Cited by 122Radiomics and Machine Learning in Medical ImagingProstate Cancer Diagnosis and TreatmentMRI in cancer diagnosis

Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness

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Vignati, A, Mazzetti, S, Giannini, V, Russo, F, Bollito, E, Porpiglia, F, Stasi, M, Regge, D·2015·Physics in Medicine & Biology

Abstract

To explore contrast (C) and homogeneity (H) gray-level co-occurrence matrix texture features on T2-weighted (T2w) Magnetic Resonance (MR) images and apparent diffusion coefficient (ADC) maps for predicting prostate cancer (PCa) aggressiveness, and to compare them with traditional ADC metrics for differentiating low- from intermediate/high-grade PCas. The local Ethics Committee approved this prospective study of 93 patients (median age, 65 years), who underwent 1.5 T multiparametric endorectal MR imaging before prostatectomy. Clinically significant (volume ≥0.5 ml) peripheral tumours were outlined on histological sections, contoured on T2w and ADC images, and their pathological Gleason Score (pGS) was recorded. C, H, and traditional ADC metrics (mean, median, 10th and 25th percentile) were calculated on the largest lesion slice, and correlated with the pGS through the Spearman correlation coefficient. The area under the receiver operating characteristic curve (AUC) assessed how parameters differentiate pGS = 6 from pGS ≥ 7. The dataset included 49 clinically significant PCas with a balanced distribution of pGS. The Spearman ρ and AUC values on ADC were: −0.489, 0.823 (mean); −0.522, 0.821 (median); −0.569, 0.854 (10th percentile); −0.556, 0.854 (25th percentile); −0.386, 0.871 (C); 0.533, 0.923 (H); while on T2w they were: −0.654, 0.945 (C); 0.645, 0.962 (H). AUC of H on ADC and T2w, and C on T2w were significantly higher than that of the mean ADC ( p = 0.05). H and C calculated on T2w images outperform ADC parameters in correlating with pGS and differentiating low- from intermediate/high-risk PCas, supporting the role of T2w MR imaging in assessing PCa biological aggressiveness.

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  • For standard ADC cutoff metrics in Vignati et al, the area under the receiver operator characteristic curve ranged from 0.82 to 0.85.

    “For standard ADCapparent diffusion coefficient cutoff metrics, the area under the receiver operator characteristic curve ranged from 0.82 to 0.85.”

    From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [58]

  • Vignati et al performed a prospective radiomic analysis of diffusion- and T2-weighted MR imaging examinations in 49 patients with prostate cancer.

    “Recently, Vignati et al performed a thorough prospective radiomic analysis of diffusion- and T2-weighted MR imaging examinations in 49 patients with prostate cancer (58).”

    From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [58]

  • When ADC and T2 maps were analyzed for heterogeneity in Vignati et al, the area under the curve improved to 0.92 and 0.96, respectively.

    “When ADCapparent diffusion coefficient and T2 maps were analyzed for heterogeneity, the area under the curve improved to impressive values of 0.92 and 0.96, respectively.”

    From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [58]

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References 47 · Cited by 122

Authors
Vignati, A., Mazzetti, S., Giannini, V, Russo, F., Bollito, E., Porpiglia, F., Stasi, M., & Regge, D.
Affiliations
  • Candiolo Cancer Institute (IT)
  • Ospedale San Luigi Gonzaga (IT)
Corresponding author
Vignati, A
Venue
Physics in Medicine & Biology
Published
2015-03-13
Volume
60
Issue
7
Pages
2685-2701
ISSN
  • 0031-9155 (ISSN-L)
  • 1361-6560
OpenAlex
W2025971174
Cited by
122
References
47
Citations by year
2026: 1 · 2025: 4 · 2024: 8 · 2023: 9 · 2022: 7 · 2021: 15 · 2020: 16 · 2019: 20 · 2018: 17 · 2017: 6 · 2016: 17 · 2015: 1
FWCI
11.10
Type
journal article
Language
en
Open access
Locations
  • Physics in Medicine and Biology (publishedVersion)
  • PubMed (publishedVersion)
  • Institutional Research Information System University of Turin (University of Turin) (submittedVersion, other-oa)
Funders
Ministero della Salute
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
Keywords
prostate cancer, T2-weighted MRI, texture analysis, gray level co-occurrence matrix, apparent diffusion coefficient, Gleason score, tumor aggressiveness, biomarkers, endorectal MRI, multiparametric MRI, clinically significant prostate cancer
MeSH terms
Aged, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Prostatic Neoplasms, Diffusion Magnetic Resonance Imaging, Neoplasm Grading

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