Repeatability of [18F]FDG PET/CT total metabolic active tumour volume and total tumour burden in NSCLC patients

Kolinger, Guilherme Domingues; García, David Vállez; Krämer, G.; Frings, Virginie; Smit, Egbert F.; Langen, Adrianus J. de; Dierckx, Rudi; Hoekstra, Otto S.; Boellaard, Ronald

doi:10.1186/s13550-019-0481-1

Cited by 33 publications

(36 citation statements)

References 37 publications

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“…The lack of reasonable training data is one drawback making the clinical implementation of AI based segmentation algorithm challenging. However, the MV2 approach used in this study was found to result in accurate and robust segmentations in a previous study [7]. Moreover, in our study it also outperformed the conventional segmentation approaches in terms of repeatability without depending on training data.…”

Section: Discussionsupporting

confidence: 50%

“…For images acquired under the same conditions as in this study (i.e. 60 minutes time between tracer injection and scan start and EARLcompliant reconstructions), Kolinger et al found with repeatability coe cients between 43-56 higher repeatability coe cients than the ones of the AI based segmentations[7]. However, Kolinger et al also reported lower repeatability coe cients for MV3 and 41%SUV MAX segmentation approaches.…”

mentioning

confidence: 52%

“…3). The MV segmentation was chosen for this task as it resulted in previous work in the most accurate segmentation when compared with manual segmentations [7] and is easy to implement.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…The underlying segmentation algorithm are explained in the supplemental Sect. 3 and are also described in previous work [7]. The two MV segmentation methods include:…”

Section: Conventional Segmentation Algorithmmentioning

confidence: 99%

“…they are time-consuming, non-reproducible and come with a high inter-observer variability [4][5][6]. A recent study also demonstrated that even the consensus of several observers results in a low repeatability [7].…”

Section: Introductionmentioning

confidence: 99%

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Repeatability of Two Artificial Intelligence Approaches for Tumor Segmentation in PET

Pfaehler

Mesotten

Kramer

et al. 2020

Preprint

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Background: Positron Emission Tomography (PET) is routinely used for cancer staging and treatment follow up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV - including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis (TLG) derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. Methods: In this study, we compare two artificial intelligence (AI) based segmentation methods with conventional segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a Convolutional Neural Network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the jaccard coefficient (JC). Additionally, the approaches are applied on a fully independent test-retest dataset. The repeatability of the methods is compared with the repeatability of two majority vote (MV2, MV3) approaches, 41%SUVMAX, and a SUV>4 segmentation (SUV4). Repeatability is assessed with test-retest coefficients (TRT%) and intraclass correlation coefficient (ICC). A TRT% of 0 indicates perfect repeatability and an ICC>0.9 was regarded as representing excellent repeatability.Results: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73) Both segmentation approaches outperformed together with the MV2 approach the other conventional segmentation methods in terms of test-retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUVMAX: 28.1%, SUV4: 18.1% ) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUVMAX: 0.68).Conclusion: The AI based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.

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Section: Discussionsupporting

confidence: 50%

mentioning

confidence: 52%

“…3). The MV segmentation was chosen for this task as it resulted in previous work in the most accurate segmentation when compared with manual segmentations [7] and is easy to implement.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…The underlying segmentation algorithm are explained in the supplemental Sect. 3 and are also described in previous work [7]. The two MV segmentation methods include:…”

Section: Conventional Segmentation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Repeatability of Two Artificial Intelligence Approaches for Tumor Segmentation in PET

Pfaehler

Mesotten

Kramer

et al. 2020

Preprint

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Plausibility and redundancy analysis to select FDG‐PET textural features in non‐small cell lung cancer

et al. 2021

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Background Radiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non‐small cell lung cancer (NSCLC) dataset. Materials and methods The proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1‐yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume. Results The number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4–10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%). Conclusion When performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the sam...

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Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images

Pfaehler

Mesotten

Kramer

et al. 2020

Communications in Computer and Information Science

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Repeatability of [18F]FDG PET/CT total metabolic active tumour volume and total tumour burden in NSCLC patients

Cited by 33 publications

References 37 publications

Repeatability of Two Artificial Intelligence Approaches for Tumor Segmentation in PET

Repeatability of Two Artificial Intelligence Approaches for Tumor Segmentation in PET

Plausibility and redundancy analysis to select FDG‐PET textural features in non‐small cell lung cancer

Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images

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