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

Fatima, Kashuf; Dasgupta, Archya; DiCenzo, Daniel; Kolios, Christopher; Quiaoit, Karina; Saifuddin, Murtuza; Sandhu, Michael; Bhardwaj, Divya; Karam, Irene; Poon, Ian; Husain, Zain; Sannachi, Lakshmanan; Czarnota, Gregory J.

doi:10.1016/j.ctro.2021.03.002

Cited by 18 publications

(28 citation statements)

References 30 publications

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“…Studies that recognize tumor recurrence in follow-up images have gradually become apparent. US-based radiomic features can identify recurrence risk in patients with head-neck squamous cell carcinoma treated with RT ( 130 ). Glioblastoma multiforme has a poor prognosis and inevitably recurs or progresses.…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

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Section: Discussionmentioning

confidence: 99%

“…Fatima et al. ( 130 ) performed radiomic analysis of US images of patients with head and neck squamous cell carcinoma to assess the recurrence status after RT. The best-selected SVM model achieved AUC and accuracy of 75% and 81% at week 1 and 80% and 82% at week 4, respectively.…”

Section: Some Case Studies and Applicationsmentioning

confidence: 99%

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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“…124,125 Delta radiomics has been proven effective in the study of immunotherapy response 126,127 or to predict recurrence in oncological patients. 128…”

Section: Delta Radiomicsmentioning

confidence: 99%

A review in radiomics: Making personalized medicine a reality via routine imaging

Guiot¹,

Vaidyanathan

Deprez

et al. 2021

Medicinal Research Reviews

131

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proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decisionmaking, from oncology and respiratory medicine to pharmacological and genotyping studies.

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

Cited by 18 publications

References 30 publications

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

A review in radiomics: Making personalized medicine a reality via routine imaging

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

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