Radiation Therapy and Hearing Loss

Department of Radiation Oncology, University of Florida College of Medicine, Gainesville, Florida 32610, USA.
International journal of radiation oncology, biology, physics (Impact Factor: 4.26). 03/2010; 76(3 Suppl):S50-7. DOI: 10.1016/j.ijrobp.2009.04.096
Source: PubMed


A review of literature on the development of sensorineural hearing loss after high-dose radiation therapy for head-and-neck tumors and stereotactic radiosurgery or fractionated stereotactic radiotherapy for the treatment of vestibular schwannoma is presented. Because of the small volume of the cochlea a dose-volume analysis is not feasible. Instead, the current literature on the effect of the mean dose received by the cochlea and other treatment- and patient-related factors on outcome are evaluated. Based on the data, a specific threshold dose to cochlea for sensorineural hearing loss cannot be determined; therefore, dose-prescription limits are suggested. A standard for evaluating radiation therapy-associated ototoxicity as well as a detailed approach for scoring toxicity is presented.

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Available from: John C Flickinger, Sep 29, 2015
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    • "+ 10x(1.8Gy+1.6Gy))[6] volume 1 cc / Dose limit = 10Gy[7] volume 1 cc / Dose limit=12Gy[8] Eyes(Retina) 40Gy (IMRT -fractionation 30x2Gy)[9] 5Gy[10] Eyes(Lens) As low as possible[9] 3Gy[10] Cochlea 45Gy (conventionally fractionated RT)[11] 12Gy[7] 10Gy[12] Chiasma 54Gy (IMRT -fractionation 30x2Gy)[9] volume 0.2CC / Dose limit = 8Gy[7] Optic Nerve 54Gy (IMRT -fractionation 30x2Gy)[9] volume 0.2CC / Dose limit = 8Gy[7, 13–15] Table 1 Dose limits for the OARs in both radiotherapy and radio-surgery. "
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    ABSTRACT: This work covers the current state of the art with regard to approaches to segment subcortical brain structures. A huge range of diverse methods have been presented in the literature during the last decade to segment not only one or a constrained number of structures, but also a complete set of these subcortical regions. Special attention has been paid to atlas based segmentation methods, statistical models and deformable models for this purpose. More recently, the introduction of machine learning techniques, such as artificial neural networks or support vector machines, has helped the researchers to optimize the classification problem. These methods are presented in this work, and their advantages and drawbacks are further discussed. Although these methods have proved to perform well, their use is often limited to those situations where either there are no lesions in the brain or the presence of lesions does not highly vary the brain anatomy. Consequently, the development of segmentation algorithms that can deal with such lesions in the brain and still provide a good performance when segmenting subcortical structures is highly required in practice by some clinical applications, such as radiotherapy or radiosurgery.
    Full-text · Article · Sep 2014
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    • "As Voet et al. conducted an analysis using AS to define nodal target volumes as well as OARs which were then used unedited for planning [23], resulting in under dosage of target volumes, we are as yet conceptually unwilling to accept unmodified AS-defined GTV/ CTV ROIs, including elective neck contours generated as AS-contoured nodal basins. Our data suggest clinically unacceptable AS segmentation for several critical OAR structures (e.g., chiasm, cochlea, and larynx), inadvertent overdosage of which might result in blindness [24], hearing loss [25], or aspiration/dysphagia [26]. Additionally, it must be carefully stressed that the criticality of ROI segmentation remains, at its most fundamental, the primary driver of subsequent planning. "
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    ABSTRACT: Background and purpose: Target volumes and organs-at-risk (OARs) for radiotherapy (RT) planning are manually defined, which is a tedious and inaccurate process. We sought to assess the feasibility, time reduction, and acceptability of an atlas-based autosegmentation (AS) compared to manual segmentation (MS) of OARs. Materials and methods: A commercial platform generated 16 OARs. Resident physicians were randomly assigned to modify AS OAR (AS+R) or to draw MS OAR followed by attending physician correction. Dice similarity coefficient (DSC) was used to measure overlap between groups compared with attending approved OARs (DSC=1 means perfect overlap). 40 cases were segmented. Results: Mean ± SD segmentation time in the AS+R group was 19.7 ± 8.0 min, compared to 28.5 ± 8.0 min in the MS cohort, amounting to a 30.9% time reduction (Wilcoxon p<0.01). For each OAR, AS DSC was statistically different from both AS+R and MS ROIs (all Steel-Dwass p<0.01) except the spinal cord and the mandible, suggesting oversight of AS/MS processes is required; AS+R and MS DSCs were non-different. AS compared to attending approved OAR DSCs varied considerably, with a chiasm mean ± SD DSC of 0.37 ± 0.32 and brainstem of 0.97 ± 0.03. Conclusions: Autosegmentation provides a time savings in head and neck regions of interest generation. However, attending physician approval remains vital.
    Full-text · Article · Sep 2014 · Radiotherapy and Oncology
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    • "This is associated with psychological and cognitive morbidity [32]. The mean dose to the cochlea should be limited to ≤45Gy (or more conservatively ≤35Gy); and when combined with cisplatin, strictly limited [33]. "
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    ABSTRACT: Background The accurate definition of organs at risk (OARs) is required to fully exploit the benefits of intensity-modulated radiotherapy (IMRT) for head and neck cancer. However, manual delineation is time-consuming and there is considerable inter-observer variability. This is pertinent as function-sparing and adaptive IMRT have increased the number and frequency of delineation of OARs. We evaluated accuracy and potential time-saving of Smart Probabilistic Image Contouring Engine (SPICE) automatic segmentation to define OARs for salivary-, swallowing- and cochlea-sparing IMRT. Methods Five clinicians recorded the time to delineate five organs at risk (parotid glands, submandibular glands, larynx, pharyngeal constrictor muscles and cochleae) for each of 10 CT scans. SPICE was then used to define these structures. The acceptability of SPICE contours was initially determined by visual inspection and the total time to modify them recorded per scan. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm created a reference standard from all clinician contours. Clinician, SPICE and modified contours were compared against STAPLE by the Dice similarity coefficient (DSC) and mean/maximum distance to agreement (DTA). Results For all investigated structures, SPICE contours were less accurate than manual contours. However, for parotid/submandibular glands they were acceptable (median DSC: 0.79/0.80; mean, maximum DTA: 1.5 mm, 14.8 mm/0.6 mm, 5.7 mm). Modified SPICE contours were also less accurate than manual contours. The utilisation of SPICE did not result in time-saving/improve efficiency. Conclusions Improvements in accuracy of automatic segmentation for head and neck OARs would be worthwhile and are required before its routine clinical implementation.
    Full-text · Article · Aug 2014 · Radiation Oncology
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