Predicting the Need for Adaptive Radiotherapy in Head and Neck Patients from CT-Based Radiomics and Pre-treatment Data

Alves, Natália; Dias, Joana; Ventura, Tiago; Mateus, J.; Capela, M.; Khouri, Leila; Lopes, Maria do Carmo

doi:10.1007/978-3-030-86976-2_29

Cited by 2 publications

(4 citation statements)

References 39 publications

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“…Alves et al investigated CT-based radiomics for electively scheduling adaptive radiotherapy, but only considered radiomic features from the gross tumor volume (GTV), and did not include dose metrics as part of the model. 29 Yu et al achieved high AUCs to predict adaptive radiotherapy eligibility,however,they used magnetic resonance-based radiomics features in a cohort of nasopharyngeal cancer patients. 11 We performed the first study to use radiomics from the primary tumor, nodal volumes, and parotid glands for the prediction of replanning for patients with OPC.…”

Section: Discussionmentioning

confidence: 99%

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Predicting the need for a replan in oropharyngeal cancer: A radiomic, clinical, and dosimetric model

Chinnery,

Lang,

Nichols

et al. 2023

Medical Physics

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BackgroundPatients with oropharyngeal cancer (OPC) treated with chemoradiation can experience weight loss and tumor shrinkage, altering the prescribed treatment. Treatment replanning ensures patients do not receive excessive doses to normal tissue. However, it is a time‐ and resource‐intensive process, as it takes 1 to 2 weeks to acquire a new treatment plan, and during this time, overtreatment of normal tissues could lead to increased toxicities. Currently, there are limited prognostic factors to determine which patients will require a replan. There remains an unmet need for predictive models to assist in identifying patients who could benefit from the knowledge of a replan prior to treatment.PurposeWe aimed to develop and evaluate a CT‐based radiomic model, integrating clinical and dosimetric information, to predict the need for a replan prior to treatment.MethodsA dataset of patients (n = 315) with OPC treated with chemoradiation was used for this study. The dataset was split into independent training (n = 220) and testing (n = 95) datasets. Tumor volumes and organs at risk (OARs) were contoured on planning CT images. PyRadiomics was used to compute radiomic image features (n = 1218) on the original and filtered images from each of the primary tumor, nodal volumes, and ipsilateral and contralateral parotid glands. Nine clinical features and nine dose features extracted from the OARs were collected and those significantly (p < 0.05) associated with the need for a replan in the training dataset were used in a baseline model. Random forest feature selection was applied to select the optimal radiomic features to predict replanning. Logistic regression, Naïve Bayes, support vector machine, and random forest classifiers were built using the non‐correlated selected radiomic, clinical, and dose features on the training dataset and performance was assessed in the testing dataset. The area under the curve (AUC) was used to assess the prognostic value.ResultsA total of 78 patients (25%) required a replan. Smoking status, nodal stage, base of tongue subsite, and larynx mean dose were found to be significantly associated with the need for a replan in the training dataset and incorporated into the baseline model, as well as into the combined models. Five predictive radiomic features were selected (one nodal volume, one primary tumor, two ipsilateral and one contralateral parotid gland). The baseline model comprised of clinical and dose features alone achieved an AUC of 0.66 [95% CI: 0.51–0.79] in the testing dataset. The random forest classifier was the top‐performing radiomics model and achieved an AUC of 0.82 [0.75–0.89] in the training dataset and an AUC of 0.78 [0.68–0.87] in the testing dataset, which significantly outperformed the baseline model (p = 0.023, testing dataset).ConclusionsThis is the first study to use radiomics from the primary tumor, nodal volumes, and parotid glands for the prediction of replanning for patients with OPC. Radiomic features augmented clinical and dose features for predicting the need for a replan in our testing dataset. Once validated, this model has the potential to assist physicians in identifying patients that may benefit from a replan, allowing for better resource allocation and reduced toxicities.

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Section: Discussionmentioning

confidence: 99%

“…Alves et al. investigated CT‐based radiomics for electively scheduling adaptive radiotherapy, but only considered radiomic features from the gross tumor volume (GTV), and did not include dose metrics as part of the model 29 . Yu et al.…”

Section: Discussionmentioning

confidence: 99%

Predicting the need for a replan in oropharyngeal cancer: A radiomic, clinical, and dosimetric model

Chinnery,

Lang,

Nichols

et al. 2023

Medical Physics

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“…Although no statistical difference was found, this fact corroborates with other studies showing that smaller “Grey Level Non-Uniformity” values display higher uniformity and therefore suggesting that “Grey Level Non-Uniformity” represents the response to cancer treatment as regards to radiotherapy [ 51 ]. As a result, other research works have proposed the employment of CT-image “Grey Level Non-Uniformity” as a predictive marker for adaptive radiotherapy and for local failure (disease persistence or reappearance) in HN cancer patients [ 28 , 52 ]. In a similar fashion, “Kurtosis” measures the peakedness of the distribution values and the complexity of the organizational structure and tumor heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

“…By incorporating radiomics features derived from CT images (one before the start of radiotherapy and one during radiotherapy for boost planning) in a deep learning design, they were able to obtain efficient predictive performance (Area Under the Curve = 0.73 to 0.75). In a similar fashion, Alves et al, 2021 [ 28 ] utilized sematic features, radiomic features (extracted from contrast-enhanced CT) and a combination of the two, to discriminate between replan (ART) and control (no-ART) groups by applying an SVM classifier. Their proposed design reported a radiomic-based mean accuracy of 0.78, with the feature combination further increasing classification performance (mean accuracy = 0.82).…”

Section: Introductionmentioning

confidence: 99%

Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach

Iliadou

Kakkos

Karaiskos

et al. 2022

Cancers

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Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.

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Predicting the Need for Adaptive Radiotherapy in Head and Neck Patients from CT-Based Radiomics and Pre-treatment Data

Cited by 2 publications

References 39 publications

Predicting the need for a replan in oropharyngeal cancer: A radiomic, clinical, and dosimetric model

Predicting the need for a replan in oropharyngeal cancer: A radiomic, clinical, and dosimetric model

Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach

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