Clinical, Pathologic, and Dosimetric Predictors of Head and Neck Lymphedema Following Definitive or Adjuvant Radiation Therapy for Head and Neck Cancer

IF 2.2 Q3 ONCOLOGY
Kevin R. Rogacki MD , P. Troy Teo PhD , Mahesh Gopalakrishnan MSc , Suvidya Lakshmi Pachigolla MD , Clayton E. Lyons MD , Mohamed E. Abazeed MD, PHD , Indra Das PhD , Bharat B. Mittal MD , Michelle Gentile MD, PhD
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引用次数: 0

Abstract

Purpose

Head and neck lymphedema (HNL) following radiation therapy for head and neck cancer (HNC) causes patient morbidity. Predicting individual patients’ risk of HNL after treatment is challenging. We aimed to identify the demographic, disease-related, and treatment-related factors associated with external and internal HNL following treatment of HNC with definitive or adjuvant radiation therapy.

Methods and Materials

Relevant clinical, pathologic, and dosimetric data for 76 consecutive patients who received definitive or adjuvant radiation ± chemotherapy were retrospectively collected from a single institution. Multivariable models predictive of external and internal lymphedema using clinicopathologic variables alone and in combination with dosimetric variables were constructed and optimized using competing risk regression.

Results

After median follow-up of 550 days, the incidence of external and internal HNL at 360 days was 70% and 34%, respectively. When evaluating clinical and treatment-related factors alone, number of lymph nodes removed and advanced adenopathy status were predictive of external lymphedema. With incorporation of dosimetric variables, the optimized model included the percentage volume of the contralateral lymph node level VII receiving 30Gy V30 ≥50%, number of lymph nodes removed, and advanced adenopathy status. For internal lymphedema, our clinicopathologic model identified both adjuvant radiation, as opposed to definitive radiation, and advanced adenopathy status. With inclusion of a dosimetric variable, the optimized model included larynx V45 ≥50% and advanced adenopathy.

Conclusions

HNL following HNC treatment is common. For both external and internal lymphedema, nodal disease burden at diagnosis predicts increased risk. For external lymphedema, increasing extent of lymph node dissection prior to adjuvant therapy increases risk. The contralateral level VII lymph node region is also predictive of external lymphedema when radiation dose to V30 is ≥50%, meriting investigation. For internal lymphedema, we confirm that increasing radiation dose to the larynx is the most significant dosimetric predictor of mucosal edema when larynx V45 is ≥50%.

头颈部癌症确定性或辅助放疗后头颈部淋巴水肿的临床、病理和剂量预测因素
目的头颈癌(HNC)放疗后的头颈淋巴水肿(HNL)会导致患者发病。预测患者治疗后发生 HNL 的风险具有挑战性。我们的目的是确定与明确或辅助放疗治疗 HNC 后内外 HNL 相关的人口统计学、疾病相关因素和治疗相关因素。方法和材料我们从一家机构回顾性收集了 76 例连续接受明确或辅助放疗 ± 化疗患者的相关临床、病理和剂量数据。结果在中位随访 550 天后,360 天时外部和内部 HNL 的发生率分别为 70% 和 34%。在单独评估临床和治疗相关因素时,淋巴结切除数量和晚期腺病状态可预测外部淋巴水肿。在纳入剂量变量后,优化模型包括接受30Gy V30的对侧淋巴结VII水平体积百分比≥50%、淋巴结切除数量和晚期腺病状态。对于内淋巴水肿,我们的临床病理模型同时识别了辅助放射(而非最终放射)和晚期腺病状态。加入剂量变量后,优化模型包括喉V45≥50%和晚期腺病。对于外部淋巴水肿和内部淋巴水肿,诊断时的结节疾病负担可预测风险的增加。就外部淋巴水肿而言,辅助治疗前淋巴结清扫范围的扩大也会增加风险。当V30的放射剂量≥50%时,对侧VII级淋巴结区域也可预测体外淋巴水肿,值得研究。对于体内淋巴水肿,我们证实,当喉V45的放射剂量≥50%时,喉部放射剂量的增加是预测粘膜水肿最重要的剂量学指标。
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来源期刊
Advances in Radiation Oncology
Advances in Radiation Oncology Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.60
自引率
4.30%
发文量
208
审稿时长
98 days
期刊介绍: The purpose of Advances is to provide information for clinicians who use radiation therapy by publishing: Clinical trial reports and reanalyses. Basic science original reports. Manuscripts examining health services research, comparative and cost effectiveness research, and systematic reviews. Case reports documenting unusual problems and solutions. High quality multi and single institutional series, as well as other novel retrospective hypothesis generating series. Timely critical reviews on important topics in radiation oncology, such as side effects. Articles reporting the natural history of disease and patterns of failure, particularly as they relate to treatment volume delineation. Articles on safety and quality in radiation therapy. Essays on clinical experience. Articles on practice transformation in radiation oncology, in particular: Aspects of health policy that may impact the future practice of radiation oncology. How information technology, such as data analytics and systems innovations, will change radiation oncology practice. Articles on imaging as they relate to radiation therapy treatment.
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