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Research Article
Gregory Czarnota
Sunnybrook Health Science Centre
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background The purpose of the study was to investigate the role of pre-treatment quantitative ultrasound (QUS)-radiomics in predicting recurrence for patients with locally advanced breast cancer (LABC).
Methods A prospective study was conducted with patients with LABC (n=83). Primary tumours were scanned using a clinical ultrasound device before starting treatment. Ninety-five imaging features were extracted-spectral features, texture, and texture-derivatives. Patients were determined to have recurrence or no recurrence based on clinical outcomes. Machine learning classifiers with k-nearest neighbour (KNN) and support vector machine (SVM) were evaluated for model development using a maximum of 3 features and leave-one-out cross-validation.
Results With a median follow up of 69 months (range 7-118 months), 28 patients had disease recurrence (local or distant). The best classification results were obtained using an SVM classifier with a sensitivity, specificity, accuracy and area under curve of 71%, 87%, 82%, and 0.76, respectively. Using the SVM model for the predicted non-recurrence and recurrence groups, the estimated 5-year recurrence-free survival was 83% and 54% (p=0.003), and the predicted 5-year overall survival was 85% and 74% (p=0.083), respectively.
Conclusion A QUS-radiomics model using higher-order texture derivatives can predict patients with LABC at higher risk of disease recurrence before starting treatment.
Keywords
Radiomics, Breast cancer, Quantitative ultrasound, Recurrence, Neoadjuvant chemotherapy, Texture analysis, Texture derivatives, Machine learning.
Figure 1
Figure 2
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
- Supplementaryfigurescombined.pdf
Supplementary Figure 1: Venn diagram representing the pattern of relapse (local, regional or distant) when first diagnosed with disease recurrence. Supplementary Figure 2: Scatter plots of all 95 features showing the distribution between the two groups (Recurrence vs Non-recurrence).
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CURRENT STATUS: Under Revision
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On 26 Apr, 2021
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Received 27 Mar, 2021
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On 11 Mar, 2021
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On 18 Dec, 2020
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This preprint is under consideration at Scientific Reports. A preprint is 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
Research Article
Gregory Czarnota
Sunnybrook Health Science Centre
Corresponding Author
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: Under Revision
Version 1
Posted 21 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 26 Apr, 2021
Reviews received
Received 22 Apr, 2021
Reviewers agreed
On 22 Apr, 2021
Reviews received
Received 27 Mar, 2021
Reviewers agreed
On 11 Mar, 2021
Reviewers invited
Invitations sent on 04 Jan, 2021
Editor assigned
On 18 Dec, 2020
Editor invited
On 18 Dec, 2020
Submission checks complete
On 18 Dec, 2020
First submitted
On 12 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background The purpose of the study was to investigate the role of pre-treatment quantitative ultrasound (QUS)-radiomics in predicting recurrence for patients with locally advanced breast cancer (LABC).
Methods A prospective study was conducted with patients with LABC (n=83). Primary tumours were scanned using a clinical ultrasound device before starting treatment. Ninety-five imaging features were extracted-spectral features, texture, and texture-derivatives. Patients were determined to have recurrence or no recurrence based on clinical outcomes. Machine learning classifiers with k-nearest neighbour (KNN) and support vector machine (SVM) were evaluated for model development using a maximum of 3 features and leave-one-out cross-validation.
Results With a median follow up of 69 months (range 7-118 months), 28 patients had disease recurrence (local or distant). The best classification results were obtained using an SVM classifier with a sensitivity, specificity, accuracy and area under curve of 71%, 87%, 82%, and 0.76, respectively. Using the SVM model for the predicted non-recurrence and recurrence groups, the estimated 5-year recurrence-free survival was 83% and 54% (p=0.003), and the predicted 5-year overall survival was 85% and 74% (p=0.083), respectively.
Conclusion A QUS-radiomics model using higher-order texture derivatives can predict patients with LABC at higher risk of disease recurrence before starting treatment.
Figure 1
Figure 2
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
- Supplementaryfigurescombined.pdf
Supplementary Figure 1: Venn diagram representing the pattern of relapse (local, regional or distant) when first diagnosed with disease recurrence. Supplementary Figure 2: Scatter plots of all 95 features showing the distribution between the two groups (Recurrence vs Non-recurrence).
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