Cribriform pattern detection in prostate histopathological images using deep learning models

Singh, Malay; Kalaw, Emarene; Wang, Jie; Al-Shabi, Mundher; Wong, Chin Fong; Giron, Danilo Medina; Chong, Kian-Tai; Tan, Maxine; Zeng, Zeng; Lee, Hwee Kuan

doi:10.48550/arxiv.1910.04030

Cited by 4 publications

(4 citation statements)

References 56 publications

(162 reference statements)

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“…It showed a high performance (sensitivity of minimum 0.94 and specificity of minimum 0.93), focusing on a high sensitivity to avoid missing small areas of cancer. Other articles focused on the detection of more specific patterns, such as cribriform patterns with an accuracy of 0.88 and AUC of 0.8 [ 50 , 51 , 52 ] or perineural invasion with an AUC of 0.95 [ 36 ]. To evaluate the generalization of their model trained on biopsies for which they obtained an AUC of 0.96, Tsuneki et al applied it to TUR-P (TransUrethral Resection of Prostate) biopsies with an AUC of 0.80.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

et al. 2023

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Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles (n = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661.

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Section: Resultsmentioning

confidence: 99%

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

et al. 2023

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“…The UNet model was trained using cross-validation, incorporating split-sample techniques, and subsequently validated using an external test set. Singh et al (39) suggest the use of deep neural networks for cribriform pattern classification. In this study, the authors introduce an automated image classification system employing deep learning and hand-crafted features to analyze prostate images.…”

Section: Related Workmentioning

confidence: 99%

A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI

Mehmood,

Abbasi,

Aurangzeb

et al. 2023

Front. Oncol.

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Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model’s ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer.

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“…Challenging histology and morphology is often met with enduringly high rates of inter- and intra-observer variability and increased time-to-diagnosis from pathologists using light microscopy [ 34 ]. Discordance is further emphasized within the focuses of genitourinary and renal pathology, where interpretation of complex grading systems, e.g., Fuhrman and Gleason, and prognostic patterns, e.g., cribriform and glomerulosclerotic, is concerningly incongruent even amongst specialists [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. Inter-pathologist grading assessments for prostate cancer grading have elicited concerning results, with kappa values reported as low as 0.3 [ 38 , 49 ].…”

Section: Realizing the Clinical Potential Of Aimentioning

confidence: 99%

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Patel

Shaker

Mohanty³

et al. 2022

Diagnostics

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Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.

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Cribriform pattern detection in prostate histopathological images using deep learning models

Cited by 4 publications

References 56 publications

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

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