scholarly journals Beam-Specific Spot Guidance and Optimization for PBS Proton Treatment of Bilateral Head and Neck Cancers

2021 ◽  
Vol 8 (1) ◽  
pp. 50-61
Author(s):  
Karla Leach ◽  
Shikui Tang ◽  
Jared Sturgeon ◽  
Andrew K. Lee ◽  
Ryan Grover ◽  
...  
Keyword(s):  
Head And Neck ◽  
Systematic Approach ◽  
Target Coverage ◽  
Distribution Characteristics ◽  
Dose Distributions ◽  
Proton Treatment ◽  
Organ At Risk ◽  
Single Field ◽  
Beam Technique ◽  
Specific Volumes

Abstract Purpose A multi-field optimization (MFO) technique that uses beam-specific spot placement volumes (SPVs) and spot avoidance volumes (SAVs) is introduced for bilateral head and neck (H&N) cancers. These beam-specific volumes are used to guide the optimizer to consistently achieve optimal organ-at-risk (OAR) sparing with target coverage and plan robustness. Materials and Methods Implementation of this technique using a 4-beam, 5-beam, and variant 5-beam arrangement is discussed. The generation of beam-specific SPVs and SAVs derived from target and OARs are shown. The SPVs for select fields are further partitioned into optimization volumes for uniform dose distributions that resemble those of single-field optimization (SFO). A conventional MFO plan that does not use beam-specific spot placement guidance (MFOcon) and an MFO plan that uses only beam-specific SPV (MFOspv) are compared with current technique (MFOspv/sav), using both simulated scenarios and forward-calculated plans on weekly verification computed tomography (VFCT) scans. Results Dose distribution characteristics of the 4-beam, 5-beam, and variant 5-beam technique are demonstrated with discussion on OAR sparing. When comparing the MFOcon, MFOspv, and MFOspv/sav, the MFOspv/sav is shown to have superior OAR sparing in 9 of the 14 OARs examined. It also shows clinical plan robustness when evaluated by using both simulated uncertainty scenarios and forward-calculated weekly VFCTs throughout the 7-week treatment course. Conclusion The MFOspv/sav technique is a systematic approach using SPVs and SAVs to guide the optimizer to consistently reach desired OAR dose values and plan robustness.

scholarly journals Impact of Geometric Uncertainties on Dose Distribution During Intensity Modulated Radiotherapy of Head-and-neck Cancer: The Need for a Planning Target Volume and A Planning Organ-at-Risk Volume

2006 ◽  
Vol 13 (3) ◽  
pp. 108-115 ◽  
Author(s):  
O. Ballivy ◽  
W. Parker ◽  
T. Vuong ◽  
G. Shenouda ◽  
H. Patrocinio
Keyword(s):  
Spinal Cord ◽  
At Risk ◽  
Neck Cancer ◽  
Intensity Modulated Radiotherapy ◽  
Target Volume ◽  
Ct Scans ◽  
Target Coverage ◽  
Geometric Uncertainties ◽  
Intensity Modulated ◽  
Organ At Risk

We assessed the effect of geometric uncertainties on target coverage and on dose to the organs at risk (OARS) during intensity-modulated radiotherapy (IMRT) for head-and-neck cancer, and we estimated the required margins for the planning target volume (PTV) and the planning organ-at-risk volume (PRV). For eight headand- neck cancer patients, we generated IMRT plans with localization uncertainty margins of 0 mm, 2.5 mm, and 5.0 mm. The beam intensities were then applied on repeat computed tomography (CT) scans obtained weekly during treatment, and dose distributions were recalculated. The dose–volume histogram analysis for the repeat CT scans showed that target coverage was adequate (V100 ≥ 95%) for only 12.5% of the gross tumour volumes, 54.3% of the upper-neck clinical target volumes (CTVS), and 27.4% of the lower-neck CTVS when no margins were added for PTV. The use of 2.5-mm and 5.0-mm margins significantly improved target coverage, but the mean dose to the contralateral parotid increased from 25.9 Gy to 29.2 Gy. Maximum dose to the spinal cord was above limit in 57.7%, 34.6%, and 15.4% of cases when 0-mm, 2.5-mm, and 5.0-mm margins (respectively) were used for PRV. Significant deviations from the prescribed dose can occur during IMRT treatment delivery for headand- neck cancer. The use of 2.5-mm to 5.0-mm margins for PTV and PRV greatly reduces the risk of underdosing targets and of overdosing the spinal cord.

scholarly journals Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

10.2196/26151 ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. e26151
Author(s):  
Stanislav Nikolov ◽  
Sam Blackwell ◽  
Alexei Zverovitch ◽  
Ruheena Mendes ◽  
Michelle Livne ◽  
...  
Keyword(s):  
Computed Tomography ◽  
At Risk ◽  
Deep Learning ◽  
Clinical Practice ◽  
Head And Neck ◽  
Organs At Risk ◽  
Data Set ◽  
Computed Tomography Scans ◽  
Neck Anatomy ◽  
Organ At Risk

Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

scholarly journals Proton therapy for non-squamous cell carcinoma of the head and neck: planning comparison and toxicity

2019 ◽  
Vol 60 (5) ◽  
pp. 612-621 ◽  
Author(s):  
Hiromitsu Iwata ◽  
Toshiyuki Toshito ◽  
Kensuke Hayashi ◽  
Maho Yamada ◽  
Chihiro Omachi ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Proton Therapy ◽  
Patient Specific ◽  
Dose Distributions

Abstract To investigate optimal treatment planning using proton beams for non-squamous cell carcinoma of the head and neck (NSCHN), the dose distributions of plans involving pencil beam scanning (PBS) with or without a patient-specific aperture system (PSAS), passive-scattering proton therapy (PSPT) and X-ray intensity-modulated radiotherapy (IMRT) were compared. As clinical results, toxicities of PBS with PSAS, including changes in quality of life, were reported. Between April 2014 and August 2016, a total of 30 patients were treated using PBS with PSAS. In 20 patients selected at random, the dose distributions of PBS with or without the PSAS, PSPT and IMRT plans were compared. Neutron exposure by proton therapy was calculated using a Monte Carlo simulation. Toxicities were scored according to CTCAE ver. 4.0. Patients completed EORTC quality of life survey forms (QLQ-C30 and QLQ-HN35) before and 0–12 months after proton therapy. The 95% conformity number of PBS with the PSAS plan was the best, and significant differences were detected among the four plans (P < 0.05, Bonferroni tests). Neutron generation by PSAS was ~1.1-fold higher, but was within an acceptable level. No grade 3 or higher acute dermatitis was observed. Pain, appetite loss and increased weight loss were more likely at the end of treatment, but recovered by the 3 month follow-up and returned to the pretreatment level at the 12 month follow-up. PBS with PSAS reduced the penumbra and improved dose conformity in the planning target volume. PBS with PSAS was tolerated well for NSCHN.

Radiology of acute mastoiditis and its complications: a pictorial review and interpretation checklist

2019 ◽  
Vol 133 (10) ◽  
pp. 856-861
Author(s):  
M W Mather ◽  
P D Yates ◽  
J Powell ◽  
I Zammit-Maempel
Keyword(s):  
Head And Neck ◽  
Surgical Planning ◽  
Systematic Approach ◽  
Acute Mastoiditis ◽  
Cross Sectional ◽  
Imaging Technology ◽  
Radiological Features ◽  
Pictorial Review ◽  
Cross Sectional Imaging ◽  
Sectional Imaging

AbstractBackgroundMastoiditis is an otological emergency, and cross-sectional imaging has a role in the diagnosis of complications and surgical planning. Advances in imaging technology are becoming increasingly sophisticated and, by the same token, the ability to accurately interpret findings is essential.MethodsThis paper reviews common and rare complications of mastoiditis using case-led examples. A radiologist-derived systematic checklist is proposed, to assist the ENT surgeon with interpreting cross-sectional imaging in emergency mastoiditis cases when the opinion of a head and neck radiologist may be difficult to obtain.ResultsA 16-point checklist (the ‘mastoid 16’) was used on a case-led basis to review the radiological features of both common and rare complications of mastoiditis; this is complemented with imaging examples.ConclusionAcute mastoiditis has a range of serious complications that may be amenable to treatment, once diagnosed using appropriate imaging. The proposed checklist provides a systematic approach to identifying complications of mastoiditis.

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