Clinical Translation of a Deep Learning Model of Radiation-Induced Lymphopenia for Esophageal Cancer

被引:0
作者
Hu, Zongsheng [1 ]
Mohan, Radhe [1 ]
Chu, Yan [1 ,2 ]
Wang, Xiaochun [1 ]
van Rossum, Peter S. N. [3 ]
Chen, Yiqing [1 ,2 ]
Grayson, Madison E. [1 ]
Gearhardt, Angela G. [1 ]
Grassberger, Clemens [4 ]
Zhi, Degui [2 ]
Hobbs, Brian P. [5 ]
Lin, Steven H. [6 ]
Cao, Wenhua [1 ]
机构
[1] Univ Texas MD Anderson Canc Ctr, Dept Radiat Phys, Houston, TX 77030 USA
[2] Univ Texas Hlth Sci Ctr Houston, Houston, TX USA
[3] Amsterdam UMC, Amsterdam, Netherlands
[4] Univ Washington, Dept Radiat Oncol, Seattle, WA USA
[5] Univ Texas Austin, Dept Populat Hlth, Austin, TX USA
[6] Univ Texas MD Anderson Canc Ctr, Dept Radiat Oncol, Houston, TX USA
基金
美国国家卫生研究院;
关键词
Deep learning; Radiation-induced lymphopenia; Esophageal cancer; Proton therapy; LYMPHOCYTE; RADIOTHERAPY; SURVIVAL; THERAPY; PROTON;
D O I
10.1016/j.ijpt.2024.100624
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Purpose: Radiation-induced lymphopenia is a common immune toxicity that adversely impacts treatment outcomes. We report here our approach to translate a deep-learning (DL) model developed to predict severe lymphopenia risk among esophageal cancer into a strategy for incorporating the immune system as an organ-at-risk (iOAR) to mitigate the risk. Materials and Methods: We conducted "virtual clinical trials" utilizing retrospective data for 10 intensitymodulated radiation therapy (IMRT) and 10 passively-scattered proton therapy (PSPT) esophageal cancer patients. For each patient, additional treatment plans of the modality other than the original were created employing standard-of-care (SOC) dose constraints. Predicted values of absolute lymphocyte count (ALC) nadir for all plans were estimated using a previously-developed DL model. The model also yielded the relative magnitudes of contributions of iOARs dosimetric factors to ALC nadir, which were used to compute iOARs dose-volume constraints, which were incorporated into optimization criteria to produce "IMRT-enhanced" and "intensitymodulated proton therapy (IMPT)-enhanced" plans. Results: Model-predicted ALC nadir for the original IMRT (IMRT-SOC) and PSPT plans agreed well with actual values. IMPT-SOC showed greater immune sparing vs IMRT and PSPT. The average mean body doses were 13.10 Gy vs 7.62 Gy for IMRT-SOC vs IMPT-SOC for patients treated with IMRT-SOC; and 8.08 Gy vs 6.68 Gy for PSPT vs IMPT-SOC for patients treated with PSPT. For IMRT patients, the average predicted ALC nadir of IMRTSOC, IMRT-enhanced, IMPT-SOC, and IMPT-enhanced was 281, 327, 351, and 392 cells/mu L, respectively. For PSPT patients, the average predicted ALC nadir of PSPT, IMPT-SOC, and IMPT-enhanced was 258, 316, and 350 cells/mu L, respectively. Enhanced plans achieved higher predicted ALC nadir, with an average improvement of 40.8 cells/mu L (20.6%). Conclusion: The proposed DL model-guided strategy to incorporate the immune system as iOAR in IMRT and IMPT optimization has the potential for radiation-induced lymphopenia mitigation. A prospective clinical trial is planned.
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页数:7
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