Dataset Information

Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer.

ABSTRACT:

Objective

To develop and validate a non-invasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy.

Methods

In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T₂WI (n = 788) and diffusion-weighted imaging (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature.

Results

Nine radiomics features were identified to form the radiomics signature. The radiomics score (Rad-score) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95% CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95% CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%.

Conclusion

The multiparametric MRI-based radiomics signature can potentially serve as a non-invasive tool for distinguishing between indolent and aggressive PCa prior to therapy.

Advances in knowledge

The multiparametric MRI-based radiomics signature has the potential to non-invasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.

SUBMITTER: Zhang L

PROVIDER: S-EPMC8978240 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer.

Zhang Liuhui L Jiang Donggen D Chen Chujie C Yang Xiangwei X Lei Hanqi H Kang Zhuang Z Huang Hai H Pang Jun J

The British journal of radiology 20210929 1131

<h4>Objective</h4>To develop and validate a non-invasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy.<h4>Methods</h4>In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (<i>n</i> = 93) and a validation set (<i>n</i> = 46). A total of 1576 radiomics features were extracted from the <i>T</i><sub>2</sub>WI (<i>n</i> = 788) and diffusion-weighted imaging (<i>n</i> = 788) for each patie ...[more]

PMID: 34289319

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Project description:ObjectivesTo create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa).MethodsThis study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3-5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets.ResultsThe best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980-0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920-0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution.ConclusionsThe phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa.Key points• T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly improve diagnostic performance. • Multivariate feature selection and extreme gradient outperform univariate feature selection and random forest. • The developed radiomics model that extracts multiparametric MRI features with an auto-fixed volume of interest may be a valuable addition to visual assessment in diagnosing clinically significant prostate cancer.

Project description:BackgroundDistinguishing men with aggressive from indolent prostate cancer is critical to decisions in the management of clinically localized prostate cancer. Molecular signatures of aggressive disease could help men overcome this major clinical challenge by reducing unnecessary treatment and allowing more appropriate treatment of aggressive disease.MethodsWe performed a mass spectrometry-based proteomic analysis of normal and malignant prostate tissues from 22 men who underwent surgery for prostate cancer. Prostate cancer samples included Grade Groups (3-5), with 8 patients experiencing recurrence and 14 without evidence of recurrence with a mean of 6.8 years of follow-up. To better understand the biological pathways underlying prostate cancer aggressiveness, we performed a systems biology analysis and gene enrichment analysis. Proteins that distinguished recurrent from nonrecurrent cancer were chosen for validation by immunohistochemical analysis on tissue microarrays containing samples from a larger cohort of patients with recurrent and nonrecurrent prostate cancer.ResultsIn all, 24,037 unique peptides (false discovery rate < 1%) corresponding to 3,313 distinct proteins were identified with absolute abundance ranges spanning seven orders of magnitude. Of these proteins, 115 showed significantly (p < 0.01) different levels in tissues from recurrent versus nonrecurrent cancers. Analysis of all differentially expressed proteins in recurrent and nonrecurrent cases identified several protein networks, most prominently one in which approximately 24% of the proteins in the network were regulated by the YY1 transcription factor (adjusted p < 0.001). Strong immunohistochemical staining levels of three differentially expressed proteins, POSTN, CALR, and CTSD, on a tissue microarray validated their association with shorter patient survival.ConclusionsThe protein signatures identified could improve understanding of the molecular drivers of aggressive prostate cancer and be used as candidate prognostic biomarkers.

Dataset Information

Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer.

Objective

Methods

Results

Conclusion

Advances in knowledge

Publications

Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer.

Similar Datasets

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