ArticleCancer imaging : the official publication of the International Cancer Imaging Society2022
Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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10 citing papers in PubMed.
- The Application of Surface Luminance Distribution Measurements to the Evaluation of Neoplastic Lesions of the Prostate Gland.Cancers · 2025Article
- Machine learning-based identification of high-risk bone metastasis factors after radical prostatectomy in prostate cancer.Frontiers in oncology · 2025Article
- A preoperative scoring system for predicting the extraprostatic extension of prostate cancer following radical prostatectomy using magnetic resonance imaging and clinical factors.Abdominal radiology (New York) · 2024Article
- What the urologist needs to know before radical prostatectomy: MRI effective support to pre-surgery planning.La Radiologia medica · 2024Review
- 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
- Can we predict hypoadrenocorticism in dogs with resting hypocortisolemia? A predictive model based on clinical, haematological, and biochemical variables.Frontiers in veterinary science · 2024Article
- Article
- Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis.Insights into imaging · 2023Review
- Radiologic-pathologic correlation of prostatic cancer extracapsular extension (ECE).Insights into imaging · 2023Review
- Correction: Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
backgroundTo construct a model based on magnetic resonance imaging (MRI) features and histological and clinical variables for the prediction of pathology-detected extracapsular extension (pECE) in patients with prostate cancer (PCa).
methodsWe performed a prospective 3 T MRI study comparing the clinical and MRI data on pECE obtained from patients treated using robotic-assisted radical prostatectomy (RARP) at our institution. The covariates under consideration were prostate-specific antigen (PSA) levels, the patient's age, prostate volume, and MRI interpretative features for predicting pECE based on the Prostate Imaging-Reporting and Data System (PI-RADS) version 2.0 (v2), as well as tumor capsular contact length (TCCL), length of the index lesion, and prostate biopsy Gleason score (GS). Univariable and multivariable logistic regression models were applied to explore the statistical associations and construct the model. We also recruited an additional set of participants-which included 59 patients from external institutions-to validate the model.
resultsThe study participants included 184 patients who had undergone RARP at our institution, 26% of whom were pECE+ (i.e., pECE positive). Significant predictors of pECE+ were TCCL, capsular disruption, measurable ECE on MRI, and a GS of ≥7(4 + 3) on a prostate biopsy. The strongest predictor of pECE+ is measurable ECE on MRI, and in its absence, a combination of TCCL and prostate biopsy GS was significantly effective for detecting the patient's risk of being pECE+. Our predictive model showed a satisfactory performance at distinguishing between patients with pECE+ and patients with pECE-, with an area under the ROC curve (AUC) of 0.90 (86.0-95.8%), high sensitivity (86%), and moderate specificity (70%).
conclusionsOur predictive model, based on consistent MRI features (i.e., measurable ECE and TCCL) and a prostate biopsy GS, has satisfactory performance and sufficiently high sensitivity for predicting pECE+. Hence, the model could be a valuable tool for surgeons planning preoperative nerve sparing, as it would reduce positive surgical margins.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.