1
|
Chen X, Chen Q, Liu Y, Qiu Y, Lv L, Zhang Z, Yin X, Shu F. Radiomics models to predict bone marrow metastasis of neuroblastoma using CT. CANCER INNOVATION 2024; 3:e135. [PMID: 38948899 PMCID: PMC11212276 DOI: 10.1002/cai2.135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 07/02/2024]
Abstract
Background Bone marrow is the leading site for metastasis from neuroblastoma and affects the prognosis of patients with neuroblastoma. However, the accurate diagnosis of bone marrow metastasis is limited by the high spatial and temporal heterogeneity of neuroblastoma. Radiomics analysis has been applied in various cancers to build accurate diagnostic models but has not yet been applied to bone marrow metastasis of neuroblastoma. Methods We retrospectively collected information from 187 patients pathologically diagnosed with neuroblastoma and divided them into training and validation sets in a ratio of 7:3. A total of 2632 radiomics features were retrieved from venous and arterial phases of contrast-enhanced computed tomography (CT), and nine machine learning approaches were used to build radiomics models, including multilayer perceptron (MLP), extreme gradient boosting, and random forest. We also constructed radiomics-clinical models that combined radiomics features with clinical predictors such as age, gender, ascites, and lymph gland metastasis. The performance of the models was evaluated with receiver operating characteristics (ROC) curves, calibration curves, and risk decile plots. Results The MLP radiomics model yielded an area under the ROC curve (AUC) of 0.97 (95% confidence interval [CI]: 0.95-0.99) on the training set and 0.90 (95% CI: 0.82-0.95) on the validation set. The radiomics-clinical model using an MLP yielded an AUC of 0.93 (95% CI: 0.89-0.96) on the training set and 0.91 (95% CI: 0.85-0.97) on the validation set. Conclusions MLP-based radiomics and radiomics-clinical models can precisely predict bone marrow metastasis in patients with neuroblastoma.
Collapse
Affiliation(s)
- Xiong Chen
- Department of Paediatric Urology, Guangzhou Women and Children's Medical CenterGuangzhou Medical UniversityGuangzhouChina
- Department of Paediatric Surgery, Guangzhou Institute of PaediatricsGuangzhou Medical UniversityGuangzhouChina
| | - Qinchang Chen
- Department of Pediatric Cardiology, Guangdong Provincial People's HospitalGuangdong Academy of Medical Sciences, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Structural Heart DiseaseGuangzhouChina
| | - Yuanfang Liu
- Department of Radiology, Sun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Ya Qiu
- Department of Radiologythe First People's Hospital of Kashi PrefectureKashiChina
| | - Lin Lv
- Medical SchoolSun Yat‐sen UniversityGuangzhouChina
| | - Zhengtao Zhang
- Department of Paediatric Urology, Guangzhou Women and Children's Medical CenterGuangzhou Medical UniversityGuangzhouChina
- Department of Paediatric Surgery, Guangzhou Institute of PaediatricsGuangzhou Medical UniversityGuangzhouChina
| | - Xuntao Yin
- Department of RadiologyGuangzhou Women and Children's Medical CenterGuangzhouChina
| | - Fangpeng Shu
- Department of Paediatric Urology, Guangzhou Women and Children's Medical CenterGuangzhou Medical UniversityGuangzhouChina
- Department of Paediatric Surgery, Guangzhou Institute of PaediatricsGuangzhou Medical UniversityGuangzhouChina
| |
Collapse
|
2
|
Shkolyar E, Zhou SR, Carlson CJ, Chang S, Laurie MA, Xing L, Bowden AK, Liao JC. Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence. Nat Rev Urol 2024:10.1038/s41585-024-00904-9. [PMID: 38982304 DOI: 10.1038/s41585-024-00904-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2024] [Indexed: 07/11/2024]
Abstract
Diagnostic cystoscopy in combination with transurethral resection of the bladder tumour are the standard for the diagnosis, surgical treatment and surveillance of bladder cancer. The ability to inspect the bladder in its current form stems from a long chain of advances in imaging science and endoscopy. Despite these advances, bladder cancer recurrence and progression rates remain high after endoscopic resection. This stagnation is a result of the heterogeneity of cancer biology as well as limitations in surgical techniques and tools, as incomplete resection and provider-specific differences affect cancer persistence and early recurrence. An unmet clinical need remains for solutions that can improve tumour delineation and resection. Translational advances in enhanced cystoscopy technologies and artificial intelligence offer promising avenues to overcoming the progress plateau.
Collapse
Affiliation(s)
- Eugene Shkolyar
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Steve R Zhou
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Camella J Carlson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Shuang Chang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Mark A Laurie
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Audrey K Bowden
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Joseph C Liao
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
| |
Collapse
|
3
|
Borna MR, Sepehri MM, Shadpour P, Khaleghi Mehr F. Enhancing bladder cancer diagnosis through transitional cell carcinoma polyp detection and segmentation: an artificial intelligence powered deep learning solution. Front Artif Intell 2024; 7:1406806. [PMID: 38873177 PMCID: PMC11169928 DOI: 10.3389/frai.2024.1406806] [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: 03/25/2024] [Accepted: 05/08/2024] [Indexed: 06/15/2024] Open
Abstract
Background Bladder cancer, specifically transitional cell carcinoma (TCC) polyps, presents a significant healthcare challenge worldwide. Accurate segmentation of TCC polyps in cystoscopy images is crucial for early diagnosis and urgent treatment. Deep learning models have shown promise in addressing this challenge. Methods We evaluated deep learning architectures, including Unetplusplus_vgg19, Unet_vgg11, and FPN_resnet34, trained on a dataset of annotated cystoscopy images of low quality. Results The models showed promise, with Unetplusplus_vgg19 and FPN_resnet34 exhibiting precision of 55.40 and 57.41%, respectively, suitable for clinical application without modifying existing treatment workflows. Conclusion Deep learning models demonstrate potential in TCC polyp segmentation, even when trained on lower-quality images, suggesting their viability in improving timely bladder cancer diagnosis without impacting the current clinical processes.
Collapse
Affiliation(s)
- Mahdi-Reza Borna
- Department of IT Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Mehdi Sepehri
- Department of IT Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Pejman Shadpour
- Hasheminejad Kidney Center (HKC), Iran University of Medical Sciences, Tehran, Iran
| | | |
Collapse
|
4
|
Wu S, Shen R, Hong G, Luo Y, Wan H, Feng J, Chen Z, Jiang F, Wang Y, Liao C, Li X, Liu B, Huang X, Liu K, Qin P, Wang Y, Xie Y, Ouyang N, Huang J, Lin T. Development and validation of an artificial intelligence-based model for detecting urothelial carcinoma using urine cytology images: a multicentre, diagnostic study with prospective validation. EClinicalMedicine 2024; 71:102566. [PMID: 38686219 PMCID: PMC11056596 DOI: 10.1016/j.eclinm.2024.102566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/01/2024] [Accepted: 03/12/2024] [Indexed: 05/02/2024] Open
Abstract
Background Urine cytology is an important non-invasive examination for urothelial carcinoma (UC) diagnosis and follow-up. We aimed to explore whether artificial intelligence (AI) can enhance the sensitivity of urine cytology and help avoid unnecessary endoscopy. Methods In this multicentre diagnostic study, consecutive patients who underwent liquid-based urine cytology examinations at four hospitals in China were included for model development and validation. Patients who declined surgery and lacked associated histopathology results, those diagnosed with rare subtype tumours of the urinary tract, or had low-quality images were excluded from the study. All liquid-based cytology slides were scanned into whole-slide images (WSIs) at 40 × magnification and the WSI-labels were derived from the corresponding histopathology results. The Precision Urine Cytology AI Solution (PUCAS) was composed of three distinct stages (patch extraction, features extraction, and classification diagnosis) and was trained to identify important WSI features associated with UC diagnosis. The diagnostic sensitivity was mainly used to validate the performance of PUCAS in retrospective and prospective validation cohorts. This study is registered with the ChiCTR, ChiCTR2300073192. Findings Between January 1, 2018 and October 31, 2022, 2641 patients were retrospectively recruited in the training cohort, and 2335 in retrospective validation cohorts; 400 eligible patients were enrolled in the prospective validation cohort between July 7, 2023 and September 15, 2023. The sensitivity of PUCAS ranged from 0.922 (95% CI: 0.811-0.978) to 1.000 (0.782-1.000) in retrospective validation cohorts, and was 0.896 (0.837-0.939) in prospective validation cohort. The PUCAS model also exhibited a good performance in detecting malignancy within atypical urothelial cells cases, with a sensitivity of over 0.84. In the recurrence detection scenario, PUCAS could reduce 57.5% of endoscopy use with a negative predictive value of 96.4%. Interpretation PUCAS may help to improve the sensitivity of urine cytology, reduce misdiagnoses of UC, avoid unnecessary endoscopy, and reduce the clinical burden in resource-limited areas. The further validation in other countries is needed. Funding National Natural Science Foundation of China; Key Program of the National Natural Science Foundation of China; the National Science Foundation for Distinguished Young Scholars; the Science and Technology Planning Project of Guangdong Province; the National Key Research and Development Programme of China; Guangdong Provincial Clinical Research Centre for Urological Diseases.
Collapse
Affiliation(s)
- Shaoxu Wu
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, China
| | - Runnan Shen
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Guibin Hong
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yun Luo
- Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Huan Wan
- Department of Pathology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiahao Feng
- CellsVision Medical Technology Services Co., Ltd., Guangzhou, China
| | - Zeshi Chen
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Fan Jiang
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yun Wang
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Chengxiao Liao
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaoyang Li
- Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bohao Liu
- Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaowei Huang
- CellsVision Medical Technology Services Co., Ltd., Guangzhou, China
| | - Kai Liu
- CellsVision Medical Technology Services Co., Ltd., Guangzhou, China
| | - Ping Qin
- Department of Pathology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yahui Wang
- Department of Urology, The Shen-Shan Central Hospital, Shanwei, China
| | - Ye Xie
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Nengtai Ouyang
- Department of Pathology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jian Huang
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, China
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, China
| |
Collapse
|
5
|
Hashemi Gheinani A, Kim J, You S, Adam RM. Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 2024; 21:214-242. [PMID: 37604982 DOI: 10.1038/s41585-023-00805-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 08/23/2023]
Abstract
The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
Collapse
Affiliation(s)
- Ali Hashemi Gheinani
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Urology, Inselspital, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Jina Kim
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rosalyn M Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
6
|
Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [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: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
Collapse
Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
| |
Collapse
|
7
|
Tano ZE, Cumpanas AD, Gorgen ARH, Rojhani A, Altamirano-Villarroel J, Landman J. Surgical Artificial Intelligence: Endourology. Urol Clin North Am 2024; 51:77-89. [PMID: 37945104 DOI: 10.1016/j.ucl.2023.06.004] [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] [Indexed: 11/12/2023]
Abstract
Endourology is ripe with information that includes patient factors, laboratory tests, outcomes, and visual data, which is becoming increasingly complex to assess. Artificial intelligence (AI) has the potential to explore and define these relationships; however, humans might not be involved in the input, analysis, or even determining the methods of analysis. Herein, the authors present the current state of AI in endourology and highlight the need for urologists to share their proposed AI solutions for reproducibility outside of their institutions and prepare themselves to properly critique this new technology.
Collapse
Affiliation(s)
- Zachary E Tano
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA.
| | - Andrei D Cumpanas
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Antonio R H Gorgen
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Allen Rojhani
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Jaime Altamirano-Villarroel
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| | - Jaime Landman
- Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA
| |
Collapse
|
8
|
Laurie MA, Zhou SR, Islam MT, Shkolyar E, Xing L, Liao JC. Bladder Cancer and Artificial Intelligence: Emerging Applications. Urol Clin North Am 2024; 51:63-75. [PMID: 37945103 PMCID: PMC10697017 DOI: 10.1016/j.ucl.2023.07.002] [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] [Indexed: 11/12/2023]
Abstract
Bladder cancer is a common and heterogeneous disease that poses a significant burden to the patient and health care system. Major unmet needs include effective early detection strategy, imprecision of risk stratification, and treatment-associated morbidities. The existing clinical paradigm is imprecise, which results in missed tumors, suboptimal therapy, and disease progression. Artificial intelligence holds immense potential to address many unmet needs in bladder cancer, including early detection, risk stratification, treatment planning, quality assessment, and outcome prediction. Despite recent advances, extensive work remains to affirm the efficacy of artificial intelligence as a decision-making tool for bladder cancer management.
Collapse
Affiliation(s)
- Mark A Laurie
- Department of Urology, Stanford University School of Medicine, 453 Quarry Road, Mail Code 5656, Palo Alto, CA 94304, USA; Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive Room G204, Stanford, CA 94305-5847, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA; Institute for Computational and Mathematical Engineering, Stanford University School of Engineering, Stanford, CA 94305, USA
| | - Steve R Zhou
- Department of Urology, Stanford University School of Medicine, 453 Quarry Road, Mail Code 5656, Palo Alto, CA 94304, USA
| | - Md Tauhidul Islam
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive Room G204, Stanford, CA 94305-5847, USA
| | - Eugene Shkolyar
- Department of Urology, Stanford University School of Medicine, 453 Quarry Road, Mail Code 5656, Palo Alto, CA 94304, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive Room G204, Stanford, CA 94305-5847, USA
| | - Joseph C Liao
- Department of Urology, Stanford University School of Medicine, 453 Quarry Road, Mail Code 5656, Palo Alto, CA 94304, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA.
| |
Collapse
|
9
|
Liu B, Sun C, Zhao X, Liu L, Liu S, Ma H. The value of multimodality MR in T staging evaluation after neoadjuvant therapy for rectal cancer. Technol Health Care 2024; 32:615-627. [PMID: 37393447 PMCID: PMC10977434 DOI: 10.3233/thc-220798] [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: 11/25/2022] [Accepted: 03/29/2023] [Indexed: 07/03/2023]
Abstract
BACKGROUND Surgery is the preferred treatment for rectal cancer, but surgical treatment alone sometimes does not achieve satisfactory results. OBJECTIVE To explore the value of multimodal Magnetic Resonance (MR) images in evaluating T staging of rectal cancer after neoadjuvant therapy and to compare and analyze with pathological results. METHODS This study retrospectively analyzed 232 patients with stage T3, T4 rectal cancer between January 1, 2017 and October 31, 2022. MR examination was performed within 3 days before surgery. Different MR sequences were used for mrT staging of rectal cancer after neoadjuvant therapy and compared with pathological pT staging. The accuracy of different MR sequences in evaluating T staging of rectal cancer was calculated, and the consistency between the two was analyzed by kappa test. The sensitivity, specificity, negative predictive value and positive predictive value of different MR sequences in evaluating rectal cancer invading mesorectal fascia after neoadjuvant therapy were calculated. RESULTS A total of 232 patients with rectal cancer were included in the study. The accuracy of high-resolution T2 WI in evaluating T staging of rectal cancer after neoadjuvant therapy was 49.57%, and the Kappa value was 0.261. The accuracy of high-resolution T2WI combined with diffusion weighted imaging (DWI) in evaluating T staging of rectal cancer after neoadjuvant therapy was 61.64%, and the Kappa value was 0.411. The accuracy of high-resolution combined with DCE-MR images in evaluating T staging of rectal cancer after neoadjuvant therapy was 80.60%, and the Kappa value was 0.706. The sensitivity and specificity of high-resolution t2-weighted imaging (HR-T2WI) combined with dynamic contrast-enhancement magnetic resonance (DCE-MR) in evaluating the invasion of mesorectal fascia were 83.46% and 95.33%, respectively. CONCLUSION Compared with HR-T2WI combined with DWI images for mrT staging of rectal cancer after neoadjuvant chemoradiotherapy (N-CRT), HR-T2WI combined with DCE-M has the highest accuracy in evaluating mrT staging of rectal cancer after neoadjuvant therapy (80.60%), and has a high consistency with pathological pT staging. It is the best sequence for T staging of rectal cancer after neoadjuvant therapy. At the same time, the sequence has high sensitivity and specificity in evaluating mesorectal fascia invasion, which can provide accurate perioperative information for the formulation of surgical plan.
Collapse
Affiliation(s)
- Bin Liu
- Department of Radiology, The Second Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
| | - Chuan Sun
- Department of Radiology, The Second Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
| | - Xinyu Zhao
- Department of Radiology, The Second Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
| | - Lingyu Liu
- Department of Radiology, The Second Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
| | - Shuang Liu
- Department of Radiology, The Second Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
| | - Haichuan Ma
- Department of Radiology, The Second Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
| |
Collapse
|
10
|
Ikeda A, Nosato H. Overview of current applications and trends in artificial intelligence for cystoscopy and transurethral resection of bladder tumours. Curr Opin Urol 2024; 34:27-31. [PMID: 37902120 DOI: 10.1097/mou.0000000000001135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
PURPOSE OF REVIEW Accurate preoperative and intraoperative identification and complete resection of bladder cancer is essential. Adequate postoperative follow-up and observation are important to identify early intravesical recurrence or progression. However, the accuracy of diagnosis and treatment is dependent on the knowledge and experience of the physicians. Artificial intelligence (AI) can be an important tool for physicians performing cystoscopies. RECENT FINDINGS Reports published over the past year and a half have identified an adequate amount of cystoscopy datasets for deep learning, with rich datasets of multiple tumour types including images of flat, carcinoma-in-situ, and elevated lesions, and more diverse applications. In addition to detecting bladder tumours, AI can assist in diagnosing interstitial cystitis. Applications of AI using conventional white-light and also to bladder endoscopy with different image enhancement techniques and manufacturers is underway. A framework has also been proposed to standardise the management of clinical data from cystoscopy to aid education and AI development and to compare with gastrointestinal endoscopic AI. Although real-world clinical applications have lagged, technological developments are progressing. SUMMARY AI-based cystoscopy is likely to become an important tool and is expected to have real-world clinical applications comprehensively linking AI and imaging, data management systems, and clinicians. VIDEO ABSTRACT http://links.lww.com/COU/A45.
Collapse
Affiliation(s)
- Atsushi Ikeda
- Department of Urology, Institute of Medicine, University of Tsukuba, Tsukuba City, Ibaraki Prefecture, Japan
| | - Hirokazu Nosato
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba City, Ibaraki Prefecture, Japan
| |
Collapse
|
11
|
Eminaga O, Lee TJ, Laurie M, Ge TJ, La V, Long J, Semjonow A, Bogemann M, Lau H, Shkolyar E, Xing L, Liao JC. Efficient Augmented Intelligence Framework for Bladder Lesion Detection. JCO Clin Cancer Inform 2023; 7:e2300031. [PMID: 37774313 PMCID: PMC10569784 DOI: 10.1200/cci.23.00031] [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: 02/28/2023] [Revised: 05/23/2023] [Accepted: 08/16/2023] [Indexed: 10/01/2023] Open
Abstract
PURPOSE Development of intelligence systems for bladder lesion detection is cost intensive. An efficient strategy to develop such intelligence solutions is needed. MATERIALS AND METHODS We used four deep learning models (ConvNeXt, PlexusNet, MobileNet, and SwinTransformer) covering a variety of model complexity and efficacy. We trained these models on a previously published educational cystoscopy atlas (n = 312 images) to estimate the ratio between normal and cancer scores and externally validated on cystoscopy videos from 68 cases, with region of interest (ROI) pathologically confirmed to be benign and cancerous bladder lesions (ie, ROI). The performance measurement included specificity and sensitivity at frame level, frame sequence (block) level, and ROI level for each case. RESULTS Specificity was comparable between four models at frame (range, 30.0%-44.8%) and block levels (56%-67%). Although sensitivity at the frame level (range, 81.4%-88.1%) differed between the models, sensitivity at the block level (100%) and ROI level (100%) was comparable between these models. MobileNet and PlexusNet were computationally more efficient for real-time ROI detection than ConvNeXt and SwinTransformer. CONCLUSION Educational cystoscopy atlas and efficient models facilitate the development of real-time intelligence system for bladder lesion detection.
Collapse
Affiliation(s)
- Okyaz Eminaga
- AI Vobis, Palo Alto, CA
- Center for Artificial Intelligence in Medicine and Imaging, Stanford University School of Medicine, Stanford, CA
| | - Timothy Jiyong Lee
- Department of Urology, Stanford University School of Medicine, Stanford, CA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA
| | - Mark Laurie
- Department of Urology, Stanford University School of Medicine, Stanford, CA
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - T. Jessie Ge
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Vinh La
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Jin Long
- Center for Artificial Intelligence in Medicine and Imaging, Stanford University School of Medicine, Stanford, CA
| | - Axel Semjonow
- Department of Urology, Muenster University Hospital, Muenster, Germany
| | - Martin Bogemann
- Department of Urology, Muenster University Hospital, Muenster, Germany
| | - Hubert Lau
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA
| | - Eugene Shkolyar
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Lei Xing
- Center for Artificial Intelligence in Medicine and Imaging, Stanford University School of Medicine, Stanford, CA
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - Joseph C. Liao
- Center for Artificial Intelligence in Medicine and Imaging, Stanford University School of Medicine, Stanford, CA
- Department of Urology, Stanford University School of Medicine, Stanford, CA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA
| |
Collapse
|
12
|
Yilmaz EC, Belue MJ, Turkbey B, Reinhold C, Choyke PL. A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging. Can Assoc Radiol J 2023; 74:534-547. [PMID: 36515576 DOI: 10.1177/08465371221135782] [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] [Indexed: 12/15/2022] Open
Abstract
Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease management and its prognosis. However, interpretation of conventional imaging methods such as CT or MR imaging (MRI) usually demonstrates variability across different readers and institutions. Artificial intelligence (AI) has emerged as a promising technology that could improve the patient care by providing helpful input to human readers through lesion detection algorithms and lesion classification systems. Moreover, the robustness of these models may be valuable in automating time-consuming tasks such as organ and lesion segmentations. Herein, we review the current state of imaging and existing challenges in GU malignancies, particularly for cancers of prostate, kidney and bladder; and briefly summarize the recent AI-based solutions to these challenges.
Collapse
Affiliation(s)
- Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Caroline Reinhold
- McGill University Health Center, McGill University, Montreal, Canada
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| |
Collapse
|
13
|
Liu X, Shi J, Li Z, Huang Y, Zhang Z, Zhang C. The Present and Future of Artificial Intelligence in Urological Cancer. J Clin Med 2023; 12:4995. [PMID: 37568397 PMCID: PMC10419644 DOI: 10.3390/jcm12154995] [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: 05/05/2023] [Revised: 07/10/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular task autonomously. Although the clinical application of AI is still immature and faces drawbacks such as insufficient data and a lack of prospective clinical trials, AI will play an essential role in individualization and the whole management of cancers as research progresses. In this review, we summarize the applications and studies of AI in major urological cancers, including tumor diagnosis, treatment, and prognosis prediction. Moreover, we discuss the current challenges and future applications of AI.
Collapse
Affiliation(s)
| | | | | | | | - Zhihong Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China; (X.L.)
| | - Changwen Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China; (X.L.)
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Eminaga O, Lee TJ, Ge J, Shkolyar E, Laurie M, Long J, Hockman LG, Liao JC. Conceptual framework and documentation standards of cystoscopic media content for artificial intelligence. J Biomed Inform 2023; 142:104369. [PMID: 37088456 PMCID: PMC10643098 DOI: 10.1016/j.jbi.2023.104369] [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: 09/10/2022] [Revised: 04/03/2023] [Accepted: 04/18/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice. METHODS A conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, reusable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure compliance with FAIR principles. RESULTS The framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion, and frame levels. CONCLUSION Our study shows that the proposed framework facilitates the storage of visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.
Collapse
Affiliation(s)
- Okyaz Eminaga
- Department of Urology, Stanford University School of Medicine, Stanford, USA; Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA, USA.
| | - Timothy Jiyong Lee
- Department of Urology, Stanford University School of Medicine, Stanford, USA
| | - Jessie Ge
- Department of Urology, Stanford University School of Medicine, Stanford, USA
| | - Eugene Shkolyar
- Department of Urology, Stanford University School of Medicine, Stanford, USA
| | - Mark Laurie
- Department of Urology, Stanford University School of Medicine, Stanford, USA
| | - Jin Long
- Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Joseph C Liao
- Department of Urology, Stanford University School of Medicine, Stanford, USA; Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA, USA.
| |
Collapse
|
16
|
Wang ZY, Guo ZH. Intelligent Chinese Medicine: A New Direction Approach for Integrative Medicine in Diagnosis and Treatment of Cardiovascular Diseases. Chin J Integr Med 2023:10.1007/s11655-023-3639-7. [PMID: 37222830 DOI: 10.1007/s11655-023-3639-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2023] [Indexed: 05/25/2023]
Abstract
High mortality rates from cardiovascular diseases (CVDs) persist worldwide. Older people are at a higher risk of developing these diseases. Given the current high treatment cost for CVDs, there is a need to prevent CVDs and or develop treatment alternatives. Western and Chinese medicines have been used to treat CVDs. However, several factors, such as inaccurate diagnoses, non-standard prescriptions, and poor adherence behavior, lower the benefits of the treatments by Chinese medicine (CM). Artificial intelligence (AI) is increasingly used in clinical diagnosis and treatment, especially in assessing efficacy of CM in clinical decision support systems, health management, new drug research and development, and drug efficacy evaluation. In this study, we explored the role of AI in CM in the diagnosis and treatment of CVDs, and discussed application of AI in assessing the effect of CM on CVDs.
Collapse
Affiliation(s)
- Zi-Yan Wang
- The First Clinical College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China
| | - Zhi-Hua Guo
- School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China.
- Hunan Key Laboratory of Colleges and Universities of Intelligent Traditional Chinese Medicine Diagnosis and Preventive Treatment of Chronic Diseases of Hunan Universities of Chinese Medicine, Changsha, 410208, China.
| |
Collapse
|
17
|
Chen X, You G, Chen Q, Zhang X, Wang N, He X, Zhu L, Li Z, Liu C, Yao S, Ge J, Gao W, Yu H. Development and evaluation of an artificial intelligence system for children intussusception diagnosis using ultrasound images. iScience 2023; 26:106456. [PMID: 37063466 PMCID: PMC10090215 DOI: 10.1016/j.isci.2023.106456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/16/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Accurate identification of intussusception in children is critical for timely non-surgical management. We propose an end-to-end artificial intelligence algorithm, the Children Intussusception Diagnosis Network (CIDNet) system, that utilizes ultrasound images to rapidly diagnose intussusception. 9999 ultrasound images of 4154 pediatric patients were divided into training, validation, test, and independent reader study datasets. The independent reader study cohort was used to compare the diagnostic performance of the CIDNet system to six radiologists. Performance was evaluated using, among others, balance accuracy (BACC) and area under the receiver operating characteristic curve (AUC). The CIDNet system performed the best in diagnosing intussusception with a BACC of 0.8464 and AUC of 0.9716 in the test dataset compared to other deep learning algorithms. The CIDNet system compared favorably with expert radiologists by outstanding identification performance and robustness (BACC:0.9297; AUC:0.9769). CIDNet is a stable and precise technological tool for identifying intussusception in ultrasound scans of children.
Collapse
Affiliation(s)
- Xiong Chen
- Department of Paediatric Urology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
- Department of Paediatric Surgery, Guangzhou Institute of Paediatrics, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Guochang You
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, P. R. China
| | - Qinchang Chen
- Department of Pediatric Cardiology, Guangdong Provincial Key Laboratory of Structural Heart Disease, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong Cardiovascular Institute, Guangzhou 510080, P. R. China
| | - Xiangxiang Zhang
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Na Wang
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Xuehua He
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Liling Zhu
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Zhouzhou Li
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Chen Liu
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Shixiang Yao
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Junshuang Ge
- Clinical Data Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
| | - Wenjing Gao
- Clinical Data Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
- Corresponding author
| | - Hongkui Yu
- Department of Ultrasound, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China
- Corresponding author
| |
Collapse
|
18
|
Wu S, Hong G, Xu A, Zeng H, Chen X, Wang Y, Luo Y, Wu P, Liu C, Jiang N, Dang Q, Yang C, Liu B, Shen R, Chen Z, Liao C, Lin Z, Wang J, Lin T. Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study. Lancet Oncol 2023; 24:360-370. [PMID: 36893772 DOI: 10.1016/s1470-2045(23)00061-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Accurate lymph node staging is important for the diagnosis and treatment of patients with bladder cancer. We aimed to develop a lymph node metastases diagnostic model (LNMDM) on whole slide images and to assess the clinical effect of an artificial intelligence-assisted (AI) workflow. METHODS In this retrospective, multicentre, diagnostic study in China, we included consecutive patients with bladder cancer who had radical cystectomy and pelvic lymph node dissection, and from whom whole slide images of lymph node sections were available, for model development. We excluded patients with non-bladder cancer and concurrent surgery, or low-quality images. Patients from two hospitals (Sun Yat-sen Memorial Hospital of Sun Yat-sen University and Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China) were assigned before a cutoff date to a training set and after the date to internal validation sets for each hospital. Patients from three other hospitals (the Third Affiliated Hospital of Sun Yat-sen University, Nanfang Hospital of Southern Medical University, and the Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong, China) were included as external validation sets. A validation subset of challenging cases from the five validation sets was used to compare performance between the LNMDM and pathologists, and two other datasets (breast cancer from the CAMELYON16 dataset and prostate cancer from the Sun Yat-sen Memorial Hospital of Sun Yat-sen University) were collected for a multi-cancer test. The primary endpoint was diagnostic sensitivity in the four prespecified groups (ie, the five validation sets, a single-lymph-node test set, the multi-cancer test set, and the subset for a performance comparison between the LNMDM and pathologists). FINDINGS Between Jan 1, 2013 and Dec 31, 2021, 1012 patients with bladder cancer had radical cystectomy and pelvic lymph node dissection and were included (8177 images and 20 954 lymph nodes). We excluded 14 patients (165 images) with concurrent non-bladder cancer and also excluded 21 low-quality images. We included 998 patients and 7991 images (881 [88%] men; 117 [12%] women; median age 64 years [IQR 56-72]; ethnicity data not available; 268 [27%] with lymph node metastases) to develop the LNMDM. The area under the curve (AUC) for accurate diagnosis of the LNMDM ranged from 0·978 (95% CI 0·960-0·996) to 0·998 (0·996-1·000) in the five validation sets. Performance comparisons between the LNMDM and pathologists showed that the diagnostic sensitivity of the model (0·983 [95% CI 0·941-0·998]) substantially exceeded that of both junior pathologists (0·906 [0·871-0·934]) and senior pathologists (0·947 [0·919-0·968]), and that AI assistance improved sensitivity for both junior (from 0·906 without AI to 0·953 with AI) and senior (from 0·947 to 0·986) pathologists. In the multi-cancer test, the LNMDM maintained an AUC of 0·943 (95% CI 0·918-0·969) in breast cancer images and 0·922 (0·884-0·960) in prostate cancer images. In 13 patients, the LNMDM detected tumour micrometastases that had been missed by pathologists who had previously classified these patients' results as negative. Receiver operating characteristic curves showed that the LNMDM would enable pathologists to exclude 80-92% of negative slides while maintaining 100% sensitivity in clinical application. INTERPRETATION We developed an AI-based diagnostic model that did well in detecting lymph node metastases, particularly micrometastases. The LNMDM showed substantial potential for clinical applications in improving the accuracy and efficiency of pathologists' work. FUNDING National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, and the Guangdong Provincial Clinical Research Centre for Urological Diseases.
Collapse
Affiliation(s)
- Shaoxu Wu
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, Guangdong, China
| | - Guibin Hong
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Abai Xu
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Hong Zeng
- Department of Pathology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xulin Chen
- Cells Vision Medical Technology, Guangzhou, Guangdong, China
| | - Yun Wang
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yun Luo
- Sun Yat-sen Memorial Hospital and Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Peng Wu
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Cundong Liu
- Department of Urology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Ning Jiang
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Qiang Dang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Cheng Yang
- Department of Urology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Bohao Liu
- Sun Yat-sen Memorial Hospital and Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Runnan Shen
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zeshi Chen
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Chengxiao Liao
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhen Lin
- Cells Vision Medical Technology, Guangzhou, Guangdong, China
| | - Jin Wang
- Cells Vision Medical Technology, Guangzhou, Guangdong, China
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, Guangdong, China.
| |
Collapse
|
19
|
Eminaga O, Lee TJ, Ge J, Shkolyar E, Laurie M, Long J, Hockman LG, Liao JC. Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence. ARXIV 2023:arXiv:2301.05991v2. [PMID: 36713258 PMCID: PMC9882574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice. METHODS A conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, re-usable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure the compliance with FAIR principles. RESULTS The framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion and frame levels. CONCLUSION Our study shows that the proposed framework facilitates the storage of the visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.
Collapse
Affiliation(s)
- Okyaz Eminaga
- Department of Urology, Stanford University School of Medicine, Stanford
- Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA
| | | | - Jessie Ge
- Department of Urology, Stanford University School of Medicine, Stanford
| | - Eugene Shkolyar
- Department of Urology, Stanford University School of Medicine, Stanford
| | - Mark Laurie
- Department of Urology, Stanford University School of Medicine, Stanford
| | - Jin Long
- Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA
| | | | - Joseph C. Liao
- Department of Urology, Stanford University School of Medicine, Stanford
- Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA
| |
Collapse
|
20
|
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.
Collapse
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.)
| |
Collapse
|
21
|
Liu Y, Li J, Ou H, Qi D, Hu B, Xu Y, Hu J, Xiong Y, Xia L, Huang JH, Hu X, Wu E. Identification of new aptamer BC-3 targeting RPS7 from rapid screening for bladder carcinoma. Genes Dis 2022. [PMID: 37492709 PMCID: PMC10363591 DOI: 10.1016/j.gendis.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Aptamers, short single DNA or RNA oligonucleotides, have shown immense application potential as molecular probes for the early diagnosis and therapy of cancer. However, conventional cell-SELEX technologies for aptamer discovery are time-consuming and laborious. Here we discovered a new aptamer BC-3 by using an improved rapid X-Aptamer selection process for human bladder carcinoma, for which there is no specific molecular probe yet. We show that BC-3 exhibited excellent affinity in bladder cancer cells but not normal cells. We demonstrate that BC-3 displayed high selectivity for tumor cells over their normal counterparts in vitro, in mice, and in patient tumor tissue specimens. Further endocytosis pathway analysis revealed that BC-3 internalized into bladder cancer cells via clathrin-mediated endocytosis. Importantly, we identified ribosomal protein S7 (RPS7) as the binding target of BC-3 via an integrated methodology (mass spectrometry, colocalization assay, and immunoblotting). Together, we report that a novel aptamer BC-3 is discovered for bladder cancer and its properties in the disease are unearthed. Our findings will facilitate the discovery of novel diagnostic and therapeutic strategies for bladder cancer.
Collapse
|
22
|
Schulz A, Loloi J, Pina Martina L, Sankin A. The Development of Non-Invasive Diagnostic Tools in Bladder Cancer. Onco Targets Ther 2022; 15:497-507. [PMID: 35529887 PMCID: PMC9075009 DOI: 10.2147/ott.s283891] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/22/2022] [Indexed: 11/23/2022] Open
Abstract
Bladder cancer is a common urinary tract cancer with a difficult clinical course. With frequent recurrence, patients with a history of bladder cancer often undergo surveillance that involves invasive cystoscopies and biopsies. Not only is this financially burdensome for patients but it is also mentally and physically intensive. Given this predicament, the field has shifted towards the use of non-invasive urinary tests to detect bladder cancer earlier in the disease course and to avoid unnecessary procedures. The first non-invasive test developed was urine cytology; however, that was found to have a low sensitivity, especially for low-grade lesions. There are many tests that are available that utilize common protein biomarkers to enhance the sensitivity of detection. However, many of these tests lack the specificity seen with cytology. With recent technological and research advancements, there are newer detection systems such as RNA sequencing and microfluidics along with novel bladder cancer biomarkers including mRNAs, methylation patterns and exosomes, which have potential to be used in clinical practice. The aim of this review is to highlight established non-invasive bladder cancer diagnostic tests as well as innovative methodologies that are on the horizon for use in bladder cancer detection.
Collapse
Affiliation(s)
- Alison Schulz
- Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Justin Loloi
- Department of Urology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 11061, USA
| | - Luis Pina Martina
- Department of Urology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 11061, USA
| | - Alexander Sankin
- Department of Urology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 11061, USA
| |
Collapse
|
23
|
Shen Z, Wu H, Chen Z, Hu J, Pan J, Kong J, Lin T. The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis. Front Oncol 2022; 12:843735. [PMID: 35299747 PMCID: PMC8921533 DOI: 10.3389/fonc.2022.843735] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 01/28/2022] [Indexed: 01/03/2023] Open
Abstract
Background With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including prostate cancer. Facts have proved that AI has broad prospects in the accurate diagnosis and treatment of prostate cancer. Objective This study mainly summarizes the research on the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores possible future research hotspots. Methods The articles and reviews regarding application of AI in prostate cancer between 1999 and 2020 were selected from Web of Science Core Collection on August 23, 2021. Microsoft Excel 2019 and GraphPad Prism 8 were applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 5.8.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field. Results A total of 2,749 articles were selected in this study. AI-related research on prostate cancer increased exponentially in recent years, of which the USA was the most productive country with 1,342 publications, and had close cooperation with many countries. The most productive institution and researcher were the Henry Ford Health System and Tewari. However, the cooperation among most institutions or researchers was not close even if the high research outputs. The result of keyword analysis could divide all studies into three clusters: “Diagnosis and Prediction AI-related study”, “Non-surgery AI-related study”, and “Surgery AI-related study”. Meanwhile, the current research hotspots were “deep learning” and “multiparametric MRI”. Conclusions Artificial intelligence has broad application prospects in prostate cancer, and a growing number of scholars are devoted to AI-related research on prostate cancer. Meanwhile, the cooperation among various countries and institutions needs to be strengthened in the future. It can be projected that noninvasive diagnosis and accurate minimally invasive treatment through deep learning technology will still be the research focus in the next few years.
Collapse
Affiliation(s)
- Zefeng Shen
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Haiyang Wu
- Graduate School, Tianjin Medical University, Tianjin, China
| | - Zeshi Chen
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jintao Hu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiexin Pan
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jianqiu Kong
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China
| | - Tianxin Lin
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China
| |
Collapse
|
24
|
Lenis AT, Litwin MS. Does Artificial Intelligence Meaningfully Enhance Cystoscopy? J Natl Cancer Inst 2022; 114:174-175. [PMID: 34473299 PMCID: PMC8826502 DOI: 10.1093/jnci/djab180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 08/30/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Andrew T Lenis
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, NY, USA
| | - Mark S Litwin
- Department of Urology, University of California Los Angeles, Los
Angeles, CA, USA
- Department of Health Policy & Management, Fielding School of Public Health,
University of California Los Angeles, Los Angeles, CA, USA
- Correspondence to: Mark S. Litwin, MD, MPH, Department of Urology, University
of California Los Angeles, 300 Stein Plaza, 3rd Floor, Los Angeles, CA, USA (e-mail:
)
| |
Collapse
|