Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation

Gaur, Sonia; Lay, Nathan; Harmon, Stephanie; Doddakashi, Sreya; Mehralivand, Sherif; Argun, Burak; Barrett, Tristan; Bednarova, Sandra; Girometti, R.; Karaarslan, Ercan; Kural, Ali Rıza; Oto, Aytekin; Purysko, Andrei S.; Antic, Tatjana; Magi‐Galluzzi, Cristina; Sağlıcan, Yeşim; Sioletic, Stefano; Warren, Anne Y.; Bittencourt, Leonardo Kayat; Fütterer, Jurgen J.; Gupta, Rajan T.; Kabakus, Ismail; Law, Yan Mee; Margolis, Daniel; Shebel, Haytham; Westphalen, Antonio C.; Wood, Bradford J.; Pinto, Peter A.; Shih, Joanna H.; Choyke, Peter L.; Summers, Ronald M.; Türkbey, Barış

doi:10.18632/oncotarget.26100

Cited by 68 publications

(83 citation statements)

References 39 publications

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“…The use of more quantitative or automated methods for PCa detection from MRI is therefore clinically desirable. The addition of quantitative metrics to subjective PI‐RADS improves accuracy with improved interobserver agreement . Comprehensive reviews of computer‐aided systems (CAD) developed for PCa detection using prostate MRI are available .…”

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confidence: 99%

“…The addition of quantitative metrics to subjective PI-RADS improves accuracy 8 with improved interobserver agreement. 9 Comprehensive reviews of computer-aided systems (CAD) developed for PCa detection using prostate MRI are available. 10,11 Generally, developed CAD methods for PCa detection require prostate zonal segmentation, feature representation within the region of interest (PZ or transition zone [TZ]), and PCa localization.…”

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confidence: 99%

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Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

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Background Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. Purpose/Hypothesis To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U‐Net‐based model. Study Type Retrospective, case–control. Population In all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively. Field Strength 3T, ADC maps. Sequence ADC map. Assessment The ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI‐radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per‐slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t‐test was used to test the differences between the performances of constituent networks of the ensemble model. Results Our developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779. Data Conclusion An ensemble U‐Net‐based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1223–1234.

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confidence: 99%

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confidence: 99%

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

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“…Standardising the interpretation of prostate MRI with the PI-RADS did not solve the problem of suboptimal inter-reader agreement [19], nor eliminate cancer missing (miss rate up to 30% in some series) [48]. This might partly depend on limitations inherent to the PI-RADS lexicon and interpretation rules [1].…”

Section: Less Variability From Human Readersmentioning

confidence: 99%

“…In most studies, this tool has been evaluated as a radiologist's assistant, with the human reader assuming the final decision on the nature of the findings prompted by CAD. There are plenty of promising results in literature in this regard, as exemplified by a recent multicentre and multireader study in which CAD-assisted readings across different vendors and institutions showed a sensitivity for index-lesions comparable to that of non-assisted readings using PI-RADS version 2 [48]. CAD also helped less experienced readers to diagnose more TZ cancers, and reduced reading time.…”

Section: Less Variability From Human Readersmentioning

confidence: 99%

“…CAD also helped less experienced readers to diagnose more TZ cancers, and reduced reading time. Whether the increase in sensitivity affects specificity is a matter of debate, with works reporting it as unaltered, improved, or decreased [48,49]. Controversial results also exist about the accuracy for TZ cancers [48], and the capability to act as a stand-alone reader Fig.…”

Section: Less Variability From Human Readersmentioning

confidence: 99%

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Evolution of prostate MRI: from multiparametric standard to less-is-better and different-is better strategies

et al. 2019

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Multiparametric magnetic resonance imaging (mpMRI) has become the standard of care to achieve accurate and reproducible diagnosis of prostate cancer. However, mpMRI is quite demanding in terms of technical rigour, patient's tolerability and safety, expertise in interpretation, and costs. This paper reviews the main technical strategies proposed as less-is-better solutions for clinical practice (non-contrast biparametric MRI, reduction of acquisition time, abbreviated protocols, computer-aided diagnosis systems), discussing them in the light of the available evidence and of the concurrent evolution of Prostate Imaging Reporting and Data System (PI-RADS). We also summarised research results on those advanced techniques representing an alternative different-is-better line of the still ongoing evolution of prostate MRI (quantitative diffusion-weighted imaging, quantitative dynamic contrast enhancement, intravoxel incoherent motion, diffusion tensor imaging, diffusional kurtosis imaging, restriction spectrum imaging, radiomics analysis, hybrid positron emission tomography/MRI).

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Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly

Retter¹,

Gong²,

Syer³

et al. 2021

Molecular Oncology

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Imaging plays a fundamental role in all aspects of the cancer management pathway. However, conventional imaging techniques are largely reliant on morphological and size descriptors that have well known limitations, particularly when considering targeted-therapy response monitoring. Thus, new imaging methods have been developed to characterise cancer and are now routinely implemented, such as diffusion weighted imaging (DWI), dynamic contrast enhancement (DCE), positron emission technology (PET) and magnetic resonance spectroscopy (MRS). However, despite the improvement these techniques have enabled, limitations still remain. Novel imaging methods are now emerging, intent on further interrogating cancers.

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Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation

Cited by 68 publications

References 39 publications

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Evolution of prostate MRI: from multiparametric standard to less-is-better and different-is better strategies

Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly

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