See the published version of this preprint at European Journal of Hybrid Imaging.
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Original article
Margarita Kirienko
Humanitas University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3923-1151
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background According to published data radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet.
Purpose The study aimed at setting up a methodological framework in radiomics applications in Hodgkin Lymphoma (HL), especially at: a) developing a novel feature selection approach; b) evaluating radiomic intra-patient lesions’ similarity; c) classifying relapsing refractory (R/R) vs non-(R/R) patients.
Methods We retrospectively included 85 patients (male:female=52:33; median age 35 years, range 19-74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis-transformed features were used to build the fingerprints, that were tested to assess lesions’ similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersamplig Boosting of Tree Ensemble (RUBTE).
Results HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity).
Conclusions Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R, performed the best when all the lesions were used.
Keywords
Lymphoma, PET/CT, Radiomics, Similarity, Feature selection, Silhouette, Response prediction, Outcome prediction
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CURRENT STATUS: Published
View the published article at European Journal of Hybrid Imaging.
No community comments so far
Editorial decision: Accept
On 06 May, 2020
No comments provided
Editorial decision: Minor revision
On 05 May, 2020
Review #2 received
Received 04 May, 2020
Review #1 received
Received 29 Apr, 2020
Reviewer #2 agreed
On 28 Apr, 2020
Reviewers invited
Invitations sent on 28 Apr, 2020
Reviewer #1 agreed
On 28 Apr, 2020
Editor assigned
On 23 Apr, 2020
Submission checks complete
On 22 Apr, 2020
Editor invited
On 22 Apr, 2020
Version 1
Posted 25 Mar, 2020
No comments provided
Editorial decision: Major revision
On 09 Apr, 2020
Review #2 received
Received 07 Apr, 2020
Review #1 received
Received 07 Apr, 2020
Reviewer #4 agreed
On 26 Mar, 2020
Editor assigned
On 25 Mar, 2020
Reviewers invited
Invitations sent on 25 Mar, 2020
Reviewer #1 agreed
On 25 Mar, 2020
Reviewer #2 agreed
On 25 Mar, 2020
Reviewer #3 agreed
On 25 Mar, 2020
Submission checks complete
On 24 Mar, 2020
Editor invited
On 24 Mar, 2020
First submitted
On 22 Mar, 2020
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See the published version of this preprint at European Journal of Hybrid Imaging.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Original article
Margarita Kirienko
Humanitas University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3923-1151
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at European Journal of Hybrid Imaging.
No community comments so far
Editorial decision: Accept
On 06 May, 2020
No comments provided
Editorial decision: Minor revision
On 05 May, 2020
Review #2 received
Received 04 May, 2020
Review #1 received
Received 29 Apr, 2020
Reviewer #2 agreed
On 28 Apr, 2020
Reviewers invited
Invitations sent on 28 Apr, 2020
Reviewer #1 agreed
On 28 Apr, 2020
Editor assigned
On 23 Apr, 2020
Submission checks complete
On 22 Apr, 2020
Editor invited
On 22 Apr, 2020
Version 1
Posted 25 Mar, 2020
No comments provided
Editorial decision: Major revision
On 09 Apr, 2020
Review #2 received
Received 07 Apr, 2020
Review #1 received
Received 07 Apr, 2020
Reviewer #4 agreed
On 26 Mar, 2020
Editor assigned
On 25 Mar, 2020
Reviewers invited
Invitations sent on 25 Mar, 2020
Reviewer #1 agreed
On 25 Mar, 2020
Reviewer #2 agreed
On 25 Mar, 2020
Reviewer #3 agreed
On 25 Mar, 2020
Submission checks complete
On 24 Mar, 2020
Editor invited
On 24 Mar, 2020
First submitted
On 22 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at European Journal of Hybrid Imaging.
Background According to published data radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet.
Purpose The study aimed at setting up a methodological framework in radiomics applications in Hodgkin Lymphoma (HL), especially at: a) developing a novel feature selection approach; b) evaluating radiomic intra-patient lesions’ similarity; c) classifying relapsing refractory (R/R) vs non-(R/R) patients.
Methods We retrospectively included 85 patients (male:female=52:33; median age 35 years, range 19-74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis-transformed features were used to build the fingerprints, that were tested to assess lesions’ similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersamplig Boosting of Tree Ensemble (RUBTE).
Results HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity).
Conclusions Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R, performed the best when all the lesions were used.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
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