A Pilot Study Evaluating the Effectiveness of Dual-Registration Image-Guided Radiotherapy in Patients with Oropharyngeal Cancer

Goldsworthy, Simon; Leslie-Dakers, Marcus; Higgins, Steven L.; Barnes, Terri; Jankowska, P.; Dogramadzi, Sanja; Latour, Jos M.

doi:10.1016/j.jmir.2017.09.004

Cited by 9 publications

(10 citation statements)

References 30 publications

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“…Fuss et al (5) reported that the translational error at the isocenter was 0.74 ± 0.53, 0.75 ± 0.60, and 0.93 ± 0.78 mm in the RL, CC, and AP directions, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, it is necessary to have high geometrical accuracy for a safe clinical application of precise radiotherapy. Some recent studies describe the process of measurement and reduction in geometrical errors [5][6][7]. There are some reviews which discuss different types of errors in radiotherapy and the process to overcome these discrepancies [3,[8][9][10].…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, geometric errors have to be identified and removed for safe radiation therapy. There are many recent studies which address issues of measurement and the reduction in geometrical errors [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature

Basu

Goldsworthy²,

Gkoutos

2021

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The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors.

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“…Fuss et al (5) reported that the translational error at the isocenter was 0.74 ± 0.53, 0.75 ± 0.60, and 0.93 ± 0.78 mm in the RL, CC, and AP directions, respectively.…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature

Basu

Goldsworthy²,

Gkoutos

2021

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“…3-5 mm margins have been reported as appropriate only where daily IGRT [45,48], or alternating day IGRT [49,43] is routine practice. Where daily IGRT is not practiced studies recommended CTV-PTV margins of greater than 5 mm due to potential sub region set up variabilities in mandible, nasal septum and neck [50][51][52][53]24,54,55] and unstable set ups in patients with a high Body Mass Index (BMI) [50]. Random errors of up to 2-5 mm have been reported which could result in geometric miss if not imaged and corrected online where standard CTV-PTV margins are used [13,56,57].…”

Section: Ctv-ptv Margins Set Up Errors and Imaging Frequencymentioning

confidence: 99%

A review of Image Guided Radiation Therapy in head and neck cancer from 2009–2019 – Best Practice Recommendations for RTTs in the Clinic

Kearney

Coffey

Leong

2020

Technical Innovations & Patient Support in Radiation Oncolo

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Radiation therapy (RT) is beneficial in Head and Neck Cancer (HNC) in both the definitive and adjuvant setting. Highly complex and conformal planning techniques are becoming standard practice in delivering increased doses in HNC. A sharp falloff in dose outside the high dose area is characteristic of highly complex techniques and geometric uncertainties must be minimised to prevent under dosage of the target volume and possible over dosage of surrounding critical structures. CTV-PTV margins are employed to account for geometric uncertainties such as set up errors and both interfraction and intrafraction motion. Robust immobilisation and Image Guided Radiation Therapy (IGRT) is also essential in this group of patients to minimise discrepancies in patient position during the treatment course. IGRT has evolved with increased 2-Dimensional (2D) and 3-Dimensional (3D) IGRT modalities available for geometric verification. 2D and 3D IGRT modalities are both beneficial in geometric verification while 3D imaging is a valuable tool in assessing volumetric changes that may have dosimetric consequences for this group of patients. IGRT if executed effectively and efficiently provides clinicians with confidence to reduce CTV-PTV margins thus limiting treatment related toxicities in patients. Accumulated exposure dose from IGRT vary considerably and may be incorporated into the treatment plan to avoid excess dose. However, there are considerable variations in the application of IGRT in RT practice. This paper aims to summarise the advances in IGRT in HNC treatment and provide clinics with recommendations for an IGRT strategy for HNC in the clinic.

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“…They reported that DR was an effective IGRT tool to ensure target coverage of inferior neck nodes as compared to SCR and have demonstrated acceptability to RT clinical practice. [11]…”

Section: Introductionmentioning

confidence: 99%

Study to compare the effect of different registration methods on patient setup uncertainties in cone-beam computed tomography during volumetric modulated arc therapy for breast cancer patients

et al. 2018

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Purpose:This study compared three different methods used in registering cone-beam computed tomography (CBCT) image set with planning CT image set for determining patient setup uncertainties during volumetric modulated arc therapy (VMAT) for breast cancer patients.Materials and Methods:Seven breast cancer patients treated with 50 Gy in 25 fractions using VMAT technique were chosen for this study. A total of 105 CBCT scans were acquired by image guidance protocol for patient setup verification. Approved plans’ CT images were used as the reference image sets for registration with their corresponding CBCT image sets. Setup errors in mediolateral, craniocaudal, and anteroposterior direction were determined using gray-scale matching between the reference CT images and onboard CBCT images. Patient setup verification was performed using clip-box registration (CBR) method during online imaging. Considering the CBR method as the reference, two more registrations were performed using mask registration (MR) method and dual registration (DR) (CBR + MR) method in the offline mode. For comparison, systematic error (∑), random error (σ), mean displacement vector (R), mean setup error (M), and registration time (Rt) were analyzed. Post hoc Tukey's honest significant difference test was performed for multiple comparisons.Results:Systematic and random errors were less in CBR as compared to MR and DR (P > 0.05). The mean displacement error and mean setup errors were less in CBR as compared to MR and DR (P > 0.05). Increased Rt was observed in DR as compared to CBR and MR (P < 0.05). In addition, multiple comparisons did not show any significant difference in patient setup error (P > 0.05).Conclusion:For breast VMAT plan delivery, all three registration methods show insignificant variation in patient setup error. One can use any of the three registration methods for patient setup verification.

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A Pilot Study Evaluating the Effectiveness of Dual-Registration Image-Guided Radiotherapy in Patients with Oropharyngeal Cancer

Cited by 9 publications

References 30 publications

A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature

A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature

A review of Image Guided Radiation Therapy in head and neck cancer from 2009–2019 – Best Practice Recommendations for RTTs in the Clinic

Study to compare the effect of different registration methods on patient setup uncertainties in cone-beam computed tomography during volumetric modulated arc therapy for breast cancer patients

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