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

Dasgupta, Archya; Fatima, Kashuf; DiCenzo, Daniel; Bhardwaj, Divya; Quiaoit, Karina; Saifuddin, Murtuza; Karam, Irene; Poon, Ian; Husain, Zain; Tran, William T.; Sannachi, Lakshmanan; Czarnota, Gregory J.

doi:10.1002/cam4.3634

Cited by 14 publications

(21 citation statements)

References 43 publications

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“…Classification the current study than the kNN in the previous study using pretreatment features alone [20]. Also, in a smaller sample size like the present study, individual classifier performances' stability should be interpreted with caution, which will be minimized as more patients are included.…”

Section: Modelmentioning

confidence: 73%

“…In previous studies, QUS-based radiomics have been shown to effectively predict the response to radical RT in patients with HNSCC [18,19]. In a recent study, we have demonstrated the use of QUS-radiomics in predicting the recurrence for HNSCC using pretreatment features [20]. QUS data was obtained from the metastatic lymph node in 51 patients with HNSCC.…”

Section: Introductionmentioning

confidence: 99%

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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: Modelmentioning

confidence: 73%

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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“…The performance of US radiomics models for prognostic applications in predicting treatment response, locoregional recurrence, and treatment outcomes are summarized in Table 2. Dasgupta et al [38] conducted a prospective study to investigate the role of US radiomics for the pretreatment prediction of recurrence in head and neck cancer patients (clinicaltrials.gov identifier NCT03908684). A total of 51 head and neck cancer patients with a primary site of oropharynx, hypopharynx, and larynx were enrolled in this study.…”

Section: Performance Of Us Radiomics For Response Prediction In Head ...mentioning

confidence: 99%

Application of Machine Learning Methods to Improve the Performance of Ultrasound in Head and Neck Oncology: A Literature Review

DeJohn

Grant

Seshadri

2022

Cancers

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Radiomics is a rapidly growing area of research within radiology that involves the extraction and modeling of high-dimensional quantitative imaging features using machine learning/artificial intelligence (ML/AI) methods. In this review, we describe the published clinical evidence on the application of ML methods to improve the performance of ultrasound (US) in head and neck oncology. A systematic search of electronic databases (MEDLINE, PubMed, clinicaltrials.gov) was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Of 15,080 initial articles identified, 34 studies were selected for in-depth analysis. Twenty-five out of 34 studies (74%) focused on the diagnostic application of US radiomics while 6 (18%) studies focused on response assessment and 3 (8%) studies utilized US radiomics for modeling normal tissue toxicity. Support vector machine (SVM) was the most commonly employed ML method (47%) followed by multivariate logistic regression (24%) and k-nearest neighbor analysis (21%). Only 11/34 (~32%) of the studies included an independent validation set. A majority of studies were retrospective in nature (76%) and based on single-center evaluation (85%) with variable numbers of patients (12–1609) and imaging datasets (32–1624). Despite these limitations, the application of ML methods resulted in improved diagnostic and prognostic performance of US highlighting the potential clinical utility of this approach.

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“…Application of quantitative texture analysis to the QUS imaging has shown to unfold critical biological information that can be modeled effectively to predict biological behavior in breast malignancies [ 12 , 13 ]. Several studies have demonstrated the utility of QUS imaging in predicting treatment response using pretreatment imaging features [ 12 , 14 , 15 , 16 ]. Similarly, the acquisition of QUS features during different treatment modalities (chemotherapy, radiotherapy) reflects ongoing microstructural changes resulting from cell death and can effectively improve the interpretation of clinical outcomes [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer

Bhardwaj

Dasgupta

DiCenzo

et al. 2022

Cancers

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Background: 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). Methods: 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-Tex1) 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-Tex1-Tex2), 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-Tex1), and texture derivatives (QUS-Tex1-Tex2) of week 4 data and week 0 data. Patients were divided into two groups: recurrence and non-recurrence. Machine learning algorithms using k-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. Results: 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-Tex1-Tex2) 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. Conclusions: 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.

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

Cited by 14 publications

References 43 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

Application of Machine Learning Methods to Improve the Performance of Ultrasound in Head and Neck Oncology: A Literature Review

Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer

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