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Margue G, Ferrer L, Etchepare G, Bigot P, Bensalah K, Mejean A, Roupret M, Doumerc N, Ingels A, Boissier R, Pignot G, Parier B, Paparel P, Waeckel T, Colin T, Bernhard JC. UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120). NPJ Precis Oncol 2024; 8:45. [PMID: 38396089 PMCID: PMC10891119 DOI: 10.1038/s41698-024-00532-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/30/2024] [Indexed: 02/25/2024] Open
Abstract
Renal cell carcinoma (RCC) is most often diagnosed at a localized stage, where surgery is the standard of care. Existing prognostic scores provide moderate predictive performance, leading to challenges in establishing follow-up recommendations after surgery and in selecting patients who could benefit from adjuvant therapy. In this study, we developed a model for individual postoperative disease-free survival (DFS) prediction using machine learning (ML) on real-world prospective data. Using the French kidney cancer research network database, UroCCR, we analyzed a cohort of surgically treated RCC patients. Participating sites were randomly assigned to either the training or testing cohort, and several ML models were trained on the training dataset. The predictive performance of the best ML model was then evaluated on the test dataset and compared with the usual risk scores. In total, 3372 patients were included, with a median follow-up of 30 months. The best results in predicting DFS were achieved using Cox PH models that included 24 variables, resulting in an iAUC of 0.81 [IC95% 0.77-0.85]. The ML model surpassed the predictive performance of the most commonly used risk scores while handling incomplete data in predictors. Lastly, patients were stratified into four prognostic groups with good discrimination (iAUC = 0.79 [IC95% 0.74-0.83]). Our study suggests that applying ML to real-world prospective data from patients undergoing surgery for localized or locally advanced RCC can provide accurate individual DFS prediction, outperforming traditional prognostic scores.
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Affiliation(s)
- Gaëlle Margue
- Bordeaux University Hospital, Urology department, Bordeaux, France.
- Kidney Cancer group of the French Association of Urology Cancer Committee, Paris, France.
| | - Loïc Ferrer
- SOPHiA GENETICS, Multimodal R&D team, Pessac, France
| | | | - Pierre Bigot
- Kidney Cancer group of the French Association of Urology Cancer Committee, Paris, France
- Angers University hospital, Urology department, Angers, France
| | - Karim Bensalah
- Rennes university hospital, Urology department, Rennes, France
| | | | - Morgan Roupret
- Kidney Cancer group of the French Association of Urology Cancer Committee, Paris, France
- La Pitié APHP, Urology department, Paris, France
| | - Nicolas Doumerc
- Kidney Cancer group of the French Association of Urology Cancer Committee, Paris, France
- Toulouse university hospital, Urology department, Toulouse, France
| | - Alexandre Ingels
- Kidney Cancer group of the French Association of Urology Cancer Committee, Paris, France
- Mondor-APHP, Urology department, Paris, France
| | - Romain Boissier
- Kidney Cancer group of the French Association of Urology Cancer Committee, Paris, France
- APHM, Urology department, Marseille, France
| | | | - Bastien Parier
- Kremlin-Bicêtre -APHP, Urology department, Paris, France
| | | | - Thibaut Waeckel
- Kidney Cancer group of the French Association of Urology Cancer Committee, Paris, France
- Caen University Hospital, Urology department, Caen, France
| | - Thierry Colin
- SOPHiA GENETICS, Multimodal R&D team, Pessac, France
| | - Jean-Christophe Bernhard
- Bordeaux University Hospital, Urology department, Bordeaux, France
- Kidney Cancer group of the French Association of Urology Cancer Committee, Paris, France
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2
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Parwani AV, Patel A, Zhou M, Cheville JC, Tizhoosh H, Humphrey P, Reuter VE, True LD. An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS). J Pathol Inform 2023; 14:100177. [PMID: 36654741 PMCID: PMC9841212 DOI: 10.1016/j.jpi.2022.100177] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 12/20/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022] Open
Abstract
Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.
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Affiliation(s)
- Anil V. Parwani
- The Ohio State University, Columbus, Ohio, USA
- Corresponding author.
| | - Ankush Patel
- The Ohio State University, 2441 60th Ave SE, Mercer Island, Washington 98040, USA
| | - Ming Zhou
- Tufts University, Medford, Massachusetts, USA
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3
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Barkan E, Porta C, Rabinovici-Cohen S, Tibollo V, Quaglini S, Rizzo M. Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma. Front Oncol 2023; 13:1021684. [PMID: 36874081 PMCID: PMC9978529 DOI: 10.3389/fonc.2023.1021684] [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: 08/17/2022] [Accepted: 01/30/2023] [Indexed: 02/18/2023] Open
Abstract
Background and objectives Investigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate the capacity of emerging Artificial Intelligence (AI) technologies to predict three- and five-year overall survival (OS) for mRCC patients starting their first-line of systemic treatment. Patients and methods The retrospective study included 322 Italian patients with mRCC who underwent systemic treatment between 2004 and 2019. Statistical analysis included the univariate and multivariate Cox proportional-hazard model and the Kaplan-Meier analysis for the prognostic factors' investigation. The patients were split into a training cohort to establish the predictive models and a hold-out cohort to validate the results. The models were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We assessed the clinical benefit of the models using decision curve analysis (DCA). Then, the proposed AI models were compared with well-known pre-existing prognostic systems. Results The median age of patients in the study was 56.7 years at RCC diagnosis and 78% of participants were male. The median survival time from the start of systemic treatment was 29.2 months; 95% of the patients died during the follow-up that finished by the end of 2019. The proposed predictive model, which was constructed as an ensemble of three individual predictive models, outperformed all well-known prognostic models to which it was compared. It also demonstrated better usability in supporting clinical decisions for 3- and 5-year OS. The model achieved (0.786 and 0.771) AUC and (0.675 and 0.558) specificity at sensitivity 0.90 for 3 and 5 years, respectively. We also applied explainability methods to identify the important clinical features that were found to be partially matched with the prognostic factors identified in the Kaplan-Meier and Cox analyses. Conclusions Our AI models provide best predictive accuracy and clinical net benefits over well-known prognostic models. As a result, they can potentially be used in clinical practice for providing better management for mRCC patients starting their first-line of systemic treatment. Larger studies would be needed to validate the developed model.
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Affiliation(s)
- Ella Barkan
- Department of Artificial Intelligence for Accelerated Healthcare & Life Sciences Discovery, IBM Research - Israel, University of Haifa Campus, Haifa, Israel
| | - Camillo Porta
- Department of Interdisciplinary Medicine, School of Medicine, University of Bari Aldo Moro, Bari, Italy.,Division of Medical Oncology, Azienda Ospedaliero Universitaria Consorziale Policlinico di Bari, Bari, Italy
| | - Simona Rabinovici-Cohen
- Department of Artificial Intelligence for Accelerated Healthcare & Life Sciences Discovery, IBM Research - Israel, University of Haifa Campus, Haifa, Israel
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Scientific Clinical Institute Maugeri (ICS Maugeri), Pavia, Italy
| | - Silvana Quaglini
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Mimma Rizzo
- Division of Medical Oncology, Azienda Ospedaliero Universitaria Consorziale Policlinico di Bari, Bari, Italy
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Patel AU, Mohanty SK, Parwani AV. Applications of Digital and Computational Pathology and Artificial Intelligence in Genitourinary Pathology Diagnostics. Surg Pathol Clin 2022; 15:759-785. [PMID: 36344188 DOI: 10.1016/j.path.2022.08.001] [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: 06/16/2023]
Abstract
As machine learning (ML) solutions for genitourinary pathology image analysis are fostered by a progressively digitized laboratory landscape, these integrable modalities usher in a revolution in histopathological diagnosis. As technology advances, limitations stymying clinical artificial intelligence (AI) will not be extinguished without thorough validation and interrogation of ML tools by pathologists and regulatory bodies alike. ML solutions deployed in clinical settings for applications in prostate pathology yield promising results. Recent breakthroughs in clinical artificial intelligence for genitourinary pathology demonstrate unprecedented generalizability, heralding prospects for a future in which AI-driven assistive solutions may be seen as laboratory faculty, rather than novelty.
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Affiliation(s)
- Ankush Uresh Patel
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Sambit K Mohanty
- Surgical and Molecular Pathology, Advanced Medical Research Institute, Plot No. 1, Near Jayadev Vatika Park, Khandagiri, Bhubaneswar, Odisha 751019. https://twitter.com/SAMBITKMohanty1
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Cooperative Human Tissue Network (CHTN) Midwestern Division Polaris Innovation Centre, 2001 Polaris Parkway Suite 1000, Columbus, OH 43240, USA.
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5
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Kim HM, Byun SS, Kim JK, Jeong CW, Kwak C, Hwang EC, Kang SH, Chung J, Kim YJ, Ha YS, Hong SH. Machine learning-based prediction model for late recurrence after surgery in patients with renal cell carcinoma. BMC Med Inform Decis Mak 2022; 22:241. [PMID: 36100881 PMCID: PMC9472380 DOI: 10.1186/s12911-022-01964-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 07/21/2022] [Indexed: 11/24/2022] Open
Abstract
Background Renal cell carcinoma is characterized by a late recurrence that occurs 5 years after surgery; hence, continuous monitoring and follow-up is necessary. Prognosis of late recurrence of renal cell carcinoma can only be improved if it is detected early and treated appropriately. Therefore, tools for rapid and accurate renal cell carcinoma prediction are essential. Methods This study aimed to develop a prediction model for late recurrence after surgery in patients with renal cell carcinoma that can be used as a clinical decision support system for the early detection of late recurrence. We used the KOrean Renal Cell Carcinoma database that contains large-scale cohort data of patients with renal cell carcinoma in Korea. From the collected data, we constructed a dataset of 2956 patients for the analysis. Late recurrence and non-recurrence were classified by applying eight machine learning models, and model performance was evaluated using the area under the receiver operating characteristic curve. Results Of the eight models, the AdaBoost model showed the highest performance. The developed algorithm showed a sensitivity of 0.673, specificity of 0.807, accuracy of 0.799, area under the receiver operating characteristic curve of 0.740, and F1-score of 0.609. Conclusions To the best of our knowledge, we developed the first algorithm to predict the probability of a late recurrence 5 years after surgery. This algorithm may be used by clinicians to identify patients at high risk of late recurrence that require long-term follow-up and to establish patient-specific treatment strategies. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01964-w.
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Affiliation(s)
- Hyung Min Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, 06591, Korea.,Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, 06591, Korea
| | - Seok-Soo Byun
- Department of Urology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, 13620, Korea
| | - Jung Kwon Kim
- Department of Urology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, 13620, Korea
| | - Chang Wook Jeong
- Department of Urology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, 03080, Korea
| | - Cheol Kwak
- Department of Urology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, 03080, Korea
| | - Eu Chang Hwang
- Department of Urology, Chonnam National University Medical School, Gwangju, 61469, Korea
| | - Seok Ho Kang
- Department of Urology, Korea University School of Medicine, Seoul, 02841, Korea
| | - Jinsoo Chung
- Department of Urology, National Cancer Center, Goyang, 10408, Korea
| | - Yong-June Kim
- Department of Urology, Chungbuk National University College of Medicine, Cheongju, 28644, Korea.,Department of Urology, College of Medicine, Chungbuk National University, Cheongju, 28644, Korea
| | - Yun-Sok Ha
- Department of Urology, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu, 41404, Korea
| | - Sung-Hoo Hong
- Department of Urology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University, Seoul, 06591, Korea.
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6
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Khene ZE, Bigot P, Doumerc N, Ouzaid I, Boissier R, Nouhaud FX, Albiges L, Bernhard JC, Ingels A, Borchiellini D, Kammerer-Jacquet S, Rioux-Leclercq N, Roupret M, Acosta O, De Crevoisier R, Bensalah K. Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma. Eur Urol Oncol 2022:S2588-9311(22)00137-7. [PMID: 35987730 DOI: 10.1016/j.euo.2022.07.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/28/2022] [Accepted: 07/21/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND Predictive tools can be useful for adapting surveillance or including patients in adjuvant trials after surgical resection of nonmetastatic renal cell carcinoma (RCC). Current models have been built using traditional statistical modelling and prespecified variables, which limits their performance. OBJECTIVE To investigate the performance of machine learning (ML) framework to predict recurrence after RCC surgery and compare them with current validated models. DESIGN, SETTING, AND PARTICIPANTS In this observational study, we derived and tested several ML-based models (Random Survival Forests [RSF], Survival Support Vector Machines [S-SVM], and Extreme Gradient Boosting [XG boost]) to predict recurrence of patients who underwent radical or partial nephrectomy for a nonmetastatic RCC, between 2013 and 2020, at 21 French medical centres. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The primary end point was disease-free survival. Model discrimination was assessed using the concordance index (c-index), and calibration was assessed using the Brier score. ML models were compared with four conventional prognostic models, using decision curve analysis (DCA). RESULTS AND LIMITATIONS A total of 4067 patients were included in this study (3253 in the development cohort and 814 in the validation cohort). Most tumours (69%) were clear cell RCC, 40% were of high grade (nuclear International Society of Urological Pathology grade 3 or 4), and 24% had necrosis. Of the patients, 4% had nodal involvement. After a median follow-up of 57 mo (interquartile range 29-76), 523 (13%) patients recurred. ML models obtained higher c-index values than conventional models. The RSF yielded the highest c-index values (0.794), followed by S-SVM (c-index 0.784) and XG boost (c-index 0.782). In addition, all models showed good calibration with low integrated Brier scores (all integrated brier scores <0.1). However, we found calibration drift over time for all models, albeit with a smaller magnitude for ML models. Finally, DCA showed an incremental net benefit from all ML models compared with conventional models currently used in practice. CONCLUSIONS Applying ML approaches to predict recurrence following surgical resection of RCC resulted in better prediction than that of current validated models available in clinical practice. However, there is still room for improvement, which may come from the integration of novel biological and/or imaging biomarkers. PATIENT SUMMARY We found that artificial intelligence algorithms could better predict the risk of recurrence after surgery for a localised kidney cancer. These algorithms may help better select patients who will benefit from medical treatment after surgery.
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Affiliation(s)
- Zine-Eddine Khene
- Department of Urology, University of Rennes 1, Rennes, France; LTSI, Inserm U1099, Université de Rennes 1, Rennes, France
| | - Pierre Bigot
- Department of Urology, University of Angers, Angers, France
| | - Nicolas Doumerc
- Department of Urology, University of Toulouse, Toulouse, France
| | - Idir Ouzaid
- Department of Urology, Bichat Claude Bernard Hospital, Paris, France
| | - Romain Boissier
- Department of Urology, Aix-Marseille University, Marseille, France
| | | | - Laurence Albiges
- Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | | | | | | | | | | | - Morgan Roupret
- Department of Urology, La Pitie Salpétrière Hospital, Paris, France
| | - Oscar Acosta
- LTSI, Inserm U1099, Université de Rennes 1, Rennes, France
| | - Renaud De Crevoisier
- LTSI, Inserm U1099, Université de Rennes 1, Rennes, France; Department of Medical Oncology, Centre Eugene Marquis, Rennes, France
| | - Karim Bensalah
- Department of Urology, University of Rennes 1, Rennes, France; LTSI, Inserm U1099, Université de Rennes 1, Rennes, France.
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7
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Patel AU, Shaker N, Mohanty S, Sharma S, Gangal S, Eloy C, Parwani AV. Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence. Diagnostics (Basel) 2022; 12:diagnostics12081778. [PMID: 35892487 PMCID: PMC9332710 DOI: 10.3390/diagnostics12081778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 11/17/2022] Open
Abstract
Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.
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Affiliation(s)
- Ankush U. Patel
- Mayo Clinic Department of Laboratory Medicine and Pathology, Rochester, MN 55905, USA
- Correspondence: ; Tel.: +1-206-451-3519
| | - Nada Shaker
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
| | - Sambit Mohanty
- CORE Diagnostics, Gurugram 122016, India; (S.M.); (S.S.)
- Advanced Medical Research Institute, Bareilly 243001, India
| | - Shivani Sharma
- CORE Diagnostics, Gurugram 122016, India; (S.M.); (S.S.)
| | - Shivam Gangal
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
- College of Engineering, Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Catarina Eloy
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Rua Júlio Amaral de Carvalho, 45, 4200-135 Porto, Portugal;
- Institute for Research and Innovation in Health (I3S Consortium), Rua Alfredo Allen, 208, 4200-135 Porto, Portugal
| | - Anil V. Parwani
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
- Cooperative Human Tissue Network (CHTN) Midwestern Division, Columbus, OH 43240, USA
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8
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Sharma R, Kannourakis G, Prithviraj P, Ahmed N. Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma. Front Med (Lausanne) 2022; 9:766869. [PMID: 35775004 PMCID: PMC9237320 DOI: 10.3389/fmed.2022.766869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 04/11/2022] [Indexed: 12/24/2022] Open
Abstract
Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.
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Affiliation(s)
- Revati Sharma
- Fiona Elsey Cancer Research Institute, Ballarat Central Technology Central Park, Ballarat Central, VIC, Australia
- School of Science, Psychology and Sport, Federation University, Mt Helen, VIC, Australia
| | - George Kannourakis
- Fiona Elsey Cancer Research Institute, Ballarat Central Technology Central Park, Ballarat Central, VIC, Australia
- School of Science, Psychology and Sport, Federation University, Mt Helen, VIC, Australia
| | - Prashanth Prithviraj
- Fiona Elsey Cancer Research Institute, Ballarat Central Technology Central Park, Ballarat Central, VIC, Australia
- School of Science, Psychology and Sport, Federation University, Mt Helen, VIC, Australia
| | - Nuzhat Ahmed
- Fiona Elsey Cancer Research Institute, Ballarat Central Technology Central Park, Ballarat Central, VIC, Australia
- School of Science, Psychology and Sport, Federation University, Mt Helen, VIC, Australia
- Centre for Reproductive Health, Hudson Institute of Medical Research and Department of Translational Medicine, Monash University, Clayton, VIC, Australia
- Department of Obstetrics and Gynaecology, University of Melbourne, Melbourne, VIC, Australia
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Artificial intelligence for renal cancer: From imaging to histology and beyond. Asian J Urol 2022; 9:243-252. [PMID: 36035341 PMCID: PMC9399557 DOI: 10.1016/j.ajur.2022.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/07/2022] [Accepted: 05/07/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.
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10
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Rasmussen R, Sanford T, Parwani AV, Pedrosa I. Artificial Intelligence in Kidney Cancer. Am Soc Clin Oncol Educ Book 2022; 42:1-11. [PMID: 35580292 DOI: 10.1200/edbk_350862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field of medicine. The diagnosis, characterization, management, and treatment of kidney cancer is ripe with areas for improvement that may be met with the promises of artificial intelligence. Here, we explore the impact of current research work in artificial intelligence for clinicians caring for patients with renal cancer, with a focus on the perspectives of radiologists, pathologists, and urologists. Promising preliminary results indicate that artificial intelligence may assist in the diagnosis and risk stratification of newly discovered renal masses and help guide the clinical treatment of patients with kidney cancer. However, much of the work in this field is still in its early stages, limited in its broader applicability, and hampered by small datasets, the varied appearance and presentation of kidney cancers, and the intrinsic limitations of the rigidly structured tasks artificial intelligence algorithms are trained to complete. Nonetheless, the continued exploration of artificial intelligence holds promise toward improving the clinical care of patients with kidney cancer.
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Affiliation(s)
- Robert Rasmussen
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Thomas Sanford
- Department of Urology, Upstate Medical University, Syracuse, NY
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Columbus, OH
| | - Ivan Pedrosa
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX.,Department of Urology, The University of Texas Southwestern Medical Center, Dallas, TX.,Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX
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11
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Staffini A, Svensson T, Chung UI, Svensson AK. Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2021; 22:s22010034. [PMID: 35009581 PMCID: PMC8747593 DOI: 10.3390/s22010034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 05/04/2023]
Abstract
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual's age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).
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Affiliation(s)
- Alessio Staffini
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Department of Economics and Finance, Catholic University of Milan, 20123 Milan, Italy
- Business Promotion Division, ALBERT Inc., Tokyo 169-0074, Japan
| | - Thomas Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- School of Health Innovation, Kanagawa University of Human Services Graduate School, Yokosuka 210-0821, Japan
- Department of Clinical Sciences, Lund University, Skåne University Hospital, 221 84 Malmo, Sweden
- Correspondence:
| | - Ung-il Chung
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- School of Health Innovation, Kanagawa University of Human Services Graduate School, Yokosuka 210-0821, Japan
- Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Akiko Kishi Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Department of Clinical Sciences, Lund University, Skåne University Hospital, 221 84 Malmo, Sweden
- Department of Diabetes and Metabolic Diseases, The University of Tokyo, Tokyo 113-8655, Japan
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