Building reliable radiomic models using image perturbation

Teng, Xinzhi; Zhang, Jiang; Zwanenburg, Alex; Sun, Jiachen; Huang, Yuhua; Lam, Saikit; Zhang, Yuanpeng; Li, Bing; Ta, Zhou; Xiao, Haonan; Liu, Chenyang; Li, Wen; Han, Xinyang; Ma, Zongrui; Li, Tian; Cai, Jing

doi:10.1038/s41598-022-14178-x

Cited by 17 publications

(16 citation statements)

References 47 publications

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“…The investigation should be reanalyzed for other radiotherapy techniques, such as Volume Modulated Radiation Therapy (VMAT) or a combination of IMRT and VMAT owing to the difference in dose distribution. Thirdly, several studies have analyzed feature repeatability and reproducibility by considering the impact of VOI segmentation and CT image acquisition [43][44][45][46][47][48]. It is worth comprehensively studying models that use robust and repeatable features before clinical use.…”

Section: Discussionmentioning

confidence: 99%

Multi-omics and Multi-VOIs to Predict Esophageal Fistula in Esophageal Cancer Patients Treated with Radiotherapy

Guo,

Li,

et al. 2023

Preprint

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Objective: This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes-of-interest (VOIs). Methods: We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised and supervised approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1-score. Results: For multi-omics using RD features, the model performance in EG model shows：AUC, 0.817±0.031; 95% CI: 0.805, 0.825; p＜0.001, which is better than single VOI (ESO or GTV). Conclusion: Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.

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Section: Discussionmentioning

confidence: 99%

Multi-omics and Multi-VOIs to Predict Esophageal Fistula in Esophageal Cancer Patients Treated with Radiotherapy

Guo,

Li,

et al. 2023

Preprint

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“…Reproducibility is a measure of the variability of repeated measurements of the same or similar quantitative imaging biomarkers in a real clinical environment and is affected by external factors that cannot be strictly controlled, such as operators, measurement systems, and measurement equipment [ 86 , 87 ]. Thus, reproducibility represents stability, so radiomics studies must ensure that the radiomics features they use have high reproducibility, such that their models generate similar classification results in different clinical environments.…”

Section: Ai-driven Radiomics Studiesmentioning

confidence: 99%

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

Zhang

Cheng

et al. 2023

Military Med Res

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Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’ anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.

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“…It was also shown that inclusion of prior knowledge, through the selection of MRI sequences or which type of robust features are used, helped to reduce the performance drop when the radiomics model developed from a single institution was tested for broader application (Suter et al 2020). A radiomics model reliability assessment method using image perturbations has also been proposed in a recent study (Teng et al 2022).…”

Section: Guideline For Clinical Trialsmentioning

confidence: 99%

Impact of image quality on radiomics applications

Cui

Yin

2022

Phys. Med. Biol.

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Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.

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Building reliable radiomic models using image perturbation

Cited by 17 publications

References 47 publications

Multi-omics and Multi-VOIs to Predict Esophageal Fistula in Esophageal Cancer Patients Treated with Radiotherapy

Multi-omics and Multi-VOIs to Predict Esophageal Fistula in Esophageal Cancer Patients Treated with Radiotherapy

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

Impact of image quality on radiomics applications

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