Szczegóły publikacji
Opis bibliograficzny
Textural features of MR images correlate with an increased risk of clinically significant cancer in patients with high PSA levels / Sebastian Gibala, Rafal Obuchowicz, Julia LASEK, Zofia SCHNEIDER, Adam PIÓRKOWSKI, Elżbieta POCIASK, Karolina Nurzynska // Journal of Clinical Medicine [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2077-0383. — 2023 — vol. 12 iss. 8 art. no. 2836, s. 1-17. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 14-17, Abstr. — Publikacja dostępna online od: 2023-04-12
Autorzy (7)
- Gibala Sebastian
- Obuchowicz Rafał
- AGHLasek Julia
- AGHSchneider Zofia
- AGHPiórkowski Adam
- AGHPociask Elżbieta
- Nurzynska Karolina
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 146217 |
|---|---|
| Data dodania do BaDAP | 2023-04-19 |
| Tekst źródłowy | URL |
| DOI | 10.3390/jcm12082836 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Clinical Medicine |
Abstract
Background: Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk. Methods: Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy. Results: Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy. Conclusions: There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.