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| 1 | Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model显示文摘Objective: To develop and validate a radiomics prediction model for individualized prediction of perineural invasion(PNI) in colorectal cancer(CRC).Methods: After computed tomography(CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort(346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen(CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation(separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results: The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index(c-index): 0.817; 95% confidence interval(95% CI): 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination(c-index: 0.803; 95% CI: 0.794–0.812).Conclusions: Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment. | Yanqi Huang Lan He Di Dong Caiyun Yang Cuishan Liang Xin Chen Zelan Ma Xiaomei Huang Su Yao Changhong Liang Jie Tian Zaiyi Liu | 2018 | Chinese Journal of Cancer Research2018，30，1： | 23 |
| 2 | Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study显示文摘Objective: The standard treatment for patients with locally advanced gastric cancer has relied on perioperative radio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of radiomics for pre-treatment computed tomography(CT) in the prediction of the pathological response of locally advanced gastric cancer with preoperative chemotherapy.Methods: Thirty consecutive patients with CT-staged II/III gastric cancer receiving neoadjuvant chemotherapy were enrolled in this study between December 2014 and March 2017. All patients underwent upper abdominal CT during the unenhanced, late arterial phase(AP) and portal venous phase(PP) before the administration of neoadjuvant chemotherapy. In total, 19,985 radiomics features were extracted in the AP and PP for each patient.Four methods were adopted during feature selection and eight methods were used in the process of building the classifier model. Thirty-two combinations of feature selection and classification methods were examined. Receiver operating characteristic(ROC) curves were used to evaluate the capability of each combination of feature selection and classification method to predict a non-good response(non-GR) based on tumor regression grade(TRG).Results: The mean area under the curve(AUC) ranged from 0.194 to 0.621 in the AP, and from 0.455 to 0.722 in the PP, according to different combinations of feature selection and the classification methods. There was only one cross-combination machine-learning method indicating a relatively higher AUC(>0.600) in the AP, while 12 cross-combination machine-learning methods presented relatively higher AUCs(all >0.600) in the PP. The feature selection method adopted by a filter based on linear discriminant analysis + classifier of random forest achieved a significantly prognostic performance in the PP(AUC, 0.722±0.108; accuracy, 0.793; sensitivity, 0.636; specificity,0.889; Z=2.039; P=0.041).Conclusions: It is possible to predict non-GR after neoadjuvant chemotherapy in locally advanced gastric cancers based on the radiomics of CT. | Zhenhui Li Dafu Zhang Youguo Dai Jian Dong Lin Wu Yajun Li Zixuan Cheng Yingying Ding Zaiyi Liu | 2018 | Chinese Journal of Cancer Research2018，30，4： | 18 |
| 3 | Radiomics approach for preoperative identification of stages Ⅰ-Ⅱand Ⅲ-Ⅳ of esophageal cancer显示文摘Objective: To predict preoperative staging using a radiomics approach based on computed tomography(CT)images of patients with esophageal squamous cell carcinoma(ESCC).Methods: This retrospective study included 154 patients(primary cohort: n=114; validation cohort: n=40) with pathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. High throughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomics signature was constructed using the least absolute shrinkage and selection operator(Lasso). Associations between radiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomics approach and tumor volume for discriminating between stages Ⅰ-Ⅱ and Ⅲ-Ⅳ was evaluated and compared using the receiver operating characteristics(ROC) curves and net reclassification improvement(NRI).Results: A total of 9,790 radiomics features were extracted. Ten features were selected to build a radiomics signature after feature dimension reduction. The radiomics signature was significantly associated with ESCC staging(P<0.001), and yielded a better performance for discrimination of early and advanced stage ESCC compared to tumor volume in both the primary [area under the receiver operating characteristic curve(AUC): 0.795 vs. 0.694,P=0.003; NRI=0.424)] and validation cohorts(AUC: 0.762 vs. 0.624, P=0.035; NRI=0.834).Conclusions: The quantitative approach has the potential to identify stage Ⅰ-Ⅱ and Ⅲ-Ⅳ ESCC before treatment. | Lei WU Cong Wang Xianzheng Tan Zixuan Cheng Ke Zhao Lifen Yan Yanli Liang Zaiyi Liu Changhong Liang | 2018 | Chinese Journal of Cancer Research2018，30，4： | 14 |
| 4 | Radiomics-based predictive risk score: A scoring system for preoperatively predicting risk of lymph node metastasis in patients with resectable non-small cell lung cancer显示文摘Objective: To develop and validate a radiomics-based predictive risk score(RPRS) for preoperative prediction of lymph node(LN) metastasis in patients with resectable non-small cell lung cancer(NSCLC).Methods: We retrospectively analyzed 717 who underwent surgical resection for primary NSCLC with systematic mediastinal lymphadenectomy from October 2007 to July 2016. By using the method of radiomics analysis, 591 computed tomography(CT)-based radiomics features were extracted, and the radiomics-based classifier was constructed. Then, using multivariable logistic regression analysis, a weighted score RPRS was derived to identify LN metastasis. Apparent prediction performance of RPRS was assessed with its calibration,discrimination, and clinical usefulness.Results: The radiomics-based classifier was constructed, which consisted of 13 selected radiomics features.Multivariate models demonstrated that radiomics-based classifier, age group, tumor diameter, tumor location, and CT-based LN status were independent predictors. When we assigned the corresponding score to each variable,patients with RPRSs of 0-3, 4-5, 6, 7-8, and 9 had distinctly very low(0%-20%), low(21%-40%), intermediate(41%-60%), high(61%-80%), and very high(81%-100%) risks of LN involvement, respectively. The developed RPRS showed good discrimination and satisfactory calibration (C-index: 0.785, 95% confidence interval(95% CI):0.780-0.790)Additionally, RPRS outperformed the clinicopathologic-based characteristics model with net reclassification index(NRI) of 0.711(95% CI: 0.555-0.867).Conclusions: The novel clinical scoring system developed as RPRS can serve as an easy-to-use tool to facilitate the preoperatively individualized prediction of LN metastasis in patients with resectable NSCLC. This stratification of patients according to their LN status may provide a basis for individualized treatment. | Lan He Yanqi Huang Lixu Yan Junhui Zheng Changhong Liang Zaiyi Liu | 2019 | Chinese Journal of Cancer Research2019，31，4： | 7 |
| 5 | Evaluation of human epidermal growth factor receptor 2 status of breast cancer using preoperative multidetector computed tomography with deep learning and handcrafted radiomics features显示文摘Objective:To evaluate the human epidermal growth factor receptor 2(HER2)status in patients with breast cancer using multidetector computed tomography(MDCT)-based handcrafted and deep radiomics features.Methods:This retrospective study enrolled 339 female patients(primary cohort,n=177;validation cohort,n=162)with pathologically confirmed invasive breast cancer.Handcrafted and deep radiomics features were extracted from the MDCT images during the arterial phase.After the feature selection procedures,handcrafted and deep radiomics signatures and the combined model were built using multivariate logistic regression analysis.Performance was assessed by measures of discrimination,calibration,and clinical usefulness in the primary cohort and validated in the validation cohort.Results:The handcrafted radiomics signature had a discriminative ability with a C-index of 0.739[95%confidence interval(95%CI):0.661-0.818]in the primary cohort and 0.695(95%CI:0.609-0.781)in the validation cohort.The deep radiomics signature also had a discriminative ability with a C-index of 0.760(95%CI:0.690-0.831)in the primary cohort and 0.777(95%CI:0.696-0.857)in the validation cohort.The combined model,which incorporated both the handcrafted and deep radiomics signatures,showed good discriminative ability with a C-index of 0.829(95%CI:0.767-0.890)in the primary cohort and 0.809(95%CI:0.740-0.879)in the validation cohort.Conclusions:Handcrafted and deep radiomics features from MDCT images were associated with HER2 status in patients with breast cancer.Thus,these features could provide complementary aid for the radiological evaluation of HER2 status in breast cancer. | Xiaojun Yang Lei Wu Ke Zhao Weitao Ye Weixiao Liu Yingyi Wang Jiao Li Hanxiao Li Xiaomei Huang Wen Zhang Yanqi Huang Xin Chen Su Yao Zaiyi Liu Changhong Liang | 2020 | Chinese Journal of Cancer Research2020，32，2： | 4 |
| 6 | Integrating pathomics with radiomics and genomics for cancer prognosis:A brief review显示文摘In the last decade,the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering'sub-visual'prognostic image cues from the histopathological image.While we are getting more knowledge and experience in digital pathology,the emerging goal is to integrate other-omics or modalities that will contribute for building a better prognostic assay.In this paper,we provide a brief review of representative works that focus on integrating pathomics with radiomics and genomics for cancer prognosis.It includes:correlation of pathomics and genomics;fusion of pathomics and genomics;fusion of pathomics and radiomics.We also present challenges,potential opportunities,and avenues for future work. | Cheng Lu Rakesh Shiradkar Zaiyi Liu | 2021 | Chinese Journal of Cancer Research2021，33，5： | 4 |
| 7 | Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma显示文摘Objectives:To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma(GA).Methods:This retrospective study enrolled 592 patients with clinicopathologically confirmed GA(low-grade:n=154;high-grade:n=438)from January 2008 to March 2018 who were divided into training(n=450)and validation(n=142)sets according to the time of computed tomography(CT)examination.Radiomic features were extracted from the portal venous phase CT images.The Mann-Whitney U test and the least absolute shrinkage and selection operator(LASSO)regression model were used for feature selection,data dimension reduction and radiomics signature construction.Multivariable logistic regression analysis was applied to develop the prediction model.The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram.The performance of the nomogram was assessed with respect to its calibration and discrimination.Results:A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA(P<0.001 for both training and validation sets).A nomogram including the radiomics signature and tumor location as predictors was developed.The model showed both good calibration and good discrimination,in which C-index in the training set,0.752[95%confidence interval(95%CI):0.701-0.803];C-index in the validation set,0.793(95%CI:0.711-0.874).Conclusions:This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures,which can be useful in facilitating preoperative individualized prediction of histologic grade of GA. | Jia Huang Huasheng Yao Yexing Li Mengyi Dong Chu Han Lan He Xiaomei Huang Ting Xia Zongjian Yi Huihui Wang Yuan Zhang Jian He Changhong Liang Zaiyi Liu | 2021 | Chinese Journal of Cancer Research2021，33，1： | 3 |
| 8 | A radiomics prognostic scoring system for predicting progression-free survival in patients with stageⅣnon-small cell lung cancer treated with platinum-based chemotherapy显示文摘Objective:To develop and validate a radiomics prognostic scoring system(RPSS)for prediction of progressionfree survival(PFS)in patients with stageⅣnon-small cell lung cancer(NSCLC)treated with platinum-based chemotherapy.Methods:In this retrospective study,four independent cohorts of stageⅣNSCLC patients treated with platinum-based chemotherapy were included for model construction and validation(Discovery:n=159;Internal validation:n=156;External validation:n=81,Mutation validation:n=64).First,a total of 1,182 three-dimensional radiomics features were extracted from pre-treatment computed tomography(CT)images of each patient.Then,a radiomics signature was constructed using the least absolute shrinkage and selection operator method(LASSO)penalized Cox regression analysis.Finally,an individualized prognostic scoring system incorporating radiomics signature and clinicopathologic risk factors was proposed for PFS prediction.Results:The established radiomics signature consisting of 16 features showed good discrimination for classifying patients with high-risk and low-risk progression to chemotherapy in all cohorts(All P<0.05).On the multivariable analysis,independent factors for PFS were radiomics signature,performance status(PS),and N stage,which were all selected into construction of RPSS.The RPSS showed significant prognostic performance for predicting PFS in discovery[C-index:0.772,95%confidence interval(95%CI):0.765-0.779],internal validation(C-index:0.738,95%CI:0.730-0.746),external validation(C-index:0.750,95%CI:0.734-0.765),and mutation validation(Cindex:0.739,95%CI:0.720-0.758).Decision curve analysis revealed that RPSS significantly outperformed the clinicopathologic-based model in terms of clinical usefulness(All P<0.05).Conclusions:This study established a radiomics prognostic scoring system as RPSS that can be conveniently used to achieve individualized prediction of PFS probability for stageⅣNSCLC patients treated with platinumbased chemotherapy,which holds promise for guiding personalized pre-therapy of stageⅣNSCLC. | Lan He Zhenhui Li Xin Chen Yanqi Huang Lixu Yan Changhong Liang Zaiyi Liu | 2021 | Chinese Journal of Cancer Research2021，33，5： | 3 |
| 9 | Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage Ⅰ-Ⅲ colorectal cancer显示文摘Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification.Methods: We retrospectively identified 161 consecutive patients with stage Ⅰ-Ⅲ CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction.Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio(HR)=6.670;95% confidence interval(95% CI): 3.433-12.956;P<0.001), external validation cohort 1(HR=2.866;95% CI: 1.646-4.990;P<0.001) and external validation cohort 2(HR=3.342;95% CI: 1.289-8.663;P=0.002).Incorporating the EcoRad signature into the prediction model presented a higher prediction ability(P<0.001) with respect to the C-index(0.813, 95% CI: 0.804-0.822 in the training cohort;0.758, 95% CI: 0.751-0.765 in the external validation cohort 1;and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis(TNM) system, as well as a better calibration,improved reclassification and superior clinical usefulness.Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage Ⅰ-Ⅲ CRC patients. | Yanqi Huang Lan He Zhenhui Li Xin Chen Chu Han Ke Zhao Yuan Zhang Jinrong Qu Yun Mao Changhong Liang Zaiyi Liu | 2022 | Chinese Journal of Cancer Research2022，34，1： | 2 |
| 10 | Angiomyolipoma with Minimal Fat显示文摘 | Lifen Yan Zaiyi Liu Guangyi Wang Yanqi Huang Yubao Liu Yuanxin Yu Changhong Liang | 2015 | Academic Radiology2015，，： | 1 |
| 11 | Leptin promotes fatty acid oxidation and OXPHOS via the c-Myc/PGC-1 pathway in cancer cells显示文摘Alteration in cellular energy metabolism plays a critical role in the development and progression of cancer. Leptin is a hormone secreted by adipose tissue. Recent reports have shown that leptin can induce cancer cell proliferation and regulate cell energy metabolism, but the regulatory mechanism is still unclear. Here, we showed that leptin could promote cell proliferation and maintain high adenosine triphosphate levels in HCT116 and MCF-7 cells. The expression levels of carnitine palmitoyl transferase 1A (CPT1A), pyruvate dehydrogenase, succinate dehydrogenase subunit A and mitochondrial respiratory chain-associated proteins NADH dehydrogenase 1 (ND1), NADH:ubiquinone oxidoreductase subunit B8, and mitochondrial transcription factor A (TFAM) were distinctly increased in leptin-treated HCT116 and MCF-7 cells, while fatty acid synthase and lactate dehydrogenase expression were downregulated. Simultaneously, we found that c-Myc and peroxisome proliferator-activated receptor gamma co-activator 1 (PGC-1) protein expression levels were significantly increased. These results indicated that leptin boosted fatty acid β-oxidation and the tricarboxylic acid cycle, enhanced oxidative phosphorylation (OXPHOS) activity, and inhibited fatty acid synthesis and glycolysis in tumor cells. Gene transfection experiments revealed that leptin could induce the expression of c-Myc. Moreover, the expressions of PGC-1, CPT1A, and TFAM proteins were downregulated in HCT116 cells with low expression of c-Myc, and the expression levels of these proteins were increased in HCT116 cells overexpressing c-Myc. These findings suggest that leptin plays an important role in the regulation of energy metabolism in tumor cells. It may regulate fatty acid oxidation and OXPHOS of tumor cells by regulating the c-Myc/PGC-1 pathway. Targeting metabolic pathways for cancer treatment has been investigated as potential preventive or therapeutic methods. This study has important implications for the clinical therapy of tumor cell metabolism through hormone regulation. | Qianqian Liu Yang Sun Zaiyi Fei Zhibin Yang Ke Duan Jiaji Zi Qinghua Cui Min Yu Wei Xiong | 2019 | Acta Biochimica et Biophysica Sinica2019，51，7： | 1 |
| 12 | MRI quantification of non‐Gaussian water diffusion in normal human kidney: a diffusional kurtosis imaging study显示文摘 | Yanqi Huang Xin Chen Zhongping Zhang Lifen Yan Dan Pan Changhong Liang Zaiyi Liu | 2015 | NMR Biomed2015，，： | 1 |
| 13 | Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images显示文摘Background:In colorectal cancer(CRC),mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype,morphology,and prognosis.However,mucinous components are present in a large number of adenocarcinomas,and the prognostic value of mucus proportion has not been investigated.Artificial intelligence provides a way to quantify mucus proportion on whole-slide images(WSIs)accurately.We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts.Methods:Deep learning was used to segment WSIs stained with hematoxylin and eosin.Mucus-tumor ratio(MTR)was defined as the proportion of mucinous component in the tumor area.A training cohort(N=419)and a validation cohort(N=315)were used to evaluate the prognostic value of MTR.Survival analysis was performed using the Cox proportional hazard model.Result:Patients were stratified tomucus-low andmucus-high groups,with 24.1%as the threshold.In the training cohort,patients with mucus-high had unfavorable outcomes(hazard ratio for high vs.low 1.88,95%confidence interval 1.18–2.99,P=0.008),with 5-year overall survival rates of 54.8%and 73.7%in mucus-high and mucus-lowgroups,respectively.The resultswere confirmed in the validation cohort(2.09,1.21–3.60,0.008;62.8%vs.79.8%).The prognostic value of MTR was maintained in multivariate analysis for both cohorts.Conclusion:The deep learning quantified MTR was an independent prognostic factor in CRC.With the advantages of advanced efficiency and high consistency,our method is suitable for clinical application and promotes precision medicine development. | Ke Zhao Lin Wu Yanqi Huang Su Yao Zeyan Xu Huan Lin Huihui Wang Yanting Liang Yao Xu Xin Chen Minning Zhao Jiaming Peng Yuli Huang Changhong Liang Zhenhui Li Yong Li Zaiyi Liu | 2021 | Precision Clinical Medicine2021，4，1： | 0 |
| 14 | Combining quantitative and qualitative magnetic resonance imaging features to differentiate anorectal malignant melanoma from low rectal cancer显示文摘Background:Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings.We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging(MRI)features could differentiate anorectal malignant melanoma from low rectal cancer.Methods:Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent preoperative rectal MRI from three hospitals were retrospectively enrolled.All patients were divided into the primary cohort(N=84)and validation cohort(N=51).Quantitative image analysiswas performed on T1-weighted(T1WI),T2-weighted(T2WI),and contrast-enhanced T1-weighted imaging(CE-T1WI).The subjective qualitative MRI findings were evaluated by two radiologists in consensus.Multivariable analysis was performed using stepwise logistic regression.The discrimination performance was assessed by the area under the receiver operating characteristic curve(AUC)with a 95%confidence interval(CI).Results:The skewness derived from T2WI(T2WI-skewness)showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer(primary cohort:AUC=0.852,95%CI 0.788–0.916;validation cohort:0.730,0.645–0.815).Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors,and incorporating both factors achieved good discrimination performance in two cohorts(primary cohort:AUC=0.913,95%CI 0.868–0.958;validation cohort:0.902,0.844–0.960).Conclusions:Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer.The quantitative image analysis helps improve diagnostic accuracy. | Zeyan Xu Ke Zhao Lujun Han Pinxiong Li Zhenwei Shi Xiaomei Huang Chu Han Huihui Wang Minglei Chen Chen Liu Yanting Liang Suyun Li Yanqi Huang Xin Chen Changhong Liang Wuteng Cao Zaiyi Liu | 2021 | Precision Clinical Medicine2021，4，2： | 0 |
| 15 | Prognostic value of a modified Immunoscore in patients with stage Ⅰ-Ⅲ resectable colon cancer显示文摘Objective: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer.However, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore(IS-mod) system for predicting overall survival(OS) in patients with stage Ⅰ-Ⅲ colon cancer.Methods: The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort(N=212) and validation cohort(N=103)from two centers were used to evaluate the prognostic value of the IS-mod.Results: In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS[adjusted hazard ratio(HR)=0.36, 95% confidence interval(95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like(IS-like) system(C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25%(C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation cohort.Conclusions: Our method simplifies the annotation and accelerates the calculation of Immunoscore method,thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set(N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer. | Ke Zhao Xiaomei Wu Zhenhui Li Yingyi Wang Zeyan Xu Yajun Li Lin Wu Su Yao Yanqi Huang Changhong Liang Zaiyi Liu | 2021 | Chinese Journal of Cancer Research2021，33，3： | 0 |
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