Early Recurrence of Hepatocellular Carcinoma After Hepatectomy: Predictive Role of Whole-Tumor Iodine Density Histogram Features and Resection Margin Distance

Early Recurrence of Hepatocellular Carcinoma After Hepatectomy: Predictive Role of Whole-Tumor Iodine Density Histogram Features and Resection Margin Distance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early Recurrence of Hepatocellular Carcinoma After Hepatectomy: Predictive Role of Whole-Tumor Iodine Density Histogram Features and Resection Margin Distance Yuan Xu, Bo Liu, Xiaojing Ming, Tiezhu Ren, Jiachen Sun, Rui Xu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7978947/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Purpose To evaluate the predictive value of whole-tumor iodine density (ID) histogram parameters and resection margin distance for early recurrence (ER) after curative resection of hepatocellular carcinoma (HCC). Methods This retrospective study included patients with HCC who underwent R0 resection and received preoperative spectral CT scans. Patients were categorized into ER+ (n = 42) and ER− (n = 43) groups. Independent predictors of recurrence-free survival (RFS) were identified using multivariate Cox regression analysis. The performance of the prediction model was assessed using time-dependent receiver operating characteristic (td-ROC) curves, calibration and decision curves analysis. Kaplan-Meier analysis was used to evaluate differences in RFS between groups. Results Multivariate Cox regression identified Max, Skewness, microvascular invasion (MVI), and resection margin distance as independent risk factors for ER. Kaplan-Meier analysis revealed significantly shorter mean RFS in patients with MVI+ (12.34 months vs. 29.02 months), extremely narrow margin (9.54 months) and narrow margin (15.42 months) compared to wide margin (28.41 months), high Max (≥ 2041.00 vs. <2041.00; 15.15 vs. 26.13 months), and high Skewness (≥ 0.22 vs. <0.22; 16.83 vs. 23.62 months) (all P < 0.05). Conclusion Whole-tumor ID histogram parameters (Max and Skewness) and clinicopathological factors (MVI and resection margin distance) are independent predictors of ER. These factors allow effective stratification of RFS and may guide individualized postoperative management. Critical relevance statement: Whole-tumor iodine density histogram features and resection margin distance provide independent predictors of early recurrence after hepatectomy in HCC, enabling improved risk stratification and guiding individualized postoperative management. Hepatocellular carcinoma Early recurrence Iodine density Histogram analysis Resection margin Figures Figure 1 Figure 2 Figure 3 Figure 4 Key Points Essential to establish noninvasive early prediction for early recurrence (ER) after curative hepatocellular carcinoma resection. Max, Skewness, microvascular invasion (MVI), and resection margin were identified as independent risk factors for predicting ER. Each independent risk factor allowed for effective stratification of recurrence-free survival (RFS). 1 Introduction Liver resection (LR) remains a first-line curative treatment option for patients with early and very early-stage hepatocellular carcinoma (HCC) [1, 2]. Despite advances in perioperative management and surgical techniques that have significantly reduced perioperative complication rates, postoperative recurrence remains a major clinical concern [3]. Among these, early recurrence (ER)—defined as recurrence within two years after curative hepatectomy—occurs in up to 50% of patients [4] and has been associated with poorer median overall survival and post-recurrence survival [5]. Therefore, accurate risk assessment of ER in HCC patients may help guide surgeons in implementing proactive antiviral or adjuvant therapies to prolong survival and improve quality of life through individualized treatment strategies. Multiple factors influence ER after hepatectomy for HCC, including preoperative patient conditions, intraoperative surgical strategies, and postoperative pathological characteristics [6]. Among these, only intraoperative factors are modifiable to some extent. Rational surgical planning and operative strategies may help reduce the risk of ER by targeting key intraoperative variables [7]. It is well established that the key to surgical success lies in determining an appropriate resection margin to balance complete tumor removal with adequate remnant liver volume and function [6]. However, the optimal width of the surgical margin remains controversial [8]. Some studies suggest that a margin width <1 cm is associated with residual microvascular invasion (MVI) and a higher risk of tumor recurrence, particularly in patients with more aggressive tumors or concomitant cirrhosis [9]. Conversely, other literature has reported that a margin of 0.5–1 cm may be oncologically safe [10]. More importantly, even among patients achieving R0 resection, those undergoing wide-margin (WM, ≥1 cm) hepatectomy may not necessarily experience improved long-term outcomes [11]. In clinical practice, many patients are unavoidably left with margins <1 cm due to tumor location, size, or limited hepatic functional reserve [12]. Therefore, the impact of specific resection margin widths—wide margin (WM, ≥1 cm), narrow margin (NM, ≥0.5 to <1 cm), and extremely narrow margin (ENM, <0.5 cm)—on ER warrants further investigation. Spectral CT, as a functional imaging modality, has demonstrated potential value in elucidating the biological characteristics of HCC [13]. Parameters such as iodine density (ID), the slope of the spectral attenuation curve, and effective atomic number (Zeff) are typically derived from manually drawn regions of interest (ROI) on a single axial slice, which are subject to interobserver variability and may compromise the robustness and reproducibility of the results [14, 15]. A recent small-sample study demonstrated that whole-tumor quantitative spectral CT parameters provide greater diagnostic value in histological grading of HCC compared to single-slice measurements [16]. In particular, ID has been shown in multiple studies to noninvasively and effectively assess MVI, therapeutic response, ER, and prognosis in HCC [17-19]. Therefore, a comprehensive assessment of whole-tumor ID provides a more objective approach that minimizes sampling bias [20]. Especially, histogram analysis of the entire tumor ID images demonstrates not only high reproducibility and minimal inter-observer variability, but also holds clinical value in reflecting intratumoral heterogeneity, including pathological risk stratification, histological grading, and immunohistochemical features [20-22]. However, the utility of ID histogram analysis in predicting ER following HCC surgery still warrants further investigation. 2 Materials and methods 2.1 Patients This study adhered to the Declaration of Helsinki, was approved by Lanzhou University Second Hospital's Ethics Committee, exempted from subjects' informed consent, and approval number: 2024A-1267. We retrospectively collected data from consecutive patients who underwent curative LR for HCC at our hospital between January 2018 and January 2023. The inclusion criteria were as follows: (1) patients aged ≥18 years who underwent initial LR; (2) histopathologically confirmed solitary HCC with R0 resection; (3) preoperative liver spectral CT performed within 2 weeks before surgery; and (4) complete clinical and pathological data. Exclusion criteria included: (1) prior treatment for HCC, such as microwave ablation, radiofrequency ablation, or transarterial chemoembolization (TACE); (2) confirmed distant metastasis before surgery; (3) coexistence of other malignancies; (4) poor CT image quality; and (5) early postoperative death due to severe complications or loss to follow-up. A total of 85 patients were ultimately included in the study. The detailed patient enrollment flowchart is shown in Fig. 1 . 2.2 Collection of data We retrospectively collected baseline admission data, including demographic characteristics, laboratory results, perioperative variables, and histopathological findings. The surgical resection margin was defined as the shortest pathological distance from the tumor edge to the liver transection line. Based on pathological reports, patients were categorized into three groups according to resection margin width: WM (≥1 cm), NM (≥0.5 cm to <1 cm), and ENM (<0.5 cm) [23, 24]. Detailed admission data are provided in Supplementary Material 1 . 2.3 Image acquisition and analysis All patients were scanned using Discovery CT 750 HD (GE Healthcare, Waukesha) and Revolution CT (GE Medical Healthcare). The detailed spectral CT scanning parameters are provided in Supplementary Material 1 . Two abdominal radiologists with 7 and 10 years of diagnostic experience, respectively, independently and blindly evaluated the patients’ CT images. The assessment focused on the presence of liver cirrhosis, splenomegaly, gastroesophageal varices (GEVs), spontaneous portosystemic shunt (SPSS), ascites, peritumoral arterial phase enhancement, tumor margin, tumor capsule, intratumoral necrosis, and radiologic vascular invasion (RVI). In cases of disagreement, a consensus was reached through discussion. The CT diagnostic criteria for clinically significant portal hypertension (CSPH) included the presence of splenomegaly along with at least one of the following: GEVs, SPSS, or ascites [25]. Detailed definitions of CT features are provided in Supplementary Material 1, Table S1 . 2.4 Histogram analysis ID images from the portal venous phase of spectral CT were stored in DICOM format and imported into FireVoxel software (FireVoxel, version 462; https://www.firevoxel.org). Two radiologists, each with over five years of experience in hepatic imaging, independently performed whole-tumor histogram analysis under blinded conditions. Any discrepancies were resolved through consensus. The entire HCC lesion was manually segmented slice by slice along its boundary, with each ROI encompassing as much of the tumor as possible, including necrotic, cystic, and hemorrhagic areas. To minimize partial volume effects, the ROI was drawn slightly smaller than the actual lesion boundary. After ROI delineation, the software automatically generated histogram parameters based on a three-dimensional volume of interest (VOI), including minimum (Min), maximum (Max), mean (Mean), standard deviation (SD), variance, skewness, kurtosis, entropy, and percentiles (1st–99th) (Fig. 2) . To ensure the consistency and reliability of the extracted histogram parameters, the intraclass correlation coefficient (ICC) was used to evaluate interobserver agreement. 2.5 Follow-up and Endpoints All patients were followed up at 1, 3, and 6 months postoperatively, and then every 6 months thereafter. Follow-up assessments included measurements of serum alpha-fetoprotein (AFP) levels, liver function tests, and imaging studies (abdominal ultrasound, contrast-enhanced CT, or MRI). Tumor recurrence was defined as intrahepatic recurrence or extrahepatic metastasis, primarily diagnosed based on imaging findings or confirmed by histopathology through liver biopsy. The primary endpoint of this study was ER, defined as the occurrence of intrahepatic or extrahepatic tumor recurrence within 2 years after curative resection. The time of confirmed recurrence and the characteristics of the recurrent lesions were recorded. Recurrence-free survival (RFS) was defined as the interval between the date of curative surgery and the date of tumor recurrence or the last follow-up. The follow-up deadline was set as February 1, 2025. 2.6 Statistical analysis Data processing and analysis were conducted using IBM SPSS Statistics (Version 26.0; IBM, New York, USA), R (Version 4.3.2; https://www.r-project.org/) and Zstats v1.0 (www.zstats.net). The inter-observer agreement between the two radiologists was assessed using Cohen's Kappa coefficient for categorical variables. The normality of continuous variables was tested using the Shapiro-Wilk test. Continuous variables with a normal distribution are presented as Mean ± SD, while non-normally distributed variables are expressed as Median (Q1, Q3). The differences between continuous variables across groups were compared using the independent samples t-test for normally distributed data and the Mann-Whitney U test for non-normally distributed data. Categorical variables are presented as frequencies (%) and analyzed using the χ² test or Fisher's exact test. Multicollinearity was evaluated by calculating the variance inflation factor (VIF). Univariate and multivariate Cox regression analyses were performed, and variables with P<0.05 in the multivariate analysis were used to construct the ER prediction model. Continuous variables were dichotomized based on the median, and Kaplan-Meier survival curves were plotted. Differences in survival curves were analyzed using the Log-rank test. The predictive performance of the model was assessed by the area under the time-dependent receiver operating characteristic (td-ROC) curve (AUC). Model calibration was evaluated using calibration curves, and the overall net benefit of the model was assessed using decision curve analysis (DCA). A P-value of <0.05 was considered statistically significant. 3 Results 3.1 Baseline clinical characteristics A total of 85 patients were included in this study, comprising 66 males (77.65%), with a median age of 57 years (range, 32–74 years). Based on follow-up results, patients were categorized into early recurrence (ER+) and non-early recurrence (ER−) groups, with 42 patients (49.41%) experiencing ER. The median RFS for the entire cohort was 24.07 months (range, 5.10–58.87 months). Among the 42 patients who experienced recurrence, the median RFS was 4.08 months (range, 5.10–34.10 months), while for the 43 patients without recurrence, the median RFS was 30.53 months (range, 23.97–58.87 months). Among all ER+ patients, 25 had only intrahepatic recurrence, 10 had intrahepatic recurrence with vascular invasion and/or extrahepatic recurrence, and 7 had only extrahepatic recurrence. A comparison of clinical characteristics between the ER+ and ER- groups revealed significant differences in prothrombin time (PT), Model for End-Stage Liver Disease (MELD) score, Tumor Burden Score (TBS), MVI, and resection margin ( P <0.05 for all). The detailed clinical characteristics comparison is shown in Table 1 . 3.2 CT Characteristics and Whole-Tumor ID Histogram Parameters The inter-observer agreement between the two radiologists for assessing imaging findings and measuring histogram parameters was good (K = 0.811–0.921; ICC = 0.817–0.939). A comparison of CT characteristics and histogram parameters between the ER+ and ER- groups revealed that the ER+ group had significantly higher values for Mean, Max, Skewness, Kurtosis, and the 75th, 90th, 95th, and 99th percentiles compared to the ER- group ( P <0.05 for all, Table 2 ). Representative cases are shown in Fig. 2 . 3.3 Cox Regression Analysis Parameters with statistically significant intergroup differences were included in the univariate Cox regression analysis, identifying 13 features significantly associated with ER ( P 10 and were excluded. The remaining 9 significant variables were subjected to multivariate Cox regression analysis. The results identified the following as independent predictors of postoperative ER: NM (HR: 2.24; 95% CI : 1.08–4.64), ENM (HR: 3.48; 95% CI : 1.25–9.69), MVI+ (HR: 3.05; 95% CI : 1.45–6.40), Max (HR: 1.06; 95% CI : 1.04–1.09), and Skewness (HR: 1.70; 95% CI : 1.05–2.77) ( P <0.05 for all, Table 3 ). Based on these four independent predictors, a combined clinical-radiological model was developed and visualized as a nomogram to predict RFS at 6, 12, and 24 months after curative hepatectomy in HCC patients ( Fig. 3A ). The td-ROC curves demonstrated strong predictive performance with AUC values of 0.885, 0.894, and 0.872 at 6, 12, and 24 months, respectively (Fig. 3B ). Calibration curves and DCA further confirmed the model’s good predictive accuracy and clinical utility ( Fig. 3C–D ). 3.4 Recurrence Risk Stratification Kaplan-Meier survival curves were plotted based on the independent predictive factors, and the results showed that the mean RFS was significantly shorter in patients with MVI+ (Positive, 12.34 months; Negative, 29.02 months; P < 0.001), narrow resection margins (ENM, 9.54 months; NM, 15.42 months; WM, 28.41 months; P < 0.001), high Max (Max ≥ 2041.00, 15.15 months; Max < 2041.00, 26.13 months; P < 0.001), and high Skewness (Skewness ≥ 0.22, 16.83 months; Skewness < 0.22, 23.62 months; P = 0.004) ( Table 4, Fig. 4 ). 4 Discussion This study aimed to predict ER following curative treatment for HCC by integrating whole-tumor ID-based histogram parameters with clinicopathological features, and to further evaluate prognostic factors associated with RFS. The results demonstrated that higher whole-tumor ID histogram values (Max ≥ 2041.00, Skewness ≥ 0.22), microvascular invasion (MVI+), and narrower resection margins (ENM and NM) were independent risk factors for ER (all P < 0.05). These parameters effectively identified patients at high risk of ER and enabled robust stratification of RFS outcomes. Resection margin width is a key intraoperative criterion for achieving R0 resection (≥1 cm) and plays a significant role in determining the timing and pattern of postoperative recurrence in HCC [26, 27]. However, there is currently no consensus on the optimal margin width [8]. In this study, patients were further categorized into three groups based on pathologically assessed resection margin width: WM (≥1 cm), NM (≥0.5 cm and <1 cm), and ENM (<0.5 cm). Our results demonstrated a progressive decline in RFS and an increasing ER rate with decreasing margin width. Notably, both NM and ENM were significantly associated with a higher risk of ER ( P < 0.05), which is consistent with previous findings [23, 24]. This may be attributed to the inability of subcentimeter margins to completely eliminate microscopic tumor spread or microinvasive lesions [28]. Previous studies have demonstrated that the risk of recurrence following curative treatment for HCC largely depends on the invasiveness and proliferation of residual tumor cells, particularly in the form of MVI [29-31]. MVI, typically found in small branches of the portal vein, is a well-established pathological indicator of tumor aggressiveness and metastatic potential [32], and it further complicates the prognostic impact of resection margin status [27]. In the presence of MVI, even when gross complete (R0) resection is achieved, residual tumor cells within the microvasculature can disseminate via intrahepatic vessels, leading to the development of portal vein tumor thrombus and distant metastases [9]. In our study, patients with MVI (+) and narrower resection margins (NM and ENM) showed a significantly higher risk of ER compared to those with WM. These findings suggest a potential synergistic effect between narrow resection margins and MVI, whereby limited resection margins may exacerbate the adverse prognostic impact of MVI. Given the inherent delay in postoperative pathological characteristics, increasing research has focused on using preoperative imaging biomarkers for prognostic prediction of the disease [33]. In clinical practice, spectral CT has been widely used to evaluate the internal characteristics of liver tumors. Studies have shown that parameters such as ID derived from manually delineated ROI on a single plane can effectively predict recurrence and treatment response [17, 34]. However, due to the high heterogeneity of HCC, a single-layer ROI may fail to reflect the overall characteristics of the lesion [16]. In contrast, histogram analysis allows for a quantitative assessment of the entire tumor, providing a more comprehensive view of its internal heterogeneity, thus offering greater clinical value [16, 21]. This study performed a full-tumor histogram analysis using preoperative ID images of HCC patients. The results showed that Max (HR: 1.06, 95% CI : 1.04–1.09) and Skewness (HR: 1.70, 95% CI : 1.05–2.77) are independent risk factors for ER, and they can effectively stratify patients. Max reflects the peak iodine distribution in the tumor region, indirectly indicating the area with the richest blood supply [35], suggesting a stronger neovascularization or abnormal vascular distribution, which is associated with more active tumor biology and a higher risk of recurrence. Skewness mainly reflects the asymmetry of voxel distribution [36]. An increase in skewness indicates a more uneven iodine distribution within the tumor, suggesting more significant structural and vascular heterogeneity [37], which is often related to more aggressive and recurrent biological characteristics. Therefore, Max and Skewness, as histogram-based indicators of iodine distribution peaks and distribution patterns, can serve as quantitative markers reflecting tumor vascular heterogeneity and invasive potential, making them independent predictors for ER. This study has certain limitations. Firstly, it is a retrospective study, which inevitably carries the risk of selection bias. Secondly, only the portal venous phase ID images were used, primarily because the portal venous phase provides better delineation of the tumor boundaries and extent, which aids in the accurate segmentation of the entire tumor. Lastly, there is insufficient automation in tumor segmentation. Although manual delineation of the tumor's VOI ensures high precision, it is time-consuming and less efficient. 5 Conclusions This study constructed a predictive model for ER after radical LR in HCC patients, based on whole-tumor histogram parameters and clinical-pathological characteristics. The model includes factors such as MVI (+), resection margin, Max, and Skewness, all of which effectively stratify RFS. In conclusion, when predicting high-risk HCC patients for ER, wide-margin liver resection or postoperative adjuvant therapy may improve patient prognosis. Abbreviations LR, Liver resection; HCC, Hepatocellular carcinoma; ER, Early recurrence; MVI, Microvascular invasion; WM, Wide margin; NM, Narrow margin; ENM, Extremely narrow margin; ID, Iodine density; ROI, Regions of interest; GEVs, Gastroesophageal varices; SPSS, Spontaneous portosystemic shunt; RVI, Radiologic vascular invasion; CSPH, Clinically significant portal hypertension; VOI, Volume of interest; ICC, Intraclass correlation coefficient; RFS, Recurrence-free survival; VIF, Variance inflation factor; td-ROC, Time-dependent receiver operating characteristic; AUC, Area Under the Curve; DCA, Decision curve analysis; PT, Prothrombin time; MELD, Model for End-Stage Liver Disease; TBS, Tumor Burden Score. Declarations Availability of data and materials The data used in this study is not a public data. The datasets used in this study are available from the corresponding authors on reasonable requests. References VOGEL A, CHAN S L, DAWSON L A, et al. 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Histogram analysis based on intravoxel incoherent motion diffusion-weighted imaging for determining the perineural invasion status of rectal cancer [J]. Quantitative imaging in medicine and surgery, 2024, 14(8): 5358-72. YAMAGATA K, YANAGAWA M, HATA A, et al. Three-dimensional iodine mapping quantified by dual-energy CT for predicting programmed death-ligand 1 expression in invasive pulmonary adenocarcinoma [J]. Sci Rep, 2024, 14(1): 18310. Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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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-7978947","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543579361,"identity":"7bbbaf88-86dc-4b76-a75e-91c8883c2733","order_by":0,"name":"Yuan Xu","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Xu","suffix":""},{"id":543579365,"identity":"248d9f8c-fcd9-4b71-b938-775d94f45249","order_by":1,"name":"Bo Liu","email":"","orcid":"","institution":"Lanzhou University Second 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17:55:04","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138863,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7978947/v1/cd22c011a433d8cd38214382.html"},{"id":96208909,"identity":"d77c00a4-8cf1-49a6-8a83-073f62cd9078","added_by":"auto","created_at":"2025-11-18 17:55:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1188611,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Flow Chart. ER, early recurrence.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7978947/v1/1b8e2f90a1d1f9f751438c4c.png"},{"id":96208915,"identity":"053c8c7f-d955-4f77-b508-5f9914c01957","added_by":"auto","created_at":"2025-11-18 17:55:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1648932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A–D, ER+ group)\u003c/strong\u003e: A 67-year-old female with a recurrence-free survival (RFS) of 7.83 months. (A–B) Pseudocolor iodine density (ID) images of the tumor. (C) Region of interest (ROI) delineation on the ID image. (D) Histogram showing the distribution of ID-related features. Preoperative ID-based histogram parameters: Max = 2064, Skewness = 0.69. Postoperative pathology revealed a resection margin of 0.2 cm and the presence of microvascular invasion (MVI+). \u003cstrong\u003e(E–H, ER− group)\u003c/strong\u003e: A 54-year-old male with an RFS of 52.23 months. (E–F) Pseudocolor ID images of the tumor. (G) ROI delineation on the ID images. (H) Corresponding histogram of ID-related features. Preoperative histogram parameters: Max = 2039, Skewness = 0.19. Postoperative pathology showed a resection margin of 1.4 cm and absence of MVI (MVI−).\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7978947/v1/29cc222aa1ad0ee1545a171a.png"},{"id":96252893,"identity":"d46b1cb3-37a0-4c03-a5d9-016d137da6b4","added_by":"auto","created_at":"2025-11-19 07:41:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":569433,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Nomogram predicting 6-, 12-, and 24-month recurrence-free survival (RFS) in hepatocellular carcinoma (HCC) patients. (B) Time-dependent receiver operating characteristic (tdROC) curves evaluating the nomogram's performance for predicting RFS at 6-, 12-, and 24-months after curative hepatectomy. (C–D) Calibration curves and decision curve analysis (DCA) demonstrating the nomogram’s predictive accuracy and clinical utility.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7978947/v1/a6fefd5553c6d947f3f57f97.png"},{"id":96208917,"identity":"e33474f9-1bec-49f3-a815-908664fb540d","added_by":"auto","created_at":"2025-11-18 17:55:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":573951,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for independent prognostic factors. Patients with (A) MVI (+), (B) resection margins (including extremely narrow margins [ENM] and narrow margins [NM]), (C) Max ≥ 2041.00, and (D) Skewness ≥ 0.22 exhibited significantly shorter mean recurrence-free survival (RFS) (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7978947/v1/80c940e3f4a348fa2f737de1.png"},{"id":96257098,"identity":"96014f4e-38b0-436b-8a10-55d7c4605d93","added_by":"auto","created_at":"2025-11-19 07:51:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4591258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7978947/v1/a67cf1bb-ba3e-4973-a24a-19b1b176f044.pdf"},{"id":96208908,"identity":"4dba6774-9dc4-4cee-9d3e-9191300eb2a1","added_by":"auto","created_at":"2025-11-18 17:55:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":38289,"visible":true,"origin":"","legend":"","description":"","filename":"1Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-7978947/v1/a6b283b585c875a69650db5b.docx"},{"id":96251466,"identity":"11bbb959-4c33-4241-8b28-a59b450e30fb","added_by":"auto","created_at":"2025-11-19 07:39:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32555,"visible":true,"origin":"","legend":"","description":"","filename":"1SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7978947/v1/3fcc7aba7348431786a5086e.docx"},{"id":96250797,"identity":"c2aae810-3229-4419-b9a7-860daf548da2","added_by":"auto","created_at":"2025-11-19 07:39:01","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17319334,"visible":true,"origin":"","legend":"","description":"","filename":"ARvisualabstract.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7978947/v1/7d00fef8060abf204d038f2f.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Recurrence of Hepatocellular Carcinoma After Hepatectomy: Predictive Role of Whole-Tumor Iodine Density Histogram Features and Resection Margin Distance","fulltext":[{"header":"Key Points","content":"\u003cul start=\"50\"\u003e\n \u003cli\u003eEssential to establish noninvasive early prediction for early recurrence (ER) after curative hepatocellular carcinoma resection.\u003c/li\u003e\n \u003cli\u003eMax, Skewness, microvascular invasion (MVI), and resection margin were identified as independent risk factors for predicting ER.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEach independent risk factor allowed for effective stratification of recurrence-free survival (RFS).\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eLiver resection (LR) remains a first-line curative treatment option for patients with early and very early-stage hepatocellular carcinoma (HCC)\u0026nbsp;[1, 2]. Despite advances in perioperative management and surgical techniques that have significantly reduced perioperative complication rates, postoperative recurrence remains a major clinical concern\u0026nbsp;[3]. Among these, early recurrence (ER)\u0026mdash;defined as recurrence within two years after curative hepatectomy\u0026mdash;occurs in up to 50% of patients\u0026nbsp;[4]\u0026nbsp;and has been associated with poorer median overall survival and post-recurrence survival\u0026nbsp;[5]. Therefore, accurate risk assessment of ER in HCC patients may help guide surgeons in implementing proactive antiviral or adjuvant therapies to prolong survival and improve quality of life through individualized treatment strategies.\u003c/p\u003e\n\u003cp\u003eMultiple factors influence ER after hepatectomy for HCC, including preoperative patient conditions, intraoperative surgical strategies, and postoperative pathological characteristics\u0026nbsp;[6]. Among these, only intraoperative factors are modifiable to some extent. Rational surgical planning and operative strategies may help reduce the risk of ER by targeting key intraoperative variables\u0026nbsp;[7]. It is well established that the key to surgical success lies in determining an appropriate resection margin to balance complete tumor removal with adequate remnant liver volume and function\u0026nbsp;[6].\u0026nbsp;However, the optimal width of the surgical margin remains controversial\u0026nbsp;[8]. Some studies suggest that a margin width \u0026lt;1 cm is associated with residual microvascular invasion (MVI) and a higher risk of tumor recurrence, particularly in patients with more aggressive tumors or concomitant cirrhosis\u0026nbsp;[9]. Conversely, other literature has reported that a margin of 0.5\u0026ndash;1 cm may be oncologically safe\u0026nbsp;[10]. More importantly, even among patients achieving R0 resection, those undergoing wide-margin (WM, \u0026ge;1 cm) hepatectomy may not necessarily experience improved long-term outcomes\u0026nbsp;[11]. In clinical practice, many patients are unavoidably left with margins \u0026lt;1 cm due to tumor location, size, or limited hepatic functional reserve\u0026nbsp;[12]. Therefore, the impact of specific resection margin widths\u0026mdash;wide margin (WM, \u0026ge;1 cm), narrow margin (NM, \u0026ge;0.5 to \u0026lt;1 cm), and extremely narrow margin (ENM, \u0026lt;0.5 cm)\u0026mdash;on ER warrants further investigation.\u003c/p\u003e\n\u003cp\u003eSpectral CT, as a functional imaging modality, has demonstrated potential value in elucidating the biological characteristics of HCC [13]. Parameters such as iodine density (ID), the slope of the spectral attenuation curve, and effective atomic number (Zeff) are typically derived from manually drawn regions of interest (ROI) on a single axial slice, which are subject to interobserver variability and may compromise the robustness and reproducibility of the results [14, 15]. A recent small-sample study demonstrated that whole-tumor quantitative spectral CT parameters provide greater diagnostic value in histological grading of HCC compared to single-slice measurements [16]. In particular, ID has been shown in multiple studies to noninvasively and effectively assess MVI, therapeutic response, ER, and prognosis in HCC [17-19]. Therefore, a comprehensive assessment of whole-tumor ID provides a more objective approach that minimizes sampling bias [20]. Especially, histogram analysis of the entire tumor ID images demonstrates not only high reproducibility and minimal inter-observer variability, but also holds clinical value in reflecting intratumoral heterogeneity, including pathological risk stratification, histological grading, and immunohistochemical features [20-22]. However, the utility of ID histogram analysis in predicting ER following HCC surgery still warrants further investigation.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adhered to the Declaration of Helsinki, was approved by Lanzhou University Second Hospital\u0026apos;s Ethics Committee, exempted from subjects\u0026apos; informed consent, and approval number: 2024A-1267.\u003c/p\u003e\n\u003cp\u003eWe retrospectively collected data from consecutive patients who underwent curative LR for HCC at our hospital between January 2018 and January 2023. The inclusion criteria were as follows: (1) patients aged \u0026ge;18 years who underwent initial LR; (2) histopathologically confirmed solitary HCC with R0 resection; (3) preoperative liver spectral CT performed within 2 weeks before surgery; and (4) complete clinical and pathological data. Exclusion criteria included: (1) prior treatment for HCC, such as microwave ablation, radiofrequency ablation, or transarterial chemoembolization (TACE); (2) confirmed distant metastasis before surgery; (3) coexistence of other malignancies; (4) poor CT image quality; and (5) early postoperative death due to severe complications or loss to follow-up. A total of 85 patients were ultimately included in the study. The detailed patient enrollment flowchart is shown in\u003cstrong\u003e\u0026nbsp;Fig. 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Collection of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe retrospectively collected baseline admission data, including demographic characteristics, laboratory results, perioperative variables, and histopathological findings. The surgical resection margin was defined as the shortest pathological distance from the tumor edge to the liver transection line. Based on pathological reports, patients were categorized into three groups according to resection margin width: WM (\u0026ge;1 cm), NM (\u0026ge;0.5 cm to \u0026lt;1 cm), and ENM (\u0026lt;0.5 cm) [23, 24]. Detailed admission data are provided in\u003cstrong\u003e\u0026nbsp;Supplementary Material 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Image acquisition and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients were scanned using Discovery CT 750 HD (GE Healthcare, Waukesha) and Revolution CT (GE Medical Healthcare). The detailed spectral CT scanning parameters are provided in \u003cstrong\u003eSupplementary Material 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTwo abdominal radiologists with 7 and 10 years of diagnostic experience, respectively, independently and blindly evaluated the patients\u0026rsquo; CT images. The assessment focused on the presence of liver cirrhosis, splenomegaly, gastroesophageal varices (GEVs), spontaneous portosystemic shunt (SPSS), ascites, peritumoral arterial phase enhancement, tumor margin, tumor capsule, intratumoral necrosis, and radiologic vascular invasion (RVI). In cases of disagreement, a consensus was reached through discussion. The CT diagnostic criteria for clinically significant portal hypertension (CSPH) included the presence of splenomegaly along with at least one of the following: GEVs, SPSS, or ascites [25]. Detailed definitions of CT features are provided in \u003cstrong\u003eSupplementary Material 1, Table S1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Histogram analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eID images from the portal venous phase of spectral CT were stored in DICOM format and imported into FireVoxel software (FireVoxel, version 462; https://www.firevoxel.org). Two radiologists, each with over five years of experience in hepatic imaging, independently performed whole-tumor histogram analysis under blinded conditions. Any discrepancies were resolved through consensus. The entire HCC lesion was manually segmented slice by slice along its boundary, with each ROI encompassing as much of the tumor as possible, including necrotic, cystic, and hemorrhagic areas. To minimize partial volume effects, the ROI was drawn slightly smaller than the actual lesion boundary. After ROI delineation, the software automatically generated histogram parameters based on a three-dimensional volume of interest (VOI), including minimum (Min), maximum (Max), mean (Mean), standard deviation (SD), variance, skewness, kurtosis, entropy, and percentiles (1st\u0026ndash;99th) \u003cstrong\u003e(Fig. 2)\u003c/strong\u003e. To ensure the consistency and reliability of the extracted histogram parameters, the intraclass correlation coefficient (ICC) was used to evaluate interobserver agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Follow-up and Endpoints\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients were followed up at 1, 3, and 6 months postoperatively, and then every 6 months thereafter. Follow-up assessments included measurements of serum alpha-fetoprotein (AFP) levels, liver function tests, and imaging studies (abdominal ultrasound, contrast-enhanced CT, or MRI). Tumor recurrence was defined as intrahepatic recurrence or extrahepatic metastasis, primarily diagnosed based on imaging findings or confirmed by histopathology through liver biopsy.\u003c/p\u003e\n\u003cp\u003eThe primary endpoint of this study was ER, defined as the occurrence of intrahepatic or extrahepatic tumor recurrence within 2 years after curative resection. The time of confirmed recurrence and the characteristics of the recurrent lesions were recorded. Recurrence-free survival (RFS) was defined as the interval between the date of curative surgery and the date of tumor recurrence or the last follow-up. The follow-up deadline was set as February 1, 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData processing and analysis were conducted using IBM SPSS Statistics (Version 26.0; IBM, New York, USA), R (Version 4.3.2; https://www.r-project.org/) and Zstats v1.0 (www.zstats.net).\u003c/p\u003e\n\u003cp\u003eThe inter-observer agreement between the two radiologists was assessed using Cohen\u0026apos;s Kappa coefficient for categorical variables. The normality of continuous variables was tested using the Shapiro-Wilk test. Continuous variables with a normal distribution are presented as Mean \u0026plusmn; SD, while non-normally distributed variables are expressed as Median (Q1, Q3). The differences between continuous variables across groups were compared using the independent samples t-test for normally distributed data and the Mann-Whitney U test for non-normally distributed data. Categorical variables are presented as frequencies (%) and analyzed using the \u0026chi;\u0026sup2; test or Fisher\u0026apos;s exact test.\u003c/p\u003e\n\u003cp\u003eMulticollinearity was evaluated by calculating the variance inflation factor (VIF). Univariate and multivariate Cox regression analyses were performed, and variables with P\u0026lt;0.05 in the multivariate analysis were used to construct the ER prediction model. Continuous variables were dichotomized based on the median, and Kaplan-Meier survival curves were plotted. Differences in survival curves were analyzed using the Log-rank test. The predictive performance of the model was assessed by the area under the time-dependent receiver operating characteristic (td-ROC) curve (AUC). Model calibration was evaluated using calibration curves, and the overall net benefit of the model was assessed using decision curve analysis (DCA). A P-value of \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 85 patients were included in this study, comprising 66 males (77.65%), with a median age of 57 years (range, 32\u0026ndash;74 years). Based on follow-up results, patients were categorized into early recurrence (ER+) and non-early recurrence (ER\u0026minus;) groups, with 42 patients (49.41%) experiencing ER. The median RFS for the entire cohort was 24.07 months (range, 5.10\u0026ndash;58.87 months). Among the 42 patients who experienced recurrence, the median RFS was 4.08 months (range, 5.10\u0026ndash;34.10 months), while for the 43 patients without recurrence, the median RFS was 30.53 months (range, 23.97\u0026ndash;58.87 months). Among all ER+ patients, 25 had only intrahepatic recurrence, 10 had intrahepatic recurrence with vascular invasion and/or extrahepatic recurrence, and 7 had only extrahepatic recurrence. A comparison of clinical characteristics between the ER+ and ER- groups revealed significant differences in prothrombin time (PT), Model for End-Stage Liver Disease (MELD) score, Tumor Burden Score (TBS), MVI, and resection margin (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 for all). The detailed clinical characteristics comparison is shown in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 CT Characteristics and Whole-Tumor ID Histogram Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inter-observer agreement between the two radiologists for assessing imaging findings and measuring histogram parameters was good (K = 0.811\u0026ndash;0.921; ICC = 0.817\u0026ndash;0.939). A comparison of CT characteristics and histogram parameters between the ER+ and ER- groups revealed that the ER+ group had significantly higher values for Mean, Max, Skewness, Kurtosis, and the 75th, 90th, 95th, and 99th percentiles compared to the ER- group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 for all, \u003cstrong\u003eTable 2\u003c/strong\u003e). Representative cases are shown in\u003cstrong\u003e\u0026nbsp;Fig. 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Cox Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParameters with statistically significant intergroup differences were included in the univariate Cox regression analysis, identifying 13 features significantly associated with ER (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Multicollinearity analysis revealed that Mean, 75th, 90th, and 95th percentile values had VIF \u0026gt;10 and were excluded. The remaining 9 significant variables were subjected to multivariate Cox regression analysis. The results identified the following as independent predictors of postoperative ER: NM (HR: 2.24; 95% \u003cem\u003eCI\u003c/em\u003e: 1.08\u0026ndash;4.64), ENM (HR: 3.48; 95% \u003cem\u003eCI\u003c/em\u003e: 1.25\u0026ndash;9.69), MVI+ (HR: 3.05; 95% \u003cem\u003eCI\u003c/em\u003e: 1.45\u0026ndash;6.40), Max (HR: 1.06; 95%\u003cem\u003e\u0026nbsp;CI\u003c/em\u003e: 1.04\u0026ndash;1.09), and Skewness (HR: 1.70; 95% \u003cem\u003eCI\u003c/em\u003e: 1.05\u0026ndash;2.77) (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 for all, \u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eBased on these four independent predictors, a combined clinical-radiological model was developed and visualized as a nomogram to predict RFS at 6, 12, and 24 months after curative hepatectomy in HCC patients (\u003cstrong\u003eFig. 3A\u003c/strong\u003e). The td-ROC curves demonstrated strong predictive performance with AUC values of 0.885, 0.894, and 0.872 at 6, 12, and 24 months, respectively \u003cstrong\u003e(Fig. 3B\u003c/strong\u003e). Calibration curves and DCA further confirmed the model\u0026rsquo;s good predictive accuracy and clinical utility (\u003cstrong\u003eFig. 3C\u0026ndash;D\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Recurrence Risk Stratification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival curves were plotted based on the independent predictive factors, and the results showed that the mean RFS was significantly shorter in patients with MVI+ (Positive, 12.34 months; Negative, 29.02 months; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), narrow resection margins (ENM, 9.54 months; NM, 15.42 months; WM, 28.41 months;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001), high Max (Max\u0026nbsp;\u0026ge;\u0026nbsp;2041.00, 15.15 months; Max \u0026lt; 2041.00, 26.13 months;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001), and high Skewness (Skewness\u0026nbsp;\u0026ge;\u0026nbsp;0.22, 16.83 months; Skewness \u0026lt; 0.22, 23.62 months; \u003cem\u003eP\u003c/em\u003e = 0.004) (\u003cstrong\u003eTable 4, Fig. 4\u003c/strong\u003e).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study aimed to predict ER following curative treatment for HCC by integrating whole-tumor ID-based histogram parameters with clinicopathological features, and to further evaluate prognostic factors associated with RFS. The results demonstrated that higher whole-tumor ID histogram values (Max \u0026ge; 2041.00, Skewness \u0026ge; 0.22), microvascular invasion (MVI+), and narrower resection margins (ENM and NM) were independent risk factors for ER (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). These parameters effectively identified patients at high risk of ER and enabled robust stratification of RFS outcomes.\u003c/p\u003e\n\u003cp\u003eResection margin width is a key intraoperative criterion for achieving R0 resection (\u0026ge;1 cm) and plays a significant role in determining the timing and pattern of postoperative recurrence in HCC [26, 27]. However, there is currently no consensus on the optimal margin width [8]. In this study, patients were further categorized into three groups based on pathologically assessed resection margin width: WM (\u0026ge;1 cm), NM (\u0026ge;0.5 cm and \u0026lt;1 cm), and ENM (\u0026lt;0.5 cm). Our results demonstrated a progressive decline in RFS and an increasing ER rate with decreasing margin width. Notably, both NM and ENM were significantly associated with a higher risk of ER (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), which is consistent with previous findings [23, 24]. This may be attributed to the inability of subcentimeter margins to completely eliminate microscopic tumor spread or microinvasive lesions [28]. Previous studies have demonstrated that the risk of recurrence following curative treatment for HCC largely depends on the invasiveness and proliferation of residual tumor cells, particularly in the form of MVI [29-31]. MVI, typically found in small branches of the portal vein, is a well-established pathological indicator of tumor aggressiveness and metastatic potential [32], and it further complicates the prognostic impact of resection margin status [27]. In the presence of MVI, even when gross complete (R0) resection is achieved, residual tumor cells within the microvasculature can disseminate via intrahepatic vessels, leading to the development of portal vein tumor thrombus and distant metastases [9]. In our study, patients with MVI (+) and narrower resection margins (NM and ENM) showed a significantly higher risk of ER compared to those with WM. These findings suggest a potential synergistic effect between narrow resection margins and MVI, whereby limited resection margins may exacerbate the adverse prognostic impact of MVI.\u003c/p\u003e\n\u003cp\u003eGiven the inherent delay in postoperative pathological characteristics, increasing research has focused on using preoperative imaging biomarkers for prognostic prediction of the disease [33]. In clinical practice, spectral CT has been widely used to evaluate the internal characteristics of liver tumors. Studies have shown that parameters such as ID derived from manually delineated ROI on a single plane can effectively predict recurrence and treatment response [17, 34]. However, due to the high heterogeneity of HCC, a single-layer ROI may fail to reflect the overall characteristics of the lesion [16]. In contrast, histogram analysis allows for a quantitative assessment of the entire tumor, providing a more comprehensive view of its internal heterogeneity, thus offering greater clinical value [16, 21]. This study performed a full-tumor histogram analysis using preoperative ID images of HCC patients. The results showed that Max (HR: 1.06, 95% \u003cem\u003eCI\u003c/em\u003e: 1.04\u0026ndash;1.09) and Skewness (HR: 1.70, 95% \u003cem\u003eCI\u003c/em\u003e: 1.05\u0026ndash;2.77) are independent risk factors for ER, and they can effectively stratify patients. Max reflects the peak iodine distribution in the tumor region, indirectly indicating the area with the richest blood supply\u0026nbsp;[35], suggesting a stronger neovascularization or abnormal vascular distribution, which is associated with more active tumor biology and a higher risk of recurrence. Skewness mainly reflects the asymmetry of voxel distribution\u0026nbsp;[36]. An increase in skewness indicates a more uneven iodine distribution within the tumor, suggesting more significant structural and vascular heterogeneity\u0026nbsp;[37], which is often related to more aggressive and recurrent biological characteristics. Therefore, Max and Skewness, as histogram-based indicators of iodine distribution peaks and distribution patterns, can serve as quantitative markers reflecting tumor vascular heterogeneity and invasive potential, making them independent predictors for ER.\u003c/p\u003e\n\u003cp\u003eThis study has certain limitations. Firstly, it is a retrospective study, which inevitably carries the risk of selection bias. Secondly, only the portal venous phase ID images were used, primarily because the portal venous phase provides better delineation of the tumor boundaries and extent, which aids in the accurate segmentation of the entire tumor. Lastly, there is insufficient automation in tumor segmentation. Although manual delineation of the tumor\u0026apos;s VOI ensures high precision, it is time-consuming and less efficient.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study constructed a predictive model for ER after radical LR in HCC patients, based on whole-tumor histogram parameters and clinical-pathological characteristics. The model includes factors such as MVI (+), resection margin, Max, and Skewness, all of which effectively stratify RFS. In conclusion, when predicting high-risk HCC patients for ER, wide-margin liver resection or postoperative adjuvant therapy may improve patient prognosis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLR, Liver resection; HCC, Hepatocellular carcinoma; ER, Early recurrence; MVI, Microvascular invasion; WM, Wide margin; NM, Narrow margin; ENM, Extremely narrow margin; ID, Iodine density; ROI, Regions of interest; GEVs, Gastroesophageal varices; SPSS, Spontaneous portosystemic shunt; RVI, Radiologic vascular invasion; CSPH, Clinically significant portal hypertension; VOI, Volume of interest; ICC, Intraclass correlation coefficient; RFS, Recurrence-free survival; VIF, Variance inflation factor; td-ROC, Time-dependent receiver operating characteristic; AUC, Area Under the Curve; DCA, Decision curve analysis; PT, Prothrombin time; MELD, Model for End-Stage Liver Disease; TBS, Tumor Burden Score.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study is not a public data. The datasets used in this study are available from the corresponding authors on reasonable requests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVOGEL A, CHAN S L, DAWSON L A, et al. Hepatocellular carcinoma: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up [J]. Annals of oncology : official journal of the European Society for Medical Oncology, 2025.\u003c/li\u003e\n\u003cli\u003eEASL Clinical Practice Guidelines on the management of hepatocellular carcinoma [J]. J Hepatol, 2025, 82(2): 315-74.\u003c/li\u003e\n\u003cli\u003eBRAY F, FERLAY J, SOERJOMATARAM I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2018, 68(6): 394-424.\u003c/li\u003e\n\u003cli\u003eHUANG Z, ZHU R H, LI S S, et al. 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World J Gastroenterol, 2023, 29(43): 5804-17.\u003c/li\u003e\n\u003cli\u003eVANDE LUNE P, ABDEL AAL A K, KLIMKOWSKI S, et al. Hepatocellular Carcinoma: Diagnosis, Treatment Algorithms, and Imaging Appearance after Transarterial Chemoembolization [J]. J Clin Transl Hepatol, 2018, 6(2): 175-88.\u003c/li\u003e\n\u003cli\u003eCUI Y, LI C, LIU Y, et al. Differentiation of prostate cancer and benign prostatic hyperplasia: comparisons of the histogram analysis of intravoxel incoherent motion and monoexponential model with in-bore MR-guided biopsy as pathological reference [J]. Abdom Radiol (NY), 2020, 45(10): 3265-77.\u003c/li\u003e\n\u003cli\u003eHE R, SONG G, FU J, et al. Histogram analysis based on intravoxel incoherent motion diffusion-weighted imaging for determining the perineural invasion status of rectal cancer [J]. Quantitative imaging in medicine and surgery, 2024, 14(8): 5358-72.\u003c/li\u003e\n\u003cli\u003eYAMAGATA K, YANAGAWA M, HATA A, et al. Three-dimensional iodine mapping quantified by dual-energy CT for predicting programmed death-ligand 1 expression in invasive pulmonary adenocarcinoma [J]. Sci Rep, 2024, 14(1): 18310.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hepatocellular carcinoma, Early recurrence, Iodine density, Histogram analysis, Resection margin","lastPublishedDoi":"10.21203/rs.3.rs-7978947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7978947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the predictive value of whole-tumor iodine density (ID) histogram parameters and resection margin distance for early recurrence (ER) after curative resection of hepatocellular carcinoma (HCC).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective study included patients with HCC who underwent R0 resection and received preoperative spectral CT scans. Patients were categorized into ER+ (n\u0026thinsp;=\u0026thinsp;42) and ER\u0026minus; (n\u0026thinsp;=\u0026thinsp;43) groups. Independent predictors of recurrence-free survival (RFS) were identified using multivariate Cox regression analysis. The performance of the prediction model was assessed using time-dependent receiver operating characteristic (td-ROC) curves, calibration and decision curves analysis. Kaplan-Meier analysis was used to evaluate differences in RFS between groups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMultivariate Cox regression identified Max, Skewness, microvascular invasion (MVI), and resection margin distance as independent risk factors for ER. Kaplan-Meier analysis revealed significantly shorter mean RFS in patients with MVI+ (12.34 months vs. 29.02 months), extremely narrow margin (9.54 months) and narrow margin (15.42 months) compared to wide margin (28.41 months), high Max (\u0026ge;\u0026thinsp;2041.00 vs. \u0026lt;2041.00; 15.15 vs. 26.13 months), and high Skewness (\u0026ge;\u0026thinsp;0.22 vs. \u0026lt;0.22; 16.83 vs. 23.62 months) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhole-tumor ID histogram parameters (Max and Skewness) and clinicopathological factors (MVI and resection margin distance) are independent predictors of ER. These factors allow effective stratification of RFS and may guide individualized postoperative management.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCritical relevance statement:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhole-tumor iodine density histogram features and resection margin distance provide independent predictors of early recurrence after hepatectomy in HCC, enabling improved risk stratification and guiding individualized postoperative management.\u003c/p\u003e","manuscriptTitle":"Early Recurrence of Hepatocellular Carcinoma After Hepatectomy: Predictive Role of Whole-Tumor Iodine Density Histogram Features and Resection Margin Distance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 17:54:58","doi":"10.21203/rs.3.rs-7978947/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-07T23:44:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T20:49:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333169220657139458419366572888467409392","date":"2026-01-03T06:43:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T17:56:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170620907915994354472121582912802383529","date":"2025-12-28T11:17:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245017768063086353909180122285221911208","date":"2025-12-26T16:50:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T09:44:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289522404236343481280574542869985746071","date":"2025-11-25T03:54:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222328302030016445643958632745586724416","date":"2025-11-24T03:13:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195722882949236478495039899343689184471","date":"2025-11-23T06:55:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137668564036889702692180603204461995637","date":"2025-11-10T11:27:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-09T17:05:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-04T13:24:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-04T06:33:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2025-10-29T10:44:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"919a72f2-601e-4bd5-908d-cc1afe9288f7","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-27T01:38:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 17:54:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7978947","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7978947","identity":"rs-7978947","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}