scholarly journals A Computed Tomography-Derived Radiomics Approach for Predicting Uncommon EGFR Mutation in Patients With NSCLC

2021 ◽  
Vol 11 ◽  
Author(s):  
Wufei Chen ◽  
Yanqing Hua ◽  
Dingbiao Mao ◽  
Hao Wu ◽  
Mingyu Tan ◽  
...  
Keyword(s):  
Egfr Mutation ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Integrated Model ◽  
Curve Analysis ◽  
Support Vector ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Nsclc Patients ◽  
Radiomics Signature

PurposeThis study aims to develop a CT-based radiomics approach for identifying the uncommon epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer (NSCLC).MethodsThis study involved 223 NSCLC patients (107 with uncommon EGFR mutation-positive and 116 with uncommon EGFR mutation-negative). A total of 1,269 radiomics features were extracted from the non-contrast-enhanced CT images after image segmentation and preprocessing. Support vector machine algorithm was used for feature selection and model construction. Receiver operating characteristic curve analysis was applied to evaluate the performance of the radiomics signature, the clinicopathological model, and the integrated model. A nomogram was developed and evaluated by using the calibration curve and decision curve analysis.ResultsThe radiomics signature demonstrated a good performance for predicting the uncommon EGFR mutation in the training cohort (area under the curve, AUC = 0.802; 95% confidence interval, CI: 0.736–0.858) and was verified in the validation cohort (AUC = 0.791, 95% CI: 0.642–0.899). The integrated model combined radiomics signature with clinicopathological independent predictors exhibited an incremental performance compared with the radiomics signature or the clinicopathological model. A nomogram based on the integrated model was developed and showed good calibration (Hosmer–Lemeshow test, P = 0.92 in the training cohort and 0.608 in the validation cohort) and discrimination capacity (AUC of 0.816 in the training cohort and 0.795 in the validation cohort).ConclusionRadiomics signature combined with the clinicopathological features can predict uncommon EGFR mutation in NSCLC patients.

scholarly journals Using contrast-enhanced CT and non-contrast-enhanced CT to predict EGFR mutation status in NSCLC patients—a radiomics nomogram analysis

2021 ◽  
Author(s):  
Xiaoyan Yang ◽  
Min Liu ◽  
Yanhong Ren ◽  
Huang Chen ◽  
Pengxin Yu ◽  
...  
Keyword(s):  
Clinical Application ◽  
Egfr Mutation ◽  
Test Set ◽  
Mutation Status ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Test Sets ◽  
Nsclc Patients ◽  
Radiomics Signature

Abstract Objectives To develop and validate a general radiomics nomogram capable of identifying EGFR mutation status in non-small cell lung cancer (NSCLC) patients, regardless of patient with either contrast-enhanced CT (CE-CT) or non-contrast-enhanced CT (NE-CT). Methods A total of 412 NSCLC patients were retrospectively enrolled in this study. Patients’ radiomics features not significantly different between NE-CT and CE-CT were defined as general features, and were further used to construct the general radiomics signature. Fivefold cross-validation was used to select the best machine learning algorithm. Finally, a general radiomics nomogram was developed using general radiomics signature, and clinical and radiological characteristics. Two groups of data collected at different time periods were used as two test sets to access the discrimination and clinical usefulness. Area under the receiver operating characteristic curve (ROC-AUC) was applied to performance evaluation. Result The general radiomics signature yielded the highest AUC of 0.756 and 0.739 in the two test sets, respectively. When applying to same type of CT, the performance of general radiomics signature was always similar to or higher than that of models built using only NE-CT or CE-CT features. The general radiomics nomogram combining general radiomics signature, smoking history, emphysema, and ILD achieved higher performance whether applying to NE-CT or CE-CT (test set 1, AUC = 0.833 and 0.842; test set 2, AUC = 0.839 and 0.850). Conclusions Our work demonstrated that using general features to construct radiomics signature and nomogram could help identify EGFR mutation status of NSCLC patients and expand its scope of clinical application. Key Points • General features were proposed to construct general radiomics signature using different types of CT of different patients at the same time to identify EGFR mutation status of NSCLC patients. • The general radiomics nomogram based on general radiomics signature, and clinical and radiological characteristics could identify EGFR mutation status of patients with NSCLC and outperformed the general radiomics signature. • The general radiomics nomogram had a wider scope of clinical application; no matter which of NE-CT and CE-CT the patient has, its EGFR mutation status could be predicted.

scholarly journals Establishment and validation of prognosis model for patients with cerebral contusion

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yufeng Zhu ◽  
Xiaoqing Jin ◽  
Lulu Xu ◽  
Pei Han ◽  
Shengwu Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Third Ventricle ◽  
Curve Analysis ◽  
Imaging Features ◽  
Lasso Regression ◽  
Cerebral Contusion ◽  
Decision Curve Analysis ◽  
Training Cohort

Abstract Background And Objective Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). In this study, the baseline data, imaging features and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a prediction model of nomogram to evaluate the clinical outcomes of patients. Methods A total of 426 patients with cerebral contusion (CC) admitted to the People’s Hospital of Qinghai Province and Affiliated Hospital of Qingdao University from January 2018 to January 2021 were included in this study, We randomly divided the cohort into a training cohort (n = 284) and a validation cohort (n = 142) with a ratio of 2:1.At Least absolute shrinkage and selection operator (Lasso) regression were used for screening high-risk factors affecting patient prognosis and development of the predictive model. The identification ability and clinical application value of the prediction model were analyzed through the analysis of receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results Twelve independent prognostic factors, including age, Glasgow Coma Score (GCS), Basal cistern status, Midline shift (MLS), Third ventricle status, intracranial pressure (ICP) and CT grade of cerebral edema,etc., were selected by Lasso regression analysis and included in the nomogram. The model showed good predictive performance, with a C index of (0.87, 95% CI, 0.026–0.952) in the training cohort and (0.93, 95% CI, 0.032–0.965) in the validation cohort. Clinical decision curve analysis (DCA) also showed that the model brought high clinical benefits to patients. Conclusion This study established a high accuracy of nomogram model to predict the prognosis of patients with CC, its low cost, easy to promote, is especially applicable in the acute environment, at the same time, CSF-glucose/lactate ratio(C-G/L), volume of contusion, and mean CT values of edema zone, which were included for the first time in this study, were independent predictors of poor prognosis in patients with CC. However, this model still has some limitations and deficiencies, which require large sample and multi-center prospective studies to verify and improve our results.

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.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

scholarly journals Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao-Yong Chen ◽  
Jin-Yuan Chen ◽  
Yin-Xing Huang ◽  
Jia-Heng Xu ◽  
Wei-Wei Sun ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Clinical Laboratory ◽  
External Validation ◽  
Postoperative Recurrence ◽  
Integrated Model ◽  
Atypical Meningioma ◽  
Tumor Diameter ◽  
Roc Curve Analysis ◽  
Training Cohort

BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen &gt;2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p &lt; 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter &gt;4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p &lt; 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.

scholarly journals Nomogram to predict primary non-response to infliximab in patients with Crohn’s disease: a multicenter study

2020 ◽  
Author(s):  
Xiao-Qi Ye ◽  
Jing Cai ◽  
Qiao Yu ◽  
Xiao-Cang Cao ◽  
Yan Chen ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Mucosal Healing ◽  
Clinical Usefulness ◽  
Secondary Outcome ◽  
Training Cohort ◽  
Graphical Tool ◽  
Therapeutic Decision Making

Abstract Background Infliximab (IFX) is effective at inducing and maintaining clinical remission and mucosal healing in patients with Crohn’s disease (CD); however, 9%–40% of patients do not respond to primary IFX treatment. This study aimed to construct and validate nomograms to predict IFX response in CD patients. Methods A total of 343 patients diagnosed with CD who had received IFX induction from four tertiary centers between September 2008 and September 2019 were enrolled in this study and randomly classified into a training cohort (n = 240) and a validation cohort (n = 103). The primary outcome was primary non-response (PNR) and the secondary outcome was mucosal healing (MH). Nomograms were constructed from the training cohort using multivariate logistic regression. Performance of nomograms was evaluated by area under the receiver-operating characteristic curve (AUC) and calibration curve. The clinical usefulness of nomograms was evaluated by decision-curve analysis. Results The nomogram for PNR was developed based on four independent predictors: age, C-reactive protein (CRP) at week 2, body mass index, and non-stricturing, non-penetrating behavior (B1). AUC was 0.77 in the training cohort and 0.76 in the validation cohort. The nomogram for MH included four independent factors: baseline Crohn’s Disease Endoscopic Index of Severity, CRP at week 2, B1, and disease duration. AUC was 0.79 and 0.72 in the training and validation cohorts, respectively. The two nomograms showed good calibration in both cohorts and were superior to single factors and an existing matrix model. The decision curve indicated the clinical usefulness of the PNR nomogram. Conclusions We established and validated nomograms for the prediction of PNR to IFX and MH in CD patients. This graphical tool is easy to use and will assist physicians in therapeutic decision-making.

scholarly journals Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model

2019 ◽  
Vol 47 (6) ◽  
pp. 1400-1411 ◽  
Author(s):  
Kai Wang ◽  
Zhen Qiao ◽  
Xiaobin Zhao ◽  
Xiaotong Li ◽  
Xin Wang ◽  
...  
Keyword(s):  
Tumor Recurrence ◽  
Validation Cohort ◽  
Radiation Necrosis ◽  
Integrated Model ◽  
Magnetic Resonance Images ◽  
Diagnostic Model ◽  
Integrated Diagnosis ◽  
Diagnosis Model ◽  
Met Pet ◽  
Radiomics Signature

Abstract Purpose To develop and validate an integrated model for discriminating tumor recurrence from radiation necrosis in glioma patients. Methods Data from 160 pathologically confirmed glioma patients were analyzed. The diagnostic model was developed in a primary cohort (n = 112). Textural features were extracted from postoperative 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), 11C-methionine (11C-MET) PET, and magnetic resonance images. The least absolute shrinkage and selection operator regression model was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a model for predicting tumor recurrence. The radiomics signature, quantitative PET parameters, and clinical risk factors were incorporated in the model. The clinical value of the model was then assessed in an independent validation cohort using the remaining 48 glioma patients. Results The integrated model consisting of 15 selected features was significantly associated with postoperative tumor recurrence (p < 0.001 for both primary and validation cohorts). Predictors contained in the individualized diagnosis model included the radiomics signature, the mean of tumor-background ratio (TBR) of 18F-FDG, maximum of TBR of 11C-MET PET, and patient age. The integrated model demonstrated good discrimination, with an area under the curve (AUC) of 0.988, with a 95% confidence interval (CI) of 0.975–1.000. Application in the validation cohort showed good differentiation (AUC of 0.914 and 95% CI of 0.881–0.945). Decision curve analysis showed that the integrated diagnosis model was clinically useful. Conclusions Our developed model could be used to assist the postoperative individualized diagnosis of tumor recurrence in patients with gliomas.

Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatectomy or liver transplantation.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4079-4079
Author(s):  
Hidetoshi Nitta ◽  
Marc Antoine Allard ◽  
Mylene Sebagh ◽  
Gabriella Pittau ◽  
Oriana Ciacio ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Predictive Model ◽  
Validation Cohort ◽  
Microvascular Invasion ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

4079 Background: Microvascular invasion (MVI) is the strongest prognostic factor following surgery of hepatocellular carcinoma (HCC). However, it is usually not available on the preoperative setting. A predictive model of MVI in patients scheduled for hepatic resection (HR) or liver transplantation (LT) would thus help guiding treatment strategy. The aim of this study was to develop a predictive model for MVI of HCC before either HR or LT. Methods: HCC patients who consecutively performed HR or LT from January 1994 to June 2016 at a single institution were subdivided into a training and validation cohort. Risk factors for MVI in the training cohort were used to develop a predictive model for MVI, to be validated in the validation cohort. The outcomes of the HR and LT patients with high or low MVI probability based on the model, were compared using propensity score matching (PSM). Cut-off values for continuous factors were determined based on ROC curve analysis. Results: A total of 910 patients (425 HR, 485 LT) were included in the training (n = 637) and validation (n = 273) cohorts. In the training cohort, multivariate analysis demonstrated that alpha-fetoprotein ≥100ng/ml ( p < 0.0001), largest tumor size ≥40mm ( p = 0.0002), non-boundary HCC type on contrast-enhanced CT ( p = 0.001), neutrophils-to-lymphocytes ratio ≥3.2 ( p = 0.002), aspartate aminotransferase ≥62U/l ( p = 0.02) were independently associated with MVI. Combinations of these 5 factors varied the MVI probability from 15.5% to 91.1%. This predictive model achieved a good c-index of 0.76 in the validation cohort. In PSM (109 HR, 109 LT), there was no difference in survival between HR and LT patients among the high MVI probability (≥50%) patients, (5y-OS; 46.3% vs 42.2%, p = 0.77, 5y-RFS; 54.0% vs 28.8%, p = 0.21). Among the low probability ( < 50%), survival was significantly decreased following HR compared with LT (5y-OS; 54.1% vs 78.8%, p = 0.007, 5y-RFS; 17.3% vs 86.1%, p< 0.0001). Conclusions: This model developed from preoperative data allows reliable prediction of MVI, and may thus help with preoperative decisions about the suitability of HR or LT in patients with HCC.

A urine exosomal circRNA classifier for detection of high-grade prostate cancer at initial biopsy: A multicenter, retrospective study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 5522-5522
Author(s):  
Liaoyuan Li ◽  
Wen Tao ◽  
Yadi He ◽  
Tao He ◽  
Qing Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Circular Rna ◽  
High Grade ◽  
Training Cohort ◽  
Potential Biomarker ◽  
Initial Biopsy

5522 Background: The low specificity of prostate-specific antigen (PSA) has resulted in the overdiagnosis and overtreatment of clinically indolent prostate cancer (PCa). We aimed to identify a urine exosomal circular RNA (circRNA) classifier that could detect high-grade (Gleason score [GS]7 or greater) PCa. Methods: We did a three-stage study that enrolled eligible participants, including PCa-free men, 45 years or older, scheduled for an initial prostate biopsy due to suspicious digital rectal examination findings and/or PSA levels (limit range, 2.0-20.0 ng/mL), from four hospitals in China. We used RNA sequencing and digital droplet polymerase chain reaction to identify 18 candidate urine exosomal circRNAs that were increased in 11 patients with high-grade PCa compared with 11 case-matched patients with benign prostatic hyperplasia. Using a training cohort of eligible participants, we built a urine exosomal circRNA classifier (Ccirc) to detect high-grade PCa. We then evaluated the classifier in discrimination of GS7 or greater from GS6 and benign disease on initial biopsy in two independent cohorts. We used the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to evaluate diagnostic performance, and compared Ccirc with standard of care (SOC) (ie, PSA level, age, race, and family history). Results: Between June 1, 2016, and July 31, 2019, we recruited 356 participants to the training cohort, and 442 and 325 participants to the two independent validation cohorts. We identified a Ccirc containing five differentially expressed circRNAs (circ_0049335, circ_0056536, circ_0004028, circ_0008475, and circ_0126027) that could detect high-grade PCa. Ccirc showed higher accuracy than SOC to distinguish individuals with high-grade PCa from controls in both the training cohort and the validation cohorts. (AUC 0.831 [95% CI 0.765-0.883] vs 0.724 [0.705-0.852], P = 0.032 in the training cohort; 0.823 [0.762-0.871] vs 0.706 [0.649-0.762], P = 0.007 in validation cohort 1; and 0.878 [0.802-0.943] vs 0.785 [0.701-0.890], P = 0.021 for validation cohort 2). In all three cohorts, Ccirc had higher sensitivity (range 71.6-87.2%) and specificity (82.3-90.7%) than did SOC (sensitivity, 42.3-68.2%; specificity, 40.1-62.3%) to detect high-grade PCa. Using a predefined cut point, 202 of 767 (26.3%) biopsies would have been avoided, missing only 6% of patients with dominant pattern 4 high-risk GS 7 disease. Conclusions: Ccirc is a potential biomarker for high-grade PCa among suspicious men.

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.

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