ArticleFrontiers in oncology2022
Application Value of Radiomic Nomogram in the Differential Diagnosis of Prostate Cancer and Hyperplasia.
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 11 papers.
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11 citing papers in PubMed.
- MRI-based habitat radiomics analysis for predicting clinically significant prostate cancer: a retrospective dual-center study.Frontiers in oncology · 2026Article
- Habitat analysis based on magnetic resonance imaging for the prediction of prostate cancer: a dual-center study.Quantitative imaging in medicine and surgery · 2025Article
- A deep learning derived prostate zonal volume-based biomarker from T2-weighted MRI to distinguish between prostate cancer and benign prostatic hyperplasia.Medical physics · 2025Article
- The Mediating Role of Depression in the Association Between Death Anxiety and Quality of Life in Elderly Prostate Cancer Patients.Actas espanolas de psiquiatria · 2025Article
- Predictive impact of T2-MRI radiomics model on initial diagnosis of bone metastasis in prostate cancer patients.BMC medical imaging · 2025Article
- Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models.Frontiers in oncology · 2025Article
- Impact of Image Enhancement on the Radiomics Stability of Diffusion-Weighted MRI Images of Cervical Cancer.Cureus · 2024Article
- Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte.Journal of translational medicine · 2023Article
- Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith.International journal of clinical practice · 2022Article
- Biparametric MRI-based radiomics classifiers for the detection of prostate cancer in patients with PSA serum levels of 4∼10 ng/mL.Frontiers in oncology · 2022Article
- Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors.Technology in cancer research & treatmentArticle
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Abstract
Objective: Prostate cancer and hyperplasia require different treatment strategies and have completely different outcomes; thus, preoperative identification of prostate cancer and hyperplasia is very important. The purpose of this study was to evaluate the application value of magnetic resonance imaging (MRI)-derived radiomic nomogram based on T2-weighted images (T2WI) in differentiating prostate cancer and hyperplasia. Materials and Methods: One hundred forty-six patients (66 cases of prostate cancer and 80 cases of prostate hyperplasia) who were confirmed by surgical pathology between September 2019 and September 2019 were selected. We manually delineated T2WI of all patients using ITK-SNAP software and radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the effective features were selected using the LASSO algorithm, and the radiomic feature model was constructed. Next, combined with independent clinical risk factors, a multivariate Logistic regression model was used to establish a radiomic nomogram. The receiver operator characteristic (ROC) curve was used to evaluate the prediction performance of the radiomic nomogram. Finally, the clinical application value of the nomogram was evaluated by decision curve analysis. Results: The PSA and the selected imaging features were significantly correlated with the differential diagnosis of prostate cancer and hyperplasia. The radiomic model had good discrimination efficiency for prostate cancer and hyperplasia. The training set (AUC = 0.85; 95% CI: 0.77-0.92) and testing set (AUC = 0.84; 95% CI: 0.72-0.96) were effective. The radiomic nomogram, combined with the radiomic characteristics of MRI and independent clinical risk factors, showed better differentiation efficiency in the training set (AUC = 0.91; 95% CI: 0.85-0.97) and testing set (AUC = 0.90; 95% CI: 0.81-0.99). The decision curve showed the clinical application value of the radiomic nomogram. Conclusion: The radiomic nomogram of T2-MRI combined with clinical risk factors can easily identify prostate cancer and hyperplasia. It also provides suggestions for further clinical events.
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