Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results

Quiaoit, Karina; DiCenzo, Daniel; Fatima, Kashuf; Bhardwaj, Divya; Sannachi, Lakshmanan; Gangeh, Mehrdad J.; Sadeghi‐Naini, Ali; Dasgupta, Archya; Kolios, Michael C.; Trudeau, Maureen; Gandhi, Sonal; Eisen, Andrea; Wright, Frances C.; Look-Hong, Nicole; Sahgal, Arjun; Stanisz, Greg J.; Brezden, Christine B.; Dinniwell, Robert; Tran, William T.; Yang, Wei Tse; Curpen, Belinda; Czarnota, Gregory J.

doi:10.1371/journal.pone.0236182

Cited by 48 publications

(43 citation statements)

References 65 publications

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“…The critical premise of quantitative ultrasound (QUS) is the processing of raw RF data from tissue backscatters, which can be used to characterize and distinguish phenotypic changes within a region of interest at a cellular level [12]. In preclinical studies, QUS had demonstrated efficacy in detecting treatment effects of various forms of cancer therapies [13][14][15][16][17]. QUS is sensitive to cell death-related structural changes even within 24 h, or clinically week 1 of treatment, arising from the changes in ultrasound scatterer elastic properties with phenomena like nuclear condensation, fragmentation, and the formation of apoptotic bodies.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

Fatima

Dasgupta

DiCenzo

et al. 2021

Clinical and Translational Radiation Oncology

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This study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC). Methods: Fifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation. Results: The median follow up for the entire group was 38 months (range 7-64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment. Conclusion: QUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC. Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

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Section: Introductionmentioning

confidence: 99%

Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

Fatima

Dasgupta

DiCenzo

et al. 2021

Clinical and Translational Radiation Oncology

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“…It would be helpful to develop imaging biomarkers to identify patients likely to benefit from NAC avoiding ineffective treatments in a selected cohort of patients having chemoresistant disease. Radiomic analysis involving a wide range of imaging modalities, including magnetic resonance imaging (MRI) [6][7][8], quantitative ultrasound (QUS) [9][10][11][12], and computed tomography (CT) [13], has shown promising results in the assessment of clinical outcomes for patients with breast cancer.…”

Section: Introductionmentioning

confidence: 99%

MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

et al. 2021

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Background: Radiomics involving quantitative analysis of imaging has shown promises in oncology to serve as non-invasive biomarkers. We investigated whether pre-treatment T2-weighted magnetic resonance imaging (MRI) can be used to predict response to neoadjuvant chemotherapy (NAC) in breast cancer.Materials and Methods: MRI scans were obtained for 102 patients with locally advanced breast cancer (LABC). All patients were treated with standard regimens of NAC as decided by the treating oncologist, followed by surgery and adjuvant treatment according to standard institutional practice. The primary tumor was segmented, and 11 texture features were extracted using the grey-level co-occurrence matrices analysis of the T2W-images from tumor cores and margins. Response assessment was done using clinical-pathological responses with patients classified into binary groups: responders and non-responders. Machine learning classifiers were used to develop a radiomics model, and a leave-one-out cross-validation technique was used to assess the performance.Results: 7 features were significantly (p < 0.05) different between the two response groups. The best classification accuracy was obtained using a k-nearest neighbor (kNN) model with sensitivity, specificity, accuracy, and area under curve of 63, 93, 87, and 0.78, respectively.Conclusions: Pre-treatment T2-weighted MRI texture features can predict NAC response with reasonable accuracy.

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“…The elastic properties of the tissue at the microcellular level, as probed by ultrasound, serve as a surrogate of biological characteristics and can be further processed to generate useful information. The various spectral parameters reflect intrinsic tissue properties like cellular density, scatterer size, and tissue organization, which correlates with biological outcomes 16‐21 . Texture analysis of the spectral features of the tissue can further help in characterizing the heterogeneity of the tumor, which often is linked to the responsiveness to treatment and clinical outcomes.…”

Section: Introductionmentioning

confidence: 99%

Quantitative ultrasound radiomics in predicting recurrence for patients with node‐positive head‐neck squamous cell carcinoma treated with radical radiotherapy

et al. 2020

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This prospective study was conducted to investigate the role of quantitative ultrasound (QUS) radiomics in predicting recurrence for patients with node‐positive head‐neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). The most prominent cervical lymph node (LN) was scanned with a clinical ultrasound device having central frequency of 6.5 MHz. Ultrasound radiofrequency data were processed to obtain 7 QUS parameters. Color‐coded parametric maps were generated based on individual QUS spectral features corresponding to each of the smaller units. A total of 31 (7 primary QUS and 24 texture) features were obtained before treatment. All patients were treated with radical RT and followed according to standard institutional practice. Recurrence (local, regional, or distant) served as an endpoint. Three different machine learning classifiers with a set of maximally three features were used for model development and tested with leave‐one‐out cross‐validation for nonrecurrence and recurrence groups. Fifty‐one patients were included, with a median follow up of 38 months (range 7–64 months). Recurrence was observed in 17 patients. The best results were obtained using a k‐nearest neighbor (KNN) classifier with a sensitivity, specificity, accuracy, and an area under curve of 76%, 71%, 75%, and 0.74, respectively. All the three features selected for the KNN model were texture features. The KNN‐model‐predicted 3‐year recurrence‐free survival was 81% and 40% in the predicted no‐recurrence and predicted‐recurrence groups, respectively. (p = 0.001). The pilot study demonstrates pretreatment QUS‐radiomics can predict the recurrence group with an accuracy of 75% in patients with node‐positive HNSCC. Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

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Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results

Cited by 48 publications

References 65 publications

Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

Quantitative ultrasound radiomics in predicting recurrence for patients with node‐positive head‐neck squamous cell carcinoma treated with radical radiotherapy

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