Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients
PurposeThis study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients.MethodsWe tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to invest...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.777793/full |
| _version_ | 1811238006211739648 |
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| author | Sebastien A. A. Gros Anand P. Santhanam Alec M. Block Bahman Emami Brian H. Lee Cara Joyce |
| author_facet | Sebastien A. A. Gros Anand P. Santhanam Alec M. Block Bahman Emami Brian H. Lee Cara Joyce |
| author_sort | Sebastien A. A. Gros |
| collection | DOAJ |
| description | PurposeThis study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients.MethodsWe tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients’ data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V95<95%) and adaptation (V95<93%) thresholds were set for PTVs, while OAR adaptation dosimetric endpoints of +10% (DE10) were set for all Dmax and Dmean DPs. Any threshold violation at end of treatment (EOT) triggered a review of the DP trends to determine the threshold-crossing fraction Fx when the violations occurred. The prediction model accuracy was determined as the difference between calculated and predicted DP values with 95% confidence intervals (CI95).ResultsRTapp was able to address the needs of treatment adaptation. Specifically, we identified 18/27 studies (67%) for violating PTV coverage or parotid Dmean at EOT. Twelve PTVs had V95<95% (mean coverage decrease of −6.8 ± 2.9%) including six flagged for adaptation at median Fx= 6 (range, 1–16). Seventeen parotids were flagged for exceeding Dmean dose constraints with a median increase of +2.60 Gy (range, 0.99–6.31 Gy) at EOT, including nine with DP>DE10. The differences between predicted and calculated PTV V95 and parotid Dmean was up to 7.6% (mean ± CI95, −2.7 ± 4.1%) and 5 Gy (mean ± CI95, 0.3 ± 1.6 Gy), respectively. The most accurate predictions were obtained closest to the threshold-crossing fraction. For parotids, the results showed that Fx ranged between fractions 1 and 23, with a lack of specific trend demonstrating that the need for treatment adaptation may be verified for every fraction.ConclusionIntegrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time. |
| first_indexed | 2024-04-12T12:34:19Z |
| format | Article |
| id | doaj.art-8e1c000d80a6462b8c8dd2e505856081 |
| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-04-12T12:34:19Z |
| publishDate | 2022-06-01 |
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| series | Frontiers in Oncology |
| spelling | doaj.art-8e1c000d80a6462b8c8dd2e5058560812022-12-22T03:32:56ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-06-011210.3389/fonc.2022.777793777793Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer PatientsSebastien A. A. Gros0Anand P. Santhanam1Alec M. Block2Bahman Emami3Brian H. Lee4Cara Joyce5Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, IL, United StatesDepartment of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United StatesLoyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, IL, United StatesLoyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, IL, United StatesLoyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, IL, United StatesDepartment of Public Health, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, United StatesPurposeThis study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients.MethodsWe tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients’ data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V95<95%) and adaptation (V95<93%) thresholds were set for PTVs, while OAR adaptation dosimetric endpoints of +10% (DE10) were set for all Dmax and Dmean DPs. Any threshold violation at end of treatment (EOT) triggered a review of the DP trends to determine the threshold-crossing fraction Fx when the violations occurred. The prediction model accuracy was determined as the difference between calculated and predicted DP values with 95% confidence intervals (CI95).ResultsRTapp was able to address the needs of treatment adaptation. Specifically, we identified 18/27 studies (67%) for violating PTV coverage or parotid Dmean at EOT. Twelve PTVs had V95<95% (mean coverage decrease of −6.8 ± 2.9%) including six flagged for adaptation at median Fx= 6 (range, 1–16). Seventeen parotids were flagged for exceeding Dmean dose constraints with a median increase of +2.60 Gy (range, 0.99–6.31 Gy) at EOT, including nine with DP>DE10. The differences between predicted and calculated PTV V95 and parotid Dmean was up to 7.6% (mean ± CI95, −2.7 ± 4.1%) and 5 Gy (mean ± CI95, 0.3 ± 1.6 Gy), respectively. The most accurate predictions were obtained closest to the threshold-crossing fraction. For parotids, the results showed that Fx ranged between fractions 1 and 23, with a lack of specific trend demonstrating that the need for treatment adaptation may be verified for every fraction.ConclusionIntegrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time.https://www.frontiersin.org/articles/10.3389/fonc.2022.777793/fulladaptive radiotherapyhead and neck cancerclinical workflowdeformable image registration (DIR)prediction model |
| spellingShingle | Sebastien A. A. Gros Anand P. Santhanam Alec M. Block Bahman Emami Brian H. Lee Cara Joyce Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients Frontiers in Oncology adaptive radiotherapy head and neck cancer clinical workflow deformable image registration (DIR) prediction model |
| title | Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients |
| title_full | Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients |
| title_fullStr | Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients |
| title_full_unstemmed | Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients |
| title_short | Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients |
| title_sort | retrospective clinical evaluation of a decision support software for adaptive radiotherapy of head and neck cancer patients |
| topic | adaptive radiotherapy head and neck cancer clinical workflow deformable image registration (DIR) prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.777793/full |
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