title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð¾ÑÐºÐ»Ð¸ÐºÐ° Ð¼ÐµÑÐ°ÑÑÐ°Ð·Ð¾Ð² Ð² Ð³Ð¾Ð»Ð¾Ð²Ð½Ð¾Ð¼ Ð¼Ð¾Ð·Ð³Ðµ Ð½Ð° Ð²ÑÑÐ¾ÐºÐ¾Ð´Ð¾Ð·Ð½Ð¾Ðµ Ð¾Ð±Ð»ÑÑÐµÐ½Ð¸Ðµ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Application of machine learning models for prediction of brain metastasis response to high-dose irradiation
Creators: name: ÐÐ½Ð´ÑÐ¾Ð½Ð¾Ð²Ð°, ÐÐ»Ð¸Ð·Ð°Ð²ÐµÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÑÐ°Ð´Ð¸Ð¾Ð¼Ð¸ÐºÐ°
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
Ð¼ÐµÑÐ°ÑÑÐ°Ð·Ñ Ð² Ð³Ð¾Ð»Ð¾Ð²Ð½Ð¾Ð¼ Ð¼Ð¾Ð·Ð³Ðµ
Ð²ÑÑÐ¾ÐºÐ¾Ð´Ð¾Ð·Ð½Ð°Ñ ÑÐ°Ð´Ð¸Ð¾ÑÐµÑÐ°Ð¿Ð¸Ñ
Ð¾ÑÐºÐ»Ð¸Ðº Ð½Ð° Ð»ÐµÑÐµÐ½Ð¸Ðµ
radiomics
machine learning
classification
regression
brain metastases
high-dose radiotherapy
response to therapy

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description: To date, plenty of studies on machine learning applications in oncology have been successfully conducted. The majority of investigations, meanwhile, refer to classification tasks, whereas more complex regression ones are less common. Therefore, in the presented work, we decided to apply ML for predicting brain metastasis response to radiotherapy in both classification and regression ways and evaluate prediction quality in these cases. The dataset of 101 metastases was involved in this investigation. For every single sample, we gathered MRI scans registered before and after radiotherapy. Radiomic features extracted from pretreatment MRI combined with some clinical data were used to train six classifiers and ten regressors. Model predictions were compared to the ground truth derived from the first follow-up MRI scans. The comparison of three standard metric values (recall, precision, and AUC) for selected classifiers revealed that among the three especially efficient models (decision tree, random forest, and k-nearest neighbours), random forest seemed the most accurate. Unfortunately, we did not manage to solve the regression problem just as successfully. Too large values of the mean absolute error corresponding to the regressors examined in this research indicated that none of them was able to solve the stated task correctly. To sum up, we succeeded in solving the classification task. However, to handle the regression problem, more powerful and sophisticated methods are required.