Intentional deep overfit learning for patient‐specific dose predictions in adaptive radiotherapy.
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| Title: | Intentional deep overfit learning for patient‐specific dose predictions in adaptive radiotherapy. |
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| Authors: | Maniscalco, Austen1 (AUTHOR), Liang, Xiao1 (AUTHOR), Lin, Mu‐Han1 (AUTHOR), Jiang, Steve1 (AUTHOR), Nguyen, Dan1 (AUTHOR) Dan.Nguyen@UTSouthwestern.edu |
| Source: | Medical Physics. Sep2023, Vol. 50 Issue 9, p5354-5363. 10p. |
| Subjects: | Deep learning, Medical dosimetry, Adaptive testing, Radiotherapy, Computed tomography, Prediction models |
| Abstract: | Background: The framework of adaptive radiation therapy (ART) was crafted to address the underlying sources of intra‐patient variation that were observed throughout numerous patients' radiation sessions. ART seeks to minimize the consequential dosimetric uncertainty resulting from this daily variation, commonly through treatment planning re‐optimization. Re‐optimization typically consists of manual evaluation and modification of previously utilized optimization criteria. Ideally, frequent treatment plan adaptation through re‐optimization on each day's computed tomography (CT) scan may improve dosimetric accuracy and minimize dose delivered to organs at risk (OARs) as the planning target volume (PTV) changes throughout the course of treatment. Purpose: Re‐optimization in its current form is time‐consuming and inefficient. In response to this ART bottleneck, we propose a deep learning based adaptive dose prediction model that utilizes a head and neck (H&N) patient's initial planning data to fine‐tune a previously trained population model towards a patient‐specific model. Our fine‐tuned, patient‐specific (FT‐PS) model, which is trained using the intentional deep overfit learning (IDOL) method, may enable clinicians and treatment planners to rapidly evaluate relevant dosimetric changes daily and re‐optimize accordingly. Methods: An adaptive population (AP) model was trained using adaptive data from 33 patients. Separately, 10 patients were selected for training FT‐PS models. The previously trained AP model was utilized as the base model weights prior to re‐initializing model training for each FT‐PS model. Ten FT‐PS models were separately trained by fine‐tuning the previous model weights based on each respective patient's initial treatment plan. From these 10 patients, 26 ART treatment plans were withheld from training as the test dataset for retrospective evaluation of dose prediction performance between the AP and FT‐PS models. Each AP and FT‐PS dose prediction was compared against the ground truth dose distribution as originally generated during the patient's course of treatment. Mean absolute percent error (MAPE) evaluated the dose differences between a model's prediction and the ground truth. Results: MAPE was calculated within the 10% isodose volume region of interest for each of the AP and FT‐PS models dose predictions and averaged across all test adaptive sessions, yielding 5.759% and 3.747% respectively. MAPE differences were compared between AP and FT‐PS models across each test session in a test of statistical significance. The differences were statistically significant in a paired t‐test with two‐tailed p‐value equal to 3.851×10−9$3.851 \times {10}^{ - 9}$ and 95% confidence interval (CI) equal to [−2.483, −1.542]. Furthermore, MAPE was calculated using each individually segmented structure as an ROI. Nineteen of 24 structures demonstrated statistically significant differences between the AP and FT‐PS models. Conclusion: We utilized the IDOL method to fine‐tune a population‐based dose prediction model into an adaptive, patient‐specific model. The averaged MAPE across the test dataset was 5.759% for the population‐based model versus 3.747% for the fine‐tuned, patient‐specific model, and the difference in MAPE between models was found to be statistically significant. Our work demonstrates the feasibility of patient‐specific models in adaptive radiotherapy, and offers unique clinical benefit by utilizing initial planning data that contains the physician's treatment intent. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 171852713 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Intentional deep overfit learning for patient‐specific dose predictions in adaptive radiotherapy. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Maniscalco%2C+Austen%22">Maniscalco, Austen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liang%2C+Xiao%22">Liang, Xiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Mu‐Han%22">Lin, Mu‐Han</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Steve%22">Jiang, Steve</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Dan%22">Nguyen, Dan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Dan.Nguyen@UTSouthwestern.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Sep2023, Vol. 50 Issue 9, p5354-5363. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+dosimetry%22">Medical dosimetry</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+testing%22">Adaptive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Radiotherapy%22">Radiotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: The framework of adaptive radiation therapy (ART) was crafted to address the underlying sources of intra‐patient variation that were observed throughout numerous patients' radiation sessions. ART seeks to minimize the consequential dosimetric uncertainty resulting from this daily variation, commonly through treatment planning re‐optimization. Re‐optimization typically consists of manual evaluation and modification of previously utilized optimization criteria. Ideally, frequent treatment plan adaptation through re‐optimization on each day's computed tomography (CT) scan may improve dosimetric accuracy and minimize dose delivered to organs at risk (OARs) as the planning target volume (PTV) changes throughout the course of treatment. Purpose: Re‐optimization in its current form is time‐consuming and inefficient. In response to this ART bottleneck, we propose a deep learning based adaptive dose prediction model that utilizes a head and neck (H&N) patient's initial planning data to fine‐tune a previously trained population model towards a patient‐specific model. Our fine‐tuned, patient‐specific (FT‐PS) model, which is trained using the intentional deep overfit learning (IDOL) method, may enable clinicians and treatment planners to rapidly evaluate relevant dosimetric changes daily and re‐optimize accordingly. Methods: An adaptive population (AP) model was trained using adaptive data from 33 patients. Separately, 10 patients were selected for training FT‐PS models. The previously trained AP model was utilized as the base model weights prior to re‐initializing model training for each FT‐PS model. Ten FT‐PS models were separately trained by fine‐tuning the previous model weights based on each respective patient's initial treatment plan. From these 10 patients, 26 ART treatment plans were withheld from training as the test dataset for retrospective evaluation of dose prediction performance between the AP and FT‐PS models. Each AP and FT‐PS dose prediction was compared against the ground truth dose distribution as originally generated during the patient's course of treatment. Mean absolute percent error (MAPE) evaluated the dose differences between a model's prediction and the ground truth. Results: MAPE was calculated within the 10% isodose volume region of interest for each of the AP and FT‐PS models dose predictions and averaged across all test adaptive sessions, yielding 5.759% and 3.747% respectively. MAPE differences were compared between AP and FT‐PS models across each test session in a test of statistical significance. The differences were statistically significant in a paired t‐test with two‐tailed p‐value equal to 3.851×10−9$3.851 \times {10}^{ - 9}$ and 95% confidence interval (CI) equal to [−2.483, −1.542]. Furthermore, MAPE was calculated using each individually segmented structure as an ROI. Nineteen of 24 structures demonstrated statistically significant differences between the AP and FT‐PS models. Conclusion: We utilized the IDOL method to fine‐tune a population‐based dose prediction model into an adaptive, patient‐specific model. The averaged MAPE across the test dataset was 5.759% for the population‐based model versus 3.747% for the fine‐tuned, patient‐specific model, and the difference in MAPE between models was found to be statistically significant. Our work demonstrates the feasibility of patient‐specific models in adaptive radiotherapy, and offers unique clinical benefit by utilizing initial planning data that contains the physician's treatment intent. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mp.16616 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 5354 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Medical dosimetry Type: general – SubjectFull: Adaptive testing Type: general – SubjectFull: Radiotherapy Type: general – SubjectFull: Computed tomography Type: general – SubjectFull: Prediction models Type: general Titles: – TitleFull: Intentional deep overfit learning for patient‐specific dose predictions in adaptive radiotherapy. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Maniscalco, Austen – PersonEntity: Name: NameFull: Liang, Xiao – PersonEntity: Name: NameFull: Lin, Mu‐Han – PersonEntity: Name: NameFull: Jiang, Steve – PersonEntity: Name: NameFull: Nguyen, Dan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 50 – Type: issue Value: 9 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
