Incomplete Radiofrequency Ablation in the Treatment of Colorectal Cancer Liver Metastasis: A Multicenter Prospective Cohort Study

Incomplete Radiofrequency Ablation in the Treatment of Colorectal Cancer Liver Metastasis: A Multicenter Prospective Cohort Study | 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 Incomplete Radiofrequency Ablation in the Treatment of Colorectal Cancer Liver Metastasis: A Multicenter Prospective Cohort Study Huilin Lu, Xuancheng Xie, Xiangwen Xia, Xiangjun Dong, Hongjie Fan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4675432/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 Radiofrequency ablation (RFA) is a commonly used interventional method for treating colorectal cancer liver metastases (CLMs), with its efficacy influenced by tumor characteristics and intrahepatic distribution. The presence of incomplete RFA (iRFA) can result in poorer prognosis and may impede the effectiveness of targeted therapies or immunotherapy. Currently, there is a lack of multicenter prospective cohort studies and predictive models for iRFA. This study prospectively included CLM patients from four medical centers who underwent percutaneous RFA to develop and validate a predictive model for iRFA. All patients were followed up, with the occurrence of new intrahepatic metastases (NIHM) and overall survival (OS) assessed using the Kaplan-Meier method. We identified independent predictors of iRFA, including perivascular tumor location (odds ratio [OR] = 3.164), tumor size ≥ 20 mm (OR = 5.639), and minimal ablative margin (OR = 0.607). The area under the receiver operating characteristic curve (AUC) was 0.884 for the developmental cohort and 0.857 for the external validation cohort. Compared to the complete RFA group, patients in the iRFA group had a higher incidence of NIHM and shorter OS. However, broader external validation and the inclusion of more variables for comprehensive analysis and balance are still needed. radiofrequency ablation colorectal cancer liver metastases overall survival new intrahepatic metastases computed tomography Figures Figure 1 Figure 2 Figure 3 Key points The incidence of incomplete ablation of CLM with percutaneous RFA was 16.3% (67/410). The perivascular tumor location, tumor size ≥ 20 mm, and minimal ablative margin were important independent predictors of iRFA. Incomplete RFA promotes the development of new metastatic tumors in the liver and shortens the overall survival of patients. Introduction Colorectal cancer (CRC) is a major contributor to the global cancer burden, ranking third in morbidity and second in mortality among all cancers 1,2 . The liver is the most common organ for metastases, and over 25% of CRC patients developing liver metastases during their disease course 3-5 . Although surgery is considered the optimal intervention for patients with colorectal cancer liver metastases (CLMs), less than 20% of patients are eligible for surgical resection 6 . Minimally invasive alternative approaches, such as selective ablation for unresectable tumors, are thought to provide satisfactory quality of life and long-term overall survival (OS) for affected patients 7 . As the primary thermal ablation technique, radiofrequency ablation (RFA) achieves disease control by using heat to kill tumor cells and is often employed in the treatment of CLMs (≤5 tumors, diameter ≤5 cm) 8 . The effectiveness of RFA in treating CLMs is affected by tissue characteristics, including thermal conductivity, peripheral blood vessels, and water content 9-11 . When the tumor is close to the main blood vessels of the porta hepatis, the ‘heat sink effect’ affects the temperature distribution, thereby weakening the therapeutic effect of RFA 12 . Additionally, the reduction of energy to avoid thermal damage to adjacent structures, the unpredictability of the ablation range, and deviations in electrode positioning can all lead to incomplete radiofrequency ablation (iRFA). The presence of residual tumor mass after iRFA can lead to early new intrahepatic metastasis (NIHM) and poor OS in patients with CLMs 13 . This process may involve the activation of proteins (e.g., phosphoprotein [ 14 ] and heat shock proteins [ 15,16 ]) and signaling pathways (e.g. the STAT3/c-Met pathway 17 ]) related to biological mechanisms such as inflammation 13 , autophagy 18 , and hypoxia 19 . Previous studies have analyzed the oncological outcomes of RFA in treating CLMs, indicating that tumor size and minimal ablative margin are independent predictors of local tumor progression-free survival and OS 6,8,20 . However, to date, no predictive model for incomplete radiofrequency ablation (iRFA) of CLMs has been reported, nor has there been a multicenter, prospective cohort study on this topic. Therefore, our aim was to develop and validate a predictive model for iRFA and to evaluate the effect of iRFA on NIHM and OS. Materials and Methods Study population Approved by the Ethics Review Committee of xxx (NO. xxxx), the study population included patients with CLMs who received percutaneous RFA treatment at four medical institutions between November 2019 and November 2022. Consent was obtained from all participants and/or their legal guardians and all methods were carried out in accordance with the ethical standards of the responsible committee on human experimentation and with the Declaration of Helsinki. The exclusion criteria for RFA treatment included uncorrected coagulopathy, Child–Pugh grade C liver function, more than five diffuse metastases and/or large tumors (>50 mm), failure of vital organs such as the heart, kidneys, and liver, and/or biliary tract infection or sepsis. Additionally, patients were excluded from the analysis based on the following criteria: (1) lack of key clinical or imaging data, (2) RFA re-treatment, and (3) follow-up period of less than 4 months. RFA procedure A whole liver dynamic contrast-enhanced (DCE) scan was obtained using a 64-row spiral computed tomography (CT) scanner (GE LightSpeed VCT, Boston, Massachusetts, USA) one week before RFA. The scan and reconstruction parameters are detailed in Supplementary Table 1. Routine preoperative laboratory tests were conducted to identify any contraindications. Percutaneous intrahepatic puncture RFA was performed using the RF-3000 radiofrequency treatment instrument (Boston Scientific Corporation, Boston, USA). For percutaneous infiltration anesthesia, 5–10 mL of 2% lidocaine (North China Pharmaceutical Co., Ltd., Hebei, China) was used, and hydromorphone hydrochloride (Renfu Pharmaceutical Co., Ltd., Hubei, China) was administered as an analgesic. A 20-row spiral CT scanner (Siemens Definition AS, Erlangen, Germany) was used to guide the radiofrequency electrode to the edge of the lesion and to deploy the sub-electrodes. RFA was performed once the sub-electrodes satisfactorily covered the lesion. The ablation defect zone extended at least 5 mm beyond the target tumor edge. Upon needle withdrawal, the electrode path was cauterized to prevent tumor spread or bleeding. DCE-CT examination was performed immediately after RFA to assess the ablation defect coverage and identify any potential complications. Definition Based on the methods and findings of previous studies, iRFA was defined as the presence of active lesions on CT or magnetic resonance (MR) images within 4 months post-RFA, either in the original ablation zone or within 1 cm of the ablation defect zone 6,8,9,13 . Otherwise, the procedure was considered complete RFA (cRFA). CLMs located near the first- or second-degree branches of the portal or hepatic vein with a diameter of ≥ 5 mm were defined as perivascular tumors. Following a previously reported method 6,8,20 , two radiologists (xxx and xxx) with more than 10 years of experience in imaging diagnosis enhanced the measurement process to determine the minimal ablative margin. Follow-up All patients underwent follow-up evaluations within 4–8 weeks after RFA to assess treatment efficacy. Repeat RFA was performed for incompletely ablated tumors based on DCE CT/MR findings. Subsequent DCE-CT/MR examinations were conducted 2–3 months later to confirm cRFA in the absence of residual tumors or recurrence. The occurrence and timing of NIHM were documented. In cases of intrahepatic tumor progression, repeat RFA was considered as needed. OS was calculated from the time of RFA to the time of patient death, while the time interval of NIHM was defined as the time from the RFA procedure to the identification of NIHM. Statistical analysis We conducted statistical analysis using R 4.0.2 21 and SPSS 24.0 (IBM, Chicago, USA). Continuous variables (e.g., age) with a normal distribution were compared using Student’s t-test, while the χ 2 test was utilized for categorical data analysis (e.g., sex). This study aimed to develop and validate a robust predictive model for iRFA, which involved three main steps 22 . Initially, independent variables were selected based on literature review, expert input, clinical relevance, and statistical significance across different groups 23 . Subsequently, predictive models were developed using data from three initial centers and validated with patients from a fourth center. In the development dataset, potential risk factors were identified through univariate analysis, and a multivariate logistic regression (MLR) model was adjusted to avoid overfitting, considering the sample size of the iRFA group and results from univariate analysis. An omnibus test compared the model's probability distribution with a null model, and the Hosmer–Lemeshow test assessed model fit. Receiver operating characteristic (ROC) curves, including area under the curve (AUC) calculations, evaluated model performance. Thirdly, a generalized linear model (GLM) was employed within a sensitivity analysis to refine the predictive model and validate key findings 24 , as depicted in Supplementary Figure 1. Kaplan–Meier survival analysis and log-rank tests were utilized to estimate and compare NIHM and OS between iRFA and cRFA groups. Statistical significance was set at P < 0.05. Results Patient characteristics During the study period, a total of 301 patients with CLMs underwent RFA, from which 101 patients were excluded due to insufficient data (n=41) or repeated RFA (n=60) (Figure 1). The final study cohort comprised 200 hospitalized patients with 410 CLMs. The average age of these patients was 61.0 ±10.5 years (range, 27–89 years), with males accounting for 63.0% (Table 1). The most common primary tumor location was the sigmoid colon (65/200), followed by the rectum (57/200). Approximately half of the patients presented with synchronous CLMs (106/200), and 72 (36.0%) patients had extrahepatic metastases, most frequently in the lung. Comorbidities were present in 101 patients (50.5%), with hypertension being the most prevalent. Pre-RFA laboratory examinations revealed elevated carcinoembryonic antigen levels (> 30 µg/mL) in 45 patients (22.5%) and elevated carbohydrate antigen 199 levels (> 37 U/mL) in 55 patients (27.5%). Fifty-five patients (27.5%) had undergone prior liver resection, and 172 (86.0%) had received chemotherapy before RFA. Major complications related to RFA occurred in 22 cases (11.0%), primarily involving pleural effusion and bleeding. In our study, iRFA was noted in 67 CLMs (16.3%). Among the patients, 246 tumors from 120 patients were randomly assigned to the development cohort, with an iRFA incidence of 37 tumors (15.0%). The internal validation cohort included 164 tumors (from 80 patients) with an iRFA incidence of 30 tumors (18.8%). No significant differences in baseline characteristics were observed between the two cohorts (Table 1). Univariate and multivariate analysis In the development cohort of 246 CLMs, the average minimal ablative margin was 5.8 ± 2.5 mm, and the average ∆CT value was 21.8 ± 12.3 Hu. Ninety-one tumors (37.0%) were located near critical organs or structures, 32 (13.0%) CLMs were in perivascular locations, 53 (21.5%) CLMs had a diameter ≥ 20 mm, and 129 (52.4%) CLMs were subcapsular metastases. Univariate analysis demonstrated significant differences in perivascular tumor location, tumor size (≥20 mm), and minimal ablative margin between the iRFA and cRFA groups (all P < 0.001) (Table 2). To ensure important predictor variables were not overlooked, subcapsular tumor location (P=0.199) and ∆CT value (P=0.154) were also included alongside the aforementioned three independent variables as potential risk factors in the multivariate analysis. Results from the MLR model (Table 2) revealed that perivascular tumor location (odds ratio [OR] = 3.164, 95% CI: 1.201–8.333, P=0.02), tumor size ≥ 20 mm (OR = 5.639, 95% CI: 2.547–12.487, P<0.001), and minimal ablative margins (OR = 0.607, 95% CI: 0.498–0.753, P<0.001) independently predicted iRFA. The omnibus test indicated that the current model significantly outperformed the null model (χ 2 = 77.974, P<0.001). Additionally, the Hosmer–Lemeshow test demonstrated good model fit (χ 2 = 4.449, P=0.815). The predictive model's performance in both the development and internal validation cohorts is illustrated in Figure 2, with corresponding AUC values of 0.884 (95% CI: 0.819–0.950, P<0.001) and 0.857 (95% CI: 0.785–0.930, P<0.001), respectively. Sensitivity analysis The goodness-of-fit of the GLM is presented in Supplementary Table 2. According to the GLM results, a minimal ablative margin (OR = 0.620, 95% CI: 0.479–0.801, P<0.001) was identified as a protective factor against iRFA, while perivascular tumor location (OR = 3.214, 95% CI: 1.860–8.543, P=0.025) and tumor size ≥ 20 mm (OR = 5.095, 95% CI: 1.815–14.300, P=0.002) were significant risk factors for iRFA. Sensitivity analyses indicated that the β regression coefficient and OR of the GLM remained stable, confirming the robustness of our findings compared to those from the MLR. OS and NIHM Follow-up was completed for 164 hospitalized patients (338 CLMs), with 36 patients lost to follow-up. The median follow-up period was 22 months. Among the patients, 50 (30.5%) died, and the cumulative OS rates at 1, 3, and 5 years after RFA were 92.2%, 59.7%, and 41.2%, respectively. Before RFA, there were no significant differences in extrahepatic metastasis (P = 0.468) between patients in the cRFA and iRFA groups. The estimated median survival time, calculated using the Kaplan–Meier method, was 45 months (95% CI: 29.348–60.652) for the entire cohort, 74 months (95% CI: 48.212–99.788) for the cRFA group, and 35 months (95% CI: 22.954–47.046) for the iRFA group. The log-rank test results indicated a significant difference in cumulative OS between the two groups (χ 2 = 12.269, P<0.001) (Fig. 3A), with a hazard ratio of 2.54 (95% CI: 1.341–4.814). During the follow-up period, a total of 98 cases (59.8%) of patients were observed to have experienced NIHM, and appropriate anti-tumor treatment strategies were implemented based on their condition, including repeated RFA (n=62), transcatheter arterial chemoembolization (n=9), stereotactic body radiotherapy (n=2), surgical resection (n=8), and other comprehensive treatments (n=17). The cumulative incidence of NIHM at 1 and 3 years after RFA was estimated at 44.2% and 65.0%, respectively. All NIHM occurrences were identified within 48 months, with five patients showing no NIHM during the follow-up period exceeding 5 years after cRFA. The median time without NIHM, estimated using Kaplan–Meier analysis, was 16 months (95% CI: 7.868–24.132) overall. In the cRFA group, NIHM was observed in 58 (50.9%) patients, with a median NIHM-free period of 31 months (95% CI: 15.818–46.182). In contrast, NIHM was detected in 40 patients in the iRFA group (80%), with a median time without NIHM of 5 months (95% CI: 3.359–6.641). The difference between the two groups was significant (χ 2 = 26.811, P<0.001) (Fig. 3B), with a hazard ratio of 2.679 (95% CI: 1.64–4.377). Discussion In this study, we identified three independent factors associated with iRFA. Our findings indicate that maintaining a sufficient minimal ablative margin is beneficial in preventing iRFA. Additionally, we observed that tumor size ≥ 20 mm and perivascular tumor location are independent risk factors for iRFA. These results suggest that iRFA may significantly impact the occurrence of NIHM and OS. Adequate ablation margins are crucial for achieving local tumor control and improving long-term patient survival 6,8,20 , a view supported by biopsy evidence from previous studies 25 . However, the measurement of the minimal ablative margin in several previous studies was not accurate because the differences between the corresponding distances (distance between the tumor and the ablation defect zone to anatomic landmarks) can only reflect the minimal ablative margins at several points 6,8,20 . In addition, Kang et al. 26 used the Cox proportional hazard model to evaluate the long-term effects of RFA treatment for perivascular and non-perivascular hepatocellular carcinoma and did not find significant differences in the cumulative local tumor progression survival and OS between the groups. In a recent study 8 , while perivascular tumor location showed significance in univariate analysis, it did not retain significance in the final predictive model following multivariate analysis. The two studies mentioned above defined tumors with blood vessels close to, greater than, or equal to 3 mm as perivascular tumors. A study showed that the “heat sink effect” of veins > 3 mm on RFA of the surrounding tissues was 50% and 100% of veins > 5 mm showed this effect 10 . Therefore, the findings from these studies support the rationale of our use of 5-mm vessel diameters to select categorical independent variables. Our study underscores that both perivascular tumor location and tumor size are pivotal factors influencing iRFA risk, emphasizing the importance of considering real-time heat distribution during RFA procedures for tumors of varying sizes. Larger tumors, which are more prone to incomplete ablation, were associated with poorer OS outcomes. Notably, while the presence of extrahepatic metastasis can impact OS, this variable did not significantly affect outcomes in the iRFA versus cRFA groups (P > 0.05) in our study. Technical failures observed on immediate post-surgical CT and during the 4-month follow-up period following each RFA procedure contributed to the increased incidence of iRFA. Moreover, to meet statistical criteria, more than 10 samples were allocated to each variable in the regression model. Given the dichotomous nature of the dependent variables, an effective sample size of 30 was estimated for the three independent variables, resulting in a well-fitting and predictive model. During the follow-up period, we observed that the majority of cases of NIHM occurred within 3 years post-RFA, with all instances of NIHM detected within 4 years post-RFA, consistent with previously reported findings 8 . This underscores the importance of regular DCE-CT/MR examinations within 4 years following RFA. Complete tumor eradication was achieved in 5 (3.0%) patients with solitary intrahepatic CLM, complete resection of the primary lesion, and no observed NIHM or local tumor progression during a follow-up period exceeding 5 years. In this study, the estimated cumulative OS rates at 1, 3, and 5 years post-RFA were comparable to those reported by Han et al. 8 but higher than those reported by Shady et al. 6 . The 5-year cumulative OS among participants was akin to that of surgical resection, while the 8-year cumulative OS was generally lower than that observed with liver resection in most cases 27 . However, methodological differences across studies and variables influencing treatment and follow-up periods should be carefully considered when directly comparing these findings. Several limitations of this study warrant acknowledgment. Firstly, the cohort used for developing the prediction model was relatively small, resulting in a model with fewer variables, potentially limiting its predictive capacity for iRFA. Secondly, the lack of extensive external validation datasets introduces uncertainty regarding the generalizability of our model to other regional or international populations. Lastly, factors such as the neutrophil-to-lymphocyte ratio and genetic mutations (e.g., KRAS), which may impact NIHM and OS, were not comprehensively analyzed or balanced due to incomplete data. In conclusion, we accurately measured the minimal ablative margin in RFA procedures and successfully developed and validated an iRFA prediction model demonstrating good performance. A sufficient minimal ablative margin emerged as an independent protective factor against iRFA, while tumor size ≥ 20 mm and perivascular tumor location were identified as independent risk factors. iRFA may potentially contribute to the occurrence of NIHM and poorer OS outcomes. This predictive model could aid clinical decision-making regarding treatment strategies for patients with CLMs based on tumor characteristics. Abbreviations AUC Area under the curve CLM Cancer liver metastases CRC Colorectal cancer CT Computed tomography DCE Dynamic contrast-enhanced GLM Generalized linear model MLR Multivariate logistic regression MR Magnetic resonance NIHM New intrahepatic metastases OR Odds ratio OS Overall survival RFA Radiofrequency ablation Declarations Funding: This work was supported by National Natural Science Foundation of China(No. 82302332; No. 82272100). Acknowledgments This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors. Authors contributions All authors made significant contributions to the conception and design of the study. H.L., Xu.X. and Xi.X. contributed equally to this work. H.L, Xu.X and H.F. were responsible for material preparation, data collection and analysis. The initial draft of the manuscript was written by H.L. and Xi.X. X.D. H.F. and S.X. were responsible for data curation, formal analysis, investigation, conceptualization, writing review and editing. All authors contributed to manuscript revision, read, and approved the submitted version. Additional information 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 and no competing interests. Data availability The datasets used and analysed during the current study available from the corresponding author (Shufeng Xu, email: [email protected] ) on reasonable request. Ethics declarations The study approved by the Ethics Review Committee of Quzhou Affiliated Hospital of Wenzhou Medical University (Approval No. Keyan20191228-35). Consent was obtained from all participants and/or their legal guardians and all methods were carried out in accordance with the ethical standards of the responsible committee on human experimentation and with the Declaration of Helsinki. References Siegel, R. L. et al. Colorectal cancer statistics, 2020. CA Cancer J Clin 70 , 145-164, doi:10.3322/caac.21601 (2020). Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68 , 394-424, doi:10.3322/caac.21492 (2018). Gonzaga MI et al. The epidemiology and survivorship of clear cell sarcoma: a National Cancer Database (NCDB) review. J Cancer Res Clin Oncol 144 , 1711-1716, doi:10.1007/s00432-018-2693-6 (2018). T Ruers, R. P. B. Treatment of liver metastases, an update on the possibilities and results. Eur J Cancer 38 , 1023–1033 (2002). Manfredi, S. et al. Epidemiology and management of liver metastases from colorectal cancer. Ann Surg 244 , 254-259, doi:10.1097/01.sla.0000217629.94941.cf (2006). Shady W, P. E., Gonen M, Erinjeri JP, Brown KT, Covey AM, Alago W, Durack JC, Maybody M, Brody LA, Siegelbaum RH, D'Angelica MI, Jarnagin WR, Solomon SB, Kemeny NE, Sofocleous CT. Percutaneous Radiofrequency Ablation of Colorectal Cancer Liver Metastases: Factors Affecting Outcomes--A 10-year Experience at a Single Center. Radiology. 278 , 601-611 (2016). Petrowsky, H. et al. Modern therapeutic approaches for the treatment of malignant liver tumours. Nat Rev Gastroenterol Hepatol 17 , 755-772, doi:10.1038/s41575-020-0314-8 (2020). Han, K. et al. A Single-Center Retrospective Analysis of Periprocedural Variables Affecting Local Tumor Progression after Radiofrequency Ablation of Colorectal Cancer Liver Metastases. Radiology 298 , 212-218, doi:10.1148/radiol.2020200109 (2021). Ahmed M et al. Image-guided tumor ablation: standardization of terminology and reporting criteria--a 10-year update. Radiology 273 , 241-260, doi:10.1148/radiol.14132958 (2014). Lu, D. et al. Effect of vessel size on creation of hepatic radiofrequency lesions in pigs: assessment of the "heat sink" effect. AJR Am J Roentgenol 178 , 47-51, doi:10.2214/ajr.178.1.1780047 (2002). Brace CL, D. T., Hinshaw JL, Lee FT Jr. Radiofrequency and microwave ablation of the liver, lung, kidney, and bone: what are the differences? Curr Probl Diagn Radiol 38 , 135-143, doi:10.1067/j.cpradiol.2007.10.001 (2009). Ahmed M, B. C., Lee FT Jr, Goldberg SN. Principles of and advances in percutaneous ablation. Radiology 258 , 351-369, doi:10.1148/radiol.10081634 (2011). Shi, L. et al. Inflammation induced by incomplete radiofrequency ablation accelerates tumor progression and hinders PD-1 immunotherapy. Nat Commun 10 , 5421, doi:10.1038/s41467-019-13204-3 (2019). Su, T. et al. Stress-induced phosphoprotein 1 mediates hepatocellular carcinoma metastasis after insufficient radiofrequency ablation. Oncogene 37 , 3514-3527, doi:10.1038/s41388-018-0169-4 (2018). Chen, F., Bao, H., Xie, H., Tian, G. & Jiang, T. Heat shock protein expression and autophagy after incomplete thermal ablation and their correlation. Int J Hyperthermia 36 , 95-103, doi:10.1080/02656736.2018.1536285 (2019). Zhang, Z. et al. Incomplete radiofrequency ablation provokes colorectal cancer liver metastases through heat shock response by PKCalpha/Fra-1 pathway. Cancer Biol Med 16 , 542-555, doi:10.20892/j.issn.2095-3941.2018.0407 (2019). Liao, H. et al. Thermal Ablation Induces Transitory Metastatic Growth by Means of the STAT3/c-Met Molecular Pathway in an Intrahepatic Colorectal Cancer Mouse Model. Radiology 294 , 464-472, doi:10.1148/radiol.2019191023 (2020). Zhao, Z. et al. Insufficient radiofrequency ablation promotes proliferation of residual hepatocellular carcinoma via autophagy. Cancer Lett 421 , 73-81, doi:10.1016/j.canlet.2018.02.024 (2018). Nijkamp, M. W. et al. CD95 is a key mediator of invasion and accelerated outgrowth of mouse colorectal liver metastases following radiofrequency ablation. J Hepatol 53 , 1069-1077, doi:10.1016/j.jhep.2010.04.040 (2010). Wang X et al. Margin size is an independent predictor of local tumor progression after ablation of colon cancer liver metastases. Cardiovasc Intervent Radiol 36 , 166-175, doi:10.1007/s00270-012-0377-1 (2013). R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Huang, Y. Q. et al. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol 34 , 2157-2164, doi:10.1200/JCO.2015.65.9128 (2016). Collins GS, Reitsma JB, Altman DG & KG, M. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 350 , g7594, doi:10.1136/bmj.g7594 (2015). Thabane, L. et al. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med Res Methodol 13 , 92, doi:10.1186/1471-2288-13-92 (2013). Sotirchos VS et al. Colorectal Cancer Liver Metastases: Biopsy of the Ablation Zone and Margins Can Be Used to Predict Oncologic Outcome. Radiology 280 , 949-959 (2016). Kang Tae Wook et al. Perivascular versus nonperivascular small HCC treated with percutaneous RF ablation: retrospective comparison of long-term therapeutic outcomes. Radiology 270 , 888-899 (2014). Aldrighetti L, C. R., Di Palo S, Arru M, Stella M, Orsenigo E, Gavazzi F, Ferla G, Di Carlo V, Staudacher C. Ten-year survival after liver resection for colorectal metastases: systematic review and meta-analysis. ISRN oncology 2011 , 763245, doi:10.5402/2011/763245 (2011). Tables Table 1 . Clinicopathological and demographic characteristics of patients in the development and internal validation cohorts. Development set Validation set t/c 2 P Patients 120 80 - - Tumors 246 164 - - Age (years) 60.6±11.0 61.7±9.6 -0.715 0.475 Sex 1.033 0.309 Male 79 (65.8) 47 (58.8) Female 41 (34.2) 33 (41.3) BMI (kg/m 2 ) 22.9±2.5 23.5±3.2 -1.588 0.154 Primary tumor location 5.687 0.224 Rectum 30 (25.0) 27 (33.8) Sigmoid colon 42 (35.0) 23 (28.8) Descending colon 13 (10.8) 14 (17.5) Transverse colon 9 (7.5) 6 (7.5) Ascending colon 26 (21.7) 10 (12.5) Differentiation (missing, n=25) 2.924 0.571 Low 15(15.2) 10 (13.2) Low to medium 8 (8.1) 8 (10.5) Medium 34 (34.3) 32 (42.1) Medium to high 29 (29.3) 21 (27.6) High 13 (13.1) 5 (6.6) Synchronous/ metachronous 3.643 0.056 Synchronous 57 (47.5) 49 (61.3) Metachronous 63 (52.5) 31 (38.8) Extrahepatic metastasis 0.438 0.508 No 79 (65.8) 49 (61.3) Yes 41 (34.2) 31 (38.8) Previous liver resection 0.104 0.747 No 88 (73.3) 57 (71.3) Yes 32 (26.7) 23 (28.8) Prior chemotherapy 0.111 0.739 No 16 (13.3) 12 (15.0) Yes 104 (86.7) 68 (85.0) Primary tumor invasion (missing, n=9) 0.056 0.813 T1–3 72 (63.7) 51 (65.4) T4 41 (36.3) 27 (34.6) Comorbidities (missing, n=1) 0.709 0.400 No 62 (51.7) 36 (45.6) Yes 58 (48.3) 43 (54.4) CEA (ng/ml) 0.119 0.730 ≤30 92 (76.7) 63 (78.8) >30 28 (23.3) 17 (21.3) CA199 (0–37 U/mL) 0.104 0.747 Normal 86 (71.7) 59 (73.8) Abnormal 34 (28.3) 21 (26.3) Primary tumor nodal status 4.409 0.110 Negative 54 (45.0) 25 (31.3) 1–3 35 (29.2) 33 (41.3) >3 31 (25.8) 22 (27.5) Adjuvant chemotherapy post- RFA 0.046 0.830 No 24 (20.0) 17 (21.3) Yes 96 (80.0) 63 (78.8) Major complications 0.136 0.712 No 106 (88.3) 72 (90.0) Yes 14 (11.7) 8 (10.0) Note: The data represent the number of patients, with percentages in parentheses, as well as the results from Pearson’s chi-square test (c 2 ) for counts data. Measured data are expressed as means ± standard deviations, and t-test results are presented for measurement data. BMI, body mass index; CEA, carcinoembryonic antigen; CA199, Carbohydrate antigen 199; iRFA, incomplete radiofrequency ablation; cRFA, complete radiofrequency ablation. Table 2 . Univariable and multivariate analyses of predictors for iRFA in the development data set. Univariable Analysis Multivariable Logistic Regression cRFA (n=209) iRFA (n=37) c 2 /t P B OR (95% CI) P Crucial location 77 (36.8) 14 (37.8) 0.013 0.908 # Perivascular 14 (6.7) 18 (48.6) 48.886 <0.001 # 1.152 3.164 (1.201, 8.333) 0.020 Subcapsular 106 (50.7) 23 (62.2) 1.651 0.199 # Size (mm) 37.038 <0.001 # 1.730 5.639 (2.547, 12.487) <0.001 <20 178 (85.2) 15 (40.5) ≥20 31 (14.8) 22 (59.5) ∆CT (Hu) 22.3±12.0 19.2±13.5 1.431 0.154 * Minimal ablative margin (mm) 6.2±2.5 3.9±1.6 5.513 <0.001 * -0.500 0.607 (0.498, 0.753) <0.001 Note. The data represent the number of tumors, with percentages in parentheses, and Pearson’s chi-square (c 2 ) test for counts data (#). Means ± standard deviations represent measured data, with t-tests comparing the ‘*’ symbols data. B, β regression coefficient; OR, odds ratio; CI, confidence interval; cRFA, complete radiofrequency ablation; iRFA, incomplete radiofrequency ablation; ∆CT, the difference in CT values between the portal vein and plain scan phase; subcapsular, CLMs with the shortest distance to the liver capsule within 5 mm; crucial location, the location of CLMs that were adjacent to an organ or structure that could affect the RFA procedure, such as the diaphragm, gastrointestinal tract, and gallbladde Additional Declarations No competing interests reported. Supplementary Files supplementaryfigure.tif Supplementary Figure 1. Parameter estimates of the generalized linear model for iRFA. Dependent variable: iRFA. Model: (intercept), minimal ablative margin, ∆CT, subcapsular, perivascular, size ≥ 20 mm, crucial location. The frequency axis is on a log scale, with 95% confidence intervals in parentheses. B, β regression coefficient; OR, odds ratio; CI, confidence interval; iRFA, incomplete radiofrequency ablation; ∆CT, the difference in CT values between the portal vein and plain scan phase; a, set to zero because this parameter was redundant. supplementarytables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4675432","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":332791648,"identity":"34459768-0c42-4357-8e28-3bfabf8e2323","order_by":0,"name":"Huilin Lu","email":"","orcid":"","institution":"Xinxiang Central Hospital / the Fourth Clinical College of Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huilin","middleName":"","lastName":"Lu","suffix":""},{"id":332791649,"identity":"519ff768-ce49-4da2-8c12-3bfaa1a5a1f5","order_by":1,"name":"Xuancheng Xie","email":"","orcid":"","institution":"The First People's Hospital of Yunnan Province / the Affiliated Hospital of Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xuancheng","middleName":"","lastName":"Xie","suffix":""},{"id":332791650,"identity":"da4b6c1b-41d8-4acb-a526-235318b54ad9","order_by":2,"name":"Xiangwen Xia","email":"","orcid":"","institution":"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiangwen","middleName":"","lastName":"Xia","suffix":""},{"id":332791651,"identity":"2e5c9576-aa34-4f8c-9560-1856f1e8a68e","order_by":3,"name":"Xiangjun Dong","email":"","orcid":"","institution":"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiangjun","middleName":"","lastName":"Dong","suffix":""},{"id":332791652,"identity":"7e285d42-6962-48d6-af6e-d705c5e8081d","order_by":4,"name":"Hongjie Fan","email":"","orcid":"","institution":"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hongjie","middleName":"","lastName":"Fan","suffix":""},{"id":332791654,"identity":"92d9a485-4abc-409f-b6d7-3915a7ab25b1","order_by":5,"name":"Shufeng Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACNv7m45///vkvJ89w+OCDhIoawlr4JI6lMfA2MBsbNh5LNnhw5hhhLXIMOWYgLYkNh8+oST5sYSbCYQxnzB5I7mBjbGw7w1aR2MDGwN/enYBfC3NbuYHhGR5mdp6zx24k7pBhkDhzdgMBWw5vkEhgk2BjnHEu7UbiGTYGA4lcQloSDCQOsBnwMNx/Y1aQ2MZMjJYUM8nGtgQJhgNnzBiI0yJxLNmY4cwBA8OGY8kSCWeO8RD0i3x/88HHDBUH6ucDo/Ljj4oaOf72XvxaMAAPacpHwSgYBaNgFGAFAPk8TiPF1ZPtAAAAAElFTkSuQmCC","orcid":"","institution":"The Quzhou Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shufeng","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-07-02 15:52:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4675432/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4675432/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61359367,"identity":"b840f9b9-8a15-42b1-8bb4-b9e1ef47b039","added_by":"auto","created_at":"2024-07-29 21:35:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134256,"visible":true,"origin":"","legend":"\u003cp\u003eFlowdiagram of the study design. CLMs, colorectal cancer liver metastases; RFA, radiofrequency ablation; OS, overall survival; NIHM, new intrahepatic metastases.\u003c/p\u003e","description":"","filename":"Figure1flowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-4675432/v1/78a316f8f18840a27d0a0295.png"},{"id":61359104,"identity":"ab89573a-c528-4ef8-b2fe-fab8caaca86b","added_by":"auto","created_at":"2024-07-29 21:27:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76804,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for the multivariate logistic regression model in the development (green) and internal validation cohorts (red), with the area under the curve (AUC) corresponding to 0.884 and 0.857, respectively.\u003c/p\u003e","description":"","filename":"Figure2ROC.png","url":"https://assets-eu.researchsquare.com/files/rs-4675432/v1/c021d31e109f34ff4866167e.png"},{"id":61359366,"identity":"c0eb22d8-f5ff-47a3-a82e-eeb3d3d53347","added_by":"auto","created_at":"2024-07-29 21:35:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114666,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan–Meier curve and log-rank (Mantel–Cox) test for overall survival (OS) and new intrahepatic metastases (NIHM) in incomplete and completeRFA.\u003c/p\u003e","description":"","filename":"Figure3OSandNIHM.png","url":"https://assets-eu.researchsquare.com/files/rs-4675432/v1/d72399d8ca568fa7d4194a97.png"},{"id":65159282,"identity":"122c837a-eab3-4673-9262-16aed2478881","added_by":"auto","created_at":"2024-09-24 08:39:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":928263,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4675432/v1/20ae54e1-2edf-4780-b345-d8e959aa1f85.pdf"},{"id":61359106,"identity":"c421b21a-d397-4b53-a52e-2fe4f6e375a1","added_by":"auto","created_at":"2024-07-29 21:27:10","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20026716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003e Parameter estimates of the generalized linear model for iRFA. Dependent variable: iRFA. Model: (intercept), minimal ablative margin, ∆CT, subcapsular, perivascular, size ≥ 20 mm, crucial location. The frequency axis is on a log scale, with 95% confidence intervals in parentheses.\u003c/p\u003e\n\u003cp\u003eB, β regression coefficient; OR, odds ratio; CI, confidence interval; iRFA, incomplete radiofrequency ablation; ∆CT, the difference in CT values between the portal vein and plain scan phase; a, set to zero because this parameter was redundant.\u003c/p\u003e","description":"","filename":"supplementaryfigure.tif","url":"https://assets-eu.researchsquare.com/files/rs-4675432/v1/f0997f86c37fb14bb69c3738.tif"},{"id":61359103,"identity":"5d1107aa-0049-40f0-bd93-5ce18cc9b7ec","added_by":"auto","created_at":"2024-07-29 21:27:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21825,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4675432/v1/551aee8dfd543db3cb920b77.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Incomplete Radiofrequency Ablation in the Treatment of Colorectal Cancer Liver Metastasis: A Multicenter Prospective Cohort Study","fulltext":[{"header":"Key points","content":"\u003cp\u003eThe incidence of incomplete ablation of CLM with percutaneous RFA was 16.3% (67/410).\u003c/p\u003e\n\u003cp\u003eThe perivascular tumor location, tumor size \u0026ge; 20 mm, and minimal ablative margin were important independent predictors of iRFA.\u003c/p\u003e\n\u003cp\u003eIncomplete RFA promotes the development of new metastatic tumors in the liver and shortens the overall survival of patients.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is a major contributor to the global cancer burden, ranking third in morbidity and second in mortality among all cancers \u003csup\u003e1,2\u003c/sup\u003e. The liver is the most common organ for metastases, and over 25% of CRC patients developing liver metastases during their disease course \u003csup\u003e3-5\u003c/sup\u003e. Although surgery is considered the optimal intervention for patients with colorectal cancer liver metastases (CLMs), less than 20% of patients are eligible for surgical resection \u003csup\u003e6\u003c/sup\u003e. Minimally invasive alternative approaches, such as selective ablation for unresectable tumors, are thought to provide satisfactory quality of life and long-term overall survival (OS) for affected patients \u003csup\u003e7\u003c/sup\u003e. As the primary thermal ablation technique, radiofrequency ablation (RFA) achieves disease control by using heat to kill tumor cells and is often employed in the treatment of CLMs (\u0026le;5 tumors, diameter \u0026le;5 cm) \u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe effectiveness of RFA in treating CLMs is affected by tissue characteristics, including thermal conductivity, peripheral blood vessels, and water content \u003csup\u003e9-11\u003c/sup\u003e . When the tumor is close to the main blood vessels of the porta hepatis, the \u0026lsquo;heat sink effect\u0026rsquo; affects the temperature distribution, thereby weakening the therapeutic effect of RFA \u003csup\u003e12\u003c/sup\u003e . Additionally, the reduction of energy to avoid thermal damage to adjacent structures, the unpredictability of the ablation range, and deviations in electrode positioning can all lead to incomplete radiofrequency ablation (iRFA). The presence of residual tumor mass after iRFA can lead to early new intrahepatic metastasis (NIHM) and poor OS in patients with CLMs \u003csup\u003e13\u003c/sup\u003e . This process may involve the activation of proteins (e.g., phosphoprotein [\u003csup\u003e14\u003c/sup\u003e] and heat shock proteins [\u003csup\u003e15,16\u003c/sup\u003e]) and signaling pathways (e.g. the STAT3/c-Met pathway \u003csup\u003e17\u003c/sup\u003e]) related to biological mechanisms such as inflammation \u003csup\u003e13\u003c/sup\u003e , autophagy\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e, and hypoxia\u0026nbsp;\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePrevious studies have analyzed the oncological outcomes of RFA in treating CLMs, indicating that tumor size and minimal ablative margin are independent predictors of local tumor progression-free survival and OS \u003csup\u003e6,8,20\u003c/sup\u003e. However, to date, no predictive model for incomplete radiofrequency ablation (iRFA) of CLMs has been reported, nor has there been a multicenter, prospective cohort study on this topic. Therefore, our aim was to develop and validate a predictive model for iRFA and to evaluate the effect of iRFA on NIHM and OS.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003eStudy population\u003c/h2\u003e\n\u003cp\u003eApproved by the Ethics Review Committee of xxx (NO. xxxx), the study population included patients with CLMs who received percutaneous RFA treatment at four medical institutions between November 2019 and November 2022. Consent was obtained from all participants and/or their legal guardians and all methods were carried out in accordance with the ethical standards of the responsible committee on human experimentation and with the Declaration of Helsinki. The exclusion criteria for RFA treatment included uncorrected coagulopathy, Child\u0026ndash;Pugh grade C liver function, more than five diffuse metastases and/or large tumors (\u0026gt;50 mm), failure of vital organs such as the heart, kidneys, and liver, and/or biliary tract infection or sepsis. Additionally, patients were excluded from the analysis based on the following criteria: (1) lack of key clinical or imaging data, (2) RFA re-treatment, and (3) follow-up period of less than 4 months.\u003c/p\u003e\n\u003ch2\u003eRFA procedure\u003c/h2\u003e\n\u003cp\u003eA whole liver dynamic contrast-enhanced (DCE) scan was obtained using a 64-row spiral computed tomography (CT) scanner (GE LightSpeed VCT, Boston, Massachusetts, USA) one week before RFA. The scan and reconstruction parameters are detailed in Supplementary Table 1. Routine preoperative laboratory tests were conducted to identify any contraindications. Percutaneous intrahepatic puncture RFA was performed using the RF-3000 radiofrequency treatment instrument (Boston Scientific Corporation, Boston, USA). For percutaneous infiltration anesthesia, 5\u0026ndash;10 mL of 2% lidocaine (North China Pharmaceutical Co., Ltd., Hebei, China) was used, and hydromorphone hydrochloride (Renfu Pharmaceutical Co., Ltd., Hubei, China) was administered as an analgesic. A 20-row spiral CT scanner (Siemens Definition AS, Erlangen, Germany) was used to guide the radiofrequency electrode to the edge of the lesion and to deploy the sub-electrodes. RFA was performed once the sub-electrodes satisfactorily covered the lesion. The ablation defect zone extended at least 5 mm beyond the target tumor edge. Upon needle withdrawal, the electrode path was cauterized to prevent tumor spread or bleeding. DCE-CT examination was performed immediately after RFA to assess the ablation defect coverage and identify any potential complications.\u003c/p\u003e\n\u003ch2\u003eDefinition\u003c/h2\u003e\n\u003cp\u003eBased on the methods and findings of previous studies, iRFA was defined as the presence of active lesions on CT or magnetic resonance (MR) images within 4 months post-RFA, either in the original ablation zone or within 1 cm of the ablation defect zone \u003csup\u003e6,8,9,13\u003c/sup\u003e. Otherwise, the procedure was considered complete RFA (cRFA). CLMs located near the first- or second-degree branches of the portal or hepatic vein with a diameter of \u0026ge; 5 mm were defined as perivascular tumors.\u0026nbsp;Following a previously reported method \u003csup\u003e6,8,20\u003c/sup\u003e, two radiologists (xxx\u0026nbsp;and xxx)\u0026nbsp;with more than 10 years of experience in imaging diagnosis enhanced the measurement process to determine the minimal ablative margin.\u003c/p\u003e\n\u003ch2\u003eFollow-up\u003c/h2\u003e\n\u003cp\u003eAll patients underwent follow-up evaluations within 4\u0026ndash;8 weeks after RFA to assess treatment efficacy. Repeat RFA was performed for incompletely ablated tumors based on DCE CT/MR findings. Subsequent DCE-CT/MR examinations were conducted 2\u0026ndash;3 months later to confirm cRFA in the absence of residual tumors or recurrence. The occurrence and timing of NIHM were documented. In cases of intrahepatic tumor progression, repeat RFA was considered as needed. OS was calculated from the time of RFA to the time of patient death, while the time interval of NIHM was defined as the time from the RFA procedure to the identification of NIHM.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eWe\u0026nbsp;conducted statistical analysis using R 4.0.2 \u003csup\u003e21\u003c/sup\u003e and SPSS 24.0 (IBM, Chicago, USA). Continuous variables (e.g., age) with a normal distribution were compared using\u0026nbsp;Student\u0026rsquo;s t-test, while the \u0026chi;\u003csup\u003e2\u003c/sup\u003e test was utilized for categorical data analysis (e.g., sex).\u0026nbsp;This study aimed to develop and validate a robust predictive model for iRFA, which involved three main steps \u003csup\u003e22\u003c/sup\u003e. Initially, independent variables were selected based on literature review, expert input, clinical relevance, and statistical significance across different groups\u0026nbsp;\u003csup\u003e23\u003c/sup\u003e. Subsequently, predictive models were developed using data from three initial centers and validated with patients from a fourth center. In the development dataset, potential risk factors were identified through univariate analysis, and a multivariate logistic regression (MLR) model was adjusted to avoid overfitting, considering the sample size of the iRFA group and results from univariate analysis. An omnibus test compared the model\u0026apos;s probability distribution with a null model, and the Hosmer\u0026ndash;Lemeshow test assessed model fit. Receiver operating characteristic (ROC) curves, including area under the curve (AUC) calculations, evaluated model performance. Thirdly, a generalized linear model (GLM) was employed within a sensitivity analysis to refine the predictive model and validate key findings \u003csup\u003e24\u003c/sup\u003e, as depicted in Supplementary Figure 1. Kaplan\u0026ndash;Meier survival analysis and log-rank tests were utilized to estimate and compare NIHM and OS between iRFA and cRFA groups. Statistical significance was set at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003ePatient characteristics\u003c/h2\u003e\n\u003cp\u003eDuring the study period, a total of 301 patients with CLMs underwent RFA, from which 101 patients were excluded due to insufficient data (n=41) or repeated RFA (n=60) (Figure 1). The final study cohort comprised 200 hospitalized patients with 410 CLMs. The average age of these patients was 61.0 \u0026plusmn;10.5 years (range, 27\u0026ndash;89 years), with males accounting for 63.0% (Table 1). The most common primary tumor location was the sigmoid colon (65/200), followed by the rectum (57/200). Approximately half of the patients presented with synchronous CLMs (106/200), and 72 (36.0%) patients had extrahepatic metastases, most frequently in the lung. Comorbidities were present in 101 patients (50.5%), with hypertension being the most prevalent. Pre-RFA laboratory examinations revealed elevated carcinoembryonic antigen levels (\u0026gt; 30 \u0026micro;g/mL) in 45 patients (22.5%) and elevated carbohydrate antigen 199 levels (\u0026gt; 37 U/mL) in 55 patients (27.5%). Fifty-five patients (27.5%) had undergone prior liver resection, and 172 (86.0%) had received chemotherapy before RFA. Major complications related to RFA occurred in 22 cases (11.0%), primarily involving pleural effusion and bleeding. In our study, iRFA was noted in 67 CLMs (16.3%). Among the patients, 246 tumors from 120 patients were randomly assigned to the development cohort, with an iRFA incidence of 37 tumors (15.0%). The internal validation cohort included 164 tumors (from 80 patients) with an iRFA incidence of 30 tumors (18.8%). No significant differences in baseline characteristics were observed between the two cohorts (Table 1).\u003c/p\u003e\n\u003ch2\u003eUnivariate and multivariate analysis\u003c/h2\u003e\n\u003cp\u003eIn the development cohort of 246 CLMs, the average minimal ablative margin was 5.8 \u0026plusmn; 2.5 mm, and the average ∆CT value was 21.8 \u0026plusmn; 12.3 Hu. Ninety-one tumors (37.0%) were located near critical organs or structures, 32 (13.0%) CLMs were in perivascular locations, 53 (21.5%) CLMs had a diameter \u0026ge; 20 mm, and 129 (52.4%) CLMs were subcapsular metastases. Univariate analysis demonstrated significant differences in perivascular tumor location, tumor size (\u0026ge;20 mm), and minimal ablative margin between the iRFA and cRFA groups (all P \u0026lt; 0.001) (Table 2).\u003c/p\u003e\n\u003cp\u003eTo ensure important predictor variables were not overlooked, subcapsular tumor location (P=0.199) and ∆CT value (P=0.154) were also included alongside the aforementioned three independent variables as potential risk factors in the multivariate analysis. Results from the MLR model (Table 2) revealed that perivascular tumor location (odds ratio [OR] = 3.164, 95% CI: 1.201\u0026ndash;8.333, P=0.02), tumor size \u0026ge; 20 mm (OR = 5.639, 95% CI: 2.547\u0026ndash;12.487, P\u0026lt;0.001), and minimal ablative margins (OR = 0.607, 95% CI: 0.498\u0026ndash;0.753, P\u0026lt;0.001) independently predicted iRFA. The omnibus test indicated that the current model significantly outperformed the null model (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 77.974, P\u0026lt;0.001). Additionally, the Hosmer\u0026ndash;Lemeshow test demonstrated good model fit (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 4.449, P=0.815). The predictive model\u0026apos;s performance in both the development and internal validation cohorts is illustrated in Figure 2, with corresponding AUC values of 0.884 (95% CI: 0.819\u0026ndash;0.950, P\u0026lt;0.001) and 0.857 (95% CI: 0.785\u0026ndash;0.930, P\u0026lt;0.001), respectively.\u003c/p\u003e\n\u003ch2\u003eSensitivity analysis\u003c/h2\u003e\n\u003cp\u003eThe goodness-of-fit of the GLM is presented in Supplementary Table 2. According to the GLM results, a minimal ablative margin (OR = 0.620, 95% CI: 0.479\u0026ndash;0.801, P\u0026lt;0.001) was identified as a protective factor against iRFA, while perivascular tumor location (OR = 3.214, 95% CI: 1.860\u0026ndash;8.543, P=0.025) and tumor size \u0026ge; 20 mm (OR = 5.095, 95% CI: 1.815\u0026ndash;14.300, P=0.002) were significant risk factors for iRFA. Sensitivity analyses indicated that the \u0026beta; regression coefficient and OR of the GLM remained stable, confirming the robustness of our findings compared to those from the MLR.\u003c/p\u003e\n\u003ch2\u003eOS and NIHM\u003c/h2\u003e\n\u003cp\u003eFollow-up was completed for 164 hospitalized patients (338 CLMs), with 36 patients lost to follow-up. The median follow-up period was 22 months. Among the patients, 50 (30.5%) died, and the cumulative OS rates at 1, 3, and 5 years after RFA were 92.2%, 59.7%, and 41.2%, respectively. Before RFA, there were no significant differences in extrahepatic metastasis (P = 0.468) between patients in the cRFA and iRFA groups. The estimated median survival time, calculated using the Kaplan\u0026ndash;Meier method, was 45 months (95% CI: 29.348\u0026ndash;60.652) for the entire cohort, 74 months (95% CI: 48.212\u0026ndash;99.788) for the cRFA group, and 35 months (95% CI: 22.954\u0026ndash;47.046) for the iRFA group. The log-rank test results indicated a significant difference in cumulative OS between the two groups (\u0026chi;\u003csup\u003e2\u003c/sup\u003e = 12.269, P\u0026lt;0.001) (Fig. 3A), with a hazard ratio of 2.54 (95% CI: 1.341\u0026ndash;4.814).\u003c/p\u003e\n\u003cp\u003eDuring the follow-up period, a total of 98 cases (59.8%) of patients were observed to have experienced NIHM, and appropriate anti-tumor treatment strategies were implemented based on their condition, including repeated RFA (n=62), transcatheter arterial chemoembolization (n=9), stereotactic body radiotherapy (n=2), surgical resection (n=8), and other comprehensive treatments (n=17).\u0026nbsp;The cumulative incidence of NIHM at 1 and 3 years after RFA was estimated at 44.2% and 65.0%, respectively. All NIHM occurrences were identified within 48 months, with five patients showing no NIHM during the follow-up period exceeding 5 years after cRFA. The median time without NIHM, estimated using Kaplan\u0026ndash;Meier analysis, was 16 months (95% CI: 7.868\u0026ndash;24.132) overall. In the cRFA group, NIHM was observed in 58 (50.9%) patients, with a median NIHM-free period of 31 months (95% CI: 15.818\u0026ndash;46.182). In contrast, NIHM was detected in 40 patients in the iRFA group (80%), with a median time without NIHM of 5 months (95% CI: 3.359\u0026ndash;6.641). The difference between the two groups was significant (\u0026chi;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 26.811, P\u0026lt;0.001) (Fig. 3B), with a hazard ratio of 2.679 (95% CI: 1.64\u0026ndash;4.377).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified three independent factors associated with iRFA. Our findings indicate that maintaining a sufficient minimal ablative margin is beneficial in preventing iRFA. Additionally, we observed that tumor size \u0026ge; 20 mm and perivascular tumor location are independent risk factors for iRFA. These results suggest that iRFA may significantly impact the occurrence of NIHM and OS.\u003c/p\u003e\n\u003cp\u003eAdequate ablation margins are crucial for achieving local tumor control and improving long-term patient survival\u0026nbsp;\u003csup\u003e6,8,20\u003c/sup\u003e, a view supported by biopsy evidence from previous studies\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e. However, the measurement of the minimal ablative margin in several previous studies\u0026nbsp;was\u0026nbsp;not accurate because the differences between the corresponding distances (distance between the tumor and the ablation defect zone to anatomic landmarks) can only reflect the minimal ablative margins at several points\u0026nbsp;\u003csup\u003e6,8,20\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, Kang et al.\u0026nbsp;\u003csup\u003e26\u003c/sup\u003e used the Cox proportional hazard model to evaluate the long-term effects of RFA treatment for perivascular and non-perivascular hepatocellular carcinoma and did not find significant differences in the cumulative local tumor progression survival and OS between the groups. In a recent study\u0026nbsp;\u003csup\u003e8\u003c/sup\u003e, while perivascular tumor location showed significance in univariate analysis, it did not retain significance in the final predictive model following multivariate analysis. The two studies mentioned above defined tumors with blood vessels close to, greater than, or equal to 3 mm as perivascular tumors. A study showed that the \u0026ldquo;heat sink effect\u0026rdquo; of veins \u0026gt; 3 mm on RFA of the surrounding tissues was 50%\u0026nbsp;and\u0026nbsp;100% of veins \u0026gt; 5 mm showed this effect\u0026nbsp;\u003csup\u003e10\u003c/sup\u003e. Therefore, the findings from these studies support the rationale of our use of 5-mm vessel diameters to select categorical independent variables. Our study underscores that both perivascular tumor location and tumor size are pivotal factors influencing iRFA risk, emphasizing the importance of considering real-time heat distribution during RFA procedures for tumors of varying sizes. Larger tumors, which are more prone to incomplete ablation, were associated with poorer OS outcomes. Notably, while the presence of extrahepatic metastasis can impact OS, this variable did not significantly affect outcomes in the iRFA versus cRFA groups (P \u0026gt; 0.05) in our study.\u0026nbsp;Technical failures observed on immediate post-surgical CT and during the 4-month follow-up period following each RFA procedure contributed to the increased incidence of iRFA. Moreover, to meet statistical criteria, more than 10 samples were allocated to each variable in the regression model. Given the dichotomous nature of the dependent variables, an effective sample size of 30 was estimated for the three independent variables, resulting in a well-fitting and predictive model.\u003c/p\u003e\n\u003cp\u003eDuring the follow-up period, we observed that the majority of cases of NIHM occurred within 3 years post-RFA, with all instances of NIHM detected within 4 years post-RFA, consistent with previously reported findings \u003csup\u003e8\u003c/sup\u003e. This underscores the importance of regular DCE-CT/MR examinations within 4 years following RFA. Complete tumor eradication was achieved in 5 (3.0%) patients with solitary intrahepatic CLM, complete resection of the primary lesion, and no observed NIHM or local tumor progression during a follow-up period exceeding 5 years.\u0026nbsp;In this study, the estimated cumulative OS rates at 1, 3, and 5 years post-RFA were comparable to those reported by Han et al. \u003csup\u003e8\u003c/sup\u003e but higher than those reported by Shady et al. \u003csup\u003e6\u003c/sup\u003e. The 5-year cumulative OS among participants was akin to that of surgical resection, while the 8-year cumulative OS was generally lower than that observed with liver resection in most cases \u003csup\u003e27\u003c/sup\u003e. However, methodological differences across studies and variables influencing treatment and follow-up periods should be carefully considered when directly comparing these findings.\u003c/p\u003e\n\u003cp\u003eSeveral limitations of this study warrant acknowledgment. Firstly, the cohort used for developing the prediction model was relatively small, resulting in a model with fewer variables, potentially limiting its predictive capacity for iRFA. Secondly, the lack of extensive external validation datasets introduces uncertainty regarding the generalizability of our model to other regional or international populations. Lastly, factors such as the neutrophil-to-lymphocyte ratio and genetic mutations (e.g., KRAS), which may impact NIHM and OS, were not comprehensively analyzed or balanced due to incomplete data.\u003c/p\u003e\n\u003cp\u003eIn conclusion, we accurately measured the minimal ablative margin in RFA procedures and successfully developed and validated an iRFA prediction model demonstrating good performance. A sufficient minimal ablative margin emerged as an independent protective factor against iRFA, while tumor size \u0026ge; 20 mm and perivascular tumor location were identified as independent risk factors. iRFA may potentially contribute to the occurrence of NIHM and poorer OS outcomes. This predictive model could aid clinical decision-making regarding treatment strategies for patients with CLMs based on tumor characteristics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area under the curve\u003c/p\u003e\n\u003cp\u003eCLM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cancer liver metastases\u003c/p\u003e\n\u003cp\u003eCRC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Colorectal cancer\u003c/p\u003e\n\u003cp\u003eCT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Computed tomography\u003c/p\u003e\n\u003cp\u003eDCE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Dynamic contrast-enhanced\u003c/p\u003e\n\u003cp\u003eGLM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Generalized linear model\u003c/p\u003e\n\u003cp\u003eMLR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Multivariate logistic regression\u003c/p\u003e\n\u003cp\u003eMR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Magnetic resonance\u003c/p\u003e\n\u003cp\u003eNIHM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;New intrahepatic metastases\u003c/p\u003e\n\u003cp\u003eOR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Odds ratio\u003c/p\u003e\n\u003cp\u003eOS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Overall survival\u003c/p\u003e\n\u003cp\u003eRFA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Radiofrequency ablation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis work was supported by National Natural Science Foundation of China(No. 82302332; No. 82272100).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made significant contributions to the conception and design of the study. H.L.,\u0026nbsp;Xu.X.\u0026nbsp;and Xi.X.\u0026nbsp;contributed equally to this work. H.L, Xu.X and H.F.\u0026nbsp;were responsible for material preparation, data collection and analysis. The initial draft of the manuscript was written by\u0026nbsp;H.L.\u0026nbsp;and Xi.X.\u0026nbsp;X.D.\u0026nbsp;H.F.\u0026nbsp;and\u0026nbsp;S.X.\u0026nbsp;were responsible for data curation, formal analysis, investigation, conceptualization, writing review and editing.\u0026nbsp;All authors contributed to manuscript revision, read, and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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\u0026nbsp;and\u0026nbsp;no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study available from the corresponding author (Shufeng Xu, email:[email protected]) on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study approved by the Ethics Review Committee of Quzhou Affiliated Hospital of Wenzhou Medical University (Approval No. Keyan20191228-35). Consent was obtained from all participants and/or their legal guardians and all methods were carried out in accordance with the ethical standards of the responsible committee on human experimentation and with the Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R. L.\u003cem\u003e et al.\u003c/em\u003e Colorectal cancer statistics, 2020. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 145-164, doi:10.3322/caac.21601 (2020).\u003c/li\u003e\n\u003cli\u003eBray, F.\u003cem\u003e et al.\u003c/em\u003e Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 394-424, doi:10.3322/caac.21492 (2018).\u003c/li\u003e\n\u003cli\u003eGonzaga MI\u003cem\u003e et al.\u003c/em\u003e The epidemiology and survivorship of clear cell sarcoma: a National Cancer Database (NCDB) review. \u003cem\u003eJ Cancer Res Clin Oncol\u003c/em\u003e \u003cstrong\u003e144\u003c/strong\u003e, 1711-1716, doi:10.1007/s00432-018-2693-6 (2018).\u003c/li\u003e\n\u003cli\u003eT Ruers, R. P. B. Treatment of liver metastases, an update on the possibilities and results. \u003cem\u003eEur J Cancer\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 1023\u0026ndash;1033 (2002).\u003c/li\u003e\n\u003cli\u003eManfredi, S.\u003cem\u003e et al.\u003c/em\u003e Epidemiology and management of liver metastases from colorectal cancer. \u003cem\u003eAnn Surg\u003c/em\u003e \u003cstrong\u003e244\u003c/strong\u003e, 254-259, doi:10.1097/01.sla.0000217629.94941.cf (2006).\u003c/li\u003e\n\u003cli\u003eShady W, P. E., Gonen M, Erinjeri JP, Brown KT, Covey AM, Alago W, Durack JC, Maybody M, Brody LA, Siegelbaum RH, D\u0026apos;Angelica MI, Jarnagin WR, Solomon SB, Kemeny NE, Sofocleous CT. Percutaneous Radiofrequency Ablation of Colorectal Cancer Liver Metastases: Factors Affecting Outcomes--A 10-year Experience at a Single Center.\u003cem\u003e Radiology. \u003c/em\u003e\u003cstrong\u003e278\u003c/strong\u003e, 601-611 (2016).\u003c/li\u003e\n\u003cli\u003ePetrowsky, H.\u003cem\u003e et al.\u003c/em\u003e Modern therapeutic approaches for the treatment of malignant liver tumours. \u003cem\u003eNat Rev Gastroenterol Hepatol\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 755-772, doi:10.1038/s41575-020-0314-8 (2020).\u003c/li\u003e\n\u003cli\u003eHan, K.\u003cem\u003e et al.\u003c/em\u003e A Single-Center Retrospective Analysis of Periprocedural Variables Affecting Local Tumor Progression after Radiofrequency Ablation of Colorectal Cancer Liver Metastases. \u003cem\u003eRadiology\u003c/em\u003e \u003cstrong\u003e298\u003c/strong\u003e, 212-218, doi:10.1148/radiol.2020200109 (2021).\u003c/li\u003e\n\u003cli\u003eAhmed M\u003cem\u003e et al.\u003c/em\u003e Image-guided tumor ablation: standardization of terminology and reporting criteria--a 10-year update. \u003cem\u003eRadiology\u003c/em\u003e \u003cstrong\u003e273\u003c/strong\u003e, 241-260, doi:10.1148/radiol.14132958 (2014).\u003c/li\u003e\n\u003cli\u003eLu, D.\u003cem\u003e et al.\u003c/em\u003e Effect of vessel size on creation of hepatic radiofrequency lesions in pigs: assessment of the \u0026quot;heat sink\u0026quot; effect. \u003cem\u003eAJR Am J Roentgenol\u003c/em\u003e \u003cstrong\u003e178\u003c/strong\u003e, 47-51, doi:10.2214/ajr.178.1.1780047 (2002).\u003c/li\u003e\n\u003cli\u003eBrace CL, D. T., Hinshaw JL, Lee FT Jr. Radiofrequency and microwave ablation of the liver, lung, kidney, and bone: what are the differences? \u003cem\u003eCurr Probl Diagn Radiol\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 135-143, doi:10.1067/j.cpradiol.2007.10.001 (2009).\u003c/li\u003e\n\u003cli\u003eAhmed M, B. C., Lee FT Jr, Goldberg SN. Principles of and advances in percutaneous ablation. \u003cem\u003eRadiology\u003c/em\u003e \u003cstrong\u003e258\u003c/strong\u003e, 351-369, doi:10.1148/radiol.10081634 (2011).\u003c/li\u003e\n\u003cli\u003eShi, L.\u003cem\u003e et al.\u003c/em\u003e Inflammation induced by incomplete radiofrequency ablation accelerates tumor progression and hinders PD-1 immunotherapy. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 5421, doi:10.1038/s41467-019-13204-3 (2019).\u003c/li\u003e\n\u003cli\u003eSu, T.\u003cem\u003e et al.\u003c/em\u003e Stress-induced phosphoprotein 1 mediates hepatocellular carcinoma metastasis after insufficient radiofrequency ablation. \u003cem\u003eOncogene\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 3514-3527, doi:10.1038/s41388-018-0169-4 (2018).\u003c/li\u003e\n\u003cli\u003eChen, F., Bao, H., Xie, H., Tian, G. \u0026amp; Jiang, T. Heat shock protein expression and autophagy after incomplete thermal ablation and their correlation. \u003cem\u003eInt J Hyperthermia\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 95-103, doi:10.1080/02656736.2018.1536285 (2019).\u003c/li\u003e\n\u003cli\u003eZhang, Z.\u003cem\u003e et al.\u003c/em\u003e Incomplete radiofrequency ablation provokes colorectal cancer liver metastases through heat shock response by PKCalpha/Fra-1 pathway. \u003cem\u003eCancer Biol Med\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 542-555, doi:10.20892/j.issn.2095-3941.2018.0407 (2019).\u003c/li\u003e\n\u003cli\u003eLiao, H.\u003cem\u003e et al.\u003c/em\u003e Thermal Ablation Induces Transitory Metastatic Growth by Means of the STAT3/c-Met Molecular Pathway in an Intrahepatic Colorectal Cancer Mouse Model. \u003cem\u003eRadiology\u003c/em\u003e \u003cstrong\u003e294\u003c/strong\u003e, 464-472, doi:10.1148/radiol.2019191023 (2020).\u003c/li\u003e\n\u003cli\u003eZhao, Z.\u003cem\u003e et al.\u003c/em\u003e Insufficient radiofrequency ablation promotes proliferation of residual hepatocellular carcinoma via autophagy. \u003cem\u003eCancer Lett\u003c/em\u003e \u003cstrong\u003e421\u003c/strong\u003e, 73-81, doi:10.1016/j.canlet.2018.02.024 (2018).\u003c/li\u003e\n\u003cli\u003eNijkamp, M. W.\u003cem\u003e et al.\u003c/em\u003e CD95 is a key mediator of invasion and accelerated outgrowth of mouse colorectal liver metastases following radiofrequency ablation. \u003cem\u003eJ Hepatol\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 1069-1077, doi:10.1016/j.jhep.2010.04.040 (2010).\u003c/li\u003e\n\u003cli\u003eWang X\u003cem\u003e et al.\u003c/em\u003e Margin size is an independent predictor of local tumor progression after ablation of colon cancer liver metastases. \u003cem\u003eCardiovasc Intervent Radiol\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 166-175, doi:10.1007/s00270-012-0377-1 (2013).\u003c/li\u003e\n\u003cli\u003eR Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.\u003c/li\u003e\n\u003cli\u003eHuang, Y. Q.\u003cem\u003e et al.\u003c/em\u003e Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. \u003cem\u003eJ Clin Oncol\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2157-2164, doi:10.1200/JCO.2015.65.9128 (2016).\u003c/li\u003e\n\u003cli\u003eCollins GS, Reitsma JB, Altman DG \u0026amp; KG, M. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. \u003cem\u003eBMJ\u003c/em\u003e \u003cstrong\u003e350\u003c/strong\u003e, g7594, doi:10.1136/bmj.g7594 (2015).\u003c/li\u003e\n\u003cli\u003eThabane, L.\u003cem\u003e et al.\u003c/em\u003e A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. \u003cem\u003eBMC Med Res Methodol\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 92, doi:10.1186/1471-2288-13-92 (2013).\u003c/li\u003e\n\u003cli\u003eSotirchos VS\u003cem\u003e et al.\u003c/em\u003e Colorectal Cancer Liver Metastases: Biopsy of the Ablation Zone and Margins Can Be Used to Predict Oncologic Outcome. \u003cem\u003eRadiology\u003c/em\u003e \u003cstrong\u003e280\u003c/strong\u003e, 949-959 (2016).\u003c/li\u003e\n\u003cli\u003eKang Tae Wook\u003cem\u003e et al.\u003c/em\u003e Perivascular versus nonperivascular small HCC treated with percutaneous RF ablation: retrospective comparison of long-term therapeutic outcomes. \u003cem\u003eRadiology\u003c/em\u003e \u003cstrong\u003e270\u003c/strong\u003e, 888-899 (2014).\u003c/li\u003e\n\u003cli\u003eAldrighetti L, C. R., Di Palo S, Arru M, Stella M, Orsenigo E, Gavazzi F, Ferla G, Di Carlo V, Staudacher C. Ten-year survival after liver resection for colorectal metastases: systematic review and meta-analysis. \u003cem\u003eISRN oncology\u003c/em\u003e \u003cstrong\u003e2011\u003c/strong\u003e, 763245, doi:10.5402/2011/763245 (2011).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Clinicopathological and demographic characteristics of patients in the development and internal validation cohorts.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003eDevelopment set\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003eValidation set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003et/c\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003ePatients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eTumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e60.6\u0026plusmn;11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e61.7\u0026plusmn;9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e-0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e79 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e47 (58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e41 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e33 (41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e22.9\u0026plusmn;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e23.5\u0026plusmn;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e-1.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary tumor location\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e5.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eRectum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e30 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e27 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eSigmoid colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e42 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e23 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eDescending colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e13 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e14 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eTransverse colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e9 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e6 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eAscending colon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e26 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e10 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eDifferentiation (missing, n=25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e2.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e15(15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e10 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eLow to medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e8 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e8 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e34 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e32 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eMedium to high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e29 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e21 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e13 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e5 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eSynchronous/ metachronous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e3.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eSynchronous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e57 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e49 (61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eMetachronous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e63 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e31 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eExtrahepatic metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e79 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e49 (61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e41 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e31 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003ePrevious liver resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e88 (73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e57 (71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e32 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e23 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003ePrior chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e16 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e12 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e104 (86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e68 (85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary tumor invasion (missing, n=9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eT1\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e72 (63.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e51 (65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e41 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e27 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eComorbidities (missing, n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e62 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e36 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e58 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e43 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eCEA (ng/ml)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e92 (76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e63 (78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e28 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e17 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eCA199 (0\u0026ndash;37 U/mL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e86 (71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e59 (73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e34 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e21 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary tumor nodal status\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e4.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e54 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e25 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003e1\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e35 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e33 (41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e31 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e22 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eAdjuvant chemotherapy post- RFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e24 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e17 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e96 (80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e63 (78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eMajor complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e106 (88.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e72 (90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.47145187601958%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.92332789559543%\" valign=\"top\"\u003e\n \u003cp\u003e14 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\" valign=\"top\"\u003e\n \u003cp\u003e8 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.809135399673735%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: The data represent the number of patients, with percentages in parentheses, as well as the results from Pearson\u0026rsquo;s chi-square test (c\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;for counts data. Measured data are expressed as means \u0026plusmn; standard deviations, and t-test results are presented for measurement data. BMI, body mass index; CEA, carcinoembryonic antigen; CA199, Carbohydrate antigen 199; iRFA, incomplete radiofrequency ablation; cRFA, complete radiofrequency ablation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eUnivariable and multivariate analyses of predictors for iRFA in the\u0026nbsp;development data set.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.12121212121212%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.75757575757576%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eUnivariable Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.121212121212125%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMultivariable Logistic Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.095310136157337%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.674735249621785%\" valign=\"top\"\u003e\n \u003cp\u003ecRFA (n=209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.46444780635401%\" valign=\"top\"\u003e\n \u003cp\u003eiRFA (n=37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e/t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7155824508320725%\" valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.02874432677761%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.472012102874432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.095310136157337%\" valign=\"top\"\u003e\n \u003cp\u003eCrucial location\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.674735249621785%\" valign=\"top\"\u003e\n \u003cp\u003e77 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.46444780635401%\" valign=\"top\"\u003e\n \u003cp\u003e14 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e0.908\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7155824508320725%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.02874432677761%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.472012102874432%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.095310136157337%\" valign=\"top\"\u003e\n \u003cp\u003ePerivascular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.674735249621785%\" valign=\"top\"\u003e\n \u003cp\u003e14 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.46444780635401%\" valign=\"top\"\u003e\n \u003cp\u003e18 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e48.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7155824508320725%\" valign=\"top\"\u003e\n \u003cp\u003e1.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.02874432677761%\" valign=\"top\"\u003e\n \u003cp\u003e3.164 (1.201, 8.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.472012102874432%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.095310136157337%\" valign=\"top\"\u003e\n \u003cp\u003eSubcapsular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.674735249621785%\" valign=\"top\"\u003e\n \u003cp\u003e106 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.46444780635401%\" valign=\"top\"\u003e\n \u003cp\u003e23 (62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e1.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e0.199\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7155824508320725%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.02874432677761%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.472012102874432%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.095310136157337%\" valign=\"top\"\u003e\n \u003cp\u003eSize (mm)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.674735249621785%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.46444780635401%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e37.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7155824508320725%\" valign=\"top\"\u003e\n \u003cp\u003e1.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.02874432677761%\" valign=\"top\"\u003e\n \u003cp\u003e5.639 (2.547, 12.487)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.472012102874432%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.095310136157337%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.674735249621785%\" valign=\"top\"\u003e\n \u003cp\u003e178 (85.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.46444780635401%\" valign=\"top\"\u003e\n \u003cp\u003e15 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7155824508320725%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.02874432677761%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.472012102874432%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.095310136157337%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.674735249621785%\" valign=\"top\"\u003e\n \u003cp\u003e31 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.46444780635401%\" valign=\"top\"\u003e\n \u003cp\u003e22 (59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7155824508320725%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.02874432677761%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.472012102874432%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.095310136157337%\" valign=\"top\"\u003e\n \u003cp\u003e∆CT (Hu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.674735249621785%\" valign=\"top\"\u003e\n \u003cp\u003e22.3\u0026plusmn;12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.46444780635401%\" valign=\"top\"\u003e\n \u003cp\u003e19.2\u0026plusmn;13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e1.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e0.154\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7155824508320725%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.02874432677761%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.472012102874432%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.095310136157337%\" valign=\"top\"\u003e\n \u003cp\u003eMinimal ablative margin (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.674735249621785%\" valign=\"top\"\u003e\n \u003cp\u003e6.2\u0026plusmn;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.46444780635401%\" valign=\"top\"\u003e\n \u003cp\u003e3.9\u0026plusmn;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e5.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.774583963691377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7155824508320725%\" valign=\"top\"\u003e\n \u003cp\u003e-0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.02874432677761%\" valign=\"top\"\u003e\n \u003cp\u003e0.607 (0.498, 0.753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.472012102874432%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. The data represent the number of tumors, with percentages in parentheses, and Pearson\u0026rsquo;s chi-square (c\u003csup\u003e2\u003c/sup\u003e) test for counts data (#). Means \u0026plusmn; standard deviations represent measured data, with t-tests comparing the \u0026lsquo;*\u0026rsquo; symbols data. B, \u0026beta; regression coefficient; OR, odds ratio; CI, confidence interval; cRFA, complete radiofrequency ablation; iRFA, incomplete radiofrequency ablation; ∆CT, the difference in CT values between the portal vein and plain scan phase; subcapsular, CLMs with the shortest distance to the liver capsule within 5 mm; crucial location, the location of CLMs that were adjacent to an organ or structure that could affect the RFA procedure, such as the diaphragm, gastrointestinal tract, and gallbladde\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"radiofrequency ablation, colorectal cancer liver metastases, overall survival, new intrahepatic metastases, computed tomography","lastPublishedDoi":"10.21203/rs.3.rs-4675432/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4675432/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Radiofrequency ablation (RFA) is a commonly used interventional method for treating colorectal cancer liver metastases (CLMs), with its efficacy influenced by tumor characteristics and intrahepatic distribution. The presence of incomplete RFA (iRFA) can result in poorer prognosis and may impede the effectiveness of targeted therapies or immunotherapy. Currently, there is a lack of multicenter prospective cohort studies and predictive models for iRFA. This study prospectively included CLM patients from four medical centers who underwent percutaneous RFA to develop and validate a predictive model for iRFA. All patients were followed up, with the occurrence of new intrahepatic metastases (NIHM) and overall survival (OS) assessed using the Kaplan-Meier method. We identified independent predictors of iRFA, including perivascular tumor location (odds ratio [OR] = 3.164), tumor size ≥ 20 mm (OR = 5.639), and minimal ablative margin (OR = 0.607). The area under the receiver operating characteristic curve (AUC) was 0.884 for the developmental cohort and 0.857 for the external validation cohort. Compared to the complete RFA group, patients in the iRFA group had a higher incidence of NIHM and shorter OS. However, broader external validation and the inclusion of more variables for comprehensive analysis and balance are still needed.","manuscriptTitle":"Incomplete Radiofrequency Ablation in the Treatment of Colorectal Cancer Liver Metastasis: A Multicenter Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 21:27:04","doi":"10.21203/rs.3.rs-4675432/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":"dfc15408-beae-4867-b1e5-2c148a08497c","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-24T08:38:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-29 21:27:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4675432","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4675432","identity":"rs-4675432","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}