A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy

Abstract Background Hepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature. Methods In all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n = 381) and the validation cohort (n = 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram. Results The radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram. Conclusion The radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy.

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A radiomics nomogram for prediction of overall survival in hepatocellular carcinoma after hepatectomy

10.21203/rs.3.rs-28085/v1 ◽

2020 ◽

Author(s):

Qinqin Liu ◽

Jing Li ◽

Fei Liu ◽

Weilin Yang ◽

Jingjing Ding ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Clinicopathological Factors ◽

Training Cohort ◽

Texture Parameters ◽

Radiomics Signature

Abstract Background Hepatocellular carcinoma (HCC) is associated with dismal prognosis, and prediction of the prognosis of HCC can assist the therapeutic decisions. More and more studies showed that the texture parameters of images can reflect the heterogeneity of the tumor, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of the study was to investigate the prognostic value of computed tomography (CT) texture parameters for patients with HCC after hepatectomy, and try to develop a radiomics nomograms by combining clinicopathological factors with radiomics signature.Methods 544 eligible patients were enrolled in the retrospective study and randomly divided into training cohort (n=381) and validation cohort (n=163). The regions of interest (ROIs) of tumor is delineated, then the corresponding texture parameters are extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in training cohort, and the radiomics score (Rad-score) was generated. According to the cut-off value of the Rad-score calculated by the receiver operating characteristic (ROC) curve, the patients were divided into high-risk group and low-risk group. The prognosis of the two groups was compared and validated in the validation cohort. Univariate and multivariable analyses by COX proportional hazard regression model were used to select the prognostic factors of overall survival (OS). The radiomics nomogram for OS were established based on the radiomics signature and clinicopathological factors. The Concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram.Result 7 texture parameters associated with OS were selected in the training, and the radiomics signature was formulated based on the texture parameters. The patients were divided into high-risk group and low-risk group by the cut-off values of the Rad-score of OS. The 1-, 3- and 5-year OS rate was 71.0%, 45.5% and 35.5% in the high-risk group, respectively, and 91.7%, 82.1% and 78.7%, in the low-risk group, respectively, with significant difference (P <0.001). COX regression model found that Rad-score was an independent prognostic factor of OS. In addition, the radiomics nomogram was developed based on five variables: α‐fetoprotein (AFP), platelet lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and Rad-score. The nomograms displayed good accuracy in predicting OS (C-index=0.747) in the training cohort and was confirmed in the validation cohort (C-index=0.777). The calibration plots also showed an excellent agreement between the actual and predicted survival probabilities. The DAC indicated that the radiomics nomogram showed better clinical usefulness than the clinicopathologic nomogram.Conclusion The radiomics signature is potential biomarkers of the prognosis of HCC after hepatectomy. Radiomics nomogram that integrated radiomics signature can provide more accurate estimate of OS for patients with HCC after hepatectomy.

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Development and validation of Nomograms for predicting overall survival and Cancer-specific survival in patients with ovarian clear cell carcinoma

Journal of Ovarian Research ◽

10.1186/s13048-020-00727-3 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Qian Chen ◽

Shu Wang ◽

Jing-He Lang

Keyword(s):

Overall Survival ◽

Cell Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Ovarian Clear Cell Carcinoma ◽

Cancer Specific Survival ◽

Training Cohort ◽

Organ Metastasis

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3- and 5-year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups. Conclusions We have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

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Prediction of Overall Survival Rate in Patients With Hepatocellular Carcinoma Using an Integrated Model Based on Autophagy Gene Marker

Frontiers in Genetics ◽

10.3389/fgene.2021.647309 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shuaiqun Wang ◽

Dalu Yang ◽

Wei Kong

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Survival Rate ◽

Clinical Data ◽

Cox Regression ◽

Cox Model ◽

Pathway Enrichment Analysis ◽

Gene Marker ◽

Overall Survival Rate ◽

Cox Regression Analysis

The autophagy cell, which can inhibit the formation of tumor in the early stage and can promote the development of tumor in the late stage, plays an important role in the development of tumor. Therefore, it has potential significance to explore the influence of autophagy-related genes (AAGs) on the prognosis of hepatocellular carcinoma (HCC). The differentially expressed AAGs are selected from HCC gene expression profile data and clinical data downloaded from the TCGA database, and human autophagy database (HADB). The role of AAGs in HCC is elucidated by GO functional annotation and KEGG pathway enrichment analysis. Combining with clinical data, we selected age, gender, grade, stage, T state, M state, and N state as Cox model indexes to construct the multivariate Cox model and survival curve of Kaplan Meier (KM) was drawn to estimate patients’ survival between high- and low-risk groups. Through an ROC curve drawn by univariate and multivariate Cox regression analysis, we found that seven genes with high expression levels, including HSP90AB1, SQSTM1, RHEB, HDAC1, ATIC, HSPB8, and BIRC5 were associated with poor prognosis of HCC patients. Then the ICGC database is used to verify the reliability and robustness of the model. Therefore, the prognosis model of HCC constructed by autophagy genes might effectively predict the overall survival rate and help to find the best personalized targeted therapy of patients with HCC, which can provide better prognosis for patients.

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An 8-lncRNA Signature Predicts Survival of Triple-Negative Breast Cancer Patients Without Germline BRCA1/2 Mutation

10.21203/rs.3.rs-60544/v1 ◽

2020 ◽

Author(s):

Minling Liu ◽

Wei Dai ◽

Mengyuan Zhu ◽

Xueying Li ◽

Shan Huang ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

Validation Cohort ◽

Functional Enrichment ◽

Biological Behavior ◽

Risk Scores ◽

Training Cohort

Abstract Background: Triple-negative breast cancer (TNBC) is a particular breast cancer subtype with poor prognosis due to its aggressive biological behavior and strong heterogeneity. TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment.Methods: 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas (TCGA) database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort (N = 59). Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The signature related mRNAs were identified using the Pearson correlation. Functional enrichment analysis of related mRNA was performed using the Metascape. The qPCR assay was performed to confirm the expressions and clinicopathological correlationsof two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm.Results:We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. In both the training and validation cohort, patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores(P=0.00018 and P =0.0068 respectively). 1, 5, 8-year AUC in the training cohort were 1.000, 1.000 and 0.908 respectively, in the validation cohort were 0.785, 0.790 and 0.892 respectively indicating that our signature has a good prognostic capacity. Signature related mRNA mainly enriched in terms include RNA metabolic process, DNA repair pathways, and so on. Two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed in TNBC without gBRCAm, and significantly associated with tumor grade and invasion.Conclusions: We constructed a novel 8-lncRNA signaturewhich significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targetswhich function needed further exploration.

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Development and Validation of a Nomogram for Predicting Overall Survival In Synchronous Peritoneal Metastasis of Colorectal Cance

10.21203/rs.3.rs-113895/v1 ◽

2020 ◽

Author(s):

Wenle Chen ◽

Zixu Yuan ◽

Aiwen Wu ◽

Ming Cui ◽

Zhongyi Yue ◽

...

Keyword(s):

Overall Survival ◽

Peritoneal Metastasis ◽

Cox Regression ◽

Validation Cohort ◽

Peritoneal Cancer Index ◽

Training Cohort ◽

Peritoneal Cancer ◽

Kaplan Meier ◽

Development And Validation ◽

Actual Survival

Abstract Background: Synchronous peritoneal metastases (PM) is a difficult issue to tackle and the prognosis is poor. The aim of this study is to construct a nomogram to predict the overall survival (OS) for synchronous colorectal peritoneal metastasis.Method: In this retrospective study, 332 patients with synchronous PM were included. The training cohort consisting of 251 patients underwent abdominal surgery from February 2007 to February 2018. The risk factors related to prognosis were analyzed by Kaplan-Meier curve and Cox regression model. 81 patients from other two hospitals were enrolled as validation cohort. The prediction effect of this nomogram was evaluated by C-index and the calibration curve. Result: Five predictors were enrolled into this nomogram after multivariate analysis, including age, peritoneal cancer index (PCI), completeness of cytoreductive surgery (CRS), CA19-9, and albumin. The nomogram showed the accuracy to predict the OS at 0.5, 1, 2, and 3 years. The C-index of the nomogram in the training cohort and validation cohort were 0.713 (95% CI, 0.674–0.752) and 0.642 (95% CI, 0.563-0.720) separately. Both training and validation cohorts showed good discrimination of the nomogram for OS. Calibration curves have shown the predicted OS of nomogram are consistent with actual survival.Conclusion: This novel nomogram, combined with age, PCI, CRS, CA19-9, and albumin, has shown good accuracy to predict OS in patients with synchronous PM, which could be used as an easy-to-use tool for clinicians and surgeons to make decisions.

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Development and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Ovarian Clear Cell Carcinoma

10.21203/rs.3.rs-39370/v2 ◽

2020 ◽

Author(s):

Qian Chen ◽

Shu Wang ◽

Jing-he Lang

Keyword(s):

Overall Survival ◽

Cell Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Ovarian Clear Cell Carcinoma ◽

Cancer Specific Survival ◽

Training Cohort ◽

Organ Metastasis

Abstract Background: Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC.Methods: Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups.Results: A total of 1541 patients from SEER registries were randomly divided into a training cohort (n=1079) and a validation cohort (n=462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3‐ and 5‐year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773-0.831] and 0.802 (0.769-0.835), respectively, in the training cohort, while 0.746 (0.691-0.801) and 0.770 (0.721-0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups.Conclusions: We have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

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Noninvasive prediction of future macrovascular invasion occurrence in hepatocellular carcinoma based on quantitative imaging analysis: A multi-center study.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e14623 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e14623-e14623

Author(s):

JingWei Wei ◽

Jie Tian ◽

Sirui Fu ◽

Ligong Lu

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Quantitative Imaging ◽

Integrated Model ◽

Preoperative Imaging ◽

Rank Test ◽

Imaging Analysis ◽

Occurrence Time ◽

Training Cohort

e14623 Background: To investigate whether preoperative imaging-based analysis could help to predict future macrovascular invasion (MaVI) occurrence in hepatocellular carcinoma (HCC). Methods: A cohort of 224 patients with HCC was enrolled from five independent medical centers (training cohort: n = 154; independent validation cohort: n = 70). Predictive clinical factors were primarily selected by uni- and multi-variable analysis. CT-based imaging analysis was performed based on extraction of 1217 radiomic features. Recursive feature elimination and random forest (RF) were chosen as the optimal radiomics modelling algorithms. A clinical-radiomics integrated model was constructed by RF modelling. Cox-regression analyses further selected risk independent factors. Risk stratification was explored by Kaplan-Meier analysis with log-rank test, regarding to MaVI occurrence time (MOT), progression free survival (PFS) and overall survival (OS). Results: The clinical-radiomics integrated model could successfully predict MaVI occurrence with areas under curve of 0.920 (training cohort, 95% confidence index [CI]: 0.875-0.965) and 0.853 (validation cohort, 95% CI: 0.737-0.970). The radiomics signature added significant improvement to the integrated model in both training and validation cohorts with p-value of 0.009 and 0.008, respectively. Radiomic features: N25_ori_gldzm_IN (hazard ratio [HR]: 0.44; p = 0.001) and N25_Coif1_ngldm_DE (HR: 0.60; p = 0.016) were selected as independent risk factors associated with MaVI occurrence time. The cox-regression model could stratified patients into high-risk and low-risk groups in MOT (p < 0.001), PFS (p = 0.003), and OS (p = 0.007). Conclusions: The noninvasive quantitative imaging analysis could enable preoperative prediction of future MaVI occurrence in HCC with prognosis implication.

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Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome

Frontiers in Oncology ◽

10.3389/fonc.2021.594510 ◽

2021 ◽

Vol 11 ◽

Author(s):

Chunyuan Cen ◽

Liying Liu ◽

Xin Li ◽

Ailan Wu ◽

Huan Liu ◽

...

Keyword(s):

Overall Survival ◽

Pancreatic Ductal Adenocarcinoma ◽

Clinical Characteristics ◽

Validation Cohort ◽

Stage I ◽

Ductal Adenocarcinoma ◽

Training Cohort ◽

Calibration Curves ◽

Kaplan Meier ◽

Radiomics Signature

ObjectivesTo construct a nomogram model that combines clinical characteristics and radiomics signatures to preoperatively discriminate pancreatic ductal adenocarcinoma (PDAC) in stage I-II and III-IV and predict overall survival.MethodsA total of 135 patients with histopathologically confirmed PDAC who underwent contrast-enhanced CT were included. A total of 384 radiomics features were extracted from arterial phase (AP) or portal venous phase (PVP) images. Four steps were used for feature selection, and multivariable logistic regression analysis were used to build radiomics signatures and combined nomogram model. Performance of the proposed model was assessed by using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was applied to analyze overall survival in the stage I-II and III-IV PDAC groups.ResultsThe AP+PVP radiomics signature showed the best performance among the three radiomics signatures [training cohort: area under the curve (AUC) = 0.919; validation cohort: AUC = 0.831]. The combined nomogram model integrating AP+PVP radiomics signature with clinical characteristics (tumor location, carcinoembryonic antigen level, and tumor maximum diameter) demonstrated the best discrimination performance (training cohort: AUC = 0.940; validation cohort: AUC = 0.912). Calibration curves and DCA verified the clinical usefulness of the combined nomogram model. Kaplan-Meier analysis showed that overall survival of patients in the predicted stage I-II PDAC group was longer than patients in stage III-IV PDAC group (p<0.0001).ConclusionsWe propose a combined model with excellent performance for the preoperative, individualized, noninvasive discrimination of stage I-II and III-IV PDAC and prediction of overall survival.

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Development and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Ovarian Clear Cell Carcinoma

10.21203/rs.3.rs-39370/v1 ◽

2020 ◽

Author(s):

Qian Chen ◽

Shu Wang ◽

Jing-he Lang

Keyword(s):

Overall Survival ◽

Cell Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Ovarian Clear Cell Carcinoma ◽

Cancer Specific Survival ◽

Training Cohort ◽

Organ Metastasis

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of useful prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO COX regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for prediction of 3- and 5‐year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in survival curves of different risk subgroups. Conclusions We constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

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Pretreatment Prediction of Relapse Risk in Patients with Osteosarcoma Using Radiomics Nomogram Based on CT: A Retrospective Multicenter Study

BioMed Research International ◽

10.1155/2021/6674471 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Jin Liu ◽

Tao Lian ◽

Haimei Chen ◽

Xiaohong Wang ◽

Xianyue Quan ◽

...

Keyword(s):

Risk Factors ◽

Multicenter Study ◽

Cox Regression ◽

Validation Cohort ◽

External Validation ◽

Training Cohort ◽

Relapse Risk ◽

Significant Difference ◽

Prediction Of Relapse ◽

Radiomics Signature

Objective. To develop and externally validate a CT-based radiomics nomogram for pretreatment prediction of relapse in osteosarcoma patients within one year. Materials and Methods. In this multicenter retrospective study, a total of 80 patients (training cohort: 63 patients from three hospitals; validation cohort: 17 patients from three other hospitals) with osteosarcoma, undergoing pretreatment CT between August 2010 and December 2018, were identified from multicenter databases. Radiomics features were extracted and selected from tumor regions on CT image, and then, the radiomics signature was constructed. The radiomics nomogram that incorporated the radiomics signature and clinical-based risk factors was developed to predict relapse risk with a multivariate Cox regression model using the training cohort and validated using the external validation cohort. The performance of the nomogram was assessed concerning discrimination, calibration, reclassification, and clinical usefulness. Results. Kaplan-Meier curves based on the radiomics signature showed a significant difference between the high-risk and the low-risk groups in both training and validation cohorts ( P < 0.001 and P = 0.015 , respectively). The radiomics nomogram achieved good discriminant results in the training cohort ( C -index: 0.779) and the validation cohort ( C -index: 0.710) as well as good calibration. Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinical-based nomogram ( P < 0.001 ). Conclusions. This multicenter study demonstrates that a radiomics nomogram incorporated the radiomics signature and clinical-based risk factors can increase the predictive value of the osteosarcoma relapse risk, which supports the clinical application in different institutions.

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