Preoperative Assessment for Event-Free Survival With Hepatoblastoma in Pediatric Patients by Developing a CT-Based Radiomics Model

Objective: To explore a CT-based radiomics model for preoperative prediction of event-free survival (EFS) in patients with hepatoblastoma and to compare its performance with that of a clinicopathologic model.Patients and Methods: Eighty-eight patients with histologically confirmed hepatoblastoma (mean age: 2.28 ± 2.72 years) were recruited from two institutions between 2002 and 2019 for this retrospective study. They were divided into a training cohort (65 patients from institution A) and a validation cohort (23 patients from institution B). Radiomics features were extracted manually from pretreatment CT images in the portal venous (PV) phase. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to construct a “radiomics signature” and radiomics score (Rad-score) for EFS prediction. Then, a nomogram incorporating the Rad-score, updated staging system, and significant variables of clinicopathologic risk (age, alpha-fetoprotein (AFP) level, histology subtype, tumor diameter) as the radiomic model, clinicopathologic model, and combined clinicopathologic-radiomic model were built for EFS estimation in the training cohort, the performance of which was assessed in an external-validation cohort with respect to clinical usefulness, discrimination, and calibration.Results: Nine survival-relevant features were selected for a radiomics signature and Rad-score building. Multivariable analysis revealed that histology subtype (P = 0.01), PV (P = 0.001) invasion, and metastasis (P = 0.047) were independent risk factors of EFS. Patients were divided into low- and high-risk groups based on the Rad-score with a cutoff of 0.08 according to survival outcome. The radiomics signature-incorporated nomogram showed good performance (P < 0.001) for EFS estimation (C-Index: 0.810; 95% CI: 0.738–0.882), which was comparable with that of the clinicopathological model for EFS estimation (C-Index: 0.81 vs. 0.85). The radiomics-based nomogram failed to show incremental prognostic value compared with that using the clinicopathologic model. The combined model (radiomics signature plus clinicopathologic parameters) showed significant improvement in the discriminatory accuracy, along with good calibration and greater net clinical benefit, of EFS (C-Index: 0.88; 95% CI: 0.829–0.933).Conclusion: The radiomics signature can be used as a prognostic indicator for EFS in patients with hepatoblastoma. A combination of the radiomics signature and clinicopathologic risk factors showed better performance in terms of EFS prediction in patients with hepatoblastoma, which enabled precise clinical decision-making.

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

Frontiers in Oncology ◽

10.3389/fonc.2021.754937 ◽

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 >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 < 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter >4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p < 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.

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

10.21203/rs.3.rs-557179/v2 ◽

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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Development and validation of nomogram combining serum biomarker for predicting survival in patients with resected rectal cancer

Bioscience Reports ◽

10.1042/bsr20192636 ◽

2019 ◽

Vol 39 (11) ◽

Cited By ~ 1

Author(s):

Shaonan Fan ◽

Ting Li ◽

Ping Zhou ◽

Qiliang Peng ◽

Yaqun Zhu

Keyword(s):

Rectal Cancer ◽

Clinical Decision Making ◽

Validation Cohort ◽

External Validation ◽

Staging System ◽

Clinical Decision ◽

Tnm Staging ◽

P Value ◽

Training Cohort ◽

American Joint Committee

Abstract Purpose: Nomogram is a widely used tool that precisely predicts individualized cancer prognoses. We aimed to develop and validate a reliable nomogram including serum tumor biomarkers to predict individual overall survival (OS) for patients with resected rectal cancer (RC) and compare the predictive value with the American Joint Committee on Cancer (AJCC) stages. Patients and methods: We analyzed 520 patients who were diagnosed with non-metastatic rectal cancer as training cohort. External validation was performed in a cohort of 11851 patients from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified and integrated to build a nomogram using the Cox proportional hazard regression model. The nomogram was evaluated by Harrell’s concordance index (C-index) and calibration plots in both training and validation cohort. Results: The calibration curves for probability of 1-, 3-, and 5-year OS in both cohorts showed favorable accordance between the nomogram prediction and the actual observation. The C-indices of the nomograms to predict OS were 0.71 in training cohort and 0.69 in the SEER cohort, which were higher than that of the seventh edition American Joint Committee on Cancer TNM staging system for predicting OS (training cohort, 0.71 vs. 0.58, respectively; P-value < 0.001; validation cohort, 0.69 vs. 0.57, respectively; P-value < 0.001). Conclusion: We developed and validated a novel nomogram based on CEA and other factors for predicting OS in patients with resected RC, which could assist clinical decision making and improvement of prognosis prediction for individual RC patients after surgery.

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Nomogram for Predicting Recurrence-Free Survival in Chinese Women with Endometrial Cancer after Initial Therapy: External Validation

Journal of Oncology ◽

10.1155/2020/2363545 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuan Cheng ◽

Yangyang Dong ◽

Wenjuan Tian ◽

Hua Zhang ◽

Xiaoping Li ◽

...

Keyword(s):

Endometrial Cancer ◽

Chinese Women ◽

External Validation ◽

Figo Stage ◽

Peritoneal Cytology ◽

Tumor Diameter ◽

Recurrence Free Survival ◽

Free Survival ◽

Training Cohort ◽

Gynecology And Obstetrics

This study aimed at developing an available recurrence-free survival (RFS) model of endometrial cancer (EC) for accurate and individualized prognosis assessment. A training cohort of 520 women with EC who underwent initial surgical treatment and an external validation cohort of 445 eligible EC patients from 2006 to 2016 were analyzed retrospectively. Multivariable Cox proportional hazards regression models were used to develop nomograms for predicting recurrence. The concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) were calculated to determine the discrimination of RFS prognostic scoring systems. Calibration plots were generated to examine the performance characteristics of the predictive nomograms. Regression analysis revealed that an advanced International Federation of Gynecology and Obstetrics (FIGO) stage, histological grade 3, primary tumor diameter ≥2 cm, and positive peritoneal cytology were independent prognostic factors for RFS in EC in the training set. The nomograms estimated RFS according to these four variables, with a C-index of 0.860, which was superior to that of FIGO stage (2009 criteria), at 0.809 (P=0.034), in the training cohort. Encouragingly, consistent results were observed in the validation set, with a C-index of 0.875 for the nomogram and a C-index of 0.833 for the FIGO staging (P=0.0137). Furthermore, the calibrations of the nomograms predicting 3- and 5-year RFS strongly corresponded to the actual survival outcome. In conclusion, this study developed an available nomogram with effective external validation and relatively appreciable discrimination and conformity for the accurate assessment of 3- and 5-year RFS in Chinese women with EC.

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JAK2V617F variant allele frequency >50% identifies patients with polycythemia vera at high risk for venous thrombosis

Blood Cancer Journal ◽

10.1038/s41408-021-00581-6 ◽

2021 ◽

Vol 11 (12) ◽

Author(s):

Paola Guglielmelli ◽

Giuseppe G. Loscocco ◽

Carmela Mannarelli ◽

Elena Rossi ◽

Francesco Mannelli ◽

...

Keyword(s):

Risk Factors ◽

Polycythemia Vera ◽

Allele Frequency ◽

Validation Cohort ◽

Multivariable Analysis ◽

Variant Allele ◽

Variant Allele Frequency ◽

Training Cohort ◽

Independent Risk Factors ◽

The Impact

AbstractArterial (AT) and venous (VT) thrombotic events are the most common complications in patients with polycythemia vera (PV) and are the leading causes of morbidity and mortality. In this regard, the impact of JAK2V617F variant allele frequency (VAF) is still debated. The purpose of the current study was to analyze the impact of JAK2V617F VAF in the context of other established risk factors for thrombosis in a total of 865 2016 WHO-defined PV patients utilizing two independent cohorts: University of Florence (n = 576) as a training cohort and Policlinico Gemelli, Catholic University, Rome (n = 289) as a validation cohort. In the training cohort VT free-survival was significantly shorter in the presence of a JAK2V617F VAF > 50% (HR 4; p < 0.0001), whereas no difference was found for AT (HR 0.9; p = 0.8). Multivariable analysis identified JAK2V617F VAF > 50% (HR 3.8, p = 0.001) and previous VT (HR 2.2; p = 0.04) as independent risk factors for future VT whereas diabetes (HR 2.4; p = 0.02), hyperlipidemia (HR 2.3; p = 0.01) and previous AT (HR 2; p = 0.04) were independent risk factors for future AT. Similarly, JAK2V617F VAF > 50% (HR 2.4; p = 0.01) and previous VT (HR 2.8; p = 0.005) were confirmed as independent predictors of future VT in the validation cohort. Impact of JAK2V617F VAF > 50% on VT was particularly significant in conventional low-risk patients, both in Florence (HR 10.6, p = 0.005) and Rome cohort (HR 4; p = 0.02). In conclusion, we identified JAK2V617F VAF > 50% as an independent strong predictor of VT, supporting that AT and VT are different entities which might require distinct management.

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Preoperative and Postoperative Nomograms in Predicting Early Recurrence of Hepatocellular Carcinoma Without Macrovascular Invasion After Curative Resection

10.21203/rs.3.rs-891054/v1 ◽

2021 ◽

Author(s):

Yanfang Zhang ◽

Liangliang Xu ◽

Mingqing Xu ◽

Hong Tang

Keyword(s):

Risk Factors ◽

Hepatocellular Carcinoma ◽

Validation Cohort ◽

Predictive Performance ◽

Early Recurrence ◽

Tumor Diameter ◽

Training Cohort ◽

Factors Associated ◽

Tumor Number ◽

Before And After

Abstract This study aimed to establish pre- and postoperative nomograms in predicting postoperative early recurrence (ER) for hepatocellular carcinoma (HCC) without macrovascular invasion. The patients who underwent curative LR for HCC from January 2012 to December 2016 in our center were divided into training and internal prospective validation cohorts. Nomograms were constructed based on the independent risk factors derived from multivariate logistic regression analyses in training cohort. The predictive performance of nomograms was validated by internal prospective validation cohort. A total of 698 patients fulfilled with eligible criteria. Among them, 265 out of 482 patients (55.0%) in training cohort and 120 out 216 (55.6%) patients in validation cohort developed ER. The preoperative risk factors associated with ER were age, alpha fetoprotein (AFP), tumor diameter, tumor number; the postoperative risk factors associated with ER were age, tumor diameter, tumor number, microvasular invasion (MVI) and differentiation. The pre- and postoperative nomograms based on these factors showed good accuracy with C-indices of 0.712 and 0.850 in training cohort, and 0.754 and 0.857 in validation cohort, respectively. The calibration curves showed optimal agreement between the prediction by the nomograms and actual observation. The area under the receiver operating characteristic curves of pre- and postoperative nomograms were 0.721 and 0.848 in training cohort, and 0.754 and 0.844 in validation cohort, respectively. Present nomograms showed good performance in predicting ER for HCC without macrovascular invasion before and after surgery, which were helpful for doctors in designation of treatments and selection of patients for regularly surveillance or administration of neoadjuvant therapies.

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Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study

British Journal of Radiology ◽

10.1259/bjr.20210188 ◽

2021 ◽

pp. 20210188

Author(s):

Feihong Yu ◽

Jing Hang ◽

Jing Deng ◽

Bin Yang ◽

Jianxiang Wang ◽

...

Keyword(s):

Breast Cancer ◽

Triple Negative ◽

Validation Cohort ◽

External Validation ◽

Disease Free Survival ◽

Internal Validation ◽

Free Survival ◽

Clinicopathological Variables ◽

Disease Free ◽

Radiomics Signature

Objectives: To explore the predictive value of radiomics nomogram using pretreatment ultrasound for disease-free survival (DFS) after resection of triple negative breast cancer (TNBC). Methods and materials: A total of 486 TNBC patients from 3 different institutions were consecutively recruited for this study. They were categorized into the primary cohort (n = 216), as well as the internal validation cohort (n = 108) and external validation cohort (n = 162). In primary cohort, least absolute shrinkage and selection operator logistic regression algorithm was used to select recurrence-related radiomics features extracted from the breast tumor and peritumor regions, and a radiomics signature was constructed derived from the grayscale ultrasound images. A radiomic nomogram integrating independent clinicopathological variables and radiomic signature was established with uni- and multivariate cox regressions. The predictive nomogram was validated using an internal cohort and an independent external cohort regarding abilities of discrimination, calibration and clinical usefulness. Results: The patients with higher Rad-score had a worse prognostic outcome than those with lower Rad-score in primary cohort and two validation cohorts (All p < 0.05).The radiomics nomogram indicated more effective prognostic performance compared with the clinicopathological model and tumor node metastasis staging system (p < 0.01), with a training C-index of 0.75 (95% confidence interval (CI), 0.71–0.80), an internal validation C-index of 0.73 (95% CI, 0.69–0.78) and an external validation 0.71 (95% CI,0.66–0.76). Moreover, the calibration curves revealed a good consistency for survival prediction of the radiomics model. Conclusions: The ultrasound-based radiomics signature was a promising biomarker for risk stratification for TNBC patients. Furthermore, the proposed radiomics modal integrating the optimal radiomics features and clinical data provided individual relapse risk accurately. Advances in knowledge: The radiomics model integrating radiomic signature and independent clinicopathological variables could improve individual prognostic evaluation and facilitate therapeutic decision-making, which demonstrated the incremental value of the radiomics signature for prognostic prediction in TNBC.

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Development, Validation, and Visualization of A Web-Based Nomogram for Predicting the Recurrence-Free Survival Rate of Patients With Desmoid Tumors

Frontiers in Oncology ◽

10.3389/fonc.2021.634648 ◽

2021 ◽

Vol 11 ◽

Author(s):

Haotian Liu ◽

Kai Huang ◽

Tao Li ◽

Tielong Yang ◽

Zhichao Liao ◽

...

Keyword(s):

Risk Factors ◽

Desmoid Tumor ◽

External Validation ◽

Roc Curves ◽

Tumor Diameter ◽

Recurrence Free Survival ◽

Web Based ◽

Free Survival ◽

Tumor Number ◽

Cox Analysis

BackgroundSurgery is an important treatment option for desmoid tumor (DT) patients, but how to decrease and predict the high recurrence rate remains a major challenge.MethodsDesmoid tumor patients diagnosed and treated at Tianjin Cancer Institute & Hospital were included, and a web-based nomogram was constructed by screening the recurrence-related risk factors using Cox regression analysis. External validation was conducted with data from the Fudan University Shanghai Cancer Center.ResultsA total of 385 patients were identified. Finally, after excluding patients without surgery, patients who were lost to follow-up, and patients without complete resection, a total of 267 patients were included in the nomogram construction. Among these patients, 53 experienced recurrence, with a recurrence rate of 19.85%. The 3-year and 5-year recurrence-free survival (RFS) rates were 82.5% and 78%, respectively. Age, tumor diameter, admission status, location, and tumor number were correlated with recurrence in univariate Cox analysis. In multivariate Cox analysis, only age, tumor diameter and tumor number were independent risk factors for recurrence and were then used to construct a web-based nomogram to predict recurrence. The concordance index (C-index) of the nomogram was 0.718, and the areas under the curves (AUCs) of the 3-year and 5-year receiver operating characteristic (ROC) curves were 0.751 and 0.761, respectively. In the external validation set, the C-index was 0.706, and the AUCs of the 3-year and 5-year ROC curves are 0.788 and 0.794, respectively.ConclusionsAge, tumor diameter, and tumor number were independent predictors of recurrence for DTs, and a web-based nomogram containing these three predictors could accurately predict RFS (https://stepforward.shinyapps.io/Desmoidtumor/).

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

10.21203/rs.3.rs-557179/v1 ◽

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 To construct a prognostic model for preoperative prediction on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC), we constructed radiomics signature with high throughput radiomics features extracted from CT images of 272 patients (204 in training and 68 in validation cohort). 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. 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. 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), which were confirmed in validation cohort. DCA showed the model will receive benefit when the threshold probability was between 0 and 0.9. Collectively, multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.

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