Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation

Yamashita, Rikiya; Perrin, Thomas; Chakraborty, Jayasree; Chou, Joanne F.; Horvat, Natally; Koszalka, Maura A; Midya, Abhishek; Gönen, Mithat; Allen, Peter J.; Jarnagin, William R.; Simpson, Amber L.; Richard, K. G.

doi:10.1007/s00330-019-06381-8

Cited by 60 publications

(49 citation statements)

References 37 publications

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“…The reason for this high interobserver agreement may be explained by the high accuracy of the semi‐automated intensity tracing algorithm for this relatively simple task to segment the phantom from the image background. In our results, it seemed that the repeatability of many radiomics features subjected more to acquisition variations than to segmentation uncertainty, which was in line with the findings recently reported in a few clinical studies 53,54 . But, this indication could still not be safely extended to generic radiomics use in MRgRT, in which target tumor and/or organs at risk (OARs) delineation relies heavily on manual segmentation, which might subject to considerable interobserver segmentation variability.…”

Section: Discussionsupporting

confidence: 87%

Longitudinal acquisition repeatability of MRI radiomics features: An ACR MRI phantom study on two MRI scanners using a 3D T1W TSE sequence

et al. 2021

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Purpose The purpose of this study was to quantitatively assess the longitudinal acquisition repeatability of MRI radiomics features in a three‐dimensional (3D) T1‐weighted (T1W) TSE sequence via a well‐controlled prospective phantom study. Methods Thirty consecutive daily datasets of an ACR‐MRI phantom were acquired on two 1.5T MRI simulators using a 3D T1W TSE sequence. Images were blindly segmented by two observers. Post‐acquisition processing was minimized but an intensity discretization (fixed bin size of 25). One hundred and one radiomics features (shape n = 12; first order n = 16; texture n = 73) were extracted. Longitudinal repeatability of each feature was evaluated by Pearson correlation and coefficient of variance (CV68%). Interobserver feature value agreement was also quantified using intraclass correlation coefficient (ICC) and Bland–Altman analysis. A most repeatable radiomics feature set on both scanners was determined by feature coefficient of variance (CV68%<5%), ICC (>0.75), and the ratio of the interobserver difference to the interobserver mean δ<5%. Results No trend of radiomics feature value changed with time. Longitudinal feature repeatability CV68% ranged 0.01–38.60% (mean/median: 12.5%/9.9%), and 0.01–40.47%, (8.49%/7.34%) on the scanners A and B. Shape features exhibited significantly better repeatability than first‐order and texture features (all P < 0.01). Significant longitudinal repeatability difference was observed in texture features (P < 0.001) between the two scanners, but not in shape and first‐order features (P > 0.30). First‐order and texture features had smaller interobserver‐dependent variation than acquisition‐dependent variation. They also showed good interobserver agreement on both scanners (A:ICC = 0.80 ± 0.23; B:ICC = 0.80 ± 0.22), independent of acquisition repeatability. The repeatable radiomics features in common on both scanners, including 12 shape features, 0 first‐order features, and 3 texture features, were determined as the most repeatable MRI radiomics feature set. Conclusions Radiomics features exhibited heterogeneous longitudinal repeatability, while the shape features were the most repeatable, in this phantom study with a 3D T1W TSE acquisition. The most repeatable radiomics feature set derived in this study should be helpful for the selection of reliable radiomics features in the future clinical use.

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Section: Discussionsupporting

confidence: 87%

Longitudinal acquisition repeatability of MRI radiomics features: An ACR MRI phantom study on two MRI scanners using a 3D T1W TSE sequence

et al. 2021

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“…Moreover, among the many confounding effects in radiomics such as scanner device, vendor, reconstruction method, image preprocessing and feature implementation, we only examined the influence of segmentation variability. Yamashita et al claimed that variations between scans had a higher impact on reproducibility than segmentation 17 . An additional aspect that was not covered in this work is the question as to what extent feature reproducibility translates into the reproducibility of a whole radiomic signature, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…Zwanenburg et al 16 assessed radiomics feature robustness by image perturbation in computed tomography (CT) images. Yamashita et al 17 found that for contrast-enhanced CT images of patients with pancreatic cancer, scan parameters had stronger influence on radiomics features than segmentation variability. Tunali et al 18 assessed reproducibility of radiomic features extracted from peritumoral regions of lung cancer lesions.…”

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confidence: 99%

Radiomics feature reproducibility under inter-rater variability in segmentations of CT images

Haarburger

Müller‐Franzes

Weninger

et al. 2020

Sci Rep

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identifying image features that are robust with respect to segmentation variability is a tough challenge in radiomics. So far, this problem has mainly been tackled in test-retest analyses. in this work we analyse radiomics feature reproducibility in two phases: first with manual segmentations provided by four expert readers and second with probabilistic automated segmentations using a recently developed neural network (pHiseg). We test feature reproducibility on three publicly available datasets of lung, kidney and liver lesions. We find consistent results both over manual and automated segmentations in all three datasets and show that there are subsets of radiomic features which are robust against segmentation variability and other radiomic features which are prone to poor reproducibility under differing segmentations. By providing a detailed analysis of robustness of the most common radiomics features across several datasets, we envision that more reliable and reproducible radiomic models can be built in the future based on this work.

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“…At present, the role of contouring in RFs reproducibility has been addressed in several studies [24][25][26][27], but to the best of knowledge, none of them concerned hepatic CRC metastases. Therefore, the aim of this work is to assess the influence of inter-reader contouring variability on the texture analysis of CRC liver metastases, focusing on the role of three-and two-dimensional segmentation in determining RFs robustness.…”

Section: Introductionmentioning

confidence: 99%

Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases

et al. 2020

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Background Radiomics is expected to improve the management of metastatic colorectal cancer (CRC). We aimed at evaluating the impact of liver lesion contouring as a source of variability on radiomic features (RFs). Methods After Ethics Committee approval, 70 liver metastases in 17 CRC patients were segmented on contrast-enhanced computed tomography scans by two residents and checked by experienced radiologists. RFs from grey level co-occurrence and run length matrices were extracted from three-dimensional (3D) regions of interest (ROIs) and the largest two-dimensional (2D) ROIs. Inter-reader variability was evaluated with Dice coefficient and Hausdorff distance, whilst its impact on RFs was assessed using mean relative change (MRC) and intraclass correlation coefficient (ICC). For the main lesion of each patient, one reader also segmented a circular ROI on the same image used for the 2D ROI. Results The best inter-reader contouring agreement was observed for 2D ROIs according to both Dice coefficient (median 0.85, interquartile range 0.78–0.89) and Hausdorff distance (0.21 mm, 0.14–0.31 mm). Comparing RF values, MRC ranged 0–752% for 2D and 0–1567% for 3D. For 24/32 RFs (75%), MRC was lower for 2D than for 3D. An ICC > 0.90 was observed for more RFs for 2D (53%) than for 3D (34%). Only 2/32 RFs (6%) showed a variability between 2D and circular ROIs higher than inter-reader variability. Conclusions A 2D contouring approach may help mitigate overall inter-reader variability, albeit stable RFs can be extracted from both 3D and 2D segmentations of CRC liver metastases.

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Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation

Cited by 60 publications

References 37 publications

Longitudinal acquisition repeatability of MRI radiomics features: An ACR MRI phantom study on two MRI scanners using a 3D T1W TSE sequence

Longitudinal acquisition repeatability of MRI radiomics features: An ACR MRI phantom study on two MRI scanners using a 3D T1W TSE sequence

Radiomics feature reproducibility under inter-rater variability in segmentations of CT images

Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases

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