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カナイ タカユキ
金井 貴幸 所属 医学部 医学科（東京女子医科大学病院） 職種 講師 |
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| 論文種別 | 原著 |
| 言語種別 | 英語 |
| 査読の有無 | 査読あり |
| 表題 | Radiation pneumonitis prediction model with integrating multiple dose-function features on 4DCT ventilation images. |
| 掲載誌名 | 正式名：Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) 略 称：Phys Med ISSNコード：1724191X/11201797 |
| 掲載区分 | 国外 |
| 巻・号・頁 | 105,pp.102505 |
| 著者・共著者 | Katsuta Yoshiyuki, Kadoya Noriyuki, Kajikawa Tomohiro, Mouri Shina, Kimura Tomoki, Takeda Kazuya, Yamamoto Takaya, Imano Nobuki, Tanaka Shohei, Ito Kengo, Kanai Takayuki, Nakajima Yujiro, Jingu Keiichi |
| 発行年月 | 2023/01 |
| 概要 | PURPOSE:Radiation pneumonitis (RP) is dose-limiting toxicity for non-small-cell cancer (NSCLC). This study developed an RP prediction model by integrating dose-function features from computed four-dimensional computed tomography (4DCT) ventilation using the least absolute shrinkage and selection operator (LASSO).METHODS:Between 2013 and 2020, 126 NSCLC patients were included in this study who underwent a 4DCT scan to calculate ventilation images. We computed two sets of candidate dose-function features from (1) the percentage volume receiving > 20 Gy or the mean dose on the functioning zones determined with the lower cutoff percentile ventilation value, (2) the functioning zones determined with lower and upper cutoff percentile ventilation value using 4DCT ventilation images. An RP prediction model was developed by LASSO while simultaneously determining the regression coefficient and feature selection through fivefold cross-validation.RESULTS:We found 39.3 % of our patients had a ≥ grade 2 RP. The mean area under the curve (AUC) values for the developed models using clinical, dose-volume, and dose-function features with a lower cutoff were 0.791, and the mean AUC values with lower and upper cutoffs were 0.814. The relative regression coefficient (RRC) on dose-function features with upper and lower cutoffs revealed a relative impact of dose to each functioning zone to RP. RRCs were 0.52 for the mean dose on the functioning zone, with top 20 % of all functioning zone was two times greater than that of 0.19 for these with 60 %-80 % and 0.17 with 40 %-60 % (P < 0.01).CONCLUSIONS:The introduction of dose-function features computed from functioning zones with lower and upper cutoffs in a machine learning framework can improve RP prediction. The RRC given by LASSO using dose-function features allows for the quantification of the RP impact of dose on each functioning zones and having the potential to support treatment planning on functional image-guided radiotherapy. |
| DOI | 10.1016/j.ejmp.2022.11.009 |
| PMID | 36535238 |