‘Line’ Constraints Optimization for Improved Dose Distribution in Locally Recurrent Nasopharyngeal Carcinoma Using Knowledge-Based Planning
Introduction To investigate the efficacy of a knowledge-based planning (KBP) model in optimizing dose distribution, and identify the inter-institutional variation in radiotherapy of recurrent nasopharyngeal carcinoma (rNPC). Methods A total of 70 rNPC patients treated with intensity-modulated radiot...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-06-01
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| Series: | Technology in Cancer Research & Treatment |
| Online Access: | https://doi.org/10.1177/15330338251351535 |
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| author | Xiaoli Yu MD Yixuan Wang MD Mingli Wang MD Huikuan Gu MD Xin Yang PhD Jiang Hu MD |
| author_facet | Xiaoli Yu MD Yixuan Wang MD Mingli Wang MD Huikuan Gu MD Xin Yang PhD Jiang Hu MD |
| author_sort | Xiaoli Yu MD |
| collection | DOAJ |
| description | Introduction To investigate the efficacy of a knowledge-based planning (KBP) model in optimizing dose distribution, and identify the inter-institutional variation in radiotherapy of recurrent nasopharyngeal carcinoma (rNPC). Methods A total of 70 rNPC patients treated with intensity-modulated radiotherapy (IMRT) were recruited to build a KBP model. Following model refinement, 36 patients were retrospectively enrolled for dosimetric comparison between manually optimized and KBP-generated plans. Ten experienced physicists from six different institutions were engaged to independently design manual plan for a representative case, to assess inter-institutional variations, and differences between KBP and manual plans. Results The refined KBP model provided significant reduced dose in brainstem D 1cc (the dose received by the “hottest”1 cm 3 volume, 41.14 ± 8.51 Gy vs 38.48 ± 8.60 Gy, P < 0.001) and spinal cord D 1cc (17.48 ± 9.38Gy vs 12.23 ± 6.56Gy, P < 0.001). In addition, The mean dose (D mean) of brainstem, spinal cord, mandible, parotid, temporomandibular joint and inner ear were statistically decreased ( P < 0.05). In validation cohort, KBP model eliminated the hotspot (0.57 ± 0.01% vs 0.00 ± 0.00%, P < 0.001), improved target homogeneity (HI: 0.06 ± 0.00 vs 0.08 ± 0.00, P < 0.001), and performed superior to the manual plans in sparing organs. While all institutions achieved comparable target coverage, manual plans exhibited substantial variability in sparing brainstem. KBP implementation reduced inter-institutional dose disparities for brainstem (46.30 ± 10.08 Gy vs 41.80 ± 5.80 Gy, P = 0.041) and spinal cord (26.08 ± 7.06 Gy vs 18.19 ± 1.98 Gy, P = 0.002). Additionally, planning efficiency increased by 48.7% (39 vs 76 min). Conclusions This KBP framework optimized rNPC reirradiation from three dimensions: 1) Enhanced OARs’ protection; 2) Improved target homogeneity; 3) Improved the multi-institutional consistency and efficiency of planning. These advancements established a clinically actionable paradigm for precision reirradiation. |
| format | Article |
| id | doaj-art-b40cf3aadbc3425089fffc8720193216 |
| institution | Kabale University |
| issn | 1533-0338 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Technology in Cancer Research & Treatment |
| spelling | doaj-art-b40cf3aadbc3425089fffc87201932162025-08-20T03:31:07ZengSAGE PublishingTechnology in Cancer Research & Treatment1533-03382025-06-012410.1177/15330338251351535‘Line’ Constraints Optimization for Improved Dose Distribution in Locally Recurrent Nasopharyngeal Carcinoma Using Knowledge-Based PlanningXiaoli Yu MD0Yixuan Wang MD1Mingli Wang MD2Huikuan Gu MD3Xin Yang PhD4Jiang Hu MD5 Department of Radiation Oncology, , Guangzhou, Guangdong, People's Republic of China State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center of Cancer Medicine, Department of Radiation Oncology, Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center of Cancer Medicine, Department of Radiation Oncology, Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center of Cancer Medicine, Department of Radiation Oncology, Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center of Cancer Medicine, Department of Radiation Oncology, Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center of Cancer Medicine, Department of Radiation Oncology, Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of ChinaIntroduction To investigate the efficacy of a knowledge-based planning (KBP) model in optimizing dose distribution, and identify the inter-institutional variation in radiotherapy of recurrent nasopharyngeal carcinoma (rNPC). Methods A total of 70 rNPC patients treated with intensity-modulated radiotherapy (IMRT) were recruited to build a KBP model. Following model refinement, 36 patients were retrospectively enrolled for dosimetric comparison between manually optimized and KBP-generated plans. Ten experienced physicists from six different institutions were engaged to independently design manual plan for a representative case, to assess inter-institutional variations, and differences between KBP and manual plans. Results The refined KBP model provided significant reduced dose in brainstem D 1cc (the dose received by the “hottest”1 cm 3 volume, 41.14 ± 8.51 Gy vs 38.48 ± 8.60 Gy, P < 0.001) and spinal cord D 1cc (17.48 ± 9.38Gy vs 12.23 ± 6.56Gy, P < 0.001). In addition, The mean dose (D mean) of brainstem, spinal cord, mandible, parotid, temporomandibular joint and inner ear were statistically decreased ( P < 0.05). In validation cohort, KBP model eliminated the hotspot (0.57 ± 0.01% vs 0.00 ± 0.00%, P < 0.001), improved target homogeneity (HI: 0.06 ± 0.00 vs 0.08 ± 0.00, P < 0.001), and performed superior to the manual plans in sparing organs. While all institutions achieved comparable target coverage, manual plans exhibited substantial variability in sparing brainstem. KBP implementation reduced inter-institutional dose disparities for brainstem (46.30 ± 10.08 Gy vs 41.80 ± 5.80 Gy, P = 0.041) and spinal cord (26.08 ± 7.06 Gy vs 18.19 ± 1.98 Gy, P = 0.002). Additionally, planning efficiency increased by 48.7% (39 vs 76 min). Conclusions This KBP framework optimized rNPC reirradiation from three dimensions: 1) Enhanced OARs’ protection; 2) Improved target homogeneity; 3) Improved the multi-institutional consistency and efficiency of planning. These advancements established a clinically actionable paradigm for precision reirradiation.https://doi.org/10.1177/15330338251351535 |
| spellingShingle | Xiaoli Yu MD Yixuan Wang MD Mingli Wang MD Huikuan Gu MD Xin Yang PhD Jiang Hu MD ‘Line’ Constraints Optimization for Improved Dose Distribution in Locally Recurrent Nasopharyngeal Carcinoma Using Knowledge-Based Planning Technology in Cancer Research & Treatment |
| title | ‘Line’ Constraints Optimization for Improved Dose Distribution in Locally Recurrent Nasopharyngeal Carcinoma
Using Knowledge-Based Planning |
| title_full | ‘Line’ Constraints Optimization for Improved Dose Distribution in Locally Recurrent Nasopharyngeal Carcinoma
Using Knowledge-Based Planning |
| title_fullStr | ‘Line’ Constraints Optimization for Improved Dose Distribution in Locally Recurrent Nasopharyngeal Carcinoma
Using Knowledge-Based Planning |
| title_full_unstemmed | ‘Line’ Constraints Optimization for Improved Dose Distribution in Locally Recurrent Nasopharyngeal Carcinoma
Using Knowledge-Based Planning |
| title_short | ‘Line’ Constraints Optimization for Improved Dose Distribution in Locally Recurrent Nasopharyngeal Carcinoma
Using Knowledge-Based Planning |
| title_sort | line constraints optimization for improved dose distribution in locally recurrent nasopharyngeal carcinoma using knowledge based planning |
| url | https://doi.org/10.1177/15330338251351535 |
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