A wavelet features derived radiomics nomogram for prediction of malignant and benign early-stage lung nodules

AbstractThis study was to develop a radiomics nomogram mainly using wavelet features for identifying malignant and benign early-stage lung nodules for high-risk screening. A total of 116 patients with early-stage solitary pulmonary nodules (SPNs) (≤ 3 cm) were divided into a training set (N = 70) and a validation set (N = 46). Radiomics features were extracted from plain LDCT images of each patient. A radiomics signature was then constructed with the LASSO with the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. This radiomics signature, consisting of one original and nine wavelet features, achieved favorable predictive efficacy than Mayo Clinic Model. The radiomics nomogram with radiomics signature and age also showed good calibration and discrimination in the training set (AUC 0.9406; 95% CI 0.8831–0.9982) and the validation set (AUC 0.8454; 95% CI 0.7196–0.9712). The decision curve indicated the clinical usefulness of our nomogram. The presented radiomics nomogram shows favorable predictive accuracy for identifying malignant and benign lung nodules in early-stage patients and is much better than the Mayo Clinic Model.

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A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas

Frontiers in Oncology ◽

10.3389/fonc.2021.632176 ◽

2021 ◽

Vol 11 ◽

Author(s):

Lei Bi ◽

Yubo Liu ◽

Jingxu Xu ◽

Ximing Wang ◽

Tong Zhang ◽

...

Keyword(s):

Logistic Regression ◽

Lymph Node ◽

Clinical Utility ◽

Clinical Decision Making ◽

Multivariate Logistic Regression Model ◽

Training Set ◽

Preoperative Prediction ◽

Validation Set ◽

Periampullary Carcinomas ◽

Radiomics Signature

PurposeTo establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.Materials and MethodsA total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.ResultsThe radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p < 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram.ConclusionOur CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.

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Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer

British Journal of Radiology ◽

10.1259/bjr.20210191 ◽

2021 ◽

pp. 20210191

Author(s):

Liuhui Zhang ◽

Donggen Jiang ◽

Chujie Chen ◽

Xiangwei Yang ◽

Hanqi Lei ◽

...

Keyword(s):

Prostate Cancer ◽

Multiparametric Mri ◽

Curve Analysis ◽

Training Set ◽

Threshold Probability ◽

Roc Curve Analysis ◽

Aggressive Prostate Cancer ◽

Therapeutic Decisions ◽

Validation Set ◽

Radiomics Signature

Objectives: To develop and validate a noninvasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy. Methods: In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T2WI (n = 788) and DWI (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic (ROC) curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature. Results: nine radiomics features were identified to form the radiomics signature. The radiomics score (Rad-score) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95% CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95% CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%. Conclusions: The multiparametric MRI-based radiomics signature can potentially serve as a noninvasive tool for distinguishing between indolent and aggressive PCa prior to therapy. Advances in knowledge: The multiparametric MRI-based radiomics signature has the potential to noninvasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.

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Cervical Spine Osteoradionecrosis or Bone Metastasis after Radiotherapy for Nasopharyngeal Carcinoma? The MRI-Based Radiomics for Characterization

10.21203/rs.3.rs-29809/v3 ◽

2020 ◽

Author(s):

Xi Zhong ◽

Li Li ◽

Huali Jiang ◽

Jinxue Yin ◽

Bingui Lu ◽

...

Keyword(s):

Cervical Spine ◽

Nasopharyngeal Carcinoma ◽

Clinical Utility ◽

Training Set ◽

Decision Curve Analysis ◽

Contrast Enhanced ◽

Validation Set ◽

Selection Operator ◽

Radiomics Signature ◽

Good Calibration

Abstract Background: To develop and validate an MRI-based radiomics nomogram for differentiation of cervical spine ORN from metastasis after radiotherapy (RT) in nasopharyngeal carcinoma (NPC).Methods: A radiomics nomogram was developed in a training set that comprised 46 NPC patients after RT with 95 cervical spine lesions (ORN, n = 51; metastasis, n = 44), and data were gathered from January 2008 to December 2012. 279 radiomics features were extracted from the axial contrast-enhanced T1-weighted image (CE-T1WI). A radiomics signature was created by using the least absolute shrinkage and selection operator (LASSO) algorithm. A nomogram model was developed based on the radiomics scores. The performance of the nomogram was determined in terms of its discrimination, calibration, and clinical utility. An independent validation set contained 25 consecutive patients with 47 lesions (ORN, n = 25; metastasis, n = 22) from January 2013 to December 2015. Results: The radiomics signature that comprised eight selected features was significantly associated with the differentiation of cervical spine ORN and metastasis. The nomogram model demonstrated good calibration and discrimination in the training set [AUC, 0.725; 95% confidence interval (CI), 0.622–0.828] and the validation set (AUC, 0.720; 95% CI, 0.573–0.867). The decision curve analysis indicated that the radiomics nomogram was clinically useful.Conclusions: MRI-based radiomics nomogram shows potential value to differentiate cervical spine ORN from metastasis after RT in NPC.

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MRI-Based Radiomics Nomogram To Predict Intraoperative Hemorrhage of Placenta Previa

10.21203/rs.3.rs-457028/v2 ◽

2021 ◽

Author(s):

Ye Song ◽

Liping Zhu ◽

Dali Chen ◽

Yongmei Li ◽

Qi Xi ◽

...

Keyword(s):

Predictive Accuracy ◽

Placenta Previa ◽

Preoperative Evaluation ◽

Curve Analysis ◽

Training Set ◽

Decision Curve Analysis ◽

Preoperative Prediction ◽

Mri Features ◽

Wide Range ◽

Validation Set

Abstract Background: Placenta previa is associated with higher percentage of intraoperative and postpartum hemorrhage, increased obstetric hysterectomy, significant maternal morbidity and mortality. We aimed to develop and validate a magnetic resonance imaging (MRI)-based nomogram to preoperative prediction of intraoperative hemorrhage (IPH) for placenta previa, which might contribute to adequate assessment and preoperative preparation for the obstetricians.Methods: Between May 2015 and December 2019, a total of 125 placenta previa pregnant women were divided into a training set (n = 80) and a validation set (n = 45). Radiomics features were extracted from MRI images of each patient. A MRI-based model comprising seven features was built for the classification of patients into IPH and non-IPH groups in a training set and validation set. Multivariate nomograms based on logistic regression analyses were built according to radiomics features. Receiver operating characteristic (ROC) curve was used to assess the model. Predictive accuracy of nomogram were assessed by calibration plots and decision curve analysis. Results: In multivariate analysis, placenta position, placenta thickness, cervical blood sinus and placental signals in the cervix were signifcantly independent predictors for IPH (all p < 0.05). The MRI-based nomogram showed favorable discrimination between IPH and non-IPH groups. The calibration curve showed good agreement between the estimated and the actual probability of IPH. Decision curve analysis also showed a high clinical benefit across a wide range of probability thresholds. The AUC was 0.918 ( 95% CI, 0.857-0.979 ) in the training set and 0.866( 95% CI, 0.748-0.985 ) in the validation set by the combination of four MRI features.Conclusions: The MRI-based nomograms might be a useful tool for the preoperative prediction of IPH outcomes for placenta previa. Our study enables obstetricians to perform adequate preoperative evaluation to minimize blood loss and reduce the rate of caesarean hysterectomy.

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A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

10.21203/rs.3.rs-41188/v2 ◽

2020 ◽

Author(s):

Hesan Luo ◽

Shao-Fu Huang ◽

Hong-Yao Xu ◽

Xu-Yuan Li ◽

Sheng-Xi Wu ◽

...

Keyword(s):

Logistic Regression ◽

Squamous Cell ◽

Cell Cancer ◽

Complete Response ◽

Clinical Staging ◽

Training Set ◽

Simple Method ◽

Validation Set ◽

The Impact ◽

Radiomics Signature

Abstract Purpose: To develop and validate a nomogram model to predict complete response (CR) after concurrent chemoradiotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features. Methods: Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomogram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions. Results: A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUC of Rad-score was 0.812 (95%CI: 0.742-0.869, p<0.001) in the training set and 0.744 (95%CI: 0.632-0.851, p=0.003) in the validation set. Multivariate analysis showed that Rad-score and clinical staging were independent predictors of CR status, with P values of 0.035 and 0.023, respectively. A nomogram model incorporating Rad-socre and clinical staging was developed and validated, with an AUC of 0.844 (95％CI: 0.779-0.897) in the training set and 0.807 (95％CI: 0.691-0.894) in the validation set．Delong test showed that the nomogram model was significantly superior to the clinical staging, with P<0.001 in the training set and P=0.026 in the validation set. The decision curve showed that the nomogram model was superior to the clinical staging when the risk threshold was greater than 25%. Conclusion: We developed and validated a nomogram model for predicting CR status of ESCC patients after CCRT. The nomogram model was combined radiomics signature Rad-score and clinical staging. This model provided us with an economical and simple method for evaluating the response of chemoradiotherapy for patients with ESCC.

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Identification of predictive biomarkers of overall survival (OS) in patients (pts) with advanced renal cell carcinoma (RCC) treated with interferon alpha (I) with or without bevacizumab (B): Results from CALGB 90206 (Alliance).

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.4520 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. 4520-4520 ◽

Cited By ~ 14

Author(s):

Andrew B. Nixon ◽

Susan Halabi ◽

Ivo Shterev ◽

Mark Starr ◽

John C Brady ◽

...

Keyword(s):

Proportional Hazards ◽

Proportional Hazards Model ◽

Predictive Accuracy ◽

Area Under The Curve ◽

Predictive Biomarkers ◽

Risk Groups ◽

Phase Iii ◽

Plasma Samples ◽

Training Set ◽

Validation Set

4520 Background: CALGB 90206 was a phase III trial of 732 pts with RCC comparing B+I versus I alone demonstrating no difference in OS. To date, there are no validated predictive biomarkers for B in RCC. For this reason, baseline plasma samples from CALGB 90206 pts were analyzed to identify and test predictive markers for B+I in RCC pts. Methods: Baseline EDTA plasma samples from 424 consenting pts were analyzed using an optimized multiplex ELISA platform for 32 candidate factors related to tumor growth, angiogenesis, and inflammation. The data were randomly split into training (n=286) and validation (n=138) sets. The proportional hazards model was used to test for treatment-marker interactions of OS. The estimated coefficients from the training set were used to compute a risk score (RS) for each pt in the validation set. The RS classified pts by risk in the validation set. The model was assessed for its predictive accuracy using area under the curve (AUC). Results: A statistically significant 3-way interaction between interleukin-6 (IL-6), hepatocyte growth factor (HGF) and treatment was observed in the training set (p<0.0001). The median levels of IL-6 and HGF in the training set were 8.4 pg/ml and 89 pg/ml, respectively. In the validation set, the RS was predictive of OS (p<0.001) with the high and low risk groups having a median OS of 10 months and 32 months, respectively. The AUC in the validation set was 0.82 (95% CI=0.77-0.88). The median OS (in months) by median levels of IL-6 and HGF stratified by treatment arm in the validation set is presented in the table with associated 95% CI (NR=not reached). Conclusions: IL-6 and HGF are predictive for OS in RCC patients treated with B+I and a RS based on these factors identified patients who benefitted most from B. If independently validated, this novel RS could guide clinical decisions and pt selection in future RCC trials. [Table: see text]

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Predictive Value of Nomogram Based on Kyoto Classification of Gastritis to Diagnosis of Gastric Cancer

10.21203/rs.3.rs-847483/v1 ◽

2021 ◽

Author(s):

Jiejun Lin ◽

Huang Su ◽

Yaqi Guan ◽

Qingjie Zhou ◽

Jie Pan ◽

...

Keyword(s):

Gastric Cancer ◽

Multivariate Analysis ◽

Calibration Curve ◽

Predictive Value ◽

Predictive Accuracy ◽

Bootstrap Method ◽

Characteristic Curve ◽

Training Set ◽

Validation Set

Abstract Background and Aim. It is of importance to predict the risk of gastric cancer (GC) for endoscopists because early detection of GC determines the determines the selection of best treatment strategy and the prognosis of patients. The aim of the study was to evaluate the utility of a predictive nomogram based on Kyoto classification of gastritis for GC. Methods. It was a retrospective study that included 2639 patients who received esophagogastroduodenoscopy and serum pepsinogen (PG) assay from January 2020 to November 2020 at the Endoscopy Center of the Department of Gastroenterology, Wenzhou Central Hospital. Routine biopsy was conducted to determine the benign and malignant lesions pathologically. All cases were randomly divided into the training set (70%) and the validation set (30%) by using bootstrap method. A nomogram was formulated according to multivariate analysis of training set. The predictive accuracy and discriminative ability of the nomogram were assessed by concordance index (C-index), area under the curve (AUC) of receiver operating characteristic curve (ROC) as well as calibration curve and were validated by validation set.Results. Multivariate analysis indicated that age, sex, PG I/II ratio and Kyoto classification scores were independent predictive variables for GC. The C-index of the nomogram of the training set was 0.79 (95% CI: 0.74 to 0.84) and the AUC of ROC is 0.79. The calibration curve of the nomogram demonstrated an optimal agreement between predicted probability and observed probability of the risk of GC. In the validation set, the C-index was 0.86 (95% CI: 0.79 to 0.94) with a calibration curve of better concurrence.Conclusion. The nomogram formulated was proven to be of high predictive value for GC.

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A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

10.21203/rs.3.rs-41188/v1 ◽

2020 ◽

Author(s):

Hesan Luo ◽

Shao-Fu Huang ◽

Hong-Yao Xu ◽

Xu-Yuan Li ◽

Sheng-Xi Wu ◽

...

Keyword(s):

Logistic Regression ◽

Squamous Cell ◽

Cell Cancer ◽

Complete Response ◽

Clinical Staging ◽

Training Set ◽

Simple Method ◽

Validation Set ◽

The Impact ◽

Radiomics Signature

Abstract Purpose To develop and validate a nomogram model to predict complete response (CR) after concurrent chemoradiotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features. Methods Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomogram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions. Results A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUCs of Rad-score was 0.812 (95%CI: 0.742–0.869) in the training set and 0.744 (95%CI: 0.632–0.851) in the validation set. Multivariate analysis showed that Rad-score and clinical staging were independent predictors of CR status, with P values of 0.035 and 0.023, respectively. A nomogram model incorporating Rad-socre and clinical staging was developed and validated, with an AUC of 0.844 (95%CI: 0.779–0.897) in the training set and 0.807 (95༅CI: 0.691–0.894) in the validation set༎Delong test showed that the nomogram model was significantly superior to the clinical staging, with P < 0.001 in the training set and P = 0.026 in the validation set. The decision curve showed that the nomogram model was superior to the clinical staging when the risk threshold was greater than 25%. Conclusion We developed and validated a nomogram model for predicting CR status of ESCC patients after CCRT. The nomogram model was combined radiomics signature Rad-score and clinical staging. This model provided us with an economical and simple method for evaluating the response of chemoradiotherapy for patients with ESCC.

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Prognostic Nomogram and a Risk Classification System for Predicting Overall Survival of Elderly Patients with Fibrosarcoma: A Population-Based Study

Journal of Oncology ◽

10.1155/2021/9984217 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Fengkai Yang ◽

Hangkai Xie ◽

Yucheng Wang

Keyword(s):

Overall Survival ◽

Elderly Patients ◽

Classification System ◽

Cox Regression ◽

Predictive Accuracy ◽

Risk Classification ◽

Seer Database ◽

Training Set ◽

Decision Curve Analysis ◽

Validation Set

Background. The objective of this study was to develop a nomogram model and risk classification system to predict overall survival in elderly patients with fibrosarcoma. Methods. The study retrospectively collected data from the Surveillance, Epidemiology, and End Results (SEER) database relating to elderly patients diagnosed with fibrosarcoma between 1975 and 2015. Independent prognostic factors were identified using univariate and multivariate Cox regression analyses on the training set to construct a nomogram model for predicting the overall survival of patients at 3, 5, and 10 years. The receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the discrimination and predictive accuracy of the model. Decision curve analysis was used for assessing the clinical utility of the model. Result. A total of 357 elderly fibrosarcoma patients from the SEER database were included in our analysis, randomly classified into a training set (252) and a validation set (105). The multivariate Cox regression analysis of the training set demonstrated that age, surgery, grade, chemotherapy, and tumor stage were independent prognostic factors. The ROC showed good model discrimination, with AUC values of 0.837, 0.808, and 0.806 for 3, 5, and 10 years in the training set and 0.769, 0.779, and 0.770 for 3, 5, and 10 years in the validation set, respectively. The calibration curves and decision curve analysis showed that the model has high predictive accuracy and a high clinical application. In addition, a risk classification system was constructed to differentiate patients into three different mortality risk groups accurately. Conclusion. The nomogram model and risk classification system constructed by us help optimize patients’ treatment decisions to improve prognosis.

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A Novel Radiomics-Platelet Nomogram for the Prediction of Gastroesophageal Varices Needing Treatment in Cirrhotic Patients

10.21203/rs.3.rs-186603/v1 ◽

2021 ◽

Author(s):

Yiken Lin ◽

Lijuan Li ◽

Dexin Yu ◽

Zhuyun Liu ◽

Shuhong Zhang ◽

...

Keyword(s):

External Validation ◽

Multivariate Logistic Regression Model ◽

Noninvasive Method ◽

Training Set ◽

Noninvasive Methods ◽

Internal Validation ◽

Gastroesophageal Varices ◽

Portal Phase ◽

Cirrhotic Patients ◽

Validation Set

Abstract Background and aimsHighly accurate noninvasive methods for predicting gastroesophageal varices needing treatment (VNT) are desired. Radiomics is a newly emerging technology of image analysis. This study aims to develop and validate a novel noninvasive method based on radiomics for predicting VNT in cirrhosis.MethodsIn this retrospective-prospective study, a total of 245 cirrhotic patients were divided as the training set, internal validation set and external validation set. Radiomics features were extracted from portal-phase computed tomography (CT) images of each patient. A radiomics signature (Rad-score) was constructed with the least absolute shrinkage and selection operator algorithm and 10-folds cross-validation in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. ResultsThe rad-score, consisting of 14 features from the gastroesophageal region and 5 from the splenic hilum region, was effective for VNT classification. The diagnostic performance was further improved by combining the rad-score with platelet counts, achieving an AUC of 0.987(95% CI, 0.969-1.00), 0.973(95% CI, 0.939-1.00) and 0.947(95% CI, 0.876-1.00) in the training set, internal validation set and external validation set respectively. In efficacy and safety assessment, the radiomics nomogram could spare more than 40% of endoscopic examinations with a low risk of missing VNT (<5%), and no more than 8.3% of unnecessary endoscopic examinations still be performed.ConclusionsIn this study, we developed and validated a novel, diagnostic radiomics-based nomogram which is a reliable and noninvasive method to predict VNT in cirrhotic patients.

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