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Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer
BACKGROUND: around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─). OBJECTIVE: To quantify the differences between MR visible (MRI+) and MRI(─) CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI). METHODS: Thi...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311200/ https://www.ncbi.nlm.nih.gov/pubmed/37396490 http://dx.doi.org/10.1016/j.ejro.2023.100496 |
Sumario: | BACKGROUND: around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─). OBJECTIVE: To quantify the differences between MR visible (MRI+) and MRI(─) CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI). METHODS: This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI(─) CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (D(v)) comprised 52 patients from a single institution, the remaining 112 patients were used for training (D(t)). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI. Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on D(t) to identify radiomic features associated with MRI(─) and MRI(+) CsPCa to generate corresponding risk scores R(MRI─) and R(MRI+). R(bpMRI) was further generated by integrating R(MRI─) and R(MRI+). Statistical significance was determined using the Wilcoxon signed-rank test. RESULTS: Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI(─) CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI(─) and MRI(+) CsPCa (p < 0.05). R(bpMRI) yielded the highest AUC of 0.82 (95 % CI 0.72–0.91) compared to AUCs of R(MRI+) 0.76 (95 % CI 0.63–0.89), and PI-RADS 0.58 (95 % CI 0.50–0.72) on D(v). R(bpMRI) correctly reclassified 10 out of 14 MRI(─) CsPCa on D(v). CONCLUSION: Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI(─) CsPCa. These features could assist in CsPCa identification on bpMRI. |
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