Preoperative MRI features for predicting response to postoperative adjuvant anti-PD-1 therapy in hepatocellular carcinoma

Preoperative MRI features for predicting response to postoperative adjuvant anti-PD-1 therapy in hepatocellular carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Preoperative MRI features for predicting response to postoperative adjuvant anti-PD-1 therapy in hepatocellular carcinoma Zhenwei Peng, Xiaofang He, Jie Zhan, Yukun Sun, Shuifang Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4593371/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 Biomarkers for predicting survival benefit of postoperative adjuvant anti-PD-1 therapy (PA-PD-1) in hepatocellular carcinoma (HCC) are scare and lack of clinical evidence currently. This study aimed to identify the value of preoperative MRI features for predicting response to PA-PD-1 in HCC. Between 2020 and 2023, 58 patients with PA-PD-1 and 110 without PA-PD-1 were retrospectively included after propensity-score matching. Patients with PA-PD-1 had significantly longer recurrence-free survival (RFS) than those without PA-PD-1 (29.50 versus 10.97 months, p = 0.005). Absence of hypointense halos and irregular rim-like hyper enhancement were identified as independent predictors for RFS. Subgroup analysis indicated that patients with absence of hypointense halos and irregular rim-like hyper enhancement achieved significantly longer RFS after PA-PD-1 compared with those without PA-PD-1. In conclusion, preoperative MRI features of absence of hypointense halos and irregular rim-like hyper enhancement were significantly associated with recurrence and potential predictors for response to PA-PD-1 in HCC. Health sciences/Oncology/Cancer/Gastrointestinal cancer/Liver cancer Health sciences/Biomarkers/Predictive markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hepatocellular carcinoma (HCC) is one of the most widespread malignancies and the third leading cause of cancer-related death in the world 1,2 . Liver resection has long been considered as the potentially curative option for HCC patients, with a 5-year survival of 70–80% 3 . However, high 5-year recurrence rate reaching up to 70% after radical hepatectomy remains a major obstacle to survival 4 . Nowadays, there is no unified or standardized adjuvant treatments to improve postoperative recurrence-free survival (RFS) in HCC patients 5 . Previous studies demonstrated that patients with high risks of recurrence could benefit from postoperative adjuvant therapies, and promising responses have been recently reported with adjuvant anti-programmed cell death (PD)-1 therapy 6–10 . High-risk recurrent factors are commonly referred to large tumor, multiple tumors, satellite nodules, microvascular invasion (MVI), or alpha-fetoprotein (AFP) > 400ng/mL 11–14 . However, only a subset of patients benefited from adjuvant anti-PD-1 therapy, and the current selected factors give inadequate attention to the intertumor biological heterogeneity. Identifying potential predictive biomarkers associated with therapeutic efficacy would allow tailoring of appropriate adjuvant immunotherapy for different patient subgroups. As non-invasive examination, enhanced MRI, especially hepatobiliary-specific contrast-enhanced MRI, has been widely-used for diagnosis, treatment planning and surveillance of HCC 15 . Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid, a hepatocyte-specific contrast agent, can be used for quantitatively and qualitatively evaluating intratumor and peritumor imaging features in HCC development 16 . Recently, imaging features displayed by MRI or CT have been found as robust markers to reflect intertumor heterogeneity and prognosis of HCC 17,18 . Previous investigations demonstrated that absence of hypodense halos, peritumor enhancement, internal arteries and nonsmooth tumor margins could reveal gene expression patterns of HCC and help develop a noninvasive molecular portrait of the tumor 19,20 . While rim arterial enhancement, arterial peritumoral enhancement, peritumoral hypointensity on the hepatobiliary phase imaging, nonsmooth tumor margin and hypointensity on T1-weighted imaging were widely recognized as the significant predictors of microvascular invasion 18,21 , which is a robust pathological characteristic reflecting poor prognosis 22 . Moreover, nonsmooth tumor margin, presence of satellite nodule and peritumoral hypointensity on hepatobiliary phase were found to be independent significant variables associated with tumor aggressiveness and recurrence 23,24 . Therefore, taking MRI features into consideration may be reasonable when predicting response to adjuvant anti-PD-1 therapy. However, there has been no related evidence so far. Hence, the current study aimed to explore the value of seven MRI features in the therapeutic selection between adjuvant anti-PD-1 therapy and non-adjuvant anti-PD-1 therapy for HCC patients with high risks for recurrence. Results Patient Demographics From January 2020 to June 2023, 987 HCC patients underwent hepatectomy in our institution. According to the inclusion and exclusion criteria, 398 HCC patients with high relapse risks were included, with 61 patients in the postoperative adjuvant anti-PD-1 therapy (PA-PD-1) group and 337 patients in the non-PA-PD-1 group (Fig. 1 ). The median follow-up time was 27.3 (95% confidence interval [CI] 25.4–32.7) months. All the baseline characteristics between these two groups were compared and represented in Supplementary Table 1. Further multivariable Cox regression analysis identified that number of lesions, maximum tumor size, level of α-Fetoprotein, adjuvant anti-PD-1 treatment, and absence of hypointense halos were independent influencing factors for RFS, while only maximum tumor size and level of α-Fetoprotein were independent influencing factors for overall survival (OS) (Supplementary Table 2). All these independent prognostic factors were presented in the baseline charateristics between subgroups. To balance the baseline differences between patients in PA-PD-1 group and non-PA-PD-1 group, 168 patients were matched after 1:2 propensity score matching (PSM), with 58 patients (mean age, 54 years; range, 25–75 years; 56 men) in the PA-PD-1 group and 110 patients (mean age, 52 years; range, 21–75 years; 101 men) in the non-PA-PD-1 group. Detailed baseline characteristics between groups before and after PSM are described in Table 1 . Before PSM, absence of hypointense halos differed obviously between the two groups (p < 0.05). After PSM, the positive rates of nonsmooth tumor margins, internal arteries, peritumoral enhancement, absence of hypointense halos, irregular rim-like hyper enhancement, satellite nodules, and peritumoral hypointensity were 93.1% (54/58), 84.5% (49/58), 81.0% (47/58), 32.8% (19/58), 55.2% (32/58), 37.9% (22/58), and 79.3% (46/58) in the PA-PD-1 group, and 80.9% (89/110), 94.5% (104/110), 53.6% (59/110), 29.1% (32/110), 67.2% (74/110), 31.8% (35/110), and 71.8% (79/110) in the non-PA-PD-1 group, respectively. Typical images of the seven MRI features were displayed in Fig. 2 . Table 1 Baseline characteristics of the PA-PD-1 group compared with the non-PA-PD-1 group before and after PSM. Variable Before PSM p value After PSM p value PA-PD-1 group (n = 61) non-PA-PD-1 group (n = 337) PA-PD-1 group (n = 58) non-PA-PD-1 group (n = 110) Mean age, y (range) 51 (25–75) 53 (21–75) 0.47 52 (25–75) 54 (21–75) 0.36 ≤ 60 years 45 (73.8%) 233 (69.1%) 43 (74.1%) 74 (67.3%) > 60 years 16 (26.2%) 104 (30.9%) 15 (25.9%) 36 (32.7%) Gender 0.21 0.24 Female 2 (3.3%) 26 (7.7%) 2 (3.4%) 9 (8.2%) Male 59 (96.7%) 311 (92.3%) 56 (96.6%) 101 (91.8%) Number of lesions 0.32 0.28 1 35 (57.4%) 216 (64.1%) 33 (56.9%) 72 (65.5%) ≥ 2 26 (42.6%) 121 (35.9%) 25 (43.1%) 38 (34.5%) Maximum tumor size (mm) 0.29 0.77 ≤ 50 11 (18.0%) 82 (24.3%) 10 (17.2%) 21 (19.1%) > 50 50 (82.0%) 255 (75.7%) 48 (82.8%) 89 (80.9%) MVI 0.28 0.64 No 20 (32.8%) 135 (40.1%) 20 (34.5%) 42 (38.2%) Yes 41 (67.2%) 202 (59.9%) 38 (65.5%) 68 (61.8%) Child-pugh class 0.27 0.23 A 57 (93.4%) 325 (96.4%) 55 (94.8%) 108 (98.2%) B7-8 4 (6.6%) 12 (3.6%) 3 (5.2%) 2 (1.8%) HbsAg 0.40 0.47 Negative 8 (13.1%) 59 (17.5%) 8 (13.8%) 20 (18.2%) Positive 53 (86.9%) 278 (82.5%) 50 (86.2%) 90 (81.8%) HCV-Ab 0.90 0.24 Negative 59 (96.7%) 327 (97.0%) 56 (96.6%) 109 (99.1%) Positive 2 (3.3%) 10 (3.0%) 2 (3.4%) 1 (0.9%) AFP (ng/ml) 0.89 0.49 ≤ 400 40 (65.6%) 218 (64.7%) 38 (65.5%) 66 (60.0%) >400 21 (34.4%) 119 (35.3%) 20 (34.5%) 44 (40.0%) Absence of hypointense halos <0.001 0.62 No 39 (63.9%) 281 (83.4%) 39 (67.2%) 78 (70.9%) Yes 22 (36.1%) 56 (16.6%) 19 (32.8%) 32 (29.1%) Abbreviations: PA-PD-1, postoperative adjuvant anti-programmed cell death-1 therapy; PSM, propensity-score matching; MVI, microvascular invasion; HbsAg, hepatitis B surface antigen; HCV-Ab, hepatitis C virus antibody; AFP, alpha-fetoprotein. Survival outcomes After PSM, the median RFS in the PA-PD-1 group was 29.50 (95%CI 12.17–29.50) months, while it was 10.97 (95%CI 6.77–14.93) months in the non-PA-PD-1 group. The corresponding 1-year and 2-year RFS% were 67.1% and 58.0% in the PA-PD-1 group, and 47.1% and 32.8% in the non-PA-PD-1 group, respectively. In the PA-PD-1 group, the RFS was longer than that in the non-PA-PD-1 group ( p = 0.005, Fig. 3 A). Both groups did not reach the median OS time. The corresponding 1-year and 2-year OS% were 91.3% and 85.4% in the PA-PD-1 group, and 86.2% and 73.1% in the non-PA-PD-1 group, respectively. Between the two groups, OS did not show a statistically significant difference ( p = 0.144, Fig. 3 B). Univariable and multivariable analysis Univariable analysis revealed that adjuvant anti-PD-1 therapy, AFP > 400 ng/ml, absence of hypointense halos, and irregular rim-like hyper enhancement tended to be factors related to RFS (all p < 0.100, Table 2 ). Multivariable analysis showed that adjuvant anti-PD-1 therapy (HR = 0.471; 95% CI = 0.300-0.741; p = 0.001), absence of hypointense halos (HR = 1.635; 95% CI = 1.066–2.510; p = 0.024), and irregular rim-like hyper enhancement (HR = 0.566; 95% CI = 0.379–0.845; p = 0.005) were independent predictors of RFS (Table 2 ). As for OS, AFP > 400 ng/ml, absence of nonsmooth tumor margins, and presence of peritumoral hypointensity tended to be factors related to OS (all p < 0.100, Table 2 ) in the univariate analysis. While multivariable analysis revealed that none of these factors were significantly related to OS. Table 2 Univariate and multivariate analyses of predictors of recurrence-free survival and overall survival after PSM. Variable Recurrence-free survival Overall survival Univariate analysis Multivariate analysis Univariate analysis Multivariate analysis p value Hazard Ratio 95% CI p value P value Hazard Ratio 95% CI p value Age (> 60 years vs ≤ 60 years) 0.67 0.90 Gender (male vs female) 0.60 0.50 Number of lesions (≥ 2 vs 1) 0.25 0.91 Maximum tumor size (> 50 mm vs ≤ 50 mm) 0.17 0.58 MVI (yes vs no) 0.12 0.41 Child-Pugh class (A vs B7-8) 0.32 0.56 Hepatitis B virus infection (Yes vs No) 0.49 0.58 Hepatitis C virus infection (Yes vs No) 0.51 0.82 AFP (> 400 ng/ml vs ≤ 400 ng/ml) 0.09 1.391 0.921–2.101 0.18 0.04 1.461 0.809–2.640 0.21 Adjuvant anti-PD-1 treatment (Yes vs No) 0.006 0.471 0.300-0.741 0.001 0.15 Nonsmooth tumor margins (Yes vs No) 0.70 0.07 2.478 0.756–8.123 0.13 Internal arteries (Presence vs absence) 0.18 0.97 Peritumoral enhancement (Yes vs No) 0.16 0.27 Absence of hypointense halos (Yes vs No) 0.06 1.635 1.066–2.510 0.024 0.60 Irregular rim-like hyper enhancement (Yes vs No) 0.03 0.566 0.379–0.845 0.005 0.48 Satellite nodules (Presence vs absence) 0.21 0.71 Peritumoral hypointensity (Yes vs No) 0.11 0.04 1.988 0.875–4.517 0.10 Abbreviations: PSM, propensity-score matching; MVI, microvascular invasion; AFP, alpha-fetoprotein; PD-1, programmed cell death-1. Subgroup analysis Subgroup analyses stratified by different clinical variables and MRI features were performed to further explore the specific subgroups of HCC patients who might benefit from PA-PD-1 treatment (Fig. 4 and Supplementary Fig. 1). Results indicated that subgroups of > 60 years (HR = 0.41, 95% CI = 0.20–0.88), more than one lesion (HR = 0.41, 95% CI = 0.17–0.99), tumor size > 50 mm (HR = 0.61, 95% CI = 0.40–0.95), tumor size ≤ 50 mm (HR = 0.33, 95% CI = 0.12–0.94), presence of MVI (HR = 0.46, 95% CI = 0.28–0.75), HBV infection (HR = 0.60, 95% CI = 0.39–0.92), AFP ≤ 400 ng/ml (HR = 0.54, 95% CI = 0.33–0.91), nonsmooth tumor margins (HR = 0.54, 95% CI = 0.35–0.82), presence of internal arterials (HR = 0.59, 95% CI = 0.39–0.89), peritumoral enhancement (HR = 0.46, 95% CI = 0.28–0.74), absence of hypointense halos (HR = 0.33, 95% CI = 0.17–0.65), irregular rim-like hyper enhancement (HR = 0.37, 95% CI = 0.21–0.64), absence of satellite nodules (HR = 0.54, 95% CI = 0.32–0.90), and peritumoral hypointensity (HR = 0.56, 95% CI = 0.36–0.88) achieved significantly longer RFS after receiving adjuvant anti-PD-1 treatment compared with those who did not (Fig. 4 ). As for patients with absence of hypointense halos and irregular rim-like hyper enhancement, PA-PD-1 group had significantly longer RFS than those in non-PA-PD-1 group (median RFS, not reached versus 4.544 months, p = 0.001; not reached versus 10.233 months, p < 0.001, respectively; Fig. 5 ). In addition, patients could benefit from PA-PD-1 treatment in OS if they had presence of MVI (HR = 0.46, 95% CI = 0.23–0.91) and peritumoral enhancement (HR = 0.48, 95% CI = 0.24–0.97) (Supplementary Fig. 1). Patterns of Recurrence Data of recurrent pattens were presented in Table 3 . In the whole cohort, recurrence occurred in 28 patients in the PA-PD-1 group and 65 patients in the non-PA-PD-1 group (48.3% versus 59.1%, p = 0.181). The proportions of patients with local recurrence only were comparable between PA-PD-1 group and non-PA-PD-1 group (32.7% versus 39.1%, p = 0.873). As for patients with absence of hypointense halos and irregular rim-like hyper enhancement, patients in the PA-PD-1 group had significantly lower rate of recurrence than those in the non-PA-PD-1 group (47.4% versus 78.1%, p = 0.026; 25.8% versus 60.6%, p = 0.001, respectively). Table 3 Patterns of recurrence. Parameters The whole cohort Patients with absence of hypointense halos Patients with irregular rim like hyper enhancement non-PA-PD-1 (n = 110) PA-PD-1 (n = 58) p value non-PA-PD-1 (n = 32) PA-PD-1 (n = 19) p value non-PA-PD-1 (n = 71) PA-PD-1 (n = 31) p value Number of recurrence 65 (59.1%) 28 (48.3%) 0.18 25 (78.1%) 9 (47.4%) 0.03 43 (60.6%) 8 (25.8%) 0.001 Patterns of recurrence 0.87 0.72 0.28 Local recurrence 43 (39.1%) 19 (32.7%) 15 (46.9%) 6 (31.6%) 25 (35.2%) 3 (9.7%) Distant recurrence 22 (20.0%) 9 (15.5%) 10 (31.3%) 3 (15.8%) 18 (25.4%) 5 (16.1%) Table 3 Patterns of recurrence (continued). Parameters Patients with satellite nodules Patients with peritumoral hypointensity Patients with nonsmooth tumor margins non-PA-PD-1 (n = 42) PA-PD-1 (n = 22) p value non-PA-PD-1 (n = 76) PA-PD-1 (n = 46) p value non-PA-PD-1 (80) PA-PD-1 (n = 54) p value Number of recurrence 28 (66.7%) 12 (54.5%) 0.35 49 (64.5%) 24 (52.2%) 0.18 49 (61.3%) 26 (48.1%) 0.14 Patterns of recurrence 0.20 0.79 0.88 Local recurrence 20 (47.6%) 6 (27.3%) 29 (38.2%) 15 (32.6%) 31 (38.8%) 16 (29.6%) Distant recurrence 8 (19.0%) 6 (27.3%) 20 (26.3%) 9 (19.6%) 18 (22.5%) 10 (18.5%) Table 3. Patterns of recurrence (continued). Parameters Patients with satellite nodules Patients with peritumoral hypointensity Patients with nonsmooth tumor margins non-PA-PD-1 (n=42) PA-PD-1 (n=22) p value non-PA-PD-1 (n=76) PA-PD-1 (n=46) p value non-PA-PD-1 (80) PA-PD-1 (n=54) p value Number of recurrence 28 (66.7%) 12 (54.5%) 0.35 49 (64.5%) 24 (52.2%) 0.18 49 (61.3%) 26 (48.1%) 0.14 Patterns of recurrence 0.20 0.79 0.88 Local recurrence 20 (47.6%) 6 (27.3%) 29 (38.2%) 15 (32.6%) 31 (38.8%) 16 (29.6%) Distant recurrence 8 (19.0%) 6 (27.3%) 20 (26.3%) 9 (19.6%) 18 (22.5%) 10 (18.5%) Discussion Biomarkers for predicting survival benefit of adjuvant anti-PD-1 therapy in HCC patients with high risks of recurrence are scare and lack of clinical evidence currently. We investigated the role of seven MRI features in predicting response to adjuvant anti-PD-1 therapy in patients with HCC. Results showed that absence of hypointense halos (HR = 1.635; 95% CI = 1.066–2.510; p = 0.024), and irregular rim-like hyper enhancement (HR = 0.566; 95% CI = 0.379–0.845; p = 0.005) were independent predictors for RFS. And patients with absence of hypointense halos or irregular rim-like hyper enhancement had significantly reduced recurrent rates after receiving PA-PD-1 compared with those who did not (47.4% versus 78.1%, p = 0.026; 25.8% versus 60.6%, p = 0.001, respectively). Furthermore, PA-PD-1 provided improved RFS than non-PA-PD-1 in patients with absence of hypointense halos, irregular rim-like hyper enhancement, nonsmooth tumor margins, presence of internal arterials, peritumoral enhancement, peritumoral hypointensity, and absence of satellite nodules, which might potentially act as predictive and response biomarkers of outcome in terms of RFS in patients treated with adjuvant anti-PD-1 therapy. Postoperative adjuvant treatment of anti-PD-1 inhibitors, such as atezolizumab or sintilimab, has recently been found to be associated with significantly improved RFS compared with active surveillance in HCC patients with high risk for recurrence after curative-intent resection or ablation 9,10 , which was consistent with the results of the previous study and the current study 6 . However, the response rates in overall patients remain unsatisfactory. Although several biomarkers, including PD-L1 expression, density of tumor infiltrating lymphocytes, tumor mutational burden, mismatch-repair deficiency, and some specific lymphocyte subtypes have been noticed to be associated with treatment effect of anti-PD-1 therapy 25–27 , their predictive efficacies are controversial and inconsistent. Robust predictors of response to PA-PD-1 are still urgently needed. Nowadays, a growing body of research supports the potential of CT or MRI feature-based method to assess the biological characteristics of HCC, and empower novel prognostic and predictive approaches in patient-tailored treatment selection 19,28,29 . CT or MRI mage allows for visualization and assessment of the whole tumor, which can capture the entire spectrum of disease within a patient and may allow for better assessment of any heterogeneity that exists within or between tumors. To the best of our knowledge, this is the first study to explore the efficacy of MRI features in predicting response to PA-PD-1 in HCC patients, which might be potential predictive biomarkers enabling the accurate selection of patients who are most likely to derive benefit from postoperative anti-PD-1 therapy. The presence of fibrous capsule is one of the characteristic findings of HCC, which was displayed as hypointense halos in MRI or hypointense halos in CT images. It is histologically composed of an inner layer rich in pure, fibrous tissue and an outer layer containing portal venules and newly formed bile ducts, which can cut off the dissemination of HCC 30 . Once the tumor cells invade into and break through the portal venules of the capsule, imaging signs of the incomplete capsule and infiltrative border appear 31 . Previous studies have demonstrated some “worrisome” radiologic features, including absence of hypointense/hypointense halos, were confirmed as reliable predictors for MVI and poor prognosis 32 . Xu et al. developed a Radiographic-Radiomic model based on 12 imaging features including hypointense halos, and announced good performance for predicting MVI and clinical outcomes 33 . Segal et al. found that absence of hypointense halos combined with presence of internal arteries were associated with a 91-gene venous invasion profile representing angiogenesis and cellular proliferation 19 . Furthermore, an increasing number of studies recently demonstrated that radiomics based on imaging biomarkers can be used to predict immunotherapy response in various kinds of cancers, especially lung cancer 34,35 . Our analysis revealed that absence of hypointense halos was an independent predictor for RFS. HCC patients with absence of hypointense halos had significantly longer RFS in PA-PD-1 group than those in non-PA-PD-1 group. Therefore, it is reasonable to take absence of hypointense halos into consideration when trying to predict survival benefit from PA-PD-1 in high-risk recurrent HCC patients. Of note, there are several limitations for the current study. First, it is a retrospective study from a single institution with unavoidable selection bias, though we included patients consecutively and applied PSM to avoid certain bias. Moreover, the sample size of patients receiving PA-PD-1 was small (58 patients after PSM) and the follow-up period was short. Therefore, prospective and larger-sample sized research with longer follow-ups are warranted to verify our results. In conclusion, our study highlighted the value of MRI features such as absence of hypointense halos and irregular rim-like hyper enhancement as novel and promising predictors for recurrence and response to PA-PD-1 in HCC patients with high risks for recurrence after curative hepatectomy, which might help clinicians implement more appropriate and individualized treatments based on the MRI image characteristics of HCC. Further studies are needed to confirm our findings. Methods Patient selection This retrospectively enrolled and prospectively followed-up observational study was conducted from Janurary 2020 to June 2023, and centrally approved by the institutional review board. Informed consent was obtained from each patient. Inclusion criteria were outlined as follows: ( 1 ) age 18–75 years; ( 2 ) received abdominal MRI evaluation within 2 weeks before liver resection; ( 3 ) underwent R0 resection and histological confirmed as HCC; ( 4 ) received abdominal CT or MRI evaluation to confirm no tumor relapse or residual within 1 month after surgery; ( 5 ) presence of one or more high risk for relapse: maximum tumor size excceding 5 cm, multiple tumor nodules (>3 nodules), MVI, satellite nodules or AFP>400ng/mL; ( 6 ) Child-Pugh A or B7; ( 7 ) Eastern Co-operative Oncology Group score 0 or 1. Patients were excluded if they showed evidence of coexistence of other malignancies, macrovascular invasion or distant metastasis on the baseline imaging, or had previously received anti-cancer therapy for HCC. MRI features MRI was performed by a 3.0-T MRI system (Achieva; Philips Healthcare, Best, the Netherlans). T1-weighted, T2-weighted, and diffusion-weighted imaging were included in the baseline MRI examination. Gadoxetic acid-enhanced imaging consisted of precontrast images, as well as arterial phase, portal phase, transitional phase, and hepatobiliary phase images, which were 2–35 seconds, 60 seconds, 3 minutes, and 20 minutes after injection of contrast material, respectively. MRI features were indepently reviewed by two senior abdominal radiologists in consensus, both having more than five years of experience interpreting liver MRI images. Altogether, seven imaging features were extracted from tumors and peritumor regions on preoperative MRI, which were demonstrated previously as robust predictors for MVI or poor prognosis. These seven features included absence of hypointense halos, irregular rim-like enhancement in arterial phase, nonsmooth tumor margins, peritumoral arterial phase hyperenhancement, peritumoral hepatobiliary phase hypointensity, satellite nodule, and internal arteries, definitions of which were described in Supplementary Data. Postoperative adjuvant anti-PD-1 antibody treatment Inculded patients were separated into two groups: ( 1 ) patients received PA-PD-1 after hepatic resection; ( 2 ) paitents did not receive PA-PD-1. The application of PA-PD-1 was decided based on the physician’s clinical knowledge and experience, as well as patient’s request. Patients in the PA-PD-1 group received intravenous PD-1 blockade therapy at an interal of 3 weeks after each regimen. PD-1 blockade treatment would be terminated if there was disease progression or unacceptable toxicity measured accoding to the CTCAE version 5.0. Follow-up and definition of outcomes Postoperative surveillance visits were scheduled 1 month postoperatively to confirm disease-free status, every 2–3 months for the first 2 years, and then every 6–12 months thereafter for each patient in both groups. Data of physical examination, laboratory tests (including peripheral blood test, liver function, AFP, and abdominal radiological examinations (ultrasound, contrastenhanced CT, or MRI) from each follow-up visit were collected. Survival outcomes included RFS and OS. RFS was defined as the interval from the date of hepatectomy to the date of first disease recurrence, death, or last follow-up. OS was defined as the interval from the date of hepatectomy to the date of death or last follow-up. Follow-up data collection was terminated on March 31, 2024. Patients sufferring from recurrence of HCC underwent surgery, radiofrequency ablation, transarterial chemoembolization, radiotherapy or systemic treatment, according to tumor location, size, and number, as well as patient’s general condition and liver function. Statistical analysis Continuous variables were represented as mean ± standard deviation and compared through the Student’s t tests or the Mann–Whitney U tests. Categorical variables, expressed as frequencies (percentages), were compared by performing through Pearson's Chi-square tests or Fisher’s exact tests. Cox regression analysis was used for univariable and multivariable analyses to find out the significant predictors for RFS and OS. To mitigate selection bias and minimize the potential influence of confounding factors, including imaging features that can affect prognosis, we employed a PSM analysis, wherein we matched patients in PA-PD-1 or non-PA-PD-1 group. Baseline variables demonstrating p-values of less than 0.2 in both groups were included in the PSM model for the calculation of propensity scores. The variable encompassed absence of hypointense halos. For the matching process, patients in the two groups were paired in a one-to-two ratio using logistic regression based on their propensity scores. Survival analysis was estimated by the Kaplan-Meier method, and differences between groups were assessed by logrank test. Subgroup analyses were performed through the Kaplan-Meier method, and the forest plot for subgroup analyses was described with estimated HRs and 95% CIs. For statistical analysis, the software of Medcal (version 20.100) was used. Rstudio, “survminer”, “survival”, and “forestplot” packages were used to analyze the data. A 2-tailed p value of < 0.05 was considered statistically significant. Declarations Data Availability All data supporting the results reported in the article can be found in the manuscript and the supplementary data. Competing interests We declare that we have no conflict of interest. Author contributions Z.P. and X.H.: Study concept and design; interpretation of data; writing of manuscript; critical review of the manuscript; study supervision; and funding acquisition. X.H. and J.Z.: Analysis and interpretation of data; critical review of the manuscript. J.Z., Y.S. and S.H.: Acquisition of data; critical review of the manuscript. All authors gave final approval of the completed version. Acknowledgements This study was supported by the National Natural Science Foundation of China (No. 82072029 and No. 82303738), the National high level talents special support plan - “Ten thousand plan” - Young top - notch talent support program, the Natural Science Foundation of Guangdong Province, China (No. 2024A1515012207), the Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515111101) and Guangzhou Science and Technology Program Project (2024A04J4634). References Vogel, A., Meyer, T., Sapisochin, G., Salem, R. & Saborowski, A. Hepatocellular carcinoma. Lancet (London, England) 400, 1345–1362, doi: 10.1016/s0140-6736(22)01200-4 (2022). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians 71, 209–249, doi: 10.3322/caac.21660 (2021). Altekruse, S. F., McGlynn, K. A., Dickie, L. 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T. & Han, J. K. Hepatocellular carcinoma: preoperative gadoxetic acid-enhanced MR imaging can predict early recurrence after curative resection using image features and texture analysis. Abdominal radiology (New York) 44, 539–548, doi: 10.1007/s00261-018-1768-9 (2019). Hectors, S. J. et al. Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging. Scientific reports 7, 2452, doi: 10.1038/s41598-017-02706-z (2017). Hong, S. B. et al. MRI Features for Predicting Microvascular Invasion of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. Liver cancer 10, 94–106, doi: 10.1159/000513704 (2021). Segal, E. et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nature biotechnology 25, 675–680, doi: 10.1038/nbt1306 (2007). Kuo, M. D. & Yamamoto, S. Next generation radiologic-pathologic correlation in oncology: Rad-Path 2.0. AJR. American journal of roentgenology 197, 990–997, doi: 10.2214/ajr.11.7163 (2011). Lee, S., Kim, S. H., Lee, J. E., Sinn, D. H. & Park, C. K. Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. Journal of hepatology 67, 526–534, doi: 10.1016/j.jhep.2017.04.024 (2017). Lim, K. C. et al. Microvascular invasion is a better predictor of tumor recurrence and overall survival following surgical resection for hepatocellular carcinoma compared to the Milan criteria. Annals of surgery 254, 108–113, doi: 10.1097/SLA.0b013e31821ad884 (2011). Wei, Y., Pei, W., Qin, Y., Su, D. & Liao, H. Preoperative MR imaging for predicting early recurrence of solitary hepatocellular carcinoma without microvascular invasion. European journal of radiology 138, 109663, doi: 10.1016/j.ejrad.2021.109663 (2021). Kim, A. Y. et al. Hepatobiliary MRI as novel selection criteria in liver transplantation for hepatocellular carcinoma. Journal of hepatology 68, 1144–1152, doi: 10.1016/j.jhep.2018.01.024 (2018). Yi, M. et al. Biomarkers for predicting efficacy of PD-1/PD-L1 inhibitors. Molecular cancer 17, 129, doi: 10.1186/s12943-018-0864-3 (2018). Liu, Z. et al. Progenitor-like exhausted SPRY1(+)CD8(+) T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma. Cancer cell 41, 1852–1870.e1859, doi: 10.1016/j.ccell.2023.09.011 (2023). Chuah, S. et al. Uncoupling immune trajectories of response and adverse events from anti-PD-1 immunotherapy in hepatocellular carcinoma. Journal of hepatology 77, 683–694, doi: 10.1016/j.jhep.2022.03.039 (2022). Greten, T. F. et al. Biomarkers for immunotherapy of hepatocellular carcinoma. Nature reviews. Clinical oncology 20, 780–798, doi: 10.1038/s41571-023-00816-4 (2023). Wei, M. et al. Role of Preoperational Imaging Traits for Guiding Treatment in Single ≤ 5 cm Hepatocellular Carcinoma. Annals of surgical oncology 29, 5144–5153, doi: 10.1245/s10434-022-11344-3 (2022). Cho, E. S. & Choi, J. Y. MRI features of hepatocellular carcinoma related to biologic behavior. Korean journal of radiology 16, 449–464, doi: 10.3348/kjr.2015.16.3.449 (2015). Wei, Y. et al. IVIM improves preoperative assessment of microvascular invasion in HCC. European radiology 29, 5403–5414, doi: 10.1007/s00330-019-06088-w (2019). Renzulli, M. et al. Can Current Preoperative Imaging Be Used to Detect Microvascular Invasion of Hepatocellular Carcinoma? Radiology 279, 432–442, doi: 10.1148/radiol.2015150998 (2016). Xu, X. et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. Journal of hepatology 70, 1133–1144, doi: 10.1016/j.jhep.2019.02.023 (2019). de Miguel-Perez, D. et al. Validation of a multiomic model of plasma extracellular vesicle PD-L1 and radiomics for prediction of response to immunotherapy in NSCLC. Journal of experimental & clinical cancer research: CR 43, 81, doi: 10.1186/s13046-024-02997-x (2024). Kothari, G. Role of radiomics in predicting immunotherapy response. Journal of medical imaging and radiation oncology 66, 575–591, doi: 10.1111/1754-9485.13426 (2022). Additional Declarations (Not answered) Supplementary Files SupplementaryData0615.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4593371","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":326149293,"identity":"0a351a06-a2d5-4a1c-8773-d045cffd511a","order_by":0,"name":"Zhenwei Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYFACHhBhw8DAzNwAEzIgRksaUAsjaVoOAzGxWsz5zx58XNh2Ppq/nbGB8WdbXWIDe/M2CYaaOzi1WDacSzae2XY7d8ZhxgZm3rbDiQ08x8okGI49w6nF4GCPmTTvttu5DSAtjG0HEhskcswkGBsO49ZymMf8N++2c7nzD8McJv+GgJZjPGbMvNsO5G4AamHgbWMG2sKDX4tlD4+xNO+/5NyNQC2Hec4dNm7jSSu2SDiGW4s5/xnDzzxn7HLnnT988OGPsjrZfvbDG298qMHjMGTOAUY2BgY2ECsBpwaMWPuDR+koGAWjYBSMWAAAkNpUvSyBQN4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0617-3805","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Zhenwei","middleName":"","lastName":"Peng","suffix":""},{"id":326149294,"identity":"b4df9d4d-1f45-4cd9-96d5-0c5c26d8141d","order_by":1,"name":"Xiaofang He","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaofang","middleName":"","lastName":"He","suffix":""},{"id":326149295,"identity":"910ea73b-ff57-43e7-be3a-bb869c122677","order_by":2,"name":"Jie Zhan","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhan","suffix":""},{"id":326149296,"identity":"cc014993-0085-4f93-8d0c-77a9f943e4b0","order_by":3,"name":"Yukun Sun","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yukun","middleName":"","lastName":"Sun","suffix":""},{"id":326149297,"identity":"a149f8ba-5fc0-43bf-818b-bf50e2c90e7c","order_by":4,"name":"Shuifang Hu","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Shuifang","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-06-17 10:00:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4593371/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4593371/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62219223,"identity":"6e8048c0-7f60-4f16-90ff-b09b0e93d717","added_by":"auto","created_at":"2024-08-11 12:11:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157961,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram.\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593371/v1/3dd4d285f6023634b2d9dca4.jpg"},{"id":62219221,"identity":"c3b0aef0-7bc0-4fca-8e4f-f26f6897cc2d","added_by":"auto","created_at":"2024-08-11 12:11:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1093504,"visible":true,"origin":"","legend":"\u003cp\u003eTypical MRI features. (A) Nonsmooth tumor margins assessed in the arterial phase; (B) Presence of internal arteries assessed in the arterial phase; (C) Peritumoral enhancement, enhancing assessed in the arterial phase; (D) Presence of satellite nodules in the arterial phase; (E) Hypointense halos assessed on unenhanced T1-weighted MRI image; (F) Absence of hypointense halos on unenhanced T1-weighted MRI image; (G) Irregular rim-like hyperenhancement assessed in the arterial phase; (H) Peritumoral hypointensity assessed in the hepatobiliary phase.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4593371/v1/ce269e6bacc306fc429aa23b.png"},{"id":62220172,"identity":"610257ce-1193-4781-8b48-8b64e858e8c5","added_by":"auto","created_at":"2024-08-11 12:19:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113655,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of recurrence-free survival (A) and overall survival (B) between the PA-PD-1 group and non-PA-PD-1 group after propensity-score matching.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4593371/v1/4c1774461bc2c433f5f0e7a1.png"},{"id":62219219,"identity":"f6f45eb8-8efd-4c99-a1e7-9c72d785dde7","added_by":"auto","created_at":"2024-08-11 12:11:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43083,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of recurrence-free survival stratified by clinical parameters between the PA-PD-1 group and non-PD-1 group after propensity-score matching.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4593371/v1/e6c8ea684fc601751716847e.png"},{"id":62220173,"identity":"77ae09b2-f8a9-48ae-b09b-d241b8ac4cee","added_by":"auto","created_at":"2024-08-11 12:19:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":107193,"visible":true,"origin":"","legend":"\u003cp\u003eRecurrence-free survival for patients with absence of hypointense halos (A) and irregular rim-like hyper enhancement (B) treated with PA-PD-1 or not.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4593371/v1/5874bc19aaf8fd4ac266ea96.png"},{"id":64652687,"identity":"3c394cea-2a9f-41ab-a1a0-000aba9d5014","added_by":"auto","created_at":"2024-09-17 05:59:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2301590,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4593371/v1/052d3db6-4bf4-41d8-bbbb-aecca98d6e37.pdf"},{"id":62219225,"identity":"3d6fe688-6921-4d6c-81bb-40322dcd229b","added_by":"auto","created_at":"2024-08-11 12:11:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1320209,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData0615.docx","url":"https://assets-eu.researchsquare.com/files/rs-4593371/v1/4ec4d65b6965f63ddef9e20c.docx"}],"financialInterests":"(Not answered)","formattedTitle":"Preoperative MRI features for predicting response to postoperative adjuvant anti-PD-1 therapy in hepatocellular carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is one of the most widespread malignancies and the third leading cause of cancer-related death in the world\u003csup\u003e1,2\u003c/sup\u003e. Liver resection has long been considered as the potentially curative option for HCC patients, with a 5-year survival of 70\u0026ndash;80%\u003csup\u003e3\u003c/sup\u003e. However, high 5-year recurrence rate reaching up to 70% after radical hepatectomy remains a major obstacle to survival\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNowadays, there is no unified or standardized adjuvant treatments to improve postoperative recurrence-free survival (RFS) in HCC patients\u003csup\u003e5\u003c/sup\u003e. Previous studies demonstrated that patients with high risks of recurrence could benefit from postoperative adjuvant therapies, and promising responses have been recently reported with adjuvant anti-programmed cell death (PD)-1 therapy\u003csup\u003e6\u0026ndash;10\u003c/sup\u003e. High-risk recurrent factors are commonly referred to large tumor, multiple tumors, satellite nodules, microvascular invasion (MVI), or alpha-fetoprotein (AFP) \u0026gt; 400ng/mL\u003csup\u003e11\u0026ndash;14\u003c/sup\u003e. However, only a subset of patients benefited from adjuvant anti-PD-1 therapy, and the current selected factors give inadequate attention to the intertumor biological heterogeneity. Identifying potential predictive biomarkers associated with therapeutic efficacy would allow tailoring of appropriate adjuvant immunotherapy for different patient subgroups.\u003c/p\u003e \u003cp\u003eAs non-invasive examination, enhanced MRI, especially hepatobiliary-specific contrast-enhanced MRI, has been widely-used for diagnosis, treatment planning and surveillance of HCC\u003csup\u003e15\u003c/sup\u003e. Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid, a hepatocyte-specific contrast agent, can be used for quantitatively and qualitatively evaluating intratumor and peritumor imaging features in HCC development\u003csup\u003e16\u003c/sup\u003e. Recently, imaging features displayed by MRI or CT have been found as robust markers to reflect intertumor heterogeneity and prognosis of HCC\u003csup\u003e17,18\u003c/sup\u003e. Previous investigations demonstrated that absence of hypodense halos, peritumor enhancement, internal arteries and nonsmooth tumor margins could reveal gene expression patterns of HCC and help develop a noninvasive molecular portrait of the tumor\u003csup\u003e19,20\u003c/sup\u003e. While rim arterial enhancement, arterial peritumoral enhancement, peritumoral hypointensity on the hepatobiliary phase imaging, nonsmooth tumor margin and hypointensity on T1-weighted imaging were widely recognized as the significant predictors of microvascular invasion\u003csup\u003e18,21\u003c/sup\u003e, which is a robust pathological characteristic reflecting poor prognosis\u003csup\u003e22\u003c/sup\u003e. Moreover, nonsmooth tumor margin, presence of satellite nodule and peritumoral hypointensity on hepatobiliary phase were found to be independent significant variables associated with tumor aggressiveness and recurrence\u003csup\u003e23,24\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, taking MRI features into consideration may be reasonable when predicting response to adjuvant anti-PD-1 therapy. However, there has been no related evidence so far. Hence, the current study aimed to explore the value of seven MRI features in the therapeutic selection between adjuvant anti-PD-1 therapy and non-adjuvant anti-PD-1 therapy for HCC patients with high risks for recurrence.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Demographics\u003c/h2\u003e \u003cp\u003eFrom January 2020 to June 2023, 987 HCC patients underwent hepatectomy in our institution. According to the inclusion and exclusion criteria, 398 HCC patients with high relapse risks were included, with 61 patients in the postoperative adjuvant anti-PD-1 therapy (PA-PD-1) group and 337 patients in the non-PA-PD-1 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median follow-up time was 27.3 (95% confidence interval [CI] 25.4\u0026ndash;32.7) months. All the baseline characteristics between these two groups were compared and represented in Supplementary Table\u0026nbsp;1. Further multivariable Cox regression analysis identified that number of lesions, maximum tumor size, level of α-Fetoprotein, adjuvant anti-PD-1 treatment, and absence of hypointense halos were independent influencing factors for RFS, while only maximum tumor size and level of α-Fetoprotein were independent influencing factors for overall survival (OS) (Supplementary Table\u0026nbsp;2). All these independent prognostic factors were presented in the baseline charateristics between subgroups.\u003c/p\u003e \u003cp\u003eTo balance the baseline differences between patients in PA-PD-1 group and non-PA-PD-1 group, 168 patients were matched after 1:2 propensity score matching (PSM), with 58 patients (mean age, 54 years; range, 25\u0026ndash;75 years; 56 men) in the PA-PD-1 group and 110 patients (mean age, 52 years; range, 21\u0026ndash;75 years; 101 men) in the non-PA-PD-1 group. Detailed baseline characteristics between groups before and after PSM are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Before PSM, absence of hypointense halos differed obviously between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After PSM, the positive rates of nonsmooth tumor margins, internal arteries, peritumoral enhancement, absence of hypointense halos, irregular rim-like hyper enhancement, satellite nodules, and peritumoral hypointensity were 93.1% (54/58), 84.5% (49/58), 81.0% (47/58), 32.8% (19/58), 55.2% (32/58), 37.9% (22/58), and 79.3% (46/58) in the PA-PD-1 group, and 80.9% (89/110), 94.5% (104/110), 53.6% (59/110), 29.1% (32/110), 67.2% (74/110), 31.8% (35/110), and 71.8% (79/110) in the non-PA-PD-1 group, respectively. Typical images of the seven MRI features were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the PA-PD-1 group compared with the non-PA-PD-1 group before and after PSM.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBefore PSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAfter PSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA-PD-1 group (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-PA-PD-1 group (n\u0026thinsp;=\u0026thinsp;337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ePA-PD-1\u003c/p\u003e \u003cp\u003egroup (n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enon-PA-PD-1\u003c/p\u003e \u003cp\u003egroup (n\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age, y (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (25\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (21\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e52 (25\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54 (21\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (69.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e43 (74.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e15 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e311 (92.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e56 (96.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101 (91.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216 (64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e33 (56.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e25 (43.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum tumor size (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e10 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (82.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e255 (75.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e48 (82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89 (80.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (40.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e20 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (59.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e38 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-pugh class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (93.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e325 (96.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e55 (94.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e108 (98.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB7-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbsAg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (86.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (82.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e50 (86.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV-Ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327 (97.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e56 (96.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e109 (99.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; 400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (65.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e38 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e20 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence of hypointense halos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (63.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281 (83.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e39 (67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78 (70.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (36.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e19 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: PA-PD-1, postoperative adjuvant anti-programmed cell death-1 therapy; PSM, propensity-score matching; MVI, microvascular invasion; HbsAg, hepatitis B surface antigen; HCV-Ab, hepatitis C virus antibody; AFP, alpha-fetoprotein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSurvival outcomes\u003c/h3\u003e\n\u003cp\u003eAfter PSM, the median RFS in the PA-PD-1 group was 29.50 (95%CI 12.17\u0026ndash;29.50) months, while it was 10.97 (95%CI 6.77\u0026ndash;14.93) months in the non-PA-PD-1 group. The corresponding 1-year and 2-year RFS% were 67.1% and 58.0% in the PA-PD-1 group, and 47.1% and 32.8% in the non-PA-PD-1 group, respectively. In the PA-PD-1 group, the RFS was longer than that in the non-PA-PD-1 group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Both groups did not reach the median OS time. The corresponding 1-year and 2-year OS% were 91.3% and 85.4% in the PA-PD-1 group, and 86.2% and 73.1% in the non-PA-PD-1 group, respectively. Between the two groups, OS did not show a statistically significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.144, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eUnivariable and multivariable analysis\u003c/h2\u003e \u003cp\u003eUnivariable analysis revealed that adjuvant anti-PD-1 therapy, AFP\u0026thinsp;\u0026gt;\u0026thinsp;400 ng/ml, absence of hypointense halos, and irregular rim-like hyper enhancement tended to be factors related to RFS (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.100, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Multivariable analysis showed that adjuvant anti-PD-1 therapy (HR\u0026thinsp;=\u0026thinsp;0.471; 95% CI\u0026thinsp;=\u0026thinsp;0.300-0.741; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), absence of hypointense halos (HR\u0026thinsp;=\u0026thinsp;1.635; 95% CI\u0026thinsp;=\u0026thinsp;1.066\u0026ndash;2.510; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), and irregular rim-like hyper enhancement (HR\u0026thinsp;=\u0026thinsp;0.566; 95% CI\u0026thinsp;=\u0026thinsp;0.379\u0026ndash;0.845; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) were independent predictors of RFS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As for OS, AFP\u0026thinsp;\u0026gt;\u0026thinsp;400 ng/ml, absence of nonsmooth tumor margins, and presence of peritumoral hypointensity tended to be factors related to OS (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.100, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in the univariate analysis. While multivariable analysis revealed that none of these factors were significantly related to OS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analyses of predictors of recurrence-free survival and overall survival after PSM.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eRecurrence-free survival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eOverall survival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026gt;\u0026thinsp;60 years vs\u0026thinsp;\u0026le;\u0026thinsp;60 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male vs female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of lesions (\u0026ge;\u0026thinsp;2 vs 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum tumor size (\u0026gt;\u0026thinsp;50 mm vs\u0026thinsp;\u0026le;\u0026thinsp;50 mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVI (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh class (A vs B7-8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis B virus infection (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis C virus infection (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP (\u0026gt;\u0026thinsp;400 ng/ml vs\u0026thinsp;\u0026le;\u0026thinsp;400 ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.921\u0026ndash;2.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.809\u0026ndash;2.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant anti-PD-1 treatment (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.300-0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonsmooth tumor margins (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.756\u0026ndash;8.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal arteries (Presence vs absence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeritumoral enhancement (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence of hypointense halos\u003c/p\u003e \u003cp\u003e(Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.066\u0026ndash;2.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular rim-like hyper enhancement (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.379\u0026ndash;0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatellite nodules (Presence vs absence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeritumoral hypointensity\u003c/p\u003e \u003cp\u003e(Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.875\u0026ndash;4.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: PSM, propensity-score matching; MVI, microvascular invasion; AFP, alpha-fetoprotein; PD-1, programmed cell death-1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eSubgroup analyses stratified by different clinical variables and MRI features were performed to further explore the specific subgroups of HCC patients who might benefit from PA-PD-1 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Fig.\u0026nbsp;1). Results indicated that subgroups of \u0026gt;\u0026thinsp;60 years (HR\u0026thinsp;=\u0026thinsp;0.41, 95% CI\u0026thinsp;=\u0026thinsp;0.20\u0026ndash;0.88), more than one lesion (HR\u0026thinsp;=\u0026thinsp;0.41, 95% CI\u0026thinsp;=\u0026thinsp;0.17\u0026ndash;0.99), tumor size\u0026thinsp;\u0026gt;\u0026thinsp;50 mm (HR\u0026thinsp;=\u0026thinsp;0.61, 95% CI\u0026thinsp;=\u0026thinsp;0.40\u0026ndash;0.95), tumor size\u0026thinsp;\u0026le;\u0026thinsp;50 mm (HR\u0026thinsp;=\u0026thinsp;0.33, 95% CI\u0026thinsp;=\u0026thinsp;0.12\u0026ndash;0.94), presence of MVI (HR\u0026thinsp;=\u0026thinsp;0.46, 95% CI\u0026thinsp;=\u0026thinsp;0.28\u0026ndash;0.75), HBV infection (HR\u0026thinsp;=\u0026thinsp;0.60, 95% CI\u0026thinsp;=\u0026thinsp;0.39\u0026ndash;0.92), AFP\u0026thinsp;\u0026le;\u0026thinsp;400 ng/ml (HR\u0026thinsp;=\u0026thinsp;0.54, 95% CI\u0026thinsp;=\u0026thinsp;0.33\u0026ndash;0.91), nonsmooth tumor margins (HR\u0026thinsp;=\u0026thinsp;0.54, 95% CI\u0026thinsp;=\u0026thinsp;0.35\u0026ndash;0.82), presence of internal arterials (HR\u0026thinsp;=\u0026thinsp;0.59, 95% CI\u0026thinsp;=\u0026thinsp;0.39\u0026ndash;0.89), peritumoral enhancement (HR\u0026thinsp;=\u0026thinsp;0.46, 95% CI\u0026thinsp;=\u0026thinsp;0.28\u0026ndash;0.74), absence of hypointense halos (HR\u0026thinsp;=\u0026thinsp;0.33, 95% CI\u0026thinsp;=\u0026thinsp;0.17\u0026ndash;0.65), irregular rim-like hyper enhancement (HR\u0026thinsp;=\u0026thinsp;0.37, 95% CI\u0026thinsp;=\u0026thinsp;0.21\u0026ndash;0.64), absence of satellite nodules (HR\u0026thinsp;=\u0026thinsp;0.54, 95% CI\u0026thinsp;=\u0026thinsp;0.32\u0026ndash;0.90), and peritumoral hypointensity (HR\u0026thinsp;=\u0026thinsp;0.56, 95% CI\u0026thinsp;=\u0026thinsp;0.36\u0026ndash;0.88) achieved significantly longer RFS after receiving adjuvant anti-PD-1 treatment compared with those who did not (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As for patients with absence of hypointense halos and irregular rim-like hyper enhancement, PA-PD-1 group had significantly longer RFS than those in non-PA-PD-1 group (median RFS, not reached versus 4.544 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; not reached versus 10.233 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In addition, patients could benefit from PA-PD-1 treatment in OS if they had presence of MVI (HR\u0026thinsp;=\u0026thinsp;0.46, 95% CI\u0026thinsp;=\u0026thinsp;0.23\u0026ndash;0.91) and peritumoral enhancement (HR\u0026thinsp;=\u0026thinsp;0.48, 95% CI\u0026thinsp;=\u0026thinsp;0.24\u0026ndash;0.97) (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatterns of Recurrence\u003c/h2\u003e \u003cp\u003eData of recurrent pattens were presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the whole cohort, recurrence occurred in 28 patients in the PA-PD-1 group and 65 patients in the non-PA-PD-1 group (48.3% versus 59.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.181). The proportions of patients with local recurrence only were comparable between PA-PD-1 group and non-PA-PD-1 group (32.7% versus 39.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.873). As for patients with absence of hypointense halos and irregular rim-like hyper enhancement, patients in the PA-PD-1 group had significantly lower rate of recurrence than those in the non-PA-PD-1 group (47.4% versus 78.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026; 25.8% versus 60.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatterns of recurrence.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eThe whole cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePatients with absence of hypointense halos\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePatients with irregular rim like hyper enhancement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003enon-PA-PD-1 (n\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePA-PD-1 (n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003enon-PA-PD-1 (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePA-PD-1 (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003enon-PA-PD-1 (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePA-PD-1 (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (78.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43 (60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatterns of recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatterns of recurrence (continued).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePatients with satellite nodules\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003ePatients with peritumoral hypointensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003ePatients with nonsmooth tumor margins\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003enon-PA-PD-1 (n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePA-PD-1 (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003enon-PA-PD-1 (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePA-PD-1 (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003enon-PA-PD-1 (80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePA-PD-1 (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatterns of recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Patterns of recurrence (continued).\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"848\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.391509433962263%\" rowspan=\"2\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.7688679245283%\" colspan=\"3\"\u003e\n \u003cp\u003ePatients with satellite nodules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.830188679245282%\" colspan=\"4\"\u003e\n \u003cp\u003ePatients with peritumoral hypointensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.00943396226415%\" colspan=\"4\"\u003e\n \u003cp\u003ePatients with nonsmooth tumor margins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.23943661971831%\"\u003e\n \u003cp\u003enon-PA-PD-1 (n=42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.971830985915492%\"\u003e\n \u003cp\u003ePA-PD-1 (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.028169014084508%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.380281690140846%\"\u003e\n \u003cp\u003enon-PA-PD-1 (n=76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.971830985915492%\"\u003e\n \u003cp\u003ePA-PD-1 (n=46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.028169014084508%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.380281690140846%\"\u003e\n \u003cp\u003enon-PA-PD-1 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.971830985915492%\"\u003e\n \u003cp\u003ePA-PD-1 (n=54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.028169014084508%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.372202591283862%\"\u003e\n \u003cp\u003eNumber of recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.071849234393405%\"\u003e\n \u003cp\u003e28 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\n \u003cp\u003e12 (54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\" colspan=\"2\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.189634864546525%\"\u003e\n \u003cp\u003e49 (64.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\n \u003cp\u003e24 (52.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\" colspan=\"2\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.189634864546525%\"\u003e\n \u003cp\u003e49 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\n \u003cp\u003e26 (48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.372202591283862%\"\u003e\n \u003cp\u003ePatterns of recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.071849234393405%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\" colspan=\"2\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.189634864546525%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\" colspan=\"2\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.189634864546525%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.372202591283862%\"\u003e\n \u003cp\u003eLocal recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.071849234393405%\"\u003e\n \u003cp\u003e20 (47.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\n \u003cp\u003e6 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.189634864546525%\"\u003e\n \u003cp\u003e29 (38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\n \u003cp\u003e15 (32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.189634864546525%\"\u003e\n \u003cp\u003e31 (38.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\n \u003cp\u003e16 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.372202591283862%\"\u003e\n \u003cp\u003eDistant recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.071849234393405%\"\u003e\n \u003cp\u003e8 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\n \u003cp\u003e6 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.189634864546525%\"\u003e\n \u003cp\u003e20 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\n \u003cp\u003e9 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.189634864546525%\"\u003e\n \u003cp\u003e18 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.011778563015312%\"\u003e\n \u003cp\u003e10 (18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.713780918727915%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.41086186540732%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.097992916174734%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.035419126328216%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.548996458087367%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.062573789846517%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.216056670602125%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.035419126328216%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.548996458087367%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.062573789846517%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.216056670602125%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.035419126328216%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.7296340023612755%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBiomarkers for predicting survival benefit of adjuvant anti-PD-1 therapy in HCC patients with high risks of recurrence are scare and lack of clinical evidence currently. We investigated the role of seven MRI features in predicting response to adjuvant anti-PD-1 therapy in patients with HCC. Results showed that absence of hypointense halos (HR\u0026thinsp;=\u0026thinsp;1.635; 95% CI\u0026thinsp;=\u0026thinsp;1.066\u0026ndash;2.510; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), and irregular rim-like hyper enhancement (HR\u0026thinsp;=\u0026thinsp;0.566; 95% CI\u0026thinsp;=\u0026thinsp;0.379\u0026ndash;0.845; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) were independent predictors for RFS. And patients with absence of hypointense halos or irregular rim-like hyper enhancement had significantly reduced recurrent rates after receiving PA-PD-1 compared with those who did not (47.4% versus 78.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026; 25.8% versus 60.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, respectively). Furthermore, PA-PD-1 provided improved RFS than non-PA-PD-1 in patients with absence of hypointense halos, irregular rim-like hyper enhancement, nonsmooth tumor margins, presence of internal arterials, peritumoral enhancement, peritumoral hypointensity, and absence of satellite nodules, which might potentially act as predictive and response biomarkers of outcome in terms of RFS in patients treated with adjuvant anti-PD-1 therapy.\u003c/p\u003e \u003cp\u003ePostoperative adjuvant treatment of anti-PD-1 inhibitors, such as atezolizumab or sintilimab, has recently been found to be associated with significantly improved RFS compared with active surveillance in HCC patients with high risk for recurrence after curative-intent resection or ablation\u003csup\u003e9,10\u003c/sup\u003e, which was consistent with the results of the previous study and the current study\u003csup\u003e6\u003c/sup\u003e. However, the response rates in overall patients remain unsatisfactory. Although several biomarkers, including PD-L1 expression, density of tumor infiltrating lymphocytes, tumor mutational burden, mismatch-repair deficiency, and some specific lymphocyte subtypes have been noticed to be associated with treatment effect of anti-PD-1 therapy \u003csup\u003e25\u0026ndash;27\u003c/sup\u003e, their predictive efficacies are controversial and inconsistent. Robust predictors of response to PA-PD-1 are still urgently needed. Nowadays, a growing body of research supports the potential of CT or MRI feature-based method to assess the biological characteristics of HCC, and empower novel prognostic and predictive approaches in patient-tailored treatment selection\u003csup\u003e19,28,29\u003c/sup\u003e. CT or MRI mage allows for visualization and assessment of the whole tumor, which can capture the entire spectrum of disease within a patient and may allow for better assessment of any heterogeneity that exists within or between tumors. To the best of our knowledge, this is the first study to explore the efficacy of MRI features in predicting response to PA-PD-1 in HCC patients, which might be potential predictive biomarkers enabling the accurate selection of patients who are most likely to derive benefit from postoperative anti-PD-1 therapy.\u003c/p\u003e \u003cp\u003eThe presence of fibrous capsule is one of the characteristic findings of HCC, which was displayed as hypointense halos in MRI or hypointense halos in CT images. It is histologically composed of an inner layer rich in pure, fibrous tissue and an outer layer containing portal venules and newly formed bile ducts, which can cut off the dissemination of HCC\u003csup\u003e30\u003c/sup\u003e. Once the tumor cells invade into and break through the portal venules of the capsule, imaging signs of the incomplete capsule and infiltrative border appear\u003csup\u003e31\u003c/sup\u003e. Previous studies have demonstrated some \u0026ldquo;worrisome\u0026rdquo; radiologic features, including absence of hypointense/hypointense halos, were confirmed as reliable predictors for MVI and poor prognosis\u003csup\u003e32\u003c/sup\u003e. Xu et al. developed a Radiographic-Radiomic model based on 12 imaging features including hypointense halos, and announced good performance for predicting MVI and clinical outcomes\u003csup\u003e33\u003c/sup\u003e. Segal et al. found that absence of hypointense halos combined with presence of internal arteries were associated with a 91-gene venous invasion profile representing angiogenesis and cellular proliferation\u003csup\u003e19\u003c/sup\u003e. Furthermore, an increasing number of studies recently demonstrated that radiomics based on imaging biomarkers can be used to predict immunotherapy response in various kinds of cancers, especially lung cancer\u003csup\u003e34,35\u003c/sup\u003e. Our analysis revealed that absence of hypointense halos was an independent predictor for RFS. HCC patients with absence of hypointense halos had significantly longer RFS in PA-PD-1 group than those in non-PA-PD-1 group. Therefore, it is reasonable to take absence of hypointense halos into consideration when trying to predict survival benefit from PA-PD-1 in high-risk recurrent HCC patients.\u003c/p\u003e \u003cp\u003eOf note, there are several limitations for the current study. First, it is a retrospective study from a single institution with unavoidable selection bias, though we included patients consecutively and applied PSM to avoid certain bias. Moreover, the sample size of patients receiving PA-PD-1 was small (58 patients after PSM) and the follow-up period was short. Therefore, prospective and larger-sample sized research with longer follow-ups are warranted to verify our results.\u003c/p\u003e \u003cp\u003eIn conclusion, our study highlighted the value of MRI features such as absence of hypointense halos and irregular rim-like hyper enhancement as novel and promising predictors for recurrence and response to PA-PD-1 in HCC patients with high risks for recurrence after curative hepatectomy, which might help clinicians implement more appropriate and individualized treatments based on the MRI image characteristics of HCC. Further studies are needed to confirm our findings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003e This retrospectively enrolled and prospectively followed-up observational study was conducted from Janurary 2020 to June 2023, and centrally approved by the institutional review board. Informed consent was obtained from each patient. Inclusion criteria were outlined as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age 18\u0026ndash;75 years; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) received abdominal MRI evaluation within 2 weeks before liver resection; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) underwent R0 resection and histological confirmed as HCC; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) received abdominal CT or MRI evaluation to confirm no tumor relapse or residual within 1 month after surgery; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) presence of one or more high risk for relapse: maximum tumor size excceding 5 cm, multiple tumor nodules (\u0026gt;3 nodules), MVI, satellite nodules or AFP\u0026gt;400ng/mL; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Child-Pugh A or B7; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Eastern Co-operative Oncology Group score 0 or 1. Patients were excluded if they showed evidence of coexistence of other malignancies, macrovascular invasion or distant metastasis on the baseline imaging, or had previously received anti-cancer therapy for HCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMRI features\u003c/h2\u003e \u003cp\u003eMRI was performed by a 3.0-T MRI system (Achieva; Philips Healthcare, Best, the Netherlans). T1-weighted, T2-weighted, and diffusion-weighted imaging were included in the baseline MRI examination. Gadoxetic acid-enhanced imaging consisted of precontrast images, as well as arterial phase, portal phase, transitional phase, and hepatobiliary phase images, which were 2\u0026ndash;35 seconds, 60 seconds, 3 minutes, and 20 minutes after injection of contrast material, respectively. MRI features were indepently reviewed by two senior abdominal radiologists in consensus, both having more than five years of experience interpreting liver MRI images. Altogether, seven imaging features were extracted from tumors and peritumor regions on preoperative MRI, which were demonstrated previously as robust predictors for MVI or poor prognosis. These seven features included absence of hypointense halos, irregular rim-like enhancement in arterial phase, nonsmooth tumor margins, peritumoral arterial phase hyperenhancement, peritumoral hepatobiliary phase hypointensity, satellite nodule, and internal arteries, definitions of which were described in Supplementary Data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePostoperative adjuvant anti-PD-1 antibody treatment\u003c/h2\u003e \u003cp\u003eInculded patients were separated into two groups: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients received PA-PD-1 after hepatic resection; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) paitents did not receive PA-PD-1. The application of PA-PD-1 was decided based on the physician\u0026rsquo;s clinical knowledge and experience, as well as patient\u0026rsquo;s request. Patients in the PA-PD-1 group received intravenous PD-1 blockade therapy at an interal of 3 weeks after each regimen. PD-1 blockade treatment would be terminated if there was disease progression or unacceptable toxicity measured accoding to the CTCAE version 5.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up and definition of outcomes\u003c/h2\u003e \u003cp\u003ePostoperative surveillance visits were scheduled 1 month postoperatively to confirm disease-free status, every 2\u0026ndash;3 months for the first 2 years, and then every 6\u0026ndash;12 months thereafter for each patient in both groups. Data of physical examination, laboratory tests (including peripheral blood test, liver function, AFP, and abdominal radiological examinations (ultrasound, contrastenhanced CT, or MRI) from each follow-up visit were collected. Survival outcomes included RFS and OS. RFS was defined as the interval from the date of hepatectomy to the date of first disease recurrence, death, or last follow-up. OS was defined as the interval from the date of hepatectomy to the date of death or last follow-up. Follow-up data collection was terminated on March 31, 2024. Patients sufferring from recurrence of HCC underwent surgery, radiofrequency ablation, transarterial chemoembolization, radiotherapy or systemic treatment, according to tumor location, size, and number, as well as patient\u0026rsquo;s general condition and liver function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared through the Student\u0026rsquo;s t tests or the Mann\u0026ndash;Whitney U tests. Categorical variables, expressed as frequencies (percentages), were compared by performing through Pearson's Chi-square tests or Fisher\u0026rsquo;s exact tests. Cox regression analysis was used for univariable and multivariable analyses to find out the significant predictors for RFS and OS. To mitigate selection bias and minimize the potential influence of confounding factors, including imaging features that can affect prognosis, we employed a PSM analysis, wherein we matched patients in PA-PD-1 or non-PA-PD-1 group. Baseline variables demonstrating p-values of less than 0.2 in both groups were included in the PSM model for the calculation of propensity scores. The variable encompassed absence of hypointense halos. For the matching process, patients in the two groups were paired in a one-to-two ratio using logistic regression based on their propensity scores. Survival analysis was estimated by the Kaplan-Meier method, and differences between groups were assessed by logrank test. Subgroup analyses were performed through the Kaplan-Meier method, and the forest plot for subgroup analyses was described with estimated HRs and 95% CIs. For statistical analysis, the software of Medcal (version 20.100) was used. Rstudio, \u0026ldquo;survminer\u0026rdquo;, \u0026ldquo;survival\u0026rdquo;, and \u0026ldquo;forestplot\u0026rdquo; packages were used to analyze the data. A 2-tailed \u003cem\u003ep\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eAll data supporting the results reported in the article can be found in the manuscript and the supplementary data.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eWe declare that we have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eZ.P. and X.H.: Study concept and design; interpretation of data; writing of manuscript; critical review of the manuscript; study supervision; and funding acquisition. X.H. and J.Z.: Analysis and interpretation of data; critical review of the manuscript. J.Z., Y.S. and S.H.: Acquisition of data; critical review of the manuscript. All authors gave final approval of the completed version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 82072029 and No. 82303738), the National high level talents special support plan - \u0026ldquo;Ten thousand plan\u0026rdquo; - Young top - notch talent support program, the Natural Science Foundation of Guangdong Province, China (No. 2024A1515012207), the Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515111101) and Guangzhou Science and Technology Program Project (2024A04J4634).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVogel, A., Meyer, T., Sapisochin, G., Salem, R. \u0026amp; Saborowski, A. Hepatocellular carcinoma. 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Role of radiomics in predicting immunotherapy response. Journal of medical imaging and radiation oncology 66, 575\u0026ndash;591, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1754-9485.13426\u003c/span\u003e\u003cspan address=\"10.1111/1754-9485.13426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4593371/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4593371/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiomarkers for predicting survival benefit of postoperative adjuvant anti-PD-1 therapy (PA-PD-1) in hepatocellular carcinoma (HCC) are scare and lack of clinical evidence currently. This study aimed to identify the value of preoperative MRI features for predicting response to PA-PD-1 in HCC. Between 2020 and 2023, 58 patients with PA-PD-1 and 110 without PA-PD-1 were retrospectively included after propensity-score matching. Patients with PA-PD-1 had significantly longer recurrence-free survival (RFS) than those without PA-PD-1 (29.50 versus 10.97 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). Absence of hypointense halos and irregular rim-like hyper enhancement were identified as independent predictors for RFS. Subgroup analysis indicated that patients with absence of hypointense halos and irregular rim-like hyper enhancement achieved significantly longer RFS after PA-PD-1 compared with those without PA-PD-1. In conclusion, preoperative MRI features of absence of hypointense halos and irregular rim-like hyper enhancement were significantly associated with recurrence and potential predictors for response to PA-PD-1 in HCC.\u003c/p\u003e","manuscriptTitle":"Preoperative MRI features for predicting response to postoperative adjuvant anti-PD-1 therapy in hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 12:11:43","doi":"10.21203/rs.3.rs-4593371/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":"e5460e50-8fac-4555-ad81-b043a4355ee5","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34513671,"name":"Health sciences/Oncology/Cancer/Gastrointestinal cancer/Liver cancer"},{"id":34513672,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2024-09-17T05:35:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-11 12:11:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4593371","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4593371","identity":"rs-4593371","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}