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

Bardis, Michelle; Houshyar, Roozbeh; Chang, Peter D.; Ushinsky, Alexander; Glavis-Bloom, Justin; Chahine, Chantal; Bui, Thanh-Lan; Rupasinghe, Mark; Filippi, Christopher G.; Chow, Daniel

doi:10.3390/cancers12051204

Cited by 41 publications

(35 citation statements)

References 109 publications

(161 reference statements)

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“…Additional work in this dataset should progress beyond prostate segmentation and detect prostate lesions. Lesion identification is a much more challenging task for AI and data augmentation with a generative adversarial network (GAN) [36] could be very useful since this technical problem lacks sufficient training data [37].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning with Limited Data: Organ Segmentation Performance by U-Net

et al. 2020

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(1) Background: The effectiveness of deep learning artificial intelligence depends on data availability, often requiring large volumes of data to effectively train an algorithm. However, few studies have explored the minimum number of images needed for optimal algorithmic performance. (2) Methods: This institutional review board (IRB)-approved retrospective review included patients who received prostate magnetic resonance imaging (MRI) between September 2014 and August 2018 and a magnetic resonance imaging (MRI) fusion transrectal biopsy. T2-weighted images were manually segmented by a board-certified abdominal radiologist. Segmented images were trained on a deep learning network with the following case numbers: 8, 16, 24, 32, 40, 80, 120, 160, 200, 240, 280, and 320. (3) Results: Our deep learning network’s performance was assessed with a Dice score, which measures overlap between the radiologist’s segmentations and deep learning-generated segmentations and ranges from 0 (no overlap) to 1 (perfect overlap). Our algorithm’s Dice score started at 0.424 with 8 cases and improved to 0.858 with 160 cases. After 160 cases, the Dice increased to 0.867 with 320 cases. (4) Conclusions: Our deep learning network for prostate segmentation produced the highest overall Dice score with 320 training cases. Performance improved notably from training sizes of 8 to 120, then plateaued with minimal improvement at training case size above 160. Other studies utilizing comparable network architectures may have similar plateaus, suggesting suitable results may be obtainable with small datasets.

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Section: Discussionmentioning

confidence: 99%

Deep Learning with Limited Data: Organ Segmentation Performance by U-Net

et al. 2020

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“…Diagnosis based on mpMRI suffers from interobserver variability, influenced by experience [ 76 ], and subtleties in differentiating benign and premalignant lesions that may closely resemble PCa [ 77 ]. Studies of AI-based radiomics have suggested that these models may become a reliable and informative biomarker complementary to human interpretation of mpMRI [ 23 , 24 , 31 ].…”

Section: Multiparametric Mri (Mpmri) Of Prostate Cancermentioning

confidence: 99%

“…The growing interest in AI techniques and their applications in medicine [ 30 ], has carried over to computer-aided diagnostic (CAD) systems to detect, grade, and introduce other classifications of PCa [ 31 , 32 , 33 , 34 , 35 , 36 ]. So far, the term of radiomic with AI represents the features extraction and interpretation of hidden quantitative imaging data to be used for CAD [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Chaddad

Kucharczyk

Cheddad

et al. 2021

Cancers

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The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.

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“…Urological Cancer 2020 contains three reviews [ 31 , 32 , 33 ] and seven articles [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ] about prostate cancer. Sarkar et al [ 31 ] reviewed the intimate mechanisms of angiogenesis in prostate cancer and their possible blockade to maximize benefits minimizing toxicity.…”

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confidence: 99%

“…In this sense, a recent study showed that PTEN heterozygosity in LKB1 -mutant mice promotes the development of a metastatic aggressive form of prostate cancer [ 41 ]. Bardis et al [ 33 ] reviewed the applications of Artificial Intelligence to multiparametric magnetic resonance imaging in prostate cancer and its interaction with radiologists’ algorithms.…”

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confidence: 99%