A predictive model of radiation-related fibrosis based on the radiomic features of magnetic resonance imaging and computed tomography

Abstract

Background: To establish a predictive model for the fibrotic level of neck muscles after radiotherapy by using radiomic features extracted from the magnetic resonance imaging (MRI) before and after radiotherapy and planning computed tomography (CT) in nasopharyngeal carcinoma patients.Methods: A total of one hundred and eighty-six patients were finally enrolled in this study. According to the specific standard, all patients were divided into three different fibrosis groups. Regions of interests (ROI), including sternocleidomastoids (SCMs), trapezius (T), levator scapulae (LS), and scalenus muscles (S), were delineated manually and used for features extraction on IBEX. XGBoost, a machine learning algorithm, was used for the establishment of the prediction model. First, the patients were divided into training cohort (80%) and testing cohort (20%) randomly. Then the image features of CT or delta changes calculated from preand post-radiotherapy MRI images on each cohort constituted training and testing datasets. Then, based on the training dataset, a well-trained prediction model was produced. We used five-fold cross-validation to validate the predictive models. Afterward, the model performance was assessed on the 'testing' set and reported in terms of area under the receiver operating characteristic curve (AUC) under five scenarios: (I) only T1 sequence, (II) only T2 sequence, (III) only T1 post-contrast (T1 + C) sequence, (IV) Combination of all MRI sequences, (V) only CT.Results: Most of the patients enrolled are male (73.1%), mean age was 47 years, receiving concurrent chemo-radiotherapy as the primary treatment (90.9%). By the end of the final follow-up, most of the patients were rated as mild fibrosis (60.8%). We found the prediction model based on the CT image features outperform all MRI features with an AUC of 0.69 and accuracy of 0.65. Contrarily, the model based on features from all MRI sequence showed lower AUC less than 0.5 and lower accuracy less than 0.6. Conclusions:The prediction model based on CT radiomics features has better performance in the prediction of the grade of post-radiotherapy neck fibrosis. This might help guide radiotherapy treatment planning to achieve a better quality of life.

Regression Analysis of Factors Based on Cluster Analysis of Acute Radiation Pneumonia due to Radiation Therapy for Lung Cancer

“…Many clinical factors have been reported to influence the development of radiation pneumonia, including age, gender, smoking history, and history of chronic obstructive pulmonary disease (COPD). Wang et al reported that age was an influential factor in the development of radiation pneumonia and that patients of advanced age were at a higher risk of developing radiation pneumonia [5]. Tinkle et al showed that smoking history was a protective factor against radiation pneumonia and that smoking could prevent radiation pneumonia [3].…”

Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

“…The studies could be categorized into 4 domains, which were auto-contouring (n=21), 15 , 16 , 18 , 22 , 24 , 30–32 , 45–55 , 67 , 69 diagnosis (n=17), 10 , 15 , 16 , 23 , 26 , 27 , 49 , 52 , 54 , 56–63 prognosis (n=20) 12–14 , 17 , 19 , 25 , 28 , 29 , 33–44 and miscellaneous applications (n=7), 11 , 20 , 21 , 64–66 , 68 which included risk factor identification, image registration and radiotherapy planning ( Figure 2A ). Five studies examined both diagnosis and auto-contouring simultaneously.…”

“… 10 , 12 , 15 , 26–28 , 35 , 43 , 44 , 49 , 54 , 56 , 60–63 Sensitivity was the most common studied parameter for diagnostic performance: 15 out of 23 papers. 10 , 15 , 16 , 23 , 26 , 27 , 49 , 52 , 54 , 56 , 59–63 The specificity was only reported for prognosis (n=7) 12 , 14 , 28 , 34 , 39 , 40 , 43 and diagnosis (n=15). 10 , 15 , 16 , 23 , 26 , 27 , 49 , 52 , 54 , 56 , 59–63 In addition, the DSC (n=20) 15 , 18 , 22 , 24 , 30–32 , 45–53 , 55 , 65 , 67 , 69 and ASSD (n=10) 18 , 22 , 24 , 31 , 32 , 45 , 46 , 48 , 51 , 69 were the primary metrics reported in studies on auto-contouring ( Figure 2B ).…”

“…Possible misidentification of endoscopy image data by experts. Wang et al (2020) 27 (China, USA and Thailand) NPC 186 (CT and MRI images) Machine learning (Radiation-induced injury diagnosis) 1. Prediction: XGBoost To assess the feasibility in developing a model for predicting radiation-related fibrosis 1.…”

“… ✓ ✓ ✓ ✓ ✓ ✓ Yes No Mohammed et al (2020) 26 ? ✓ ✓ ✓ ✓ ✓ ✓ Yes No Wang et al (2020) 27 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Wang et al (2020) 48 ✓ ✓ ✓ ? ✓ ✓ ✓ Yes No Xie et al (2020) 37 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Xue et al (2020) 69 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Xue et al (2020) 46 ✓ ✓ ✓ ?…”