A MRI-based radiomics model for preoperative prediction of five-year survival status in hepatocellular carcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15596-e15596
Author(s):  
Xiao-Hang Wang ◽  
Liu-Hua Long ◽  
Yong Cui ◽  
Angela Y Jia ◽  
Xiang-Gao Zhu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Model Performance ◽  
Area Under The Curve ◽  
Clinical Risk Factor ◽  
Multivariable Logistic Regression Analysis ◽  
Survival Status ◽  
Clinical Risk ◽  
Preoperative Prediction ◽  
Magnetic Resonance Imaging Mri ◽  
Radiomics Signature

e15596 Background: Recurrence is the major cause of mortality in resected hepatocellular carcinoma (HCC) patients. However, without a standard approach to evaluate prognosis, it is difficult to select potential candidates for additional therapy. We aim to develop and evaluate a magnetic resonance imaging (MRI)-based radiomics model to predict 5-year survival status of HCC patients in the preoperative setting. Methods: A total of 201 HCC patients who were followed up for at least 5 years (unless death occurred) after curative hepatectomy were enrolled in this retrospective multicenter study. 3144 radiomics features were extracted from four conventional sequences of preoperative MRI (T1WI, T2WI, DWI and dynamic contrast-enhanced MRI). The random forest method was used for feature selection and radiomics signature building. 5-fold cross validation was used for robust estimation. A radiomics model incorporating the radiomics signature and clinical risk factors was developed. The model performance was evaluated by its discrimination and calibration. Results: Patients were divided into survivor (n = 97) and non-survivor (n = 104) groups based on survival status at 5 years from surgery. The 30 most survival-related radiomics features were selected to develop the radiomics signature. The preoperative alpha-fetoprotein level was integrated into the model as an independent clinical risk factor in multivariable logistic regression analysis (OR = 3.764; 95% CI 1.997-7.096). The radiomics model demonstrated good calibration and satisfactory discrimination, with the mean area under the curve of 0.9340 (95% CI 0.9222-0.9458) in training set and 0.7383 (95% CI 0.6914-0.7852) in validation set. Conclusions: The MRI-based radiomics model represents a valid method to predict 5-year survival status in HCC patients in the preoperative setting, and may be used to guide neoadjuvant or adjuvant treatment decisions in high-risk patients.

scholarly journals Prediction of Post-hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma Based on Radiomics Using Gd-EOB-DTPA-Enhanced MRI: The Liver Failure Model

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuyan Chen ◽  
Zelong Liu ◽  
Yunxian Mo ◽  
Bin Li ◽  
Qian Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Failure ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Training Cohort ◽  
Clinical Model ◽  
Preoperative Prediction ◽  
Medical Centers ◽  
Appropriate Treatment ◽  
Magnetic Resonance Imaging Mri

Objectives: Preoperative prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) is significant for developing appropriate treatment strategies. We aimed to establish a radiomics-based clinical model for preoperative prediction of PHLF in HCC patients using gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI).Methods: A total of 144 HCC patients from two medical centers were included, with 111 patients as the training cohort and 33 patients as the test cohort, respectively. Radiomics features and clinical variables were selected to construct a radiomics model and a clinical model, respectively. A combined logistic regression model, the liver failure (LF) model that incorporated the developed radiomics signature and clinical risk factors was then constructed. The performance of these models was evaluated and compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) with 95% confidence interval (CI).Results: The radiomics model showed a higher AUC than the clinical model in the training cohort and the test cohort for predicting PHLF in HCC patients. Moreover, the LF model had the highest AUCs in both cohorts [0.956 (95% CI: 0.955–0.962) and 0.844 (95% CI: 0.833–0.886), respectively], compared with the radiomics model and the clinical model.Conclusions: We evaluated quantitative radiomics features from MRI images and presented an externally validated radiomics-based clinical model, the LF model for the prediction of PHLF in HCC patients, which could assist clinicians in making treatment strategies before surgery.

scholarly journals Development of a radiomics model to diagnose pheochromocytoma preoperatively: a multicenter study with prospective validation

2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Jianqiu Kong ◽  
Junjiong Zheng ◽  
Jieying Wu ◽  
Shaoxu Wu ◽  
Jinhua Cai ◽  
...  
Keyword(s):  
External Validation ◽  
Area Under The Curve ◽  
Multivariable Logistic Regression Analysis ◽  
Lasso Regression ◽  
Clinical Predictors ◽  
Internal Validation ◽  
Clinical Model ◽  
Magnetic Resonance Imaging Mri ◽  
Individualized Prediction ◽  
Radiomics Signature

Abstract Background Preoperative diagnosis of pheochromocytoma (PHEO) accurately impacts preoperative preparation and surgical outcome in PHEO patients. Highly reliable model to diagnose PHEO is lacking. We aimed to develop a magnetic resonance imaging (MRI)-based radiomic-clinical model to distinguish PHEO from adrenal lesions. Methods In total, 305 patients with 309 adrenal lesions were included and divided into different sets. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction, feature selection, and radiomics signature building. In addition, a nomogram incorporating the obtained radiomics signature and selected clinical predictors was developed by using multivariable logistic regression analysis. The performance of the radiomic-clinical model was assessed with respect to its discrimination, calibration, and clinical usefulness. Results Seven radiomics features were selected among the 1301 features obtained as they could differentiate PHEOs from other adrenal lesions in the training (area under the curve [AUC], 0.887), internal validation (AUC, 0.880), and external validation cohorts (AUC, 0.807). Predictors contained in the individualized prediction nomogram included the radiomics signature and symptom number (symptoms include headache, palpitation, and diaphoresis). The training set yielded an AUC of 0.893 for the nomogram, which was confirmed in the internal and external validation sets with AUCs of 0.906 and 0.844, respectively. Decision curve analyses indicated the nomogram was clinically useful. In addition, 25 patients with 25 lesions were recruited for prospective validation, which yielded an AUC of 0.917 for the nomogram. Conclusion We propose a radiomic-based nomogram incorporating clinically useful signatures as an easy-to-use, predictive and individualized tool for PHEO diagnosis.

scholarly journals P13.12 Development and validation of a radiomic model for the preoperative prediction of transsphenoidal surgical response in patients with acromegaly

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii65-iii65
Author(s):  
Y Fan ◽  
M Feng ◽  
R Wang
Keyword(s):  
Clinical Features ◽  
Validation Cohort ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Support Vector ◽  
Individual Treatment ◽  
Multivariable Logistic Regression Analysis ◽  
Operating Characteristics ◽  
Preoperative Prediction ◽  
Characteristics Analysis

Abstract BACKGROUND The preoperative prediction of transsphenoidal surgical (TSS) response is important for determining individual treatment strategies for acromegaly. Therefore, this study aimed to predict TSS response in a non-invasive way based on radiomic analysis. MATERIAL AND METHODS 273 patients with acromegaly were enrolled and divided into primary (n=180) and validation cohorts (n=93) according to time point. Radiomic features were extracted from the MR images and determined using the ‘Elastic Net’ feature selection algorithm. A radiomic signature was built using a support vector machine. Subsequently, multivariable logistic regression analysis was used to select the most informative clinical features, and a radiomic model, incorporating the radiomic signature and selected clinical features, was constructed and used as the final predictive model. The performance of this radiomic model was validated using receiver operating characteristics analysis, and its calibration, discriminating ability, and clinical usefulness were assessed. RESULTS The radiomic signature, which was constructed with six radiomic features selected using the primary cohort, showed a favorable discriminatory ability in the validation cohort. The radiomic model incorporating the radiomic signature and three selected clinical features showed good discrimination abilities and calibration, with an area under the curve (AUC) of 0.93 for the primary cohort and 0.89 for the validation cohort. The radiomic model better estimated the treatment responses of patients with acromegaly than did the clinical features. Decision curve analysis showed the radiomic model was clinically useful. CONCLUSION This radiomic model could aid neurosurgeons in the preoperative prediction of TSS response in patients with acromegaly, and could contribute to determining individual treatment strategies.

scholarly journals Effectiveness of hysteroscopic resection of a uterine caesarean niche can be predicted: a prospective cohort study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qian Zhu ◽  
Xiaoqing He ◽  
Ling Jiang ◽  
Guiling Liang ◽  
Chenfeng Zhu ◽  
...  
Keyword(s):  
Predictive Model ◽  
Prospective Cohort ◽  
Multivariable Logistic Regression Analysis ◽  
Mri Findings ◽  
Internal Validation ◽  
Preoperative Prediction ◽  
Tenfold Cross Validation ◽  
Magnetic Resonance Imaging Mri ◽  
Individualized Prediction ◽  
Hysteroscopic Resection

Abstract This study aimed to develop and validate a model for the preoperative prediction of the effectiveness of hysteroscopic resection of a uterine cesarean niche in patients with postmenstrual spotting. The predictive model was developed in a primary prospective cohort consisting of 208 patients with niche treated by hysteroscopic resection. Multivariable logistic regression analysis was performed to develop the predictive model, which incorporated preoperative menstrual characteristics and magnetic resonance imaging (MRI) findings. Surgical efficacy was defined as a decrease in postmenstrual spotting duration of at least 3 days at the 3-month follow-up compared with baseline. The predictive model was presented with a nomogram, and the performance was assessed with respect to its calibration, discrimination, and clinical use. Internal validation was performed using tenfold cross-validation. The predictive factors in the final model were as follows: preoperative menstrual duration, thickness of the residual myometrium (TRM), length, TRM/thickness of the adjacent myometrium ratio, angle γ, area, and presence of a lateral branch of the niche. The model showed good performance in predicting the effectiveness of hysteroscopic niche resection. Incorporating the preoperative duration of the menstrual period and MRI findings of the niche into an easy-to-use nomogram facilitates the individualized prediction of the effectiveness of a hysteroscopic niche resection by 26 Fr resectoscope, but multicenter prospective studies are needed to validate it.

Deep-Learning Based Analysis of Preoperative MRI Predicts Microvascular Invasion and Outcome in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Bao-Ye sun ◽  
Pei-Yi Gu ◽  
Ruo-Yu Guan ◽  
Cheng Zhou ◽  
Jian-Wei Lu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Predictive Model ◽  
Clinical Outcomes ◽  
Area Under The Curve ◽  
Microvascular Invasion ◽  
Dce Mri ◽  
Preoperative Prediction ◽  
Model Combining ◽  
Contrast Enhanced

Abstract Background & Aims: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC. Methods We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression. Results Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and AFP were independently associated with MVI: DL-MVI (odds ratio [OR]=35.738; 95% confidence interval [CI]: 14.027-91.056; p<0.001), AFP (OR=4.634, 95% CI: 2.576-8.336; p<0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824. Conclusions Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC.

scholarly journals Predicting 6-Month Mortality in Incident Elderly Dialysis Patients: A Simple Prognostic Score

2019 ◽  
Vol 45 (1) ◽  
pp. 38-50
Author(s):  
Josefina Santos ◽  
Pedro Oliveira ◽  
Jorge Malheiro ◽  
Andreia Campos ◽  
Sofia Correia ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Prognostic Score ◽  
Treatment Options ◽  
Laboratory Data ◽  
Model Performance ◽  
Area Under The Curve ◽  
Predictive Score ◽  
Multivariable Logistic Regression Analysis ◽  
Patient Centered ◽  
Internal Validation

Aim: Mortality in end-stage renal disease (ESRD) remains high, particularly among elderly, who represents the most rapidly growing segment of the ESRD population in wealthier countries. We developed and validated a risk score in elderly patients to predict 6-month mortality after dialysis initiation. Methods: We used data from a cohort of 421 patients, aged 65 years and over who started dialysis between 2009 and 2016, in our Nephrology department. The predictive score was developed using a multivariable logistic regression analysis. A bootstrapping technique was used for internal validation. Results: The overall mortality within 6 months was 14.0%. Five independent predictors were identified, and a points system was constructed: age 75 years or older (2 points), coronary artery disease (2), cerebrovascular disease with hemiplegia (2), time of nephrology care before dialysis (<3.0 months [2]; ≥3 to <12 months [1]), and serum albumin levels (3.0–3.49 g/dL [1]; <3.0 g/dL [2]). A score of 6 identified patients with a 70% risk of 6-month mortality. Model performance was good in both discrimination (area under the curve of 0.793; [95% CI 0.73–0.86]) and validation (concordance statistics of 0.791 [95% CI 0.73–0.85]). Conclusions: We developed a simple prediction score based on readily available clinical and laboratory data that can be a practical and useful tool to assess short-term prognosis in elderly patients starting dialysis. It may help to inform patients and their families about ESRD treatment options and provide a more patient-centered overall approach to care.

scholarly journals Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Yun Bian ◽  
Shiwei Guo ◽  
Hui Jiang ◽  
Suizhi Gao ◽  
Chengwei Shao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Validation Cohort ◽  
Ductal Adenocarcinoma ◽  
Arterial Phase ◽  
Multivariable Logistic Regression Analysis ◽  
Preoperative Prediction ◽  
Multivariable Logistic Regression ◽  
Radiomics Signature

Abstract Purpose To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC). Materials and methods In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA). Results The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68–0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69–0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful. Conclusions The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC.

MRI-based radiomics analysis to evaluate the clinicopathological characteristics of cervical carcinoma: a multicenter study

2021 ◽  
pp. 028418512110651
Author(s):  
Yi Liu ◽  
Ting Song ◽  
Tian-Fa Dong ◽  
Wei Zhang ◽  
Ge Wen
Keyword(s):  
Lymph Node ◽  
Area Under The Curve ◽  
Tumor Grade ◽  
Figo Stage ◽  
Clinicopathological Characteristics ◽  
Preoperative Prediction ◽  
Invasive Method ◽  
Treatment Plans ◽  
Magnetic Resonance Imaging Mri ◽  
Selection Operator

Background Preoperative prediction of clinical pathological indicators of cervical cancer (CC) is of great significance to the formulation of personalized treatment plans for CC. Purpose To investigate magnetic resonance imaging (MRI) radiomics analysis for the evaluation of pathological types, tumor grade, FIGO stage, and lymph node metastasis (LNM) of CC. Material and Methods A total of 235 patients with CC from three institutes were enrolled in the study. All patients underwent T2/SPAIR and contrast-enhanced T1-weighted (CE-T1WI) imaging scans before radical hysterectomy by pelvic lymph node dissection surgery. Radiomics features extracted from T2/SPAIR and CE-T1WI imaging were selected by the least absolute shrinkage and selection operator (LASSO) methods for further radiomics signature calculation. These radiomic features were used to construct regression and decision tree models to evaluate the performance of radiomic features in distinguishing clinicopathological indicators. Results The area under the curve (AUC) of T2/SPAIR and CE-T1WI imaging were 0.777 and 0.750, respectively, for differentiating between adenocarcinoma and squamous cell carcinoma. From the two sequences, the AUC of the verification group that distinguished low FIGO stage from high FIGO stage was 0.716 and 0.676, respectively. The AUC for moderately well and poorly differentiated tumors were 0.729 on T2/SPAIR and 0.749 on CE-T1WI imaging. The AUC of the verification groups for LNM was 0.730 and 0.618 on T2/SPAIR and CE-T1WI imaging, respectively. Conclusion MRI radiomics features can be used as a non-invasive method to evaluate the clinicopathological indexes of CC and provide an important auxiliary examination method for patients to determine individualized treatment plans before operation.

scholarly journals Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeoung Kun Kim ◽  
Yoo Jin Choo ◽  
Hyunkwang Shin ◽  
Gyu Sang Choi ◽  
Min Cheol Chang
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Early Stage ◽  
Model Performance ◽  
Area Under The Curve ◽  
Brain Magnetic Resonance Imaging ◽  
Validation Dataset ◽  
Corona Radiata ◽  
Magnetic Resonance Imaging Mri ◽  
Validation Set

AbstractDeep learning (DL) is an advanced machine learning approach used in diverse areas such as bioinformatics, image analysis, and natural language processing. Here, using brain magnetic resonance imaging (MRI) data obtained at early stages of infarcts, we attempted to develop a convolutional neural network (CNN) to predict the ambulatory outcome of corona radiata infarction at six months after onset. We retrospectively recruited 221 patients with corona radiata infarcts. A favorable outcome of ambulatory function was defined as a functional ambulation category (FAC) score of ≥ 4 (able to walk without a guardian’s assistance), and a poor outcome of ambulatory function was defined as an FAC score of < 4. We used a CNN algorithm. Of the included subjects, 69.7% (n = 154) were assigned randomly to the training set and the remaining 30.3% (n = 67) were assigned to the validation set to measure the model performance. The area under the curve was 0.751 (95% CI 0.649–0.852) for the prediction of ambulatory function with the validation dataset using the CNN model. We demonstrated that a CNN model trained using brain MRIs captured at an early stage after corona radiata infarction could be helpful in predicting long-term ambulatory outcomes.

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