The factors of fiducial marker motions and individual margin assessment in postoperative breast cancer radiotherapy.

Background: As a surrogate for the breast tumor bed, individual fiducial markers frequently move during radiotherapy. This study aimed to classify the motions and calculate the individualized target margin. Methods: The mammary basal diameters (D) and heights (H) were measured to represent breast sizes for 15 patients after breast-conserving surgery. The clinical target volume (CTV) was divided into 4 quadrants by a coordinate system with the center of mass of the tumor bed as the origin. The lateral, anteroposterior, and craniocaudal motions of markers were calculated (MLR, MAP, MSI) based on the difference of the setup errors between the spine matching and the fiducial markers matching. The distances between markers and the innermost, foremost, and uppermost borders of CTV (DSLR, DSAP, DSSI) were recorded. Results: In the first quadrant, MAP was strongly correlated with D×H (r>0.80) when D×H <99.89 cm2. Both MLR and MAP were positively linearly related to DSLR, DSAP, DSSI (r>0.85, R2>0.75). MSI was also positively linearly correlated with DSAP and DSLR (r>0.90, R2>0.80). In the fourth quadrant with D×H <90.71 cm2, only MLR and DSLR showed a linear positive correlation (r>0.90, R2>0.75), whereas the others showed linear negative correlations (r>-0.90, R2>0.80). The planning target volume (PTV) margin varied significantly between the first and fourth quadrant (P<0.05), and the largest margin was 12.4 mm in the craniocaudal direction of the first quadrant with D×H ≥99.89 cm2. Conclusions: Fiducial motion is susceptible to breast size and fiducial position, and the individualized PTV margins should take the above factors into account.

Correlation between the γ passing rates of IMRT plans and the volumes of air cavities and bony structures in head and neck cancer.

BACKGROUND: Both patient-specific dose recalculation and γ passing rate analysis are important for the quality assurance (QA) of intensity modulated radiotherapy (IMRT) plans. The aim of this study was to analyse the correlation between the γ passing rates and the volumes of air cavities (Vair) and bony structures (Vbone) in target volume of head and neck cancer. METHODS: Twenty nasopharyngeal carcinoma and twenty nasal natural killer T-cell lymphoma patients were enrolled in this study. Nine-field sliding window IMRT plans were produced and the dose distributions were calculated by anisotropic analytical algorithm (AAA), Acuros XB algorithm (AXB) and SciMoCa based on the Monte Carlo (MC) technique. The dose distributions and γ passing rates of the targets, organs at risk, air cavities and bony structures were compared among the different algorithms. RESULTS: The γ values obtained with AAA and AXB were 95.6 ± 1.9% and 96.2 ± 1.7%, respectively, with 3%/2 mm criteria (p > 0.05). There were significant differences (p < 0.05) in the γ values between AAA and AXB in the air cavities (86.6 ± 9.4% vs. 98.0 ± 1.7%) and bony structures (82.7 ± 13.5% vs. 99.0 ± 1.7%). Using AAA, the γ values were proportional to the natural logarithm of Vair (R2 = 0.674) and inversely proportional to the natural logarithm of Vbone (R2 = 0.816). When the Vair in the targets was smaller than approximately 80 cc or the Vbone in the targets was larger than approximately 6 cc, the γ values of AAA were below 95%. Using AXB, no significant relationship was found between the γ values and Vair or Vbone. CONCLUSION: In clinical head and neck IMRT QA, greater attention should be paid to the effect of Vair and Vbone in the targets on the γ passing rates when using different dose calculation algorithms.

Cancer risk assessment in modern radiotherapy workflow with medical big data.

Modern radiotherapy (RT) is being enriched by big digital data and intensive technology. Multimodality image registration, intelligence-guided planning, real-time tracking, image-guided RT (IGRT), and automatic follow-up surveys are the products of the digital era. Enormous digital data are created in the process of treatment, including benefits and risks. Generally, decision making in RT tries to balance these two aspects, which is based on the archival and retrieving of data from various platforms. However, modern risk-based analysis shows that many errors that occur in radiation oncology are due to failures in workflow. These errors can lead to imbalance between benefits and risks. In addition, the exact mechanism and dose-response relationship for radiation-induced malignancy are not well understood. The cancer risk in modern RT workflow continues to be a problem. Therefore, in this review, we develop risk assessments based on our current knowledge of IGRT and provide strategies for cancer risk reduction. Artificial intelligence (AI) such as machine learning is also discussed because big data are transforming RT via AI.