CT-Based Radiomics Nomogram for Differentiation of Anterior Mediastinal Thymic Cyst From Thymic Epithelial Tumor

ObjectivesThis study aimed to distinguish preoperatively anterior mediastinal thymic cysts from thymic epithelial tumors via a computed tomography (CT)-based radiomics nomogram.MethodsThis study analyzed 74 samples of thymic cysts and 116 samples of thymic epithelial tumors as confirmed by pathology examination that were collected from January 2014 to December 2020. Among the patients, 151 cases (scanned at CT 1) were selected as the training cohort, and 39 cases (scanned at CT 2 and 3) served as the validation cohort. Radiomics features were extracted from pre-contrast CT images. Key features were selected by SelectKBest and least absolute shrinkage and selection operator and then used to build a radiomics signature (Rad-score). The radiomics nomogram developed herein via multivariate logistic regression analysis incorporated clinical factors, conventional CT findings, and Rad-score. Its performance in distinguishing the samples of thymic cysts from those of thymic epithelial tumors was assessed via discrimination, calibration curve, and decision curve analysis (DCA).ResultsThe radiomics nomogram, which incorporated 16 radiomics features and 3 conventional CT findings, including lesion edge, lobulation, and CT value, performed better than Rad-score, conventional CT model, and the clinical judgment by radiologists in distinguishing thymic cysts from thymic epithelial tumors. The area under the receiver operating characteristic (ROC) curve of the nomogram was 0.980 [95% confidence interval (CI), 0.963–0.993] in the training cohort and 0.992 (95% CI, 0.969–1.000) in the validation cohort. The calibration curve and the results of DCA indicated that the nomogram has good consistency and valuable clinical utility.ConclusionThe CT-based radiomics nomogram presented herein may serve as an effective and convenient tool for differentiating thymic cysts from thymic epithelial tumors. Thus, it may aid in clinical decision-making.

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Development and Validation of a Clinical Score for Predicting the Risk of Tympanosclerosis Before Surgery

Ear Nose & Throat Journal ◽

10.1177/0145561320951676 ◽

2020 ◽

pp. 014556132095167

Author(s):

Zhihuai Dong ◽

Mingguang Zhou ◽

Gaofei Ye ◽

Jing Ye ◽

Mang Xiao

Keyword(s):

Otitis Media ◽

Calibration Curve ◽

Validation Cohort ◽

Predictive Accuracy ◽

Strong Predictor ◽

Multivariate Logistic Regression Analysis ◽

Clinical Score ◽

Type I ◽

Discriminative Ability ◽

Training Cohort

Objective: To develop and validate a clinical score to predict the risk of tympanosclerosis before surgery. Methods: A sample of 404 patients who underwent middle ear microsurgery for otitis media was enrolled. These patients were randomly divided into 2 cohorts: the training cohort (n = 243, 60%) and the validation cohort (n = 161, 40%). The preoperative predictors of tympanosclerosis were determined by multivariate logistic regression analysis and implemented using a clinical score tool. The predictive accuracy and discriminative ability of the clinical score were determined by the area under the curve (AUC) and the calibration curve. Results: The multivariate analysis in the training cohort (n = 243, 60%) identified independent factors for tympanosclerosis as the female sex (odds ratio [OR]: 3.83; 95% CI: 1.66-9.37), the frequency-specific air-bone gap at 250 Hz ≥ 45 dB HL (OR: 3.68; 95% CI: 1.68-8.57), aditus ad antrum blockage (OR: 3.29; 95% CI: 1.38-8.43), type I eardrum calcification (OR: 25.37; 95% CI: 8.41-88.91) or type II eardrum calcification (OR: 18.86; 95% CI: 6.89-58.77), and a history of otitis media ≥ 10 years (OR: 4.10; 95% CI: 1.58-11.83), which were all included in the clinical score tool. The AUC of the clinical score for predicting tympanosclerosis was 0.89 (95% CI: 0.85-0.93) in the training cohort and 0.89 (95% CI: 0.84-0.95) in the validation cohort. The calibration curve also showed good agreement between the predicted and observed probability. Conclusions: The clinical score achieved an optimal prediction of tympanosclerosis before surgery. The presence of calcification pearls on the promontorium tympani is a strong predictor of tympanosclerosis with stapes fixation.

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Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

Cancer Imaging ◽

10.1186/s40644-021-00395-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sijia Cui ◽

Tianyu Tang ◽

Qiuming Su ◽

Yajie Wang ◽

Zhenyu Shu ◽

...

Keyword(s):

Calibration Curve ◽

Main Pancreatic Duct ◽

Validation Cohort ◽

External Validation ◽

High Grade ◽

Intraductal Papillary Mucinous Neoplasms ◽

Training Cohort ◽

Independent Validation ◽

Medical Centers ◽

Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

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Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study

Frontiers in Oncology ◽

10.3389/fonc.2021.719494 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiangming Cai ◽

Junhao Zhu ◽

Jin Yang ◽

Chao Tang ◽

Feng Yuan ◽

...

Keyword(s):

Risk Factors ◽

Cerebrospinal Fluid ◽

Prediction Model ◽

Calibration Curve ◽

Validation Cohort ◽

Laboratory Data ◽

Pituitary Surgery ◽

Cerebrospinal Fluid Leakage ◽

Training Cohort ◽

Preoperative Prediction

BackgroundPituitary adenomas (PAs) are the most common tumor of the sellar region. PA resection is the preferred treatment for patients with clear indications for surgery. Intraoperative cerebrospinal fluid (iCSF) leakage is a major complication of PA resection surgery. Risk factors for iCSF leakage have been studied previously, but a predictive nomogram has not yet been developed. We constructed a nomogram for preoperative prediction of iCSF leakage in endoscopic pituitary surgery.MethodsA total of 232 patients who underwent endoscopic PA resection at the Department of Neurosurgery in Jinling Hospital between January of 2018 and October of 2020 were enrolled in this retrospective study. Patients treated by a board-certified neurosurgeon were randomly classified into a training cohort or a validation cohort 1. Patients treated by other qualified neurosurgeons were included in validation cohort 2. A range of demographic, clinical, radiological, and laboratory data were acquired from the medical records. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and uni- and multivariate logistic regression were utilized to analyze these features and develop a nomogram model. We used a receiver operating characteristic (ROC) curve and calibration curve to evaluate the predictive performance of the nomogram model.ResultsVariables were comparable between the training cohort and validation cohort 1. Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. The calibration curve showed satisfactory homogeneity between the predicted probability and actual observations. Nomogram performance was stable in the subgroup analysis.ConclusionsTumor height and albumin were the independent risk factors for iCSF leakage. The prediction model developed in this study is the first nomogram developed as a practical and effective tool to facilitate the preoperative prediction of iCSF leakage in endoscopic pituitary surgery, thus optimizing treatment decisions.

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

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e16641 ◽

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.

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Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study

PeerJ ◽

10.7717/peerj.12626 ◽

2021 ◽

Vol 9 ◽

pp. e12626

Author(s):

Jiaojiao Zhong ◽

Yunan He ◽

Jianchi Ma ◽

Siyao Lu ◽

Yushi Wu ◽

...

Keyword(s):

Protective Factor ◽

Validation Cohort ◽

Malignant Tumors ◽

Univariate Analysis ◽

Multivariate Logistic Regression Analysis ◽

Area Under The Curve ◽

Poor Prognostic Factor ◽

Training Cohort ◽

Discrimination Ability ◽

Logistic Analysis

Background Dermatomyositis accompanied with malignancy is a common poor prognostic factor of dermatomyositis. Thus, the early prediction of the risk of malignancy in patients with dermatomyositis can significantly improve the prognosis of patients. However, the identification of antibodies related to malignancy in dermatomyositis patients has not been widely implemented in clinical practice. Herein, we established a predictive nomogram model for the diagnosis of dermatomyositis associated with malignancy. Methods We retrospectively analyzed 240 cases of dermatomyositis patients admitted to Sun Yat-sen Memorial Hospital, Sun Yat-sen University from January 2002 to December 2019. According to the year of admission, the first 70% of the patients were used to establish a training cohort, and the remaining 30% were assigned to the validation cohort. Univariate analysis was performed on all variables, and statistically relevant variables were further included in a multivariate logistic regression analysis to screen for independent predictors. Finally, a nomogram was constructed based on these independent predictors. Bootstrap repeated sampling calculation C-index was used to evaluate the model’s calibration, and area under the curve (AUC) was used to evaluate the model discrimination ability. Results Multivariate logistic analysis showed that patients older than 50-year-old, dysphagia, refractory itching, and elevated creatine kinase were independent risk factors for dermatomyositis associated with malignancy, while interstitial lung disease was a protective factor. Based on this, we constructed a nomogram using the above-mentioned five factors. The C-index was 0.780 (95% CI [0.690–0.870]) in the training cohort and 0.756 (95% CI [0.618–0.893]) in the validation cohort, while the AUC value was 0.756 (95% CI [0.600–0.833]). Taken together, our nomogram showed good calibration and was effective in predicting which dermatomyositis patients were at a higher risk of developing malignant tumors.

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Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

Frontiers in Medicine ◽

10.3389/fmed.2021.680679 ◽

2021 ◽

Vol 8 ◽

Author(s):

Haisheng You ◽

Mengmeng Teng ◽

Chun Xia Gao ◽

Bo Yang ◽

Sasa Hu ◽

...

Keyword(s):

Decision Making ◽

Prognostic Factors ◽

Elderly Patients ◽

Clinical Decision Making ◽

Cox Regression ◽

Validation Cohort ◽

Discrimination Performance ◽

Seer Database ◽

Histologic Subtype ◽

Training Cohort

Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

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Prognostic Nomogram for Postoperative Patients With Gastroesophageal Junction Cancer of No Distant Metastasis

Frontiers in Oncology ◽

10.3389/fonc.2021.643261 ◽

2021 ◽

Vol 11 ◽

Author(s):

Qiang Guo ◽

YuanYuan Peng ◽

Heng Yang ◽

JiaLong Guo

Keyword(s):

Regression Analysis ◽

Surgical Resection ◽

Distant Metastasis ◽

Calibration Curve ◽

Cox Regression ◽

Validation Cohort ◽

Predictive Accuracy ◽

Gastroesophageal Junction ◽

Training Cohort ◽

Cox Regression Analysis

BackgroundGastroesophageal junction (GEJ) was one of the most common malignant tumors. However, the value of clinicopathological features in predicting the prognosis of postoperative patients with GEJ cancer and without distant metastasis was still unclear.MethodsThe 3425 GEJ patients diagnosed and underwent surgical resection without distant metastasis in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015 were enrolled,and they were randomly divided into training and validation cohorts with 7:3 ratio. Univariate and multivariate Cox regression analysis were used to determine the predictive factors that constituted the nomogram. The predictive accuracy and discriminability of Nomogram were determined by the area under the curve (AUC), C index, and calibration curve, and the influence of various factors on prognosis was explored.Results2,400 patients were designed as training cohort and 1025 patients were designed as validation cohort. The percentages of the distribution of demographic and clinicopathological characteristics in the training and validation cohorts tended to be the same. In the training cohort, multivariate Cox regression analysis revealed that the age, tumor grade, T stage and N stage were independent prognostic risk factors for patients with GEJ cancer without distant metastasis. The C index of nomogram model was 0.667. The AUC of the receiver operating characteristic (ROC) analysis for 3- and 5-year overall survival (OS) were 0.704 and 0.71, respectively. The calibration curve of 3- and 5-year OS after operation showed that there was the best consistency between nomogram prediction and actual observation. In the validation cohort, the C index of nomogram model, the AUC of 3- and 5-year OS, and the calibration curve were similar to the training cohort.ConclusionsNomogram could evaluate the prognosis of patients with GEJ cancer who underwent surgical resection without distant metastasis.

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Growth of thymic epithelial tumors and thymic cysts: Differential radiological points

Thoracic Cancer ◽

10.1111/1759-7714.13016 ◽

2019 ◽

Vol 10 (4) ◽

pp. 864-871 ◽

Cited By ~ 1

Author(s):

Hyungjin Kim ◽

Soon Ho Yoon ◽

Jihang Kim ◽

Kyung Won Lee ◽

Ye Ra Choi ◽

...

Keyword(s):

Thymic Epithelial Tumors ◽

Epithelial Tumors ◽

Thymic Cysts

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A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data

Biomarker Research ◽

10.1186/s40364-020-00219-y ◽

2020 ◽

Vol 8 (1) ◽

Author(s):

Jin-Cheng Wang ◽

Rao Fu ◽

Xue-Wen Tao ◽

Ying-Fan Mao ◽

Fei Wang ◽

...

Keyword(s):

Clinical Decision Making ◽

Multivariate Logistic Regression Analysis ◽

Support Vector ◽

Features Selection ◽

Clinical Predictors ◽

Training Cohort ◽

Discrimination Ability ◽

Clinical Benefits ◽

Contrast Ct ◽

Radiomics Signature

Abstract Background To establish and validate a radiomics-based model for predicting liver cirrhosis in patients with hepatitis B virus (HBV) by using non-contrast computed tomography (CT). Methods This retrospective study developed a radiomics-based model in a training cohort of 144 HBV-infected patients. Radiomic features were extracted from abdominal non-contrast CT scans. Features selection was performed with the least absolute shrinkage and operator (LASSO) method based on highly reproducible features. Support vector machine (SVM) was adopted to build a radiomics signature. Multivariate logistic regression analysis was used to establish a radiomics-based nomogram that integrated radiomics signature and other independent clinical predictors. Performance of models was evaluated through discrimination ability, calibration and clinical benefits. An internal validation was conducted in 150 consecutive patients. Results The radiomics signature comprised 25 cirrhosis-related features and showed significant differences between cirrhosis and non-cirrhosis cohorts (P < 0.001). A radiomics-based nomogram that integrates radiomics signature, alanine transaminase, aspartate aminotransferase, globulin and international normalized ratio showed great calibration and discrimination ability in the training cohort (area under the curve [AUC]: 0.915) and the validation cohort (AUC: 0.872). Decision curve analysis confirmed the most clinical benefits can be provided by the nomogram compared with other methods. Conclusions Our developed radiomics-based nomogram can successfully diagnose the status of cirrhosis in HBV-infected patients, that may help clinical decision-making.

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Individualized predictions of early isolated distal deep vein thrombosis in patients with acute ischemic stroke: a retrospective study

BMC Geriatrics ◽

10.1186/s12877-021-02088-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Hao-Ran Cheng ◽

Gui-Qian Huang ◽

Zi-Qian Wu ◽

Yue-Min Wu ◽

Gang-Qiang Lin ◽

...

Keyword(s):

Deep Vein Thrombosis ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Validation Cohort ◽

Characteristic Curve ◽

Multivariate Logistic Regression Analysis ◽

Training Cohort ◽

Vein Thrombosis ◽

Distal Deep Vein Thrombosis ◽

Deep Vein

Abstract Background Although isolated distal deep vein thrombosis (IDDVT) is a clinical complication for acute ischemic stroke (AIS) patients, very few clinicians value it and few methods can predict early IDDVT. This study aimed to establish and validate an individualized predictive nomogram for the risk of early IDDVT in AIS patients. Methods This study enrolled 647 consecutive AIS patients who were randomly divided into a training cohort (n = 431) and a validation cohort (n = 216). Based on logistic analyses in training cohort, a nomogram was constructed to predict early IDDVT. The nomogram was then validated using area under the receiver operating characteristic curve (AUROC) and calibration plots. Results The multivariate logistic regression analysis revealed that age, gender, lower limb paralysis, current pneumonia, atrial fibrillation and malignant tumor were independent risk factors of early IDDVT; these variables were integrated to construct the nomogram. Calibration plots revealed acceptable agreement between the predicted and actual IDDVT probabilities in both the training and validation cohorts. The nomogram had AUROC values of 0.767 (95% CI: 0.742–0.806) and 0.820 (95% CI: 0.762–0.869) in the training and validation cohorts, respectively. Additionally, in the validation cohort, the AUROC of the nomogram was higher than those of the other scores for predicting IDDVT. Conclusions The present nomogram provides clinicians with a novel and easy-to-use tool for the prediction of the individualized risk of IDDVT in the early stages of AIS, which would be helpful to initiate imaging examination and interventions timely.

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