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Butt MA, Kaleem MF, Bilal M, Hanif MS. Using multi-label ensemble CNN classifiers to mitigate labelling inconsistencies in patch-level Gleason grading. PLoS One 2024; 19:e0304847. [PMID: 38968206 PMCID: PMC11226137 DOI: 10.1371/journal.pone.0304847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 05/21/2024] [Indexed: 07/07/2024] Open
Abstract
This paper presents a novel approach to enhance the accuracy of patch-level Gleason grading in prostate histopathology images, a critical task in the diagnosis and prognosis of prostate cancer. This study shows that the Gleason grading accuracy can be improved by addressing the prevalent issue of label inconsistencies in the SICAPv2 prostate dataset, which employs a majority voting scheme for patch-level labels. We propose a multi-label ensemble deep-learning classifier that effectively mitigates these inconsistencies and yields more accurate results than the state-of-the-art works. Specifically, our approach leverages the strengths of three different one-vs-all deep learning models in an ensemble to learn diverse features from the histopathology images to individually indicate the presence of one or more Gleason grades (G3, G4, and G5) in each patch. These deep learning models have been trained using transfer learning to fine-tune a variant of the ResNet18 CNN classifier chosen after an extensive ablation study. Experimental results demonstrate that our multi-label ensemble classifier significantly outperforms traditional single-label classifiers reported in the literature by at least 14% and 4% on accuracy and f1-score metrics respectively. These results underscore the potential of our proposed machine learning approach to improve the accuracy and consistency of prostate cancer grading.
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Affiliation(s)
- Muhammad Asim Butt
- Department of Electrical Engineering, University of Management and Technology, Lahore, Pakistan
| | | | - Muhammad Bilal
- Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muhammad Shehzad Hanif
- Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
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Aupperle-Lellbach H, Kehl A, de Brot S, van der Weyden L. Clinical Use of Molecular Biomarkers in Canine and Feline Oncology: Current and Future. Vet Sci 2024; 11:199. [PMID: 38787171 PMCID: PMC11126050 DOI: 10.3390/vetsci11050199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
Molecular biomarkers are central to personalised medicine for human cancer patients. It is gaining traction as part of standard veterinary clinical practice for dogs and cats with cancer. Molecular biomarkers can be somatic or germline genomic alterations and can be ascertained from tissues or body fluids using various techniques. This review discusses how these genomic alterations can be determined and the findings used in clinical settings as diagnostic, prognostic, predictive, and screening biomarkers. We showcase the somatic and germline genomic alterations currently available to date for testing dogs and cats in a clinical setting, discussing their utility in each biomarker class. We also look at some emerging molecular biomarkers that are promising for clinical use. Finally, we discuss the hurdles that need to be overcome in going 'bench to bedside', i.e., the translation from discovery of genomic alterations to adoption by veterinary clinicians. As we understand more of the genomics underlying canine and feline tumours, molecular biomarkers will undoubtedly become a mainstay in delivering precision veterinary care to dogs and cats with cancer.
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Affiliation(s)
- Heike Aupperle-Lellbach
- Laboklin GmbH&Co.KG, Steubenstr. 4, 97688 Bad Kissingen, Germany; (H.A.-L.); (A.K.)
- School of Medicine, Institute of Pathology, Technical University of Munich, Trogerstr. 18, 80333 München, Germany
| | - Alexandra Kehl
- Laboklin GmbH&Co.KG, Steubenstr. 4, 97688 Bad Kissingen, Germany; (H.A.-L.); (A.K.)
- School of Medicine, Institute of Pathology, Technical University of Munich, Trogerstr. 18, 80333 München, Germany
| | - Simone de Brot
- Institute of Animal Pathology, COMPATH, University of Bern, 3012 Bern, Switzerland;
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González-Castro A, Leirós-Rodríguez R, Prada-García C, Benítez-Andrades JA. The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review. J Med Internet Res 2024; 26:e54934. [PMID: 38684088 DOI: 10.2196/54934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/30/2024] [Accepted: 02/13/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. OBJECTIVE The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. METHODS A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. RESULTS We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI. CONCLUSIONS The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. TRIAL REGISTRATION PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv.
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Affiliation(s)
- Ana González-Castro
- Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain
| | - Raquel Leirós-Rodríguez
- SALBIS Research Group, Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain
| | - Camino Prada-García
- Department of Preventive Medicine and Public Health, Universidad de Valladolid, Valladolid, Spain
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Zhang C, Gao X, Fan B, Guo S, Lyu X, Shi J, Fu Y, Zhang Q, Liu P, Guo H. Highly accurate and effective deep neural networks in pathological diagnosis of prostate cancer. World J Urol 2024; 42:93. [PMID: 38386116 DOI: 10.1007/s00345-024-04775-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 01/08/2024] [Indexed: 02/23/2024] Open
Abstract
PURPOSE To established an AI system to make the pathological diagnosis of prostate cancer. METHODS Prostate histopathological whole mount (WM) sections from patients underwent robot-assisted laparoscopic prostatectomy were prepared. All the prostate WM pathological sections were converted to digital image data and marked with different colors on the basis of the ISUP Gleason grade group. The image was then fed into a segmentation algorithm. We chose modified U-Net as our fundamental network architecture. RESULTS 172 patients were involved in this study. 896 pieces of prostate WM pathological sections from 160 patients, in which 826 pieces of WM sections from 148 patients were assigned to the training set randomly. After image segmentation there were totally 2,138,895 patches, of which 1,646,535 patches were valid for training. The other WM section was arranged for testing. Based on the whole image testing, AI and pathologists presented the same answers among 21 of 22 pieces of sections. To evaluate the diagnostic results at the pixel level, we anticipated correct cancer or non-cancer diagnose from this AI system. The area under the ROC curve as 96.8%. The value of pixel accuracy of three methods (binary analysis, clinically oriented analysis and analysis for different ISUP Gleason grade) were 96.93%, 95.43% and 93.88%, respectively. The value of frequency weighted IoU were 94.32%, 92.13% and 90.21%, respectively. CONCLUSIONS This AI system is able to assist pathologists to make a final diagnosis, indicating the great potential and a wide-range of applications of AI in the medical field.
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Affiliation(s)
- Chengwei Zhang
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Xiubin Gao
- Nanjing Innovative Data Technologies, Inc., Nanjing, 210014, Jiangsu, China
| | - Bo Fan
- Department of Urology, The First People's Hospital of Changshu, The Changshu Hospital Affiliated to Soochow University, Changshu, 215500, China
| | - Suhan Guo
- College of Global Public Health, New York University, NY, 10012, USA
| | - Xiaoyu Lyu
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Jiong Shi
- Department of Pathology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Yao Fu
- Department of Pathology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Qing Zhang
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
| | - Peng Liu
- Nanjing Innovative Data Technologies, Inc., Nanjing, 210014, Jiangsu, China.
| | - Hongqian Guo
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
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