Investigating the role of DCE-MRI, over T2 and DWI, in accurate PI-RADS v2 assessment of clinically significant peripheral zone prostate lesions as defined at radical prostatectomy

Taghipour, Mehdi; Ziaei, Alireza; Alessandrino, Francesco; Hassanzadeh, Elmira; Harisinghani, Mukesh G.; Vangel, Mark; Tempany, Clare M.; Fennessy, Fiona M.

doi:10.1007/s00261-018-1807-6

Cited by 29 publications

(16 citation statements)

References 42 publications

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“…Providing a large amount of anatomical and functional information, mp-MRI is a promising method for the non-invasive evaluation of PCa. Many previous studies have evaluated the performance of mp-MRIbased models for prediction csPCa [11,[21][22][23][24]. Yim JH et al combined Prostate Imaging Reporting and Data System scores and the tumour ADC value reaching an AUC of 0.803 for predicting patients with ciPCa [21].…”

Section: Discussionmentioning

confidence: 99%

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method

Min

Dong

et al. 2019

European Journal of Radiology

113

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To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa). Materials and methods: Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient. The minority group in the training cohort was balanced via the synthetic minority over-sampling technique (SMOTE) method. We used minimum-redundancy maximum-relevance (mRMR) selection and the LASSO algorithm for feature selection and radiomics signature building. The classification performance of the radiomics signature for csPCa and ciPCa was evaluated by receiver operating characteristic curve analysis in the training and test cohorts. Results: Nine features were selected for the radiomics signature building. Significant differences in the radiomics signature existed between the csPCa and ciPCa groups in both the training and test cohorts (p < 0.01 for both). The AUC, sensitivity and specificity of the radiomics signature were 0.872 (95% CI: 0.823−0.921), 0.883, and 0.753, respectively, in the training cohort, and 0.823 (95% CI: 0.669−0.976), 0.841, and 0.727, respectively, in the test cohort. Conclusion: Mp-MRI-based radiomics signature have the potential to noninvasively discriminate between csPCa and ciPCa.

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Section: Discussionmentioning

confidence: 99%

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method

Min

Dong

et al. 2019

European Journal of Radiology

113

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“…These results suggest that the inclusion of radiomic features derived from DCE and DKI models does not provide a clear added value for PCa detection. On one hand, this would justify the choice to exclude DCE from dominant sequences for PI-RADSv2 score assessment, which is still a topic of discussion [34][35][36]; on the other hand, this would be food for thought regarding the complex debate of the financial benefits of including time-and computational-demanding DKI acquisition in prostate mpMRI acquisition protocol [14]. Regarding the comparison of the 3D and 2D advanced models, high diagnostic performances were shown by both advanced models, although the sensitivity was slightly lower and the specificity higher for the model built using 2D ROI, even though the resulting AUCs were similar and above 0.99, thereby suggesting great accuracy for the quantitative parameters extracted from VOI.…”

Section: Discussionmentioning

confidence: 99%

Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol

Monti

Brancato

Costanzo

et al. 2020

Cancers

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Prostate cancer (PCa) is a disease affecting an increasing number of men worldwide. Several efforts have been made to identify imaging biomarkers to non-invasively detect and characterize PCa, with substantial improvements thanks to multiparametric Magnetic Resonance Imaging (mpMRI). In recent years, diffusion kurtosis imaging (DKI) was proposed to be directly related to tissue physiological and pathological characteristic, while the radiomic approach was proven to be a key method to study cancer imaging phenotypes. Our aim was to compare a standard radiomic model for PCa detection, built using T2-weighted (T2W) and Apparent Diffusion Coefficient (ADC), with an advanced one, including DKI and quantitative Dynamic Contrast Enhanced (DCE), while also evaluating differences in prediction performance when using 2D or 3D lesion segmentation. The obtained results in terms of diagnostic accuracy were high for all of the performed comparisons, reaching values up to 0.99 for the area under a receiver operating characteristic curve (AUC), and 0.98 for both sensitivity and specificity. In comparison, the radiomic model based on standard features led to prediction performances higher than those of the advanced model, while greater accuracy was achieved by the model extracted from 3D segmentation. These results provide new insights into active topics of discussion, such as choosing the most convenient acquisition protocol and the most appropriate postprocessing pipeline to accurately detect and characterize PCa.

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“…Algunos trabajos ya han demostrado y cuantificado la utilidad de la secuencia contrastada (8,9,10) . El artículo de Taghipour et al (10), que compara resultados de RM-mp versus hallazgos histológicos de prostatectomía radical (PR), mostró la utilidad de la DCE en un 21,5% de los casos y en estos comprobó que incrementa correctamente el PI-RADS de 3 a 4 en un 68,9%. El trabajo de Visschere et al (9) , mostró similares resultados a pesar de tener diferencias metodológicas.…”

Section: La Secuencia Dinámica Contrastada (Dce)unclassified

“…Los valores predictivos positivos (VPP) variaron entre 54% a 97% y los valores predictivos negativos (VPN) entre 26% a 92%. Un estudio más reciente, Taghipour et al (10) mostraron una sensibilidad de 89,2%, especificidad de 57,6%, VPP de 88,3% y VPN de 59,6% con una exactitud diagnóstica de un 82,3% en la detección de cánceres clínicamente significativos.…”

Section: Rendimiento Del Pi-rads V2unclassified

Cáncer de próstata, el problema del diagnóstico ¿Es la resonancia multiparamétrica de próstata la solución?

Messina

2019

Rev. chil. radiol.

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Prostate cancer, the problem of diagnosis. Is multiparametric prostate magnetic resonance imaging the solution?Resumen: Junto con un aumento sostenido en la incidencia de cáncer de próstata en los países desarrollados ha habido un cambio en el enfrentamiento diagnóstico en estos pacientes. El diagnóstico tradicional basado en la medición del antígeno prostático específico y la biopsia randomizada ha mostrado tener muchas falencias ya que sobre-diagnostica cánceres no significativos y sub-diagnostica cánceres clínicamente significativos. La resonancia magnética de próstata multiparamétrica ha demostrado ser útil ya que disminuye este tipo de falencias. En este artículo se revisará la historia de la resonancia magnética de próstata y del PI-RADS, con el objeto de revisar el rendimiento de estos métodos y sus indicaciones actuales. Palabras clave: Antígeno prostático específico, Cáncer de próstata, Resonancia multiparamétrica de próstata, Vigilancia activa cáncer de próstata.Abstract: Along with a sustained increase in the incidence of prostate cancer in developed countries, there has been a change in the diagnostic approach in these patients. The traditional diagnosis based on the measurement of the specific prostate antigen and randomized biopsy has shown to have many shortcomings as it leads to overdiagnosis of non-significant cancers and underdiagnosis of clinically significant cancers. The multiparametric prostate magnetic resonance imaging has proven to be useful since it diminishes these shortcomings. In this article we will review part of the history of prostate magnetic resonance imaging and PI-RADS, in order to review the performance of these methods and their current indications.

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Investigating the role of DCE-MRI, over T2 and DWI, in accurate PI-RADS v2 assessment of clinically significant peripheral zone prostate lesions as defined at radical prostatectomy

Cited by 29 publications

References 42 publications

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method

Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol

Cáncer de próstata, el problema del diagnóstico ¿Es la resonancia multiparamétrica de próstata la solución?

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