The utility of quantitative<scp>CT</scp>radiomics features for improved prediction of radiation pneumonitis

Krafft, Shane P.; Rao, Arvind; Stingo, Francesco C.; Briere, Tina Marie; Court, Laurence Edward; Liao, Zhongxing; Martel, Mary K.

doi:10.1002/mp.13150

Cited by 93 publications

(82 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The addition of normal lung image features produced superior model performance with respect to traditional dosimetric and clinical predictors of radiation pneumonitis (RP), suggesting that pretreatment CT radiomic features should be considered in the context of RP prediction. CT radiomic features were extracted from the total lung volume defined using the treatment‐planning scan for RP …”

Section: Overview Of Research and Clinical Applications Of Cancer Radmentioning

confidence: 99%

Machine and deep learning methods for radiomics

et al. 2020

Self Cite

View full text Add to dashboard Cite

Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three‐dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open‐source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics‐based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.

show abstract

Section: Overview Of Research and Clinical Applications Of Cancer Radmentioning

confidence: 99%

Machine and deep learning methods for radiomics

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Radiomics is a developing research area that aims to extract more complex information from conventional medical images, such as computed tomography (CT) and magnetic resonance imaging, using features not easily visible to or quantifiable by the human eye . These features can then be used to build models of clinical outcomes, including diagnostic, prognostic, and predictive models …”

Section: Introductionmentioning

confidence: 99%

“…Parmar et al showed that radiomics‐based prediction with random forest machine learning had the highest area under the receiver operating characteristic (ROC) curve (AUC) of 0.66 . Although radiomics‐based approaches using various texture‐based features, such as intensity histograms, absolute gradients, nearest gray tone difference metrices, and gray‐level co‐occurrence matrices, have shown great potential for predicting the prognosis of cancer patients, there is still room for improving prediction accuracy …”

Section: Introductionmentioning

confidence: 99%

Homology‐based radiomic features for prediction of the prognosis of lung cancer based on CT‐based radiomics

et al. 2020

View full text Add to dashboard Cite

Purpose: Radiomics is a new technique that enables noninvasive prognostic prediction by extracting features from medical images. Homology is a concept used in many branches of algebra and topology that can quantify the contact degree. In the present study, we developed homology-based radiomic features to predict the prognosis of non-small-cell lung cancer (NSCLC) patients and then evaluated the accuracy of this prediction method. Methods: Four datasets were used: two to provide training and test data and two for the selection of robust radiomic features. All the datasets were downloaded from The Cancer Imaging Archive (TCIA). In two-dimensional cases, the Betti numbers consist of two values: b 0 (zero-dimensional Betti number), which is the number of isolated components, and b 1 (one-dimensional Betti number), which is the number of one-dimensional or "circular" holes. For homology-based evaluation, computed tomography (CT) images must be converted to binarized images in which each pixel has two possible values: 0 or 1. All CT slices of the gross tumor volume were used for calculating the homology histogram. First, by changing the threshold of the CT value (range: À150 to 300 HU) for all its slices, we developed homology-based histograms for b 0 , b 1 , and b 1 /b 0 using binarized images. All histograms were then summed, and the summed histogram was normalized by the number of slices. 144 homology-based radiomic features were defined from the histogram. To compare the standard radiomic features, 107 radiomic features were calculated using the standard radiomics technique.To clarify the prognostic power, the relationship between the values of the homology-based radiomic features and overall survival was evaluated using LASSO Cox regression model and the Kaplan-Meier method. The retained features with nonzero coefficients calculated by the LASSO Cox regression model were used for fitting the regression model. Moreover, these features were then integrated into a radiomics signature. An individualized rad score was calculated from a linear combination of the selected features, which were weighted by their respective coefficients. Results: When the patients in the training and test datasets were stratified into high-risk and low-risk groups according to the rad scores, the overall survival of the groups was significantly different. The C-index values for the homology-based features (rad score), standard features (rad score), and tumor size were 0.625, 0.603, and 0.607, respectively, for the training datasets and 0.689, 0.668, and 0.667 for the test datasets. This result showed that homology-based radiomic features had slightly higher prediction power than the standard radiomic features. Conclusions: Prediction performance using homology-based radiomic features had a comparable or slightly higher prediction power than standard radiomic features. These findings suggest that homology-based radiomic features may have great potential for improving the prognostic prediction accuracy of CT-based radiomics. In this result, i...

show abstract

“…Multiple manuscripts have been published using radiomics to predict radiation response, in some cases with prediction power outperforming standard clinical variables (77)(78)(79)(80)(81)(82), though not in all (83). Radiomicsbased statistical approaches can predict various radiation normal tissue complication probabilities including radiation pneumonitis, xerostomia, and rectal wall toxicity (84)(85)(86)(87)(88)(89). Radiomics data, coupled with genomic data and increasingly computable clinical record data, may escort radiation oncology into a new epoch of truly personalized radiation plans based on patient-specific knowledge.…”

Section: Tumor Control Probability and Normal Tissue Complication Promentioning

confidence: 99%

Artificial intelligence in radiation oncology treatment planning: a brief overview

Kiser¹,

Fuller²,

Reed³

2019

J Med Artif Intell

View full text Add to dashboard Cite

Among medical specialties, radiation oncology has long been an innovator and early adopter of therapeutic technologies. This specialty is now situated in prime position to be revolutionized by advances in artificial intelligence (AI), especially machine and deep learning. AI has been investigated by radiation oncologists and physicists in both general and niche radiotherapy planning tasks and has often demonstrated performance that is indistinguishable from human experts, while substantially shortening the time required to complete these tasks. We sought to review applications of AI to domains germane to radiation oncology, namely: image segmentation, treatment plan generation and optimization, normal tissue complication probability modeling, quality assurance (QA), and adaptive re-planning. We sought likewise to consider obstacles to AI adoption in the radiotherapy clinic, now primarily political, legal, and ethical rather than technical in nature.

show abstract

The utility of quantitativeCTradiomics features for improved prediction of radiation pneumonitis

Cited by 93 publications

References 43 publications

Machine and deep learning methods for radiomics

Machine and deep learning methods for radiomics

Homology‐based radiomic features for prediction of the prognosis of lung cancer based on CT‐based radiomics

Artificial intelligence in radiation oncology treatment planning: a brief overview

Contact Info

Product

Resources

About