PI-RADS Version 2 Is an Excellent Screening Tool for Clinically Significant Prostate Cancer as Designated by the Validated International Society of Urological Pathology Criteria: A Retrospective Analysis

Daun, Mitchell; Fardin, Sara; Ushinsky, Alexander; Batra, Sahil; Nguyentat, Michael; Lee, Thomas; Uchio, Edward; Lall, Chandana; Houshyar, Roozbeh

doi:10.1067/j.cpradiol.2019.06.010

Cited by 6 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can accurately identify clinically relevant cancer. The combination of T2W, DWI, and DCE has high specificity, sensitivity, and negative predictive value in detecting PCa [43][44][45]. The use of all three functional sequences has been found to have a positive predictive value for PCa of 98% [46].…”

Section: Multiparametric Magnetic Resonance Imagingmentioning

confidence: 91%

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Bardis

Houshyar

Chang

et al. 2020

Cancers

Self Cite

View full text Add to dashboard Cite

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists’ accuracy and speed.

show abstract

Section: Multiparametric Magnetic Resonance Imagingmentioning

confidence: 91%

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Bardis

Houshyar

Chang

et al. 2020

Cancers

Self Cite

View full text Add to dashboard Cite

show abstract

“…When the latter metrics were not explicitly reported, they were estimated from ROC or FROC curves. In the case that an ROC curve was provided for the radiologists instead of explicit PI-RADS thresholds, performance was estimated from the ROC curves at specificity cutoffs derived from the literature (18.5% for PI-RADS ≥ 3 and 67.5% for PI-RADS ≥ 4) [ 4 ]. If the results of multiple radiologists were separately reported, performance metrics were extracted for the most senior radiologist.…”

Section: Methodsmentioning

confidence: 99%

“… Sensitivity and specificity of DL systems for the diagnosis of csPCa at the patient level, compared to the respective radiologist benchmarks [ 22 , 23 , 24 , 27 , 28 ]. (*) The radiologist’s performance in Hiremath et al [ 22 ] was estimated from ROC curves at specificity estimates derived from the literature [ 4 ]. …”

Section: Figurementioning

confidence: 99%

“…The PI-RADS (v2) guidelines were introduced as a standardized system for reading and reporting findings at prostate MRI and are widely used by radiologists in clinical practice [ 3 ]. Studies have shown a high sensitivity and moderate specificity for PI-RADS [ 4 ]. However, the ability of PI-RADS to detect csPCa is highly dependent on reader experience and has limited reproducibility due to high intra- and inter-reader variability [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Performance of Deep Learning and Radiologists for the Diagnosis and Localization of Clinically Significant Prostate Cancer at MRI: A Systematic Review

Roest

Fransen

Kwee

et al. 2022

Life

View full text Add to dashboard Cite

Background: Deep learning (DL)-based models have demonstrated an ability to automatically diagnose clinically significant prostate cancer (PCa) on MRI scans and are regularly reported to approach expert performance. The aim of this work was to systematically review the literature comparing deep learning (DL) systems to radiologists in order to evaluate the comparative performance of current state-of-the-art deep learning models and radiologists. Methods: This systematic review was conducted in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Studies investigating DL models for diagnosing clinically significant (cs) PCa on MRI were included. The quality and risk of bias of each study were assessed using the checklist for AI in medical imaging (CLAIM) and QUADAS-2, respectively. Patient level and lesion-based diagnostic performance were separately evaluated by comparing the sensitivity achieved by DL and radiologists at an identical specificity and the false positives per patient, respectively. Results: The final selection consisted of eight studies with a combined 7337 patients. The median study quality with CLAIM was 74.1% (IQR: 70.6–77.6). DL achieved an identical patient-level performance to the radiologists for PI-RADS ≥ 3 (both 97.7%, SD = 2.1%). DL had a lower sensitivity for PI-RADS ≥ 4 (84.2% vs. 88.8%, p = 0.43). The sensitivity of DL for lesion localization was also between 2% and 12.5% lower than that of the radiologists. Conclusions: DL models for the diagnosis of csPCa on MRI appear to approach the performance of experts but currently have a lower sensitivity compared to experienced radiologists. There is a need for studies with larger datasets and for validation on external data.

show abstract

“…To evaluate the prostate, multiparametric magnetic resonance imaging (mpMRI) is increasingly employed and consists of T1-weighted images (T1WI), T2-weighted images (T2WI), diffusion-weighted images (DWI), apparent diffusion coefficient maps (ADC), and dynamic contrastenhanced images (DCE) [5]. It has high sensitivity, specificity, and negative predictive value in detecting prostate cancer [6][7][8]. Prostate mpMRI images are interpreted and reported using the Prostate Imaging-Reporting and Data System (PI-RADSv2), which evaluates lesions utilizing T2WI, DWI, and DCE and produces an overall risk assessment ranging from 1 to 5 [9].…”

Section: Introductionmentioning

confidence: 99%

Prostate minimally invasive procedures: complications and normal vs. abnormal findings on multiparametric magnetic resonance imaging (mpMRI)

et al. 2021

Self Cite

View full text Add to dashboard Cite

Minimally invasive alternatives to traditional prostate surgery are increasingly utilized to treat benign prostatic hyperplasia and localized prostate cancer in select patients. Advantages of these treatments over prostatectomy include lower risk of complication, shorter length of hospital stay, and a more favorable safety profile. Multiparametric magnetic resonance imaging (mpMRI) has become a widely accepted imaging modality for evaluation of the prostate gland and provides both anatomical and functional information. As prostate mpMRI and minimally invasive prostate procedure volumes increase, it is important for radiologists to be familiar with normal post-procedure imaging findings and potential complications. This paper reviews the indications, procedural concepts, common post-procedure imaging findings, and potential complications of prostatic artery embolization, prostatic urethral lift, irreversible electroporation, photodynamic therapy, high-intensity focused ultrasound, focal cryotherapy, and focal laser ablation.

show abstract

PI-RADS Version 2 Is an Excellent Screening Tool for Clinically Significant Prostate Cancer as Designated by the Validated International Society of Urological Pathology Criteria: A Retrospective Analysis

Cited by 6 publications

References 20 publications

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Comparative Performance of Deep Learning and Radiologists for the Diagnosis and Localization of Clinically Significant Prostate Cancer at MRI: A Systematic Review

Prostate minimally invasive procedures: complications and normal vs. abnormal findings on multiparametric magnetic resonance imaging (mpMRI)

Contact Info

Product

Resources

About