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Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer.

ABSTRACT: BACKGROUND:To investigate quantitative ultrasound (QUS) based higher-order texture derivatives in predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC). MATERIALS AND METHODS:100 Patients with LABC were scanned before starting NAC. Five QUS parametric image-types were generated from radio-frequency data over the tumor volume. From each QUS parametric-image, 4 grey level co-occurrence matrix-based texture images were derived (20 QUS-Tex1), which were further processed to create texture derivatives (80 QUS-Tex1-Tex2). Patients were classified into responders and non-responders based on clinical/pathological responses to treatment. Three machine learning algorithms based on linear discriminant (FLD), k-nearest-neighbors (KNN), and support vector machine (SVM) were used for developing radiomic models of response prediction. RESULTS:A KNN-model provided the best results with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 81%, 82%, and 0.86, respectively. The most helpful features in separating the two response groups were QUS-Tex1-Tex2 features. The 5-year recurrence-free survival (RFS) calculated for KNN predicted responders and non-responders using QUS-Tex1-Tex2 model were comparable to RFS for the actual response groups. CONCLUSIONS:We report the first study demonstrating QUS texture-derivative methods in predicting NAC responses in LABC, which leads to better results compared to using texture features alone.

SUBMITTER: Dasgupta A

PROVIDER: S-EPMC7584238 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer.

Dasgupta Archya A Brade Stephen S Sannachi Lakshmanan L Quiaoit Karina K Fatima Kashuf K DiCenzo Daniel D Osapoetra Laurentius O LO Saifuddin Murtuza M Trudeau Maureen M Gandhi Sonal S Eisen Andrea A Wright Frances F Look-Hong Nicole N Sadeghi-Naini Ali A Tran William T WT Curpen Belinda B Czarnota Gregory J GJ

Oncotarget 20201020 42

<h4>Background</h4>To investigate quantitative ultrasound (QUS) based higher-order texture derivatives in predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).<h4>Materials and methods</h4>100 Patients with LABC were scanned before starting NAC. Five QUS parametric image-types were generated from radio-frequency data over the tumor volume. From each QUS parametric-image, 4 grey level co-occurrence matrix-based texture images were derive ...[more]

PMID: 33144919

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Project description:Introduction: MicroRNAs (miRNAs) are small non-coding RNA that play a vital role in cancer progression. Neo-adjuvant chemotherapy (NAC) has become the standard of care for locally advanced breast cancer. The aim of this study was to evaluate miRNA alterations during NAC using multiple samples of tissue and serum to correlate miRNA expression with clinico-pathological features and patient outcomes. Methods: Tissue and serum samples were collected from patients with locally advanced breast cancer undergoing NAC at four time points: time of diagnosis, after the first and fourth cycle of doxorubicin/cyclophosphamide treatment, and after the fourth cycle of docetaxel administration. First, we evaluated the miRNA expression profiles in tissue and correlated expression with clinico-pathological features. Then, a panel of four miRNAs (miR-451, miR-3200, miR-21, and miR-205) in serum samples was further validated using quantitative reverse-transcription polymerase chain reaction (RT-qPCR). The alterations in serum levels of miRNA, associations with clinical and pathological responses, correlation with clinico-pathological features, and survival outcomes were studied using Friedman, Mann-Whitney U, Spearman, Wilcoxon signed-ranks tests. Pâ¤0.05 was considered statistically significant. Results: We analyzed 32 tissue samples and 108 serum samples from 8 patients and 27 patients, respectively. MicroRNA expression profiling of tumor versus normal tissue revealed more than 100 differentially expressed miRNAs. Serum miR-451 levels were significantly decreased during treatment, and higher serum levels were associated with improved clinical and pathological responses and disease-free survival. The serum levels of miR-3200 declined during NAC, and higher serum levels were associated with lower residual breast cancer burden and relapse rates. Conclusion: Variations in serum miRNA levels during NAC treatment may be therapeutically significant for predicting response and survival outcomes. Serum and tissue from a total of 27 consecutive patients diagnosed with locally advanced breast cancer undergoing NAC treatment were collected however microarray data were generated from tissue biopsy of 5 patients at four timepoints for both Normal and tumor. Additionally 3 tissue from only tumor at four timepoints and 6 patients at two timepoint T1 and T4

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Dataset Information

Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer.

Publications

Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer.

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