Circulating tumor cells as prognostic marker in pancreatic cancer.

In this editorial we comment on the article by Zhang published in the recent issue of the . Pancreatic cancer is the fourth most common cause of cancer-related mortality and has the lowest survival rate among all solid cancers. It causes 227000 deaths annually worldwide, and the 5-year survival rate is very low due to early metastasis, which is 4.

Prognostic factors of breast cancer brain metastasis.

In this editorial we comment on the article by Chen published in the recent issue of the . Brain metastasis is one of the most serious complications of breast cancer and causes high morbidity and mortality. Brain metastases may involve the brain parenchyma and/or leptomeninges.

Radiotherapy for benign diseases in Turkey: a patterns of care survey of the Turkish Society for Radiation Oncology (TROD 05-002).

Purpose: We aimed to investigate the patterns of radiotherapy (RT) care in cases of benign diseases in Turkey.

Methods: A questionnaire survey was sent to all radiation oncology (RO) departments in Turkey. The number of patients treated for benign disease between 2015 and 2020 was requested.

Evaluation of acute hematological toxicity by machine learning in gynecologic cancers using postoperative radiotherapy.

Background: The aim of the study is to investigate the factors affecting acute hematologic toxicity (HT) in the adjuvant radiotherapy (RT) of gynecologic cancers by machine learning.

Methods: Between January 2015 and September 2018, 121 patients with endometrium and cervical cancer who underwent adjuvant RT with volumetric-modulated arc therapy (VMAT) were evaluated. The relationship between patient and treatment characteristics and acute HT was investigated using machine learning techniques, namely Logistic Regression, XGBoost, Artificial Neural Network, Random Forest, Naive Bayes, Support Vector Machine (SVM), and Gaussian Naive Bayes (GaussianNB) algorithms.

Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study.

Background: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient and tumor characteristics, dosimetric parameters, and treatment features are intertwined, and it is not always possible to associate RP with a single parameter. This study aimed to determine the algorithm that most accurately predicted RP development with machine learning.