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Mastroianni R, Chiacchio G, Perpepaj L, Tuderti G, Brassetti A, Anceschi U, Ferriero M, Misuraca L, D’Annunzio S, Bove AM, Guaglianone S, Flammia RS, Proietti F, Pula M, Milanese G, Leonardo C, Galosi AB, Simone G. Comparison of Perioperative, Functional, and Oncologic Outcomes of Open vs. Robot-Assisted Off-Clamp Partial Nephrectomy: A Propensity Scored Match Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:2822. [PMID: 38732928 PMCID: PMC11086121 DOI: 10.3390/s24092822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/15/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024]
Abstract
Off-clamp partial nephrectomy represents one of the latest developments in nephron-sparing surgery, with the goal of preserving renal function and reducing ischemia time. The aim of this study was to evaluate and compare the functional, oncologic, and perioperative outcomes between off-clamp robot-assisted partial nephrectomy (off-C RAPN) and off-clamp open partial nephrectomy (off-C OPN) through a propensity score-matched (PSM) analysis. A 1:1 PSM analysis was used to balance variables potentially affecting postoperative outcomes. To report surgical quality, 1 year trifecta was used. Univariable Cox regression analysis was performed to identify predictors of trifecta achievement. The Kaplan-Meier method was used to compare cancer-specific survival (CSS), overall survival (OS), disease-free survival (DFS), and metastasis-free survival (MFS) probabilities between groups. Overall, 542 patients were included. After PSM analysis, two homogeneous cohorts of 147 patients were obtained. The off-C RAPN cohort experienced shorter length of stay (LoS) (3.4 days vs. 5.4 days; p < 0.001), increased likelihoods of achieving 1 year trifecta (89.8% vs. 80.3%; p = 0.03), lower postoperative Clavien-Dindo ≤ 2 complications (1.3% vs. 18.3%, p < 0.001), and lower postoperative transfusion rates (3.4% vs. 12.2%, p = 0.008). At univariable analysis, the surgical approach (off-C RAPN vs. off-C OPN, OR 2.22, 95% CI 1.09-4.46, p = 0.02) was the only predictor of 1 year trifecta achievement. At Kaplan-Meier analysis, no differences were observed between the two groups in terms of OS (log-rank p = 0.451), CSS (log-rank p = 0.476), DFS (log-rank p = 0.678), and MFS (log-rank p = 0.226). Comparing RAPN and OPN in a purely off-clamp scenario, the minimally invasive approach proved to be a feasible and safe surgical approach, with a significantly lower LoS and minor rate of postoperative complications and transfusions as a result of improved surgical quality expressed by higher 1 year trifecta achievement.
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Affiliation(s)
- Riccardo Mastroianni
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Giuseppe Chiacchio
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
- Urology Division, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, 60126 Ancona, Italy; (L.P.); (G.M.); (A.B.G.)
| | - Leonard Perpepaj
- Urology Division, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, 60126 Ancona, Italy; (L.P.); (G.M.); (A.B.G.)
| | - Gabriele Tuderti
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Aldo Brassetti
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Umberto Anceschi
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Mariaconsiglia Ferriero
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Leonardo Misuraca
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Simone D’Annunzio
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Alfredo Maria Bove
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Salvatore Guaglianone
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Rocco Simone Flammia
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Flavia Proietti
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Marco Pula
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Giulio Milanese
- Urology Division, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, 60126 Ancona, Italy; (L.P.); (G.M.); (A.B.G.)
| | - Costantino Leonardo
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
| | - Andrea Benedetto Galosi
- Urology Division, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, 60126 Ancona, Italy; (L.P.); (G.M.); (A.B.G.)
| | - Giuseppe Simone
- Urology, IRCCS “Regina Elena” National Cancer Institute, 00128 Rome, Italy; (R.M.); (G.T.); (A.B.); (U.A.); (M.F.); (L.M.); (S.D.); (A.M.B.); (S.G.); (R.S.F.); (F.P.); (M.P.); (C.L.); (G.S.)
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Zhang M, Ye Z, Yuan E, Lv X, Zhang Y, Tan Y, Xia C, Tang J, Huang J, Li Z. Imaging-based deep learning in kidney diseases: recent progress and future prospects. Insights Imaging 2024; 15:50. [PMID: 38360904 PMCID: PMC10869329 DOI: 10.1186/s13244-024-01636-5] [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: 11/19/2023] [Accepted: 01/27/2024] [Indexed: 02/17/2024] Open
Abstract
Kidney diseases result from various causes, which can generally be divided into neoplastic and non-neoplastic diseases. Deep learning based on medical imaging is an established methodology for further data mining and an evolving field of expertise, which provides the possibility for precise management of kidney diseases. Recently, imaging-based deep learning has been widely applied to many clinical scenarios of kidney diseases including organ segmentation, lesion detection, differential diagnosis, surgical planning, and prognosis prediction, which can provide support for disease diagnosis and management. In this review, we will introduce the basic methodology of imaging-based deep learning and its recent clinical applications in neoplastic and non-neoplastic kidney diseases. Additionally, we further discuss its current challenges and future prospects and conclude that achieving data balance, addressing heterogeneity, and managing data size remain challenges for imaging-based deep learning. Meanwhile, the interpretability of algorithms, ethical risks, and barriers of bias assessment are also issues that require consideration in future development. We hope to provide urologists, nephrologists, and radiologists with clear ideas about imaging-based deep learning and reveal its great potential in clinical practice.Critical relevance statement The wide clinical applications of imaging-based deep learning in kidney diseases can help doctors to diagnose, treat, and manage patients with neoplastic or non-neoplastic renal diseases.Key points• Imaging-based deep learning is widely applied to neoplastic and non-neoplastic renal diseases.• Imaging-based deep learning improves the accuracy of the delineation, diagnosis, and evaluation of kidney diseases.• The small dataset, various lesion sizes, and so on are still challenges for deep learning.
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Affiliation(s)
- Meng Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
- Medical Equipment Innovation Research Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
- Med+X Center for Manufacturing, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Enyu Yuan
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Xinyang Lv
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Yiteng Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Yuqi Tan
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Jing Tang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
| | - Jin Huang
- Medical Equipment Innovation Research Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
- Med+X Center for Manufacturing, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
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Khene ZE, Kutikov A, Campi R. Machine learning in renal cell carcinoma research: the promise and pitfalls of 'renal-izing' the potential of artificial intelligence. BJU Int 2023; 132:231-232. [PMID: 37005367 DOI: 10.1111/bju.16016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Affiliation(s)
- Zine-Eddine Khene
- Department of Urology, Rennes University Hospital, Rennes, France
- Signal and Image Processing Laboratory (LTSI), University of Rennes INSERM, Rennes, France
| | - Alexander Kutikov
- Division of Urologic Oncology, Department of Surgical Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Riccardo Campi
- Unit of Urological Minimally Invasive, Robotic Surgery and Kidney Transplantation, Careggi Hospital, University of Florence, Florence, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- European Association of Urology (EAU) Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, The Netherlands
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Cabral BP, Braga LAM, Syed-Abdul S, Mota FB. Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers. Curr Oncol 2023; 30:3432-3446. [PMID: 36975473 PMCID: PMC10047823 DOI: 10.3390/curroncol30030260] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.
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Affiliation(s)
| | - Luiza Amara Maciel Braga
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: (S.S.-A.); (F.B.M.)
| | - Fabio Batista Mota
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
- Correspondence: (S.S.-A.); (F.B.M.)
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Tsai MC, Lu HHS, Chang YC, Huang YC, Fu LS. Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach. JMIR Med Inform 2022; 10:e40878. [PMID: 36322109 PMCID: PMC9669887 DOI: 10.2196/40878] [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: 07/12/2022] [Revised: 09/16/2022] [Accepted: 10/02/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND In recent years, the progress and generalization surrounding portable ultrasonic probes has made ultrasound (US) a useful tool for physicians when making a diagnosis. With the advent of machine learning and deep learning, the development of a computer-aided diagnostic system for screening renal US abnormalities can assist general practitioners in the early detection of pediatric kidney diseases. OBJECTIVE In this paper, we sought to evaluate the diagnostic performance of deep learning techniques to classify kidney images as normal and abnormal. METHODS We chose 330 normal and 1269 abnormal pediatric renal US images for establishing a model for artificial intelligence. The abnormal images involved stones, cysts, hyperechogenicity, space-occupying lesions, and hydronephrosis. We performed preprocessing of the original images for subsequent deep learning. We redefined the final connecting layers for classification of the extracted features as abnormal or normal from the ResNet-50 pretrained model. The performances of the model were tested by a validation data set using area under the receiver operating characteristic curve, accuracy, specificity, and sensitivity. RESULTS The deep learning model, 94 MB parameters in size, based on ResNet-50, was built for classifying normal and abnormal images. The accuracy, (%)/area under curve, of the validated images of stone, cyst, hyperechogenicity, space-occupying lesions, and hydronephrosis were 93.2/0.973, 91.6/0.940, 89.9/0.940, 91.3/0.934, and 94.1/0.996, respectively. The accuracy of normal image classification in the validation data set was 90.1%. Overall accuracy of (%)/area under curve was 92.9/0.959.. CONCLUSIONS We established a useful, computer-aided model for automatic classification of pediatric renal US images in terms of normal and abnormal categories.
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Affiliation(s)
- Ming-Chin Tsai
- Department of Pediatrics, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsing-chu, Taiwan
| | - Yueh-Chuan Chang
- Institute of Electrical & Control Engineering, National Yang Ming Chiao Tung University, Hsing-chu, Taiwan
| | - Yung-Chieh Huang
- Department of Pediatrics, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Pediatrics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Lin-Shien Fu
- Department of Pediatrics, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Pediatrics, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
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Xu C, Zhou Q, Liu W, Li W, Dong S, Li W, Xu X, Qiao X, Jiang Y, Chen J, Yin C. Dynamic Predictive Models with Visualized Machine Learning for Assessing the Risk of Lung Metastasis in Kidney Cancer Patients. JOURNAL OF ONCOLOGY 2022; 2022:5798602. [PMID: 36276292 PMCID: PMC9586755 DOI: 10.1155/2022/5798602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/06/2022] [Accepted: 06/30/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVE To establish and verify the clinical prediction model of lung metastasis in renal cancer patients. METHOD Kidney cancer patients from January 1, 2010, to December 31, 2017, in the SEER database were enrolled in this study. In the first section, LASSO method was adopted to select variables. Independent influencing factors were identified after multivariate logistic regression analysis. In the second section, machine learning (ML) algorithms were implemented to establish models and 10-foldcross-validation was used to train the models. Finally, receiver operating characteristic curves, probability density functions, and clinical utility curve were applied to estimate model's performance. The final model was shown by a website calculator. RESULT Lung metastasis was confirmed in 7.43% (3171 out of 42650) of study population. In multivariate logistic regression, bone metastasis, brain metastasis, grade, liver metastasis, N stage, T stage, and tumor size were independent risk factors of lung metastasis in renal cancer patients. Primary site and sequence number were independent protection factors of LM in renal cancer patients. The above 9 impact factors were used to develop the prediction models, which included random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), and logistic regression (LR). In 10-foldcross-validation, the average area under curve (AUC) ranked from 0.907 to 0.934. In ROC curve analysis, AUC ranged from 0.879-0.922. We found that the XGB model performed best, and a Web-based calculator was done according to XGB model. CONCLUSION This study provided preliminary evidence that the ML algorithm can be used to predict lung metastases in patients with kidney cancer. This low cost, noninvasive and easy to implement diagnostic method is useful for clinical work. Of course this model still needs to undergo more real-world validation.
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Affiliation(s)
- Chan Xu
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Qian Zhou
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Chong Qing Liang Jiang New Area, Chongqing, China
| | - Wencai Liu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wenle Li
- Xiamen University, Molecular Imaging and Translational Medicine Research Center, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen, China
| | - Shengtao Dong
- Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian 116000, China
| | - Wanying Li
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Xiaofeng Xu
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
- Department of Urology, Xianyang Central Hospital, Xianyang, China
| | - Ximin Qiao
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
- Department of Urology, Xianyang Central Hospital, Xianyang, China
| | - Youli Jiang
- Hengyang Medical School, School of Nursing, University of South China, Hengyang, Hunan, China
| | - Jingfang Chen
- Hengyang Medical School, School of Nursing, University of South China, Hengyang, Hunan, China
- National Clinical Research Center for Infectious Diseases, The Third People's Hospital of Shenzhen, Shenzhen, Guangdong, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
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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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An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer. BMC Med Imaging 2022; 22:84. [PMID: 35538520 PMCID: PMC9087958 DOI: 10.1186/s12880-022-00813-6] [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: 01/28/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022] Open
Abstract
Objective To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery. Methods A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validation set (60 patients). We extracted the radiomic features from the largest gray ultrasound image of the RC lesion. The intraclass correlation coefficient (ICC) was applied to test the repeatability of the radiomic features. The least absolute shrinkage and selection operator (LASSO) was used to reduce the data dimension and select significant features. Logistic regression (LR) analysis was applied to establish the radiomics model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the comprehensive performance of the model. Results Among the 203 patients, 33 (16.7%) were LVI positive and 170 (83.7%) were LVI negative. A total of 5350 (90.1%) radiomic features with ICC values of ≥ 0.75 were reported, which were subsequently subjected to hypothesis testing and LASSO regression dimension reduction analysis. Finally, 15 selected features were used to construct the radiomics model. The area under the curve (AUC) of the training set was 0.849, and the AUC of the validation set was 0.781. The calibration curve indicated that the radiomics model had good calibration, and DCA demonstrated that the model had clinical benefits. Conclusion The proposed endorectal ultrasound-based radiomics model has the potential to predict LVI preoperatively in RC. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00813-6.
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Roussel E, Capitanio U, Kutikov A, Oosterwijk E, Pedrosa I, Rowe SP, Gorin MA. Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review. Eur Urol 2022; 81:476-488. [PMID: 35216855 PMCID: PMC9844544 DOI: 10.1016/j.eururo.2022.01.040] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 01/08/2022] [Accepted: 01/21/2022] [Indexed: 01/19/2023]
Abstract
CONTEXT The incidental detection of localized renal masses has been rising steadily, but a significant proportion of these tumors are benign or indolent and, in most cases, do not require treatment. At the present time, a majority of patients with an incidentally detected renal tumor undergo treatment for the presumption of cancer, leading to a significant number of unnecessary surgical interventions that can result in complications including loss of renal function. Thus, there exists a clinical need for improved tools to aid in the pretreatment characterization of renal tumors to inform patient management. OBJECTIVE To systematically review the evidence on noninvasive, imaging-based tools for solid renal mass characterization. EVIDENCE ACQUISITION The MEDLINE database was systematically searched for relevant studies on novel imaging techniques and interpretative tools for the characterization of solid renal masses, published in the past 10 yr. EVIDENCE SYNTHESIS Over the past decade, several novel imaging tools have offered promise for the improved characterization of indeterminate renal masses. Technologies of particular note include multiparametric magnetic resonance imaging of the kidney, molecular imaging with targeted radiopharmaceutical agents, and use of radiomics as well as artificial intelligence to enhance the interpretation of imaging studies. Among these, 99mTc-sestamibi single photon emission computed tomography/computed tomography (CT) for the identification of benign renal oncocytomas and hybrid oncocytic chromophobe tumors, and positron emission tomography/CT imaging with radiolabeled girentuximab for the identification of clear cell renal cell carcinoma, are likely to be closest to implementation in clinical practice. CONCLUSIONS A number of novel imaging tools stand poised to aid in the noninvasive characterization of indeterminate renal masses. In the future, these tools may aid in patient management by providing a comprehensive virtual biopsy, complete with information on tumor histology, underlying molecular abnormalities, and ultimately disease prognosis. PATIENT SUMMARY Not all renal tumors require treatment, as a significant proportion are either benign or have limited metastatic potential. Several innovative imaging tools have shown promise for their ability to improve the characterization of renal tumors and provide guidance in terms of patient management.
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Affiliation(s)
- Eduard Roussel
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Umberto Capitanio
- Department of Urology, University Vita-Salute, San Raffaele Scientific Institute, Milan, Italy; Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alexander Kutikov
- Division of Urology, Department of Surgery, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA, USA
| | - Egbert Oosterwijk
- Department of Urology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences (RIMLS), Nijmegen, The Netherlands
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA; Advanced Imaging Research Center. University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael A Gorin
- Urology Associates and UPMC Western Maryland, Cumberland, MD, USA; Department of Urology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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10
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Solomonides AE, Koski E, Atabaki SM, Weinberg S, McGreevey JD, Kannry JL, Petersen C, Lehmann CU. Defining AMIA's artificial intelligence principles. J Am Med Inform Assoc 2022; 29:585-591. [PMID: 35190824 PMCID: PMC8922174 DOI: 10.1093/jamia/ocac006] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 01/14/2022] [Indexed: 08/08/2023] Open
Abstract
Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including "safe failure"; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence-aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications.
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Affiliation(s)
| | - Eileen Koski
- Center for Computational Health, IBM T. J. Watson Research Center, Yorktown Heights, New York, USA
| | - Shireen M Atabaki
- Pediatrics; Emergency Medicine, The George Washington University School of Medicine Children s National Hospital, Washington, District of Columbia, USA
| | - Scott Weinberg
- Public Policy, American Medical Informatics Association, Rockville, Maryland, USA
| | - John D McGreevey
- Center for Applied Health Informatics and Office of the Chief Medical Information Officer, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | - Joseph L Kannry
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Carolyn Petersen
- Health Education & Content Services, Mayo Clinic, Rochester, Minnesota, USA
| | - Christoph U Lehmann
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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