In Medical physics ; h5-index 59.0
BACKGROUND : Hematologic toxicity (HT) is a common adverse tissue reaction during radiotherapy for rectal cancer patients, which may lead to various negative effects such as reduced therapeutic effect, prolonged treatment period and increased treatment cost. Therefore, predicting the occurrence of HT before radiotherapy is necessary but still challenging.
PURPOSE : This study proposes a hybrid machine learning model to predict the symptomatic radiation hematologic toxicity in rectal cancer patients using the combined demographic, clinical, dosimetric, and Radiomics features, and ascertains the most effective regions of interest (ROI) in CT images and predictive feature sets.
METHODS : A discovery dataset of 240 rectal cancer patients, including 145 patients with hematologic toxicity (HT) symptoms and a validation dataset of 96 patients (63 patients with HT) with different dose prescription were retrospectively enrolled. Eight ROIs were contoured on patient CT images to derive Radiomics features, which were then respectively combined with the demographic, clinical and dosimetric features to classify patients with HT symptoms. Moreover, the survival analysis was performed on risky patients with HT in order to understand the HT progression.
RESULTS : The classification models in ROIs of bone marrow and femoral head exhibited relatively high accuracies (accuracy = 0.765 and 0.725) in the discovery dataset as well as comparable performances in the validation dataset (accuracy = 0.758 and 0.714). When combining the two ROIs together, the model performance was the best in both discovery and validation datasets (accuracy = 0.843 and 0.802). In the survival analysis test, only the bone marrow ROI achieved statistically significant performance in accessing risky HT (C-index = 0.658, p = 0.03). Most of the discriminative features were Radiomics features, and only gender and the mean dose in Irradvolume was involved in HT.
CONCLUSIONS : The results reflect that the Radiomics features of bone marrow is significantly correlated with HT occurrence and progression in rectal cancer. The proposed Radiomics-based model may help the early detection of radiotherapy induced HT in rectal cancer patients and thus improve the clinical outcome in future. This article is protected by copyright. All rights reserved.
Yue Haizhen, Geng Jianhao, Gong Liqing, Li Yongheng, Windsor Gabrielle, Liu Jiacheng, Pu Yichen, Du Yi, Wang Ruoxi, Wu Hao, Jiao Zhicheng, Bai Harrison, Jing Bin
2023-Feb-13
Hematologic toxicity, Machine learning, Radiomics, Rectal cancer
