CT radiomics and PET radiomics: ready for clinical implementation?

Bogowicz, Marta; Vuong, Diem; Huellner, Martin W.; Pavic, Matea; Andratschke, Nicolaus; Gabryś, Hubert S.; Gückenberger, Matthias; Tanadini‐Lang, Stephanie

doi:10.23736/s1824-4785.19.03192-3

Cited by 57 publications

(51 citation statements)

References 173 publications

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“…Radiomics has attracted increased attention in recent years, and several studies show that radiomics can be of benefit in terms of prognosis and diagnosis of multiple diseases, [21][22][23]. These studies have shown that radiomics features show great potential to serve as surrogate imaging markers for tissue biopsies [40] and reliably predict outcome [41][42][43][44] and drug response [45,46].…”

Section: Discussionmentioning

confidence: 99%

“…Due to the high objectivity and reliability of data, radiomics shows great potential as support for clinical decision-making [20]. Radiomics has attracted increased attention in recent years, and several studies show that radiomics can be of benefit in terms of prognosis and diagnosis of multiple diseases, especially malignancies [21][22][23]. In SSc-ILD, to the best of our knowledge, radiomics analyses have not yet been performed.…”

Section: Introductionmentioning

confidence: 99%

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Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept

et al. 2020

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Objective To retrospectively evaluate if texture-based radiomics features are able to detect interstitial lung disease (ILD) and to distinguish between the different disease stages in patients with systemic sclerosis (SSc) in comparison with mere visual analysis of high-resolution computed tomography (HRCT). Methods Sixty patients (46 females, median age 56 years) with SSc who underwent HRCT of the thorax were retrospectively analyzed. Visual analysis was performed by two radiologists for the presence of ILD features. Gender, age, and pulmonary function (GAP) stage was calculated from clinical data (gender, age, pulmonary function test). Data augmentation was performed and the balanced dataset was split into a training (70%) and a testing dataset (30%). For selecting variables that allow classification of the GAP stage, single and multiple logistic regression models were fitted and compared by using the Akaike information criterion (AIC). Diagnostic accuracy was evaluated from the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, and diagnostic sensitivity and specificity were calculated. Results Values for some radiomics features were significantly lower (p < 0.05) and those of other radiomics features were significantly higher (p = 0.001) in patients with GAP2 compared with those in patients with GAP1. The combination of two specific radiomics features in a multivariable model resulted in the lowest AIC of 10.73 with an AUC of 0.96, 84% sensitivity, and 99% specificity. Visual assessment of fibrosis was inferior in predicting individual GAP stages (AUC 0.86; 83% sensitivity; 74% specificity). Conclusion The correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features indicating severity of SSc-ILD on HRCT, which are not recognized by visual analysis. Key Points • Radiomics features can predict GAP stage with a sensitivity of 84% and a specificity of almost 100%. • Extent of fibrosis on HRCT and a combined model of different visual HRCT-ILD features perform worse in predicting GAP stage. • The correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features on HRCT, which are not recognized by visual analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept

et al. 2020

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“…Radiomics is a technique that is used to extract large amounts of quantitative information from routine medical images that decode information about a region of interest (ROI). The majority of radiomics articles published concerns its application in the oncological field 1 – 4 . Here, radiomics bears the advantage of non-invasively quantifying the underlying phenotype of the entire tumor for multiple lesions simultaneously, in contrast to tissue biopsy, which samples only a small part of a single (often heterogeneous) tumor 2 , 5 .…”

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

Granzier

Verbakel

Ibrahim

et al. 2020

Sci Rep

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Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness. Therefore, our study aim was to determine the robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI. A total of 129 histologically confirmed breast tumors were segmented manually in three dimensions on the first post-contrast T1-weighted MR exam by four observers: a dedicated breast radiologist, a resident, a Ph.D. candidate, and a medical student. Robust features were assessed using the intraclass correlation coefficient (ICC > 0.9). The inter-observer variability was evaluated by the volumetric Dice Similarity Coefficient (DSC). The mean DSC for all tumors was 0.81 (range 0.19-0.96), indicating a good spatial overlap of the segmentations based on observers of varying expertise. In total, 41.6% (552/1328) and 32.8% (273/833) of all RadiomiX and Pyradiomics features, respectively, were identified as robust and were independent of inter-observer manual segmentation variability. Radiomics is a technique that is used to extract large amounts of quantitative information from routine medical images that decode information about a region of interest (ROI). The majority of radiomics articles published concerns its application in the oncological field 1-4. Here, radiomics bears the advantage of non-invasively quantifying the underlying phenotype of the entire tumor for multiple lesions simultaneously, in contrast to tissue biopsy, which samples only a small part of a single (often heterogeneous) tumor 2,5. The ability to characterize the tumor and to establish links to the underlying biology 6 and ultimately clinical outcomes, allows a more patienttailored treatment 7 , enabling 'precision medicine' 8,9. Recently, several articles have outlined the potential clinical applicability of radiomics in the field of breast cancer for different purposes, e.g. diagnosis 10,11 , tumor response prediction 12-14 , prediction of molecular tumor subtype 15,16 , and prediction of axillary lymph node metastases 17,18. Although these results are promising, issues regarding features robustness as well as the comparability of results, including inter-observer segmentation variability, need to be addressed 19-24. In order to extract clinically useful information from medical images and to use features as clinical biomarkers, it is important that extracted

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“…Retrospective nature of data collection together with rapid development of detector technology and reconstruction methods makes collection of large and homogenous datasets difficult. Therefore, we think that the recently introduced, specialized PET radiomics phantoms depicting heterogeneity of PET tracer uptake are key tools for robustness studies [32]. One further limitation of this study is the low patient number analyzed.…”

Section: Discussionmentioning

confidence: 93%

“…As already stated, various factors can influence image quality and therefore have an impact on the results of a radiomics model on CT as well as on FDG PET images [25][26][27][28][29][30][31]. Acquisition of FDG PET/CT was done on different machines with different parameter settings, which can affect the robustness of radiomic features [32]. Unfortunately, a small size of subcohorts with homogenous acquisition and reconstruction protocols prevented us from studying this effect in more detail or to apply correction methods, such as ComBat [33].…”

Section: Discussionmentioning

confidence: 99%

FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients

et al. 2020

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Background: Careful selection of malignant pleural mesothelioma (MPM) patients for curative treatment is of highest importance, as the multimodal treatment regimen is challenging for patients and harbors a high risk of substantial toxicity. Radiomics-a quantitative method for image analysis-has shown its prognostic ability in different tumor entities and could therefore play an important role in optimizing patient selection for radical cancer treatment. So far, radiomics as a prognostic tool in MPM was not investigated. Materials and methods: This study is based on 72 MPM patients treated with surgery in a curative intent at our institution between 2009 and 2017. Pre-treatment Fluorine-18 fluorodeoxyglucose (FDG) PET and CT scans were used for radiomics outcome modeling. After extraction of 1404 CT and 1410 FDG PET features from each image, a preselection by principal component analysis was performed to include only robust, non-redundant features for the cox regression to predict the progression-free survival (PFS) and the overall survival (OS). Results were validated on a separate cohort. Additionally, SUVmax and SUVmean, and volume were tested for their prognostic ability for PFS and OS. Results: For the PFS a concordance index (c-index) of 0.67 (95% CI 0.52-0.82) and 0.66 (95% CI 0.57-0.78) for the training cohort (n = 36) and internal validation cohort (n = 36), respectively, were obtained for the PET radiomics model. The PFS advantage of the low-risk group translated also into an OS advantage. On CT images, no radiomics model could be trained. SUV max and SUV mean were also not prognostic in terms of PFS and OS. Conclusion: We were able to build a successful FDG PET radiomics model for the prediction of PFS in MPM. Radiomics could serve as a tool to aid clinical decision support systems for treatment of MPM in future.

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CT radiomics and PET radiomics: ready for clinical implementation?

Cited by 57 publications

References 173 publications

Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept

Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients

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