A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning

Cureus, Год журнала: 2024, Номер unknown

Опубликована: Сен. 16, 2024

Background To assess the feasibility of a machine learning (ML) approach using radiomics features perfusion defects on rest myocardial imaging (MPI) to detect presence hibernating myocardium. Methodology Data patients who underwent 99mTc-sestamibi MPI and 18F-FDG PET/CT for viability assessment were retrieved. Rest data processed ECToolbox, polar maps saved NFile PMap tool. The reference standard defining myocardium was mismatched perfusion-metabolism defect with impaired contractility at rest. Perfusion delineated regions interest (ROIs) after spatial resampling intensity discretization. Replicable random sampling allocated 80% (257) from January 2017 September 2022 training set remaining 20% (64) validation set. An independent dataset 29 consecutive October 2023 used as testing model evaluation. One hundred ten first second-order texture extracted each ROI. After feature normalization imputation, 14 best-ranked selected multistep selection process including Logistic Regression Fast Correlation-Based Filter. Thirteen supervised ML algorithms trained stratified five-fold cross-validation validated Log Loss <0.688 <0.672 in steps evaluated Performance matrices assessed included area under curve (AUC), classification accuracy (CA), F1 score, precision, recall, specificity. provide transparency interpretability, SHapley Additive exPlanations (SHAP) values depicted beeswarm plots. Results Two thirty-nine (214 males; mean age 56 ± 11 years) enrolled study. There 371 (321 sets; 50 set). Based standard, 168 had (139 On cross-validation, six AUC >0.800. validation, 10 value <0.672, among which evaluation models unseen set, nine >0.800 Gradient Boosting Random Forest (xgboost) [GB RF (xgboost)] achieving highest 0.860 could 21/29 (72.4%) precision 87.5% (21/24), specificity 85.7% (18/21), CA 78.0% (39/50) Score 0.792. Four clear pattern interpretability based SHAP These GB (xgboost), (scikit-learn), Forest. Conclusion Our study demonstrates potential detecting images. This proof-of-concept underscores notion that capture nuanced information beyond what is perceptible human eye, offering promising avenues improved assessment.

Язык: Английский

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Machine Learning-based Overall Survival Prediction in GBM Patients Using MRI Radiomics

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Год журнала: 2022, Номер unknown, С. 1 - 3

Опубликована: Ноя. 5, 2022

Glioblastoma multiforme (GBM) is regarded as the most prevalent primary tumor of central nervous system in brain. However, due to lack information, it too hard understand underlying progression patterns and prognosis patients. In this study, we evaluated overall survival predictive (time event analysis) power radiomic features extracted from MRI, along with help feature selection (FS) machine learning (ML) algorithms. The MR images 119 patients their status were obtained. data randomly split into 70% 30%, indicating training testing datasets, respectively. Twelve preprocessing methods (e.g., bin discretization, Laplacian Gaussian, wavelet transform), 5 FS Boruta, Cindex, Random Survival Forest), 7 ML Glmnet, CoxBoost) algorithms recruited form a total 420 models. models C-index method showed more decent results than others. highest-achieving model (C-index = 0.72) was combination LOG sigma 1 mm preprocessing, selector, Coxph algorithm. Our findings represent be utilized prediction glioblastoma prognostication general.

Язык: Английский

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Cardiac SPECT Radiomic Features Reproducibility: Patient study

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Год журнала: 2022, Номер unknown, С. 1 - 4

Опубликована: Ноя. 5, 2022

Heart disease is one of the leading causes death worldwide. Among various methods used to assess heart function, MPI SPECT method a valuable and non-invasive that brings high-quality images with low radiation exposure. Radiomics has been developed extract quantitative features from medical images. These can be predict diagnosis treatment in science. To use these clinic, they need reliable; other words, repeatable reproducible. Various factors, including different reconstructions, affect repeatability reproducibility radiomic features. Twenty patients who underwent stress rest were this study. As result, 40 existing reconstructed 15 modes. Finally, 600 unique reconstructions obtained, segmentation process was conducted using 3D-Slicer program. Feature extraction done LIFEx, finally, coefficient variance (COV) check reproducibility. The most robust FO_Kurtosis, GLCM_Entropy_log10, GLCM_Entropy_log2, GLRLM_SRE, GLRLM_LRE, GLRLM_RP, GLZLM_SZE, GLZLM_HGZE. change order reconstruction parameter only case caused least feature variation. This study planned reliability over changes parameters.

Язык: Английский

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Density Dedicated Deep Learning Model for Mammogram Malignancy Classification

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Год журнала: 2022, Номер unknown, С. 1 - 3

Опубликована: Ноя. 5, 2022

A body of literature has reported the promising performance deep learning models when applied to mammograms for different clinical tasks. However, a major pitfall is they are bearing high-density breasts since overlapping high-dense tissue can cover lesion and make diagnosing interpreting it difficult. Thus, analysis dedicated trained low- be great importance. In this study, we aimed develop deep-learning classifying breast masses on into benign malignant cases. Curated Breast Imaging Subset Digital Database Screening Mammography (CBIS-DDSM) dataset, including mammograms, cropped masses, pathologic diagnoses, were adopted study. For models, dataset was split low-(BI-RADS density1 2) (BI-RADS density3 4) groups. Contrast-limited adaptive histogram equalization (CLAHE) enhance contrast images, then all images resized 255 × matrix size normalized. modified DenseNet-201 neural network with rate starting at 0.0001 decreased in piecewise manner every epoch RMSProp optimizer. Fifteen percent training data excluded validation, continued 100 epochs. Data augmentation, rotation, flipping, scaling, implemented prevent overfitting. The model evaluated using test set. Accuracy (ACC), area under receiver operating characteristic curve (AUC), sensitivity (SEN), specificity (SPE) general 0.720, 0.771, 0.732, 0.701, Low-density 0.788, 0.818, 0.824, 0.742, High-density 0.712, 0.621, 0.962, 0.180, respectively. Our study highlights importance developing tailored nature improve overall accuracy. We also suggest performing specific preprocessing breasts, such as region-wise enhancement regions high-intensity values (with gamma filter, etc.).

Язык: Английский

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PET and CT Information Fusion and Quality Assessment Toward Optimized Radiomic Features Extraction

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Год журнала: 2022, Номер unknown, С. 1 - 3

Опубликована: Ноя. 5, 2022

In this study, we performed two experiments to explore radiomic features and multi-modality medical image fusion (IF). the first experiment, investigated performance of multiple IF algorithms for reflecting from both PET CT modalities in a single scan. second if can serve as an objective quality assessment (QA) metric. Experiments were on PET/CT images 328 histologically proven head neck tumors segmented by experienced radiologist. The iterative metal artifact reduction (iMAR) algorithm was applied images, their Hounsfield Unit range clipped [-500,500], then all resized isotropic voxel size 1 × mm3, quantized normalized integer values [0, 255]. To have comprehensive analysis, fused using 11 different covering almost categories, 13 metrics categories calculated each fusion. Ninety-four textural extracted regarding Image Biomarker Standardization Initiative (IBSI) guidelines. For Spearman correlation feature between its fused-set CT- PET-sets, coefficients higher than 0.7 considered significant. A "preserved" it correlated with peer sets. QA significant 0.7. Among methods GFF (guided filtering fusion) FPDE (fourth-order partial differential equation) had best results conserving 22 19 features, respectively, showing ability reflect maximum information GLCM least preserved across fusions. Several Radiomic showed peak signal-to-noise ratio (PSNR) root mean square error (RMSE) metric methods, while no entropy (EN), SSIM (structural similarity index measure), AG (average gradient), EI (edge intensity), SD (standard deviation), SF (spatial frequency), Qcv (Chen-Varshney metric).

Язык: Английский

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