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BACKGROUND: The significance of local radiotherapy (RT) in advanced non-small-cell lung cancer (NSCLC) is well documented. However, the advent of immunotherapy has raised questions regarding the synergistic survival benefits or potential adverse effects. OBJECTIVE: This study aimed to explore whether a combination of RT and systematic immune checkpoint inhibitors (ICIs) can improve the survival outcomes for NSCLC patients. METHODS: Based on collected data patients who received RT were defined as the RT group, and those who had not for any site were defined as the non-RT group. Propensity score matching (PSM) was employed to mitigate bias. The primary endpoint was progression-free survival (PFS), with secondary endpoints including overall survival (OS) and treatment-related adverse events (AEs). RESULTS: Out of 709 patients (235 in RT group and 474 in non-RT group) were included, with 213 patients per group. The median PFS of the RT group was better than that of the non-RT group (13.8 months versus 9.5 months; p < 0.0001), although no superiority in median overall survival (OS) of the RT group was observed (p = 0.715). However, among the cohort of patients with ≤3 metastases, the median OS of the RT group improved significantly (HR = 0.60, [95% CI 0.44-0.83]; p = 0.004). Treatment-related AEs occurred in 94.5% of RT group patients and in 94.9% of non-RT group patients (p = 0.792), which indicated no observable increase in AEs from RT. CONCLUSIONS: These results demonstrate the tolerability of RT when administered along with immunotherapy, suggesting its potential to positively impact the survival outcomes of NSCLC patients.
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PURPOSE: The goal of this study was to propose a knowledge-based planning system which could automatically design plans for lung cancer patients treated with intensity-modulated radiotherapy (IMRT). METHODS AND MATERIALS: From May 2018 to June 2020, 612 IMRT treatment plans of lung cancer patients were retrospectively selected to construct a planning database. Knowledge-based planning (KBP) architecture named αDiar was proposed in this study. It consisted of two parts separated by a firewall. One was the in-hospital workstation, and the other was the search engine in the cloud. Based on our previous study, ANet in the in-hospital workstation was used to generate predicted virtual dose images. A search engine including a three-dimensional convolutional neural network (3D CNN) was constructed to derive the feature vectors of dose images. By comparing the similarity of the features between virtual dose images and the clinical dose images in the database, the most similar feature was found. The optimization parameters (OPs) of the treatment plan corresponding to the most similar feature were assigned to the new plan, and the design of a new treatment plan was automatically completed. After αDiar was developed, we performed two studies. The first retrospective study was conducted to validate whether this architecture was qualified for clinical practice and involved 96 patients. The second comparative study was performed to investigate whether αDiar could assist dosimetrists in improving the quality of planning for the patients. Two dosimetrists were involved and designed plans for only one trial with and without αDiar; 26 patients were involved in this study. RESULTS: The first study showed that about 54% (52/96) of the automatically generated plans would achieve the dosimetric constraints of the Radiation Therapy Oncology Group (RTOG) and about 93% (89/96) of the automatically generated plans would achieve the dosimetric constraints of the National Comprehensive Cancer Network (NCCN). The second study showed that the quality of treatment planning designed by junior dosimetrists was improved with the help of αDiar. CONCLUSIONS: Our results showed that αDiar was an effective tool to improve planning quality. Over half of the patients' plans could be designed automatically. For the remaining patients, although the automatically designed plans did not fully meet the clinical requirements, their quality was also better than that of manual plans.
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Context: Although programmed death 1 (PD-1) inhibitors are a standard second-line treatment for esophageal squamous cell carcinoma (ESCC), their efficacy when used in combination with chemotherapy or anti-angiogenesis targeted therapy is unclear. Aim: To compare the efficacy and safety of PD-1 inhibitor monotherapy with that of combination therapy. Setting and Design: A retrospective study was conducted at the Shandong Cancer Hospital. Materials and Methods: Based on records, patients with advanced ESCC, treated with second-line or above PD-1 inhibitor-containing regimens from August 15, 2019 to April 12, 2021 were divided into combination (PD-1 inhibitors plus chemotherapy or anti-angiogenesis targeted therapy) and monotherapy groups. The primary endpoints were progression-free survival (PFS) and overall survival (OS). Statistical Analysis Used: The baseline differences between subgroups were assessed using the χ2-test, Fisher's exact test, or Student's t-test. Follow-up period, PFS, OS, median survival, and 95% confidence intervals (CIs) were estimated using KaplanâMeier analysis. The log-rank test was used to compare subgroups. Results: In the 169 patients included, clinical features were well balanced between both groups. The median PFS of the combination group was better than that of the monotherapy group (8.5 months [95%CI 6.3-10.7] vs. 3.2 months [95%CI 0.0-6.5]; hazard ratio (HR) = 0.34 [95%CI 0.13-0.92]; P < 0.001). The median OS showed the same trend (18.9 months [95%CI 14.4-23.3] vs. 9.8 months [95%CI 6.3-13.2]; HR = 0.47 [95%CI 0.21-1.04]; P = 0.010). Conclusion: Using PD-1 inhibitors in a combination treatment may improve PFS and OS, with acceptable toxicities.