Postoperative radiotherapy timing, molecular subgroups and treatment outcomes of Thai pediatric patients with medulloblastoma.

IntroductionMedulloblastoma (MB) is the most common childhood malignant brain tumor worldwide. Recently, molecular classification was established and started to play a role in the management of MB; however, studies involving molecular defined MB in Southeast Asia have been limited. We aimed to describe, and correlate clinical characteristics and molecular subgroups with therapeutic outcomes of Thai pediatric patients with MB.Materials and methodsPediatric MB patients treated at King Chulalongkorn Memorial Hospital in Thailand from 2006 to 2018 were recruited. Patients were classified by clinical characteristics into standard- and high-risk groups, which determined treatment regimen. Retrospectively, available tumor tissues were classified into 3 molecular subgroups using immunohistochemistry: 1) WNT, 2) SHH, and 3) non-WNT/non-SHH. The primary outcome was 5-year overall survival (OS). Risk factors associated with OS were analyzed using cox regression analysis.ResultsFifty-three Thai pediatric patients with MB were enrolled. The median follow-up time was 60 months. The 5-year OS for all patients, and patients with standard-risk and high-risk were 74.2%, 76.3% and 71.4%, respectively. Tumor tissues of 24 patients were available, of which 23 could be molecularly classified. Two, one and 20 were in the WNT, SHH and non-WNT/non-SHH subtypes with 5-year OS of 100%, 100% and 78.9%, respectively. Using multivariate analysis, the interval of more than 8 weeks between surgery and radiotherapy was significantly correlated with a decrease in the 5-year OS.ConclusionInterval between surgery and radiotherapy within 8 weeks was associated with good therapeutic outcomes among Thai pediatric patients with MB. Simplified molecular subtyping combined with clinical characteristics is practical in risk classification of patients with MB in institutes with limited resources

Molecular classification of the medulloblastoma pediatric patients.

Molecular classification of the medulloblastoma pediatric patients.</p

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

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 pre- and 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 \u27testing\u27 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

The participants’ flow chart.

The participants’ flow chart.</p

Demographic and clinical characteristics of the 53 pediatric patients with medulloblastoma.

Demographic and clinical characteristics of the 53 pediatric patients with medulloblastoma.</p

The Kaplan-Meier curve for overall survival (A) and event free survival (B) of medulloblastoma by molecular subtypes.

The Kaplan-Meier curve for overall survival (A) and event free survival (B) of medulloblastoma by molecular subtypes.</p