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Rabilloud N, Allaume P, Acosta O, De Crevoisier R, Bourgade R, Loussouarn D, Rioux-Leclercq N, Khene ZE, Mathieu R, Bensalah K, Pecot T, Kammerer-Jacquet SF. Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review. Diagnostics (Basel) 2023; 13:2676. [PMID: 37627935 PMCID: PMC10453406 DOI: 10.3390/diagnostics13162676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/09/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
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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Affiliation(s)
- Noémie Rabilloud
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes University, 35033 Rennes, France (S.-F.K.-J.)
| | - Pierre Allaume
- Department of Pathology, Rennes University Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.)
| | - Oscar Acosta
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes University, 35033 Rennes, France (S.-F.K.-J.)
| | - Renaud De Crevoisier
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes University, 35033 Rennes, France (S.-F.K.-J.)
- Department of Radiotherapy, Centre Eugène Marquis, 35033 Rennes, France
| | - Raphael Bourgade
- Department of Pathology, Nantes University Hospital, 44000 Nantes, France
| | | | - Nathalie Rioux-Leclercq
- Department of Pathology, Rennes University Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.)
| | - Zine-eddine Khene
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes University, 35033 Rennes, France (S.-F.K.-J.)
- Department of Urology, Rennes University Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Romain Mathieu
- Department of Urology, Rennes University Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Karim Bensalah
- Department of Urology, Rennes University Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Thierry Pecot
- Facility for Artificial Intelligence and Image Analysis (FAIIA), Biosit UAR 3480 CNRS-US18 INSERM, Rennes University, 2 Avenue du Professeur Léon Bernard, 35042 Rennes, France
| | - Solene-Florence Kammerer-Jacquet
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes University, 35033 Rennes, France (S.-F.K.-J.)
- Department of Pathology, Rennes University Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.)
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Guan Y, Li H, Yi D, Zhang D, Yin C, Li K, Zhang P. A survival model generalized to regression learning algorithms. ACTA ACUST UNITED AC 2021; 1:433-440. [PMID: 34312611 DOI: 10.1038/s43588-021-00083-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Survival prediction is an important problem that is encountered widely in industry and medicine. Despite the explosion of artificial intelligence technologies, no uniformed method allows the application of any type of regression learning algorithm to a survival prediction problem. Here, we present a statistical modeling method that is generalized to all types of regression learning algorithm, including deep learning. We present its empirical advantage when it is applied to traditional survival problems. We demonstrate its expanded applications in different types of regression learning algorithm, such as gradient boosted trees, convolutional neural networks and recurrent neural networks. Additionally, we demonstrate its application in clinical informatic data, pathological images and the hardware industry. We expect that this algorithm will be widely applicable for diverse types of survival data, including discrete data types and those suitable for deep learning such as those with time or spatial continuity.
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Affiliation(s)
- Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.,Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Hongyang Li
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Daiyao Yi
- Department of Biomedical Engineering, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Dongdong Zhang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Changchang Yin
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
| | - Keyu Li
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ping Zhang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.,Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
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Ma B, Guo Y, Hu W, Yuan F, Zhu Z, Yu Y, Zou H. Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach. Front Pharmacol 2020; 11:572372. [PMID: 33132910 PMCID: PMC7562716 DOI: 10.3389/fphar.2020.572372] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 09/08/2020] [Indexed: 12/23/2022] Open
Abstract
Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.
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Affiliation(s)
- Bowei Ma
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Yucheng Guo
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Weian Hu
- Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Fei Yuan
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhenggang Zhu
- Department of General Surgery, Ruijin Hospital, Shanghai Institute of Digestive Surgery, Shanghai Key Lab for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingyan Yu
- Department of General Surgery, Ruijin Hospital, Shanghai Institute of Digestive Surgery, Shanghai Key Lab for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Zou
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
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