Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI

BMC Medical Imaging, Journal Year: 2023, Volume and Issue: 23(1)

Published: Nov. 22, 2023

Abstract Background The purpose of this study is to investigate the use radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach called multi-flavored feature extraction or tensor, which combines four mpMRI images using eight different fusion techniques create 52 datasets each patient. evaluate effectiveness in cancer compare it traditional methods. Methods used PROSTATEx-2 dataset consisting 111 patients’ T2W-transverse, T2W-sagittal, DWI, ADC images. merge T2W, images, namely Laplacian Pyramid, Ratio low-pass pyramid, Discrete Wavelet Transform, Dual-Tree Complex Curvelet Fusion, Weighted Principal Component Analysis. Prostate were manually segmented, extracted Pyradiomics library Python. also an Autoencoder extraction. five sets train classifiers: all features, linked with PCA, combination features. processed data, including balancing, standardization, correlation, Least Absolute Shrinkage Selection Operator (LASSO) regression. Finally, we nine classifiers classify Gleason grades. Results Our results show that SVM classifier PCA achieved most promising results, AUC 0.94 balanced accuracy 0.79. Logistic regression performed best when only 0.93 0.76. Gaussian Naive Bayes had lower performance compared other classifiers, while KNN high PCA. Random Forest well achieving Voting showed higher 2 highest performance, 0.95 0.78. Conclusion concludes proposed tensor can be effective method findings suggest may more than alone accurately classifying

Language: Английский

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Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

Cancers, Journal Year: 2023, Volume and Issue: 15(15), P. 3839 - 3839

Published: July 28, 2023

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this is effective, non-invasive methods such as radiomics have gained popularity extracting features to develop predictive models clinical tasks. aim minimize invasive processes improved management cancer (PCa). This study reviews recent research progress MRI-based PCa, including the pipeline potential factors affecting personalized diagnosis. integration artificial intelligence (AI) with medical also discussed, line development trend radiogenomics multi-omics. survey highlights need more data from multiple institutions avoid bias generalize model. AI-based model considered promising tool good prospects application.

Language: Английский

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Artificial intelligence in multiparametric magnetic resonance imaging: A review

Medical Physics, Journal Year: 2022, Volume and Issue: 49(10)

Published: Aug. 18, 2022

Abstract Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for diagnosis and treatment planning of various diseases. Machine learning–based artificial intelligence (AI) methods, especially those adopting deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, super‐resolution. The current availabilities increasing computational power fast‐improving AI algorithms empowered numerous computer‐based systems applying disease diagnosis, imaging‐guided radiotherapy, patient risk overall survival time prediction, development advanced quantitative technology fingerprinting. However, wide application these developed clinic still limited by a number factors, including robustness, reliability, interpretability. This survey aims provide overview new researchers field as well radiologists with hope that they can understand general concepts, main scenarios, remaining challenges mpMRI.

Language: Английский

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Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods

Diagnostics, Journal Year: 2023, Volume and Issue: 13(4), P. 806 - 806

Published: Feb. 20, 2023

Prostate cancer is the second leading cause of cancer-related death in men. Its early and correct diagnosis particular importance to controlling preventing disease from spreading other tissues. Artificial intelligence machine learning have effectively detected graded several cancers, prostate cancer. The purpose this review show diagnostic performance (accuracy area under curve) supervised algorithms detecting using multiparametric MRI. A comparison was made between performances different machine-learning methods. This study performed on recent literature sourced scientific citation websites such as Google Scholar, PubMed, Scopus, Web Science up end January 2023. findings reveal that techniques good with high accuracy curve for prediction MR imaging. Among methods, deep learning, random forest, logistic regression appear best performance.

Language: Английский

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Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography

Current Oncology, Journal Year: 2023, Volume and Issue: 30(1), P. 839 - 853

Published: Jan. 7, 2023

breast cancer (BC) is the world's most prevalent in female population, with 2.3 million new cases diagnosed worldwide 2020. The great efforts made to set screening campaigns, early detection programs, and increasingly targeted treatments led significant improvement patients' survival. Full-Field Digital Mammograph (FFDM) considered gold standard method for diagnosis of BC. From several previous studies, it has emerged that density (BD) a risk factor development BC, affecting periodicity plans present today at an international level.in this study, focus mammographic image processing techniques allow extraction indicators derived from textural patterns mammary parenchyma indicative BD factors.a total 168 patients were enrolled internal training test while 51 compose external validation cohort. Different Machine Learning (ML) have been employed classify breasts based on values tissue density. Textural features extracted only which train classifiers, thanks aid ML algorithms.the accuracy different tested classifiers varied between 74.15% 93.55%. best results reached by Support Vector (accuracy 93.55% percentage true positives negatives equal TPP = 94.44% TNP 92.31%). was not influenced choice selection approach. Considering cohort, SVM, as classifier 7 selected wrapper method, showed 0.95, sensitivity 0.96, specificity 0.90.our preliminary Radiomics analysis approach us objectively identify BD.

Language: Английский

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MRI-based Radiomics for Predicting Prostate Cancer Grade Groups: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies

Academic Radiology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

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Investigating feature extraction by SIFT methods for prostate cancer early detection

Egyptian Informatics Journal, Journal Year: 2025, Volume and Issue: 29, P. 100607 - 100607

Published: Jan. 7, 2025

Language: Английский

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Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study

Insights into Imaging, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 15, 2025

Abstract Objective To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting aggressiveness prostate cancer (PCa). Materials and methods A total 878 PCa patients from 4 hospitals were retrospectively collected, all whom had pathological results after radical prostatectomy (RP). pre-trained AI algorithm was used to select suspected lesions extract lesion model development. The study evaluated five prediction methods, including (1) clinical-imaging clinical selected by algorithm, (2) PIRADS category, (3) a conventional radiomics model, (4) deep-learning bases (5) biopsy pathology. Results In externally validated dataset, deep learning-based showed highest area under curve (AUC 0.700 0.791). It exceeded 0.597 0.718), radiomic 0.566 0.632), score 0.554 0.613), pathology 0.537 0.578). AUC predicted did not show statistically significant difference among three verified ( p > 0.05). Conclusion Deep-learning models AI-extracted bpMRI images can potentially be predict aggressiveness, demonstrating generalized ability external validation. Critical relevance statement Predicting (PCa) is important formulating best treatment plan patients. based on learning expected provide an objective non-invasive method evaluating PCa. Key Points obtain options. with high accuracy. has good universality when tested multiple datasets. Graphical

Language: Английский

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Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 4, 2025

In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant (CsPCa). The imaging–reporting data system (PI-RADS) is combined with variables predominantly based logistic regression models. This study explores modeling using regularization techniques such as ridge regression, LASSO, elastic net, classification tree, tree ensemble models like random forest or XGBoost, neural networks to predict CsPCa in a dataset 4799 patients Catalonia (Spain). An 80–20% split was employed for training validation. We used predictor age, prostate-specific antigen (PSA), volume, PSA density (PSAD), digital rectal exam (DRE) findings, family history PCa, previous negative biopsy, PI-RADS categories. When considering sensitivity 0.9, the validation set, XGBoost model outperforms others specificity 0.640, followed closely by (0.638), network (0.634), (0.620). terms utility, 10% missclassification CsPCa, can avoid 41.77% unnecessary biopsies, (41.67%) (41.46%), while has lower rate 40.62%. Using SHAP values explainability, emerges most influential factor, particularly individuals 4 5. Additionally, positive examination proves highly certain individuals, biopsy serves protective factor others.

Language: Английский

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Combining Radiomics and Connectomics in MRI Studies of the Human Brain: A Systematic Literature Review

Computer Methods and Programs in Biomedicine, Journal Year: 2025, Volume and Issue: unknown, P. 108771 - 108771

Published: April 1, 2025

Language: Английский

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