scholarly journals Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB–IV) Lung Adenocarcinoma Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Duo Hong ◽  
Lina Zhang ◽  
Ke Xu ◽  
Xiaoting Wan ◽  
Yan Guo
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Clinical Factors ◽  
Combined Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Pre Treatment ◽  
Off Time ◽  
Radiomics Signature

PurposeThe purpose of this study was to investigate the prognostic value of pre-treatment CT radiomics and clinical factors for the overall survival (OS) of advanced (IIIB–IV) lung adenocarcinoma patients.MethodsThis study involved 165 patients with advanced lung adenocarcinoma. The Lasso–Cox regression model was used for feature selection and radiomics signature building. Then a clinical model was built based on clinical factors; a combined model in the form of nomogram was constructed with both clinical factors and the radiomics signature. Harrell’s concordance index (C-Index) and Receiver operating characteristic (ROC) curves at cut-off time points of 1-, 2-, and 3- year were used to estimate and compare the predictive ability of all three models. Finally, the discriminatory ability and calibration of the nomogram were analyzed.ResultsThirteen significant features were selected to build the radiomics signature whose C-indexes were 0.746 (95% CI, 0.699 to 0.792) in the training cohort and 0.677 (95% CI, 0.597 to 0.766) in the validation cohort. The C-indexes of combined model achieved 0.799 (95% CI, 0.757 to 0.84) in the training cohort and 0.733 (95% CI, 0.656 to 0.81) in the validation cohort, which outperformed the clinical model and radiomics signature. Moreover, the areas under the curve (AUCs) of the radiomic signature for 2-year prediction was superior to that of the clinical model. The combined model had the best AUCs for 2- and 3-year predictions.ConclusionsRadiomic signatures and clinical factors have prognostic value for OS in advanced (IIIB–IV) lung adenocarcinoma patients. The optimal model should be selected according to different cut-off time points in clinical application.

scholarly journals A Radiomics Signature-Based Nomogram to Predict the Progression-Free Survival of Patients With Hepatocellular Carcinoma After Transcatheter Arterial Chemoembolization Plus Radiofrequency Ablation

2021 ◽  
Vol 8 ◽  
Author(s):  
Shiji Fang ◽  
Linqiang Lai ◽  
Jinyu Zhu ◽  
Liyun Zheng ◽  
Yuanyuan Xu ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Progression Free Survival ◽  
Clinical Factors ◽  
Combined Model ◽  
Free Survival ◽  
Training Cohort ◽  
Clinical Model ◽  
Cox Analysis ◽  
Radiomics Signature ◽  
Arterial Chemoembolization

Objective: The study aims to establish an magnetic resonance imaging radiomics signature-based nomogram for predicting the progression-free survival of intermediate and advanced hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) plus radiofrequency ablationMaterials and Methods: A total of 113 intermediate and advanced HCC patients treated with TACE and RFA were eligible for this study. Patients were classified into a training cohort (n = 78 cases) and a validation cohort (n = 35 cases). Radiomics features were extracted from contrast-enhanced T1W images by analysis kit software. Dimension reduction was conducted to select optimal features using the least absolute shrinkage and selection operator (LASSO). A rad-score was calculated and used to classify the patients into high-risk and low-risk groups and further integrated into multivariate Cox analysis. Two prediction models based on radiomics signature combined with or without clinical factors and a clinical model based on clinical factors were developed. A nomogram comcined radiomics signature and clinical factors were established and the concordance index (C-index) was used for measuring discrimination ability of the model, calibration curve was used for measuring calibration ability, and decision curve and clinical impact curve are used for measuring clinical utility.Results: Eight radiomics features were selected by LASSO, and the cut-off of the Rad-score was 1.62. The C-index of the radiomics signature for PFS was 0.646 (95%: 0.582–0.71) in the training cohort and 0.669 (95% CI:0.572–0.766) in validation cohort. The median PFS of the low-risk group [30.4 (95% CI: 19.41–41.38)] months was higher than that of the high-risk group [8.1 (95% CI: 4.41–11.79)] months in the training cohort (log rank test, z = 16.58, p < 0.001) and was verified in the validation cohort. Multivariate Cox analysis showed that BCLC stage [hazard ratio (HR): 2.52, 95% CI: 1.42–4.47, p = 0.002], AFP level (HR: 2.01, 95% CI: 1.01–3.99 p = 0.046), time interval (HR: 0.48, 95% CI: 0.26–0.87, p = 0.016) and radiomics signature (HR 2.98, 95% CI: 1.60–5.51, p = 0.001) were independent prognostic factors of PFS in the training cohort. The C-index of the combined model in the training cohort was higher than that of clinical model for PFS prediction [0.722 (95% CI: 0.657–0.786) vs. 0.669 (95% CI: 0.657–0.786), p＜0.001]. Similarly, The C-index of the combined model in the validation cohort, was higher than that of clinical model [0.821 (95% CI: 0.726–0.915) vs. 0.76 (95% CI: 0.667–0.851), p = 0.004]. The calibration curve, decision curve and clinical impact curve showed that the nomogram can be used to accurately predict the PFS of patients.Conclusion: The radiomics signature was a prognostic risk factor, and a nomogram combined radiomics and clinical factors acts as a new strategy for predicted the PFS of intermediate and advanced HCC treated with TACE plus RFA.

Development and validation of a CT-based radiomic model combined with margin-related radiomic features to distinguish precancerous lesions from early-stage lung adenocarcinoma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21044-e21044
Author(s):  
Luyu Huang ◽  
Haiyu Zhou ◽  
Herui Yao ◽  
Yunfang Yu ◽  
Hongyuan Zhu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Validation Cohort ◽  
Early Stage ◽  
Precancerous Lesions ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Combined Model ◽  
Training Cohort ◽  
Pulmonary Lesions ◽  
Preoperative Ct

e21044 Background: The purpose of this study was to investigate whether the combined radiomic model based on tumor-associated and margin-related (5mm) radiomic features can effectively improve prediction performance of distinguishing precancerous lesions from early stage lung adenocarcinoma. Methods: 264 patients underwent preoperative chest CT in Guangdong Provincial People’s hospital from March 1, 2015 to December 31,2019 were sorted by three cohorts. All lesions were pathologically confirmed as precancerous lesions or Stage I lung adenocarcinoma and a total of 861 analyzable radiomic features were extracted from two segmented lesions including pulmonary lesions and margins, using PyRadiomics by two senior radiologists. In training cohort, 145 patients (70%) are selected randomly from the single-nodular patients (N = 207). As for the validation cohorts, the models were validated using the resting 62 patients from single-nodular cohort and multi-nodular cohort (n = 57) respectively. Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination were used for feature selection. ROC analysis and AUC were used to evaluate the performance of three models which were developed by multiple logistic regression on distinguishing the precancerous lesions from early stage lung adenocarcinoma. Results: Selected features from pulmonary lesions and pericarcinous tissue were developed into two independent radiomic models and a combined model. Margin-related radiomic model performs well in three validation cohorts. The AUC Brock of single-nodular cohort in training cohort was 0.912 (95% CI: 0.876-0.948), while in single-nodular validation cohort was 0.93 (95% CI: 0.862-0.966). Multi-nodular validation cohort in this model shows an AUC of 0.891 (95% CI = 0.824–0.943). Comparing combined model and tumor-associated radiomic model, it is found that the AUC of combined model was improved from 0.865 (95% CI: 0.767-0.963) to 0.94 (95% CI: 0.767-0.963) for single-nodular validation cohort. Respectively, this combined model also performs well in multi-nodular validation cohort. Conclusions: This study demonstrated the potential of margin-related radiomic features based on preoperative CT scans to distinguish precancerous lesions from early stage lung adenocarcinoma. The constructed radiomic model provided an easy-to-use, preoperative tool for surgeons to develop accurate therapeutic strategies for multi-nodular patients.

scholarly journals Computed Tomography-Based Radiomics Nomogram: Potential to Predict Local Recurrence of Gastric Cancer After Radical Resection

2021 ◽  
Vol 11 ◽  
Author(s):  
Liebin Huang ◽  
Bao Feng ◽  
Yueyue Li ◽  
Yu Liu ◽  
Yehang Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Computed Tomography ◽  
Validation Cohort ◽  
Radical Resection ◽  
Curve Analysis ◽  
Internal Validation ◽  
Training Cohort ◽  
Clinical Model ◽  
Radiomics Signature

ObjectiveAccurate prediction of postoperative recurrence risk of gastric cancer (GC) is critical for individualized precision therapy. We aimed to investigate whether a computed tomography (CT)-based radiomics nomogram can be used as a tool for predicting the local recurrence (LR) of GC after radical resection.Materials and Methods342 patients (194 in the training cohort, 78 in the internal validation cohort, and 70 in the external validation cohort) with pathologically proven GC from two centers were included. Radiomics features were extracted from the preoperative CT imaging. The clinical model, radiomics signature, and radiomics nomogram, which incorporated the radiomics signature and independent clinical risk factors, were developed and verified. Furthermore, the performance of these three models was assessed by using the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).ResultsThe radiomics signature, which was comprised of two selected radiomics features, namely, contrast_GLCM and dissimilarity_GLCM, showed better performance than the clinical model in predicting the LR of GC, with AUC values of 0.83 in the training cohort, 0.84 in the internal validation cohort, and 0.73 in the external cohort, respectively. By integrating the independent clinical risk factors (N stage, bile acid duodenogastric reflux and nodular or irregular outer layer of the gastric wall) into the radiomics signature, the radiomics nomogram achieved the highest accuracy in predicting LR, with AUC values of 0.89, 0.89 and 0.80 in the three cohorts, respectively. DCA in the validation cohort showed that radiomics nomogram added more net benefit than the clinical model within the range of 0.01-0.98.ConclusionThe CT-based radiomics nomogram has the potential to predict the LR of GC after radical resection.

scholarly journals A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer

2021 ◽  
Author(s):  
Yumin Hu ◽  
Qiaoyou Weng ◽  
Haihong Xia ◽  
Tao Chen ◽  
Chunli Kong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Clinical Data ◽  
Arterial Phase ◽  
Multivariable Logistic Regression Analysis ◽  
Clinical Factors ◽  
Good Discrimination ◽  
Training Cohort ◽  
Ovarian Cancers ◽  
Radiomics Signature

Abstract Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated. Results Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram. Conclusion The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.

Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15565-e15565
Author(s):  
Qiqi Zhu ◽  
Du Cai ◽  
Wei Wang ◽  
Min-Er Zhong ◽  
Dejun Fan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Related Gene ◽  
Training Cohort ◽  
Gene Pairs ◽  
Immune Related Gene

e15565 Background: Few robust predictive biomarkers have been applied in clinical practice due to the heterogeneity of metastatic colorectal cancer (mCRC) . Using the gene pair method, the absolute expression value of genes can be converted into the relative order of genes, which can minimize the influence of the sequencing platform difference and batch effects, and improve the robustness of the model. The main objective of this study was to establish an immune-related gene pairs signature (IRGPs) and evaluate the impact of the IRGPs in predicting the prognosis in mCRC. Methods: A total of 205 mCRC patients containing overall survival (OS) information from the training cohort ( n = 119) and validation cohort ( n = 86) were enrolled in this study. LASSO algorithm was used to select prognosis related gene pairs. Univariate and multivariate analyses were used to validate the prognostic value of the IRGPs. Gene sets enrichment analysis (GSEA) and immune infiltration analysis were used to explore the underlying biological mechanism. Results: An IRGPs signature containing 22 gene pairs was constructed, which could significantly separate patients of the training cohort ( n = 119) and validation cohort ( n = 86) into the low-risk and high-risk group with different outcomes. Multivariate analysis with clinical factors confirmed the independent prognostic value of IRGPs that higher IRGPs was associated with worse prognosis (training cohort: hazard ratio (HR) = 10.54[4.99-22.32], P < 0.001; validation cohort: HR = 3.53[1.24-10.08], P = 0.012). GSEA showed that several metastasis and immune-related pathway including angiogenesis, TGF-β-signaling, epithelial-mesenchymal transition and inflammatory response were enriched in the high-risk group. Through further analysis of the immune factors, we found that the proportions of CD4+ memory T cell, regulatory T cell, and Myeloid dendritic cell were significantly higher in the low-risk group, while the infiltrations of the Macrophage (M0) and Neutrophil were significantly higher in the high-risk group. Conclusions: The IRGPs signature could predict the prognosis of mCRC patients. Further prospective validations are needed to confirm the clinical utility of IRGPs in the treatment decision.

scholarly journals The Prognostic Significance of Tumor Deposit Count for Colorectal Cancer Patients after Radical Surgery

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Kuo Zheng ◽  
Nanxin Zheng ◽  
Cheng Xin ◽  
Leqi Zhou ◽  
Ge Sun ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Cutoff Point ◽  
Disease Specific Survival ◽  
Optimal Cutoff ◽  
Training Cohort ◽  
Tumor Deposit ◽  
Optimal Cutoff Point ◽  
Disease Specific

Background. The prognostic value of tumor deposit (TD) count in colorectal cancer (CRC) patients has been rarely evaluated. This study is aimed at exploring the prognostic value of TD count and finding out the optimal cutoff point of TD count to differentiate the prognoses of TD-positive CRC patients. Method. Patients diagnosed with CRC from Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2010, to December 31, 2012, were analyzed. X-tile program was used to identify the optimal cutoff point of TD count in training cohort, and a validation cohort was used to test this cutoff point after propensity score matching (PSM). Univariate and multivariate Cox proportional hazard models were used to assess the risk factors of survival. Results. X-tile plots identified 3 (P<0.001) as the optimal cutoff point of TD count to divide the patients of training cohort into high and low risk subsets in terms of disease-specific survival (DSS). This cutoff point was validated in validation cohort before and after PSM (P<0.001, P=0.002). More TD count, which was defined as more than 3, was validated as an independent risk prognostic factor in univariate and multivariate analysis (P<0.001). Conclusion. More TD count (TD count≥4) was significantly associated with poor disease-specific survival in CRC patients.

A novel pretreatment model identifying high-risk of refractoriness after transcatheter arterial chemoembolization in unresectable hepatocellular carcinoma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16641-e16641
Author(s):  
Xin Yin ◽  
Ke Shu Hu ◽  
Shen Xin Lu ◽  
Bei Tang ◽  
Zhenggang Ren
Keyword(s):  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Confidence Interval ◽  
Clinical Decision Making ◽  
Transcatheter Arterial Chemoembolization ◽  
Validation Cohort ◽  
Treatment Model ◽  
Training Cohort ◽  
Pre Treatment ◽  
Arterial Chemoembolization

e16641 Background: Refractoriness to transcatheter arterial chemoembolization is common during the therapeutic process of hepatocellular carcinoma, which is an intractable issue and may compromise the prognosis. We aim to establish a pre-treatment model to identify patients with high risks of refractoriness. Methods: From 2010 to 2016, 824 patients who had initially underwent at least two sessions of transcatheter arterial chemoembolization in Zhongshan Hospital, Fudan University were retrospectively enrolled. These patients were randomly allocated into a training cohort and a validation cohort. The pre-treatment scoring model was established based on the clinical and radiological variables using logistic regression and nomogram. The discrimination and calibration of the model were also evaluated. Results: Logistic regression identified vascularization pattern, ALBI grade, serum alpha-fetoprotein level, serum γ- glutamyl transpeptidase level and major tumor size as the key parameters related to refractoriness. The p-TACE model was established using these variables (risk score range: 0-19.5). Patients were divided into six risk subgroups based on their scores ( < 4, ≥4, ≥7, ≥10, ≥13, ≥16). The discriminative ability, as determined by the area under receiver operating characteristic curve was 0.784 (95% confidence interval: 0.741-0.827) in the training cohort and 0.743 (95% confidence interval: 0.696-0.789) in the validation cohort. Moreover, satisfactory calibration was confirmed by Hosmer-Lemeshow test with P values of 0.767 and 0.913 in the training cohort and validation cohort. Conclusions: This study presents a pre-treatment model to identify patients with high risks of refractoriness after transcatheter arterial chemoembolization and shed light on clinical decision making.

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

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.

scholarly journals Development and validation of a radiomics-based model to predict local progression-free survival after chemo-radiotherapy in patients with esophageal squamous cell cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
He-San Luo ◽  
Ying-Ying Chen ◽  
Wei-Zhen Huang ◽  
Sheng-Xi Wu ◽  
Shao-Fu Huang ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Local Recurrence ◽  
Squamous Cell ◽  
Cox Regression ◽  
Validation Cohort ◽  
Cell Cancer ◽  
Clinical Factors ◽  
Free Survival ◽  
Training Cohort ◽  
Cox Regression Analysis

Abstract Purpose To develop a nomogram model for predicting local progress-free survival (LPFS) in esophageal squamous cell carcinoma (ESCC) patients treated with concurrent chemo-radiotherapy (CCRT). Methods We collected the clinical data of ESCC patients treated with CCRT in our hospital. Eligible patients were randomly divided into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) with COX regression was performed to select optimal radiomic features to calculate Rad-score for predicting LPFS in the training cohort. The univariate and multivariate analyses were performed to identify the predictive clinical factors for developing a nomogram model. The C-index was used to assess the performance of the predictive model and calibration curve was used to evaluate the accuracy. Results A total of 221 ESCC patients were included in our study, with 155 patients in training cohort and 66 patients in validation cohort. Seventeen radiomic features were selected by LASSO COX regression analysis to calculate Rad-score for predicting LPFS. The patients with a Rad-score ≥ 0.1411 had high risk of local recurrence, and those with a Rad-score < 0.1411 had low risk of local recurrence. Multivariate analysis showed that N stage, CR status and Rad-score were independent predictive factors for LPFS. A nomogram model was built based on the result of multivariate analysis. The C-index of the nomogram was 0.745 (95% CI 0.7700–0.790) in training cohort and 0.723(95% CI 0.654–0.791) in validation cohort. The 3-year LPFS rate predicted by the nomogram model was highly consistent with the actual 3-year LPFS rate both in the training cohort and the validation cohort. Conclusion We developed and validated a prediction model based on radiomic features and clinical factors, which can be used to predict LPFS of patients after CCRT. This model is conducive to identifying the patients with ESCC benefited more from CCRT.

scholarly journals A Radiomics Nomogram for Non-Invasive Prediction of Progression-Free Survival in Esophageal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Ting Yan ◽  
lili liu ◽  
Meilan Peng ◽  
Zhenpeng Yan ◽  
Qingyu Wang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Validation Cohort ◽  
Progression Free Survival ◽  
Tnm Staging ◽  
Free Survival ◽  
Training Cohort ◽  
Radiomics Signature

Abstract Objectives: To construct a prognostic model for preoperative prediction based on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC). Methods: Radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) with high throughput radiomics features that extracted from the CT images of 272 patients (204 in training and 68 in validation cohort), who were pathologically confirmed ESCC. Multivariable logistic regression was adopted to build the radiomics signature and another predictive nomogram model, which was composed with radiomics signature, traditional TNM stage and clinical features. Then its performance was assessed by the calibration and decision curve analysis (DCA). Results: 16 radiomics features were selected from 954 to build a radiomics signature，which were significantly associated with progression-free survival (PFS) (p<0.001). The area under the curve (AUC) of performance was 0.891 (95% CI: 0.845-0.936) for training cohort and 0.706 (95% CI: 0.583-0.829) for validation cohort. The radscore of signatures’ combination showed significant discrimination for survival status in both two cohort. Kaplan-Meier survival curve further confirmed the radscore has a better prognostic performance in training cohort. Radiomics nomogram combined radscore with TNM staging showed significant improvement over TNM staging alone in training cohort (C-index, 0.802 vs 0.628; p<0.05), and it is the same with clinical data (C-index, 0.798 vs 0.660; p<0.05). Findings were confirmed in the validation cohort. DCA showed CT-based radiomics model will receive benefit when the threshold probability was between 0 and 0.9. Heat maps revealed associations between radiomics features and tumor stages.Conclusions: Multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.

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