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Reirradiation using robotic image-guided stereotactic radiotherapy of recurrent head and neck cancer.

ABSTRACT: The purpose of this study was to examine the prognosis for patients with head and neck cancer after reirradiation using Cyberknife stereotactic body irradiation with special focus on mucosal ulceration. We conducted a retrospective multi-institutional review of 107 patients with previously irradiated head and neck cancer. The median follow-up time for all patients was 15 months, and the 2-year overall survival rate was 35%. Significant prognostic factors for overall survival were primary site (nasopharynx versus other sites), presence of ulceration, and PTV volume. Detailed analysis of ulceration showed a lower response rate (28%) in the ulceration (+) group than the ulceration (-) group (63%; P = 0.0045). The 2-year overall survival rates were 8% in the ulceration (+) group and 42.7% (P = 0.0001) in the ulceration (-) group, respectively. We recorded 22 severe toxicities, including 11 patients with carotid blow-out syndrome (CBOS), which was fatal in 9 patients. CBOS occurred in 6 patients with ulceration (6/25; 24%), and 5 patients experienced CBOS without ulceration (5/82; 6%; P=0.027). In conclusion, ulceration is an important prognostic factor, not only for adverse events but also for survival after reirradiation using CyberKnife.

SUBMITTER: Yamazaki H

PROVIDER: S-EPMC4915543 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Reirradiation using robotic image-guided stereotactic radiotherapy of recurrent head and neck cancer.

Yamazaki Hideya H Ogita Mikio M Himei Kengo K Nakamura Satoaki S Suzuki Gen G Yoshida Ken K Kotsuma Tadayuki T Yoshioka Yasuo Y

Journal of radiation research 20160316 3

The purpose of this study was to examine the prognosis for patients with head and neck cancer after reirradiation using Cyberknife stereotactic body irradiation with special focus on mucosal ulceration. We conducted a retrospective multi-institutional review of 107 patients with previously irradiated head and neck cancer. The median follow-up time for all patients was 15 months, and the 2-year overall survival rate was 35%. Significant prognostic factors for overall survival were primary site (n ...[more]

PMID: 26983982

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Project description:ImportancePersonalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions.ObjectiveTo explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy.Design, setting, and participantsThis quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who received intensity-modulated radiation therapy between October 2013 and February 2020. Fifteen different OARs were manually annotated by expert readers and radiation oncologists. The models were trained on a subset of the data set to automatically delineate OARs and evaluated on both internal and external data sets. Data analysis was conducted October 2019 to September 2020.Main outcomes and measuresThe autocontouring solution was evaluated on external data sets, and its accuracy was quantified with volumetric agreement and surface distance measures. Models were benchmarked against expert annotations in an interobserver variability (IOV) study. Clinical utility was evaluated by measuring time spent on manual corrections and annotations from scratch.ResultsA total of 519 participants' (519 [100%] men; 390 [75%] aged 62-75 years) pelvic CT images and 242 participants' (184 [76%] men; 194 [80%] aged 50-73 years) head and neck CT images were included. The models achieved levels of clinical accuracy within the bounds of expert IOV for 13 of 15 structures (eg, left femur, κ = 0.982; brainstem, κ = 0.806) and performed consistently well across both external and internal data sets (eg, mean [SD] Dice score for left femur, internal vs external data sets: 98.52% [0.50] vs 98.04% [1.02]; P = .04). The correction time of autogenerated contours on 10 head and neck and 10 prostate scans was measured as a mean of 4.98 (95% CI, 4.44-5.52) min/scan and 3.40 (95% CI, 1.60-5.20) min/scan, respectively, to ensure clinically accepted accuracy. Manual segmentation of the head and neck took a mean 86.75 (95% CI, 75.21-92.29) min/scan for an expert reader and 73.25 (95% CI, 68.68-77.82) min/scan for a radiation oncologist. The autogenerated contours represented a 93% reduction in time.Conclusions and relevanceIn this study, the models achieved levels of clinical accuracy within expert IOV while reducing manual contouring time and performing consistently well across previously unseen heterogeneous data sets. With the availability of open-source libraries and reliable performance, this creates significant opportunities for the transformation of radiation treatment planning.

Dataset Information

Reirradiation using robotic image-guided stereotactic radiotherapy of recurrent head and neck cancer.

Publications

Reirradiation using robotic image-guided stereotactic radiotherapy of recurrent head and neck cancer.

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