Machine learning for radiation outcome modeling and prediction

Luo, Yi; Chen, Shifeng; Valdés, Gilmer

doi:10.1002/mp.13570

Cited by 33 publications

(20 citation statements)

References 97 publications

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“…This goes in line with the findings from more fundamental ML research studies, which have reported RFs as one of the best classical learning algorithms [133]. However, many other works in the medical field have also compared the accuracy of RFs against more complex or simpler ML classifiers, and it is well known that their performance may vary for different applications [103,113,132,[134][135][136][137][138][139] and even for different datasets within the same application [131,132]. This makes it hard to conclude on the absolute superiority of RFs algorithm over other ML classifiers.…”

Section: Random Forests (Rfs)supporting

confidence: 80%

Artificial intelligence and machine learning for medical imaging: A technology review

Montero¹,

Javaid²,

Valdés

et al. 2021

Physica Medica

193

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Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.

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Section: Random Forests (Rfs)supporting

confidence: 80%

Artificial intelligence and machine learning for medical imaging: A technology review

Montero¹,

Javaid²,

Valdés

et al. 2021

Physica Medica

193

View full text Add to dashboard Cite

show abstract

“…Therefore, interpretable models are required to establish correlations between quantitative formula-derived radiomics features and genetic subtypes. Representative classification methods include conventional logistic regression ( 45 ) and advanced machine learning techniques ( 46 ), such as decision trees and random forests, support vector machines, and deep neural networks ( 47 ), which are able to emulate human intelligence by acquiring knowledge of the surrounding environment from the input data and detect nonlinear complex patterns in the data.…”

Section: Development Of Radiomics Prediction Modelsmentioning

confidence: 99%

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

et al. 2021

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With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

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“…However, there was also evidence that the superiority of ML in outcome prediction is not always supported ( Christodoulou et al, 2019 ). In addition, the ML models and algorithms have their own limitations, notably, overfitting ( Zhen et al, 2017 ), and lack of interpretability ( Luo et al, 2019 ; Luo et al, 2020 ). Overfitting would undermine predictive performance.…”

Section: Discussionmentioning

confidence: 99%

“…In this regard, artificial intelligence especially machine learning (ML) has a great capacity to process huge and complex data and thus has been used in many areas including medicine. Recently, ML has been introduced into radiation oncology to predict clinical outcomes ( Kang et al, 2015 ; Luo et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning

Luo

2021

Front. Artif. Intell.

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Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for outcome prediction and obtained promising results. Compared to conventional statistical models, machine learning has demonstrated advantages in dealing with the complexity in large-scale data and discovering prognostic factors. It has great potential in clinical application and improving cervical cancer management. However, the limitations of prediction studies and prediction models including simplification, insufficient data, overfitting and lack of interpretability, indicate that more work is needed to make clinical outcome prediction more accurate, more reliable, and more practical for clinical use.

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Machine learning for radiation outcome modeling and prediction

Cited by 33 publications

References 97 publications

Artificial intelligence and machine learning for medical imaging: A technology review

Artificial intelligence and machine learning for medical imaging: A technology review

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning

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