Patient setup will influence the treatment of the breast cancer in radiation therapy. Improving the accuracy of the tumor target localization is vital for the cancer treatment. In this study, we focus on the breast patient setup and develop an accurate tumor localization method based on the deep learning in radiation therapy. The proposed method used a double residual neural network model to achieve the high precision and efficiency patient tumor localization. In the network training, the model attempt to localize the breast and then detect the landmarks inside the localized region. After the model training, we used an iterative filter scheme for calculating a transformation to the daily CT. Therefore, the gray value distribution can match well with the training image. The final landmark positions were obtained after the iteration. The translation errors in the daily CT were determined using the detected landmarks. We used the digital CT phantom images and the real patient CT images to evaluate the proposed method. Then result of the breast patient setup was shown to be clinically acceptable. The mean and standard deviation setup errors were 0.64 ± 1.40 mm, 0.15 ± 1.28 mm, -0.46 ± 1.17 mm in the anterior-posterior, left-right, and superior-inferior, respectively. In conclusion, we proposed an accurate patient setup method, which shown a very promising alternative for marker-free breast auto-setup.
Checkpoint inhibitor-related pneumonitis (CIP) is one of the most important immune checkpoint inhibitors side effects, and it is rare but fatal. Identifying patients at risk of refractory CIP before the start of CIP therapy is important for controlling CIP. We retrospectively analyzed the clinical data of 60 patients with lung cancer who developed CIP. Refractory CIP was defined as CIP with poor response to corticosteroid treatment, including CIP not relieved with corticosteroid administration or CIP recurrence during the corticosteroid tapering period. We analyzed clinical characteristics, peripheral blood biomarkers, treatment, and outcomes in nonrefractory and refractory CIP. Risk factors associated with refractory CIP were assessed. Among 60 patients with CIP, 16 (26.7%) had refractory CIP. The median onset time for patients with nonrefractory and those with refractory CIP was 16.57 (interquartile range [IQR], 6.82-28.14) weeks and 7.43 (IQR, 2.71-19.1) weeks, respectively. The level of lactate dehydrogenase (LDH) was significantly higher in the refractory CIP group at baseline (255 [222, 418] vs. 216 [183, 252], P = 0.031) and at CIP onset (321.5 [216.75, 487.5] vs. 219 [198. 241], P = 0.019). An LDH level > 320 U/L at CIP onset was an independent risk factor of refractory CIP (odds ratio [OR], 8.889; 95% confidence interval [CI]: 1.294-61.058; P = 0.026). The incidence of refractory CIP is high among patients with CIP. An increased LDH level at CIP onset is independently associated with refractory CIP. Monitoring LDH levels during immune checkpoint inhibitors treatment is recommended.
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