ArticleFrontiers in oncology2022
Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence-Enabled Imaging for Predicting Preoperative Extraprostatic Extension in Prostate Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- MRI-based radiomics for prediction of extraprostatic extension of prostate cancer: a systematic review and meta-analysis.La Radiologia medica · 2024Pooled it
- Preoperative prediction of positive surgical margins in prostate cancer using multimodal deep learning model: a multicenter study.Abdominal radiology (New York) · 2026Article
- Pathomic model to predict the expression of UQCRH and overall survival of lung adenocarcinoma patients.Diagnostic pathology · 2026Article
- Review
- Quantification of head and neck cancer patients' anatomical changes during radiotherapy: Toward the prediction of replanning need.Journal of applied clinical medical physics · 2026Article
- A novel MRI-based habitat analysis and deep learning for predicting perineural invasion in prostate cancer: a two-center study.BMC cancer · 2025Article
- Radiomics to predict PNI in ESCC.Abdominal radiology (New York) · 2025Article
- Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer.Frontiers in oncology · 2025Article
- Machine-Learning Based Computed Tomography Radiomics Nomgram For Predicting Perineural Invasion In Gastric Cancer.Current medical imaging · 2025Article
- Clinical value of a radiomics model based on machine learning for the prediction of prostate cancer.The Journal of international medical research · 2024Article
- Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review.Insights into imaging · 2024Review
- Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions.Journal of clinical medicine · 2024Review
- Article
- Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study.Discover oncology · 2024Article
- Clinical application of machine learning models in patients with prostate cancer before prostatectomy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Observational
- Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames.Frontiers in endocrinology · 2024Article
- CT-based radiomics for predicting pathological grade in hepatocellular carcinoma.Frontiers in oncology · 2024Article
- Radiomics-based model for prediction of TGF-β1 expression in head and neck squamous cell carcinoma.American journal of nuclear medicine and molecular imaging · 2024Article
- Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy.Scientific reports · 2023Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer.
methodsA total of 252 patients were retrospectively included who underwent radical prostatectomy and MP-MRI examinations. The prediction characteristics of this study were as follows: Ki67, S100, extracapsular extension (ECE), perineural invasion (PNI), and surgical margin (SM). Patients were divided into training cohorts and validation cohorts in the ratio of 4:1 for each group. After lesion segmentation manually, radiomic features were extracted from MP-MRI images and some clinical factors were also included. Max relevance min redundancy (mRMR) and recursive feature elimination (RFE) based on random forest (RF) were adopted to select features. Six classifiers were included (SVM, KNN, RF, decision tree, logistic regression, XGBOOST) to find the best diagnostic performance among them. The diagnostic efficiency of the construction models was evaluated by ROC curves and quantified by AUC.
resultsRF performed best among the six classifiers for the four groups according to AUC values (Ki67 = 0.87, S100 = 0.80, ECE = 0.85, PNI = 0.82). The performance of SVM was relatively the best for SM (AUC = 0.77). The number and importance of DCE features ranked first in the models of each group. The combined models of MP-MRI and clinical characteristics showed no significant difference compared with MP-MRI models according to Delong's tests.
conclusionsRadiomics models based on MP-MRI have the potential to predict biological characteristics and are expected to be a noninvasive method to evaluate the risk stratification of prostate cancer.
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