Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer.
Lymph nodes
Machine learning
Multiparametric magnetic resonance imaging
Prostatectomy
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Aug 2022
Aug 2022
Historique:
received:
30
08
2021
accepted:
02
02
2022
revised:
01
02
2022
pubmed:
4
3
2022
medline:
16
7
2022
entrez:
3
3
2022
Statut:
ppublish
Résumé
To identify which patient with prostate cancer (PCa) could safely avoid extended pelvic lymph node dissection (ePLND) by predicting lymph node invasion (LNI), via a radiomics-based machine learning approach. An integrative radiomics model (IRM) was proposed to predict LNI, confirmed by the histopathologic examination, integrating radiomics features, extracted from prostatic index lesion regions on MRI images, and clinical features via SVM. The study cohort comprised 244 PCa patients with MRI and followed by radical prostatectomy (RP) and ePLND within 6 months between 2010 and 2019. The proposed IRM was trained in training/validation set and evaluated in an internal independent testing set. The model's performance was measured by area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). AUCs were compared via Delong test with 95% confidence interval (CI), and the rest measurements were compared via chi-squared test or Fisher's exact test. Overall, 17 (10.6%) and 14 (16.7%) patients with LNI were included in training/validation set and testing set, respectively. Shape and first-order radiomics features showed usefulness in building the IRM. The proposed IRM achieved an AUC of 0.915 (95% CI: 0.846-0.984) in the testing set, superior to pre-existing nomograms whose AUCs were from 0.698 to 0.724 (p < 0.05). The proposed IRM could be potentially feasible to predict the risk of having LNI for patients with PCa. With the improved predictability, it could be utilized to assess which patients with PCa could safely avoid ePLND, thus reduce the number of unnecessary ePLND. • The combination of MRI-based radiomics features with clinical information improved the prediction of lymph node invasion, compared with the model using only radiomics features or clinical features. • With improved prediction performance on predicting lymph node invasion, the number of extended pelvic lymph node dissection (ePLND) could be reduced by the proposed integrative radiomics model (IRM), compared with the existing nomograms.
Identifiants
pubmed: 35238971
doi: 10.1007/s00330-022-08625-6
pii: 10.1007/s00330-022-08625-6
pmc: PMC9283224
mid: NIHMS1786311
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5688-5699Subventions
Organisme : NCI NIH HHS
ID : R01 CA248506
Pays : United States
Organisme : NIBIB NIH HHS
ID : T32 EB005970
Pays : United States
Organisme : NIH HHS
ID : R01-CA248506
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive licence to European Society of Radiology.
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