Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges

Bruixola, Gema; Remacha, Elena; Jiménez-Pastor, Ana; Dualde, D.; Viala, Alba; Montón, Jose Vicente; Ibarrola‐Villava, Maider

doi:10.1016/j.ctrv.2021.102263

Cited by 37 publications

(21 citation statements)

References 105 publications

(175 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Current implementations of the omics results are mainly focused on treatment outcome prediction and risk of recurrence rather than on direct personalisation of individual patient therapy [56]. It is expected that future developments in the discussed omics fields will influence the treatment of the individual patient from first contact with the healthcare system by allowing clinicians to deploy a truly individualised treatment based on radiomic analysis of diagnostic imaging, on detailed dosiomic analysis to target the tumour volume with utmost precision, alleviating side effects on healthy tissues, and on pharmacogenetic tests that enable the choice of the most effective drug family for the individual patient.…”

Section: Discussionmentioning

confidence: 99%

Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy

Marcu

2021

JPM

View full text Add to dashboard Cite

Chemoradiotherapy remains the most common management of locally advanced head and neck cancer. While both treatment components have greatly developed over the years, the quality of life and long-term survival of patients undergoing treatment for head and neck malignancies are still poor. Research in head and neck oncology is equally focused on the improvement of tumour response to treatment and on the limitation of normal tissue toxicity. In this regard, personalised therapy through a multi-omics approach targeting patient management from diagnosis to treatment shows promising results. The aim of this paper is to discuss the latest results regarding the personalised approach to chemoradiotherapy of head and neck cancer by gathering the findings of the newest omics, involving radiotherapy (dosiomics), chemotherapy (pharmacomics), and medical imaging for treatment monitoring (radiomics). The incorporation of these omics into head and neck cancer management offers multiple viewpoints to treatment that represent the foundation of personalised therapy.

show abstract

Section: Discussionmentioning

confidence: 99%

Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy

Marcu

2021

JPM

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, the xerostomia predictive models proposed in the literature are designed for head and neck tumors which include several locations anatomical sites of the primary tumor. There is a lack of models tailored for patients with OPC (15,16). Compared to treatment in other areas of the head and neck, the oropharynx represents the most frequently treated site for which the definition of a plan that preserves the functionality of the parotid is more complex (17,18).…”

Section: Introductionmentioning

confidence: 99%

Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer

et al. 2022

View full text Add to dashboard Cite

Background and purposeAlthough the latest breakthroughs in radiotherapy (RT) techniques have led to a decrease in adverse event rates, these techniques are still associated with substantial toxicity, including xerostomia. Imaging biomarkers could be useful to predict the toxicity risk related to each individual patient. Our preliminary work aims to develop a radiomic-based support tool exploiting pre-treatment CT images to predict late xerostomia risk in 3 months after RT in patients with oropharyngeal cancer (OPC).Materials and methodsWe performed a multicenter data collection. We enrolled 61 patients referred to three care centers in Apulia, Italy, out of which 22 patients experienced at least mild xerostomia 3 months after the end of the RT cycle. Pre-treatment CT images, clinical and dose features, and alcohol-smoking habits were collected. We proposed a transfer learning approach to extract quantitative imaging features from CT images by means of a pre-trained convolutional neural network (CNN) architecture. An optimal feature subset was then identified to train an SVM classifier. To evaluate the robustness of the proposed model with respect to different manual contouring practices on CTs, we repeated the same image analysis pipeline on “fake” parotid contours.ResultsThe best performances were achieved by the model exploiting the radiomic features alone. On the independent test, the model reached median AUC, accuracy, sensitivity, and specificity values of 81.17, 83.33, 71.43, and 90.91%, respectively. The model was robust with respect to diverse manual parotid contouring procedures.ConclusionRadiomic analysis could help to develop a valid support tool for clinicians in planning radiotherapy treatment, by providing a risk score of the toxicity development for each individual patient, thus improving the quality of life of the same patient, without compromising patient care.

show abstract

“…Thus, the average RGB color for each image was calculated; then, the color consistency was calculated by adjusting the scale for each image [ 21 ]. Finally, artifacts were removed, image contrast increased, and the edges of regions of interest were revealed by Gaussian and Laplacian filters [ 22 ]. Next, the images were passed over a Gaussian noise filter by removing high-frequency data and passing (retaining) the low-frequency data.…”

Section: Methodsmentioning

confidence: 99%

Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches

2022

View full text Add to dashboard Cite

Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%.

show abstract

Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges

Cited by 37 publications

References 105 publications

Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy

Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy

Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer

Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches

Contact Info

Product

Resources

About