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. 2024 Feb 15:29:100554.
doi: 10.1016/j.phro.2024.100554. eCollection 2024 Jan.

Predicting cervical cancer target motion using a multivariate regression model to enable patient selection for adaptive external beam radiotherapy

Lei Wang  1 , Dualta McQuaid  1 , Matthew Blackledge  1 , Helen McNair  1 , Emma Harris  1 , Susan Lalondrelle  1
Affiliations

Predicting cervical cancer target motion using a multivariate regression model to enable patient selection for adaptive external beam radiotherapy

Lei Wang et al. Phys Imaging Radiat Oncol. .

Abstract

Background and purpose: Interfraction motion during cervical cancer radiotherapy is substantial in some patients, minimal in others. Non-adaptive plans may miss the target and/or unnecessarily irradiate normal tissue. Adaptive radiotherapy leads to superior dose-volume metrics but is resource-intensive. The aim of this study was to predict target motion, enabling patient selection and efficient resource allocation.

Materials and methods: Forty cervical cancer patients had CT with full-bladder (CT-FB) and empty-bladder (CT-EB) at planning, and daily cone-beam CTs (CBCTs). The low-risk clinical target volume (CTVLR) was contoured. Mean coverage of the daily CTVLR by the CT-FB CTVLR was calculated for each patient. Eighty-three investigated variables included measures of organ geometry, patient, tumour and treatment characteristics. Models were trained on 29 patients (171 fractions). The Two-CT multivariate model could use all available data. The Single-CT multivariate model excluded data from the CT-EB. A univariate model was trained using the distance moved by the uterine fundus tip between CTs, the only method of patient selection found in published cervix plan-of-the-day studies. Models were tested on 11 patients (68 fractions). Accuracy in predicting mean coverage was reported as mean absolute error (MAE), mean squared error (MSE) and R2.

Results: The Two-CT model was based upon rectal volume, dice similarity coefficient between CT-FB and CT-EB CTVLR, and uterine thickness. The Single-CT model was based upon rectal volume, uterine thickness and tumour size. Both performed better than the univariate model in predicting mean coverage (MAE 7 %, 7 % and 8 %; MSE 82 %2, 65 %2, 110 %2; R2 0.2, 0.4, -0.1).

Conclusion: Uterocervix motion is complex and multifactorial. We present two multivariate models which predicted motion with reasonable accuracy using pre-treatment information, and outperformed the only published method.

Keywords: Adaptive radiotherapy; Cervical cancer; Image guided radiotherapy; Interfraction motion; Mathematical modelling.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Lei Wang is part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London; and part-funded by Elekta Ltd. Helen McNair is funded by a National Institute for Health Research and Health Education England (HEE/NIHR) Senior Clinical Lectureship (ICA-SCL-2018–04-ST2-002). Emma Harris has received research funding from Elekta Ltd and Cancer Research UK Programme Foundation Award A23557. Susan Lalondrelle has received research funding and speaking fees from Elekta Ltd.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Two-CT multivariate model. A: Performance on the training set (n = 29). B: Performance on the test set (n = 11). This model is based on mean rectal volume at planning, uterine body thickness and dice similarity coefficient at planning. The diagonal line represents perfect predictions. MAE; mean absolute error (%).
Fig. 1
Fig. 1
Two-CT multivariate model. A: Performance on the training set (n = 29). B: Performance on the test set (n = 11). This model is based on mean rectal volume at planning, uterine body thickness and dice similarity coefficient at planning. The diagonal line represents perfect predictions. MAE; mean absolute error (%).
Fig. 2
Fig. 2
Single-CT multivariate model. A: Performance on the training set (n = 29). B: Performance on the test set (n = 11). This model is based on rectal volume on the full-bladder CT, uterine body thickness and tumour size on diagnostic MRI. The diagonal line represents perfect predictions. MAE; mean absolute error (%).
Fig. 2
Fig. 2
Single-CT multivariate model. A: Performance on the training set (n = 29). B: Performance on the test set (n = 11). This model is based on rectal volume on the full-bladder CT, uterine body thickness and tumour size on diagnostic MRI. The diagonal line represents perfect predictions. MAE; mean absolute error (%).
Fig. 3
Fig. 3
Univariate model. A: Performance on the training set (n = 29). B: Performance on the test set (n = 11). This model is based on the distance moved by the tip of the uterine fundus between CT-FB and CT-EB. The diagonal line represents perfect predictions. MAE; mean absolute error (%).
Fig. 3
Fig. 3
Univariate model. A: Performance on the training set (n = 29). B: Performance on the test set (n = 11). This model is based on the distance moved by the tip of the uterine fundus between CT-FB and CT-EB. The diagonal line represents perfect predictions. MAE; mean absolute error (%).
Fig. 4
Fig. 4
Accuracy of predictions at an example threshold. Blue line; a threshold chosen to select a third of patients for adaptive radiotherapy. Orange dots; patients predicted as movers (patients below the blue line are true movers). Blue dots; patients predicted as non-movers (patients above the blue line are true non-movers). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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References

    1. Potter R., Tanderup K., Kirisits C., de Leeuw A., Kirchheiner K., Nout R., et al. The EMBRACE II study: the outcome and prospect of two decades of evolution within the GEC-ESTRO GYN working group and the EMBRACE studies. Clin Transl Radiat Oncol. 2018;9:48–60. - PMC - PubMed
    1. Taylor A., Powell M.E. An assessment of interfractional uterine and cervical motion: implications for radiotherapy target volume definition in gynaecological cancer. Radiother Oncol. 2008;88:250–257. - PubMed
    1. Wang L., Alexander S., Mason S., Blasiak-Wal I., Harris E., McNair H., et al. Carpe diem: making the Most of plan-of-the-day for cervical cancer radiation therapy. Pract Radiat Oncol. 2023;13:132–147. - PubMed
    1. Keall P.J., Nguyen D.T., O'Brien R., Caillet V., Hewson E., Poulsen P.R., et al. The first clinical implementation of real-time image-guided adaptive radiotherapy using a standard linear accelerator. Radiother Oncol. 2018;127:6–11. - PubMed
    1. Sibolt P., Andersson L.M., Calmels L., Sjostrom D., Bjelkengren U., Geertsen P., et al. Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region. Phys Imaging Radiat Oncol. 2021;17:1–7. - PMC - PubMed