Feasibility of image-based data mining in breast radiotherapy
Jaikuna, T. Aznar, Marianne Camille Hoskin, Peter J Van Herk, Marcel David, A. Gutierrez-Enriquez, S. Rancati, T. Rosenstein, B. S. de Ruysscher, D.
Jaikuna, T.
Aznar, Marianne Camille
Hoskin, Peter J
Van Herk, Marcel
David, A.
Gutierrez-Enriquez, S.
Rancati, T.
Rosenstein, B. S.
de Ruysscher, D.
Citations
Altmetric:
Abstract
Purpose or Objective
Breast pain following cancer treatment reduces the patient’s quality of life. Although the correlation between pain and
radiation hotspot has been established in previous studies, the impact of the location of the hotspot has not been
identified. Voxel-wise image-based data mining (IBDM) can identify specific dose patterns correlated with radiotherapy
toxicity and identify sensitive sub-regions. However, IBDM has not been applied to breast radiotherapy as a consequence
of variations in volumes (breast size) and set-up positions in this patient population. We aimed to demonstrate the feasibility
of applying IBDM to breast cancer patients treated with supine post-lumpectomy radiotherapy in a large multi-centre study.
Materials and Methods
IBDM was applied to 177 patients from 8 centres in the REQUITE study (www.requite.eu). All patients were treated supine
with different arm positions; both arms up (n=118), left arm up (n=32), or right arm up (n=27). Planning dose distributions
were normalised to a single reference patient using NiftyReg deformable image registration (DIR). The region of interest
was the breast +/-5 cm in the superior-inferior direction. DIR performance was assessed using the normalised correlation
coefficient (NCC). The dose in each voxel was converted into EQD2 using α/β=1.7 for moderate or marked normal tissue
toxicity in the breast or chest wall. For this feasibility study, the outcome of interest was patient-reported pain at 1-year
post-radiotherapy (any pain vs no pain). The region associated with pain was identified using voxel-wise t-test and
permutation testing (n=1000). The correlation between breast pain and mean and max EQD2 in the identified region, as
well as other clinical variables were investigated using univariable and multivariable logistic regression analysis (SPSS v.25).
Results
Patient characteristics are summarised in Table 1. DIR accuracy on the breast was acceptable (NCC=0.92 (IQR 0.14), where
1 is the ideal value), but was considerably affected by the difference in arm positions (Mann-Whitney test between both
arms up and ipsilateral arm up only, p=0.05). IBDM identified a sensitive region which correlated with breast pain in the
upper part of the treated breast (Figure 1.A-B). The mean and max EQD2 within the identified region, fractionation, and
breast cup size were significant in univariable analysis (Figure 1.C). However, only fractionation and breast cup size
remained significant on multivariable analysis. Conclusion
IBDM is feasible in breast radiotherapy and the accuracy of DIR was found acceptable. Nevertheless, larger cohorts of
patients are needed to clarify the existence of a sensitive sub-region related to breast pain and/or other side effects.
Further work will investigate increasing the power of the analysis by mirroring the patients’ anatomy to overlay left/right sided dose distribution, and including patients treated in the prone position.
Authors
Jaikuna, T.
Aznar, Marianne Camille
Hoskin, Peter J
Van Herk, Marcel
West, Catharine M L
David, A.
Gutierrez-Enriquez, S.
Rancati, T.
Rosenstein, B. S.
de Ruysscher, D.
Sperk, E.
Symonds, P.
Talbot, C. J.
De Santis, M. C.
Vega, A.
Webb, A.
Chang-Claude, J.
Seibold, P.
Lingard, Z.
Vasquez Osorio, Eliana
Aznar, Marianne Camille
Hoskin, Peter J
Van Herk, Marcel
West, Catharine M L
David, A.
Gutierrez-Enriquez, S.
Rancati, T.
Rosenstein, B. S.
de Ruysscher, D.
Sperk, E.
Symonds, P.
Talbot, C. J.
De Santis, M. C.
Vega, A.
Webb, A.
Chang-Claude, J.
Seibold, P.
Lingard, Z.
Vasquez Osorio, Eliana
Description
Date
2022
Publisher
Collections
Keywords
Type
Meetings and Proceedings
Citation
Jaikuna T, Aznar M, Hoskin P, Van Herk M, West CML, David A, et al. Feasibility of Image-Based Data Mining in Breast Radiotherapy. Radiotherapy and Oncology. 2022 May;170:S138-S40. PubMed PMID: WOS:000806759200138.