Work

44P Machine learning-based prediction of survival in patients with metastatic renal cell carcinoma receiving first-line immunotherapy

Year: 2024

Type: article

Abstract: developed using five different machine learning algorithms.The RF model showed the best predictive performance, with average area under the curve (AUC) of 0.99 and 0.72, and ACC values of 0.99 and 0.66 for the training and validation sets, respectively.CC and DCA further demonstrated the robust performance of the RF model in predicting treatment response.Conclusions: Machine learning models based on clinical and radiomics features were successfully constructed to predict the efficacy of LEN and TIS based therapy in uHCC pts.These models offer potential guidance for personalized treatment strategies.Clinical trial identification: NCT05543304.

Source: Immuno-Oncology Technology

DOI: 10.1016/j.iotech.2024.101004

Authors: A. Elgebaly, A. Abdelrahman, G. Abdelkhalek, M. Imam, R. Hammad, H. Abdalla

Institutions: University of East London, Al-Azhar University, St Peter's Hospital, Ashford and St Peter's Hospitals NHS Foundation Trust

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,170