Head-to-Head Comparison Between Biparametric and Multiparametric MRI for the Diagnosis of Prostate Cancer: A Systematic Review and Meta-Analysis

Woo, Sungmin; Suh, Chong Hyun; Kim, Sang-Youn; Cho, Jeong Yeon; Kim, Seung Hyup; Moon, Min Hoan

doi:10.2214/ajr.18.19880

Cited by 125 publications

(76 citation statements)

References 50 publications

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“…A total of 150 two-dimensional (2D) radiomic texture features (including first-order statistics, statistical, gray-level co-occurrence, steerable Gabor, co-occurrence of local anisotropic gradient orientations (CoLlAGe [34]), and Laws' energy [35]) were extracted for from T2W images and ADC maps within each delineated PCa ROI (IT) and the peri-tumoral ROI (PT). A summary of the radiomic features and their significance in characterizing PCa is provided in Table 4.…”

Section: Radiomic Feature Extractionmentioning

confidence: 99%

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

Algohary

Shiradkar

Pahwa

et al. 2020

Cancers

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Background: Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification. Purpose: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D’Amico Risk Classification System. Materials and Methods: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (n = 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1, N = 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, N = 115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores. Results: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3–6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set. Conclusion: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment.

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Section: Radiomic Feature Extractionmentioning

confidence: 99%

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

Algohary

Shiradkar

Pahwa

et al. 2020

Cancers

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“…Woo et al reported a pooled sensitivity of 0.74 and a speci city of 0.90 for bpMRI and a pooled sensitivity of 0.76 and a speci city of 0.89 for mpMRI. Therefore, the authors conclude that bpMRI has the same diagnostic performance as mpMRI for the detection of PCa 30 .…”

Section: Discussionmentioning

confidence: 79%

Bi-parametric MRI/TRUS fusion targeted repeat biopsy after systematic 10-12 core TRUS-guided biopsy reveals more significant prostate cancer especially in anteriorly located tumours

Häggman¹,

Ahlberg²,

Dahlman³

et al. 2020

Preprint

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Abstract Background To demonstrate possible advantages with Bi-parametric MRI fusion-guided repeat biopsy over systematic 10-12-core biopsy for diagnosis of prostate cancer. Methods All patients who underwent fusion guided biopsy between February 2015 to February 2017 were included. Four hundred twenty-three consecutive men, with previous systematic10-12-core TRUS-guided biopsy, with suspicion of or low-risk prostate cancer underwent fusion-guided prostate biopsy. The material was retrospectively assessed. In 220 cases no previous cancer was diagnosed and in 203 cases confirmatory fusion guided biopsy was performed prior to active monitoring. MRI was done and classified according to PI-RADS. Region of Interest (ROI) was marked. Systematic biopsy was compared to fusion guided biopsy for detection of cancer, as well as PI-RADS was compared to Gleason score. Results Fusion guided biopsy detected significantly more cancers than systematic (p < 0.001). Gleason scores were higher in the fusion biopsy group (p < 0,001). PI-RADS score correlated with the presence of cancer in fusion biopsies. PI-RADS 2 yielded 19% cancer and PI-RADS 3 56%, PI-RADS 4–5 were cancerous in 83%. Only 3 out of 53 biopsies from PI-RADS 2 lesions showed high grade cancer (Gleason score 4 + 3 and higher) compared to 10/133 (7,5%) from PI-RADS 3 lesions and 54/239 (23%) from PI-RADS 4–5. Conclusion These results show superior detection rate and grading of bi-parametric MRI/TRUS fusion targeted repeat biopsy over systematic 10–12 core biopsies. Fusion guided biopsy detects more significant cancers despite using fewer cores. There is significant impact on known cases of low-risk PCa with change of risk group for many patients initially selected for active surveillance.

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“…118,119 Other challenges at the moment are involved with the use of biparametric MRI as opposed to mpMRI, although the sensitivity and specificity of the modalities are similar. 120 At the moment, there are ongoing several population-based screening studies next to the previous discussed PROBASE trial in Germany. In Sweden, two trials are recruiting.…”

Section: Future Of Screeningmentioning

confidence: 99%

Personalized strategies in population screening for prostate cancer

Remmers

Roobol

2020

Intl Journal of Cancer

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This review discusses evidence for population-based screening with contemporary screening tools. In Europe, prostate-specific antigen (PSA)-based screening led to a relative reduction of prostate cancer (PCa) mortality, but also to a substantial amount of overdiagnosis and unnecessarily biopsies. Risk stratification based on a single variable (a clinical variable or based on the presence of a lesion on prostate imaging) or based on multivariable approaches can aid in reducing unnecessary prostate biopsies and overdiagnosis by selecting men who can benefit from further clinical assessment. Multivariable approaches include clinical variables, and biomarkers, often combined in risk calculators or nomograms. These risk calculators can also incorporate the result of MRI imaging. In general, as compared to a purely PSA based approach, the combination of relevant prebiopsy information results in superior selection of men at higher risk of harboring clinically significant prostate cancer. Currently, it is not possible to draw any conclusions on the superiority of these multivariable risk-based approaches since head-to-head comparisons are virtually lacking. Recently initiated large population-based screening studies in Finland, Germany and Sweden, incorporating various multivariable risk stratification approaches will hopefully give more insight in whether the harm-benefit ratio can be improved, that is, maintain (or improving) the ability to reduce metastatic disease and prostate cancer mortality while reducing harm caused by unnecessary testing and overdiagnosis including related overtreatment.

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Head-to-Head Comparison Between Biparametric and Multiparametric MRI for the Diagnosis of Prostate Cancer: A Systematic Review and Meta-Analysis

Cited by 125 publications

References 50 publications

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

Bi-parametric MRI/TRUS fusion targeted repeat biopsy after systematic 10-12 core TRUS-guided biopsy reveals more significant prostate cancer especially in anteriorly located tumours

Personalized strategies in population screening for prostate cancer

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