Literature DB >> 26306678

Accurate prediction of target dose-escalation and organ-at-risk dose levels for non-small cell lung cancer patients.

Steven F Petit1, Wouter van Elmpt2.   

Abstract

PURPOSE/
OBJECTIVE: To develop a method to predict feasible organ-at-risk (OAR) and tumour dose levels of non-small cell lung cancer (NSCLC) patients prior to the start of treatment planning. MATERIALS/
METHODS: Included were NSCLC patients treated with volumetric modulated arc therapy according to an institutional isotoxic dose-escalation protocol. A training cohort (N=50) was used to calculate the average dose inside the OARs as a function of the distance to the planning target volume (PTV). These dose-distance relations were used in a validation cohort (N=39) to predict dose-volume histograms (DVHs) of OARs and PTV as well as the maximum individualized PTV dose escalation.
RESULTS: The validation cohort showed that predicted and achieved MLD were in agreement with each other (difference: -0.1±1.9 Gy, p=0.81). The spinal cord was dose limiting in only two patients, which was correctly predicted. The achieved mean PTV dose varied from 52 to 73 Gy and was predicted correctly with an accuracy better than 2 Gy (i.e. 1 fraction) for 79% of the patients.
CONCLUSION: We have shown that the MLD and the prescribed PTV dose could be accurately predicted for NSCLC patients. This method can guide the treatment planner to achieve optimal OAR sparing and tumour dose escalation.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Dose prediction; Dose–distance relation; Knowledge based treatment planning; NSCLC dose escalation; Treatment planning QA

Mesh:

Year:  2015        PMID: 26306678     DOI: 10.1016/j.radonc.2015.07.040

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  5 in total

Review 1.  Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations.

Authors:  Mohammad Hussein; Ben J M Heijmen; Dirk Verellen; Andrew Nisbet
Journal:  Br J Radiol       Date:  2018-09-04       Impact factor: 3.039

2.  Predicting Patient-specific Dosimetric Benefits of Proton Therapy for Skull-base Tumors Using a Geometric Knowledge-based Method.

Authors:  David C Hall; Alexei V Trofimov; Brian A Winey; Norbert J Liebsch; Harald Paganetti
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-02-14       Impact factor: 7.038

3.  Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics.

Authors:  Ana Vaniqui; Richard Canters; Femke Vaassen; Colien Hazelaar; Indra Lubken; Kirsten Kremer; Cecile Wolfs; Wouter van Elmpt
Journal:  Phys Imaging Radiat Oncol       Date:  2020-10-19

4.  Contour-based lung dose prediction for breast proton therapy.

Authors:  Chuan Zeng; Kevin Sine; Dennis Mah
Journal:  J Appl Clin Med Phys       Date:  2018-08-23       Impact factor: 2.102

Review 5.  Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Authors:  Chunhao Wang; Xiaofeng Zhu; Julian C Hong; Dandan Zheng
Journal:  Technol Cancer Res Treat       Date:  2019-01-01
  5 in total

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