On High Grade Kidney Cancer and Machine Learning

Curci, Nicole

doi:10.1016/j.acra.2019.10.009

Cited by 2 publications

(2 citation statements)

References 15 publications

(15 reference statements)

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“…Renal cell carcinoma (RCC) is the most frequent malignant renal tumour in adults and surgical resection is the main valuable approach; other options, comprising RFA, cytoablation and even active surveillance have been employed [141][142][143][144][145][146][147][148][149][150][151]. In patients unfit for surgery, systemic therapy including targeted agents, immunotherapy and chemotherapy were employed to improve the overall survival (OS) [141][142][143][144][145][146][147][148][149][150][151]. In this context, lesion detection and identification of histologic grades has clinical significance in establishing prognosis.…”

Section: Renal Tumoursmentioning

confidence: 99%

Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when

Granata

Fusco²,

Belli

et al. 2022

Infect Agents Cancer

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This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm2 and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.

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Section: Renal Tumoursmentioning

confidence: 99%

Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when

Granata

Fusco²,

Belli

et al. 2022

Infect Agents Cancer

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“…[18][19][20] Building a machine-learning model for kidney cancer screening is expected to solve this challenge, and many papers have been published in this area. [21][22][23] The eld of nephrology is not limited to the study of individual kidney diseases; it also encompasses investigating other organs and identifying severe complications. Consequently, the application of machine learning in the prevention and treatment of kidney disease exempli es the signi cance of interdisciplinary collaboration.…”

mentioning

confidence: 99%

Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024

Li,

Hu,

Luo

2024

Preprint

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Background The kidney, an essential organ of the human body, can suffer pathological damage that can potentially have serious adverse consequences on the human body and even affect life. Furthermore, the majority of kidney-induced illnesses are frequently not readily identifiable in their early stages. Once they have progressed to a more advanced stage, they impact the individual's quality of life and burden the family and broader society. In recent years, to solve this challenge well, the application of machine learning techniques in renal medicine has received much attention from researchers, and many results have been achieved in disease diagnosis and prediction. Nevertheless, studies that have conducted a comprehensive bibliometric analysis of the field have yet to be identified. Objectives This study employs bibliometric and visualization analyses to assess the progress of the application of machine learning in the renal field and to explore research trends and hotspots in the field. Methods A search was conducted using the Web of Science Core Collection database, which yielded articles and review articles published from the database's inception to May 12, 2024. The data extracted from these articles and review articles were then analyzed. A bibliometric and visualization analysis was conducted using the VOSviewer, CiteSpace, and Bibliometrics (R-Tool of R-Studio) software. Results 2,358 papers were retrieved and analyzed for this topic. From 2013 to 2024, the number of publications and the frequency of citations in the relevant research areas have exhibited a consistent and notable increase annually. The data set comprises 3734 institutions in 91 countries and territories, with 799 journals publishing the results. The total number of authors contributing to the data set is 14,396. China and the United States have the highest number of published papers, with 721 and 525 papers, respectively. Harvard University and the University of California System exert the most significant influence at the institutional level. In terms of authors, Cheungpasitporn, Wisit, and Thongprayoon Charat of the Mayo Clinic organization were the most prolific researchers, with 23 publications each. It is noteworthy that researcher Breiman I had the highest co-citation frequency. The journal with the most published papers was "Scientific Reports," while "PLoS One" had the highest co-citation frequency. In this field of machine learning applied to renal medicine, the article "A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury" by Tomasev N et al., published in NATURE in 2019, emerged as the most influential article with the highest co-citation frequency. A keyword and reference co-occurrence analysis reveals that current research trends and frontiers in nephrology are the management of patients with renal disease, prediction and diagnosis of renal disease, imaging of renal disease, and the development of personalized treatment plans for patients with renal disease. "Acute kidney injury", "chronic kidney disease" and "kidney tumors" are the most discussed diseases in medical research. Conclusions The field of renal medicine is witnessing a surge in the application of machine learning. On the one hand, this study offers a novel perspective on the application of machine learning techniques to kidney-related diseases based on bibliometric analysis. This analysis provides a comprehensive overview of the current status and emerging research areas in the field, as well as future trends and frontiers. Conversely, this study furnishes data on collaboration and exchange between countries and regions, institutions, journals, authors, keywords, and reference co-citations. This information can facilitate the advancement of future research endeavors, which aim to enhance interdisciplinary collaboration, optimize data sharing and quality, and further advance the application of machine learning in the renal field.

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On High Grade Kidney Cancer and Machine Learning

Cited by 2 publications

References 15 publications

Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when

Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when

Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024

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