ArticleAbdominal radiology (New York)2022
Preoperative prediction of pelvic lymph nodes metastasis in prostate cancer using an ADC-based radiomics model: comparison with clinical nomograms and PI-RADS assessment.
Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- The diagnostic value of radiomics-based machine learning for lymph node metastasis in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Application of radiomics-based prediction model to predict preoperative lymph node metastasis in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Diagnostic accuracy of MRI radiomics in predicting lymph node metastasis in prostate cancer: A systematic review.European journal of radiology open · 2025Review
- Bi-parametric MRI-based quantification radiomics model for the noninvasive prediction of histopathology and biochemical recurrence after prostate cancer surgery: a multicenter study.Abdominal radiology (New York) · 2025Article
- Deep learning-based fully automated detection and segmentation of pelvic lymph nodes on diffusion-weighted images for prostate cancer: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- The influence of extraprostatic extension grade on the detection of pelvic lymph node metastasis in prostate cancer.Frontiers in oncology · 2025Article
- Article
- Can Machine Learning Models Detect and Predict Lymph Node Involvement in Prostate Cancer? A Comprehensive Systematic Review.Journal of clinical medicine · 2023Review
- The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature.Diagnostics (Basel, Switzerland) · 2023Review
- Beyond diagnosis: is there a role for radiomics in prostate cancer management?European radiology experimental · 2023Review
- Semiautomated pelvic lymph node treatment response evaluation for patients with advanced prostate cancer: based on MET-RADS-P guidelines.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
Corrections and comments
- Commented on byImaging.2023
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and test radiomics models based on manually corrected or automatically gained masks on ADC maps for pelvic lymph node metastasis (PLNM) prediction in patients with prostate cancer (PCa).
methodsA primary cohort of 474 patients with PCa who underwent prostate mpMRI were retrospectively enrolled for PLNM prediction between January 2017 and January 2020. They were then randomly split into training/validation (n = 332) and test (n = 142) groups for model development and internal testing. Four radiomics models were developed using four masks (manually corrected/automatic prostate gland and PCa lesion segmentation) based on the ADC maps using the primary cohort. Another cohort of 128 patients who underwent radical prostatectomy (RP) with extended pelvic lymph node dissection (ePLND) for PCa was used as the testing cohort between February 2020 and October 2021. The performance of the models was evaluated in terms of discrimination and clinical usefulness using the area under the curve (AUC) and decision curve analysis (DCA). The optimal radiomics model was further compared with Memorial Sloan Kettering Cancer Center (MSKCC) and Briganti 2017 nomograms, and PI-RADS assessment.
results17 (13.28%) Patients with PLNM were included in the testing cohort. The radiomics model based on the mask of automatically segmented prostate obtained the highest AUC among the four radiomics models (0.73 vs. 0.63 vs. 0.70 vs. 0.56). Briganti 2017, MSKCC nomograms, and PI-RADS assessment-yielded AUCs of 0.69, 0.71, and 0.70, respectively, and no significant differences were found compared with the optimal radiomics model (P = 0.605-0.955).
conclusionThe radiomics model based on the mask of automatically segmented prostate offers a non-invasive method to predict PLNM for patients with PCa. It shows comparable accuracy to the current MKSCC and Briganti nomograms.
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