{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bhardwaj D"],"funding":["Terry Fox Foundation","Canadian Institute of Health Research"],"pagination":["1247"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8909335"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(5)"],"pubmed_abstract":["<h4>Background</h4>This study was conducted to explore the use of quantitative ultrasound (QUS) in predicting recurrence for patients with locally advanced breast cancer (LABC) early during neoadjuvant chemotherapy (NAC).<h4>Methods</h4>Eighty-three patients with LABC were scanned with 7 MHz ultrasound before starting NAC (week 0) and during treatment (week 4). Spectral parametric maps were generated corresponding to tumor volume. Twenty-four textural features (QUS-Tex<sup>1</sup>) were determined from parametric maps acquired using grey-level co-occurrence matrices (GLCM) for each patient, which were further processed to generate 64 texture derivatives (QUS-Tex<sup>1</sup>-Tex<sup>2</sup>), leading to a total of 95 features from each time point. Analysis was carried out on week 4 data and compared to baseline (week 0) data. ∆Week 4 data was obtained from the difference in QUS parameters, texture features (QUS-Tex<sup>1</sup>), and texture derivatives (QUS-Tex<sup>1</sup>-Tex<sup>2</sup>) of week 4 data and week 0 data. Patients were divided into two groups: recurrence and non-recurrence. Machine learning algorithms using <i>k</i>-nearest neighbor (k-NN) and support vector machines (SVMs) were used to generate radiomic models. Internal validation was undertaken using leave-one patient out cross-validation method.<h4>Results</h4>With a median follow up of 69 months (range 7-118 months), 28 patients had disease recurrence. The k-NN classifier was the best performing algorithm at week 4 with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 75%, 81%, and 0.83, respectively. The inclusion of texture derivatives (QUS-Tex<sup>1</sup>-Tex<sup>2</sup>) in week 4 QUS data analysis led to the improvement of the classifier performances. The AUC increased from 0.70 (0.59 to 0.79, 95% confidence interval) without texture derivatives to 0.83 (0.73 to 0.92) with texture derivatives. The most relevant features separating the two groups were higher-order texture derivatives obtained from scatterer diameter and acoustic concentration-related parametric images.<h4>Conclusions</h4>This is the first study highlighting the utility of QUS radiomics in the prediction of recurrence during the treatment of LABC. It reflects that the ongoing treatment-related changes can predict clinical outcomes with higher accuracy as compared to pretreatment features alone."],"journal":["Cancers"],"pubmed_title":["Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer."],"pmcid":["PMC8909335"],"funding_grant_id":["PJT 159759","Hecht Foundation (grant number 1083)"],"pubmed_authors":["Bhardwaj D","Eisen A","DiCenzo D","Sannachi L","Brade S","Trudeau M","Look-Hong N","Curpen B","Gandhi S","Fatima K","Czarnota GJ","Dasgupta A","Quiaoit K","Wright F"],"additional_accession":[]},"is_claimable":false,"name":"Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer.","description":"<h4>Background</h4>This study was conducted to explore the use of quantitative ultrasound (QUS) in predicting recurrence for patients with locally advanced breast cancer (LABC) early during neoadjuvant chemotherapy (NAC).<h4>Methods</h4>Eighty-three patients with LABC were scanned with 7 MHz ultrasound before starting NAC (week 0) and during treatment (week 4). Spectral parametric maps were generated corresponding to tumor volume. Twenty-four textural features (QUS-Tex<sup>1</sup>) were determined from parametric maps acquired using grey-level co-occurrence matrices (GLCM) for each patient, which were further processed to generate 64 texture derivatives (QUS-Tex<sup>1</sup>-Tex<sup>2</sup>), leading to a total of 95 features from each time point. Analysis was carried out on week 4 data and compared to baseline (week 0) data. ∆Week 4 data was obtained from the difference in QUS parameters, texture features (QUS-Tex<sup>1</sup>), and texture derivatives (QUS-Tex<sup>1</sup>-Tex<sup>2</sup>) of week 4 data and week 0 data. Patients were divided into two groups: recurrence and non-recurrence. Machine learning algorithms using <i>k</i>-nearest neighbor (k-NN) and support vector machines (SVMs) were used to generate radiomic models. Internal validation was undertaken using leave-one patient out cross-validation method.<h4>Results</h4>With a median follow up of 69 months (range 7-118 months), 28 patients had disease recurrence. The k-NN classifier was the best performing algorithm at week 4 with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 75%, 81%, and 0.83, respectively. The inclusion of texture derivatives (QUS-Tex<sup>1</sup>-Tex<sup>2</sup>) in week 4 QUS data analysis led to the improvement of the classifier performances. The AUC increased from 0.70 (0.59 to 0.79, 95% confidence interval) without texture derivatives to 0.83 (0.73 to 0.92) with texture derivatives. The most relevant features separating the two groups were higher-order texture derivatives obtained from scatterer diameter and acoustic concentration-related parametric images.<h4>Conclusions</h4>This is the first study highlighting the utility of QUS radiomics in the prediction of recurrence during the treatment of LABC. It reflects that the ongoing treatment-related changes can predict clinical outcomes with higher accuracy as compared to pretreatment features alone.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2026-04-08T17:25:06.694Z","creation":"2025-02-19T05:04:47.657Z"},"accession":"S-EPMC8909335","cross_references":{"pubmed":["35267555"],"doi":["10.3390/cancers14051247"]}}