Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging

Roy, Sudipta; Whitehead, Terence R.; Quirk, James D.; Salter, Amber; Ademuyiwa, Foluso O.; Li, Shunqiang; An, Hongyu; Shoghi, Kooresh I.

doi:10.1016/j.ebiom.2020.102963

Cited by 75 publications

(52 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To our knowledge, this study represents the first such effort to optimize radiomic features in preclinical PET imaging to predict/assess response to therapy in TNBC PDX. We recently characterized the dependency of preclinical MR radiomic features on tumor volume [36]. In this work, we confirmed dependency of preclinical PET radiomic features on tumor volume with strikingly similar clinical parallels.…”

Section: Discussionsupporting

confidence: 76%

Co-clinical FDG-PET Radiomic Signature in Predicting Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

Roy

Whitehead

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Purpose. We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to optimize robust radiomic features associated with response to therapy in the context of a co-clinical trial and implement PDX-optimized image features in the corresponding clinical study to predict and assess response to therapy using machine-learning (ML) algorithms. Methods. TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging study to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naive Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass normalized SULpeak measures. Results. Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak, measures. Conclusions. We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in a context of a co-clinical imaging trial.

show abstract

Section: Discussionsupporting

confidence: 76%

Co-clinical FDG-PET Radiomic Signature in Predicting Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

Roy

Whitehead

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…None of the second- or higher-order features extracted from MR images of our homogenous phantom achieved excellent agreement in the OCCCs. These parameters identified as volume confounded in our study were also reported unstable in the in vivo MRI study by Roy et al, who investigated stability across different tumor volumes on breast cancer patients with T1w and T2w MR sequences [ 29 ]. Therefore, these features do not seem reliable for use in MRI-based texture analysis from differently sized ROIs, and studies based on MR-derived second- and higher-order features should be scrutinized.…”

Section: Discussionsupporting

confidence: 61%

“…In many studies, lesions were marked with ROIs, but the lesions and consecutively the ROIs had different sizes. Considering our results, however, it has to be validated if the ROI size is a pivotal influencing factor in radiomics, for example, by sorting lesions by volume and voxel size and comparing heterogeneities of the radiomic features or by normalizing the features by voxel count or volume [ 17 , 29 , 42 ]. Thus, before applying radiomics in clinical routine, volume as a confounding factor needs to be investigated further.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in a study investigating radiation-induced lung disease in CT scans, Choi et al found that only 16 of 27 texture features were robust across different tumor sizes [ 28 ]. Roy et al found 16 radiomic features dependent on tumor size in breast cancer lesions and suggested normalization for volume dependency to be used for the confounded features [ 29 ]. Traverso et al investigated volume-confounding in 841 radiomic features derived from lung and head and neck tumors and found nearly 30% strongly correlated with tumor volume [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stability of Radiomic Features across Different Region of Interest Sizes—A CT and MR Phantom Study

et al. 2021

View full text Add to dashboard Cite

We aimed to evaluate radiomic features’ stability across different region of interest (ROI) sizes in CT and MR images. We chose a phantom with a homogenous internal structure so no differences for a feature extracted from ROIs of different sizes would be expected. For this, we scanned a plastic cup filled with sodium chloride solution ten times in CT and per MR sequence (T1-weighted-gradient-echo and T2-weighted-turbo-inversion-recovery-magnitude). We placed sphere-shaped ROIs of different diameters (4, 8, and 16 mm, and 4, 8, and 16 pixels) into the phantom’s center. Features were extracted using PyRadiomics. We assessed feature stability across ROI sizes with overall concordance correlation coefficients (OCCCs). Differences were tested for significance with the Mann–Whitney U-test. Of 93 features, 87 T1w-derived, 87 TIRM-derived, and 70 CT-derived features were significantly different between ROI sizes. Among MR-derived features, OCCCs showed excellent (>0.90) agreement for mean, median, and root mean squared for ROI sizes between 4 and 16 mm and pixels. We further observed excellent agreement for 10th and 90th percentile in T1w and 10th percentile in T2w TIRM images. There was no excellent agreement among the OCCCs of CT-derived features. In summary, many features indicated significant differences and only few showed excellent agreement across varying ROI sizes, although we examined a homogenous phantom. Since we considered a small phantom in an experimental setting, further studies to investigate this size effect would be necessary for a generalization. Nevertheless, we believe knowledge about this effect is crucial in interpreting radiomics studies, as features that supposedly discriminate disease entities may only indicate a systematic difference in ROI size.

show abstract

“…However, it is difficult to identify whether pSPN is invasive before operation (39). Radiomic analysis has been proposed as a step towards realization of precision medicine by providing means to interrogate the spatial complexity of tumors in vivo (40). Therefore, we try to identify the invasiveness of pSPN by radiomics features and comprehensively consider the effectiveness of different contrastenhanced phases and 2D or 3D segmentation.…”

Section: Discussionmentioning

confidence: 99%

Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm

Huang

Liu²,

Han

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

PurposeIt is challenging for traditional CT signs to predict invasiveness of pancreatic solid pseudopapillary neoplasm (pSPN). We aim to develop and evaluate CT-based radiomics signature to preoperatively predict invasive behavior in pSPN.MethodsEighty-five patients who had pathologically confirmed pSPN and preoperative contrasted-enhanced CT imaging in our hospital were retrospectively analyzed (invasive: 24; non-invasive: 61). 1316 radiomics features were separately extracted from delineated 2D or 3D ROIs in arterial and venous phases. 200% (SMOTE) was used to generate balanced dataset (invasive: 72, non-invasive: 96) for each phase, which was for feature selection and modeling. The model was internally validated in the original dataset. Inter-observer consistency analysis, spearman correlation, univariate analysis, LASSO regression and backward stepwise logical regression were mainly applied to screen the features, and 6 logistic regression models were established based on multi-phase features from 2D or 3D segmentations. The ROC analysis and Delong’s test were mainly used for model assessment and AUC comparison.ResultsIt retained 11, 8, 7 and 7 features to construct 3D-arterial, 3D-venous, 2D-arterial and 2D-venous model. Based on 3D ROIs, the arterial model (AUC: 0.914) performed better than venous (AUC: 0.815) and the arterial-venous combined model was slightly improved (AUC: 0.918). Based on 2D ROIs, the arterial model (AUC: 0.814) performed better than venous (AUC:0.768), while the arterial-venous combined model (AUC:0.893) performed better than any single-phase model. In addition, the 3D arterial model performed better than the best combined 2D model. The Delong’s test showed that the significant difference of model AUC existed in arterial models in original dataset (p = 0.019) while not in arterial-venous combined model (p=0.49) as comparing 2D and 3D ROIs.ConclusionThe arterial radiomics model constructed by 3D-ROI feature is potential to predict the invasiveness of pSPN preoperatively.

show abstract

Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging

Cited by 75 publications

References 48 publications

Co-clinical FDG-PET Radiomic Signature in Predicting Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

Co-clinical FDG-PET Radiomic Signature in Predicting Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

Stability of Radiomic Features across Different Region of Interest Sizes—A CT and MR Phantom Study

Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm

Contact Info

Product

Resources

About