Publications by authors named "Masoom A Haider"

165 Publications

Avoiding Unnecessary Biopsy: MRI-based Risk Models versus a PI-RADS and PSA Density Strategy for Clinically Significant Prostate Cancer.

Radiology 2021 May 25:204112. Epub 2021 May 25.

From the Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany (D.D.); Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Ave, Toronto, ON, Canada M5G 1X5 (D.D., G.M.H., X.D., E.S.M., A.Z., M.A.H.); Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada (D.D., G.M.H., S.G., E.S.M., A.T., M.A.H.); Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada (N.F., R.H., G.K., A.Z., A.F., N.P.); Department of Pathology, Laboratory Medicine Program, University Health Network, Toronto, ON, Canada (T.v.d.K.); and Department of Surgery, Division of Urology, Mount Sinai Hospital, Toronto, ON, Canada (A.Z.).

Background In validation studies, risk models for clinically significant prostate cancer (csPCa; Gleason score ≥3+4) combining multiparametric MRI and clinical factors have demonstrated poor calibration (over- and underprediction) and limited use in avoiding unnecessary prostate biopsies. Purpose MRI-based risk models following local recalibration were compared with a strategy that combined Prostate Imaging Data and Reporting System (PI-RADS; version 2) and prostate-specific antigen density (PSAd) to assess the potential reduction of unnecessary prostate biopsies. Materials and Methods This retrospective study included 385 patients without prostate cancer diagnosis who underwent multipara-metric MRI (PI-RADS category ≥3) and MRI-targeted biopsy between 2015 and 2019. Recalibration and selection of the best-performing MRI model (MRI-European Randomized Study of Screening for Prostate Cancer [ERSPC], van Leeuwen, Radtke, and Mehralivand models) were undertaken in cohort C1 ( = 242; 2015-2017). The impact on biopsy decisions was compared with an alternative strategy (no biopsy for PI-RADS category 3 plus PSAd < 0.1 ng/mL per milliliter) in cohort C2 ( = 143; 2018-2019). Discrimination, calibration, and clinical utility were assessed by using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. Results The prevalence of csPCa was 38% (93 of 242 patients) and 45% (64 of 143 patients) in cohorts C1 and C2, respectively. Decision curve analysis demonstrated the highest net benefit for the van Leeuwen and Mehralivand models in C1. Used for biopsy decisions in C2, van Leeuwen (AUC, 0.84; 95% CI: 0.77, 0.9) and Mehralivand (AUC, 0.79; 95% CI: 0.72, 0.86) enabled no net benefit at a risk threshold of 10%. Up to a risk threshold of 15%, net benefit remained inferior to the PI-RADS plus PSAd strategy, which avoided biopsy in 63 per 1000 men, without missing csPCa. Without prior recalibration in C1, three of four models (MRIERSPC, Radtke, Mehralivand) were poorly calibrated and not clinically useful in C2. Conclusion The number of unnecessary prostate biopsies in men with positive MRI may be safely reduced by using a prostate-specific antigen density-based strategy. In a risk-averse scenario, this strategy enabled better biopsy decisions compared with MRI-based risk models. ©RSNA, 2021 .
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http://dx.doi.org/10.1148/radiol.2021204112DOI Listing
May 2021

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging.

Eur Radiol 2021 May 15. Epub 2021 May 15.

Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. KEY POINTS: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.
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http://dx.doi.org/10.1007/s00330-021-08021-6DOI Listing
May 2021

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

Abdom Radiol (NY) 2021 May 11. Epub 2021 May 11.

Department of Radiological Sciences, University of California, Irvine, Orange, CA, 92868-3201, USA.

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.
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http://dx.doi.org/10.1007/s00261-021-03097-6DOI Listing
May 2021

Prognostic value of early changes in CT-measured body composition in patients receiving chemotherapy for unresectable pancreatic cancer.

Eur Radiol 2021 May 2. Epub 2021 May 2.

Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.

Objectives: Skeletal muscle mass is a prognostic factor in pancreatic ductal adenocarcinoma (PDAC). However, it remains unclear whether changes in body composition provide an incremental prognostic value to established risk factors, especially the Response Evaluation Criteria in Solid Tumors version 1.1 (RECISTv1.1). The aim of this study was to determine the prognostic value of CT-quantified body composition changes in patients with unresectable PDAC starting chemotherapy.

Methods: We retrospectively evaluated 105 patients with unresectable (locally advanced or metastatic) PDAC treated with FOLFIRINOX (n = 64) or gemcitabine-based (n = 41) first-line chemotherapy within a multicenter prospective trial. Changes (Δ) in skeletal muscle index (SMI), subcutaneous (SATI), and visceral adipose tissue index (VATI) between pre-chemotherapy and first follow-up CT were assessed. Cox regression models and covariate-adjusted survival curves were used to identify predictors of overall survival (OS).

Results: At multivariable analysis, adjusting for RECISTv1.1-response at first follow-up, ΔSMI was prognostic for OS with a hazard ratio (HR) of 1.2 (95% CI: 1.08-1.33, p = 0.001). No significant association with OS was observed for ΔSATI (HR: 1, 95% CI: 0.97-1.04, p = 0.88) and ΔVATI (HR: 1.01, 95% CI: 0.99-1.04, p = 0.33). At an optimal cutoff of 2.8 cm/m per 30 days, the median survival of patients with high versus low ΔSMI was 143 versus 233 days (p < 0.001).

Conclusions: Patients with a lower rate of skeletal muscle loss at first follow-up demonstrated improved survival for unresectable PDAC, regardless of their RECISTv1.1-category. Assessing ΔSMI at the first follow-up CT may be useful for prognostication, in addition to routine radiological assessment.

Key Points: • In patients with unresectable pancreatic ductal adenocarcinoma, change of skeletal muscle index (ΔSMI) in the early phase of chemotherapy is prognostic for overall survival, even after adjusting for Response Evaluation Criteria in Solid Tumors version 1.1 (RECISTv1.1) assessment at first follow-up. • Changes in adipose tissue compartments at first follow-up demonstrated no significant association with overall survival. • Integrating ΔSMI into routine radiological assessment may improve prognostic stratification and impact treatment decision-making at the first follow-up.
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http://dx.doi.org/10.1007/s00330-021-07899-6DOI Listing
May 2021

Beyond the : "Population-Based Prostate Cancer Screening With Magnetic Resonance Imaging or Ultrasonography: The IP1-PROSTAGRAM Study".

AJR Am J Roentgenol 2021 Apr 28. Epub 2021 Apr 28.

Joint Department of Medical Imaging, University of Toronto, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada.

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http://dx.doi.org/10.2214/AJR.21.26048DOI Listing
April 2021

Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma.

Front Artif Intell 2020 5;3:550890. Epub 2020 Oct 5.

Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.

Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited. Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts. The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15-3.53, -value: 0.04). This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies.
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http://dx.doi.org/10.3389/frai.2020.550890DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861273PMC
October 2020

Prostate Magnetic Resonance Imaging for Local Recurrence Reporting (PI-RR): International Consensus -based Guidelines on Multiparametric Magnetic Resonance Imaging for Prostate Cancer Recurrence after Radiation Therapy and Radical Prostatectomy.

Eur Urol Oncol 2021 Feb 10. Epub 2021 Feb 10.

Department of Radiology and Nuclear Medicine, Radboudumc, Nijmegen, The Netherlands.

Background: Imaging techniques are used to identify local recurrence of prostate cancer (PCa) for salvage therapy and to exclude metastases that should be addressed with systemic therapy. For magnetic resonance imaging (MRI), a reduction in the variability of acquisition, interpretation, and reporting is required to detect local PCa recurrence in men with biochemical relapse after local treatment with curative intent.

Objective: To propose a standardised method for image acquisition and assessment of PCa local recurrence using MRI after radiation therapy (RP) and radical prostatectomy (RT).

Evidence Acquisition: Prostate Imaging for Recurrence Reporting (PI-RR) was formulated using the existing literature. An international panel of experts conducted a nonsystematic review of the literature. The PI-RR system was created via consensus through a combination of face-to-face and online discussions.

Evidence Synthesis: Similar to with PI-RADS, based on the best available evidence and expert opinion, the minimum acceptable MRI parameters for detection of recurrence after radiation therapy and radical prostatectomy are set. Also, a simplified and standardised terminology and content of the reports that use five assessment categories to summarise the suspicion of local recurrence (PI-RR) are designed. PI-RR scores of 1 and 2 are assigned to lesions with a very low and low likelihood of recurrence, respectively. PI-RR 3 is assigned if the presence of recurrence is uncertain. PI-RR 4 and 5 are assigned for a high and very high likelihood of recurrence, respectively. PI-RR is intended to be used in routine clinical practice and to facilitate data collection and outcome monitoring for research.

Conclusions: This paper provides a structured reporting system (PI-RR) for MRI evaluation of local recurrence of PCa after RT and RP.

Patient Summary: A new method called PI-RR was developed to promote standardisation and reduce variations in the acquisition, interpretation, and reporting of magnetic resonance imaging for evaluating local recurrence of prostate cancer and guiding therapy.
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http://dx.doi.org/10.1016/j.euo.2021.01.003DOI Listing
February 2021

Comparison of Multiparametric Magnetic Resonance Imaging-Targeted Biopsy With Systematic Transrectal Ultrasonography Biopsy for Biopsy-Naive Men at Risk for Prostate Cancer: A Phase 3 Randomized Clinical Trial.

JAMA Oncol 2021 Apr;7(4):534-542

Toronto General Hospital, Department of Radiology, University of Toronto, Toronto, Ontario, Canada.

Importance: Magnetic resonance imaging (MRI) with targeted biopsy is an appealing alternative to systematic 12-core transrectal ultrasonography (TRUS) biopsy for prostate cancer diagnosis, but has yet to be widely adopted.

Objective: To determine whether MRI with only targeted biopsy was noninferior to systematic TRUS biopsies in the detection of International Society of Urological Pathology grade group (GG) 2 or greater prostate cancer.

Design, Setting, And Participants: This multicenter, prospective randomized clinical trial was conducted in 5 Canadian academic health sciences centers between January 2017 and November 2019, and data were analyzed between January and March 2020. Participants included biopsy-naive men with a clinical suspicion of prostate cancer who were advised to undergo a prostate biopsy. Clinical suspicion was defined as a 5% or greater chance of GG2 or greater prostate cancer using the Prostate Cancer Prevention Trial Risk Calculator, version 2. Additional criteria were serum prostate-specific antigen levels of 20 ng/mL or less (to convert to micrograms per liter, multiply by 1) and no contraindication to MRI.

Interventions: Magnetic resonance imaging-targeted biopsy (MRI-TB) only if a lesion with a Prostate Imaging Reporting and Data System (PI-RADS), v 2.0, score of 3 or greater was identified vs 12-core systematic TRUS biopsy.

Main Outcome And Measures: The proportion of men with a diagnosis of GG2 or greater cancer. Secondary outcomes included the proportion who received a diagnosis of GG1 prostate cancer; GG3 or greater cancer; no significant cancer but subsequent positive MRI results and/or GG2 or greater cancer detected on a repeated biopsy by 2 years; and adverse events.

Results: The intention-to-treat population comprised 453 patients (367 [81.0%] White, 19 [4.2%] African Canadian, 32 [7.1%] Asian, and 10 [2.2%] Hispanic) who were randomized to undergo TRUS biopsy (226 [49.9%]) or MRI-TB (227 [51.1%]), of which 421 (93.0%) were evaluable per protocol. A lesion with a PI-RADS score of 3 or greater was detected in 138 of 221 men (62.4%) who underwent MRI, with 26 (12.1%), 82 (38.1%), and 30 (14.0%) having maximum PI-RADS scores of 3, 4, and 5, respectively. Eighty-three of 221 men who underwent MRI-TB (37%) had a negative MRI result and avoided biopsy. Cancers GG2 and greater were identified in 67 of 225 men (30%) who underwent TRUS biopsy vs 79 of 227 (35%) allocated to MRI-TB (absolute difference, 5%, 97.5% 1-sided CI, -3.4% to ∞; noninferiority margin, -5%). Adverse events were less common in the MRI-TB arm. Grade group 1 cancer detection was reduced by more than half in the MRI arm (from 22% to 10%; risk difference, -11.6%; 95% CI, -18.2% to -4.9%).

Conclusions And Relevance: Magnetic resonance imaging followed by selected targeted biopsy is noninferior to initial systematic biopsy in men at risk for prostate cancer in detecting GG2 or greater cancers.

Trial Registration: ClinicalTrials.gov Identifier: NCT02936258.
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http://dx.doi.org/10.1001/jamaoncol.2020.7589DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863017PMC
April 2021

MRI-guided Focused Ultrasound Ablation for Localized Intermediate-Risk Prostate Cancer: Early Results of a Phase II Trial.

Radiology 2021 Mar 2;298(3):695-703. Epub 2021 Feb 2.

From the Joint Department of Medical Imaging (S.G., R.C., E.H., M.A.H., W.K.), Division of Urology, Department of Surgical Oncology (A.F., K.C., S.J., A.K., A.R.Z., R.J.H., N.P.), Biostatistics Department, Princess Margaret Cancer Centre (X.L.), Department of Anaesthesia (S.M.), and Department of Pathology, Laboratory Medicine Program (T.H.v.d.K.), University Health Network-Mount Sinai Hospital-Women's, College Hospital, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2; and Department of Urology, Oakville Trafalgar Memorial Hospital, Toronto, Canada (P.F.I.).

Background To reduce adverse effects of whole-gland therapy, participants with localized clinically significant prostate cancer can undergo MRI-guided focal therapy. Purpose To explore safety and early oncologic and functional outcomes of targeted focal high-intensity focused ultrasound performed under MRI-guided focused ultrasound for intermediate-risk clinically significant prostate cancer. Materials and Methods In this prospective phase II trial, between February 2016 and July 2019, men with unifocal clinically significant prostate cancer visible at MRI were treated with transrectal MRI-guided focused ultrasound. The primary end point was the 5-month biopsy (last recorded in December 2019) with continuation to the 24-month follow-up projected to December 2021. Real-time ablation monitoring was performed with MR thermography. Nonperfused volume was measured at treatment completion. Periprocedural complications were recorded. Follow-up included International Prostate Symptom Score (IPSS) and International Index of Erectile Function-15 (IIEF-15) score at 6 weeks and 5 months, and multiparametric MRI and targeted biopsy of the treated area at 5 months. The generalized estimating equation model was used for statistical analysis, and the Holm method was used to adjust value. Results Treatment was successfully completed in all 44 men, 36 with grade group (GG) 2 and eight with GG 3 disease (median age, 67 years; interquartile range [IQR], 62-70 years). No major treatment-related adverse events occurred. Forty-one of 44 participants (93%; 95% CI: 82, 98) were free of clinically significant prostate cancer (≥6 mm GG 1 disease or any volume ≥GG 2 disease) at the treatment site at 5-month biopsy (median, seven cores). Median IIEF-15 and IPSS scores were similar at baseline and at 5 months (IIEF-15 score at baseline, 61 [IQR, 34-67] and at 5 months, 53 [IQR, 24-65.5], = .18; IPSS score at baseline, 3.5 [IQR, 1.8-7] and at 5 months, 6 [IQR, 2-7.3], = .43). Larger ablations (≥15 cm) compared with smaller ones were associated with a decline in IIEF-15 scores at 6 weeks (adjusted < .01) and at 5 months (adjusted = .07). Conclusion Targeted focal therapy of intermediate-risk prostate cancer performed with MRI-guided focused ultrasound ablation was safe and had encouraging early oncologic and functional outcomes. © RSNA, 2021 See also the editorial by Tempany-Afdhal in this issue.
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http://dx.doi.org/10.1148/radiol.2021202717DOI Listing
March 2021

Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images.

Sci Rep 2021 Jan 14;11(1):1378. Epub 2021 Jan 14.

Department of Medical Imaging, University of Toronto, 686 Bay Street, Toronto, ON, M5G 0A4, Canada.

As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. On the other hand, recent advances in deep learning and transfer learning have shown significant potential in the quantitative medical imaging field, raising the research question of whether deep transfer learning features have predictive information in addition to radiomics features. In this study, using CT images from Pancreatic Ductal Adenocarcinoma (PDAC) patients recruited in two independent hospitals, we discovered most transfer learning features have weak linear relationships with radiomics features, suggesting a potential complementary relationship between these two feature sets. We also tested the prognostic performance for overall survival using four feature fusion and reduction methods for combining radiomics and transfer learning features and compared the results with our proposed risk score-based feature fusion method. It was shown that the risk score-based feature fusion method significantly improves the prognosis performance for predicting overall survival in PDAC patients compared to other traditional feature reduction methods used in previous radiomics studies (40% increase in area under ROC curve (AUC) yielding AUC of 0.84).
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http://dx.doi.org/10.1038/s41598-021-80998-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809062PMC
January 2021

Validation of Prognostic Radiomic Features From Resectable Pancreatic Ductal Adenocarcinoma in Patients With Advanced Disease Undergoing Chemotherapy.

Can Assoc Radiol J 2020 Nov 5:846537120968782. Epub 2020 Nov 5.

Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, Toronto, Ontario, Canada.

Background: Radiomic features in pancreatic ductal adenocarcinoma (PDAC) often lack validation in independent test sets or are limited to early or late stage disease. Given the lethal nature of PDAC it is possible that there are similarities in radiomic features of both early and advanced disease reflective of aggressive biology.

Purpose: To assess the performance of prognostic radiomic features previously published in patients with resectable PDAC in a test set of patients with unresectable PDAC undergoing chemotherapy.

Methods: The pre-treatment CT of 108 patients enrolled in a prospective chemotherapy trial were used as a test cohort for 2 previously published prognostic radiomic features in resectable PDAC (Sum Entropy and Cluster Tendency with square-root filter[Sqrt]). We assessed the performance of these 2 radiomic features for the prediction of overall survival (OS) and time to progression (TTP) using Cox proportional-hazard models.

Results: Sqrt Cluster Tendency was significantly associated with outcome with a hazard ratio (HR) of 1.27(for primary pancreatic tumor plus local nodes), (Confidence Interval(CI):1.01 -1.6, -value = 0.039) for OS and a HR of 1.25(CI:1.00 -1.55, -value = 0.047) for TTP. Sum entropy was not associated with outcomes. Sqrt Cluster Tendency remained significant in multivariate analysis.

Conclusion: The CT radiomic feature Sqrt Cluster Tendency, previously demonstrated to be prognostic in resectable PDAC, remained a significant prognostic factor for OS and TTP in a test set of unresectable PDAC patients. This radiomic feature warrants further investigation to understand its biologic correlates and CT applicability in PDAC patients.
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http://dx.doi.org/10.1177/0846537120968782DOI Listing
November 2020

Reply by Authors.

J Urol 2020 12 24;204(6):1194. Epub 2020 Sep 24.

Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.

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http://dx.doi.org/10.1097/JU.0000000000001157.02DOI Listing
December 2020

PI-RADS Committee Position on MRI Without Contrast Medium in Biopsy-Naive Men With Suspected Prostate Cancer: Narrative Review.

AJR Am J Roentgenol 2021 01 19;216(1):3-19. Epub 2020 Nov 19.

Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, United Kingdom.

The steadily increasing demand for diagnostic prostate MRI has led to concerns regarding the lack of access to and the availability of qualified MRI scanners and sufficiently experienced radiologists, radiographers, and technologists to meet the demand. Solutions must enhance operational benefits without compromising diagnostic performance, quality, and delivery of service. Solutions should also mitigate risks such as decreased reader confidence and referrer engagement. One approach may be the implementation of MRI without the use gadolinium-based contrast medium (bipara-metric MRI), but only if certain prerequisites such as high-quality imaging, expert interpretation quality, and availability of patient recall or on-table monitoring are mandated. Alternatively, or in combination, a clinical risk-based approach could be used for protocol selection, specifically, which biopsy-naive men need MRI with contrast medium (multiparametric MRI). There is a need for prospective studies in which biopsy decisions are made according to MRI without contrast enhancement. Such studies must define clinical and operational benefits and identify which patient groups can be scanned successfully without contrast enhancement. These higher-quality data are needed before the Prostate Imaging Reporting and Data System (PI-RADS) Committee can make evidence-based recommendations about MRI without contrast enhancement as an initial diagnostic approach for prostate cancer workup.
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http://dx.doi.org/10.2214/AJR.20.24268DOI Listing
January 2021

Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?

Eur Radiol 2021 Jan 4;31(1):244-255. Epub 2020 Aug 4.

Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada.

Objective: To differentiate combined hepatocellular cholangiocarcinoma (cHCC-CC) from cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on MRI and CT radiomics features.

Methods: This retrospective study included 85 patients aged 32 to 86 years with 86 histopathology-proven liver cancers: 24 cHCC-CC, 24 CC, and 38 HCC who had MRI and CT between 2004 and 2018. Initial CT reports and morphological evaluation of MRI features were used to assess the performance of radiologists read. Following tumor segmentation, 1419 radiomics features were extracted using PyRadiomics library and reduced to 20 principle components by principal component analysis. Support vector machine classifier was utilized to evaluate MRI and CT radiomics features for the prediction of cHCC-CC vs. non-cHCC-CC and HCC vs. non-HCC. Histopathology was the reference standard for all tumors.

Results: Radiomics MRI features demonstrated the best performance for differentiation of cHCC-CC from non-cHCC-CC with the highest AUC of 0.77 (SD 0.19) while CT was of limited value. Contrast-enhanced MRI phases and pre-contrast and portal-phase CT showed excellent performance for the differentiation of HCC from non-HCC (AUC of 0.79 (SD 0.07) to 0.81 (SD 0.13) for MRI and AUC of 0.81 (SD 0.06) and 0.71 (SD 0.15) for CT phases, respectively). The misdiagnosis of cHCC-CC as HCC or CC using radiologists read was 69% for CT and 58% for MRI.

Conclusions: Our results demonstrate promising predictive performance of MRI and CT radiomics features using machine learning analysis for differentiation of cHCC-CC from HCC and CC with potential implications for treatment decisions.

Key Points: • Retrospective study demonstrated promising predictive performance of MRI radiomics features in the differentiation of cHCC-CC from HCC and CC and of CT radiomics features in the differentiation of HCC from cHCC-CC and CC. • With future validation, radiomics analysis has the potential to inform current clinical practice for the pre-operative diagnosis of cHCC-CC and to enable optimal treatment decisions regards liver resection and transplantation.
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http://dx.doi.org/10.1007/s00330-020-07119-7DOI Listing
January 2021

Small Renal Mass Surveillance: Histology-specific Growth Rates in a Biopsy-characterized Cohort.

Eur Urol 2020 09 14;78(3):460-467. Epub 2020 Jul 14.

Division of Urology, Department of Surgery, Princess Margaret Cancer Centre and the University Health Network, University of Toronto, Toronto, ON, Canada.

Background: Most reports of active surveillance (AS) of small renal masses (SRMs) lack biopsy confirmation, and therefore include benign tumors and different subtypes of renal cell carcinoma (RCC).

Objective: We compared the growth rates and progression of different histologic subtypes of RCC SRMs (SRM) in the largest cohort of patients with biopsy-characterized SRMs on AS.

Design, Setting, And Participants: Data from patients in a multicenter Canadian trial and a Princess Margaret cohort were combined to include 136 biopsy-proven SRM lesions managed by AS, with treatment deferred until progression or patient/surgeon decision.

Outcome Measurements And Statistical Analysis: Growth curves were estimated from serial tumor size measures. Tumor progression was defined by sustained size ≥4 cm or volume doubling within 1 yr.

Results And Limitations: Median follow-up for patients who remained on AS was 5.8 yr (interquartile range 3.4-7.5 yr). Clear cell RCC SRMs (SRM) grew faster than papillary type 1 SRMs (0.25 and 0.02 cm/yr on average, respectively, p =  0.0003). Overall, 60 SRM lesions progressed: 49 (82%) by rapid growth (volume doubling), seven (12%) increasing to ≥4 cm, and four (6.7%) by both criteria. Six patients developed metastases, and all were of clear cell RCC histology. Limitations include the use of different imaging modalities and a lack of central imaging review.

Conclusions: Tumor growth varies between histologic subtypes of SRM and among SRM, which likely reflects individual host and tumor biology. Without validated biomarkers that predict this variation, initial follow-up of histologically characterized SRMs can inform personalized treatment for patients on AS.

Patient Summary: Many small kidney cancers are suitable for surveillance and can be monitored over time for change. We demonstrate that different types of kidney cancers grow at different rates and are at different risks of progression. These results may guide better personalized treatment.
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http://dx.doi.org/10.1016/j.eururo.2020.06.053DOI Listing
September 2020

Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment.

Eur Radiol 2020 Dec 26;30(12):6867-6876. Epub 2020 Jun 26.

Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.

Objectives: To benchmark the performance of a calibrated 3D convolutional neural network (CNN) applied to multiparametric MRI (mpMRI) for risk assessment of clinically significant prostate cancer (csPCa) using decision curve analysis (DCA).

Methods: We retrospectively analyzed 499 patients who had positive mpMRI (PI-RADSv2 ≥ 3) and MRI-targeted biopsy. The training cohort comprised 449 men, including a calibration set of 50 men. Biopsy decision strategies included using risk estimates from the CNN (original and calibrated), to perform biopsy in men with PI-RADSv2 ≥ 4 only, or additionally in men with PI-RADSv2 3 and PSA density (PSAd) ≥ 0.15 ng/ml/ml. Discrimination, calibration and clinical usefulness in the unseen test cohort (n = 50) were assessed using C-statistic, calibration plots and DCA, respectively.

Results: The calibrated CNN achieved moderate calibration (Hosmer-Lemeshow calibration test, p = 0.41) and good discrimination (C = 0.85). DCA revealed consistently higher net benefit and net reduction in biopsies for the calibrated CNN compared with the original CNN, PI-RADSv2 ≥ 4 and the combined strategy of PI-RADSv2 and PSAd. Original CNN predictions were severely miscalibrated (p < 0.0001) resulting in net harm compared with a 'biopsy all' patients strategy. At-risk thresholds ≥ 10% using the calibrated CNN and the combined strategy reduced the number of biopsies by an estimated 201 and 55 men, respectively, per 1000 men at risk, without missing csPCa, while original CNN and PI-RADSv2 ≥ 4 could not achieve a net reduction in biopsies.

Conclusions: DCA revealed that our calibrated 3D-CNN resulted in fewer unnecessary biopsies compared with using PI-RADSv2 alone or in combination with PSAd. CNN calibration is important in achieving clinical utility.

Key Points: • A 3D deep learning model applied to multiparametric MRI may help to prevent unnecessary prostate biopsies in patients eligible for MRI-targeted biopsy. • Owing to miscalibration, original risk estimates by the deep learning model require prior calibration to enable clinical utility. • Decision curve analysis confirmed a net benefit of using our calibrated deep learning model for biopsy decisions compared with alternative strategies, including PI-RADSv2 alone and in combination with prostate-specific antigen density.
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http://dx.doi.org/10.1007/s00330-020-07030-1DOI Listing
December 2020

Does the Visibility of Grade Group 1 Prostate Cancer on Baseline Multiparametric Magnetic Resonance Imaging Impact Clinical Outcomes?

J Urol 2020 12 4;204(6):1187-1194. Epub 2020 Jun 4.

Division of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.

Purpose: We assessed whether the visibility of Grade Group (GG) 1 prostate cancer on baseline multiparametric magnetic resonance imaging affects clinical outcomes.

Materials And Methods: We evaluated 454 men who underwent multiparametric magnetic resonance imaging between 2006 and 2018 with maximum GG1 prostate cancer inclusive of magnetic resonance imaging targeted biopsy. Multiparametric magnetic resonance imaging was graded as negative, equivocal or positive. Assessed outcomes were treatment-free survival, biopsy upgrade-free survival and unfavorable disease at radical prostatectomy (pT 3 or greater and/or GG3 or greater). Kaplan-Meier and multivariable Cox proportional hazard analyses were used to estimate the impact of multiparametric magnetic resonance imaging and clinicopathological variables (age, year, prostate specific antigen density and measures of tumor volume on biopsy) on outcomes.

Results: During followup (median 45.2 months) 61 men had disease upgraded on followup biopsy and 139 underwent definitive treatment. In men with negative, equivocal and positive baseline multiparametric magnetic resonance imaging at 5 years, treatment-free survival was 79%, 73% and 49% (p <0.0001), treatment-free survival was 89%, 82% and 70% (p=0.002), and survival without unfavorable disease at radical prostatectomy was 98%, 98% and 86% (p=0.007), respectively. At multivariable analysis positive (HR 1.93, 95% CI 1.21-3.09, p=0.006) and equivocal multiparametric magnetic resonance imaging (HR 2.02, 95% CI 1.11-3.68, p=0.02) were associated with shorter treatment-free survival, and positive multiparametric magnetic resonance imaging was a significant prognostic factor for upgrade-free survival (HR 2.03, 95% CI 1.06-3.86, p=0.03) and unfavorable disease at radical prostatectomy (HR 4.45, 95% CI 1.39-18.17, p=0.01).

Conclusions: Men with positive multiparametric magnetic resonance imaging and GG1 prostate cancer on magnetic resonance imaging targeted biopsy are at increased risk for intervention, upgrading and unfavorable disease at radical prostatectomy compared to those with multiparametric magnetic resonance imaging invisible GG1 prostate cancer.
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http://dx.doi.org/10.1097/JU.0000000000001157DOI Listing
December 2020

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J Urol 2020 06 16;203(6):1093. Epub 2020 Mar 16.

Urology Division, Surgical Oncology Department, Princess Margaret Cancer Center, University Health Network, University of Toronto, Toronto, Ontario, Canada.

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http://dx.doi.org/10.1097/JU.0000000000000595.02DOI Listing
June 2020

Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing.

Sensors (Basel) 2020 Mar 10;20(5). Epub 2020 Mar 10.

Vision and Image Processing Research Group, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Prostate cancer is the most commonly diagnosed cancer in North American men; however, prognosis is relatively good given early diagnosis. This motivates the need for fast and reliable prostate cancer sensing. Diffusion weighted imaging (DWI) has gained traction in recent years as a fast non-invasive approach to cancer sensing. The most commonly used DWI sensing modality currently is apparent diffusion coefficient (ADC) imaging, with the recently introduced computed high-b value diffusion weighted imaging (CHB-DWI) showing considerable promise for cancer sensing. In this study, we investigate the efficacy of ADC and CHB-DWI sensing modalities when applied to zone-level prostate cancer sensing by introducing several radiomics driven zone-level prostate cancer sensing strategies geared around hand-engineered radiomic sequences from DWI sensing (which we term as Zone-X sensing strategies). Furthermore, we also propose Zone-DR, a discovery radiomics approach based on zone-level deep radiomic sequencer discovery that discover radiomic sequences directly for radiomics driven sensing. Experimental results using 12,466 pathology-verified zones obtained through the different DWI sensing modalities of 101 patients showed that: (i) the introduced Zone-X and Zone-DR radiomics driven sensing strategies significantly outperformed the traditional clinical heuristics driven strategy in terms of AUC, (ii) the introduced Zone-DR and Zone-SVM strategies achieved the highest sensitivity and specificity, respectively for ADC amongst the tested radiomics driven strategies, (iii) the introduced Zone-DR and Zone-LR strategies achieved the highest sensitivities for CHB-DWI amongst the tested radiomics driven strategies, and (iv) the introduced Zone-DR, Zone-LR, and Zone-SVM strategies achieved the highest specificities for CHB-DWI amongst the tested radiomics driven strategies. Furthermore, the results showed that the trade-off between sensitivity and specificity can be optimized based on the particular clinical scenario we wish to employ radiomic driven DWI prostate cancer sensing strategies for, such as clinical screening versus surgical planning. Finally, we investigate the critical regions within sensing data that led to a given radiomic sequence generated by a Zone-DR sequencer using an explainability method to get a deeper understanding on the biomarkers important for zone-level cancer sensing.
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http://dx.doi.org/10.3390/s20051539DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085575PMC
March 2020

CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging.

BMC Med Imaging 2020 02 3;20(1):11. Epub 2020 Feb 3.

Institute of Medical Science, University of Toronto, Toronto, ON, Canada.

Background: Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis. In quantitative medical imaging (radiomics) studies, CPH plays an important role in feature reduction and modeling. However, the underlying linear assumption of CPH model limits the prognostic performance. In this work, using transfer learning, a convolutional neural network (CNN) based survival model was built and tested on preoperative CT images of resectable Pancreatic Ductal Adenocarcinoma (PDAC) patients.

Results: The proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index and index of prediction accuracy, providing a better fit for patients' survival patterns.

Conclusions: The proposed CNN-based survival model outperforms CPH-based radiomics pipeline in PDAC prognosis. This approach offers a better fit for survival patterns based on CT images and overcomes the limitations of conventional survival models.
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http://dx.doi.org/10.1186/s12880-020-0418-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6998249PMC
February 2020

Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century.

Br J Radiol 2020 Feb;93(1106):20190855

Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.
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http://dx.doi.org/10.1259/bjr.20190855DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055429PMC
February 2020

Prostate Cancer Detection using Deep Convolutional Neural Networks.

Sci Rep 2019 12 20;9(1):19518. Epub 2019 Dec 20.

Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.

Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer. With deep convolutional neural networks (CNNs) significant success in computer vision tasks such as object detection and segmentation, different CNN architectures are increasingly investigated in medical imaging research community as promising solutions for designing more accurate CAD tools for cancer detection. In this work, we developed and implemented an automated CNN-based pipeline for detection of clinically significant prostate cancer (PCa) for a given axial DWI image and for each patient. DWI images of 427 patients were used as the dataset, which contained 175 patients with PCa and 252 patients without PCa. To measure the performance of the proposed pipeline, a test set of 108 (out of 427) patients were set aside and not used in the training phase. The proposed pipeline achieved area under the receiver operating characteristic curve (AUC) of 0.87 (95[Formula: see text] Confidence Interval (CI): 0.84-0.90) and 0.84 (95[Formula: see text] CI: 0.76-0.91) at slice level and patient level, respectively.
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http://dx.doi.org/10.1038/s41598-019-55972-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925141PMC
December 2019

Comparison of Magnetic Resonance Imaging and Transrectal Ultrasound Informed Prostate Biopsy for Prostate Cancer Diagnosis in Biopsy Naïve Men: A Systematic Review and Meta-Analysis.

J Urol 2020 06 14;203(6):1085-1093. Epub 2019 Oct 14.

Urology Division, Surgical Oncology Department, Princess Margaret Cancer Center, University Health Network, University of Toronto, Toronto, Ontario, Canada.

Purpose: Multiparametric magnetic resonance imaging with informed targeted biopsies has changed the paradigm of prostate cancer diagnosis. Randomized studies have demonstrated a diagnostic benefit of clinical significance for targeted biopsy compared to standard systematic biopsies. We evaluated whether multiparametric magnetic resonance imaging informed targeted biopsy has superior diagnosis rates of any, clinically significant, high grade and clinically insignificant prostate cancer compared to systematic biopsy in biopsy naïve men.

Materials And Methods: Data were searched in Medline®, Embase®, Web of Science and Evidence-Based Medicine Reviews-Cochrane Database of Systematic Reviews from database inception until 2019. Studies were selected by 2 authors independently, with disagreements resolved by consensus with a third author. Overall 1,951 unique references were identified and 100 manuscripts underwent full-text review. Data were pooled using random effects models. The meta-analysis is reported according to the PRISMA statement and the study protocol is registered with PROSPERO (CRD42019128468).

Results: Overall 29 studies (13,845 patients) were analyzed. Compared to systematic biopsy, use of multiparametric magnetic resonance imaging informed targeted biopsy was associated with a 15% higher rate of any prostate cancer diagnosis (95% CI 10-20, p <0.00001). This relationship was not affected by the study methodology (p=0.11). Diagnoses of clinically significant and high grade prostate cancer were more common in the multiparametric magnetic resonance imaging informed targeted biopsy group (risk difference 11%, 95% CI 0-20, p=0.05 and 2%, 95% CI 1-4, p=0.005, respectively) while there was no difference in diagnosis of clinically insignificant prostate cancer (risk difference 0, 95% CI -3 to 3, p=0.96). Notably, the exclusion of systematic biopsy in the multiparametric magnetic resonance imaging informed targeted biopsy arm significantly modified the association between a multiparametric magnetic resonance imaging strategy and lower rates of clinically insignificant prostate cancer diagnosis (p=0.01) without affecting the diagnosis rates of clinically significant or high grade prostate cancer.

Conclusions: Compared to systematic biopsy a multiparametric magnetic resonance imaging informed targeted biopsy strategy results in a significantly higher diagnosis rate of any, clinically significant and high grade prostate cancer. Excluding systematic biopsy from multiparametric magnetic resonance imaging informed targeted biopsy was associated with decreased rates of clinically insignificant prostate cancer diagnosis without affecting diagnosis of clinically significant or high grade prostate cancer.
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http://dx.doi.org/10.1097/JU.0000000000000595DOI Listing
June 2020

Reducing Unnecessary Prostate Multiparametric Magnetic Resonance Imaging by Using Clinical Parameters to Predict Negative and Indeterminate Findings.

J Urol 2020 02 3;203(2):292-298. Epub 2019 Sep 3.

Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.

Purpose: We sought to develop a triage strategy to reduce negative and indeterminate multiparametric magnetic resonance imaging scans in patients at risk for prostate cancer.

Materials And Methods: In this retrospective study we evaluated 865 patients with no prior prostate cancer diagnosis who underwent prostate multiparametric magnetic resonance imaging between 2009 and 2017. Age, prostate volume, prostate specific antigen and prostate specific antigen density were assessed as predictors of positive multiparametric magnetic resonance imaging, defined as PI-RADS™ (Prostate Imaging Reporting and Data System) version 2/Likert score 4 or greater. The cohort was split into a training cohort of 605 patients and a validation cohort of 260. The optimal threshold to rule out positive multiparametric magnetic resonance imaging was chosen to achieve a negative predictive value greater than 90%.

Results: All clinical variables were significant predictors of positive multiparametric magnetic resonance imaging (p <0.05). Prostate specific antigen density outperformed other parameters in diagnostic accuracy and did not significantly differ compared to a multivariate model (AUC=0.74 vs 0.75). At prostate specific antigen density greater than 0.078 ng/ml sensitivity, specificity, positive and negative predictive values were 94%, 29%, 22% and 95%, respectively, resulting in 25% fewer scans (64 of 260). In the multivariate model sensitivity, specificity, positive and negative predictive values were 85%, 32%, 22% and 91%, respectively, resulting in 29% fewer scans (75 of 260). Biopsies in men who would not have undergone multiparametric magnetic resonance imaging according to our proposed strategies revealed 2 clinically significant prostate cancers using prostate specific antigen density and 1 using the multivariate model.

Conclusions: In patients at risk for prostate cancer applying a multivariate prediction model or a prostate specific antigen density cutoff of 0.078 ng/ml resulted in 25% to 29% fewer multiparametric magnetic resonance imaging scans performed while missing only a minimal number of clinically significant prostate cancers. Further prospective validation is required.
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http://dx.doi.org/10.1097/JU.0000000000000518DOI Listing
February 2020

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J Urol 2019 12 30;202(6):1165. Epub 2019 Aug 30.

Joint Dept of Medical Imaging, University Health Network, Mt Sinai Hospital, Women's College Hospital, University of Toronto, Toronto, Ontario, Canada.

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http://dx.doi.org/10.1097/01.JU.0000581804.47392.6fDOI Listing
December 2019

Negative Predictive Value of Prostate Multiparametric Magnetic Resonance Imaging among Men with Negative Prostate Biopsy and Elevated Prostate Specific Antigen: A Clinical Outcome Retrospective Cohort Study.

J Urol 2019 12 12;202(6):1159-1165. Epub 2019 Jun 12.

Joint Dept of Medical Imaging, University Health Network, Mt Sinai Hospital, Women's College Hospital, University of Toronto, Toronto, Ontario, Canada.

Purpose: We estimated the negative predictive value of prostate multiparametric magnetic resonance imaging to detect clinically significant (Gleason 7 or greater) prostate cancer at long-term followup (median 6.7 years, range 2.6 to 10.7), in men with negative biopsy findings before magnetic resonance imaging. We also assessed the diagnostic performance of multiparametric magnetic resonance imaging to detect clinically significant prostate cancer during this time.

Materials And Methods: Following Institutional Research Ethics Board approval we retrospectively identified men who underwent prostate multiparametric magnetic resonance imaging after biopsy between 2004 and 2009 using a cancer registry database and magnetic resonance imaging reports. Multiparametric magnetic resonance imaging sequences comprised T2-weighted and dynamic contrast-enhanced series from 2004 to 2005 with diffusion-weighted imaging from 2006 and thereafter. Clinical outcomes were assessed up to July 2015 by reviewing subsequent pathology results, prostate specific antigen levels and electronic patient records. The primary outcome was clinically significant prostate cancer diagnosis during followup. We also estimated the sensitivity, specificity, and positive and negative predictive values of all prostate multiparametric magnetic resonance imaging during this period.

Results: A total of 502 multiparametric magnetic resonance imaging scans with a prior biopsy were included in study. Of these scans 121 were done in men with a prior systematic biopsy negative for cancer. In these men median prostate specific antigen was 9.5 ng/dl and median age was 60 years. At a median followup of 6.7 years (95% CI 2.6 to 10.7) 70 of 73 (96%) men with negative multiparametric magnetic resonance imaging findings remained free of clinically significant prostate cancer. In this period the overall negative and positive predictive values of multiparametric magnetic resonance imaging were 86% (range 80% to 91%) and 54% (range 52% to 57%), respectively, in the entire cohort regardless of biopsy status before magnetic resonance imaging.

Conclusions: Prostate multiparametric magnetic resonance imaging has high clinical negative predictive value. In men with a negative biopsy before magnetic resonance imaging and negative magnetic resonance imaging findings the risk of clinically significant prostate cancer was extremely low at a median of 6.7 years.
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http://dx.doi.org/10.1097/JU.0000000000000388DOI Listing
December 2019

PI-RADS Steering Committee: The PI-RADS Multiparametric MRI and MRI-directed Biopsy Pathway.

Radiology 2019 08 11;292(2):464-474. Epub 2019 Jun 11.

From the Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Rickmansworth Rd, Northwood, Middlesex HA6 2RN, England (A.R.P.); Department of Radiology and Nuclear Medicine Radboud University Medical Center, Nijmegen, the Netherlands (J.B.); Department of Radiology, Ghent University Hospital, Ghent, Belgium (G.V.); Department of Radiology, NYU Langone Medical Center, New York, NY (A.B.R.); Weill Cornell Imaging, Cornell University, New York, NY (D.J.M.); Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.); Department of Radiology, Hôpital Cantonal de Fribourg HFR, University of Fribourg, Fribourg, Switzerland (H.C.T.); Paris Descartes University, Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, France (F.C.); University of Toronto, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada (M.A.H.); Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (K.J.M.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (C.M.T.); Department of Radiology, University of Cincinnati, College of Medicine, Cincinnati, Ohio (S.V.); and Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Conn (J.C.W.).

High-quality evidence shows that MRI in biopsy-naive men can reduce the number of men who need prostate biopsy and can reduce the number of diagnoses of clinically insignificant cancers that are unlikely to cause harm. In men with prior negative biopsy results who remain under persistent suspicion, MRI improves the detection and localization of life-threatening prostate cancer with greater clinical utility than the current standard of care, systematic transrectal US-guided biopsy. Systematic analyses show that MRI-directed biopsy increases the effectiveness of the prostate cancer diagnosis pathway. The incorporation of MRI-directed pathways into clinical care guidelines in prostate cancer detection has begun. The widespread adoption of the Prostate Imaging Reporting and Data System (PI-RADS) for multiparametric MRI data acquisition, interpretation, and reporting has promoted these changes in practice. The PI-RADS MRI-directed biopsy pathway enables the delivery of key diagnostic benefits to men suspected of having cancer based on clinical suspicion. Herein, the PI-RADS Steering Committee discusses how the MRI pathway should be incorporated into routine clinical practice and the challenges in delivering the positive health impacts needed by men suspected of having clinically significant prostate cancer.
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http://dx.doi.org/10.1148/radiol.2019182946DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677282PMC
August 2019

A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach.

Invest Radiol 2019 07;54(7):437-447

Department of Radiology and Nuclear Medicine, Ghent University Hospital, Gent, Belgium.

Objectives: The aims of this study were to assess the discriminative performance of quantitative multiparametric magnetic resonance imaging (mpMRI) between prostate cancer and noncancer tissues and between tumor grade groups (GGs) in a multicenter, single-vendor study, and to investigate to what extent site-specific differences affect variations in mpMRI parameters.

Materials And Methods: Fifty patients with biopsy-proven prostate cancer from 5 institutions underwent a standardized preoperative mpMRI protocol. Based on the evaluation of whole-mount histopathology sections, regions of interest were placed on axial T2-weighed MRI scans in cancer and noncancer peripheral zone (PZ) and transition zone (TZ) tissue. Regions of interest were transferred to functional parameter maps, and quantitative parameters were extracted. Across-center variations in noncancer tissues, differences between tissues, and the relation to cancer grade groups were assessed using linear mixed-effects models and receiver operating characteristic analyses.

Results: Variations in quantitative parameters were low across institutes (mean [maximum] proportion of total variance in PZ and TZ, 4% [14%] and 8% [46%], respectively). Cancer and noncancer tissues were best separated using the diffusion-weighted imaging-derived apparent diffusion coefficient, both in PZ and TZ (mean [95% confidence interval] areas under the receiver operating characteristic curve [AUCs]; 0.93 [0.89-0.96] and 0.86 [0.75-0.94]), followed by MR spectroscopic imaging and dynamic contrast-enhanced-derived parameters. Parameters from all imaging methods correlated significantly with tumor grade group in PZ tumors. In discriminating GG1 PZ tumors from higher GGs, the highest AUC was obtained with apparent diffusion coefficient (0.74 [0.57-0.90], P < 0.001). The best separation of GG1-2 from GG3-5 PZ tumors was with a logistic regression model of a combination of functional parameters (mean AUC, 0.89 [0.78-0.98]).

Conclusions: Standardized data acquisition and postprocessing protocols in prostate mpMRI at 3 T produce equivalent quantitative results across patients from multiple institutions and achieve similar discrimination between cancer and noncancer tissues and cancer grade groups as in previously reported single-center studies.
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http://dx.doi.org/10.1097/RLI.0000000000000558DOI Listing
July 2019

Prognostic Value of CT Radiomic Features in Resectable Pancreatic Ductal Adenocarcinoma.

Sci Rep 2019 04 1;9(1):5449. Epub 2019 Apr 1.

Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.

In this work, we assess the reproducibility and prognostic value of CT-derived radiomic features for resectable pancreatic ductal adenocarcinoma (PDAC). Two radiologists contoured tumour regions on pre-operative CT of two cohorts from two institutions undergoing curative-intent surgical resection for PDAC. The first (n = 30) and second cohorts (n = 68) were used for training and validation of proposed prognostic model for overall survival (OS), respectively. Radiomic features were extracted using PyRadiomics library and those with weak inter-reader reproducibility were excluded. Through Cox regression models, significant features were identified in the training cohort and retested in the validation cohort. Significant features were then fused via Cox regression to build a single radiomic signature in the training cohort, which was validated across readers in the validation cohort. Two radiomic features derived from Sum Entropy and Cluster Tendency features were both robust to inter-reader reproducibility and prognostic of OS across cohorts and readers. The radiomic signature showed prognostic value for OS in the validation cohort with hazard ratios of 1.56 (P = 0.005) and 1.35 (P = 0.022), for the first and second reader, respectively. CT-based radiomic features were shown to be prognostic in patients with resectable PDAC. These features may help stratify patients for neoadjuvant or alternative therapies.
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http://dx.doi.org/10.1038/s41598-019-41728-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443807PMC
April 2019