PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology

Sollini, Martina; Cozzi, Luca; Antunovic, Lidija; Chiti, Arturo; Kirienko, Margarita

doi:10.1038/s41598-017-00426-y

Cited by 145 publications

(104 citation statements)

References 82 publications

(137 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, our purpose was not to use specific anatomic-driven image acquisition protocols, but rather to test a wide enough range of acquisition parameter variations to be the most comprehensive and generalizable. Second, the number of RFs could be expanded with wavelet or Laplacian of Gaussian transformations, among others (32), but a recent study demonstrated higher concordance correlation coefficient values for unfiltered CT images than did the wavelet-filtered ones (33). A multitude of RFs have been proposed and evaluated (32), ranging from 15 RFs (10) to more than 1000 RFs (33), with Breast Imaging Reporting and Data System, Prostate Imaging Reporting and Data System, or Visually Accessible Rembrandt Images being semantic RFs.…”

Section: Discussionmentioning

confidence: 99%

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters

et al. 2018

View full text Add to dashboard Cite

Purpose To identify the reproducible and nonredundant radiomics features (RFs) for computed tomography (CT). Materials and Methods Two phantoms were used to test RF reproducibility by using test-retest analysis, by changing the CT acquisition parameters (hereafter, intra-CT analysis), and by comparing five different scanners with the same CT parameters (hereafter, inter-CT analysis). Reproducible RFs were selected by using the concordance correlation coefficient (as a measure of the agreement between variables) and the coefficient of variation (defined as the ratio of the standard deviation to the mean). Redundant features were grouped by using hierarchical cluster analysis. Results A total of 177 RFs including intensity, shape, and texture features were evaluated. The test-retest analysis showed that 91% (161 of 177) of the RFs were reproducible according to concordance correlation coefficient. Reproducibility of intra-CT RFs, based on coefficient of variation, ranged from 89.3% (151 of 177) to 43.1% (76 of 177) where the pitch factor and the reconstruction kernel were modified, respectively. Reproducibility of inter-CT RFs, based on coefficient of variation, also showed large material differences, from 85.3% (151 of 177; wood) to only 15.8% (28 of 177; polyurethane). Ten clusters were identified after the hierarchical cluster analysis and one RF per cluster was chosen as representative. Conclusion Many RFs were redundant and nonreproducible. If all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs (each representing a cluster) because of redundancy.

show abstract

Section: Discussionmentioning

confidence: 99%

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The complexity of the radiomics workflow increases the need to standardize computation methods (16)(17)(18)(19). Since September 2016, about 55 researchers from 19 institutions in 8 countries have participated in the Image Biomarker Standardization Initiative (IBSI), which aims at standardizing both the computation of features and the image-processing steps required before feature extraction (e.g., image interpolation and discretization).…”

Section: Guidelines For Improving Quality Of Radiomics Analysesmentioning

confidence: 99%

Responsible Radiomics Research for Faster Clinical Translation

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Consequently, shape-and volume-related features may constitute confounding factors. Additionally, the rationale for volume-related criteria was related to the fact that in HL typically small and large lesions co-exist, and that size affects lesion's heterogeneity (larger lesions have been reported to be more heterogeneous than the smaller ones [14][15][16]). Therefore, the rationale for volume-related criteria was aimed at identifying all potentially relevant information and discard collinear variables, without ignoring volume component that may be relevant to predict disease aggressiveness.…”

Section: Feature Selectionmentioning

confidence: 99%

Methodological framework for radiomics applications in Hodgkin Lymphoma

Sollini

Kirienko

Cavinato

et al. 2020

Preprint

View full text Add to dashboard Cite

Background According to published data radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet.Purpose The study aimed at setting up a methodological framework in radiomics applications in Hodgkin Lymphoma (HL), especially at: a) developing a novel feature selection approach; b) evaluating radiomic intra-patient lesions’ similarity; c) classifying relapsing refractory (R/R) vs non-(R/R) patients.Methods We retrospectively included 85 patients (male:female=52:33; median age 35 years, range 19-74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis-transformed features were used to build the fingerprints, that were tested to assess lesions’ similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersamplig Boosting of Tree Ensemble (RUBTE). Results HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity). Conclusions Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R, performed the best when all the lesions were used.

show abstract

PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology

Cited by 145 publications

References 82 publications

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters

Responsible Radiomics Research for Faster Clinical Translation

Methodological framework for radiomics applications in Hodgkin Lymphoma

Contact Info

Product

Resources

About