Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer
This work was conducted in order to validate a pre-treatment quantitative ultrasound (QUS) and texture derivative analyses-based prediction model proposed in our previous study to identify responders and non-responders to neoadjuvant chemotherapy in patients with breast cancer. The validation cohort...
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MDPI AG
2025-04-01
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| Series: | Journal of Imaging |
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| author | Adrian Wai Chan Lakshmanan Sannachi Daniel Moore-Palhares Archya Dasgupta Sonal Gandhi Rossanna Pezo Andrea Eisen Ellen Warner Frances C. Wright Nicole Look Hong Ali Sadeghi-Naini Mia Skarpathiotakis Belinda Curpen Carrie Betel Michael C. Kolios Maureen Trudeau Gregory J. Czarnota |
| author_facet | Adrian Wai Chan Lakshmanan Sannachi Daniel Moore-Palhares Archya Dasgupta Sonal Gandhi Rossanna Pezo Andrea Eisen Ellen Warner Frances C. Wright Nicole Look Hong Ali Sadeghi-Naini Mia Skarpathiotakis Belinda Curpen Carrie Betel Michael C. Kolios Maureen Trudeau Gregory J. Czarnota |
| author_sort | Adrian Wai Chan |
| collection | DOAJ |
| description | This work was conducted in order to validate a pre-treatment quantitative ultrasound (QUS) and texture derivative analyses-based prediction model proposed in our previous study to identify responders and non-responders to neoadjuvant chemotherapy in patients with breast cancer. The validation cohort consisted of 56 breast cancer patients diagnosed between the years 2018 and 2021. Among all patients, 53 were treated with neoadjuvant chemotherapy and three had unplanned changes in their chemotherapy cycles. Radio Frequency (RF) data were collected volumetrically prior to the start of chemotherapy. In addition to tumour region (core), a 5 mm tumour-margin was also chosen for parameters estimation. The prediction model, which was developed previously based on quantitative ultrasound, texture derivative, and tumour molecular subtypes, was used to identify responders and non-responders. The actual response, which was determined by clinical and pathological assessment after lumpectomy or mastectomy, was then compared to the predicted response. The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score for determining chemotherapy response of all patients in the validation cohort were 94%, 67%, 96%, 57%, and 95%, respectively. Removing patients who had unplanned changes in their chemotherapy resulted in a sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of all patients in the validation cohort of 94%, 100%, 100%, 50%, and 97%, respectively. Explanations for the misclassified cases included unplanned modifications made to the type of chemotherapy during treatment, inherent limitations of the predictive model, presence of DCIS in tumour structure, and an ill-defined tumour border in a minority of cases. Validation of a model was conducted in an independent cohort of patient for the first time to predict the tumour response to neoadjuvant chemotherapy using quantitative ultrasound, texture derivate, and molecular features in patients with breast cancer. Further research is needed to improve the positive predictive value and evaluate whether the treatment outcome can be improved in predicted non-responders by switching to other treatment options. |
| format | Article |
| id | doaj-art-54b9b62d2b59459aa9fb24b5bc8794fc |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-54b9b62d2b59459aa9fb24b5bc8794fc2025-08-20T02:17:59ZengMDPI AGJournal of Imaging2313-433X2025-04-0111410910.3390/jimaging11040109Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast CancerAdrian Wai Chan0Lakshmanan Sannachi1Daniel Moore-Palhares2Archya Dasgupta3Sonal Gandhi4Rossanna Pezo5Andrea Eisen6Ellen Warner7Frances C. Wright8Nicole Look Hong9Ali Sadeghi-Naini10Mia Skarpathiotakis11Belinda Curpen12Carrie Betel13Michael C. Kolios14Maureen Trudeau15Gregory J. Czarnota16Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaPhysical Sciences, Sunnybrook Research Institute, Toronto, ON M4N 3M5, CanadaDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDivision of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDivision of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDivision of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDivision of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDivision of General Surgery, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDivision of General Surgery, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Physics, Ryerson University, Toronto, ON M5B 2K3, CanadaDivision of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaThis work was conducted in order to validate a pre-treatment quantitative ultrasound (QUS) and texture derivative analyses-based prediction model proposed in our previous study to identify responders and non-responders to neoadjuvant chemotherapy in patients with breast cancer. The validation cohort consisted of 56 breast cancer patients diagnosed between the years 2018 and 2021. Among all patients, 53 were treated with neoadjuvant chemotherapy and three had unplanned changes in their chemotherapy cycles. Radio Frequency (RF) data were collected volumetrically prior to the start of chemotherapy. In addition to tumour region (core), a 5 mm tumour-margin was also chosen for parameters estimation. The prediction model, which was developed previously based on quantitative ultrasound, texture derivative, and tumour molecular subtypes, was used to identify responders and non-responders. The actual response, which was determined by clinical and pathological assessment after lumpectomy or mastectomy, was then compared to the predicted response. The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score for determining chemotherapy response of all patients in the validation cohort were 94%, 67%, 96%, 57%, and 95%, respectively. Removing patients who had unplanned changes in their chemotherapy resulted in a sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of all patients in the validation cohort of 94%, 100%, 100%, 50%, and 97%, respectively. Explanations for the misclassified cases included unplanned modifications made to the type of chemotherapy during treatment, inherent limitations of the predictive model, presence of DCIS in tumour structure, and an ill-defined tumour border in a minority of cases. Validation of a model was conducted in an independent cohort of patient for the first time to predict the tumour response to neoadjuvant chemotherapy using quantitative ultrasound, texture derivate, and molecular features in patients with breast cancer. Further research is needed to improve the positive predictive value and evaluate whether the treatment outcome can be improved in predicted non-responders by switching to other treatment options.https://www.mdpi.com/2313-433X/11/4/109radiomicsmachine learningquantitative ultrasound |
| spellingShingle | Adrian Wai Chan Lakshmanan Sannachi Daniel Moore-Palhares Archya Dasgupta Sonal Gandhi Rossanna Pezo Andrea Eisen Ellen Warner Frances C. Wright Nicole Look Hong Ali Sadeghi-Naini Mia Skarpathiotakis Belinda Curpen Carrie Betel Michael C. Kolios Maureen Trudeau Gregory J. Czarnota Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Journal of Imaging radiomics machine learning quantitative ultrasound |
| title | Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer |
| title_full | Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer |
| title_fullStr | Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer |
| title_full_unstemmed | Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer |
| title_short | Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer |
| title_sort | validation of quantitative ultrasound and texture derivative analyses based model for upfront prediction of neoadjuvant chemotherapy response in breast cancer |
| topic | radiomics machine learning quantitative ultrasound |
| url | https://www.mdpi.com/2313-433X/11/4/109 |
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