Edited by: Yuming Jiang, Stanford University, United States
Reviewed by: Lejia Sun, Peking Union Medical College Hospital (CAMS), China; Jingjing Xie, University of California, Davis, United States
*Correspondence: Ying-Bin Liu,
†These authors have contributed equally to this work and share first authorship
This article was submitted to Gastrointestinal Cancers, a section of the journal Frontiers in Oncology
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
To investigate the prognostic significance of the systemic immune-inflammation index (SII) in patients after radical cholecystectomy for gallbladder cancer (GBC) using overall survival (OS) as the primary outcome measure.
Based on data from a multi-institutional registry of patients with GBC, significant prognostic factors after radical cholecystectomy were identified by multivariate Cox proportional hazards model. A novel staging system was established, visualized as a nomogram. The response to adjuvant chemotherapy was compared between patients in different subgroups according to the novel staging system.
Of the 1072 GBC patients enrolled, 691 was randomly selected in the discovery cohort and 381 in the validation cohort. SII>510 was found to be an independent predictor of OS (hazard ratio [HR] 1.90, 95% confidence interval [CI] 1.42-2.54). Carbohydrate antigen 199(CA19-9), tumor differentiation, T stage, N stage, margin status and SII were involved in the nomogram. The nomogram showed a superior prediction compared with models without SII (1-, 3-, 5-year integrated discrimination improvement (IDI):2.4%, 4.1%, 5.4%, P<0.001), and compared to TNM staging system (1-, 3-, 5-year integrated discrimination improvement (IDI):5.9%, 10.4%, 12.2%, P<0.001). The C-index of the nomogram in predicting OS was 0.735 (95% CI 0.683-0.766). The novel staging system based on the nomogram showed good discriminative ability for patients with T2 or T3 staging and with negative lymph nodes after R0 resection. Adjuvant chemotherapy offered significant survival benefits to these patients with poor prognosis.
SII was an independent predictor of OS in patients after radical cholecystectomy for GBC. The new staging system identified subgroups of patients with T2 or T3 GBC with negative lymph nodes who benefited from adjuvant chemotherapy.
Gallbladder cancer (GBC) is a highly malignant tumor that accounts for 80%-95% of biliary tract malignancies (
Nomogram, as a predictive statistical model for individual patients (
This study aimed to compare the prognostic value of several inflammatory indices, and to develop a nomogram by combining preoperative examinations and clinicopathological factors. Moreover, whether adjuvant therapy is necessary for patients after R0 radical cholecystectomy with negative regional lymph nodes is still controversial. The American Hepato-Pancreato-Biliary Association (AHPBA) consensus recommends adjuvant therapy for patients with stage II GBC or higher, but whether adjuvant chemotherapy or chemoradiation should be given is unknown (
The Chinese Research Group of Gallbladder Cancer (CRGGC) is a multi-institutional registry cohort that retrospectively collected medical records of GBC patients in China, with a standardized protocol detailed in (
To verify the response to adjuvant chemotherapy of different subgroups in the novel staging system, a different cohort of patients who underwent radical cholecystectomy between January 2008 and June 2019 at Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were retrospectively studied. The selection criteria for inclusion were patients: (1) with AJCC 8thT2 or T3 GBC; (2) with specific information of adjuvant chemotherapy. The exclusion criteria were patients with: (1) histologically confirmed positive lymph nodes; (2) chemotherapeutic agents beyond the NCCN 2021 V.2 guideline; (3) long-term oral administration of Traditional Chinese Medicine after surgery; (4) OS of less than 3 months.
Available pre-operative laboratory examination within a week before the data of surgery were collected. Results of pre-operative laboratory examination were identified from medical records, including: total bilirubin, CA19-9, absolute neutrophil, absolute lymphocyte, platelet, and absolute monocyte counts. The inflammatory indices were defined as neutrophil-to-lymphocyte ratio (NLR, absolute neutrophil count divided by absolute lymphocyte count), platelet-to-lymphocyte ratio (PLR, absolute platelet count divided by absolute lymphocyte count), lymphocyte-to-monocyte ratio (LMR, absolute lymphocyte count divided by absolute monocyte count), and SII (platelet count times NLR).
Overall survival (OS) was calculated from the date of surgery to the date of death or last follow-up, whichever came last. This study was censored on June 2020. Pathologic staging was done following the AJCC 8th Staging System. For each variable, we required duplicated entry by two trained professionals. If any discrepancies were found, a third specialist would be brought in for discussion and make a final decision.
Continuous variables were transformed into categorical variables based on routine cutoffs in clinical application. Parameters such as NLR and SII were grouped as high and low by optimal cut-off points using the cut p function (R package survMisc).
Continuous data were compared using the unpaired t test, and categorical data using the chi-square test or Fisher’s exact test. Ordinal categorical variables were compared by Wilcoxon rank sum test. OS was examined by the Kaplan-Meier method and compared using the log-rank test. The associations of bilirubin and CA19-9 with SII levels were estimated using the Spearman rank-correlation coefficient.
Univariable Cox proportional hazards models were applied to select covariates with a significance level <0.05 into the following multivariate model. Harrell’s concordance index (C-index) was calculated for each model. Patients with missing data for covariates of interest were excluded.
Multivariable Cox regression was applied to establish a prediction model, then visualized by a nomogram. The final prediction model was selected by the backward stepdown selection process with the Akaike information criterion (AIC). The predictive accuracy and discriminative ability were determined by C-index and calibration curve, and assessed by comparing the nomogram-predicted against observed survival on application of bootstrapping with 1000 resamples. The integrated discrimination improvement (IDI) and decision curve analysis (DCA) was used to evaluate the predictive performance of different models. The total point of each patient in the validation cohort was calculated according to the established nomogram, and then Cox regression was performed for this cohort using the total points as a variate. Each patient was then assigned a score based on the nomogram, and the cut-off points were calculated using the spline curve.
All tests were two-sided, and P values of less than 0.05 were considered statistically significant. All statistical analyses were performed using the software R version 3.6.1.
Of the 1072 patients included in this study, 691 patients (64.5%) were randomly assigned into the discovery cohort, and 381 (35.5%) in the validation cohort. The median OS for the discovery and validation cohorts were 40.4 months (95% confidence interval [CI], 32.0-51.4 months) and 41.4 months (95% CI, 31.9-53.5 months), respectively. The corresponding median follow-up times for the 2 cohorts were 53.8 (range 3 months to 18 years) and 52.9 months (range 3 months to 12.6 years). The baseline clinicopathological characteristics were summarized in
Clinicopathological Characteristics of the GBC Patients in the Discovery and Validation Cohorts.
Discovery cohort (N=691) | Validation cohort (N=381) | P value | |
---|---|---|---|
Age† | 62(57-69.5) | 63(57-70) | 0.48 |
Sex | 0.68 | ||
Male | 270 | 144 | |
Female | 421 | 237 | |
CA19-9 | 0.96 | ||
≤40 U/ml | 350 | 192 | |
>40 U/m | 221 | 122 | |
NA | 120 | 67 | |
Surgical approach | 0.78 | ||
RC | 405 | 240 | |
ERC | 38 | 23 | |
LC+RC | 127 | 67 | |
NA | 121 | 51 | |
Total bilirubin | 0.96 | ||
≤35 μmol/L | 553 | 311 | |
>35 μmol/L | 108 | 60 | |
NA | 30 | 10 | |
Margin status | 0.30 | ||
R0 | 65 | 27 | |
R1 | 567 | 334 | |
Rx | 59 | 20 | |
Pathological type | 0.97 | ||
ADC | 569 | 310 | |
ADSC | 35 | 13 | |
PADC | 27 | 16 | |
NEC | 10 | 7 | |
Other | 50 | 35 | |
Tumor differentiation | 0.96 | ||
Low | 145 | 76 | |
Low to medium | 118 | 68 | |
Medium | 256 | 130 | |
Medium to high | 40 | 23 | |
High | 62 | 34 | |
T stage‡ | 0.17 | ||
T1 | 91 | 52 | |
T2 | 74 | 55 | |
T3 | 526 | 274 | |
N stage‡ | 0.62 | ||
N0 | 304 | 164 | |
N1 | 159 | 85 | |
N2 | 41 | 22 | |
Nx | 187 | 110 | |
Microvascular invasion | 0.64 | ||
Yes | 68 | 31 | |
No | 550 | 308 | |
NA | 73 | 42 | |
Perineural invasion | 0.43 | ||
Yes | 118 | 54 | |
No | 519 | 294 | |
NA | 54 | 33 | |
Platelets | 0.02 | ||
≤300*10^9/L | 591 | 306 | |
>300*10^9/L | 100 | 75 | |
NLR | 0.14 | ||
≤2.3 | 331 | 165 | |
>2.3 | 360 | 216 | |
LMR | 0.22 | ||
≤10 | 400 | 206 | |
>10 | 291 | 175 | |
PLR | 0.56 | ||
≤144 | 370 | 197 | |
>144 | 321 | 184 | |
SII | 0.40 | ||
≤510 | 343 | 179 | |
>510 | 348 | 202 |
†Age is presented as the median (first quartile-third quartile).
‡T stage and N stage was classified according to the AJCC 8th edition staging system.
ADC, adenocarcinoma; ADSC, adenosquamous carcinoma;PADC, papillary adenocarcinoma; NEC, neuroendocrine carcinoma; LC, laparoscopic cholecystectomy; RC, radical cholecystectomy; ERC, extended radical cholecystectomy; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; SII, systemic immune-inflammation index.
The optimal cut-offs were 510 for SII, 2.3 for NLR, 144 for PLR, and 10 for LMR. The following predictors were associated with worse OS: age, CA19-9>40 U/ml, total bilirubin>35 µmol/L, more advanced surgical approach, R1 resection margin status, pathological type, poor tumor differentiation, microvascular invasion, perineural invasion, advanced T staging, advanced N staging, PLR>144, NLR>2.3, LMR>10, and SII>510 (
Cox Proportional Hazards Regression Models for Predictor Selection and Model Building.
Univariate Analysis | Multivariate Analysis | Selected Factors for Building the Model | ||||
---|---|---|---|---|---|---|
HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |
Age | ||||||
>60 versus ≤60 years | 1.32 (1.06-1.64) | 0.01 | 1.31 (0.92-1.86) | 0.13 | NA | |
Sex | ||||||
Female versus Male | 0.95 (0.77-1.17) | 0.63 | NA | NA | ||
CA19-9 | ||||||
>40 versus ≤40 U/ml | 2.19 (1.74-2.76) | <0.001 | 1. 59 (1.08-2.34) | 0.01 | 1.67 (1.25-2.24) | <0.001 |
Total bilirubin | ||||||
>35 versus ≤35 µmol/L | 2.00 (1.55-2.58) | <0.001 | 1.32 (0.83-2.10) | 0.22 | NA | |
Surgical approach | ||||||
ERC versus RC | 1.76 (1.20-2.61) | 0.003 | 1.69 (0.92-3.10) | 0.08 | ||
LC+RC versus RC | 0.97 (0.74-1.28) | 0.87 | 1.16 (0.70-1.91) | 0.56 | ||
Margin status | ||||||
R1 versus R0 | 2.63 (1.94-3.57) | <0.001 | 2.10 (1.19-3.68) | 0.009 | 1.55 (1.01-2.39) | 0.04 |
Pathological type | ||||||
ADSC versus ADC | 1.23 (0.77-1.96) | 0.37 | 1.05 (0.51-2.14) | 0.88 | NA | |
PADC versus ADC | 0.24 (0.10-0.59) | 0.001 | 0.45 (0.05-3.57) | 0.45 | ||
NEC versus ADC | 1.60 (0.75-3.39) | 0.21 | 4.61 (0.84-25.21) | 0.07 | ||
Else versus ADC | 1.14(0.78-1.67) | 0.48 | 1.20 (0.54-2.66) | 0.64 | ||
Tumor differentiation | ||||||
Low to medium versus Low | 0.84 (0.62-1.15) | 0.28 | 0.59 (0.35-1.01) | 0.054 | 0.63 (0.42-0.96) | 0.03 |
Medium versus Low | 0.61 (0.47-0.80) | <0.001 | 0.53 (0.33-0.82) | 0.005 | 0.53 (0.37-0.75) | <0.001 |
Medium to high versus Low | 0.41 (0.24-0.70) | 0.001 | 0.43 (0.18-1.04) | 0.06 | 0.42 (0.21-0.84) | 0.01 |
High versus Low | 0.25 (0.15-0.42) | <0.001 | 0.38 (0.17-0.85) | 0.01 | 0.40 (0.19-0.83) | 0.01 |
T stage | ||||||
T2 versus T1 | 2.08 (1.25-3.47) | 0.004 | 1.71 (0.70-4.12) | 0.23 | 1.39 (0.66-2.92) | 0.38 |
T3 versus T1 | 3.10 (2.05-4.67) | <0.001 | 2.71 (1.18-6.24) | 0. 01 | 2.58 (1.31-5.10) | 0.006 |
N stage | ||||||
N1 versus N0 | 1.80 (1.38-2.35) | <0.001 | 1.14 (0.72-1.80) | 0.56 | 1.11 (0.77-1.59) | 0.56 |
N2 versus N0 | 3.34 (2.26-4.94) | <0.001 | 2.84 (1.50-5.39) | 0.001 | 2.43 (1.42-4.17) | 0.001 |
Nx† versus N0 | 1.53 (1.18-1.98) | 0.001 | 1.42 (0.88-2.28) | 0.14 | 1.40 (0.97-2.01) | 0.06 |
Microvascular invasion | ||||||
Yes versus No | 2.12 (1.55-2.90) | <0.001 | 1.73 (0.97-3.08) | 0.06 | ||
Perineural invasion | ||||||
Yes vs No | 1.76 (1.35-2.28) | <0.001 | 1.13 (0.69-1.85) | 0.61 | ||
Platelets | ||||||
>300 versus ≤300×109/L | 1.19 (0.89-1.60) | 0.31 | NA | |||
LMR | ||||||
>10 versus ≤10 | 1.39 (1.13-1.71) | 0.001 | NA | |||
NLR | ||||||
>2.3 versus ≤2.3 | 1.97 (1.59-2.44) | <0.001 | NA | |||
PLR | ||||||
>144 versus ≤144 | 1.58 (1.28-1.94) | <0.001 | NA | |||
SII | ||||||
>510 versus ≤510 | 1.99 (1.61-2.45) | <0.001 | NA | 1.90(1.42-2.54) | <0.001 |
†Nx means that the N stage could not be judged from the pathological record or the surgeon did not obtain a sufficient number of lymph nodes.
HR, hazard ratio; CI, confidence interval; LC, laparoscopic cholecystectomy; RC, radical cholecystectomy; ERC, extended radical cholecystectomy; NA, not applicable.
As NLR, PLR, LMR and SII were predictors of OS on univariate analysis, multivariate models were compared to find out which index to include for further multivariate analysis (
Patients’ clinicopathologic characteristics categorized by high and low SII are summarized in
The associations between SII and OS were found in the discovery (
Forest plot of the association between systemic immune-inflammation index (SII) and overall survival (OS), according to different subgroups.
Of the 661 patients in the discovery cohort with available data on preoperative bilirubin levels, 108 presented with obstructive jaundice. Patients with SII<510 had significantly lower mean bilirubin levels than those with SII>510 (30.1μmol/L vs 58.5μmol/L, P<0.001). SII was correlated with pre-operative bilirubin levels (rs =0.19, P<0.001). The associations between OS and SII in the settings of low (<35 µmol/L) and high (>35 µmol/L) bilirubin levels are shown in
CA19-9 levels were found having similar association with the prognostic ability of SII. Patients with SII<510 had significantly lower mean CA19-9 levels than those with SII>510 (145.6 U/ml versus 238.1 U/ml, P=0.04). SII was correlated with CA19-9 levels (rs =0.21, P<0.001). The prognostic ability of SII was then examined in different CA19-9 groups (≤40 U/ml versus >40 U/ml). We observed that the prognostic role of SII did not persist at high CA19-9 levels in the discovery (
The prognostic ability of SII was also examined in patients with or without microvascular invasion in the discovery (
As we observed an interaction between CA19-9 and SII (p for interaction <0.05), the interaction term was included into multivariable Cox analysis. The HR of SII*CA19-9 was 0.88 (95% CI 0.50-1.55), with a P value=0.67 (
Nomogram for predicting overall survival in GBC patients.
The calibration curve plots showed agreement between nomogram predictions and actual observations in the discovery cohort, and acceptable consistency in the validation cohort (
Based on the nomogram, an individual predictive score of OS for each patient can be estimated. When patients with scores of 0-90, 90-200, 200-310, and ≥310 were classified into stages I (n=70), II (n=274), II (n=280) and IV (n=61), respectively (
Kaplan-Meier curves of overall survival for patients classified by TNM staging system and the novel staging system.
The patients with T2N0 and T3N0 GBC who underwent R0 radical cholecystectomy in the total cohort were stratified using the nomogram to study whether there were subgroups of patients with significantly worse long-term survival. For patients with T2N0 GBC after radical cholecystectomy, the nomogram stratified these patients into separate groups with distinguished prognosis, with 5-year OS rates for stages I, II, Ш being 74%, 36%, and 33% (P=0.018,
Till now, there is no definitive conclusion whether postoperative adjuvant therapy is necessary for T2N0 or T3N0 patients after radical cholecystectomy. As the novel staging system resulted in good stratification of these patients, this system was hypothesized to be able to identify patients who could benefit from adjuvant chemotherapy. To verify this hypothesis, another cohort of patients (
Kaplan-Meier curves of overall survival for patients with surgery only versus surgery and chemotherapy.
Mounting evidences have accumulated to support that inflammatory biomarker plays an important role in prognostication of cancers. Prior studies focused on NLR, PLR and LMR showed these inflammatory biomarkers to be of prognostic value in GBC (
The relation between SII and bilirubin levels has been studied in pancreatic cancer (
After radical cholecystectomy for GBC, long-term survival in an individual patient stratified by the TNM system can vary tremendously and is difficult to predict. This study sought to develop a nomogram by combining various factors in predicting long-term survival outcomes after radical cholecystectomy for GBC. This nomogram performed well in predicting survival, as supported by the C-indexes of 0.735 and 0.686 for the discovery and validation cohorts, respectively.
Researchers have been actively finding effective adjuvant therapies for GBC which has a high recurrence rate after radical cholecystectomy (
Prior studies on nomograms for GBC have identified different significant factors in predicting long-term survival outcomes (
Incorporating prognostic biomarkers in clinical practice is challenging. SII cannot be used to select patients to undergo radical cholecystectomy, and surgical resection is still the only curative treatment for GBC patients. This study did not compare the differences in SII between surgical and non-surgical patients, and whether to perform operation was dependent on the TNM staging (
Surveillance, Epidemiology, and End Results (SEER) has been one of the most commonly used cancer registry database which included 13373 gallbladder cancer patients. However, preoperative blood tests of patients are not available. The Chinese Research Group of Gallbladder Cancer (CRGGC) Project is a national multicenter retrospective tumor registry which has collected from 2000 to 2019 data of more than 9496 patients, and the number is still increasing. This project contains preoperative laboratory tests in calculating inflammatory indices.
There are several limitations of this study. First, it is a retrospective study with its inherent defects, including limited availability of laboratory data at various preoperative time points. Second, some pathological features, including vascular invasion and nerve invasion, are not available. Third, as there are no universally accepted standards on the cut-off points used in converting continuous variables into category variables, this study calculated the cut-off point of SII based on statistical methods, and it was different from the cut-off points used for other cancers (
In conclusion, this study is a large study on the value of SII in patients with GBC after radical cholecystectomy. A nomogram was constructed by combining both preoperative and pathological features. This nomogram showed good accuracy in predicting long-term survival outcomes of patients with GBC after radical cholecystectomy; and the new staging system could be used to identify groups of lymph node negative patients with T2 and T3 GBC with unfavorable prognosis who could benefit from adjuvant therapy. Further prospective studies are needed to confirm the findings of this study.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethical approval was obtained from the Ethics Committee of Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine.
LL: Data curation, formal analysis, investigation, methodology, project administration, software, validation, visualization, writing – original draft, and writing – review and editing. TR: Methodology, project administration, software, validation, visualization, writing – original draft, and writing – review and editing. KL: Methodology, project administration, software, validation, visualization, and writing – original draft, writing – review and editing. M-LL: Data curation, formal analysis, validation, visualization, and writing – review and editing. YY: Methodology, software, visualization, and writing – review and editing. H-FL: Funding acquisition, resources, supervision, and writing – review and editing. X-CL: Methodology, software, visualization, and writing – review and editing. R-FB: Methodology, resources, and supervision. Y-JS: Resources and supervision. HW: Methodology, software, and visualization. WG: Project administration, resources, validation, and writing – review and editing. WL: Project administration, resources, validation, and writing – review and editing. X-SW: Conceptualization, funding acquisition, investigation, methodology, supervision, validation, and writing – review and editing. Y-BL: Conceptualization, funding acquisition, project administration, resources, supervision, and writing – review and editing. All authors contributed to the article and approved the submitted version.
This study was supported by Shanghai Key Laboratory of Biliary Tract Disease Research Foundation(17DZ2260200), the National Natural Science Foundation of China (No. 81502433, 81773043, 91440203, 81702315), Clinical Research Program of Xinhua Hospital (19XHCR3D), Multi-center Clinical Research Project of Shanghai Jiaotong University School of Medicine(DLY201507), the Project of Excellent Young Scholars from Shanghai Municipal Health and Family Planning Commission (No. 2018YQ10), the Talent Development Fund from Shanghai Municipal Human Resources and Social Security Bureau (No. 2018048), and the Project of Experimental Animal Research from Science and Technology Commission Shanghai Municipality (No. 19140902700) and Shanghai Sailing Program (No.21YF1428700), Shanghai national science foundation (20ZR1435200) and Shanghai joint research projects on emerging frontier technologies (SHDC12018107).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank our collaborators for their contributions to the CRGGC study, as follows: Professor Houbao Liu at Zhongshan Hospital, Professor Chong Jin at Taizhou Municipal Hospital, Professor Xiaoyong Li at The Fifth Affiliated Hospital of Zhengzhou University, Professor Xiaoliang Chen at Jiangxi Provincial People’s Hospital, Professor Xueli Zhang at Shanghai Fengxian District Central Hospital, Professor Jianfeng Gu at Changshu No.1 People’s Hospital, Professor Yuzhen Xu at Xuzhou Central Hospital, Professor Zhewei Fei at Xinhua (Chongming) Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Professor Yudong Qiu at Nanjing Drum Tower Hospital, Professor Xuewen Zhang at The Second Hospital of Jilin University, Professor Hongyu Cai at Nantong Tumor Hospital, Professor Yi Wang at The First People’s Hospital of Taicang, Professor Zaiyang Zhang at Shaoxing Second Hospital, Professor Kunhua Wang at The First Affiliated Hospital of Kunming Medical University, Professor Xiaoqing Jiang at Eastern Hepatobiliary Surgery Hospital, Professor Hong Cao at China-Japan Union Hospital of Jilin University, Professor Defei Hong at Sir Run Run Shaw Hospital, Professor Yongjun Chen at Ruijin Hospital, Professor Bei Sun at The First Affiliated Hospital of Harbin Medical University, Professor Chunfu Zhu at Changzhou No.2 People’s Hospital, Professor Qiyun Li at Jiangxi Cancer Hospital, Professor Jingyu Cao at The Affiliated Hospital of Qingdao University, Professor Chaoliu Dai at Shengjing Hospital of China Medical University, Professor Yunfu Cui at The Second Affiliated Hospital of Harbin Medical University, Professor Jihui Hao at Tianjin Medical University Cancer Hospital, Professor Bing Li at Harbin Medical University Cancer Hospital, Professor Linhui Zheng at The First Affiliated Hospital of Nanchang University, Professor Yeben Qian at The First Affiliated Hospital of Anhui Medical University, Dr Bo Yang at The First Affiliated Hospital of Wenzhou Medical University, Professor Chang Liu at The First Affiliated Hospital of Xi’an Jiaotong University, Professor Jun Liu at Shangdong Provincial Hospital, Professor Changjun Liu at People’s Hospital of Hunan Province, Professor Xueyi Dang at Shanxi Provincial Cancer Hospital and Professor Lin Wang at Xijing Hospital.
The Supplementary Material for this article can be found online at: