Dataset Information

PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability.

ABSTRACT: BACKGROUND:PET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of user-interaction. METHODS:Twenty PET images of bulky tumors were delineated independently by six observers using four strategies: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (Select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences. RESULTS:The results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other segmentation strategies (p-value<0.01). Interactive threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation. CONCLUSIONS:FDG PET segmentations of bulky tumors using strategies with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the Select-the-best workflow did outperform the other strategies tested and may be a good candidate for fast and reliable labeling of bulky and heterogeneous tumors.

SUBMITTER: Pfaehler E

PROVIDER: S-EPMC7105134 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability.

Pfaehler Elisabeth E Burggraaff Coreline C Kramer Gem G Zijlstra Josée J Hoekstra Otto S OS Jalving Mathilde M Noordzij Walter W Brouwers Adrienne H AH Stevenson Marc G MG de Jong Johan J Boellaard Ronald R

PloS one 20200330 3

<h4>Background</h4>PET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumor ...[more]

PMID: 32226030

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Project description:Besides basic measurements as maximum standardized uptake value (SUV)max or SUVmean derived from 18F-FDG positron emission tomography (PET) scans, more advanced quantitative imaging features (i.e. "Radiomics" features) are increasingly investigated for treatment monitoring, outcome prediction, or as potential biomarkers. With these prospected applications of Radiomics features, it is a requisite that they provide robust and reliable measurements. The aim of our study was therefore to perform an integrated stability analysis of a large number of PET-derived features in non-small cell lung carcinoma (NSCLC), based on both a test-retest and an inter-observer setup.Eleven NSCLC patients were included in the test-retest cohort. Patients underwent repeated PET imaging within a one day interval, before any treatment was delivered. Lesions were delineated by applying a threshold of 50% of the maximum uptake value within the tumor. Twenty-three NSCLC patients were included in the inter-observer cohort. Patients underwent a diagnostic whole body PET-computed tomography (CT). Lesions were manually delineated based on fused PET-CT, using a standardized clinical delineation protocol. Delineation was performed independently by five observers, blinded to each other. Fifteen first order statistics, 39 descriptors of intensity volume histograms, eight geometric features and 44 textural features were extracted. For every feature, test-retest and inter-observer stability was assessed with the intra-class correlation coefficient (ICC) and the coefficient of variability, normalized to mean and range. Similarity between test-retest and inter-observer stability rankings of features was assessed with Spearman's rank correlation coefficient.Results showed that the majority of assessed features had both a high test-retest (71%) and inter-observer (91%) stability in terms of their ICC. Overall, features more stable in repeated PET imaging were also found to be more robust against inter-observer variability.Results suggest that further research of quantitative imaging features is warranted with respect to more advanced applications of PET imaging as being used for treatment monitoring, outcome prediction or imaging biomarkers.

Project description:INTRODUCTION:Strong correlation has been demonstrated between tumor dose and response and between healthy liver dose and side effects. Individualized dosimetry is increasingly recommended in the current clinical routine. However, hepatic and tumor segmentations could be complex in some cases. The aim of this study is to assess the reproducibility of the tumoral and non-tumoral liver dosimetry in selective internal radiation therapy (SIRT). MATERIAL AND METHODS:Twenty-three patients with hepatocellular carcinoma (HCC) who underwent SIRT with glass microspheres were retrospectively included in the study. Tumor (TV) and total liver volumes (TLV), and mean absorbed doses in tumoral liver (TD) and non-tumoral liver (THLD) were determined on the 90Y PET/CT studies using Simplicit90YTM software, by three independent observers. Dosimetry datasets were obtained by a medical physicist helped by a nuclear medicine (NM) physician with 10 years of experience (A), by a NM physician with 4-year experience (B), and by a resident who first performed 10 dosimetry assessments as a training (C). Inter-observer agreement was evaluated using intra-class correlation coefficients (ICC), coefficients of variation (CV), Bland-Altman plots, and reproducibility coefficient (RDC). RESULTS:A strong agreement was observed between all three readers for estimating TLV (ICC 0.98) and THLD (ICC 0.97). Agreement was lower for TV delineation (ICC 0.94) and particularly for TD (ICC 0.73), especially for the highest values. Regarding TD, the CV (%) was 26.5, 26.9, and 20.2 between observers A and B, A and C, and B and C, respectively, and the RDC was 1.5. Regarding THLD, it was 8.5, 12.7, and 9.4, and the RDC was 1.3. CONCLUSION:Using a standardized methodology, and regardless of the different experiences of the observers, the estimation of THLD is highly reproducible. Although the reproducibility of the assessment of tumor irradiation is overall quite high, large variations may be observed in a limited number of patients.

Project description:ObjectivesEstablishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning.MethodsFour independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots.ResultsPre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement.ConclusionsManual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models.Key points• Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.

Dataset Information

PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability.

Publications

PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability.

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