AI predictive modeling of survival outcomes for renal cancer patients undergoing targeted therapy.
Humans
Kidney Neoplasms
/ drug therapy
Carcinoma, Renal Cell
/ drug therapy
Female
Male
Middle Aged
Artificial Intelligence
Aged
Molecular Targeted Therapy
/ methods
Treatment Outcome
Prognosis
Tomography, X-Ray Computed
Algorithms
Quality of Life
Adult
Precision Medicine
/ methods
Biomarkers, Tumor
Artificial intelligence
CT
Imaging analysis
Renal clear cell cancer
Targeted therapy
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 10 2024
30 10 2024
Historique:
received:
10
06
2024
accepted:
24
10
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
Renal clear cell cancer (RCC) is a complex disease that is challenging to predict patient outcomes. Despite improvements with targeted therapy, personalized treatment planning is still needed. Artificial intelligence (AI) can help address this challenge by developing predictive models that accurately forecast patient survival periods. With AI-powered decision support, clinicians can provide patients with tailored treatment plans, enhancing treatment efficacy and quality of life. The study analyzed 267 patients with renal clear cell carcinoma, focusing on 26 who received targeted drug therapy. The data was refined by excluding 8 patients without enhanced CT scans. The research team categorized patients into two groups based on their expected lifespan: Group 1 (over 3 years) and Group 2 (under 3 years). The UPerNet algorithm was used to extract features from CT tumor markers, validating their effectiveness. These features were then used to develop an AI-based predictive model trained on the dataset. The developed AI model demonstrated remarkable accuracy, achieving a rate of 93.66% in Group 1 and 94.14% in Group 2. In conclusion, our study demonstrates the potential of AI technology in predicting the survival time of RCC patients undergoing targeted drug therapy. The established prediction model exhibits high predictive accuracy and stability, serving as a valuable tool for clinicians to facilitate the development of more personalized treatment plans for patients. This study highlights the importance of integrating AI technology in clinical decision-making, enabling patients to receive more effective and targeted treatment plans that enhance their overall quality of life.
Identifiants
pubmed: 39478092
doi: 10.1038/s41598-024-77638-6
pii: 10.1038/s41598-024-77638-6
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
26156Subventions
Organisme : scientific research of the Heilongjiang Provincial Health Commission
ID : 20220404050660
Organisme : scientific research of the Heilongjiang Provincial Health Commission
ID : 20210404050337
Organisme : scientific research of the Heilongjiang Provincial Health Commission
ID : 20220404050752
Informations de copyright
© 2024. The Author(s).
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