Visibility of significant prostate cancer on multiparametric magnetic resonance imaging (MRI)—do we still need contrast media?

Abstract Objectives To assess the visibility of clinically significant prostate cancer (PCA) lesions on the sequences multiparametric MRI of the prostate (mpMRI) and to evaluate whether the addition of dynamic contrast–enhanced imaging (DCE) improves the overall visibility. Methods We retrospectively evaluated multiparametric MRI images of 119 lesions in 111 patients with biopsy-proven clinically significant PCA. Three readers assigned visual grading scores for visibility on each sequence, and a visual grading characteristic analysis was performed. Linear regression was used to explore which factors contributed to visibility in individual sequences. Results The visibility of lesions was significantly better with mpMRI when compared to biparametric MRI in visual grading characteristic (VGC) analysis, with an AUCVGC of 0.62 (95% CI 0.55–0.69; p < 0.001). This benefit was seen across all readers. Multivariable linear regression revealed that a location in the peripheral zone was associated with better visibility on T2-weighted imaging (T2w). A higher Prostate Imaging-Reporting and Data System (PI-RADS) score was associated with better visibility on both diffusion-weighted imaging (DWI) and DCE. Increased lesion size was associated with better visibility on all sequences. Conclusions Visibility of clinically significant PCA is improved by using mpMRI. DCE and DWI images independently improve lesion visibility compared to T2w images alone. Further research into the potential of DCE to impact on clinical decision-making is suggested. Key Points • DCE and DWI images independently improve clinically significant prostate cancer lesion visibility compared to T2w images alone. • Multiparametric MRI (DCE, DWI, T2w) achieved significantly higher visibility scores than biparametric MRI (DWI, T2w). • Location in the transition zone is associated with poor visibility on T2w, while it did not affect visibility on DWI or DCE.

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Multiparametric prostate MRI and structured reporting: benefits and challenges in the PI-RADS era

Chinese Journal of Academic Radiology ◽

10.1007/s42058-021-00059-1 ◽

2021 ◽

Vol 4 (1) ◽

pp. 21-40

Author(s):

Sanas Mir-Bashiri ◽

Kaneschka Yaqubi ◽

Piotr Woźnicki ◽

Niklas Westhoff ◽

Jost von Hardenberg ◽

...

Keyword(s):

Prostate Cancer ◽

Clinical Decision Making ◽

Imaging Modality ◽

Treatment Strategies ◽

High Sensitivity ◽

Clinical Decision ◽

Prostate Mri ◽

Radiology Reports ◽

Local Staging ◽

Clinically Significant

AbstractProstate cancer (PCa) is the second most frequent cancer diagnosis in men and the sixth leading cause of cancer death worldwide with increasing numbers globally. Therefore, differentiated diagnostic imaging and risk-adapted therapeutic approaches are warranted. Multiparametric magnetic resonance imaging (mpMRI) of the prostate supports the diagnosis of PCa and is currently the leading imaging modality for PCa detection, characterization, local staging and image-based therapy planning. Due to the combination of different MRI sequences including functional MRI methods such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), mpMRI enables a high sensitivity and specificity for the detection of PCa. The rising demand for individualized treatment strategies requires methods to ensure reproducibility, completeness, and quality of prostate MRI report data. The PI-RADS (Prostate Imaging Reporting and Data System) 2.1 classification represents the classification system that is internationally recommended for MRI-based evaluation of clinically significant prostate cancer. PI-RADS facilitates clinical decision-making by providing clear reporting parameters based on clinical evidence and expert consensus. Combined with software-based solutions, structured radiology reports form the backbone to integrate results from radiomics analyses or AI-applications into radiological reports and vice versa. This review provides an overview of imaging methods for PCa detection and local staging while placing special emphasis on mpMRI of the prostate. Furthermore, the article highlights the benefits of software-based structured PCa reporting solutions implementing PI-RADS 2.1 for the integration of structured data into decision support systems, thereby paving the way for workflow automation in radiology.

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Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

Cancers ◽

10.3390/cancers13163944 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3944

Author(s):

Daniele Corradini ◽

Leonardo Brizi ◽

Caterina Gaudiano ◽

Lorenzo Bianchi ◽

Emanuela Marcelli ◽

...

Keyword(s):

Prostate Cancer ◽

Artificial Intelligence ◽

Clinical Decision Making ◽

Clinical Decision ◽

Future Perspective ◽

Imaging Data ◽

Mr Images ◽

Evaluation Time ◽

Inter Observer Variability ◽

Clinically Significant

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.

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Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

European Radiology ◽

10.1007/s00330-020-07064-5 ◽

2020 ◽

Vol 30 (12) ◽

pp. 6757-6769 ◽

Cited By ~ 1

Author(s):

Simon Bernatz ◽

Jörg Ackermann ◽

Philipp Mandel ◽

Benjamin Kaltenbach ◽

Yauheniya Zhdanovich ◽

...

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Clinical Significance ◽

Clinical Assessment ◽

Diagnostic Performance ◽

Learning Algorithms ◽

Peripheral Zone ◽

Multiparametric Mri ◽

Machine Learning Algorithms ◽

Clinically Significant

Abstract Objectives To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. Key Points • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.

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Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

European Radiology ◽

10.1007/s00330-019-06488-y ◽

2019 ◽

Vol 30 (3) ◽

pp. 1313-1324 ◽

Cited By ~ 4

Author(s):

Jeroen Bleker ◽

Thomas C. Kwee ◽

Rudi A. J. O. Dierckx ◽

Igle Jan de Jong ◽

Henkjan Huisman ◽

...

Keyword(s):

Prostate Cancer ◽

Feature Selection ◽

Peripheral Zone ◽

Visual Assessment ◽

Multiparametric Mri ◽

Dynamic Contrast Enhanced Imaging ◽

Fixed Volume ◽

Volume Of Interest ◽

Contrast Enhanced ◽

Clinically Significant

Abstract Objectives To 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). Methods This 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. Results The 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. Conclusions The 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.

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Catheter to vein ratio and risk of peripherally inserted central catheter (PICC)-associated thrombosis according to diagnostic group: a retrospective cohort study

BMJ Open ◽

10.1136/bmjopen-2020-045895 ◽

2021 ◽

Vol 11 (7) ◽

pp. e045895

Author(s):

Rebecca Sharp ◽

Peter Carr ◽

Jessie Childs ◽

Andrew Scullion ◽

Mark Young ◽

...

Keyword(s):

Cohort Study ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

Clinical Decision Making ◽

Clinical Decision ◽

Primary Outcome Measure ◽

Diagnostic Group ◽

Peripherally Inserted Central Catheter ◽

Central Catheter ◽

Characteristic Analysis

ObjectivesDetermine the effect of the catheter to vein ratio (CVR) on rates of symptomatic thrombosis in individuals with a peripherally inserted central catheter (PICC) and identify the optimal CVR cut-off point according to diagnostic group.DesignRetrospective cohort study.Setting4 tertiary hospitals in Australia and New Zealand.ParticipantsAdults who had undergone PICC insertion.Primary outcome measureSymptomatic thrombus of the limb in which the PICC was inserted.Results2438 PICC insertions were included with 39 cases of thrombosis (1.6%; 95% CI 1.14% to 2.19%). Receiver operator characteristic analysis was unable to be performed to determine the optimal CVR overall or according to diagnosis. The association between risk of thrombosis and CVR cut-offs commonly used in clinical practice were analysed. A 45% cut-off (≤45% versus ≥46%) was predictive of thrombosis, with those with a higher ratio having more than twice the risk (relative risk 2.30; 95% CI 1.202 to 4.383; p=0.01). This pattern continued when only those with malignancy were included in the analysis, those with cancer had twice the risk of thrombosis with a CVR greater than 45%. Whereas the 33% CVR cut-off was not associated with statistically significant results overall or in those with malignancy. Neither the 33% or 45% CVR cut-off produced statistically significant results in those with infection or other non-malignant conditions.ConclusionsAdherence to CVR cut-offs are an important component of PICC insertion clinical decision making to reduce the risk of thrombosis. These results suggest that in individuals with cancer, the use of a CVR ≤45% should be considered to minimise risk of thrombosis. Further research is needed to determine the risk of thrombosis according to malignancy type and the optimal CVR for those with a non-malignant diagnosis.

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Study protocol for a single-centre non-inferior randomised controlled trial on a novel three-dimensional matrix positioning-based cognitive fusion-targeted biopsy and software-based fusion-targeted biopsy for the detection rate of clinically significant prostate cancer in men without a prior biopsy

BMJ Open ◽

10.1136/bmjopen-2020-041427 ◽

2021 ◽

Vol 11 (2) ◽

pp. e041427

Author(s):

Biming He ◽

Rongbing Li ◽

Dongyang Li ◽

Liqun Huang ◽

Xiaofei Wen ◽

...

Keyword(s):

Prostate Cancer ◽

Detection Rate ◽

Three Dimensional ◽

Multiparametric Mri ◽

Fusion Method ◽

Targeted Biopsy ◽

Cognitive Fusion ◽

Single Centre ◽

Clinically Significant ◽

Randomised Controlled

IntroductionThe classical pathway for diagnosing prostate cancer is systematic 12-core biopsy under the guidance of transrectal ultrasound, which tends to underdiagnose the clinically significant tumour and overdiagnose the insignificant disease. Another pathway named targeted biopsy is using multiparametric MRI to localise the tumour precisely and then obtain the samples from the suspicious lesions. Targeted biopsy, which is mainly divided into cognitive fusion method and software-based fusion method, is getting prevalent for its good performance in detecting significant cancer. However, the preferred targeted biopsy technique in detecting clinically significant prostate cancer between cognitive fusion and software-based fusion is still beyond consensus.Methods and analysisThis trial is a prospective, single-centre, randomised controlled and non-inferiority study in which all men suspicious to have clinically significant prostate cancer are included. This study aims to determine whether a novel three-dimensional matrix positioning cognitive fusion-targeted biopsy is non-inferior to software-based fusion-targeted biopsy in the detection rate of clinically significant cancer in men without a prior biopsy. The main inclusion criteria are men with elevated serum prostate-specific antigen above 4–20 ng/mL or with an abnormal digital rectal examination and have never had a biopsy before. A sample size of 602 participants allowing for a 10% loss will be recruited. All patients will undergo a multiparametric MRI examination, and those who fail to be found with a suspicious lesion, with the anticipation of half of the total number, will be dropped. The remaining participants will be randomly allocated to cognitive fusion-targeted biopsy (n=137) and software-based fusion-targeted biopsy (n=137). The primary outcome is the detection rate of clinically significant prostate cancer for cognitive fusion-targeted biopsy and software-based fusion-targeted biopsy in men without a prior biopsy. The clinically significant prostate cancer will be defined as the International Society of Urological Pathology grade group 2 or higher.Ethics and disseminationEthical approval was obtained from the ethics committee of Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China. The results of the study will be disseminated and published in international peer-reviewed journals.Trial registration numberClinicalTrials.gov Registry (NCT04271527).

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Quantitative and Qualitative Analysis of Blood-based Liquid Biopsies to Inform Clinical Decision-making in Prostate Cancer

European Urology ◽

10.1016/j.eururo.2020.12.037 ◽

2021 ◽

Author(s):

Irene Casanova-Salas ◽

Alejandro Athie ◽

Paul C. Boutros ◽

Marzia Del Re ◽

David T. Miyamoto ◽

...

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Qualitative Analysis ◽

Clinical Decision Making ◽

Clinical Decision ◽

Liquid Biopsies

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4971Blood viscosity and its relevance to the diagnosis and management of pulmonary hypertension: a new elephant in the cathlab

European Heart Journal ◽

10.1093/eurheartj/ehz746.0030 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Author(s):

A Kempny ◽

K Dimopoulos ◽

A E Fraisse ◽

G P Diller ◽

L C Price ◽

...

Keyword(s):

Pulmonary Hypertension ◽

Cardiac Catheterization ◽

Clinical Decision Making ◽

Clinical Decision ◽

Pulmonary Vasculature ◽

Pulmonary Vascular Disease ◽

Significant Difference ◽

The Difference ◽

Clinically Significant ◽

The Relationship

Abstract Background Pulmonary vascular resistance (PVR) is an essential parameter assessed during cardiac catheterization. It is used to confirm pulmonary vascular disease, to assess response to targeted pulmonary hypertension (PH) therapy and to determine the possibility of surgery, such as closure of intra-cardiac shunt or transplantation. While PVR is believed to mainly reflect the properties of the pulmonary vasculature, it is also related to blood viscosity (BV). Objectives We aimed to assess the relationship between measured (mPVR) and viscosity-corrected PVR (cPVR) and its impact on clinical decision-making. Methods We assessed consecutive PH patients undergoing cardiac catheterization. BV was assessed using the Hutton method. Results We included 465 patients (56.6% female, median age 63y). The difference between mPVR and cPVR was highest in patients with abnormal Hb levels (anemic patients: 5.6 [3.4–8.0] vs 7.8Wood Units (WU) [5.1–11.9], P<0.001; patients with raised Hb: 10.8 [6.9–15.4] vs. 7.6WU [4.6–10.8], P<0.001, respectively). Overall, 33.3% patients had a clinically significant (>2.0WU) difference between mPVR and cPVR, and this was more pronounced in those with anemia (52.9%) or raised Hb (77.6%). In patients in the upper quartile for this difference, mPVR and cPVR differed by 4.0WU [3.4–5.2]. Adjustment of PVR required Conclusions We report, herewith, a clinically significant difference between mPVR and cPVR in a third of contemporary patients assessed for PH. This difference is most pronounced in patients with anemia, in whom mPVR significantly underestimates PVR, whereas in most patients with raised Hb, mPVR overestimates it. Our data suggest that routine adjustment for BV is necessary.

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Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Diagnostics ◽

10.3390/diagnostics11060973 ◽

2021 ◽

Vol 11 (6) ◽

pp. 973

Author(s):

Valentina Giannini ◽

Simone Mazzetti ◽

Giovanni Cappello ◽

Valeria Maria Doronzio ◽

Lorenzo Vassallo ◽

...

Keyword(s):

Prostate Cancer ◽

Computer Aided Diagnosis ◽

Multiparametric Mri ◽

Performance Study ◽

Predictive Values ◽

Cad System ◽

Computer Aided ◽

Clinically Significant ◽

Sensitivity Specificity ◽

Aided Diagnosis

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

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Single-cell Spatial Proteomic Revelations on the Multiparametric MRI Heterogeneity of Clinically Significant Prostate Cancer

Clinical Cancer Research ◽

10.1158/1078-0432.ccr-20-4217 ◽

2021 ◽

Author(s):

Russell K. Pachynski ◽

Eric H. Kim ◽

Natalia Miheecheva ◽

Nikita Kotlov ◽

Akshaya Ramachandran ◽

...

Keyword(s):

Prostate Cancer ◽

Single Cell ◽

Multiparametric Mri ◽

Clinically Significant

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