Evaluating principal component analysis models for representing anatomical changes in head and neck radiotherapy

Perez, R. Argota; Robbins, Jennifer; Green, Andrew; Herk, Marcel van; Korreman, S.; Vásquez-Osorio, Eliana

doi:10.1016/j.phro.2022.04.002

Cited by 6 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Statistical population models have also been proposed to estimate the anatomical changes of cancer patients during radiotherapy (Ehrhardt et al 2010, Budiarto et al 2011, Szeto 2017, Rigaud 2019, Argota-Perez 2022, Zhang 2022. Most methods do not differentiate between systematic progressive and random day to day changes, so they cannot model the patterns in the systematic changes over treatment.…”

Section: Introductionmentioning

confidence: 99%

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Nikou,

Thompson,

Nisbet

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

Head and neck cancer patients experience systematic anatomical changes as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes. A patient specific (SM) and population average (AM) model are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based SMs and AMs. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models. Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons. The large patient variability highlights the need for more complex, capable population models.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Nikou,

Thompson,

Nisbet

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

Deep learning method for predicting weekly anatomical changes in patients with nasopharyngeal carcinoma during radiotherapy

Yang,

Liu,

Wei

et al. 2024

Medical Physics

View full text Add to dashboard Cite

BackgroundPatients may undergo anatomical changes during radiotherapy, leading to an underdosing of the target or overdosing of the organs at risk (OARs).PurposeThis study developed a deep‐learning method to predict the tumor response of patients with nasopharyngeal carcinoma (NPC) during treatment. This method can predict the anatomical changes of a patient.MethodsThe participants included 230 patients with NPC. The data included planning computed tomography (pCT) and routine cone‐beam CT (CBCT) images. The CBCT image quality was improved to the CT level using an advanced method. A long short‐term memory network‐generative adversarial network (LSTM‐GAN) is proposed, which can harness the forecasting ability of LSTM and the generation ability of GAN. Four models were trained to predict the anatomical changes that occurred in weeks 3–6 and named LSTM‐GAN‐week 3 to LSTM‐GAN‐week 6. The pCT and CBCT were used as input, and the tumor target volumes (TVs) and OARs were delineated on the predicted and real images (ground truth). Finally, the models were evaluated using contours and dosimetry parameters.ResultsThe proposed method predicted the anatomical changes, with a dice similarity coefficient above 0.94 and 0.90 for the TVs and surrounding OARs, respectively. The dosimetry parameters were close between the prediction and ground truth. The deviations in the prescription, minimum, and maximum doses of the tumor targets were below 0.5 Gy. For serial organs (brain stem and spinal cord), the deviations in the maximum dose were below 0.6 Gy. For parallel organs (bilateral parotid glands), the deviations in the mean dose were below 0.8 Gy.ConclusionThe proposed method can predict the tumor response to radiotherapy in the future such that adaptation can be scheduled on time. This study provides a proactive mechanism for planning adaptation, which can enable personalized treatment and save clinical time by anticipating and preparing for treatment strategy adjustments.

show abstract

Probabilistic evaluation of plan quality for time-dependent anatomical deformations in head and neck cancer patients

Robbins¹,

Herk²,

Eiben³

et al. 2023

Physica Medica

View full text Add to dashboard Cite

Evaluating principal component analysis models for representing anatomical changes in head and neck radiotherapy

Cited by 6 publications

References 24 publications

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Deep learning method for predicting weekly anatomical changes in patients with nasopharyngeal carcinoma during radiotherapy

Probabilistic evaluation of plan quality for time-dependent anatomical deformations in head and neck cancer patients

Contact Info

Product

Resources

About