Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters

Oyaga-Iriarte, Esther; Insausti, Asier; Sayar, Onintza; Aldaz, Azucena

doi:10.1016/j.jphs.2019.03.004

Cited by 41 publications

(46 citation statements)

References 40 publications

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“…Consequently, these AI applications may help physicians to provide more effective treatment strategies even at the early stages of CRT. Interestingly, using machine learning techniques, Oyaga-Iriarte et al [156] conducted a study to predict whether metastatic CRC patients would suffer from high degrees of toxicity of a drug referred to as irinotecan (showing accuracy of 76%, 75% and 91% for leukopenia, neutropenia and diarrhea, respectively).…”

Section: Chemotherapymentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

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The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

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Section: Chemotherapymentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

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“…Their model predicted rat carcinogenicity with 69% accuracy and 70% AUC. AI has also been used to predict the severity of adverse effects produced by drugs used in chemotherapy treatments for cancer 86–88 …”

Section: Ai‐based Toxicity Predictionmentioning

confidence: 99%

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

Pérez‐Santín

Rodríguez‐Solana

García

et al. 2021

WIREs Comput Mol Sci

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The use and production of chemical compounds are subjected to strong legislative pressure. Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory aspects for a multitude of industries, such as chemical, pharmaceutical, or food, due to direct harm to humans, animals, plants, or the environment. Simultaneously, there are growing demands on the authorities to replace traditional in vivo toxicity tests carried out on laboratory animals (e.g., European Union REACH/3R principles, Tox21 and ToxCast by the U.S. government, etc.) with in silica computational models. This is not only for ethical aspects, but also because of its greater economic and time efficiency, as well as more recently because of their superior reliability and robustness than in vivo tests, mainly since the entry into the scene of artificial intelligence (AI)‐based models, promoting and setting the necessary requirements that these new in silico methodologies must meet. This review offers a multidisciplinary overview of the state of the art in the application of AI‐based methodologies for the fulfillment of regulatory‐related toxicological issues. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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“…An example of AI in toxicology is the prediction of irinotecan toxicity in metastatic colorectal cancer patients using a machine-learning approach with pharmacokinetic parameters. 1…”

Section: Introductionmentioning

confidence: 99%

An artificial intelligence algorithm for analyzing acetaminophen-associated toxic hepatitis

et al. 2021

Hum Exp Toxicol

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Introduction: Very little artificial intelligence (AI) work has been performed to investigate acetaminophen-associated hepatotoxicity. The objective of this study was to develop an AI algorithm for analyzing weighted features for toxic hepatitis after acetaminophen poisoning. Methods: The medical records of 187 patients with acetaminophen poisoning treated at Chang Gung Memorial Hospital were reviewed. Patients were sorted into two groups according to their status of toxic hepatitis. A total of 40 clinical and laboratory features recorded on the first day of admission were selected for algorithm development. The random forest classifier (RFC) and logistic regression (LR) were used for artificial intelligence algorithm development. Results: The RFC-based AI model achieved the following results: accuracy = 92.5 ± 2.6%; sensitivity = 100%; specificity = 60%; precision = 92.3 ± 3.4%; and F1 = 96.0 ± 1.8%. The area under the receiver operating characteristic curve (AUROC) was approximately 0.98. The LR-based AI model achieved the following results: accuracy = 92.00 ± 2.9%; sensitivity = 100%; specificity = 20%; precision = 92.8 ± 3.4%; recall = 98.8 ± 3.4%; and F1 = 95.6 ± 1.5%. The AUROC was approximately 0.68. The weighted features were calculated, and the 10 most important weighted features for toxic hepatitis were aspartate aminotransferase (ALT), prothrombin time, alanine aminotransferase (AST), time to hospital, platelet count, lymphocyte count, albumin, total bilirubin, body temperature and acetaminophen level. Conclusion: The top five weighted features for acetaminophen-associated toxic hepatitis were ALT, prothrombin time, AST, time to hospital and platelet count.

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Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters

Cited by 41 publications

References 40 publications

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

An artificial intelligence algorithm for analyzing acetaminophen-associated toxic hepatitis

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