Review
Urology & Nephrology
Yuankai Huo, Ruining Deng, Quan Liu, Agnes B. Fogo, Haichun Yang
Summary: The explosive growth of AI technologies, particularly in deep learning, has led to revolutionary applications in AI-assisted healthcare, including in renal pathology. However, successful integration of AI in renal pathology requires close interdisciplinary collaborations between computer scientists and renal pathologists. Understanding the high-level principles of AI technologies and optimizing AI techniques for renal pathology are crucial for future applications in this field.
KIDNEY INTERNATIONAL
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Tao Zhou, Jian Guan, Bao Feng, Huimin Xue, Jin Cui, Qionglian Kuang, Yehang Chen, Kuncai Xu, Fan Lin, Enming Cui, Wansheng Long
Summary: This study developed a machine learning model based on CT images to differentiate benign renal tumors from renal cell carcinomas (RCCs) and improve radiologists' diagnostic performance. The results showed that the ML model based on 3D images performed better than that based on 2D images, with the highest AUC of 0.81 and accuracy of 0.86. Radiologists achieved better performance by referring to the classifier's decision.
EUROPEAN RADIOLOGY
(2023)
Review
Chemistry, Analytical
Roberto Magherini, Elisa Mussi, Yary Volpe, Rocco Furferi, Francesco Buonamici, Michaela Servi
Summary: There is a growing interest in applying machine learning techniques to nephrology, providing physicians with additional tools for accurate and faster diagnoses, although the major limitation remains in creating public databases.
Review
Oncology
Zijie Wang, Xiaofei Zhang, Xinning Wang, Jianfei Li, Yuhao Zhang, Tianwei Zhang, Shang Xu, Wei Jiao, Haitao Niu
Summary: This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). It is the first review of deep learning in RCC applications. The review shows the great promise of deep learning technologies in RCC diagnosis and anticipates more research results for the benefit of RCC patients.
FRONTIERS IN ONCOLOGY
(2023)
Review
Medicine, General & Internal
Alessandro Martinino, Mohammad Aloulou, Surobhi Chatterjee, Juan Pablo Scarano Pereira, Saurabh Singhal, Tapan Patel, Thomas Paul-Emile Kirchgesner, Salvatore Agnes, Salvatore Annunziata, Giorgio Treglia, Francesco Giovinazzo
Summary: The systematic review analyzed articles on the application of artificial intelligence in HCC detection and characterization, showing a constant improvement in publication quality over time. Different AI methods were mainly applied in CT, US, and MRI, indicating the reliability and advancement of medical imaging technology in HCC diagnosis.
JOURNAL OF CLINICAL MEDICINE
(2022)
Review
Genetics & Heredity
Xiaomin Shen, Jinxin Wu, Junwei Su, Zhenyu Yao, Wei Huang, Li Zhang, Yiheng Jiang, Wei Yu, Zhao Li
Summary: Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), an automated approach to HCC status classification is promising.
FRONTIERS IN GENETICS
(2023)
Article
Urology & Nephrology
N. Heller, R. Tejpaul, F. Isensee, T. Benidir, M. Hofmann, P. Blake, Z. Rengal, K. Moore, N. Sathianathen, A. Kalapara, J. Rosenberg, S. Peterson, E. Walczak, A. Kutikov, R. G. Uzzo, D. A. Palacios, E. M. Remer, S. C. Campbell, N. Papanikolopoulos, Christopher J. Weight
Summary: The purpose of this study was to develop an artificial intelligence algorithm for automated scoring of R.E.N.A.L. nephrometry scores and evaluate its predictive ability for oncologic and perioperative outcomes. The results showed that AI-scores were comparable to H-scores and predicted a wide variety of meaningful patient outcomes.
JOURNAL OF UROLOGY
(2022)
Article
Multidisciplinary Sciences
Thomas McGrath, Andrei Kapishnikov, Nenad Tomasev, Adam Pearce, Martin Wattenberg, Demis Hassabis, Been Kim, Ulrich Paquet, Vladimir Kramnik
Summary: AlphaZero, a neural network engine that learns chess solely by playing against itself, acquires knowledge that enables it to outperform human chess players. Despite training without access to human games or guidance, it appears to learn concepts similar to those used by human chess players, as evidenced by linear probes and behavioral analysis.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Review
Gastroenterology & Hepatology
Julien Calderaro, Tobias Paul Seraphin, Tom Luedde, Tracey G. Simon
Summary: Hepatocellular carcinoma (HCC) is a growing malignancy with increasing incidence and mortality rates globally. Artificial intelligence (AI) provides a unique opportunity to enhance HCC risk prediction, diagnosis, and prognostication. Machine learning (ML) and deep learning (DL) models, applied to diverse data sources, have shown promising results in improving accuracy of HCC risk prediction, detection, and treatment response prediction. However, further research is needed to address challenges related to standardizing AI data and ensuring interpretability of results.
JOURNAL OF HEPATOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Khaled Rjoob, Raymond Bond, Dewar Finlay, Victoria McGilligan, Stephen J. Leslie, Ali Rababah, Aleeha Iftikhar, Daniel Guldenring, Charles Knoery, Anne McShane, Aaron Peace, Peter W. Macfarlane
Summary: This study reviewed the application of machine learning (ML) in analyzing electrocardiogram (ECG) data and used time series analysis to study the changing popularity of different ML algorithms over the past two decades. The results showed that the use of ML in analyzing ECG data has been increasing, especially in heart abnormality classification. Support vector machine has been the most commonly used technique in the past decade, but deep learning has been trending upwards since 2018 and is likely to become the leading technique in the coming years. Accuracy was the most frequently used evaluation metric in the reviewed studies.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Medicine, General & Internal
Bettina Katalin Budai, Robert Stollmayer, Aladar David Ronaszeki, Borbala Kormendy, Zita Zsombor, Lorinc Palotas, Bence Fejer, Attila Szendroi, Eszter Szekely, Pal Maurovich-Horvat, Pal Novak Kaposi
Summary: This study aimed to construct a radiomics-based machine learning model for differentiation between non-clear cell and clear cell renal cell carcinomas. The model is robust against institutional imaging protocols and scanners. By collecting CT scans from 209 patients, 107 radiomics features were extracted and used to train a support vector machine classifier. The model achieved comparable performance with an expert radiologist in differentiating the two types of renal cell carcinomas.
FRONTIERS IN MEDICINE
(2022)
Review
Environmental Sciences
Rajnish Kumar, Garima Yadav, Mohammed Kuddus, Ghulam Md Ashraf, Rachana Singh
Summary: The metagenomics approach revolutionized the study of genetic information from uncultured microbes and complex microbial communities. In silico research allowed for a better understanding of protein interactions, drug design, and microbial evolution. Artificial intelligence, particularly machine learning and deep learning, has enabled the analysis and utilization of large datasets generated from nucleic acid sequencing and proteomics, leading to breakthroughs in microbiology research.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Orthopedics
Prem N. Ramkumar, Bryan C. Luu, Heather S. Haeberle, Jaret M. Karnuta, Benedict U. Nwachukwu, Riley J. Williams
Summary: Artificial intelligence (AI) has great potential in the field of medicine, particularly in orthopaedics and sports medicine. Its applications in orthopaedic surgery and sports medicine can predict athlete injury risk, enhance patient experience, automate tasks, but physicians need to understand the strengths, limitations, and applications of AI tools.
AMERICAN JOURNAL OF SPORTS MEDICINE
(2022)
Editorial Material
Environmental Sciences
Ying-Nong Chen, Kuo-Chin Fan, Yang-Lang Chang, Toshifumi Moriyama
Summary: Remote sensing is widely used and AI-based models, especially DL models, are utilized to enhance its performance. This paper reviews nine articles in a Special Issue on remote sensing, which mainly focus on DL and satellite data, reflecting the prevailing trends in remote sensing research. The analysis and explanation of DL models have become a hot topic in AI research. While DL methods outperform traditional machine learning methods, they remain a black box, making it difficult to understand the decision-making process. Therefore, it is important to explore explainable DL methods for remote sensing applications.
Article
Dermatology
Dennis H. Murphree, Pranav Puri, Huma Shamim, Spencer A. Bezalel, Lisa A. Drage, Michael Wang, Mark R. Pittelkow, Rickey E. Carter, Mark D. P. Davis, Alina G. Bridges, Aaron R. Mangold, James A. Yiannias, Megha M. Tollefson, Julia S. Lehman, Alexander Meves, Clark C. Otley, Olayemi Sokumbi, Matthew R. Hall, Nneka Comfere
Summary: Artificial intelligence, particularly deep learning, has generated significant interest in the field of medicine, especially in automated image analysis. Although it will not replace experts, it will certainly impact the specialty of dermatology. This article aims to lay the groundwork for effective communication between clinicians and technical colleagues.
JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY
(2022)
Article
Oncology
Eddie Zhang, Karen J. Ruth, Mark K. Buyyounouski, Robert A. Price Jr, Robert G. Uzzo, Mark L. Sobczak, Alan Pollack, J. . Karen Wong, David Y. T. Chen, Mark A. Hallman, Richard E. Greenberg, Deborah Watkins-Bruner, Tahseen Al-Saleem, Eric M. Horwitz
Summary: This study aimed to compare the preservation of erectile function between limiting the doses delivered to the penile bulb and corporal bodies with intensity modulated radiation therapy (IMRT) and standard IMRT in men with prostate cancer. The results showed that there was no significant difference in the preservation of erectile function between the two treatment methods.
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2023)
Article
Urology & Nephrology
Randall A. A. Lee, Robert G. Uzzo, Jordan Anaokar, Ashanth Thomas, Shuanzeng Wei, Benjamin T. Ristau, Andrew McIntosh, Matthew Lee, David Y. T. Chen, Richard E. Greenberg, Rosalia Viterbo, Marc C. Smaldone, Andres Correa, Jared Schober, Kevin Ginsburg, Laura Bukavina, Diana Magee, Nicole Uzzo, Phyllis Parkansky, Karen Ruth, Alexander Kutikov
Summary: The study found a discrepancy between imaging-based cystic designation and pathology for renal lesions. More than 80% of radiographic Bosniak cystic lesions were not described as cystic on pathology reports. Over one in five resected cystic renal lesions demonstrated high-grade disease. However, judiciously managed active surveillance +/- delayed intervention is a safe and effective management option for most radiographic cystic renal masses.
JOURNAL OF UROLOGY
(2023)
Article
Oncology
Kevin B. Ginsburg, Marshall Strother, Jared P. Schober, Alberto Andres Castro Bigalli, Karen Ruth, David YT. Chen, Richard E. Greenberg, Marc C. Smaldone, Rosalia Viterbo, Robert G. Uzzo, Andres F. Correa, Alexander Kutikov
Summary: A study compared the renal function outcomes of patients with oncocytomas undergoing active surveillance and partial nephrectomy, and found no significant difference between the two groups.
UROLOGIC ONCOLOGY-SEMINARS AND ORIGINAL INVESTIGATIONS
(2023)
Letter
Oncology
Laura Bukavina, Mohit Sindhani, Shreyas S. Joshi, Elizabeth Plimack, Andres Correa, Nicole Uzzo, Alexander Kutikov, Robert Uzzo
ANNALS OF SURGICAL ONCOLOGY
(2023)
Article
Oncology
Antoin Douglawi, Alireza Ghoreifi, Umberto Carbonara, Wesley Yip, Robert G. Uzzo, Vitaly Margulis, Matteo Ferro, Ottavio De Cobelli, Zhenjie Wu, Giuseppe Simone, Riccardo Mastroianni, Koon H. Rha, Daniel D. Eun, Adam C. Reese, James R. Porter, Ithaar Derweesh, Reza Mehrazin, Giuseppe Rosiello, Riccardo Tellini, Marcus Jamil, Alexander Kenigsberg, Jason M. Farrow, William P. Schrock, Giovanni Cacciamani, Abhishek Srivastava, Amit S. Bhattu, Alexandre Mottrie, Mark L. Gonzalgo, Chandru P. Sundaram, Firas Abdollah, Andrea Minervini, Riccardo Autorino, Hooman Djaladat
Summary: This study retrospectively analyzed data of UTUC patients who underwent RNU and found that VH was associated with increased risk of metastasis and death.
CLINICAL GENITOURINARY CANCER
(2023)
Article
Oncology
Laura Bukavina, Spencer Bell, Vignesh T. Packiam, Marc Smaldone, Philip Abbosh, Robert Uzzo, Alexander Kutikov, Andres F. Correa, Diana E. Magee
Summary: This study evaluated the cost effectiveness of BCG compared to sequential gemcitabine-docetaxel in high-risk NMIBC patients. The results showed that gemcitabine-docetaxel had similar effectiveness but lower cost than BCG, making it a potential alternative as a first-line intravesical therapy.
UROLOGIC ONCOLOGY-SEMINARS AND ORIGINAL INVESTIGATIONS
(2023)
Article
Oncology
Marshall Strother, Robert N. Uzzo, Elizabeth Handorf, Robert G. Uzzo
Summary: The study aimed to validate the overflowing beer sign (OBS) for distinguishing between lipid-poor angiomyolipoma (AML) and renal cell carcinoma, and to determine whether it improves the detection of lipid-poor AML when added to the angular interface sign. The results showed that both OBS and angular interface sign were strongly associated with AML and had high specificity in the lipid-poor population. Using both signs together increased the sensitivity of AML detection without significantly reducing specificity.
UROLOGIC ONCOLOGY-SEMINARS AND ORIGINAL INVESTIGATIONS
(2023)
Article
Multidisciplinary Sciences
Nityam Rathi, Worapat Attawettayanon, Yosuke Yasuda, Kieran Lewis, Gustavo Roversi, Snehi Shah, Andrew Wood, Carlos Munoz-Lopez, Diego A. Palacios, Jianbo Li, Nour Abdallah, Jared P. Schober, Marshall Strother, Alexander Kutikov, Robert Uzzo, Christopher J. Weight, Mohamed Eltemamy, Venkatesh Krishnamurthi, Robert Abouassaly, Steven C. Campbell
Summary: Accurate prediction of NBGFR after RN is crucial for clinical management and patient counseling. Preoperative global GFR, SRF, and RFC are important factors for this prediction. In this study, the accuracy of three methods (NRS, software-derived PVA, and LWH measurements) for determining SRF and predicting NBGFR were compared. The results showed that software-derived PVA had the highest accuracy and precision in estimating SRF and predicting NBGFR.
SCIENTIFIC REPORTS
(2023)
Article
Urology & Nephrology
Jared P. Schober, Kevin B. Ginsburg, Alexander Kutikov, Eric Y. Cho, Matt Loecher, David Strauss, Alberto Andres Castro Bigalli, Elizabeth Handorf, Mengying Deng, Jordan Anaokar, David Y. T. Chen, Richard E. Greenberg, Marc C. Smaldone, Rosalia Viterbo, Andres F. Correa, Robert G. Uzzo, Marshall Strother
Summary: This study aimed to evaluate whether a simple point-of-care measurement system could be used to replace nuclear medicine renal scintigraphy (NMRS) in predicting estimated glomerular filtration rate (eGFR) after nephrectomy. The results showed that the point-of-care tool (RENAL-MS) performed equally well as NMRS in predicting postoperative eGFR and de novo Stage 3 chronic kidney disease (CKD), suggesting that NMRS may not be necessary in this setting.
Article
Urology & Nephrology
Deepak K. Pruthi, Gregor Miller, Donna P. Ankerst, Matthias Neumair, Umberto Capitanio, Andres F. Correa, Brian R. Lane, Eduard Roussel, Thomas B. Mcgregor, Ithaar H. Derweesh, Mauricio Cordeiro, Phillip M. Pierorazio, Carlos Calvo, Hai Bi, Sabrina L. Noyes, Margaret Meagher, Alexander Kutikov, Robert G. Uzzo, Hendrik Van Poppel, Alessandro Larcher, Francesco Montorsi, Michael W. Kattan, Dharam Kaushik, Michael A. Liss
Summary: This study aimed to investigate whether clinical risk factors and morphometric features on preoperative imaging can predict pathological upstaging in cT1 renal cell carcinoma patients. Through a retrospective study, we found that increasing preoperative tumor size, increasing age, diabetes, higher BMI, and male sex were associated with pathological upstaging. Additionally, hilar tumors were more likely to be upstaged.
JOURNAL OF UROLOGY
(2023)
Article
Medicine, General & Internal
Christopher W. Ryan, Catherine M. Tangen, Elisabeth Heath, Mark N. Stein, Maxwell Meng, Ajjai S. Alva, Sumanta K. Pal, Igor Puzanov, Joseph Clark, Toni K. Choueiri, Neeraj Agarwal, Robert G. Uzzo, Naomi B. Haas, Timothy W. Synold, Melissa Plets, Ulka N. Vaishampayan, Brian M. Shuch, Ian M. Thompson Jr, Primo N. Lara Jr
Summary: Postoperative administration of everolimus did not show improvement in recurrence-free survival compared to placebo for renal cell carcinoma patients at high risk of recurrence after nephrectomy. These results do not provide support for the adjuvant use of everolimus in the treatment of renal cell carcinoma following surgery.
Meeting Abstract
Urology & Nephrology
Laura Bukavina, Mohit Sindhani, Henkel Valentine, Daniel Ranti, Kirtishri Mishra, Alexander Kutikov, Shilpa Gupta, John Sfakianos, Mahmoud Ghannoum, Mauricio Retuerto, Andres Correa, Robert Uzzo, Philip Abbosh
JOURNAL OF UROLOGY
(2023)
Meeting Abstract
Urology & Nephrology
Nityam Rathi, Kieran Lewis, Worapat Attawettayanon, Carlos Munoz-Lopez, Yosuke Yasuda, Diego Aguilar Palacios, Jianbo Li, Nour Abdallah, Jared Schober, Marshall Strother, Alexander Kutikov, Robert Uzzo, Christopher Weight, Mohamed Eltemamy, Venkatesh Krishnamurthi, Robert Abouassaly, Steven Campbell
JOURNAL OF UROLOGY
(2023)
Meeting Abstract
Urology & Nephrology
Diana Magee, Kevin Ginsburg, Jared Schober, Laura Bukavina, Spencer Bell, Elizabeth Handorf, Alexander Kutikov, Robert Uzzo, Andres Correa
JOURNAL OF UROLOGY
(2023)
Article
Urology & Nephrology
Clara Cerrato, Devin Patel, Riccardo Autorino, Giuseppe Simone, Bo Yang, Robert Uzzo, Francesco Porpiglia, Umberto Capitanio, James Porter, Alp Tuna Beksac, Andrea Minervini, Alessandro Antonelli, Maria Angela Cerruto, Clayton Lau, Akbar Ashrafi, Daniel Eun, Alexandre Mottrie, Carmen Mir, Margaret F. Meagher, Dhruv Puri, Mimi Nguyen, Sohail Dhanji, Franklin Liu, Savio D. Pandolfo, Alexander Kutikov, Francesco Montorsi, Inderbir S. Gill, Chandru Sundaram, Jihad Kaouk, Ithaar H. Derweesh
Summary: This study compares the outcomes of robotic-assisted partial nephrectomy (RAPN) and minimally invasive radical nephrectomy (MIS-RN) for complex renal masses (CRM). The results indicate that RAPN is not associated with increased risk of complications or worsened oncological outcomes compared to MIS-RN, and may be preferred when clinically indicated.
WORLD JOURNAL OF UROLOGY
(2023)
