Circulating microvesicles correlate with radiation proctitis complication after radiotherapy

Radiation-induced gastrointestinal and cutaneous injuries: understanding models, pathologies, assessments, and clinically accepted practices

PURPOSE

A U. S. and European joint effort fostering the development of medical countermeasures (MCMs) operable in case of radiological or nuclear emergencies.

METHODS

Based on the joint engagement between the U.S. National Institute of Allergy and Infectious Diseases (NIAID) and the French Institut de Radioprotection et de Sûreté Nucléaire (IRSN), a Statement of Intent to Collaborate was signed in 2014 and a series of working group meeting were established. In December 2022, the NIAID and IRSN hosted a five-day, U.S./European meeting titled 'Radiation-Induced Cutaneous and Gastrointestinal Injuries: Advances in Understanding Pathologies, Assessment, and Clinically Accepted Practices' in Paris, France. The goals of the meeting were to bring together U.S. and European investigators to explore new research avenues for the medical management of skin and gastrointestinal injuries, including specific diagnostics for each organ system, animal models, and promising medical countermeasures (MCMs) to mitigate radiation damage. There was also an emphasis on exploring additional areas of medicine and response to understand best practices from other emergency scenarios, which could be leveraged to improve radiation preparedness, and the importance of accurate dosimetry in preclinical work.

RESULTS

Subsequent to the workshop, seven collaborative projects, funded by both organizations, were established on topics ranging from MCMs and predictive biomarkers, and using physical methods to assess cutaneous radiation injuries, to mechanistic studies to understand radiation-induced damage in multiple organ systems. The importance of accurate dosimetry in preclinical works was highlighted and two recently published U.S./European commentaries that focus on the need for dosimetry standardization in the reported literature had their origins in this meeting. This commentary summarizes the workshop and open discussions among academic investigators, industry researchers, and U.S. and IRSN program representatives.

CONCLUSIONS

Given the substantive progress made due to these interactions, both groups plan to expand out these meetings by incorporating high-level investigators from across the globe, while endeavoring to maintain the informal setting that was conducive to in-depth scientific discussion and enhanced the state of the science in radiation research.

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study

Purpose.This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients.Methods.A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity.Results.A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5-1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5-2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT.Conclusion.Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.