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He C, Xu H, Yuan E, Ye L, Chen Y, Yao J, Song B. The accuracy and quality of image-based artificial intelligence for muscle-invasive bladder cancer prediction. Insights Imaging 2024; 15:185. [PMID: 39090234 PMCID: PMC11294512 DOI: 10.1186/s13244-024-01780-y] [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: 04/09/2024] [Accepted: 07/10/2024] [Indexed: 08/04/2024] Open
Abstract
PURPOSE To evaluate the diagnostic performance of image-based artificial intelligence (AI) studies in predicting muscle-invasive bladder cancer (MIBC). (2) To assess the reporting quality and methodological quality of these studies by Checklist for Artificial Intelligence in Medical Imaging (CLAIM), Radiomics Quality Score (RQS), and Prediction model Risk of Bias Assessment Tool (PROBAST). MATERIALS AND METHODS We searched Medline, Embase, Web of Science, and The Cochrane Library databases up to October 30, 2023. The eligible studies were evaluated using CLAIM, RQS, and PROBAST. Pooled sensitivity, specificity, and the diagnostic performances of these models for MIBC were also calculated. RESULTS Twenty-one studies containing 4256 patients were included, of which 17 studies were employed for the quantitative statistical analysis. The CLAIM study adherence rate ranged from 52.5% to 75%, with a median of 64.1%. The RQS points of each study ranged from 2.78% to 50% points, with a median of 30.56% points. All models were rated as high overall ROB. The pooled area under the curve was 0.85 (95% confidence interval (CI) 0.81-0.88) for computed tomography, 0.92 (95% CI 0.89-0.94) for MRI, 0.89 (95% CI 0.86-0.92) for radiomics and 0.91 (95% CI 0.88-0.93) for deep learning, respectively. CONCLUSION Although AI-powered muscle-invasive bladder cancer-predictive models showed promising performance in the meta-analysis, the reporting quality and the methodological quality were generally low, with a high risk of bias. CRITICAL RELEVANCE STATEMENT Artificial intelligence might improve the management of patients with bladder cancer. Multiple models for muscle-invasive bladder cancer prediction were developed. Quality assessment is needed to promote clinical application. KEY POINTS Image-based artificial intelligence models could aid in the identification of muscle-invasive bladder cancer. Current studies had low reporting quality, low methodological quality, and a high risk of bias. Future studies could focus on larger sample sizes and more transparent reporting of pathological evaluation, model explanation, and failure and sensitivity analyses.
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Affiliation(s)
- Chunlei He
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, 572000, China
| | - Hui Xu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Enyu Yuan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Lei Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, 572000, China.
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Yu J, Cai L, Chen C, Zou Y, Xiao Y, Fu X, Wang L, Yang X, Liu P, Lu Q, Sun X, Shao Q. A novel predict method for muscular invasion of bladder cancer based on 3D mp-MRI feature fusion. Phys Med Biol 2024; 69:055011. [PMID: 38306973 DOI: 10.1088/1361-6560/ad25c7] [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: 07/25/2023] [Accepted: 02/01/2024] [Indexed: 02/04/2024]
Abstract
Objective. To assist urologist and radiologist in the preoperative diagnosis of non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), we proposed a combination models strategy (CMS) utilizing multiparametric magnetic resonance imaging.Approach. The CMS includes three components: image registration, image segmentation, and multisequence feature fusion. To ensure spatial structure consistency of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE), a registration network based on patch sampling normalized mutual information was proposed to register DWI and DCE to T2WI. Moreover, to remove redundant information around the bladder, we employed a segmentation network to obtain the bladder and tumor regions from T2WI. Using the coordinate mapping from T2WI, we extracted these regions from DWI and DCE and integrated them into a three-branch dual-channel input. Finally, to fully fuse low-level and high-level features of T2WI, DWI, and DCE, we proposed a distributed multilayer fusion model for preoperative MIBC prediction with five-fold cross-validation.Main results. The study included 436 patients, of which 404 were for the internal cohort and 32 for external cohort. The MIBC was confirmed by pathological examination. In the internal cohort, the area under the curve, accuracy, sensitivity, and specificity achieved by our method were 0.928, 0.869, 0.753, and 0.929, respectively. For the urologist and radiologist, Vesical Imaging-Reporting and Data System score >3 was employed to determine MIBC. The urologist demonstrated an accuracy, sensitivity, and specificity of 0.842, 0.737, and 0.895, respectively, while the radiologist achieved 0.871, 0.803, and 0.906, respectively. In the external cohort, the accuracy of our method was 0.831, which was higher than that of the urologist (0.781) and the radiologist (0.813).Significance. Our proposed method achieved better diagnostic performance than urologist and was comparable to senior radiologist. These results indicate that CMS can effectively assist junior urologists and radiologists in diagnosing preoperative MIBC.
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Affiliation(s)
- Jie Yu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Lingkai Cai
- Department of Urology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, People's Republic of China
| | - Chunxiao Chen
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Yuan Zou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Yueyue Xiao
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Xue Fu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Liang Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China
| | - Xiao Yang
- Department of Urology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, People's Republic of China
| | - Peikun Liu
- Department of Urology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, People's Republic of China
| | - Qiang Lu
- Department of Urology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, People's Republic of China
| | - Xueying Sun
- Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, People's Republic of China
| | - Qiang Shao
- Department of Urology, the Affiliated Suzhou Hospital of Nanjing Medical University, People's Republic of China
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Akin O, Lema-Dopico A, Paudyal R, Konar AS, Chenevert TL, Malyarenko D, Hadjiiski L, Al-Ahmadie H, Goh AC, Bochner B, Rosenberg J, Schwartz LH, Shukla-Dave A. Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers (Basel) 2023; 15:5468. [PMID: 38001728 PMCID: PMC10670574 DOI: 10.3390/cancers15225468] [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: 09/15/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.
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Affiliation(s)
- Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Alfonso Lema-Dopico
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
| | | | | | - Dariya Malyarenko
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hikmat Al-Ahmadie
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Alvin C. Goh
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Bernard Bochner
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jonathan Rosenberg
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lawrence H. Schwartz
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
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Ferro M, Falagario UG, Barone B, Maggi M, Crocetto F, Busetto GM, Giudice FD, Terracciano D, Lucarelli G, Lasorsa F, Catellani M, Brescia A, Mistretta FA, Luzzago S, Piccinelli ML, Vartolomei MD, Jereczek-Fossa BA, Musi G, Montanari E, Cobelli OD, Tataru OS. Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement. Diagnostics (Basel) 2023; 13:2308. [PMID: 37443700 DOI: 10.3390/diagnostics13132308] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | - Ugo Giovanni Falagario
- Department of Urology and Organ Transplantation, University of Foggia, 71121 Foggia, Italy
| | - Biagio Barone
- Urology Unit, Department of Surgical Sciences, AORN Sant'Anna e San Sebastiano, 81100 Caserta, Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, 00161 Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, 71121 Foggia, Italy
| | - Francesco Del Giudice
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, 00161 Rome, Italy
| | - Daniela Terracciano
- Department of Translational Medical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy
| | - Francesco Lasorsa
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy
| | - Michele Catellani
- Department of Urology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Antonio Brescia
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Stefano Luzzago
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Mattia Luca Piccinelli
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | | | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- Division of Radiation Oncology, IEO-European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Gennaro Musi
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca' Granda-Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Octavian Sabin Tataru
- Department of Simulation Applied in Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mures, 540142 Târgu Mures, Romania
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5
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Li J, Qiu Z, Cao K, Deng L, Zhang W, Xie C, Yang S, Yue P, Zhong J, Lyu J, Huang X, Zhang K, Zou Y, Huang B. Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107466. [PMID: 36907040 DOI: 10.1016/j.cmpb.2023.107466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/09/2023] [Accepted: 03/03/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES Radiomics and deep learning are two popular technologies used to develop computer-aided detection and diagnosis schemes for analysing medical images. This study aimed to compare the effectiveness of radiomics, single-task deep learning (DL) and multi-task DL methods in predicting muscle-invasive bladder cancer (MIBC) status based on T2-weighted imaging (T2WI). METHODS A total of 121 tumours (93 for training, from Centre 1; 28 for testing, from Centre 2) were included. MIBC was confirmed with pathological examination. A radiomics model, a single-task model, and a multi-task model based on T2WI were constructed in the training cohort with five-fold cross-validation, and validation was conducted in the external test cohort. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of each model. DeLong's test and a permutation test were used to compare the performance of the models. RESULTS The area under the ROC curve (AUC) values of the radiomics, single-task and multi-task models in the training cohort were: 0.920, 0.933 and 0.932, respectively; and were 0.844, 0.884 and 0.932, respectively, in the test cohort. The multi-task model achieved better performance in the test cohort than did the other models. No statistically significant differences in AUC values and Kappa coefficients were observed between pairwise models, in either the training or test cohorts. According to the Grad-CAM feature visualization results, the multi-task model focused more on the diseased tissue area in some samples of the test cohort compared with the single-task model. CONCLUSIONS The T2WI-based radiomics, single-task, and multi-task models all exhibited good diagnostic performance in preoperatively predicting MIBC, in which the multi-task model had the best diagnostic performance. Compared with the radiomics method, our multi-task DL method had the advantage of saving time and effort. Compared with the single-task DL method, our multi-task DL method had the advantage of being more lesion-focused and more reliable for clinical reference.
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Affiliation(s)
- Jianpeng Li
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Zhengxuan Qiu
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Kangyang Cao
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Lei Deng
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Weijing Zhang
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chuanmiao Xie
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Shuiqing Yang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Peiyan Yue
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Jian Zhong
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Jiegeng Lyu
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Xiang Huang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Kunlin Zhang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Yujian Zou
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China.
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
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Panic J, Defeudis A, Balestra G, Giannini V, Rosati S. Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:67-76. [PMID: 37283773 PMCID: PMC10241248 DOI: 10.1109/ojemb.2023.3271455] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/18/2023] [Accepted: 04/25/2023] [Indexed: 06/08/2023] Open
Abstract
Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.
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Affiliation(s)
- Jovana Panic
- Department of Surgical Science, and Polytechnic of Turin, Department of Electronics and TelecommunicationsUniversity of Turin10129TurinItaly
| | - Arianna Defeudis
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Gabriella Balestra
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
| | - Valentina Giannini
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Samanta Rosati
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
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Zhao Y, Wang X, Che T, Bao G, Li S. Multi-task deep learning for medical image computing and analysis: A review. Comput Biol Med 2023; 153:106496. [PMID: 36634599 DOI: 10.1016/j.compbiomed.2022.106496] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/06/2022] [Accepted: 12/27/2022] [Indexed: 12/29/2022]
Abstract
The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.
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Affiliation(s)
- Yan Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, 2008, Australia.
| | - Tongtong Che
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Guoqing Bao
- School of Computer Science, The University of Sydney, Sydney, NSW, 2008, Australia
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
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8
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Zheng Q, Yang R, Ni X, Yang S, Xiong L, Yan D, Xia L, Yuan J, Wang J, Jiao P, Wu J, Hao Y, Wang J, Guo L, Jiang Z, Wang L, Chen Z, Liu X. Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides. Cancers (Basel) 2022; 14:cancers14235807. [PMID: 36497289 PMCID: PMC9737237 DOI: 10.3390/cancers14235807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022] Open
Abstract
(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.
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Affiliation(s)
- Qingyuan Zheng
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Rui Yang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Xinmiao Ni
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Song Yang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lin Xiong
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Dandan Yan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lingli Xia
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jingsong Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Panpan Jiao
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jiejun Wu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yiqun Hao
- Division of Nephrology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jianguo Wang
- Department of Hepatic-Biliary-Pancreatic Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Liantao Guo
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhengyu Jiang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lei Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhiyuan Chen
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Correspondence: (Z.C.); (X.L.)
| | - Xiuheng Liu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Correspondence: (Z.C.); (X.L.)
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