Prediction of response to Stereotactic Radiotherapy for Nonfunctioning Pituitary Adenoma using radiomic features

Abstract Background: For individually predicting preoperative response to Stereotactic radiotherapy for Nonfunctioning pituitary Adenoma with the use of a radiomics approach.Methods: 93 cases (training set: n = 62; test set: n = 31) were recruited with contrast-enhanced T1-weighted MRI (CE-T1) before stereotactic radiotherapy. All of these patients received another MRI scan to assess sensitivity of radiotherapy after 12 to 18 months. The shrinkage and no increase in tumor volume are regarded as sensitive to gamma knife radiotherapy. According to CE-T1 images, we extracted 1208 quantitative imaging features totally. Support vector machine (SVM) combined with recursive feature elimination (RFE) and grid-search trained a four-feature prediction mode verified with an assay of receiver operating characteristics (ROC) for an individual set of test. In addition, a ROC curves with individual feature and signature bar were constructed for prediction.Results: The cross-validation area under the curve (AUC) on the three-fold train set is 0.991,0.843 and 0.889. In terms of the test and training sets, T1-CE image features led to 0.897 and 0.914 AUC, separately. Conclusions: With the use of a radiomics method, the response to Stereotactic Radiotherapy for Nonfunctioning Pituitary Adenoma was primarily predicted before the operation. The built mode performed well, suggesting that radiomics is promising to preoperatively predict sensitivity to radiotherapy in NFPA.

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MRI-based Radiomics Model Can Improve the Predictive Performance of Postlaminar Optic Nerve Invasion in Retinoblastoma

10.21203/rs.3.rs-328789/v1 ◽

2021 ◽

Author(s):

Zhenzhen Li ◽

Jian Guo ◽

Xiaolin Xu ◽

Wenbin Wei ◽

Junfang Xian

Keyword(s):

Optic Nerve ◽

Quantitative Imaging ◽

Area Under The Curve ◽

Predictive Performance ◽

Visual Assessment ◽

Recursive Feature Elimination ◽

Imaging Features ◽

Training Set ◽

Nerve Invasion ◽

Validation Set

Abstract Purpose: To develop an MRI-based radiomics model to predict postlaminar optic nerve invasion (PLONI) in retinoblastoma (RB) and to compare its predictive performance with that of subjective radiologists’ assessment.Methods: We retrospectively enrolled 124 patients with pathologically proven RB (90 in the training set and 34 in the validation set) who had MRI scans before surgery in this retrospective study. A radiomics model for predicting PLONI was developed by extracting 2058 quantitative imaging features from axial T2-weighted images and contrast-enhanced T1-weighted images in the training set. The Kruskal-Wallis test, least absolute shrinkage and selection operator regression, and recursive feature elimination were used for feature selection, whereupon a radiomics model was built with a logistic regression (LR) classifier. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the accuracy were assessed to evaluate the predictive performance of PLONI in the training set and validation set. The performance of the radiomics model was compared to radiologists’ assessment.Results: The AUC of the radiomics model for the prediction of PLONI according to ROC analysis was 0.928 in the training set and 0.841 in the validation set. In all 124 patients, the AUC of the radiomics model was 0.897, while that of radiologists’ assessment was 0.674 (p< 0.001).Conclusions: By incorporating MRI-based radiomics features, we constructed a radiomics model to predict PLONI in patients with RB, and it was shown to be superior to visual assessment and may serve as a potential tool to guide personalized treatment.

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MRI-based radiomics model can improve the predictive performance of postlaminar optic nerve invasion in retinoblastoma

British Journal of Radiology ◽

10.1259/bjr.20211027 ◽

2021 ◽

Author(s):

Zhenzhen Li ◽

Jian Guo ◽

Xiaolin Xu ◽

Wenbin Wei ◽

Junfang Xian

Keyword(s):

Optic Nerve ◽

Quantitative Imaging ◽

Area Under The Curve ◽

Predictive Performance ◽

Visual Assessment ◽

Recursive Feature Elimination ◽

Imaging Features ◽

Training Set ◽

Nerve Invasion ◽

Validation Set

Objectives: To develop an MRI-based radiomics model to predict postlaminar optic nerve invasion (PLONI) in retinoblastoma (RB) and compare its predictive performance with subjective radiologists’ assessment. Methods: We retrospectively enrolled 124 patients with pathologically proven RB (90 in training set and 34 in validation set) who had MRI scans before surgery. A radiomics model for predicting PLONI was developed by extracting quantitative imaging features from axial T2-weighted images and contrast-enhanced T1-weighted images in the training set. The Kruskal-Wallis test, least absolute shrinkage and selection operator regression, and recursive feature elimination were used for feature selection, whereupon a radiomics model was built with a logistic regression (LR) classifier. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the accuracy were assessed to evaluate the predictive performance in the training and validation set. The performance of the radiomics model was compared to radiologists’ assessment by DeLong test. Results: The AUC of the radiomics model for the prediction of PLONI was 0.928 in the training set and 0.841 in the validation set. Radiomics model produced better sensitivity than radiologists’ assessment (81.1% vs 43.2% in training set, 82.4vs 52.9% in validation set). In all 124 patients, the AUC of the radiomics model was 0.897, while that of radiologists’ assessment was 0.674 (p < 0.001, DeLong test). Conclusion: MRI-based radiomics model to predict PLONI in RB patients was shown to be superior to visual assessment with improved sensitivity and AUC, may serve as a potential tool to guide personalized treatment.

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Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Abdominal Radiology ◽

10.1007/s00261-019-02269-9 ◽

2019 ◽

Vol 45 (10) ◽

pp. 3193-3201 ◽

Cited By ~ 3

Author(s):

Yajuan Li ◽

Xialing Huang ◽

Yuwei Xia ◽

Liling Long

Keyword(s):

Machine Learning ◽

Differential Diagnosis ◽

Cell Carcinoma ◽

Area Under The Curve ◽

Image Features ◽

Renal Tumors ◽

Support Vector ◽

Svm Classifier ◽

Renal Oncocytoma ◽

Lasso Regression

Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

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Radiomics in radiation oncology—basics, methods, and limitations

Strahlentherapie und Onkologie ◽

10.1007/s00066-020-01663-3 ◽

2020 ◽

Vol 196 (10) ◽

pp. 848-855

Author(s):

Philipp Lohmann ◽

Khaled Bousabarah ◽

Mauritius Hoevels ◽

Harald Treuer

Keyword(s):

Mathematical Models ◽

Quantitative Imaging ◽

Human Perception ◽

Image Features ◽

Radiotherapy Planning ◽

Large Field ◽

Imaging Features ◽

Imaging Data ◽

Complete Evaluation ◽

Vast Number

Abstract Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

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Predictive Model and Feature Importance for Early Detection of Type II Diabetes Mellitus

10.21203/rs.3.rs-172421/v1 ◽

2021 ◽

Author(s):

Eric Adua ◽

Emmanuel Awuni Kolog ◽

Ebenezer Afrifa-Yamoah ◽

Bright Amankwah ◽

Christian Obirikorang ◽

...

Keyword(s):

Predictive Model ◽

Test Data ◽

Type Ii Diabetes ◽

Local Community ◽

Confusion Matrix ◽

Recursive Feature Elimination ◽

Support Vector ◽

Svm Classifier ◽

Type Ii ◽

Operating Characteristics

Abstract BackgroundAccurate prediction and early recognition of type II diabetes (T2DM) will lead to timely and meaningful interventions, while preventing T2DM associated complications. In this context, machine learning (ML) is promising, as it can transform vast amount of T2DM data into clinically relevant information. This study compares multiple ML techniques for predictive modelling based on different T2DM associated variables in an African population, Ghana. MethodsThe study involves 219 T2DM patients and 219 healthy individuals who were recruited from the hospital and the local community, respectively. Anthropometric and biochemical information including glycated haemoglobin (HbA1c), body mass index (BMI), blood pressure, fasting blood sugar (FBS), serum lipids [(total cholesterol (TC), triglycerides (TG), high and low-density lipoprotein cholesterol (HDL-c and LDL-c)] were collected. From this data, four ML classification algorithms including Naïve-Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machines (SVM) and Decision Tree (DT) were used to predict T2DM. Precision, Recall, F1-Scores, Receiver Operating Characteristics (ROC) scores and the confusion matrix were computed to determine the performance of the various algorithms while the importance of the feature attributes was determined by recursive feature elimination technique.ResultsAll the classifiers performed beyond the acceptable threshold of 70% for the Precision, Recall, F-score and Accuracy. After building the predictive model, 82% of diabetic test data was detected by the NB classifier, of which 93% were accurately predicted. The SVM classifier was the second-best performing classifier which yielded an overall accuracy of 84%. The non-T2DM test data yielded an accurate prediction score of 75% from the 98% of the proportion of the non-T2DM test data. KNN and DT yielded accuracies of 83% and 81%, respectively. NB has the best performance (AUC=0.87) followed by SVM (AUC= 0.84), KNN (AUC= 0.85) and DT (AUC= 0.81). The best three feature attributes, in order of importance, are HbA1c, TC and BMI whereas the least three importance of the features are Age, HDL-c and LDL-c.ConclusionBased on the predictive performance and high accuracy, the study has shown the potential of ML as a robust forecasting tool for T2DM. Our results can be a benchmark for guiding policy decisions in T2DM surveillance in resource and medical expertise limited countries such as Ghana.

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Predictive Model and Feature Importance for Early Detection of Type II Diabetes Mellitus

10.21203/rs.3.rs-150169/v1 ◽

2021 ◽

Author(s):

Eric Adua ◽

Emmanuel Awuni Kolog ◽

Ebenezer Afrifa-Yamoah ◽

Bright Amankwah ◽

Christian Obirikorang ◽

...

Keyword(s):

Predictive Model ◽

Test Data ◽

Type Ii Diabetes ◽

Confusion Matrix ◽

Accurate Prediction ◽

Recursive Feature Elimination ◽

Support Vector ◽

Svm Classifier ◽

Type Ii ◽

Operating Characteristics

Abstract Background Accurate prediction and early recognition of type II diabetes (T2DM) will lead to timely and meaningful interventions, while preventing T2DM associated complications. In this context, machine learning (ML) is promising, as it can transform vast amount of T2DM data into clinically relevant information. This study compares multiple ML techniques for predictive modelling based on different T2DM associated variables in an African population, Ghana. Methods The study involves 219 T2DM patients and 219 healthy individuals who were recruited from the hospital and the local community, respectively. Anthropometric and biochemical information including glycated haemoglobin (HbA1c), body mass index (BMI), blood pressure, fasting blood sugar (FBS), serum lipids [(total cholesterol (TC), triglycerides (TG), high and low-density lipoprotein cholesterol (HDL-c and LDL-c)] were collected. From this data, four ML classification algorithms including Naïve-Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machines (SVM) and Decision Tree (DT) were used to predict T2DM. Precision, Recall, F1-Scores, Receiver Operating Characteristics (ROC) scores and the confusion matrix were computed to determine the performance of the various algorithms while the importance of the feature attributes was determined by recursive feature elimination technique. Results All the classifiers performed beyond the acceptable threshold of 70% for the Precision, Recall, F-score and Accuracy. After building the predictive model, 82% of diabetic test data was detected by the NB classifier, of which 93% were accurately predicted. The SVM classifier was the second-best performing classifier which yielded an overall accuracy of 84%. The non-T2DM test data yielded an accurate prediction score of 75% from the 98% of the proportion of the non-T2DM test data. KNN and DT yielded accuracies of 83% and 81%, respectively. NB has the best performance (AUC = 0.87) followed by SVM (AUC = 0.84), KNN (AUC = 0.85) and DT (AUC = 0.81). The best three feature attributes, in order of importance, are HbA1c, TC and BMI whereas the least three importance of the features are Age, HDL-c and LDL-c. Conclusion Based on the predictive performance and high accuracy, the study has shown the potential of ML as a robust forecasting tool for T2DM. Our results can be a benchmark for guiding policy decisions in T2DM surveillance in resource and medical expertise limited countries such as Ghana.

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Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19

Frontiers in Medicine ◽

10.3389/fmed.2021.730441 ◽

2021 ◽

Vol 8 ◽

Author(s):

Xiao-Hui Wang ◽

Xiaopan Xu ◽

Zhi Ao ◽

Jun Duan ◽

Xiaoli Han ◽

...

Keyword(s):

Model Development ◽

Area Under The Curve ◽

Predictive Performance ◽

Recursive Feature Elimination ◽

Group Differences ◽

Support Vector ◽

Nasopharyngeal Swab ◽

Feature Subset ◽

Optimal Subset ◽

Discharged Patients

Objective: A considerable part of COVID-19 patients were found to be re-positive in the SARS-CoV-2 RT-PCR test after discharge. Early prediction of re-positive COVID-19 cases is of critical importance in determining the isolation period and developing clinical protocols.Materials and Methods: Ninety-one patients discharged from Wanzhou Three Gorges Central Hospital, Chongqing, China, from February 10, 2020 to March 3, 2020 were administered nasopharyngeal swab SARS-CoV-2 tests within 12–14 days, and 50 eligible patients (32 male and 18 female) with completed data were enrolled. Average age was 48 ± 11.5 years. All patients underwent non-enhanced chest CT on admission. A total of 568 radiomics features were extracted from the CT images, and 17 clinical factors were collected based on the medical record. Student's t-test and support vector machine–based recursive feature elimination (SVM-RFE) method were used to determine an optimal subset of features for the discriminative model development.Results: After Student's t-test, 62 radiomics features showed significant inter-group differences (p < 0.05) between the re-positive and negative cases, and none of the clinical features showed significant differences. These significant features were further selected by SVM-RFE algorithm, and a more compact feature subset containing only two radiomics features was finally determined, achieving the best predictive performance with the accuracy and area under the curve of 72.6% and 0.773 for the identification of the re-positive case.Conclusion: The proposed radiomics method has preliminarily shown potential in identifying the re-positive cases among the recovered COVID-19 patients after discharge. More strategies are to be integrated into the current pipeline to improve its precision, and a larger database with multi-clinical enrollment is required to extensively verify its performance.

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

Neuro-Oncology ◽

10.1093/neuonc/noz126.233 ◽

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.

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Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques

Land ◽

10.3390/land9100368 ◽

2020 ◽

Vol 9 (10) ◽

pp. 368

Author(s):

Abazar Esmali Ouri ◽

Mohammad Golshan ◽

Saeid Janizadeh ◽

Artemi Cerdà ◽

Assefa M. Melesse

Keyword(s):

Data Mining ◽

Soil Erosion ◽

Land Degradation ◽

Slope Angle ◽

Area Under The Curve ◽

Rainfall Simulation ◽

Support Vector ◽

Susceptibility Map ◽

Operating Characteristics ◽

Adaptive Boosting

Soil erosion determines landforms, soil formation and distribution, soil fertility, and land degradation processes. In arid and semiarid ecosystems, soil erosion is a key process to understand, foresee, and prevent desertification. Addressing soil erosion throughout watersheds scales requires basic information to develop soil erosion control strategies and to reduce land degradation. To assess and remediate the non-sustainable soil erosion rates, restoration programs benefit from the knowledge of the spatial distribution of the soil losses to develop maps of soil erosion. This study presents Support Vector Machine (SVM), Random Forest (RF), and adaptive boosting (AdaBoost) data mining models to map soil erosion susceptibility in Kozetopraghi watershed, Iran. A soil erosion inventory map was prepared from field rainfall simulation experiments on 174 randomly selected points along the Kozetopraghi watershed. In previous studies, this map has been prepared using indirect methods such as the Universal Soil Loss Equation to assess soil erosion. Direct field measurements for mapping soil erosion susceptibility have so far not been carried out in our study site in the past. The soil erosion rate data generated by simulated rainfall in 1 m2 plots at rainfall rate of 40 mmh−1 was used to develop the soil erosion map. Of the available data, 70% and 30% were randomly classified to calibrate and validate the models, respectively. As a result, the RF model with the highest area under the curve (AUC) value in a receiver operating characteristics (ROC) curve (0.91), and the lowest mean square error (MSE) value (0.09), has the most concordance and spatial differentiation. Sensitivity analysis by Jackknife and IncNodePurity methods indicates that the slope angle is the most important factor within the soil erosion susceptibility map. The RF susceptibility map showed that the areas located in the center and near the watershed outlet have the most susceptibility to soil erosion. This information can be used to support the development of sustainable restoration plans with more accuracy. Our methodology has been evaluated and can be also applied in other regions.

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