The Use of Artificial Intelligence in Predicting Chemotherapy-Induced Toxicities in Metastatic Colorectal Cancer: A Data-Driven Approach for Personalized Oncology.

Autor: Froicu EM; Department of Medical Oncology, Regional Institute of Oncology, 700483 Iasi, Romania.; Department of Oncology, Faculty of Medicine, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania.; 2nd Internal Medicine Department, Faculty of Medicine, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania., Oniciuc OM; Faculty of Computer Science, 'Alexandru Ioan Cuza' University, 700506 Iasi, Romania., Afrăsânie VA; Department of Medical Oncology, Regional Institute of Oncology, 700483 Iasi, Romania.; Department of Oncology, Faculty of Medicine, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania., Marinca MV; Department of Medical Oncology, Regional Institute of Oncology, 700483 Iasi, Romania.; Department of Oncology, Faculty of Medicine, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania., Riondino S; Department of Systems Medicine, Medical Oncology, Tor Vergata Clinical Center, University of Rome 'Tor Vergata', Viale Oxford 81, 00133 Rome, Italy., Dumitrescu EA; Department of Oncology, Faculty of Medicine, 'Carol Davila' University of Medicine and Pharmacy, 050474 Bucharest, Romania.; Institute of Oncology Prof. Dr. Alexandru Trestioreanu, Șoseaua Fundeni, 022328 Bucharest, Romania., Alexa-Stratulat T; Department of Medical Oncology, Regional Institute of Oncology, 700483 Iasi, Romania.; Department of Oncology, Faculty of Medicine, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania., Radu I; First Surgical Oncology Unit, Department of Surgery, Regional Institute of Oncology, 700483 Iasi, Romania.; Department of Surgery, Faculty of Medicine, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania., Miron L; Department of Medical Oncology, Regional Institute of Oncology, 700483 Iasi, Romania.; Department of Oncology, Faculty of Medicine, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania., Bacoanu G; 2nd Internal Medicine Department, Faculty of Medicine, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania.; Department of Palliative Care, Regional Institute of Oncology, 700483 Iasi, Romania., Poroch V; 2nd Internal Medicine Department, Faculty of Medicine, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania.; Department of Palliative Care, Regional Institute of Oncology, 700483 Iasi, Romania., Gafton B; Department of Medical Oncology, Regional Institute of Oncology, 700483 Iasi, Romania.; Department of Oncology, Faculty of Medicine, 'Grigore T. Popa' University of Medicine and Pharmacy, 700115 Iasi, Romania.
Jazyk: angličtina
Zdroj: Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2024 Sep 19; Vol. 14 (18). Date of Electronic Publication: 2024 Sep 19.
DOI: 10.3390/diagnostics14182074
Abstrakt: Background: Machine learning models learn about general behavior from data by finding the relationships between features. Our purpose was to develop a predictive model to identify and predict which subset of colorectal cancer patients are more likely to experience chemotherapy-induced toxicity and to determine the specific attributes that influence the presence of treatment-related side effects.
Methods: The predictor was general toxicity, and for the construction of our data training, we selected 95 characteristics that represent the health state of 74 patients prior to their first round of chemotherapy. After the data were processed, Random Forest models were trained to offer an optimal balance between accuracy and interpretability.
Results: We constructed a machine learning predictor with an emphasis on assessing the importance of numerical and categorical variables in relation to toxicity.
Conclusions: The incorporation of artificial intelligence in personalizing colorectal cancer management by anticipating and overseeing toxicities more effectively illustrates a pivotal shift towards more personalized and precise medical care.
Databáze: MEDLINE
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