Statistical Analysis of Treatment Planning Parameters for Prediction of Delivery Quality Assurance Failure for Helical Tomotherapy

Kyung Hwan Chang, Young Hyun Lee, Byung Hun Park, Min Cheol Han, Jihun Kim, Hojin Kim, Min Seok Cho, Hyokyeong Kang, Ho Lee, Dong Wook Kim, Kwangwoo Park, Jaeho Cho, Yong Bae Kim, Jin Sung Kim, Chae Seon Hong

Research output: Contribution to journalArticlepeer-review

Abstract

PURPOSE: This study aimed to investigate the parameters with a significant impact on delivery quality assurance (DQA) failure and analyze the planning parameters as possible predictors of DQA failure for helical tomotherapy. METHODS: In total, 212 patients who passed or failed DQA measurements were retrospectively included in this study. Brain (n = 43), head and neck (n = 37), spinal (n = 12), prostate (n = 36), rectal (n = 36), pelvis (n = 13), cranial spinal irradiation and a treatment field including lymph nodes (n = 24), and other types of cancer (n = 11) were selected. The correlation between DQA results and treatment planning parameters were analyzed using logistic regression analysis. Receiver operating characteristic (ROC) curves, areas under the curves (AUCs), and the Classification and Regression Tree (CART) algorithm were used to analyze treatment planning parameters as possible predictors for DQA failure. RESULTS: The AUC for leaf open time (LOT) was 0.70, and its cut-off point was approximately 30%. The ROC curve for the predicted probability calculated when the multivariate variable model was applied showed an AUC of 0.815. We confirmed that total monitor units, total dose, and LOT were significant predictors for DQA failure using the CART. CONCLUSIONS: The probability of DQA failure was higher when the percentage of LOT below 100 ms was higher than 30%. The percentage of LOT below 100 ms should be considered in the treatment planning process. The findings from this study may assist in the prediction of DQA failure in the future.

Original languageEnglish
Pages (from-to)1533033820979692
JournalTechnology in Cancer Research and Treatment
Volume19
DOIs
Publication statusPublished - 2020 Jan 1

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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