Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound
Bibliographic record
Abstract
// Archya Dasgupta 1 , 2 , 3 , Divya Bhardwaj 3 , Daniel DiCenzo 3 , Kashuf Fatima 3 , Laurentius Oscar Osapoetra 3 , Karina Quiaoit 3 , Murtuza Saifuddin 3 , Stephen Brade 3 , Maureen Trudeau 4 , 5 , Sonal Gandhi 4 , 5 , Andrea Eisen 4 , 5 , Frances Wright 6 , 7 , Nicole Look-Hong 6 , 7 , Ali Sadeghi-Naini 1 , 3 , 8 , 9 , Belinda Curpen 10 , 11 , Michael C. Kolios 12 , Lakshmanan Sannachi 3 and Gregory J. Czarnota 1 , 2 , 3 , 8 1 Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada 2 Department of Radiation Oncology, University of Toronto, Toronto, Canada 3 Physical Sciences, Sunnybrook Research Institute, Toronto, Canada 4 Department of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada 5 Department of Medicine, University of Toronto, Toronto, Canada 6 Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada 7 Department of Surgery, University of Toronto, Toronto, Canada 8 Department of Medical Biophysics, University of Toronto, Toronto, Canada 9 Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada 10 Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada 11 Department of Medical Imaging, University of Toronto, Toronto, Canada 12 Department of Physics, Ryerson University, Toronto, Canada Correspondence to: Gregory J. Czarnota, email: gregory.czarnota@sunnybrook.ca Keywords: radiomics; breast cancer; quantitative ultrasound; recurrence; machine learning Received: August 23, 2021     Accepted: November 10, 2021     Published: December 07, 2021 Copyright: © 2021 Dasgupta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT 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). Materials and Methods: A prospective study was conducted in 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. Conclusions: A QUS-radiomics model using higher-order texture derivatives can identify patients with LABC at higher risk of disease recurrence before starting treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".