Validation of in-house knowledge-based planning model for predicting change in target coverage during VMAT radiotherapy to in-operable advanced-stage NSCLC patients

Nilesh S Tambe; Isabel M Pires; Craig Moore; Andrew Wieczorek; Sunil Upadhyay; Andrew W Beavis

doi:10.1088/2057-1976/ac1f94

Validation of in-house knowledge-based planning model for predicting change in target coverage during VMAT radiotherapy to in-operable advanced-stage NSCLC patients

Biomed Phys Eng Express. 2021 Sep 2;7(6). doi: 10.1088/2057-1976/ac1f94.

Authors

Nilesh S Tambe^{1

2}, Isabel M Pires², Craig Moore¹, Andrew Wieczorek³, Sunil Upadhyay³, Andrew W Beavis^{1

2

4}

Affiliations

¹ Radiotherapy Physics, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom.
² Faculty of Health Sciences, University of Hull, Cottingham road, Hull, HU16 7RX, United Kingdom.
³ Clinical Oncology, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom.
⁴ Faculty of Health and Well Being, Sheffield-Hallam University, Collegiate Crescent, Sheffield, S10 2BP, United Kingdom.

PMID: 34415240
DOI: 10.1088/2057-1976/ac1f94

Abstract

Objectives. anatomical changes are inevitable during the course of radiotherapy treatments and, if significant, can severely alter expected dose distributions and affect treatment outcome. Adaptive radiotherapy (ART) is employed to maintain the planned distribution and minimise detriment to predicted treatment outcome. Typically, patients who may benefit from adaptive planning are identified via a re-planning process, i.e., re-simulation, re-contouring, re-planning and treatment plan quality assurance (QA). This time-intensive process significantly increases workload, can introduce delays and increases unnecessary stress to those patients who will not actually gain benefit. We consider it crucial to develop efficient models to predict changes to target coverage and trigger ART, without the need for re-planning.Methods.knowledge-based planning (KBP) models were developed using data for 20 patients' (400 fractions) to predict changes in PTV V₉₅coverageΔV95PTV.Initially, this change in coverage was calculated on the synthetic computerised tomography (sCT) images produced using the Velocity adaptive radiotherapy software. Models were developed using patient (cell death bio-marker) and treatment fraction (PTV characteristic) specific parameters to predictΔV95PTVand verified using five patients (100 fractions) data.Results. three models were developed using combinations of patient and fraction specific terms. The prediction accuracy of the model developed using biomarker (PD-L1 expression) and the difference in 'planning' and 'fraction' PTV centre of the mass (characterised by mean square difference, MSD) had the higher prediction accuracy, predicting theΔV95PTVwithin ± 1.0% for 77% of the total fractions; with 59% for the model developed using, PTV size, PD-L1 and MSD and 48% PTV size and MSD respectively.Conclusion. the KBP models can predictΔV95PTVvery effectively and efficiently for advanced-stage NSCLC patients treated using volumetric modulated arc therapy and to identify patients who may benefit from adaption for a specific fraction.

Keywords: adaptive radiotherapy; knowledge-based planning; non-small cell lung cancer; treatment planning; volumetric modulated arc therapy.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

B7-H1 Antigen
Carcinoma, Non-Small-Cell Lung* / radiotherapy
Humans
Lung Neoplasms* / radiotherapy
Organs at Risk
Radiotherapy Dosage
Radiotherapy, Intensity-Modulated*

Substances

B7-H1 Antigen