Predicting radiotherapy-induced xerostomia in head and neck cancer patients using day-to-day kinetics of radiomics features

Berger, Thomas; Noble, D.; Shelley, Leila E.A.; McMullan, Thomas; Bates, A.; Thomas, Simon; Carruthers, Linda; Beckett, George; Duffton, A.; Paterson, C.; Jena, R.; McLaren, Duncan B.; Burnet, N.G.; Nailon, William H.

doi:10.1016/j.phro.2022.10.004

Cited by 14 publications

(10 citation statements)

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“…Herein, we did not consider the temporal kinetics of RFs during RT. More recently, studies have shown that longitudinal analysis of radiation-induced changes of CT RFs can improve the identification of individuals at high risk ahead of developing RT-induced toxicity [ [48] , [49] , [50] ]. Therefore, further research is warranted to investigate whether the temporal kinetics of RFs during RT can improve the performance of our proposed machine learning classifier trained on the integrated features.…”

Section: Discussionmentioning

confidence: 99%

Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach

Agheli,

Siavashpour,

Reiazi

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach

Agheli,

Siavashpour,

Reiazi

et al. 2024

Heliyon

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“…The severity of dry mouth was clinically assessed 1 month after the end of RT by a radiotherapist with 10-years' experience using the U.S. National Cancer Institute's CTCAE v4. This score scale has three increasing levels of severity [39]. Accordingly, we decided to divide the patients into three groups:…”

Section: Patients' Differentiations Into Groups Based On Clinical Eva...mentioning

confidence: 99%

“…The severity of dry mouth was clinically assessed 1 month after the end of RT by a radiotherapist with 10-years' experience using the U.S. National Cancer Institute's CTCAE v4. This score scale has three increasing levels of severity [39]. Accordingly, we Patients who did not undergo MRI examinations or did so in other institutes from the native DCE-PWI images by drawing a ROI including the largest gland section possible (Figure 2).…”

Section: Patients' Differentiations Into Groups Based On Clinical Eva...mentioning

confidence: 99%

Morphological, Functional and Texture Analysis Magnetic Resonance Imaging Features in the Assessment of Radiotherapy-Induced Xerostomia in Oropharyngeal Cancer

et al. 2023

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The aim of this single-center, observational, retrospective study was to investigate magnetic resonance imaging (MRI) biomarkers for the assessment of radiotherapy (RT)-induced xerostomia. Twenty-seven patients who underwent radiation therapy for oropharyngeal cancer were divided into three groups according to the severity of their xerostomia—mild, moderate, and severe—clinically confirmed with the Common Terminology Criteria for Adverse Events (CTCAE). No severe xerostomia was found. Conventional and functional MRI (perfusion- and diffusion- weighted imaging) performed both pre- and post-RT were studied for signal intensity, mean apparent diffusion coefficient (ADC) values, k-trans, and area under the perfusion curves. Contrast-enhanced T1 images and ADC maps were imported into 3D slicer software, and salivary gland volumes were segmented. A total of 107 texture features were derived. T-Student and Wilcoxon signed-rank tests were performed on functional MRI parameters and texture analysis features to identify the differences between pre- and post-RT populations. A p-value < 0.01 was defined as acceptable. Receiver operating characteristic (ROC) curves were plotted for significant parameters to discriminate the severity of xerostomia in the pre-RT population. Conventional and functional MRI did not yield statistically significant results; on the contrary, five texture features showed significant variation between pre- and post-RT on the ADC maps, of which only informational measure of correlation 1 (IMC 1) was able to discriminate the severity of RT-induced xerostomia in the pre-RT population (area under the curve (AUC) > 0.7). Values lower than the cut-off of −1.473 × 10−11 were associated with moderate xerostomia, enabling the differentiation of mild xerostomia from moderate xerostomia with a 73% sensitivity, 75% specificity, and 75% diagnostic accuracy. Therefore, the texture feature IMC 1 on the ADC maps allowed the distinction between different degrees of severity of RT-induced xerostomia in the pre-RT population. Accordingly, texture analysis on ADC maps should be considered a useful tool to evaluate salivary gland radiosensitivity and help identify patients at risk of developing more serious xerostomia before radiation therapy is administered.

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“…The fused model seems to offer superior predictive capacity for primary tumor (sensitivity of 78.3% and specificity of 90.9%), as well as for lymph nodes failure (sensitivity of 100.0% and specificity of 68.0%). Even if the study cannot demonstrate a net superiority of the radiomic and Δ radiomic algorithm compared to the clinical model, the fused model, including both clinical Δ radiomic data, is considered superior for the prediction of loco-regional failure including primary tumor, but also lymph nodes, relapse [ 46 , 47 , 48 , 49 , 50 ].…”

Section: δ Radiomics In Hnc—from Image-guided Radiotherapy (Igrt) And...mentioning

confidence: 99%

Image Guided Radiotherapy (IGRT) and Delta (Δ) Radiomics—An Urgent Alliance for the Front Line of the War against Head and Neck Cancers

Mireștean

Iancu

2023

Diagnostics

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The identification of a biomarker that is response predictive could offer a solution for the stratification of the treatment of head and neck cancers (HNC) in the context of high recurrence rates, especially those associated with loco-regional failure. Delta (Δ) radiomics, a concept based on the variation of parameters extracted from medical imaging using artificial intelligence (AI) algorithms, demonstrates its potential as a predictive biomarker of treatment response in HNC. The concept of image-guided radiotherapy (IGRT), including computer tomography simulation (CT) and position control imaging with cone-beam-computed tomography (CBCT), now offers new perspectives for radiomics applied in radiotherapy. The use of Δ features of texture, shape, and size, both from the primary tumor and from the tumor-involved lymph nodes, demonstrates the best predictive accuracy. If, in the case of treatment response, promising Δ radiomics results could be obtained, even after 24 h from the start of treatment, for radiation-induced xerostomia, the evaluation of Δ radiomics in the middle of treatment could be recommended. The fused models (clinical and Δ radiomics) seem to offer benefits, both in comparison to the clinical model and to the radiomic model. The selection of patients who benefit from induction chemotherapy is underestimated in Δ radiomic studies and may be an unexplored territory with major potential. The advantage offered by “in house” simulation CT and CBCT favors the rapid implementation of Δ radiomics studies in radiotherapy departments. Positron emission tomography (PET)-CT Δ radiomics could guide the new concepts of dose escalation on radio-resistant sub-volumes based on radiobiological criteria, but also guide the “next level” of HNC adaptive radiotherapy (ART).

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Predicting radiotherapy-induced xerostomia in head and neck cancer patients using day-to-day kinetics of radiomics features

Cited by 14 publications

References 50 publications

Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach

Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach

Morphological, Functional and Texture Analysis Magnetic Resonance Imaging Features in the Assessment of Radiotherapy-Induced Xerostomia in Oropharyngeal Cancer

Image Guided Radiotherapy (IGRT) and Delta (Δ) Radiomics—An Urgent Alliance for the Front Line of the War against Head and Neck Cancers

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