Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images

Artificial intelligence (AI) for prostate magnetic resonance imaging (MRI) is starting to play a clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing workflow reduction. A total of 3,369 multi-vendor prostate MRI cases are available in open datasets, acquired from 2003 to 2021 in Europe or USA at 3 T (n = 3,018; 89.6%) or 1.5 T (n = 296; 8.8%), 346 cases scanned with endorectal coil (10.3%), 3,023 (89.7%) with phased-array surface coils; 412 collected for anatomical segmentation tasks, 3,096 for PCa detection/classification; for 2,240 cases lesions delineation is available and 56 cases have matching histopathologic images; for 2,620 cases the PSA level is provided; the total size of all open datasets amounts to approximately 253 GB. Of note, quality of annotations provided per dataset highly differ and attention must be paid when using these datasets (e.g., data overlap). Seven grand challenges and commercial applications from eleven vendors are here considered. Few small studies provided prospective validation. More work is needed, in particular validation on large-scale multi-institutional, well-curated public datasets to test general applicability. Moreover, AI needs to be explored for clinical stages other than detection/characterization (e.g., follow-up, prognosis, interventions, and focal treatment).

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“…PROSTATEx [22] is currently the dataset that is most commonly used for development of AI for detection of csPCa (e.g. [45][46][47]).…”

Section: Table 1 Summary Of Prostate Mri Public Datasetsmentioning

confidence: 99%

Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges

Sunoqrot

Saha²,

Hosseinzadeh³

et al. 2022

Eur Radiol Exp

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“…In recent years, the breakthrough of deep learning in the field of image processing has radically altered prostate cancer detection and grading using MRI images [24,25]. In the literature, different related attempts on PCa were published [13,[26][27][28][29]. The prostate CAD in [13] deployed a fully automatic mono-parametric MRI malignant PCa identification and localization system, where authors proposed a new 3D sliding window technique, that preserved the 2D domain complexity while utilizing 3D information.…”

Section: Related Workmentioning

confidence: 99%

“…However, there was a big difference in performance evaluation between radiologist and their CAD. Another recent study [29], proposed a Retina U-Net detection framework to locate the lesions and expected their most likely Gleason grade. They worked on both the lesion level and the patient level on two different datasets.…”

Section: Related Workmentioning

confidence: 99%

A New Framework for Precise Identification of Prostatic Adenocarcinoma

Ayyad

Badawy

Shehata

et al. 2022

Sensors

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Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the prostate and integrated with PSA screening results. The dataset presented in the paper includes 80 biopsy confirmed patients, with a mean age of 65.7 years (43 benign prostatic hyperplasia, 37 prostatic carcinomas). Experiments were conducted using different well-known machine learning approaches including support vector machines (SVM), random forests (RF), decision trees (DT), and linear discriminant analysis (LDA) classification models to study the impact of different feature sets that lead to better identification of prostatic adenocarcinoma. Using a leave-one-out cross-validation approach, the diagnostic results obtained using the SVM classification model along with the combined feature set after applying feature selection (88.75% accuracy, 81.08% sensitivity, 95.35% specificity, and 0.8821 AUC) indicated that the system’s performance, after integrating and reducing different types of feature sets, obtained an enhanced diagnostic performance compared with each individual feature set and other machine learning classifiers. In addition, the developed diagnostic system provided consistent diagnostic performance using 10-fold and 5-fold cross-validation approaches, which confirms the reliability, generalization ability, and robustness of the developed system.

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“…As T2 and DWI have arbitrary non-quantitative image amplitudes, we apply interquartile range (IQR)-based intraimage normalization to address the relative nature of MR image intensity values both within and across research sites and to eliminate outlying values created by imaging artifacts. Specifically, each image was normalized to the image-level IQR computed within the 3D prostate gland (annotated by a radiologist or previously developed neural network segmentation model [13]) according to [14]:…”

Section: Gland Segmentation and Contrast Normalizationmentioning

confidence: 99%

Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology

Rajagopal¹,

Redekop²,

Kemisetti³

et al. 2022

Preprint

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Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their generalization capability both within-and across clinics. To enable this for prototypestage algorithms, where the majority of existing research remains, in this paper we introduce a flexible federated learning framework for cross-site training, validation, and evaluation of deep prostate cancer detection algorithms. Our approach utilizes an abstracted representation of the model architecture and data, which allows unpolished prototype deep learning models to be trained without modification using the NVFlare federated learning framework. Our results show increases in prostate cancer detection and classification accuracy using a specialized neural network model and diverse prostate biopsy data collected at two University of California research hospitals, demonstrating the efficacy of our approach in adapting to different datasets and improving MR-biomarker discovery. We opensource our FLtools system, which can be easily adapted to other deep learning projects for medical imaging.

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Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images

Cited by 62 publications

References 57 publications

Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges

Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges

A New Framework for Precise Identification of Prostatic Adenocarcinoma

Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology

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