Prognostic CT Features and Prediction Model of Patients With Primary Hepatocellular Carcinomas Undergoing Partial Hepatectomy

Prognostic CT Features and Prediction Model of Patients With Primary Hepatocellular Carcinomas Undergoing Partial Hepatectomy | 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 Prognostic CT Features and Prediction Model of Patients With Primary Hepatocellular Carcinomas Undergoing Partial Hepatectomy Cuiping Zhou, Xiaohua Ban, Huijun Hu, Qiuxia Yang, Rong Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-949721/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Hepatocellular carcinoma (HCC) is the most common primary malignant tumor in the liver. Partial hepatectomy is one of the most effective therapies for HCC but suffer from the high recurrence rate. At present, the studies of association between clinical outcomes and CT features of patients with HCCs undergoing partial hepatectomy are still limited. The purpose of this study is to determine the predictive CT features and establish a model for predicting relapse or metastasis in patients with primary hepatocellular carcinomas (HCCs) undergoing partial hepatectomy. Methods: The clinical data and CT features of 112 patients with histopathologically confirmed primary HCCs were retrospectively reviewed. The clinical outcomes were categorized into two groups according to whether relapse or metastasis occurred within 2 years after partial hepatectomy. The association between clinical outcomes and CT features including tumour size, margin, shape, vascular invasion (VI), arterial phase hyperenhancement, washout appearance, capsule appearance, satellite lesion, involvement segment, cirrhosis, peritumoral enhancement and necrosis was analyzed using univariate analysis and binary logistic regression. Then establish logistic regression model, followed by receiver operating characteristic (ROC) curve analysis. Results: CT features including tumor size, margin, shape, VI, washout appearance, satellite lesion, involvement segment, peritumoral enhancement and necrosis were associated with clinical outcomes, as determined by univariate analysis ( P <0.05). Only tumor margin and VI remained independent risk factors in binary logistic regression analysis ( OR =6.41 and 10.92 respectively). The logistic regression model was logit(p)=-1.55+1.86 margin +2.39 VI. ROC curve analysis showed that the area under curve of the obtained logistic regression model was 0.887(95% CI：0.827-0.947). Conclusion: Patients with ill-defined margin or VI of HCCs were independent risk predictors of poor clinical outcome after partial hepatectomy. The model as logit(p)= -1.55+1.86 margin +2.39 VI was a good predictor of the clinical outcomes. Nuclear Medicine & Medical Imaging Abdominal neoplasm Hepatocellular carcinomas Primary Prognosis Computed tomography Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Hepatocellular carcinoma (HCC) is the most common primary malignant tumor in the liver. HCC is the fourth most common cause of cancer-related death worldwide [ 1 ]. Partial hepatectomy is one of the most effective therapies for HCC, but the recurrence rate is as high as 60%-70% [ 2 ]. At present, computed tomography (CT) is the most common imaging modality for HCC diagnosis in China. The imaging findings of HCC have been extensively described [ 3 – 9 ]. Previous studies have found that poor survival of patients with HCCs is associated with clinical and histological factors concluding tumor size >5 cm, presence of satellite lesions, vascular invasion, KIF3B expression [ 10 – 12 ]. Recently, some studies have reported that gross vascular invasion, irregular tumour margin and peripheral ragged enhancement, location in the liver, and nodule size on CT/MRI for HCC undergoing chemoembolization were independently associated with overall survival [ 13 , 14 ]. To date, the reports of association between clinical outcomes and CT features of patients with HCCs undergoing partial hepatectomy are limited [ 15 , 16 ]. Hence, more prognostic CT features of patients with HCCs undergoing partial hepatectomy should be assessed, and prediction model should be established to predict the clinical outcome. In this study, we retrospectively reviewed various CT findings in a series of 112 patients with partial hepatectomy and pathologically proved HCCs, and used logistic regression analysis to identify the predictive CT features and set up a prediction model for predicting relapse or metastasis in these patients. 2. Materials And Methods 2.1 Patients Between June 2007 and March 2016, 112 patients with pathologically confirmed primary hepatocellular carcinomas of our hospital were enrolled in this retrospective study. Patients were included if they had a pathologically confirmed HCC, partial hepatectomy, primary lesion in liver without extrahepatic metastasis, and abdomen CT examination before treatment. This study was approved by the Institutional Review Board of the University of Hong Kong-Shenzhen Hospital, and patient informed consent was waived for this type of review. Clinical data including clinical outcome, presentation, age, sex and treatment were reviewed. Medical records of all patients were well maintained. The patients were follow-up of 3 to 92 months (mean 33.4 months). Sixty-seven patients had local relapse or metastases within 2 years after surgery, and forty-six patients survived without any evidence of relapse or metastasis at least 2 years after therapy. There were 88 males and 34 females, aged from 19 years to 81 years with a mean of 52.3 years. The main symptoms were weight loss (n =59), abdominal pain (n=35), fatigue (n =25) and jaundice (n=14). Nine patients were detected during a regular medical checkup. All patients were treated with hepatic resection. After surgery, tumor specimens were processed for histologic examination. 2.2 CT imaging All patients had abdomen CT imaging before surgery. CT imaging in 64 patients was performed using a 64-slice spiral CT (LightSpeed VCT, GE Medical Systems, Milwaukee, WI, USA), and in the remaining 48 patients using a 4-slice spiral CT (Somatom volume zoom; Siemens Medical Systems, Erlangen, Germany). The imaging parameters included a tube voltage of 120 kV, 300-350 effective mAs, a field of view of 380 mm, a pitch of 0.9-1.2, and a matrix of 512×512. Axial, sagittal and coronal multiplanar reconstructions (MPR) images with 2–5 mm thick were obtained with soft tissue kernels. After acquisition of unenhanced images, triphasic contrast-enhanced CT imaging was obtained after intravenous injection of contrast media (Iopamiro 370; Bracco S.P.A., Milan, Italy) through a dual-head injector at flow rate of 3.5 ml/s and followed by 20-mL saline flush, with a dosage of 1.2 ml/kg of body weight. A bolus-tracking technique was used to determine the timing for the hepatic arterial phase (HAP) scanning, with a region of interest in the descending aorta. After achieving enhancement of the descending aorta up to 100 HU, the HAP images were acquired with the scanning delay of 5 s. Portal venous phase (PVP) images and equilibrium phase were obtained with a delayed time of 30 s and 120 s, respectively. When the 4-slice spiral CT was used, CT scans were obtained at 30 sec (HAP) and at 60 sec (PVP) after intravenous injection of contrast media. 2.3 Imaging analysis CT scan data were reviewed on PACS system for all patients by two experienced radiologists in consensus (C.Z. with more than 13 years of experience with diagnostic imaging, and X.D. with 10 years of experience with diagnostic imaging). CT imaging features including primary tumour size, margin, shape, vascular invasion (VI), arterial phase hyperenhancement, washout appearance, capsule appearance, satellite lesion, involvement segment, cirrhosis, peritumoral enhancement and necrosis. Tumor size was measured in maximal dimension on the transverse plane. The margin of the lesion was considered as well-defined or ill-defined. The shape of the lesion was classified as irregular or regular. The regular shape was defined as round/ovoid, and irregular was lobulated. VI defined as if lesion invaded hepatic vein or portal vein. Washout appearance was defined as a visually assessed reduction in enhancement relative to surrounding liver in PVP. Density of the lesion was classified as hypodensity, isodensity or hyperdensity compared with liver on unenhanced CT image. Involvement segment classified as single or multiple. Peritumoral enhancement was defined as peritumoral enhancement in the HAP or PVP was greater than that of surrounding liver. The degree of tumor necrosis was categorized as absent, mild (50% necrosis of the tumor) in contrast-enhanced PVP CT scan. 2.4 Statistical analysis To determine the prognostic value of the CT features, the clinical outcomes of patients were categories into two groups: poor outcome if relapse or metastases occurred within 2 years after partial hepatectomy; good outcome if patients survived for more than 2 years with no evidence of relapse or metastasis after partial hepatectomy. The CT imaging features included for analysis were categorized as follows: lesion size (>5cm or ≤5 cm), 10,12 margin (ill-defined or well-defined), shape (irregular or regular), VI (presence or absence), arterial phase hyperenhancement (presence or absence), washout appearance (presence or absence), capsule appearance (presence or absence), satellite lesion (presence or absence), involvement segment (single or multiple), cirrhosis (presence or absence), peritumoral enhancement (presence or absence) and necrosis (absence, mild or severe). Univariate analysis was applied to compare the frequency of these imaging findings between good and poor outcome groups by using χ 2 test. Variables with P value less than 0.05 as determined by univariate analysis were subsequently used binary logistic regression analysis to determine association between the clinical outcomes and CT features. Variables with P value less than 0.05 as determined by the logistic regression analysis were chosen as the independent predictor for clinical outcomes, Odds ratios (OR) as estimates of relative risk with 95% confidence intervals (CI) were obtained for each risk factor. A two-side P value of less than 0.05 was considered statistically significant. Then, establish logistic regression model of patients with HCCs. Furthermore, receiver operating curve (ROC) analysis was used to evaluate the obtained logistic regression model. All statistical tests were performed by using software (SPSS, version 22.0, Chicago, IL, USA). 3. Results 3.1 Imaging features The main CT characteristics of 112 patients are summarized in Table 1 . The size of the lesions ranged from 1.1 to 11.2 cm, with a mean of 5.90 cm. Lesions were irregular in 68 patients (60.7%) (Figs. 1 , 2 ), while regular in other 44 patients (39.3%) (Fig. 3 ). Ill-defined margin of lesions was found (Figs. 1 , 2 ) in 55 patients (49.1%) and well-defined (Fig. 3 ) in the remaining 57 patients (50.9%). VI of HCCs was found in 28 patients (Fig. 1 , 2 ). Arterial phase hyperenhancement (Fig. 2 , 3 ) was found in 82 patients (73.2%), washout appearance (Fig. 1 , 2 ) was found in 104 (92.9%), and the combination of them was found in 74 (66.07%). Capsule appearance was found in 47 patients (42.0%, Fig. 3 )). Satellite lesion was found in 17 patients (15.2%). Lesion involved single segment was found in 53 patients (47.3%), and involved multiple segments in the other 59 patients (52.7%). Primary lesions showed isodensity in 1 patient, hypodensity in 104 patients and hyperdensity in 7 patients on unenhanced CT images. Tumor internal fat density was found in 1 patient. Table 1 CT features of 112 patients with primary hepatocellular carcinoma Characteristics No. of patients % Tumor size (cm)* 5.90±3.14 (range,1.1-16.2) Tumor margin Well-defined 57 50.9 Ill-defined 55 49.1 Tumor shape Irregular 68 60.7 Regular 44 39.3 Necrosis Absent 28 25.0 Mild 71 63.4 Severe 13 11.6 Vascular invasion Present 28 25.0 Absent 84 75.0 Arterial phase hyperenhancement Present 82 73.2 Absent 30 26.8 Washout appearance Present 104 92.9 Absent 8 7.1 Capsule appearance Present 47 42.0 Absent 65 58.0 Satellite lesion Present 17 15.2 Absent 95 84.8 Involvement segment Multiple 59 52.7 Single 53 47.3 Cirrhosis Present 70 62.5 Absent 42 37.5 Peritumoral enhancement Present 40 35.7 Absent 72 62.3 *Mean±SD; Table 2 Univariate analyses of CT factors Factor Category No. of poor outcome vs. good outcome Tumor size ≥5.0cm <5.0cm 45:15* 21:31 Tumor margin Ill-defined Well-defined 49:6** 17:40 Tumor shape Irregular Regular 53:15** 13:31 Vascular invasion Presence Absence 27:1** 39:45 Arterial phase hyperenhancement Presence Absence 50:32 16:14 Washout appearance Presence Absence 65:39* 1:7 Capsule appearance Presence Absence 28:19 38:27 Involvement segment Multiple Single 42:17* 24:29 Cirrhosis Presence Absence 44:26 22:20 Peritumoral enhancement Presence Absence 32:8* 34:38 Necrosis Severe Mild Absence 12:1* 43:28 11:17 * P <0.05; ** P <0.01 3.2 Predictive value of imaging features Sixty-six patients (58.93 %) had local relapse or metastases within 2 years after partial hepatectomy, and were categorized as the poor outcome group. Forty-six patients (41.07 %) survived without any evidence of relapse or metastasis at least 2 years after partial hepatectomy, and were classified as the good outcome group. CT features including tumor size, margin, shape, VI, washout appearance, satellite lesion, involvement segment, peritumoral enhancement and necrosis were associated with clinical outcome, as determined by univariate analysis ( P <0.05) (Table 2 ). In logistic regression analysis, only tumor margin and VI of the primary HCC remained significantly independent predictors of clinical outcome ( P <0.05) (Table 3 ). The patients with ill-defined margin or VI of HCCs were likely to have poorer outcome than those with well-defined margin or without VI (OR=6.41 and 10.92 respectively). The logistic regression model was logit(p)= -1.55+1.86 margin +2.39 VI. Further ROC curve analysis showed that the area under curve (AUC) of the obtained logistic regression model was 0.887 (95% CI: 0.827-0.947), which indicated that the logistic regression model was a good predictor of the clinical outcomes (Fig. 4 ). Table 3 Binary regression analysis of various CT factors Factors Category β value P value OR (95% CI) Tumor size ≥5.0cm 0.001 0.999 1.00 (0.29, 3.46) Tumor margin Ill-defined 1.86 0.002 6.41(1.93, 21.31) Tumor shape Irregular 0.64 0.302 1.89 (0.56, 6.35) Vascular invasion Presence 2.39 0.032 10.92(1.23,97.17) Washout appearance Presence 1.41 0.258 4.11(0.35,47.69) Satellite lesion Presence 0.50 0.575 1.65(0.29, 9.35) Involvement segment Multiple -0.01 0.980 0.97(0.32,3.02) Peritumoral enhancement Presence 0.50 0.436 1.65(0.47,5.76) Necrosis Severe -1.14 0.372 0.32(0.03,3.94) Mild -1.32 0.274 0.27(0.03,2.85) 4. Discussion HCC is a major health problem worldwide. Chronic infection by the hepatitis B virus is the most common cause of this disease. The peak age of incidence is 50–70 years, with a male predominance [12, 16]. There are several treatment strategies available for HCC. Resection is the first-line of therapy in patients with HCCs and well-preserved liver function. However, the recurrence rate after surgery is high [2, 12]. In our study, the recurrence or metastases rate was 58.93% within 2 years after surgery. HCC recurrence after hepatic resection is divided into early recurrence (within 1 or 2 years after surgery) and late recurrence (greater than these temporal end-points) [17,18]. Early recurrences are considered to result from metastasis of the primary HCC and are mainly affected by adverse tumor features, whereas late recurrences should be considered as de novo HCCs and are mainly affected by the underlying liver status [17,18]. In this study, we retrospectively reviewed various CT findings of 112 patients with primary HCCs to assess the association between CT features and early clinical outcome after surgery. Multiphasic helical CT is often used as the first-line diagnostic modality for detection of HCC. The sensitivity of CT for HCC of all size is 63%-76% [6]. The accuracy of CT in the diagnosis of HCC was 94.29%, and that of lesion with a diameter ≤1 cm was 84.00% [5]. Hepatic artery is the primary feeder to the HCC. The hallmark diagnostic CT features of HCC are arterial phase hyperenhancement followed by portal venous or delayed phase washout appearance [3-6]. The arterial phase hyperenhancement is characteristic but nonspecific to radiological diagnosis of HCC, as it can also be observed in focal nodular hyperplasia, hemangiomas, hypervascular metastases and hepatic adenoma. While the combination of arterial phase hyperenhancement and washout appearance is highly specific for HCC in patients with risk factors for HCC [19, 20]. In our series, the combination of them was found in 66.07% patients. Capsule appearance is another important imaging feature for HCC, and is observed in about 42% of cases [19]. Consistently, in our study, capsule appearance was found in 47 patients (42.0%). According to the diagnostic systems [21], a mass 2 cm or larger with arterial phase hyperenhancement and capsule appearance can be diagnosed definitively as HCC even in the absence of washout appearance; for 10- to 19- mm masses with arterial phase hyperenhancement, both capsule appearance and washout appearance are required. Previously, many factors concluding tumor size >5 cm, presence satellite lesions, vascular invasion, KIF3B expression, α-fetoprotein level have been described as important prognostic factors for poor clinical outcome [10-12, 22], but tumor capsule has been described as a protect effect for clinical outcome [12]. Other than these clinical and histological factors, Baek et al. reported that increased 18F-FDG uptake of HCCs, especially high tumor-to-muscle might be correlated with microvascular invasion and poor differentiation, and tends to have a risk for recurrence in HCC [23]. Kitao et al. reported that hypervascular HCCs that hyperintensity relative to the surrounding liver on hepatobiliary phase gadoxetic acid-enhanced MR images demonstrate a significantly higher grade of differentiation, rarer portal vein invasion and lower recurrence rate than those of hypointense HCCs [24]. In addition, Honda et al. reported that the combination of normal hepatic arterial degeneration and preserved portal veins results in low attenuation on CT arteriography and isoattenuation on CT arterioportography in well differentiated lesions, and the combination of neoplastic (abnormal) arterial development by angiogenesis and obliteration of portal veins results in high attenuation on CT arteriography and low attenuation on CT arterioportography in advanced HCC [25]. Recently, some studies have reported that gross vascular invasion, irregular tumour margin and peripheral ragged enhancement, location in the liver and nodule size on CT/MRI for HCC undergoing chemoembolization were independently associated with poor overall survival [13, 14]. Moreover, there is reported that lobular configurations on CT was important independent factor for long-term survival after resection [15]. Li et al. reported that corona enhancement on CT for patients with a single HCC>5cm without extrahepatic metastasis was a significant factor for overall survival [16]. As more prognostic CT features of patients with HCCs undergoing hepatic resection are need to determined, we reviewed various CT findings including tumour size, margin, shape, VI, arterial phase hyperenhancement, washout appearance, capsule appearance, satellite lesion, involvement segment, cirrhosis, peritumoral enhancement and necrosis, and used logistic regression analysis to identify the predictive CT features and set up a prediction model. Univariate analysis showed that CT features including tumor size, margin, shape, VI, washout appearance, satellite lesion, involvement segment, peritumoral enhancement and necrosis were associated with clinical outcome, but only tumor margin and VI of the primary HCC remained independent predictors of clinical outcome in logistic regression analysis. Hence, patients with ill-defined HCCs were more likely to have local relapse or metastases within 2 years after surgery than those with well-defined HCCs, with OR of 6.41. Meanwhile, patients with VI of HCCs were more likely to have local relapse or metastases within 2 years after surgery than those without VI, with OR of 10.92. The logistic regression model was logit(p)= -1.55+1.86 margin +2.39 VI. Further ROC curve analysis showed that the area under curve (AUC) of the obtained logistic regression model was 0.887(95% CI：0.827-0.947)., which indicated that the prediction model was a good predictor of the clinical outcomes. There were several limitations in our study. First, as a large number of patients are needed for logistic regression analysis, we enrolled patients using 4 or 64-slice spiral CT. Second, no state-of-art CT was used in our study, which might represent more valuable parameters for HCC. It is well known that dual-energy CT is an excellent qualitative as well as a quantitative tool for assessing and predicting hepatocellular carcinoma [26,27]. Further prospective study using state-of-art CT may provide additional information of radiologic risk predictors. Conclusions In conclusion, our study results suggest that tumor margin and VI of the primary HCC are independent risk factor for the clinical outcome after partial hepatectomy. The model as logit(p)= -1.55+1.86 margin +2.39 VI was a good predictor of the clinical outcomes after partial hepatectomy. Abbreviations HCC: Hepatocellular carcinoma; CT: Computed tomography; MPR: Multiplanar reconstructions; HAP: Hepatic arterial phase; PVP: Portal venous phase; VI: Vascular invasion; ROC: Receiver operating curve; OR: Odds ratios; CI: Confidence intervals. Declarations Acknowledgement Not applicable. Authors’ contributions C.Z. and X.B.: conceptualization and design, visualization, methodology, writing- original draft preparation. H.H., Q.Y., R.Z., C.X. L.Y.: methodology, validation, data curation, formal analysis, investigation. X.S., X.D.: conceptualization and design, methodology, writing- review & editing. All authors read and approved the final manuscript. C.Z. and X.B. contributed equally to this work. Funding None. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was approved by the Institutional Review Board of the University of Hong Kong-Shenzhen Hospital, and patient informed consent was waived for this type of review. The methods in this study were carried out in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Department of Radiology, The University of Hong Kong-Shenzhen Hospital, No.1, Haiyuan road Futian District, Shenzhen 518000, People’s Republic of China. 2 Department of Medical Imaging Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong 510060, People’s Republic of China. 3 Department of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, Guangdong 510120, People’s Republic of China. References Yang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A, Roberts LR. A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol. 2019;16 (10):589–604. Llovet JM, Bruix J. 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Radiology. 2012;265 (3):780–9. Honda H, Tajima T, Taguchi K, Kuroiwa T, Yoshimitsu K, Irie H, et al. Recent developments in imaging diagnostics for HCC: CT arteriography and CT arterioportography evaluation of vascular changes in premalignant and malignant hepatic nodules. J Hepatobiliary Pancreat Surg. 2000;7 (3):245–51. Pfeiffer D, Parakh A, Patino M, Kambadakone A, Rummeny EJ, Sahani DV. Iodine material density images in dual-energy CT: quantification of contrast uptake and washout in HCC. Abdom Radiol (NY). 2018;43 (12):3317–23. Mulé S, Pigneur F, Quelever R, Tenenhaus A, Baranes L, Richard P, et al. Can dual-energy CT replace perfusion CT for the functional evaluation of advanced hepatocellular carcinoma? Eur Radiol. 2018;28 (5):1977–85. 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-949721","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":55776890,"identity":"8b456438-3947-4574-8816-11e8f5f1d7b6","order_by":0,"name":"Cuiping Zhou","email":"","orcid":"","institution":"The University of Hong Kong-Shenzhen Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cuiping","middleName":"","lastName":"Zhou","suffix":""},{"id":55776891,"identity":"c16f76c7-6af0-42e8-9b77-56ba7b1fa12d","order_by":1,"name":"Xiaohua Ban","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaohua","middleName":"","lastName":"Ban","suffix":""},{"id":55776893,"identity":"e9a087f8-ad8f-4b12-8866-85c3f9dcb783","order_by":2,"name":"Huijun Hu","email":"","orcid":"","institution":"Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huijun","middleName":"","lastName":"Hu","suffix":""},{"id":55776896,"identity":"07b5207f-f944-4ea8-83cb-04167b13a023","order_by":3,"name":"Qiuxia Yang","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiuxia","middleName":"","lastName":"Yang","suffix":""},{"id":55776898,"identity":"c2429e59-bbbc-4eec-9989-2a418c6b574b","order_by":4,"name":"Rong Zhang","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Zhang","suffix":""},{"id":55776900,"identity":"bf9a5982-82ad-4657-9b41-9854cec62c35","order_by":5,"name":"Chuanmiao Xie","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chuanmiao","middleName":"","lastName":"Xie","suffix":""},{"id":55776902,"identity":"827483b5-5620-46dd-9303-cf5ee412082e","order_by":6,"name":"Lingjie Yang","email":"","orcid":"","institution":"Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lingjie","middleName":"","lastName":"Yang","suffix":""},{"id":55776904,"identity":"84c87af3-fa8e-4121-b85b-d2bcc7a9f53a","order_by":7,"name":"Xinping Shen","email":"","orcid":"","institution":"The University of Hong Kong-Shenzhen Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinping","middleName":"","lastName":"Shen","suffix":""},{"id":55776906,"identity":"36ff0ff2-7614-4706-86d8-5df99ad3e597","order_by":8,"name":"Xiaohui Duan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDCCAyDCAIQYGxgSKmzk2NjbD5Cg5cGZNGM+njMJRGiB6GJgfNh2OHGehIMBXh18N5KfPeYpsJMzl0gGOowtLb1NgiGB4UfFNpxaJG+kmRvOMEg2tpyRCNTCY5PbJt14gLHnzG2cWgxuJJhJfDBgTtxwI7H9R4JEWm6bzIEEZsY2fFrSv0kkGNSDtABtMTiczgbkEtCSA7LlMFRLwuEEglokz7wpk5xhcNzY4MxDoJYDaYZtwEA+iM8vfMfTt0nz/KmWMzie/oDx5z8befn29oMPflTg1oIdHCBR/SgYBaNgFIwCNAAAzDRb/3iIxYEAAAAASUVORK5CYII=","orcid":"","institution":"Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Duan","suffix":""}],"badges":[],"createdAt":"2021-09-29 17:14:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-949721/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-949721/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14359430,"identity":"cef7fdb5-e82f-4a8b-ad19-de4298fc2de6","added_by":"auto","created_at":"2021-10-08 20:49:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6267359,"visible":true,"origin":"","legend":"A 47-year-old man with HCC had relapse 6 months after partial hepatectomy. Axial non-contrast CT image (A) shows a hypodense and irregular mass in the liver. Arterial phase contrast-enhanced CT image (B) shows enlarged vessels feeding or drain the mass (arrow). Axial (C) and coronal (D) portal venous phase contrast-enhanced CT images show a mass with ill-defined margin, washout appearance and vascular invasion (arrow).","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-949721/v1/bc6c783753003c1582490243.png"},{"id":14359490,"identity":"178cd3a9-22e1-4411-91b2-370f7324ac39","added_by":"auto","created_at":"2021-10-08 20:52:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2577806,"visible":true,"origin":"","legend":"A 50-year-old man with HCC had relapse 6 months after partial hepatectomy. Axial non-contrast CT image (A), arterial phase contrast-enhanced CT image (B) and portal venous phase contrast-enhanced CT image (C) shows an ill-defined, irregular mass in the segment Ⅱ and Ⅲ of the liver. The tumour shows hypodensity on non-contrast CT image, enlarged vessels feeding or drain the mass (arrow) and heterogeneously hyperenhancement on arterial phase contrast-enhanced CT image, washout appearance, vascular invasion (arrow) and mild necrosis on portal venous phase contrast-enhanced CT image.","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-949721/v1/d6533925b99b3ff448f2db64.png"},{"id":14359428,"identity":"c8c2f228-7585-4a83-a335-66f5f82a688c","added_by":"auto","created_at":"2021-10-08 20:49:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2342895,"visible":true,"origin":"","legend":"A 50-year-old man with HCC survived for 4 years after surgery without any evidence of relapse or metastasis. Axial non-contrast CT image (A), arterial phase contrast-enhanced CT image (B) and portal venous phase contrast-enhanced CT image (C) show a well-defined, regular mass with capsule appearance (arrow head) in the segment Ⅵ of the liver. The tumour shows hypodensity on non-contrast CT image, heterogeneously hyperenhancement on arterial phase contrast-enhanced CT image and no washout appearance on portal venous phase contrast-enhanced CT image.","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-949721/v1/1e8ca1e4169ab93d90c07db8.png"},{"id":14359431,"identity":"ca856f90-159a-46fe-8b7f-2d42aef81af8","added_by":"auto","created_at":"2021-10-08 20:49:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":266314,"visible":true,"origin":"","legend":"Graph shows the receiver operating characteristic (ROC) curve of the logistic regression model. The area under the curve (AUC) was 0.887.","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-949721/v1/31460f390ad3323c53411833.png"},{"id":15530186,"identity":"bc1aec3b-35e1-4ead-9a82-82a442a05d4a","added_by":"auto","created_at":"2021-11-15 05:14:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2408759,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-949721/v1/0aab1807-643d-4961-8c0e-14b66011ddc9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrognostic CT Features and Prediction Model of Patients With Primary Hepatocellular Carcinomas Undergoing Partial Hepatectomy\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the most common primary malignant tumor in the liver. HCC is the fourth most common cause of cancer-related death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Partial hepatectomy is one of the most effective therapies for HCC, but the recurrence rate is as high as 60%-70% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. At present, computed tomography (CT) is the most common imaging modality for HCC diagnosis in China. The imaging findings of HCC have been extensively described [\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous studies have found that poor survival of patients with HCCs is associated with clinical and histological factors concluding tumor size \u0026gt;5 cm, presence of satellite lesions, vascular invasion, KIF3B expression [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recently, some studies have reported that gross vascular invasion, irregular tumour margin and peripheral ragged enhancement, location in the liver, and nodule size on CT/MRI for HCC undergoing chemoembolization were independently associated with overall survival [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To date, the reports of association between clinical outcomes and CT features of patients with HCCs undergoing partial hepatectomy are limited [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Hence, more prognostic CT features of patients with HCCs undergoing partial hepatectomy should be assessed, and prediction model should be established to predict the clinical outcome. In this study, we retrospectively reviewed various CT findings in a series of 112 patients with partial hepatectomy and pathologically proved HCCs, and used logistic regression analysis to identify the predictive CT features and set up a prediction model for predicting relapse or metastasis in these patients.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eBetween June 2007 and March 2016, 112 patients with pathologically confirmed primary hepatocellular carcinomas of our hospital were enrolled in this retrospective study. Patients were included if they had a pathologically confirmed HCC, partial hepatectomy, primary lesion in liver without extrahepatic metastasis, and abdomen CT examination before treatment. This study was approved by the Institutional Review Board of the University of Hong Kong-Shenzhen Hospital, and patient informed consent was waived for this type of review.\u003c/p\u003e \u003cp\u003eClinical data including clinical outcome, presentation, age, sex and treatment were reviewed. Medical records of all patients were well maintained. The patients were follow-up of 3 to 92 months (mean 33.4 months). Sixty-seven patients had local relapse or metastases within 2 years after surgery, and forty-six patients survived without any evidence of relapse or metastasis at least 2 years after therapy. There were 88 males and 34 females, aged from 19 years to 81 years with a mean of 52.3 years. The main symptoms were weight loss (n =59), abdominal pain (n=35), fatigue (n =25) and jaundice (n=14). Nine patients were detected during a regular medical checkup. All patients were treated with hepatic resection. After surgery, tumor specimens were processed for histologic examination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 CT imaging\u003c/h2\u003e \u003cp\u003eAll patients had abdomen CT imaging before surgery. CT imaging in 64 patients was performed using a 64-slice spiral CT (LightSpeed VCT, GE Medical Systems, Milwaukee, WI, USA), and in the remaining 48 patients using a 4-slice spiral CT (Somatom volume zoom; Siemens Medical Systems, Erlangen, Germany). The imaging parameters included a tube voltage of 120 kV, 300-350 effective mAs, a field of view of 380 mm, a pitch of 0.9-1.2, and a matrix of 512\u0026times;512. Axial, sagittal and coronal multiplanar reconstructions (MPR) images with 2\u0026ndash;5 mm thick were obtained with soft tissue kernels. After acquisition of unenhanced images, triphasic contrast-enhanced CT imaging was obtained after intravenous injection of contrast media (Iopamiro 370; Bracco S.P.A., Milan, Italy) through a dual-head injector at flow rate of 3.5 ml/s and followed by 20-mL saline flush, with a dosage of 1.2 ml/kg of body weight. A bolus-tracking technique was used to determine the timing for the hepatic arterial phase (HAP) scanning, with a region of interest in the descending aorta. After achieving enhancement of the descending aorta up to 100 HU, the HAP images were acquired with the scanning delay of 5 s. Portal venous phase (PVP) images and equilibrium phase were obtained with a delayed time of 30 s and 120 s, respectively. When the 4-slice spiral CT was used, CT scans were obtained at 30 sec (HAP) and at 60 sec (PVP) after intravenous injection of contrast media.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Imaging analysis\u003c/h2\u003e \u003cp\u003e CT scan data were reviewed on PACS system for all patients by two experienced radiologists in consensus (C.Z. with more than 13 years of experience with diagnostic imaging, and X.D. with 10 years of experience with diagnostic imaging). CT imaging features including primary tumour size, margin, shape, vascular invasion (VI), arterial phase hyperenhancement, washout appearance, capsule appearance, satellite lesion, involvement segment, cirrhosis, peritumoral enhancement and necrosis. Tumor size was measured in maximal dimension on the transverse plane. The margin of the lesion was considered as well-defined or ill-defined. The shape of the lesion was classified as irregular or regular. The regular shape was defined as round/ovoid, and irregular was lobulated. VI defined as if lesion invaded hepatic vein or portal vein. Washout appearance was defined as a visually assessed reduction in enhancement relative to surrounding liver in PVP. Density of the lesion was classified as hypodensity, isodensity or hyperdensity compared with liver on unenhanced CT image. Involvement segment classified as single or multiple. Peritumoral enhancement was defined as peritumoral enhancement in the HAP or PVP was greater than that of surrounding liver. The degree of tumor necrosis was categorized as absent, mild (\u0026lt;50% necrosis of the tumor), and severe (\u0026gt;50% necrosis of the tumor) in contrast-enhanced PVP CT scan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo determine the prognostic value of the CT features, the clinical outcomes of patients were categories into two groups: poor outcome if relapse or metastases occurred within 2 years after partial hepatectomy; good outcome if patients survived for more than 2 years with no evidence of relapse or metastasis after partial hepatectomy. The CT imaging features included for analysis were categorized as follows: lesion size (\u0026gt;5cm or \u0026le;5 cm),\u003csup\u003e10,12\u003c/sup\u003e margin (ill-defined or well-defined), shape (irregular or regular), VI (presence or absence), arterial phase hyperenhancement (presence or absence), washout appearance (presence or absence), capsule appearance (presence or absence), satellite lesion (presence or absence), involvement segment (single or multiple), cirrhosis (presence or absence), peritumoral enhancement (presence or absence) and necrosis (absence, mild or severe). Univariate analysis was applied to compare the frequency of these imaging findings between good and poor outcome groups by using χ\u003csup\u003e2\u003c/sup\u003e test. Variables with \u003cem\u003eP\u003c/em\u003e value less than 0.05 as determined by univariate analysis were subsequently used binary logistic regression analysis to determine association between the clinical outcomes and CT features. Variables with P value less than 0.05 as determined by the logistic regression analysis were chosen as the independent predictor for clinical outcomes, Odds ratios (OR) as estimates of relative risk with 95% confidence intervals (CI) were obtained for each risk factor. A two-side \u003cem\u003eP\u003c/em\u003e value of less than 0.05 was considered statistically significant. Then, establish logistic regression model of patients with HCCs. Furthermore, receiver operating curve (ROC) analysis was used to evaluate the obtained logistic regression model. All statistical tests were performed by using software (SPSS, version 22.0, Chicago, IL, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e3.1 Imaging features\u003c/h2\u003e\n \u003cp\u003eThe main CT characteristics of 112 patients are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The size of the lesions ranged from 1.1 to 11.2 cm, with a mean of 5.90 cm. Lesions were irregular in 68 patients (60.7%) (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), while regular in other 44 patients (39.3%) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Ill-defined margin of lesions was found (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) in 55 patients (49.1%) and well-defined (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) in the remaining 57 patients (50.9%). VI of HCCs was found in 28 patients (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Arterial phase hyperenhancement (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) was found in 82 patients (73.2%), washout appearance (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) was found in 104 (92.9%), and the combination of them was found in 74 (66.07%). Capsule appearance was found in 47 patients (42.0%, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)). Satellite lesion was found in 17 patients (15.2%). Lesion involved single segment was found in 53 patients (47.3%), and involved multiple segments in the other 59 patients (52.7%). Primary lesions showed isodensity in 1 patient, hypodensity in 104 patients and hyperdensity in 7 patients on unenhanced CT images. Tumor internal fat density was found in 1 patient.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCT features of 112 patients with primary hepatocellular carcinoma\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of patients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size (cm)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.90\u0026plusmn;3.14\u003c/p\u003e\n \u003cp\u003e(range,1.1-16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor margin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWell-defined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIll-defined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNecrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArterial phase hyperenhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWashout appearance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCapsule appearance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvolvement segment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeritumoral enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e*Mean\u0026plusmn;SD;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate analyses of CT factors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of poor outcome vs. good outcome\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;5.0cm\u003c/p\u003e\n \u003cp\u003e\u0026lt;5.0cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45:15*\u003c/p\u003e\n \u003cp\u003e21:31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor margin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIll-defined\u003c/p\u003e\n \u003cp\u003eWell-defined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49:6**\u003c/p\u003e\n \u003cp\u003e17:40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53:15**\u003c/p\u003e\n \u003cp\u003e13:31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003cp\u003eAbsence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27:1**\u003c/p\u003e\n \u003cp\u003e39:45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArterial phase hyperenhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003cp\u003eAbsence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50:32\u003c/p\u003e\n \u003cp\u003e16:14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWashout appearance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003cp\u003eAbsence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65:39*\u003c/p\u003e\n \u003cp\u003e1:7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCapsule appearance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003cp\u003eAbsence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28:19\u003c/p\u003e\n \u003cp\u003e38:27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvolvement segment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42:17*\u003c/p\u003e\n \u003cp\u003e24:29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003cp\u003eAbsence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44:26\u003c/p\u003e\n \u003cp\u003e22:20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeritumoral enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003cp\u003eAbsence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32:8*\u003c/p\u003e\n \u003cp\u003e34:38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNecrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003cp\u003eAbsence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12:1*\u003c/p\u003e\n \u003cp\u003e43:28\u003c/p\u003e\n \u003cp\u003e11:17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e3.2 Predictive value of imaging features\u003c/h2\u003e\n \u003cp\u003eSixty-six patients (58.93 %) had local relapse or metastases within 2 years after partial hepatectomy, and were categorized as the poor outcome group. Forty-six patients (41.07 %) survived without any evidence of relapse or metastasis at least 2 years after partial hepatectomy, and were classified as the good outcome group. CT features including tumor size, margin, shape, VI, washout appearance, satellite lesion, involvement segment, peritumoral enhancement and necrosis were associated with clinical outcome, as determined by univariate analysis (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In logistic regression analysis, only tumor margin and VI of the primary HCC remained significantly independent predictors of clinical outcome (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The patients with ill-defined margin or VI of HCCs were likely to have poorer outcome than those with well-defined margin or without VI (OR=6.41 and 10.92 respectively). The logistic regression model was logit(p)= -1.55+1.86 margin +2.39 VI. Further ROC curve analysis showed that the area under curve (AUC) of the obtained logistic regression model was 0.887 (95% CI: 0.827-0.947), which indicated that the logistic regression model was a good predictor of the clinical outcomes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBinary regression analysis of various CT factors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta; value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;5.0cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00 (0.29, 3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor margin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIll-defined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.41(1.93, 21.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.89 (0.56, 6.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.92(1.23,97.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWashout appearance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.11(0.35,47.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.65(0.29, 9.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvolvement segment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97(0.32,3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeritumoral enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.65(0.47,5.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNecrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32(0.03,3.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27(0.03,2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHCC is a major health problem worldwide. Chronic infection by the hepatitis B virus is the most common cause of this disease. The peak age of incidence is 50\u0026ndash;70 years, with a male predominance\u0026nbsp;[12, 16]. There are several treatment strategies available for HCC. Resection is the first-line of therapy in patients with HCCs and well-preserved liver function. However, the recurrence rate after surgery is high\u0026nbsp;[2, 12]. In our study, the recurrence or metastases rate was 58.93% within 2 years after surgery. HCC recurrence after hepatic resection is divided into early recurrence (within 1 or 2 years after surgery) and late recurrence (greater than these temporal end-points)\u0026nbsp;[17,18]. Early recurrences are considered to result from metastasis of the primary HCC and are mainly affected by adverse tumor features, whereas late recurrences should be considered as de novo HCCs and are mainly affected by the underlying liver status\u0026nbsp;[17,18]. In this study,\u0026nbsp;we retrospectively reviewed various CT findings of 112 patients with primary HCCs to assess the association between CT features and early clinical outcome after surgery.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Multiphasic helical CT is often used as the first-line diagnostic modality for detection of HCC. The sensitivity of CT for HCC of all size is\u0026nbsp;63%-76%\u0026nbsp;[6]. The accuracy of CT in the diagnosis of HCC was 94.29%, and that of lesion with a diameter \u0026le;1 cm was 84.00%\u0026nbsp;[5]. Hepatic artery is the primary feeder to the HCC. The hallmark diagnostic CT features of HCC are arterial phase hyperenhancement followed by portal venous or delayed phase washout appearance\u0026nbsp;[3-6].\u0026nbsp;The\u0026nbsp;arterial phase hyperenhancement\u0026nbsp;is characteristic but nonspecific to radiological diagnosis of HCC, as it can also be observed in focal nodular hyperplasia, hemangiomas, hypervascular metastases and hepatic adenoma. While the combination of arterial phase hyperenhancement and washout appearance is highly specific for HCC in patients with risk factors for HCC\u0026nbsp;[19, 20]. In our series, the combination of them was found in 66.07% patients. Capsule appearance is another important imaging feature for HCC, and is observed in about 42% of cases\u0026nbsp;[19]. Consistently, in our study, capsule appearance was found in 47 patients (42.0%). According to the diagnostic systems\u0026nbsp;[21], a mass 2 cm or larger with arterial phase hyperenhancement and capsule appearance can be diagnosed definitively as HCC even in the absence of washout appearance; for 10- to 19- mm masses with arterial phase hyperenhancement, both capsule appearance and washout appearance are required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Previously, many\u0026nbsp;factors concluding tumor size \u0026gt;5 cm, presence satellite lesions, vascular invasion, KIF3B expression, \u0026alpha;-fetoprotein level have been described as important prognostic factors for poor clinical outcome [10-12, 22], but tumor capsule has been described as a protect effect for clinical outcome [12]. Other than these clinical and histological factors, Baek et al. reported that increased 18F-FDG uptake of HCCs, especially high tumor-to-muscle might be correlated with microvascular invasion and poor differentiation, and tends to have a risk for recurrence in HCC [23]. Kitao et al. reported that hypervascular HCCs that hyperintensity relative to the surrounding liver on hepatobiliary phase gadoxetic acid-enhanced MR images demonstrate a significantly higher grade of differentiation, rarer portal vein invasion and lower recurrence rate than those of hypointense HCCs [24]. In addition, Honda et al. reported that the combination of normal hepatic arterial degeneration and preserved portal veins results in low attenuation on CT arteriography and isoattenuation on CT arterioportography in well differentiated lesions, and the combination of neoplastic (abnormal) arterial development by angiogenesis and obliteration of portal veins results in high attenuation on CT arteriography and low attenuation on CT arterioportography in advanced HCC [25].\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eRecently, some studies have reported that gross vascular invasion, irregular tumour margin and peripheral ragged enhancement, location in the liver and nodule size on CT/MRI for HCC undergoing chemoembolization were independently associated with poor overall survival [13, 14].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eMoreover, there is reported that lobular configurations on CT was important independent factor for long-term survival after resection [15]. Li et al. reported that corona enhancement on CT for patients with a single HCC\u0026gt;5cm without extrahepatic metastasis was a significant factor for overall survival [16]. As more prognostic CT features of patients with HCCs undergoing hepatic\u0026nbsp;resection\u0026nbsp;are need to determined, we reviewed various CT findings including\u0026nbsp;tumour size, margin, shape, VI, arterial phase hyperenhancement, washout appearance, capsule appearance,\u0026nbsp;satellite lesion,\u0026nbsp;involvement segment,\u0026nbsp;cirrhosis, peritumoral enhancement and necrosis, and used logistic regression analysis to identify the predictive CT features and set up a\u0026nbsp;prediction model.\u0026nbsp;Univariate analysis showed that\u0026nbsp;CT features including tumor size, margin, shape, VI, washout appearance,\u0026nbsp;satellite lesion,\u0026nbsp;involvement segment,\u0026nbsp;peritumoral enhancement and necrosis were associated with clinical outcome, but only tumor margin and VI of the primary HCC\u0026nbsp;remained independent predictors of clinical outcome in logistic regression analysis. Hence, patients with ill-defined HCCs were more likely to\u0026nbsp;have\u0026nbsp;local relapse or metastases within 2 years after surgery than those\u0026nbsp;with well-defined HCCs, with OR of 6.41. Meanwhile,\u0026nbsp;patients with\u0026nbsp;VI of\u0026nbsp;HCCs\u0026nbsp;were more likely to have\u0026nbsp;local relapse or metastases within 2 years after surgery than those without VI, with OR of 10.92. The logistic regression model was logit(p)= -1.55+1.86 margin +2.39 VI. Further ROC curve analysis showed that the area under curve (AUC) of the obtained logistic regression model was 0.887(95% CI：0.827-0.947)., which indicated that the prediction model was a good predictor of the clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;There were several limitations in our study. First, as a large number of patients are needed for logistic regression analysis, we enrolled patients using 4 or 64-slice spiral CT. Second, no state-of-art CT was used in our study, which might represent more valuable parameters for HCC. It is well known that dual-energy CT is an excellent qualitative as well as a quantitative tool for assessing and predicting hepatocellular carcinoma [26,27]. Further prospective study using state-of-art CT may provide additional information of radiologic risk predictors.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e\u0026nbsp; \u0026nbsp;In conclusion, our study results suggest that tumor margin and VI of the primary HCC are independent risk factor for the clinical outcome after partial hepatectomy. The model as logit(p)= -1.55+1.86 margin +2.39 VI was a good predictor of the clinical outcomes after partial hepatectomy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHCC: Hepatocellular carcinoma; CT: Computed tomography; MPR: Multiplanar reconstructions; HAP: Hepatic arterial phase; PVP: Portal venous phase; VI: Vascular invasion; ROC: Receiver operating curve; OR: Odds ratios; CI: Confidence intervals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.Z. and X.B.: conceptualization and design, visualization, methodology, writing- original draft preparation. H.H., Q.Y., R.Z., C.X. L.Y.: methodology, validation, data curation, formal analysis, investigation. X.S., X.D.: conceptualization and design, methodology, writing- review \u0026amp; editing. All authors read and approved the final manuscript. C.Z. and X.B. contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of the University of Hong Kong-Shenzhen Hospital, and patient informed consent was waived for this type of review. The methods in this study were carried out in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor details\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eDepartment of Radiology, The University of Hong Kong-Shenzhen Hospital, No.1, Haiyuan road Futian District, Shenzhen 518000, People\u0026rsquo;s Republic of China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eDepartment of Medical Imaging Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong 510060, People\u0026rsquo;s Republic of China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eDepartment of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, Guangdong 510120, People\u0026rsquo;s Republic of China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A, Roberts LR. 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Computed tomographic features of hepatocellular carcinoma predict long-term survival after hepatic resection. Eur J Surg Oncol. 2002;28 (3):235\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Xin Y, Fu S, Liu Z, Li Y, Hu B, et al. Corona enhancement and mosaic architecture for prognosis and selection between of liver resection versus transcatheter arterial chemoembolization in single hepatocellular carcinomas \u0026gt;5 cm without extrahepatic metastases: an imaging-based retrospective study. Medicine (Baltimore). 2016;95 (2):e2458.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorimoto O, Nagano H, Sakon M, Fujiwara Y, Yamada T, Nakagawa H, et al. Diagnosis of intrahepatic metastasis and multicentric carcinogenesis by microsatellite loss of heterozygosity in patients with multiple and recurrent hepatocellular carcinomas. J Hepatol. 2003;39 (2):215\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImamura H, Matsuyama Y, Tanaka E, Ohkubo T, Hasegawa K, Miyagawa S, et al. Risk factors contributing to early and late phase intrahepatic recurrence of hepatocellular carcinoma after hepatectomy. J Hepatol. 2003;38 (2):200\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuca A, Caruso S, Milazzo M, Mamone G, Marrone G, Miraglia R, et al. Multidetector-row computed tomography (MDCT) for the diagnosis of hepatocellular carcinoma in cirrhotic candidates for liver transplantation: prevalence of radiological vascular patterns and histological correlation with liver explants. Eur Radiol. 2010;20 (4):898\u0026ndash;907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForner A, Vilana R, Ayuso C, Bianchi L, Sol\u0026eacute; M, Ayuso JR, et al. 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Hepatogastroenterology. 2015;62 (138):383\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitao A, Matsui O, Yoneda N, Kozaka K, Kobayashi S, Koda W, et al. Hypervascular hepatocellular carcinoma: correlation between biologic features and signal intensity on gadoxetic acid-enhanced MR images. Radiology. 2012;265 (3):780\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHonda H, Tajima T, Taguchi K, Kuroiwa T, Yoshimitsu K, Irie H, et al. Recent developments in imaging diagnostics for HCC: CT arteriography and CT arterioportography evaluation of vascular changes in premalignant and malignant hepatic nodules. J Hepatobiliary Pancreat Surg. 2000;7 (3):245\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePfeiffer D, Parakh A, Patino M, Kambadakone A, Rummeny EJ, Sahani DV. Iodine material density images in dual-energy CT: quantification of contrast uptake and washout in HCC. Abdom Radiol (NY). 2018;43 (12):3317\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMul\u0026eacute; S, Pigneur F, Quelever R, Tenenhaus A, Baranes L, Richard P, et al. Can dual-energy CT replace perfusion CT for the functional evaluation of advanced hepatocellular carcinoma? Eur Radiol. 2018;28 (5):1977\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Abdominal neoplasm, Hepatocellular carcinomas, Primary, Prognosis, Computed tomography","lastPublishedDoi":"10.21203/rs.3.rs-949721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-949721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHepatocellular carcinoma (HCC) is the most common primary malignant tumor in the liver. Partial hepatectomy is one of the most effective therapies for HCC but suffer from the high recurrence rate. At present, the studies of association between clinical outcomes and CT features of patients with HCCs undergoing partial hepatectomy are still limited. The purpose of this study is to determine the predictive CT features and establish a model for predicting relapse or metastasis in patients with primary hepatocellular carcinomas (HCCs) undergoing partial hepatectomy.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe clinical data and CT features of 112 patients with histopathologically confirmed primary HCCs were retrospectively reviewed. The clinical outcomes were categorized into two groups according to whether relapse or metastasis occurred within 2 years after partial hepatectomy. The association between clinical outcomes and CT features including tumour size, margin, shape, vascular invasion (VI), arterial phase hyperenhancement, washout appearance, capsule appearance, satellite lesion, involvement segment, cirrhosis, peritumoral enhancement and necrosis was analyzed using univariate analysis and binary logistic regression. Then establish logistic regression model, followed by receiver operating characteristic (ROC) curve analysis.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e CT features including tumor size, margin, shape, VI, washout appearance, satellite lesion, involvement segment, peritumoral enhancement and necrosis were associated with clinical outcomes, as determined by univariate analysis (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Only tumor margin and VI remained independent risk factors in binary logistic regression analysis (\u003cem\u003eOR\u003c/em\u003e=6.41 and 10.92 respectively). The logistic regression model was logit(p)=-1.55+1.86 margin +2.39 VI. ROC curve analysis showed that the area under curve of the obtained logistic regression model was 0.887(95% CI：0.827-0.947).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Patients with ill-defined margin or VI of HCCs were independent risk predictors of poor clinical outcome after partial hepatectomy. The model as logit(p)= -1.55+1.86 margin +2.39 VI was a good predictor of the clinical outcomes.\u003c/p\u003e","manuscriptTitle":"Prognostic CT Features and Prediction Model of Patients With Primary Hepatocellular Carcinomas Undergoing Partial Hepatectomy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-10-08 20:49:53","doi":"10.21203/rs.3.rs-949721/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":"05c39f27-eec3-41bd-a562-6a0980e26aec","owner":[],"postedDate":"October 8th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":7725417,"name":"Nuclear Medicine \u0026 Medical Imaging"}],"tags":[],"updatedAt":"2021-11-15T05:14:10+00:00","versionOfRecord":[],"versionCreatedAt":"2021-10-08 20:49:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-949721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-949721","identity":"rs-949721","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}