Hyperfractionated Accelerated Radiotherapy Versus Stereotactic Body Radiotherapy in the Treatment of Limited-Stage Small Cell Lung Cancer: A Matched-Pair Analysis.

BACKGROUND: Concurrent chemoradiotherapy based on hyperfractionated accelerated radiotherapy (HART) is the first-line recommended regimen for the treatment of small-cell lung cancer (SCLC). However, Stereotactic Body Radiotherapy (SBRT) is also regarded as an effective treatment for limited-stage (LS) SCLC, and the efficacy and safety of HART versus SBRT stay controversial. METHODS: In this study, 188 LS-SCLC patients were retrospectively divided into two groups receiving chemotherapy combined with either HART or SBRT. In HART group, patients received 4500 cGy in 30 fractions, administered twice daily for 3 weeks. Whereas in the SBRT group, a total radiation dose of 4000-4500 cGy was delivered in 10 fractions over 2 weeks. Thirty-three pairs of patients were finally included for next analysis. RESULTS: The estimated objective response rates were 63.6 % (21/33) and 78.8 % (26/33) in HART group and SBRT group, respectively (P = 0.269). Furthermore, there was no significant difference between HART and SBRT groups in overall survival (26 months vs. 29 months, P = 0.362) and progression free survival (11 months vs. 15 months, P = 0.223). As for the adverse events, toxicity of both groups is similar and slight that no grade 4 event was observed. Grade 3 pneumonitis cases were all occurred in the HART group (9.1%, 3/33, P = 0.238), and grade 3 esophagitis cases were all occurred in the SBRT group (6.1%, 2/33, P = 0.492). CONCLUSION: Compared with HART, SBRT could be another effective treatment with satisfactory safety for the concurrent chemoradiotherapy in patients with LS-SCLC.

Evaluation of Auto-Planning for Left-Side Breast Cancer After Breast-Conserving Surgery Based on Geometrical Relationship.

PURPOSE: This study aimed to evaluate (1) the performance of the Auto-Planning module embedded in the Pinnacle treatment planning system (TPS) with 30 left-side breast cancer plans and (2) the dose-distance correlations between dose-based patients and overlap volume histogram-based (OVH) patients. METHOD: A total of 30 patients with left-side breast cancer after breast-conserving surgery were enrolled in this study. The clinical manual-planning (MP) and the Auto-Planning (AP) plans were generated by Monaco and by the Auto-Planning module in Pinnacle respectively. The geometric information between organ at risk (OAR) and planning target volume (PTV) of each patient was described by the OVH. The AP and MP plans were ranked to compare with the geometry-based patients from OVH. The Pearson product-moment correlation coefficient (R) was used to describe the correlations between dose-based patients (APs and MPs) and geometry-based patients (OVH). Dosimetric differences between MP and AP plans were evaluated with statistical analysis. RESULT: The correlation coefficient (mean R = 0.71) indicated that the AP plans have a high correlation with geometry-based patients from OVH, whereas the correlation coefficient (mean R = 0.48) shows a weak correlation between MP plans and geometry-based patients. The dosimetric comparison revealed a statistically significant improvement in the ipsilateral lung V5Gy and V10Gy, and in the heart V5Gy of AP plans compared to MP plans, while statistical reduction was seen in PTV V107% for MP plans compared to AP plans. CONCLUSION: The overall results of AP plans were superior to MP plans. The dose distribution in AP plans was more consistent with the distance-dose relationship described by OVH. After eliminating the interference of human factors, the AP was able to provide more stable and objective plans for radiotherapy patients.

The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

Evaluation of the body mass index in breast cancer prognosis in a cohort of small-stature overweight patients: multi-center study in China.

BACKGROUND: Overweight and obesity have become a major health issue in the past 30 years. Several studies have already shown that obesity is significantly associated with a higher risk of developing breast cancer. However, few studies have assessed the prognostic value of the body mass index (BMI) in Asian populations. The purpose of this study was to retrospectively analyze the impact of BMI on the prognosis of breast cancer in overweight, under 160 cm tall patients from southern China. METHODS: We retrospectively analyzed data from 525 breast cancer patients diagnosed between 2003 to 2010 in a multi-center of China. After applying the exclusion criteria, 315 patients with complete data were retained. Their clinical and pathological characteristics were compared using the chi-square test. Survival analysis was performed with the Kaplan-Meier method. Univariate and multivariate analyses were performed using Cox regression to calculate hormone receptor status, HER-2 status, lymph node status, age, BMI and tumor size hazard ratio (HR), and 95% confidence intervals (95% CI). RESULTS: There was a strong correlation between BMI and age in the baseline feature analysis (P=0.001). After grouping the patients according to the molecular type of cancer, we found that in Luminal A and B, the BMI was related to age (P=0.002, P=0.010). The disease-free survival (DFS) and overall survival (OS) of patients with different BMI were not significantly different. This conclusion was also reached by pairwise comparison of subgroups. There was no significant difference in recurrence in patients from different BMI groups. We did not find a critical weight threshold associated with higher risk of recurrence. There were no statistically significant differences in treatment among the three BMI groups of overweight patients. CONCLUSIONS: We found that the BMI of Chinese breast cancer patients is related to age but not prognosis.