AIMC Topic: Urologic Neoplasms
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Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets.
BMC medical imaging
May 30, 2025
BACKGROUND: To investigate a non-invasive radiomics-based machine learning algorithm to differentiate upper urinary tract urothelial carcinoma (UTUC) from renal cell carcinoma (RCC) prior to surgical intervention.
Development of an artificial intelligence-generated, explainable treatment recommendation system for urothelial carcinoma and renal cell carcinoma to support multidisciplinary cancer conferences.
European journal of cancer (Oxford, England : 1990)
Mar 15, 2025
BACKGROUND: Decisions on the best available treatment in clinical oncology are based on expert opinions in multidisciplinary cancer conferences (MCC). Artificial intelligence (AI) could increase evidence-based treatment by generating additional treat...
Generative artificial intelligence in oncology.
Current opinion in urology
Feb 28, 2025
PURPOSE OF REVIEW: By leveraging models such as large language models (LLMs) and generative computer vision tools, generative artificial intelligence (GAI) is reshaping cancer research and oncologic practice from diagnosis to treatment to follow-up. ...
Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning.
Analytical chemistry
Jan 6, 2025
Bladder, kidney, and prostate cancers are prevalent urinary cancers, and developing efficient detection methods is of significance for the early diagnosis of them. However, noninvasive and sensitive detection of urinary cancers still challenges tradi...
Modification and Validation of the System Causability Scale Using AI-Based Therapeutic Recommendations for Urological Cancer Patients: A Basis for the Development of a Prospective Comparative Study.
Current oncology (Toronto, Ont.)
Nov 11, 2024
The integration of artificial intelligence, particularly Large Language Models (LLMs), has the potential to significantly enhance therapeutic decision-making in clinical oncology. Initial studies across various disciplines have demonstrated that LLM-...
Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer.
European urology focus
Jun 21, 2024
BACKGROUND: The IDENTIFY study developed a model to predict urinary tract cancer using patient characteristics from a large multicentre, international cohort of patients referred with haematuria. In addition to calculating an individual's cancer risk...
Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.
Nature communications
Jun 1, 2024
Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful de...
Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma: a step towards virtual biopsy.
International journal of surgery (London, England)
Jun 1, 2024
OBJECTIVES: Upper tract urothelial carcinoma (UTUC) is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour gra...
Re: David Musheyev, Alexander Pan, Stacy Loeb, Abdo E. Kabarriti. How Well Do Artificial Intelligence Chatbots Respond to the Top Search Queries About Urological Malignancies? Eur Urol 2023;85:13-6.
European urology
Apr 21, 2024
[What contribution can make artificial intelligence to urinary cytology?].
Annales de pathologie
Apr 12, 2024
Urinary cytology using the Paris system is still the method of choice for screening high-grade urothelial carcinomas. However, the use of the objective criteria described in this terminology shows a lack of inter- and intra-observer reproducibility. ...
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