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Knudsen JE, Rich JM, Ma R. Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma. Urol Clin North Am 2024; 51:47-62. [PMID: 37945102 DOI: 10.1016/j.ucl.2023.06.002] [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
The integration of artificial intelligence (AI) with histopathology images and gene expression patterns has led to the emergence of the dynamic fields of pathomics and genomics. These fields have revolutionized renal cell carcinoma (RCC) diagnosis and subtyping and improved survival prediction models. Machine learning has identified unique gene patterns across RCC subtypes and grades, providing insights into RCC origins and potential treatments, as targeted therapies. The combination of pathomics and genomics using AI opens new avenues in RCC research, promising future breakthroughs and innovations that patients and physicians can anticipate.
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Affiliation(s)
- J Everett Knudsen
- Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, Center for Robotic Simulation & Education, University of Southern California, Los Angeles, CA, USA
| | - Joseph M Rich
- Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, Center for Robotic Simulation & Education, University of Southern California, Los Angeles, CA, USA
| | - Runzhuo Ma
- Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, Center for Robotic Simulation & Education, University of Southern California, Los Angeles, CA, USA.
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2
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Ivanova E, Fayzullin A, Grinin V, Ermilov D, Arutyunyan A, Timashev P, Shekhter A. Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis. Biomedicines 2023; 11:2875. [PMID: 38001875 PMCID: PMC10669631 DOI: 10.3390/biomedicines11112875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 09/26/2023] [Accepted: 10/09/2023] [Indexed: 11/26/2023] Open
Abstract
Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and efficient diagnostic methods to guide treatment decisions. Traditional pathology practices have limitations, including interobserver variability and time-consuming evaluations. In recent years, digital pathology tools emerged as a promising solution to enhance the diagnosis and management of renal cancer. This review aims to provide a comprehensive overview of the current state and potential of digital pathology in the context of renal cell carcinoma. Through advanced image analysis algorithms, artificial intelligence (AI) technologies facilitate quantification of cellular and molecular markers, leading to improved accuracy and reproducibility in renal cancer diagnosis. Digital pathology platforms empower remote collaboration between pathologists and help with the creation of comprehensive databases for further research and machine learning applications. The integration of digital pathology tools with other diagnostic modalities, such as radiology and genomics, enables a novel multimodal characterization of different types of renal cell carcinoma. With continuous advancements and refinement, AI technologies are expected to play an integral role in diagnostics and clinical decision-making, improving patient outcomes. In this article, we explored the digital pathology instruments available for clear cell, papillary and chromophobe renal cancers from pathologist and data analyst perspectives.
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Affiliation(s)
- Elena Ivanova
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., Moscow 119991, Russia; (E.I.); (A.F.); (P.T.)
- B. V. Petrovsky Russian Research Center of Surgery, 2 Abrikosovskiy Lane, Moscow 119991, Russia
| | - Alexey Fayzullin
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., Moscow 119991, Russia; (E.I.); (A.F.); (P.T.)
| | - Victor Grinin
- PJSC VimpelCom, 10, 8th March Street, Moscow 127083, Russia; (V.G.); (D.E.); (A.A.)
| | - Dmitry Ermilov
- PJSC VimpelCom, 10, 8th March Street, Moscow 127083, Russia; (V.G.); (D.E.); (A.A.)
| | - Alexander Arutyunyan
- PJSC VimpelCom, 10, 8th March Street, Moscow 127083, Russia; (V.G.); (D.E.); (A.A.)
| | - Peter Timashev
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., Moscow 119991, Russia; (E.I.); (A.F.); (P.T.)
| | - Anatoly Shekhter
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., Moscow 119991, Russia; (E.I.); (A.F.); (P.T.)
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Froń A, Semianiuk A, Lazuk U, Ptaszkowski K, Siennicka A, Lemiński A, Krajewski W, Szydełko T, Małkiewicz B. Artificial Intelligence in Urooncology: What We Have and What We Expect. Cancers (Basel) 2023; 15:4282. [PMID: 37686558 PMCID: PMC10486651 DOI: 10.3390/cancers15174282] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
INTRODUCTION Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. METHODOLOGY We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. RESULTS Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. CONCLUSIONS AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.
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Affiliation(s)
- Anita Froń
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Alina Semianiuk
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Uladzimir Lazuk
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Kuba Ptaszkowski
- Department of Physiotherapy, Wroclaw Medical University, 50-368 Wroclaw, Poland;
| | - Agnieszka Siennicka
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-556 Wroclaw, Poland;
| | - Artur Lemiński
- Department of Urology and Urological Oncology, Pomeranian Medical University, 70-111 Szczecin, Poland;
| | - Wojciech Krajewski
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Tomasz Szydełko
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Bartosz Małkiewicz
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
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Wessels F, Schmitt M, Krieghoff-Henning E, Nientiedt M, Waldbillig F, Neuberger M, Kriegmair MC, Kowalewski KF, Worst TS, Steeg M, Popovic ZV, Gaiser T, von Kalle C, Utikal JS, Fröhling S, Michel MS, Nuhn P, Brinker TJ. A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma. World J Urol 2023; 41:2233-2241. [PMID: 37382622 PMCID: PMC10415487 DOI: 10.1007/s00345-023-04489-7] [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: 02/07/2023] [Accepted: 06/10/2023] [Indexed: 06/30/2023] Open
Abstract
PURPOSE To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC). METHODS Digitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision transformer (ViT) to extract image features with a self-supervised model called DINO (self-distillation with no labels). Extracted features were used in Cox regression models to prognosticate OS and DSS. Kaplan-Meier for univariable evaluation and Cox regression analyses for multivariable evaluation of the DINO-ViT risk groups were performed for prediction of OS and DSS. For validation, a cohort from a tertiary care centre was used. RESULTS A significant risk stratification was achieved in univariable analysis for OS and DSS in the training (n = 443, log rank test, p < 0.01) and validation set (n = 266, p < 0.01). In multivariable analysis, including age, metastatic status, tumour size and grading, the DINO-ViT risk stratification was a significant predictor for OS (hazard ratio [HR] 3.03; 95%-confidence interval [95%-CI] 2.11-4.35; p < 0.01) and DSS (HR 4.90; 95%-CI 2.78-8.64; p < 0.01) in the training set but only for DSS in the validation set (HR 2.31; 95%-CI 1.15-4.65; p = 0.02). DINO-ViT visualisation showed that features were mainly extracted from nuclei, cytoplasm, and peritumoural stroma, demonstrating good interpretability. CONCLUSION The DINO-ViT can identify high-risk patients using histological images of ccRCC. This model might improve individual risk-adapted renal cancer therapy in the future.
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Affiliation(s)
- Frederik Wessels
- Digital Biomarkers for Oncology Group, National Centre for Tumour Diseases (NCT), German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Max Schmitt
- Digital Biomarkers for Oncology Group, National Centre for Tumour Diseases (NCT), German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Eva Krieghoff-Henning
- Digital Biomarkers for Oncology Group, National Centre for Tumour Diseases (NCT), German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Malin Nientiedt
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Frank Waldbillig
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Manuel Neuberger
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Maximilian C Kriegmair
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Karl-Friedrich Kowalewski
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Thomas S Worst
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Matthias Steeg
- Institute of Pathology, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Zoran V Popovic
- Institute of Pathology, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Timo Gaiser
- Institute of Pathology, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Christof von Kalle
- Department of Clinical-Translational Sciences, Berlin Institute of Health (BIH), Charité University Medicine, Berlin, Germany
| | - Jochen S Utikal
- Skin Cancer Unit, German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Department of Dermatology, Venereology and Allergology, University Medical Centre Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Stefan Fröhling
- National Centre for Tumour Diseases, German Cancer Research Centre, Heidelberg, Germany
| | - Maurice S Michel
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Philipp Nuhn
- Department of Urology and Urological Surgery, Medical Faculty Mannheim of Heidelberg University, University Medical Centre Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, National Centre for Tumour Diseases (NCT), German Cancer Research Centre (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
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Distante A, Marandino L, Bertolo R, Ingels A, Pavan N, Pecoraro A, Marchioni M, Carbonara U, Erdem S, Amparore D, Campi R, Roussel E, Caliò A, Wu Z, Palumbo C, Borregales LD, Mulders P, Muselaers CHJ. Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives. Diagnostics (Basel) 2023; 13:2294. [PMID: 37443687 DOI: 10.3390/diagnostics13132294] [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: 05/31/2023] [Revised: 07/01/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems' outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.
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Affiliation(s)
- Alfredo Distante
- Department of Urology, Catholic University of the Sacred Heart, 00168 Roma, Italy
- Department of Urology, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands
| | - Laura Marandino
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, 20132 Milan, Italy
| | - Riccardo Bertolo
- Department of Urology, San Carlo Di Nancy Hospital, 00165 Rome, Italy
| | - Alexandre Ingels
- Department of Urology, University Hospital Henri Mondor, APHP (Assistance Publique-Hôpitaux de Paris), 94000 Créteil, France
| | - Nicola Pavan
- Department of Surgical, Oncological and Oral Sciences, Section of Urology, University of Palermo, 90133 Palermo, Italy
| | - Angela Pecoraro
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, 10043 Turin, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d'Annunzio University of Chieti, 66100 Chieti, Italy
| | - Umberto Carbonara
- Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation-Urology, University of Bari, 70121 Bari, Italy
| | - Selcuk Erdem
- Division of Urologic Oncology, Department of Urology, Istanbul University Istanbul Faculty of Medicine, Istanbul 34093, Turkey
| | - Daniele Amparore
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, 10043 Turin, Italy
| | - Riccardo Campi
- Urological Robotic Surgery and Renal Transplantation Unit, Careggi Hospital, University of Florence, 50121 Firenze, Italy
| | - Eduard Roussel
- Department of Urology, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Anna Caliò
- Section of Pathology, Department of Diagnostic and Public Health, University of Verona, 37134 Verona, Italy
| | - Zhenjie Wu
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Carlotta Palumbo
- Division of Urology, Maggiore della Carità Hospital of Novara, Department of Translational Medicine, University of Eastern Piedmont, 13100 Novara, Italy
| | - Leonardo D Borregales
- Department of Urology, Well Cornell Medicine, New York-Presbyterian Hospital, New York, NY 10032, USA
| | - Peter Mulders
- Department of Urology, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands
| | - Constantijn H J Muselaers
- Department of Urology, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands
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Ohe C, Yoshida T, Amin MB, Uno R, Atsumi N, Yasukochi Y, Ikeda J, Nakamoto T, Noda Y, Kinoshita H, Tsuta K, Higasa K. Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma. Hum Pathol 2023; 131:68-78. [PMID: 36372298 DOI: 10.1016/j.humpath.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022]
Abstract
We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated with prognosis and the response to angiogenesis inhibition and checkpoint blockade. This study aims to objectively show the diagnostic utility of clear or eosinophilic phenotypes of ccRCC by developing an artificial intelligence (AI) model using the TCGA-ccRCC dataset and to demonstrate if the clear or eosinophilic predicted phenotypes correlate with pathological factors and gene signatures associated with angiogenesis and cancer immunity. Before the development of the AI model, histological evaluation using hematoxylin and eosin whole-slide images of the TCGA-ccRCC cohort (n = 435) was performed by a urologic pathologist. The AI model was developed as follows. First, the highest-grade area on each whole slide image was captured for image processing. Second, the selected regions were cropped into tiles. Third, the AI model was trained using transfer learning on a deep convolutional neural network, and clear or eosinophilic predictions were scaled as AI scores. Next, we verified the AI model using a validation cohort (n = 95). Finally, we evaluated the accuracy of the prognostic predictions of the AI model and revealed that the AI model detected clear and eosinophilic phenotypes with high accuracy. The AI model stratified the patients' outcomes, and the predicted eosinophilic phenotypes correlated with adverse clinicopathological characteristics and high immune-related gene signatures. In conclusion, the AI-based histologic subclassification accurately predicted clear or eosinophilic phenotypes of ccRCC, allowing for consistently reproducible stratification for prognostic and therapeutic stratification.
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Affiliation(s)
- Chisato Ohe
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan.
| | - Takashi Yoshida
- Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Mahul B Amin
- Department of Pathology and Laboratory Medicine, University of Tennessee Health Sciences Center, 930 Madison Avenue, Memphis, TN 38163, USA; Department of Urology, University of Southern California, 1441 Eastlake Avenue, Los Angeles, CA 90033, USA
| | - Rena Uno
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan; Department of Pathology, Hyogo Cancer Center, Akashi, Hyogo 673-8558, Japan
| | - Naho Atsumi
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Yoshiki Yasukochi
- Department of Genome Analysis, Institute of Biomedical Science, Kansai Medical University, Hirakata, Osaka 573-1191, Japan
| | - Junichi Ikeda
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan; Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Takahiro Nakamoto
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan; Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Yuri Noda
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Hidefumi Kinoshita
- Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Koji Tsuta
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Koichiro Higasa
- Department of Genome Analysis, Institute of Biomedical Science, Kansai Medical University, Hirakata, Osaka 573-1191, Japan
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Zeng Y, Zhuang Y, Vinod B, Guo X, Mitra A, Chen P, Saggio I, Shivashankar GV, Gao W, Zhao W. Guiding Irregular Nuclear Morphology on Nanopillar Arrays for Malignancy Differentiation in Tumor Cells. NANO LETTERS 2022; 22:7724-7733. [PMID: 35969027 DOI: 10.1021/acs.nanolett.2c01849] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
For more than a century, abnormal nuclei in tumor cells, presenting subnuclear invaginations and folds on the nuclear envelope, have been known to be associated with high malignancy and poor prognosis. However, current nuclear morphology analysis focuses on the features of the entire nucleus, overlooking the malignancy-related subnuclear features in nanometer scale. The main technical challenge is to probe such tiny and randomly distributed features inside cells. We here employ nanopillar arrays to guide subnuclear features into ordered patterns, enabling their quantification as a strong indicator of cell malignancy. Both breast and liver cancer cells were validated as well as the quantification of nuclear abnormality heterogeneity. The alterations of subnuclear patterns were also explored as effective readouts for drug treatment. We envision that this nanopillar-enabled quantification of subnuclear abnormal features in tumor cells opens a new angle in characterizing malignant cells and studying the unique nuclear biology in cancer.
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Affiliation(s)
- Yongpeng Zeng
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 637457, Singapore
| | - Yinyin Zhuang
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 637457, Singapore
| | - Benjamin Vinod
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 637457, Singapore
| | - Xiangfu Guo
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 637457, Singapore
| | - Aninda Mitra
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 637457, Singapore
| | - Peng Chen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 637457, Singapore
- Institute for Digital Molecular Analytics and Science, Nanyang Technological University, 636921, Singapore
| | - Isabella Saggio
- Dipartimento di Biologia e Biotecnologie Charles Darwin, Sapienza Università di Roma, 00185, Roma, Italy
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
- CNR Institute of Molecular Biology and Pathology, 00185, Rome, Italy
| | - G V Shivashankar
- Department of Health Sciences & Technology (D-HEST), ETH Zurich, 8093, Zurich, Switzerland
- Paul Scherrer Institute, 5232, Villigen, Switzerland
| | - Weibo Gao
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
- The Photonics Institute and Centre for Disruptive Photonic Technologies, Nanyang Technological University, 637371, Singapore
| | - Wenting Zhao
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 637457, Singapore
- Institute for Digital Molecular Analytics and Science, Nanyang Technological University, 636921, Singapore
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8
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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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Winters DA, Soukup T, Sevdalis N, Green JSA, Lamb BW. The cancer multidisciplinary team meeting: in need of change? History, challenges and future perspectives. BJU Int 2021; 128:271-279. [PMID: 34028162 DOI: 10.1111/bju.15495] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Two decades since their inception, multidisciplinary teams (MDTs) are widely regarded as the 'gold standard' of cancer care delivery. Benefits of MDT working include improved patient outcomes, adherence to guidelines, and even economic benefits. Benefits to MDT members have also been demonstrated. An increasing body of evidence supports the use of MDTs and provides guidance on best practise. The system of MDTs in cancer care has come under increasing pressure of late, due to the increasing incidence of cancer, the popularity of MDT working, and financial pressures. This pressure has resulted in recommendations by national bodies to implement streamlining to reduce workload and improve efficiency. In the present review we examine the historical evidence for MDT working, and the scientific developments that dictate best practise. We also explore how streamlining can be safely and effectively undertaken. Finally, we discuss the future of MDT working including the integration of artificial intelligence and decision support systems and propose a new model for improving patient centredness.
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Affiliation(s)
- David A Winters
- Department of Urology, Barts Health NHS Trust, Whipps Cross University Hospital, London, UK
| | - Tayana Soukup
- Centre for Implementation Science, Health Service and Population Research Department, King's College London, London, UK
| | - Nick Sevdalis
- Department of Urology, Barts Health NHS Trust, Whipps Cross University Hospital, London, UK.,Centre for Implementation Science, Health Service and Population Research Department, King's College London, London, UK
| | - James S A Green
- Centre for Implementation Science, Health Service and Population Research Department, King's College London, London, UK
| | - Benjamin W Lamb
- Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.,Faculty of Health, Education, Medicine and Social Care, School of Allied Health, Anglia Ruskin University, Cambridge, UK
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10
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Brodie A, Dai N, Teoh JYC, Decaestecker K, Dasgupta P, Vasdev N. Artificial intelligence in urological oncology: An update and future applications. Urol Oncol 2021; 39:379-399. [PMID: 34024704 DOI: 10.1016/j.urolonc.2021.03.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 12/20/2020] [Accepted: 03/21/2021] [Indexed: 01/16/2023]
Abstract
There continues to be rapid developments and research in the field of Artificial Intelligence (AI) in Urological Oncology worldwide. In this review we discuss the basics of AI, application of AI per tumour group (Renal, Prostate and Bladder Cancer) and application of AI in Robotic Urological Surgery. We also discuss future applications of AI being developed with the benefits to patients with Urological Oncology.
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Affiliation(s)
- Andrew Brodie
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Nick Dai
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Jeremy Yuen-Chun Teoh
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Prokar Dasgupta
- Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Nikhil Vasdev
- Hertfordshire and Bedfordshire Urological Cancer Centre, Department of Urology, Lister Hospital, Stevenage, United Kingdom; School of Medicine and Life Sciences, University of Hertfordshire, Hatfield, United Kingdom.
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11
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Daisy PS, Anitha TS. Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy? Med Oncol 2021; 38:53. [PMID: 33811540 DOI: 10.1007/s12032-021-01500-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/20/2021] [Indexed: 12/17/2022]
Abstract
Gliomas are one of the most devastating primary brain tumors which impose significant management challenges to the clinicians. The aggressive behaviour of gliomas is mainly attributed to their rapid proliferation, unravelled genomics and the blood-brain barrier which protects the tumor cells from chemotherapeutic regimens. Suspects of brain tumors are usually assessed by magnetic resonance imaging and computed tomography. These images allow surgeons to decide on the tumor grading, intra-operative pathology, feasibility of surgery, and treatment planning. All these data are compiled manually by physicians, wherein it takes time for the validation of results and concluding the treatment modality. In this context, the arrival of artificial intelligence in this era of personalized medicine, has proven promising performance in the diagnosis and management of gliomas. Starting from grading prediction till outcome evaluation, artificial intelligence-based forefronts have revolutionized oncological research. Interestingly, this approach has also been able to precisely differentiate tumor lesion from healthy tissues. However, till date, their utility in neuro-oncological field remains limited due to the issues pertaining to their reliability and transparency. Hence, to shed novel insights on the "clinical utility of this novel approach on glioma management" and to reveal "the black-boxes that have to be solved for fruitful application of artificial intelligence in neuro-oncology research", we provide in this review, a succinct description of the potential gear of artificial intelligence-based avenues in glioma treatment and the barriers that impede their rapid implementation in neuro-oncology.
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Affiliation(s)
- Precilla S Daisy
- Central Inter-Disciplinary Research Facility, School of Biological Sciences, Sri Balaji Vidyapeeth (Deemed to-be University), Pillaiyarkuppam, Puducherry, India
| | - T S Anitha
- Central Inter-Disciplinary Research Facility, School of Biological Sciences, Sri Balaji Vidyapeeth (Deemed to-be University), Pillaiyarkuppam, Puducherry, India. .,Central Inter-Disciplinary Research Facility, School of Biological Sciences, Sri Balaji Vidyapeeth, Mahatma Gandhi Medical College and Research Institute Campus, Pillaiyarkuppam, Puducherry, 607403, India.
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12
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Byun SS, Heo TS, Choi JM, Jeong YS, Kim YS, Lee WK, Kim C. Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma. Sci Rep 2021; 11:1242. [PMID: 33441830 PMCID: PMC7806580 DOI: 10.1038/s41598-020-80262-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 12/15/2020] [Indexed: 12/30/2022] Open
Abstract
Survival analyses for malignancies, including renal cell carcinoma (RCC), have primarily been conducted using the Cox proportional hazards (CPH) model. We compared the random survival forest (RSF) and DeepSurv models with the CPH model to predict recurrence-free survival (RFS) and cancer-specific survival (CSS) in non-metastatic clear cell RCC (nm-cRCC) patients. Our cohort included 2139 nm-cRCC patients who underwent curative-intent surgery at six Korean institutions between 2000 and 2014. The data of two largest hospitals’ patients were assigned into the training and validation dataset, and the data of the remaining hospitals were assigned into the external validation dataset. The performance of the RSF and DeepSurv models was compared with that of CPH using Harrel’s C-index. During the follow-up, recurrence and cancer-specific deaths were recorded in 190 (12.7%) and 108 (7.0%) patients, respectively, in the training-dataset. Harrel’s C-indices for RFS in the test-dataset were 0.794, 0.789, and 0.802 for CPH, RSF, and DeepSurv, respectively. Harrel’s C-indices for CSS in the test-dataset were 0.831, 0.790, and 0.834 for CPH, RSF, and DeepSurv, respectively. In predicting RFS and CSS in nm-cRCC patients, the performance of DeepSurv was superior to that of CPH and RSF. In no distant time, deep learning-based survival predictions may be useful in RCC patients.
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Affiliation(s)
- Seok-Soo Byun
- Department of Urology, Seoul National University Bundang Hospital, Seongnam, 13620, Korea
| | - Tak Sung Heo
- Department of Convergence Software, Hallym University, Chuncheon, 24252, Korea
| | - Jeong Myeong Choi
- Department of Convergence Software, Hallym University, Chuncheon, 24252, Korea
| | | | - Yu Seop Kim
- College of Software, Hallym University, Chuncheon, 24252, Korea
| | - Won Ki Lee
- Department of Urology, College of Medicine, Hallym University, Chuncheon Sacred Heart Hospital, 153, Kyo-dong, Chuncheon, 24253, Korea.
| | - Chulho Kim
- Department of Neurology, College of Medicine, Hallym University, Chuncheon Sacred Heart Hospital, 153, Kyo-dong, Chuncheon, 24253, Korea. .,Chuncheon Translational Research Center, Hallym University, Chuncheon, 24252, Korea.
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13
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Yu SH, Kim MS, Chung HS, Hwang EC, Jung SI, Kang TW, Kwon D. Early experience with Watson for Oncology: a clinical decision-support system for prostate cancer treatment recommendations. World J Urol 2020; 39:407-413. [PMID: 32335733 DOI: 10.1007/s00345-020-03214-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 04/13/2020] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Urological oncologists have difficulty providing optimal personalized care due to rapid alterations in scientific research results, medical advancements, and treatment guidelines. IBM's Watson for Oncology (WFO) is an artificial intelligence clinical decision-support system that assists oncologists with evidence-based treatment recommendations. In the present study, we examined the level of concordance between the treatment recommendations for prostate cancer according to WFO and the actual treatments that the patients received in the department of urology. METHODS We enrolled 201 patients who received prostate cancer treatment between January 2018 and June 2018. WFO provided treatment recommendations using clinical data in three categories: recommended, for consideration, and not recommended. These were compared with the actual treatments received by patients. Prostate cancer treatments were considered concordant if the received treatments were included in the "recommended" or "for consideration" categories by WFO. RESULTS The patients' mean age was 71.2 years. There were 60 (29.9%) and 114 (56.7%) patients with an Eastern Cooperative Oncology Group (ECOG) performance score ≥ 1 and non-organ confined disease (stage III/IV), respectively. The overall prostate cancer treatment concordance rate was 73.6% ("recommended": 53.2%; "for consideration": 20.4%). An ECOG performance score ≥ 1 and older age (≥ 75 years) were significantly associated with discordance (p = 0.001 and p = 0.026, respectively) on multivariate analysis. CONCLUSION In the present study, the treatment recommendations by WFO and the actual received treatments in the department of urology showed a relatively high concordance rate in prostate cancer patients.
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Affiliation(s)
- Seong Hyeon Yu
- Department of Urology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Myung Soo Kim
- Department of Urology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Ho Seok Chung
- Department of Urology, Chonnam National University Medical School, Gwangju, Republic of Korea.
| | - Eu Chang Hwang
- Department of Urology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Seung Il Jung
- Department of Urology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Taek Won Kang
- Department of Urology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Dongdeuk Kwon
- Department of Urology, Chonnam National University Medical School, Gwangju, Republic of Korea
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14
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Suarez-Ibarrola R, Hein S, Reis G, Gratzke C, Miernik A. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol 2019; 38:2329-2347. [PMID: 31691082 DOI: 10.1007/s00345-019-03000-5] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 10/25/2019] [Indexed: 01/15/2023] Open
Abstract
PURPOSE The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date. METHODS A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa). RESULTS In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods. CONCLUSIONS The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.
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Affiliation(s)
- Rodrigo Suarez-Ibarrola
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany.
| | - Simon Hein
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Gerd Reis
- Department Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
| | - Christian Gratzke
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Arkadiusz Miernik
- Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany
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15
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Tian K, Rubadue CA, Lin DI, Veta M, Pyle ME, Irshad H, Heng YJ. Automated clear cell renal carcinoma grade classification with prognostic significance. PLoS One 2019; 14:e0222641. [PMID: 31581201 PMCID: PMC6776313 DOI: 10.1371/journal.pone.0222641] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 09/04/2019] [Indexed: 01/31/2023] Open
Abstract
We developed an automated 2-tiered Fuhrman's grading system for clear cell renal cell carcinoma (ccRCC). Whole slide images (WSI) and clinical data were retrieved for 395 The Cancer Genome Atlas (TCGA) ccRCC cases. Pathologist 1 reviewed and selected regions of interests (ROIs). Nuclear segmentation was performed. Quantitative morphological, intensity, and texture features (n = 72) were extracted. Features associated with grade were identified by constructing a Lasso model using data from cases with concordant 2-tiered Fuhrman's grades between TCGA and Pathologist 1 (training set n = 235; held-out test set n = 42). Discordant cases (n = 118) were additionally reviewed by Pathologist 2. Cox proportional hazard model evaluated the prognostic efficacy of the predicted grades in an extended test set which was created by combining the test set and discordant cases (n = 160). The Lasso model consisted of 26 features and predicted grade with 84.6% sensitivity and 81.3% specificity in the test set. In the extended test set, predicted grade was significantly associated with overall survival after adjusting for age and gender (Hazard Ratio 2.05; 95% CI 1.21-3.47); manual grades were not prognostic. Future work can adapt our computational system to predict WHO/ISUP grades, and validating this system on other ccRCC cohorts.
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Affiliation(s)
- Katherine Tian
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
- The Harker School, San Jose, CA, United States of America
| | - Christopher A. Rubadue
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Douglas I. Lin
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Mitko Veta
- Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Michael E. Pyle
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Humayun Irshad
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Yujing J. Heng
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, United States of America
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