Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using <scp>FDG</scp>‐<scp>PET</scp>/<scp>CT</scp> in Hodgkin and non‐Hodgkin lymphomas

Sadik, May; Lind, Erica; Polymeri, Eirini; Enqvist, Olof; Ulén, Johannes; Trägårdh, Elin

doi:10.1111/cpf.12546

Cited by 19 publications

(18 citation statements)

References 14 publications

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“…Sadik et al [11] developed automated segmentation of the liver and thoracic aorta as a first step towards an automated method for evaluating treatment response in patients with lymphoma, since those organs are reference organs in the Deauville 5-point scale. The CNN-method showed good agreement with results obtained by experienced radiologists who had manually segmented the CT images.…”

Section: Discussionmentioning

confidence: 99%

“…These studies were approved by the Regional Ethical Review Board (#295/08) and were performed following the Declaration of Helsinki. Patients and image acquisition have been described previously [ 7 , 8 , 10 , 11 ].…”

Section: Methodsmentioning

confidence: 99%

“…The CNN-based organ segmentation in CT studies in RECOMIA has been used in multiple studies [7][8][9][10][11][12]. These studies were approved by the Regional Ethical Review Board (#295/08) and were performed following the Declaration of Helsinki.…”

Section: Patients and Manual Segmentationsmentioning

confidence: 99%

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RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

et al. 2020

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Background: Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image. Results: The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones). Conclusion: The paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request at www.recomia.org for research purposes.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

et al. 2020

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“…A machine learning method described previously [12] was used to segment the liver and the mediastinal blood pool (thoracic part of the aorta) in the CT images. One radiology resident and one specialist in radiology and nuclear medicine corrected the automated segmentations when needed.…”

Section: Methodsmentioning

confidence: 99%

The use of a proposed updated EARL harmonization of 18F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations

Minarik

Edenbrandt

et al. 2019

EJNMMI Res

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Background The Deauville score (DS) is a clinical tool, based on the comparison between lesion and reference organ uptake of 18 F-fluorodeoxyglucose (FDG), used to stratify patients with lymphoma into categories reflecting their disease status. With a plethora of positron emission tomography with computed tomography (PET-CT) hard- and software algorithms, standard uptake value (SUV) in lesions and reference organs may differ which affects DS classification and therefore medical treatment. The EANM Research Ltd. (EARL) harmonization program from the European Association of Nuclear Medicine (EANM) partly mitigates this issue, but local preferences are common in clinical practice. We have investigated the discordance in DS calculated from patients with lymphoma referred for 18 F-FDG PET-CT reconstructed with three different algorithms: the newly introduced block-sequential regularization expectation-maximization algorithm commercially sold as Q. Clear (QC, GE Healthcare, Milwaukee, WI, USA), compliant with the newly proposed updated EARL recommendations, and two settings compliant with the current EARL recommendations (EARL lower and EARL upper , representing the lower and upper limit of the EARL recommendations). Methods Fifty-two patients with non-Hodgkin and Hodgkin lymphoma were included (18 females and 34 males). Segmentation of mediastinal blood pool and liver were semi-automatically performed, whereas segmentation of lesions was done manually. From these segmentations, SUV max and SUV peak were obtained and DS calculated. Results There was a significant difference in DS between the QC algorithm and EARL lower /EARL upper ( p < 0.0001 for both) but not between EARL lower and EARL upper ( p = 0.102) when SUV max was used. For SUV peak , there was a significant difference between QC and EARL lower ( p = 0.001), but not for QC vs EARL upper ( p = 0.071) or EARL lower vs EARL upper ( p = 0.102). Five non-responders (DS 4–5) for QC were classified as responders (DS 1–3) when EARL lower /EARL upper was used, both when SUV max and SUV peak were investigated. Conclusion Using the proposed updated EARL recommendations compared with the current recommendations will significantly change DS classification. In select cases, the discordance would affect the c...

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“…Limitations related to the gold standard are that the 5-PS and the subsequent Lugano assessment are a qualitative gold standard based on visual assessment, which results influenced by inter-observer variability due to the subjectivity of the interpretation [23]. We could have been accompanied this qualitative assessment with a semiquantitative one based on Standardized Uptake Value (SUV) values.…”

Section: Discussionmentioning

confidence: 99%

Automatic Prediction and Assessment of Treatment Response in Patients with Hodgkin’s Lymphoma Using a Whole-Body DW-MRI Based Approach

et al. 2020

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The lack of validation and standardization represents the main drawback for a clear role of whole-body diffusion weighted imaging (WB-DWI) for prediction and assessment of treatment response in Hodgkin’s lymphoma (HL). We explored the reliability of an automatic approach based on the WB-DWI technique for prediction and assessment of response to treatment in patients with HL. The study included 20 HL patients, who had whole-body positron emission tomography (PET)/ magnetic resonance Imaging (MRI) performed before, during and after chemotherapy. Using the syngo.via MR Total Tumor Load tool, we automatically extracted values of diffusion volume (DV) and its associated histogram features by WB-DWI images, and evaluated their utility in predicting and assessing interim and end-of-treatment (EOT) response. The Mann–Whitney test followed by receiver operator characteristic (ROC) analysis was performed between features and their inter-time point percentage differences for patients having a complete or partial treatment response, revealing that several WB-DWI associated features allowed for prediction of interim response and both prediction and assessment of EOT response. Our proposed method offers huge advantages in terms of saving time and work, enabling clinicians to draw conclusions relating to HL treatment response in a fully automatic way, and encloses, also, all DWI advantages compared to PET/ computed tomography (CT).

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Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG‐PET/CT in Hodgkin and non‐Hodgkin lymphomas

Cited by 19 publications

References 14 publications

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

The use of a proposed updated EARL harmonization of 18F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations

Automatic Prediction and Assessment of Treatment Response in Patients with Hodgkin’s Lymphoma Using a Whole-Body DW-MRI Based Approach

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