinki. The experimental protocols were approved by the Institutional Review Board of Chang Gung Memorial Hospital\u0026ndash;Kaohsiung Branch (reference number: 202201189B0). All experiments were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003cp\u003eData were extracted from the Kaohsiung Chang Gung Memorial Hospital HCC registry database. Our HCC registry has adopted the original Barcelona Clinic Liver Cancer (BCLC) staging [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. BCLC stage 0 is defined as a single tumor of \u0026le;\u0026thinsp;2.0 cm without macrovascular invasion or extrahepatic metastasis, with an Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 0 and Child\u0026ndash;Pugh class A liver disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. BCLC stage A is defined as a single tumor of 2\u0026ndash;5 cm or \u0026lt;\u0026thinsp;3 tumor nodules of \u0026le;\u0026thinsp;3 cm without macrovascular invasion or extrahepatic metastasis, with an ECOG-PS of 0 and Child\u0026ndash;Pugh class A or B liver disease.\u003c/p\u003e \u003cp\u003eThe criterion for inclusion in this study was patients with newly diagnosed early-stage HCC (i.e., within Milan criteria) and Child\u0026ndash;Pugh class A liver disease undergoing percutaneous RFA. The exclusion criterion was patients with incomplete ablation. The diagnosis of HCC was according to guideline recommendations. \u003csup\u003e1, 2\u003c/sup\u003e The flowchart of patient enrollment is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eWorkup before RFA\u003c/h2\u003e \u003cp\u003eContrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) of the abdomen and chest X-ray were performed for tumor staging of all patients. Chest CT or bone scan was performed if lung or bone metastasis was suspected. Serum alpha-fetoprotein (AFP), hepatitis B virus surface antigen (HBsAg), and anti-hepatitis C virus (HCV) antibody levels, blood biochemistry, complete blood cell count, and prothrombin time were obtained within one week of RFA. The formula for Model for End-Stage Liver Disease (MELD) was 9.57 \u0026times; log\u003csub\u003ee\u003c/sub\u003e(creatinine)\u0026thinsp;+\u0026thinsp;3.78 \u0026times; log\u003csub\u003ee\u003c/sub\u003e(total bilirubin)\u0026thinsp;+\u0026thinsp;11.2 \u0026times; log\u003csub\u003ee\u003c/sub\u003e(international normalized ratio [INR])\u0026thinsp;+\u0026thinsp;6.43 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRFA procedure\u003c/h2\u003e \u003cp\u003eAll patients underwent general anesthesia or sedation. Anesthesia or sedation was described in detail in our previous study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. All patients underwent ultrasound-guided RFA. Patients with a tumor size of \u0026le;\u0026thinsp;3.0 cm were ablated with a single electrode device and patients with a tumor size of 3.0\u0026ndash;5.0 cm were ablated with a multiple-electrode switching system (Medtronic, Minneapolis, MN, USA; RF Medical Co., Seoul, Korea; STARmed, Seoul, South Korea). Contrast-enhanced CT or MRI scans were performed to assess treatment effects one month after RFA. Complete ablation was considered if no enhanced area was demonstrated at the site of the index tumor. After RFA, all patients were followed up with serum AFP plus ultrasound every three months and contrast-enhanced CT every 6\u0026ndash;12 months.\u003c/p\u003e \u003cp\u003eThe primary endpoint was RFS, which was measured from the date of RFA treatment to the date of first recurrence or the last follow-up date. Overall survival (OS) was calculated as the time from the date of RFA treatment until death or the last follow-up date. Recurrence was defined according to guideline recommendations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eData are presented as number (percentage) or median (interquartile\u003c/p\u003e \u003cp\u003erange [IQR]). OS and RFS were compared between groups using the Kaplan\u0026ndash;Meier estimator and log-rank test. To identify prognostic factors, a multivariate regression analysis was performed using univariate Cox proportional hazards analysis, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the significance level. The risk score was derived using the beta coefficient estimated from Cox regression analysis. We then divided the study cohort into three risk groups using the 50th and 85th percentiles of the estimated risk score based on early recurrence [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]: low-risk (minimum risk) group of patients with their risk scores within the 50th percentile; intermediate-risk (medium risk) group with their risk scores between the 51th and 85th percentiles; and high-risk (maximum risk)\u003c/p\u003e \u003cp\u003egroup with their risk scores above the 85th percentile. Internal validation was performed by using bootstrapping with 200 resamplings. The model was calibrated using calibration plots and by comparing predicted and observed survival curves. All p-values were two-tailed, and a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using SPSS Statistics software (version 25; IBM Corp., Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of patients with early-stage HCC undergoing RFA\u003c/h2\u003e \u003cp\u003eIn the cohort of patients in this study, 389 (49.2%) were aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years; 495 (62.6%) were male; 162 (20.5%) were with multiple tumors; 459 (58.0%) were with tumors\u0026thinsp;\u0026gt;\u0026thinsp;20 mm in size; 289 (36.5%) were BCLC stage 0; 502 (63.5%) were BCLC stage A; 289 (36.5%) had an AFP level of \u0026ge;\u0026thinsp;20 ng/ml; 330 (41.7%) were HBsAg positive; 376 (47.5%) were anti-HCV positive; 378 (47.8%) were with HCCs diagnosed by pathology; the median (IQR) body mass index was 25.2 (22.8\u0026ndash;27.9); and the median (IQR) MELD score was 8.2 (6.8\u0026ndash;10.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFive-year OS and RFS in this cohort\u003c/h2\u003e \u003cp\u003eThe five-year OS was 60% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and the five-year RFS was 34% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The median (IQR) follow-up period was 2.3 (0.9\u0026ndash;4.9) years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and multivariate Cox proportional hazards analyses of two-year RFS\u003c/h2\u003e \u003cp\u003eUnivariate analyses showed that multiple tumors (HR\u0026thinsp;=\u0026thinsp;1.387; 95% CI\u0026thinsp;=\u0026thinsp;1.052\u0026ndash;1.828; p\u0026thinsp;=\u0026thinsp;0.020), AFP\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/ml (HR\u0026thinsp;=\u0026thinsp;1.574; 95% CI\u0026thinsp;=\u0026thinsp;1.239\u0026ndash;2.000; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MELD score per one increase (HR\u0026thinsp;=\u0026thinsp;1.025; 95% CI\u0026thinsp;=\u0026thinsp;1.002\u0026ndash;1.048; p\u0026thinsp;=\u0026thinsp;0.030), and HBsAg positivity (HR\u0026thinsp;=\u0026thinsp;0.768; 95% CI\u0026thinsp;=\u0026thinsp;0.600\u0026ndash;0.983; p\u0026thinsp;=\u0026thinsp;0.036) were associated with inferior two-year RFS (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Multivariate analyses showed that multiple tumors (HR\u0026thinsp;=\u0026thinsp;1.450; 95% CI\u0026thinsp;=\u0026thinsp;1.098\u0026ndash;1.914; p\u0026thinsp;=\u0026thinsp;0.009), AFP\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/ml (HR\u0026thinsp;=\u0026thinsp;1.614; 95% CI\u0026thinsp;=\u0026thinsp;1.268\u0026ndash;2.054; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and MELD score per one increase (HR\u0026thinsp;=\u0026thinsp;1.026; 95% CI\u0026thinsp;=\u0026thinsp;1.003\u0026ndash;1.049; p\u0026thinsp;=\u0026thinsp;0.025) were associated with inferior two-year RFS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Cox proportional hazards analysis of two-year recurrence-free survival.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\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\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.980 (0.772\u0026ndash;1.244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.868 (0.681\u0026ndash;1.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.387 (1.052\u0026ndash;1.828)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u0026thinsp;\u0026gt;\u0026thinsp;20 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.275 (0.997\u0026ndash;1.630)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.574 (1.239\u0026ndash;2.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD, per one increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.025 (1.002\u0026ndash;1.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBsAg positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.768 (0.600\u0026ndash;0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-HCV positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.241 (0.978\u0026ndash;1.575)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAFP, alpha-fetoprotein; HBsAg, hepatitis B virus surface antigen, HCV, hepatitis C virus; MELD, Model for End-Stage Liver Disease; HR, hazard ratio; CI,\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003econfidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox proportional hazards analysis of two-year recurrence-free survival.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebeta coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\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\u003eMultiple tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.098\u0026ndash;1.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.268\u0026ndash;2.054\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\u003eMELD score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.003\u0026ndash;1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAFP, alpha-fetoprotein; MELD, Model for End-Stage Liver Disease; HR, hazard ratio; CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the model for predicting early recurrence\u003c/h2\u003e \u003cp\u003eWe used the beta coefficients of the multivariate Cox regression analyses to predict early recurrence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Three predictive factors\u0026mdash;AFP level, tumor number, and MELD score\u0026mdash;were included in the final model. The formula for the risk score was as follows: risk score\u0026thinsp;=\u0026thinsp;0.371 \u0026times; tumor number (multiple vs single)\u0026thinsp;+\u0026thinsp;0.479 \u0026times; AFP level (\u0026ge;\u0026thinsp;20 vs\u0026thinsp;\u0026lt;\u0026thinsp;20 ng/ml)\u0026thinsp;+\u0026thinsp;0.026 \u0026times; MELD score. A value of 1 was assigned to patients with multiple tumors and an AFP level of \u0026ge;\u0026thinsp;20 ng/mL. A value of 0 was assigned to patients with a single tumor and an AFP level of \u0026lt;\u0026thinsp;20 ng/mL. For example, for a patient with HCC and a single tumor, AFP level of \u0026ge;\u0026thinsp;20 ng/ml and MELD score of 9, the risk score\u0026thinsp;=\u0026thinsp;0.371 \u0026times; 0\u0026thinsp;+\u0026thinsp;0.479 \u0026times; 1\u0026thinsp;+\u0026thinsp;0.026 \u0026times; 9\u0026thinsp;=\u0026thinsp;0.713.\u003c/p\u003e \u003cp\u003eAfter excluding four patients with missing MELD scores and one patient with missing AFP level, data for the remaining 786 patients were applied to the risk score to predict early recurrence. The patient-specific linear prediction was calculated and cutoff values were applied to classify patients into three prognostic groups. Patients with scores\u0026thinsp;\u0026le;\u0026thinsp;0.5382 were classified as low risk, those with scores 0.5382\u0026ndash;0.7624 as medium risk, and those with scores\u0026thinsp;\u0026gt;\u0026thinsp;0.7624 as high risk; 400 patients (50.9%) were at low risk, 269 (34.2%) at medium risk, and 117 (14.9%) at high risk. During the follow-up period, 114 (28.5%) patients showed early recurrence in the low-risk group, 96 (35.7%) in the intermediate-risk group, and 60 (51.3%) in the high-risk group. Their two-year RFS was 63%, 57%, and 40% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Two-year RFS was significantly different between the low- and medium-risk groups (p\u0026thinsp;=\u0026thinsp;0.039), between the low- and high-risk groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and between the medium and high-risk groups (p\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the follow-up period, 75 (18.8%) patients died within five years after RFA in the low-risk group, 84 (31.2%) in the intermediate-risk group, and 45 (38.5%) in the high-risk group. This model also provided three risk strata for five-year OS: low risk, with five-year OS of 70%; intermediate risk, with five-year OS of 55%; and high risk, with five-year OS of 40% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Five-year OS was significantly different between the low- and medium-risk groups (p\u0026thinsp;=\u0026thinsp;0.001), between the low- and high-risk groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and between the medium and high-risk groups (p\u0026thinsp;=\u0026thinsp;0.045; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCalibration\u003c/h2\u003e \u003cp\u003eThe calibration plots showed overall high agreement between the predictions made by the model and observed outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe developed a model with three variables—AFP level, tumor number, and MELD score—to predict early recurrence. This model could stratify patients into three groups based on early recurrence. Internal validation with bootstrapping showed overall high agreement between the predictions made by the model and observed outcomes. In addition, it could also stratify patients into three groups based on five-year OS.\u003c/p\u003e \u003cp\u003eElevated AFP levels and multiple tumors are well-known prognostic factors of patients with HCC [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Our study showed that an AFP level of ≥ 20 ng/mL was associated with early recurrence. This cutoff was set according to the American Association for the Study of Liver Diseases guidelines, which recommend that patients at risk of and undergoing surveillance for HCC with an AFP level of ≥ 20 ng/mL should be examined using contrast-enhanced CT or MRI [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Late recurrence is regarded as de novo tumor growth due to underlying liver disease [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, poor liver function reserve is assumed to be associated with late recurrence. However, a higher MELD score was associated with early recurrence in the present study. A similar finding was noted in previous studies, which also showed that poor liver function reserve (i.e., a lower albumin level and higher albumin–bilirubin grade) was associated with early recurrence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTumor size is a well-known prognostic factor of patients with HCC [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. We used 20 mm as the cutoff tumor size in the univariate analysis for two-year RFS due to multiple studies showing that a tumor size of \u0026gt; 20 mm is a significant predictive factor of local tumor progression after RFA [\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, this variable was not statistically significant in the univariate analysis of two-year RFS (p = 0.053).\u003c/p\u003e \u003cp\u003eThe model in this study also provided three risk strata for OS—low risk, with five-year OS of 70%; intermediate risk, with five-year OS of 55%; and high risk, with five-year OS of 40% (p \u0026lt; 0.001). We believe that high-risk patients may be ideal candidates for adjuvant treatment due to poor five-year OS; low-risk patients may not benefit from adjuvant treatment due to satisfactory five-year OS, whereas the benefit for the intermediate group is debatable. Therefore, our model could be applied to clinical practice.\u003c/p\u003e \u003cp\u003eThe IMbrave050 trial enrolled patients undergoing ablation with a single tumor of \u0026gt; 2 cm but ≤ 5 cm or multiple tumors (up to four), all ≤ 5 cm. This trial excluded patients with BCLC stage 0 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, according to our predictive model, a patient with BCLC stage 0 presenting with an AFP level of ≥ 20 ng/ml and a MELD score of ≥ 11 is at high risk of early recurrence. Therefore, we believe that BCLC stage 0 patients with AFP elevation and poor liver function reserve may benefit from adjuvant therapy.\u003c/p\u003e \u003cp\u003eAlthough multiple studies have reported predictive factors of RFS of patients with early-stage HCC undergoing RFA [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e–\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e],few have reported predictive factors of early recurrence. Xin et al. used the systemic inflammation response index (defined as neutrophil count × monocyte count/lymphocyte count), AFP, and tumor number and size to predict two-year RFS. \u003csup\u003e11\u003c/sup\u003e Yang et al. used multiple tumors and AFP, gamma-glutamyltransferase (γ-GT), and serum albumin levels to predict early recurrence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Cha et al. predicted early recurrence using variables including age, albumin–bilirubin grade, AFP, protein induced by vitamin K absence or antagonist-II, and variables from image studies, such as non-rim hyperenhancement, enhancing capsule, low signal intensity of the lesion on hepatobiliary phase (HBP) on gadoxetic acid-enhanced MRI, etc. Although Cha et al.’s model is complicated, the addition of image features that suggest risk of microvascular invasion, i.e., peritumoral parenchymal enhancement and peritumoral hypointensity on HBP, may increase the predictive accuracy of early recurrence [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strength of the present study is its large sample size. However, it also has limitations: being a retrospective single-center study and the model lacking external validation.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe have developed a risk prediction model to forecast early recurrence in patients with early-stage HCC undergoing RFA. This model can be applied to ascertain patients with HCC who might benefit from adjuvant treatment. However, external validation of our model is necessary.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors have no conflicts of interest to disclose for all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eall data is available\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e The requirement of informed consent was waived by the Institutional Review Board of Chang Gung Memorial Hospital\u0026ndash;Kaohsiung Branch (reference number: 202201189B0) due to the retrospective nature of this study and anonymous analysis of data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate.\u003c/strong\u003e Written consents were waived by the IRB due to the retrospective nature of this study and anonymous analysis of data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u0026nbsp;\u003c/strong\u003eAll authors agree to publication if the paper is accepted\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Cancer Center, Kaohsiung Chang Gung Memorial Hospital for the provision of HCC registry data.\u0026nbsp;The authors\u0026nbsp;thank Chih-Yun Lin and Nien-Tzu Hsu and the Biostatistics Center, Kaohsiung Chang Gung Memorial Hospital for statistics work.\u0026nbsp;This study was supported by\u0026nbsp;Grant CMRPG8N1131\u0026nbsp;from the\u0026nbsp;Chang Gung Memorial Hospital-Kaohsiung Medical Center, Taiwan.\u0026nbsp;No conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conception and design:\u0026nbsp;YHY\u003c/p\u003e\n\u003cp\u003eAcquisition of data: all authors\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of data: CYL\u003c/p\u003e\n\u003cp\u003eDrafting of manuscript: YHY\u003c/p\u003e\n\u003cp\u003eCritical revision of manuscript: all authors\u003c/p\u003e\n\u003cp\u003eApproval of manuscript: all authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data for the cohort involved in this study is available via the following digital object identifier: https: https://www.dropbox.com/scl/fi/unezavq59ethkc6sprtu9/raw-data.xlsx?rlkey=cwu1zq6f9bguxiq0ghqqfvvh3\u0026amp;dl=0\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Grant CMRPG8N1131 from the Chang Gung Memorial Hospital-Kaohsiung Medical Center, Taiwan. No conflict of interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEuropean Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol 2018 ;69:182-236. doi: 10.1016/j.jhep.2018.03.019.\u003c/li\u003e\n\u003cli\u003eMarrero JA, Kulik LM, Sirlin CB, et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology 2018;68:723-750. doi: 10.1002/hep.29913.\u003c/li\u003e\n\u003cli\u003eNault JC, Sutter O, Nahon P, et al. Percutaneous treatment of hepatocellular carcinoma: State of the art and innovations. J Hepatol 2018;68:783-797. doi: 10.1016/j.jhep.2017.10.004.\u003c/li\u003e\n\u003cli\u003eRossi S, Ravetta V, Rosa L, et al. Repeated radiofrequency ablation for management of patients with cirrhosis with small hepatocellular carcinomas: A long-term cohort study. 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A Systemic Inflammation Response Index (SIRI)-Based Nomogram for Predicting the Recurrence of Early Stage Hepatocellular Carcinoma After Radiofrequency Ablation.Cardiovasc Intervent Radiol. 2022;45:43-53. doi: 10.1007/s00270-021-02965-4. \u003c/li\u003e\n\u003cli\u003eCha DI, Ahn SH, Lee MW, et al. Risk Group Stratification for Recurrence-Free Survival and Early Tumor Recurrence after Radiofrequency Ablation for Hepatocellular Carcinoma. Cancers (Basel) 2023;15:687. doi: 10.3390/cancers15030687.\u003c/li\u003e\n\u003cli\u003eLlovet JM, Bru C, Bruix J. Prognosis of hepatocellular carcinoma: the BCLC staging classification. Semin Liver Dis 1999;19:329\u0026ndash;338. doi: 10.1055/s-2007-1007122.\u003c/li\u003e\n\u003cli\u003ePugh RN, Murray-Lyon IM, Dawson JL, et al. Transection of the oesophagus for bleeding oesophageal varices. The British Journal of Surgery 1973; 60: 646\u0026ndash;9. doi:10.1002/bjs.1800600817\u003c/li\u003e\n\u003cli\u003eMalinchoc M, Kamath PS, Gordon FD, et al. 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Initial response to percutaneous ablation predicts survival in patients with hepatocellular carcinoma. Hepatology 2004;40:1352-60. doi: 10.1002/hep.20465.\u003c/li\u003e\n\u003cli\u003eLee DH, Lee JM, Lee JY, et al. Radiofrequency ablation of hepatocellular carcinoma as first-line treatment: long-term results and prognostic factors in 162 patients with cirrhosis. Radiology 2014 ;270:900-9. doi: 10.1148/radiol.13130940. \u003c/li\u003e\n\u003cli\u003eZhang F, Lu SX, Hu KS, et al. Albumin-to-alkaline phosphatase ratio as a predictor of tumor recurrence and prognosis in patients with early-stage hepatocellular carcinoma undergoing radiofrequency ablation as initial therapy. Int J Hyperthermia. 2021;38:1-10. doi: 10.1080/02656736.2020.1850885.\u003c/li\u003e\n\u003cli\u003eTan Y, Wang X, Ma K, et al. Risk factors for the recurrence of early hepatocellular carcinoma treated by percutaneous radiofrequency ablation with a multiple-electrode switching system: a multicenter prospective study. Int J Hyperthermia. 2022;39:190-199. doi: 10.1080/02656736.2021.2024279.\u003c/li\u003e\n\u003cli\u003eYoo J, Lee MW, Lee DH, et al. Evaluation of a serum tumour marker-based recurrence prediction model after radiofrequency ablation for hepatocellular carcinoma. Liver Int. 2020 May;40(5):1189-1200. doi: 10.1111/liv.14406. \u003c/li\u003e\n\u003cli\u003eKim CG, Lee HW, Choi HJ, et al. Development and validation of a prognostic model for patients with hepatocellular carcinoma undergoing radiofrequency ablation. Cancer Med. 2019;8:5023-5032. doi: 10.1002/cam4.2417. \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":"radiofrequency ablation, hepatocellular carcinoma, early recurrence","lastPublishedDoi":"10.21203/rs.3.rs-3901300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3901300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFew studies have reported models predicting early recurrence in patients undergoing radiofrequency ablation (RFA) for early-stage hepatocellular carcinoma (HCC). We aim to present such a model. We enrolled 791 patients with newly diagnosed early-stage HCC (i.e., within Milan criteria) and Child\u0026ndash;Pugh class A liver disease undergoing percutaneous RFA. Survival analysis was performed using the Kaplan\u0026thinsp;\u0026minus;\u0026thinsp;Meier method with the log-rank test. Cox proportional hazards analysis was used to identify prognostic factors associated with early recurrence (i.e., recurrence within two years after RFA). Internal validation was performed with a bootstrapping method. Early recurrence was identified in 270 (34.1%) patients. Multivariate analysis showed that multiple tumors (HR\u0026thinsp;=\u0026thinsp;1.450; 95% CI\u0026thinsp;=\u0026thinsp;1.098\u0026ndash;1.914; p\u0026thinsp;=\u0026thinsp;0.009), alpha-fetoprotein (AFP)\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/ml (HR\u0026thinsp;=\u0026thinsp;1.614; 95% CI\u0026thinsp;=\u0026thinsp;1.268\u0026ndash;2.054; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Model for End-Stage Liver Disease (MELD) score (HR\u0026thinsp;=\u0026thinsp;1.026; 95% CI\u0026thinsp;=\u0026thinsp;1.003\u0026ndash;1.049; p\u0026thinsp;=\u0026thinsp;0.025) were associated with early recurrence. We constructed a predictive model with these variables. This model provided three risk strata for recurrence-free survival (RFS): low risk, intermediate risk, and high risk, with two-year RFS of 63%, 57%, and 40%, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Calibration plots showed overall high agreement between the predictions made by the model and observed outcomes. In conclusion, we developed a risk prediction model to predict early recurrence in patients undergoing RFA for early-stage HCC.\u003c/p\u003e","manuscriptTitle":"A model for predicting risk of early recurrence in patients undergoing radiofrequency ablation for early-stage hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-16 16:48:40","doi":"10.21203/rs.3.rs-3901300/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":"17fd2adf-864f-4e4f-85fb-03b9c2ba91f5","owner":[],"postedDate":"February 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-30T04:51:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-16 16:48:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3901300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3901300","identity":"rs-3901300","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}