Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma

Ni, Ruiyan; Ta, Zhou; Ren, Ge; Zhang, Yuanpeng; Yang, Dongrong; Tam, Victor C. W.; Leung, Wan Shun; Ge, Hong; Lee, Shara W. Y.; Cai, Jing

doi:10.1016/j.ijrobp.2022.03.011

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…This may cause a partial loss of information related to prognosis, which leads to a limited performance of the model. Simultaneously, there are several studies using DL to establish a prognostic prediction model for NPC in which the original image was incorporated for model training [4][5][6][7]. However, original images contain a large amount of noise, such as uninvaded cerebellar, nasal, and temporal regions far from the tumor, which may provide only very limited prognostic information, and most of the pixels in these regions are noise.…”

Section: Discussionmentioning

confidence: 99%

“…Convolutional neural network (CNN), owing to its remarkable image feature extraction capability, is one of the most used AI techniques in medical imaging. Research on the application of AI based on CNN technology in the field of nasopharyngeal carcinoma, including image segmentation [2], image classification and recognition [3], drug efficacy prediction [4,5], and prognosis prediction [6,7], has also gradually increased in recent years and has generally shown better performance than traditional machine learning methods. Many studies have reported its prediction performance in nasopharyngeal carcinoma (NPC) prognosis to exceed the traditional TNM staging system [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued

et al. 2023

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Background The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC. Methods A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes. Five datasets were constructed by expanding 5, 10, 20, 40, and 60 pixels outward from the edge of the automatically segmented region. Inception-Resnet-V2, ECA-ResNet50t, EfficientNet-B3, and EfficientNet-B0 were trained with the original, segmented, and the five new constructed datasets to establish the classification models. The receiver operating characteristic curve was used to evaluate the performance of each model. Results The Dice coefficients of Deeplab v3 and U-Net were 0.741(95%CI:0.722–0.760) and 0.737(95%CI:0.720–0.754), respectively. The average areas under the curve (aAUCs) of deep learning models for classification trained with the original and segmented images and with images expanded by 5, 10, 20, 40, and 60 pixels were 0.717 ± 0.043, 0.739 ± 0.016, 0.760 ± 0.010, 0.768 ± 0.018, 0.802 ± 0.013, 0.782 ± 0.039, and 0.753 ± 0.014, respectively. The models trained with the images expanded by 20 pixels obtained the best performance. Conclusions The peritumoral region NPC contains information related to prognosis, and the incorporation of this region could improve the performance of deep learning models for prognosis prediction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued

et al. 2023

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“…The training set is used to construct the model, and the internal test set is used for model validation and evaluation. To reduce the impact of unbalanced data on the machine learning partitioned dataset and subsequent validation, we use the synthetic minority oversampling technique (SMOTE) for the training set (15). Heat maps were plotted to determine the association between the variables.…”

Section: Discussionmentioning

confidence: 99%

Machine-learning model of eye metastasis in nasopharyngeal carcinoma based on the AdaBoost method

Rong,

Wen,

et al. 2023

Preprint

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Background Nasopharyngeal carcinoma (NPC) is a common cancer of the head and neck, and the eye is a common metastatic site of NPC. This study aimed to use machine learning (ML) to establish a clinical prediction model for ocular metastasis (OM) in NPC patients. Methods We retrospectively collected clinical data from 1,855 patients with NPC who were randomized to a training set and internal test set. Patients with NPC were divided into the OM group or the non-ocular metastasis (NOM) group. Independent risk factors for NPC-related hypertension risk were screened with multivariate logistic regression models. Six ML algorithms were used, including AdaBoost (AB), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), bagging (BAG), and XGBoost (XGB). The training set was used to tune the model parameters to determine the final prediction model, and the test set was used to evaluate the training model. We compared the accuracy, sensitivity, area under the ROC curve, F1 score, and specificity of the models to determine the best machine-learning algorithm for predicting the probability of OM in NPC patients. In addition, a web calculator was developed to facilitate its clinical application. Results Among these six models, the AB model had the best differential diagnostic ability (F1 score = 0.773, area under the curve = 0.995, accuracy = 0.983, sensitivity = 0.833, and specificity = 0.985). Based on this model, an online web calculator was constructed to calculate the probability of OM in NPC patients to help clinicians differentially diagnose the disease. Finally, the Shapley Supplementary Interpretation library was used to screen the five most important risk factors for OM in NPC patients: TG, Cyfra 21 1, CA199, Hb, TC, and Pathology type. Conclusion We developed a risk prediction model for OM in NPC patients using ML methods and demonstrated that the AB model performed best among six ML models. This prediction model can help to identify patients with OM from NPC, provide early and individualized diagnosis and treatment plans, protect patients from OM from NPC, and minimize the burden on society.

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Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review

Tan

Nasir

Manan

et al. 2023

Cancer/Radiothérapie

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Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma

Cited by 5 publications

References 37 publications

Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued

Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued

Machine-learning model of eye metastasis in nasopharyngeal carcinoma based on the AdaBoost method

Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review

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