Radiomics in radiotherapy: Applications and future challenges

Qiu, Qingtao; Duan, Jinghao; Yin, Yong

doi:10.1002/pro6.1087

Cited by 12 publications

(5 citation statements)

References 43 publications

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“…However, although cranial MRI is considered the gold standard for diagnosing occult BM, it is not suitable for patients with heart pacemakers, claustrophobia, metal implants, or low Karnofsky Performance Status (KPS) (13)(14)(15). Advances in computational image analysis, such as radiomics, involve the use of large amounts of quantitative imaging features derived from medical images to decode tumor pathology or heterogeneity (16)(17)(18). Recently, many studies aimed to construct CT radiomic models to assess the relationship between lung cancer and BM (19,20).…”

Section: Introductionmentioning

confidence: 99%

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Cong¹,

Qiu²,

Li³

et al. 2021

Transl Cancer Res TCR

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Background: To develop and validate a radiomics model using computed tomography (CT) images acquired from the first diagnosis to estimate the status of occult brain metastases (BM) in patients with stage IV lung adenocarcinoma (LADC).Methods: One hundred and ninety-three patients who were first diagnosed with stage IV LADC were enrolled and divided into a training cohort (n=135) and a validation cohort (n=58). Then, 725 radiomic features were extracted from contoured primary tumor volumes of LADCs. Intra-and interobserver reliabilities were calculated, and the least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Subsequently, a radiomics signature (Rad-Score) was built. To improve performance, a nomogram incorporating a radiomics signature and an independent clinical predictor was developed. Finally, the established signature and nomogram were assessed using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Both empirical and α-binomial model-based ROCs and PRCs were plotted, and the area under the curve (AUC) and average precision (AP) of ROCs and PRCs were calculated and compared.Results: A radiomics signature and Rad-Score were constructed using eight radiomic features, and these had significant correlations with occult BM status. A nomogram was developed by incorporating a Rad-Score and the primary tumor location. The nomogram yielded an optimal AUC of 0.911 [95% confidence interval (CI), 0.903-0.919)] and an AP of 0.885 (95% CI, 0.876-0.894) in the training cohort, and an AUC of 0.873 (95% CI, 0.866-0.80) and an AP of 0.827 (95% CI, 0.820-0.834) in the validation cohort using α-binomial model-based method. The calibration curve demonstrated that the nomogram showed high agreement between the actual occult BM probability and predicted by the nomogram (P=0.427). Conclusions:The nomogram incorporating a radiomics signature and a clinical risk factor achieved optimal performance after holistic assessment using unbiased indexes for diagnosing occult BM of patients who were first diagnosed with stage IV LADC.

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Section: Introductionmentioning

confidence: 99%

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Cong¹,

Qiu²,

Li³

et al. 2021

Transl Cancer Res TCR

Self Cite

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“…Translating this method and applying it to oncology can be done by incorporating tissue radiomics data and cancer treatment outcomes, for example, instead of arbitrary features and colour classification. Certainly, machine learning has been applied to radiomics data in the context of radiotherapy for several applications [81]. To date, however, these powerful tools have not been applied to predicting tumour responses specifically in LACC BT.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Predicting Tumour Response With Radiomics and Machine Learning in MR-Guided Cervix Brachytherapy

Bellis

2024

Preprint

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This study seeks to determine if radiomic features extracted from whole or part of the gross tumour volume of locally advanced cervical cancer (LACC) patients can be used to predict tumour response prior to brachytherapy treatment. 12 machine learning algorithms were tested with 5-fold cross validation using 1183 radiomic features extracted from 20 patients from T1, T2 and diffusion-weighted MR images. Recursive Feature Elimination was used to indicate the most predictive radiomic features of the most accurate models. Several models, particularly Ensemble Methods, performed with accuracies of up to 85%. After combining the 11 most predictive features into a single dataset, a random forest model achieved an accuracy of 93%. Overall, this study showed that machine learning models coupled with radiomic features are capable of accurately predicting LACC tumour response prior to administering the first fraction of brachytherapy treatment.

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“…Treatment planning is another promising application of radiomics in GBM management. For instance, radiotherapy stands to benefit from the integration of radiomics into clinical practice [ 15 , 65 , 66 ]. Radiomics may assist in the segmentation of radiotherapy target volumes and in measuring and predicting treatment response [ 67 ].…”

Section: Current State Of Research In Gbm Radiomicsmentioning

confidence: 99%

Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation

Martin

Holloway

Metcalfe

et al. 2022

Cancers

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Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.

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Radiomics in radiotherapy: Applications and future challenges

Cited by 12 publications

References 43 publications

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Predicting Tumour Response With Radiomics and Machine Learning in MR-Guided Cervix Brachytherapy

Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation

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