Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework

Ibrahim, Abdalla; Primakov, Sergey; Beuque, Manon; Woodruff, Henry C.; Halilaj, Iva; Wu, Guangyao; Refaee, Turkey; Granzier, R.; Widaatalla, Yousif; Hustinx, Roland; Mottaghy, Felix M.; Lambin, Philippe

doi:10.1016/j.ymeth.2020.05.022

Cited by 138 publications

(119 citation statements)

References 65 publications

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“…In addition, each time an observation is added to the data being harmonized, ComBat has to be re-performed, and models have to be refitted, as the estimated batch effects will change each time. Therefore, the harmonization of patient RFs should follow the process of estimating fixed batch effects on phantom data, then applying the location/scale shift estimated from the phantom data on patient data, as previously described by Ibrahim et al [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, we are not aware of any study that has performed a systematic evaluation of the performance of ComBat harmonization across variations between imaging parameters, which is the one of the objectives of this study. Ibrahim et al (2020) [22] have proposed a new radiomics workflow (Fig 1) that tries to address the challenges current radiomics analyses face. The framework was proposed based on mathematical considerations of the complexity of medical imaging, and RFs' mathematical definitions.…”

Section: Plos Onementioning

confidence: 99%

“…Ibrahim et al (2020) [ 22 ] have proposed a new radiomics workflow ( Fig 1 ) that tries to address the challenges current radiomics analyses face. The framework was proposed based on mathematical considerations of the complexity of medical imaging, and RFs’ mathematical definitions.…”

Section: Introductionmentioning

confidence: 99%

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The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset

et al. 2021

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Radiomics–the high throughput extraction of quantitative features from medical images and their correlation with clinical and biological endpoints- is the subject of active and extensive research. Although the field shows promise, the generalizability of radiomic signatures is affected significantly by differences in scan acquisition and reconstruction settings. Previous studies reported on the sensitivity of radiomic features (RFs) to test-retest variability, inter-observer segmentation variability, and intra-scanner variability. A framework involving robust radiomics analysis and the application of a post-reconstruction feature harmonization method using ComBat was recently proposed to address these challenges. In this study, we investigated the reproducibility of RFs across different scanners and scanning parameters using this framework. We analysed thirteen scans of a ten-layer phantom that were acquired differently. Each layer was subdivided into sixteen regions of interest (ROIs), and the scans were compared in a pairwise manner, resulting in seventy-eight different scenarios. Ninety-one RFs were extracted from each ROI. As hypothesized, we demonstrate that the reproducibility of a given RF is not a constant but is dependent on the heterogeneity found in the data under analysis. The number (%) of reproducible RFs varied across the pairwise scenarios investigated, having a wide range between 8 (8.8%) and 78 (85.7%) RFs. Furthermore, in contrast to what has been previously reported, and as hypothesized in the robust radiomics analysis framework, our results demonstrate that ComBat cannot be applied to all RFs but rather on a percentage of those–the “ComBatable” RFs–which differed depending on the data being harmonized. The number (%) of reproducible RFs following ComBat harmonization varied across the pairwise scenarios investigated, ranging from 14 (15.4%) to 80 (87.9%) RFs, and was found to depend on the heterogeneity in the data. We conclude that the standardization of image acquisition protocols remains the cornerstone for improving the reproducibility of RFs, and the generalizability of the signatures developed. Our proposed approach helps identify the reproducible RFs across different datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset

et al. 2021

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“…These features capture information on the underlying heterogeneous structure of the region of interest (ROI), describing volume and shape, intensities and textures [ 12 ]. Radiomics’ non-invasive ability to characterize the three-dimensional ROI, combined with the availability of ever-growing amounts of (longitudinal) imaging data and its cost-effectiveness, all contribute to the potential use of radiomics in personalized medicine [ 13 , 14 , 15 , 16 ]. The emergence of radiomics has so far mainly been applied in the field of clinical oncology and has also permeated breast cancer research.…”

Section: Introductionmentioning

confidence: 99%

MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study

Granzier

Ibrahim

Primakov

et al. 2021

Cancers

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This retrospective study investigated the value of pretreatment contrast-enhanced Magnetic Resonance Imaging (MRI)-based radiomics for the prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients. A total of 292 breast cancer patients, with 320 tumors, who were treated with neo-adjuvant systemic therapy and underwent a pretreatment MRI exam were enrolled. As the data were collected in two different hospitals with five different MRI scanners and varying acquisition protocols, three different strategies to split training and validation datasets were used. Radiomics, clinical, and combined models were developed using random forest classifiers in each strategy. The analysis of radiomics features had no added value in predicting pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients compared with the clinical models, nor did the combined models perform significantly better than the clinical models. Further, the radiomics features selected for the models and their performance differed with and within the different strategies. Due to previous and current work, we tentatively attribute the lack of improvement in clinical models following the addition of radiomics to the effects of variations in acquisition and reconstruction parameters. The lack of reproducibility data (i.e., test-retest or similar) meant that this effect could not be analyzed. These results indicate the need for reproducibility studies to preselect reproducible features in order to properly assess the potential of radiomics.

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“…Furthermore, certain parameters (functions, indicators) -image biomarkers (IBMs) -are selected in the processed statistical model. Work on images includes various stages of preliminary and subsequent processing [16][17][18].…”

Section: Basic Terms Of Radiomicsmentioning

confidence: 99%

Radiomics and Digital Image Texture Analysis in Oncology (Review)

Litvin¹,

Burkin²,

Kropinov³

et al. 2021

Sovrem Tehnol Med

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One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of "virtual biopsy" is of particular relevance in oncology.The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered.Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.

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Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework

Cited by 138 publications

References 65 publications

The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset

The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset

MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study

Radiomics and Digital Image Texture Analysis in Oncology (Review)

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