Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy
For 18 months following clinical introduction of deep-learning auto-segmentation (DLAS), an audit of organ at risk (OAR) contour editing was performed, including 1255 patients from a single institution and the majority of tumour sites. Mean surface-Dice similarity coefficient increased from 0.87 to...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Physics and Imaging in Radiation Oncology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405631625000211 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832557523318603776 |
|---|---|
| author | Josh Mason Jack Doherty Sarah Robinson Meagan de la Bastide Jack Miskell Ruth McLauchlan |
| author_facet | Josh Mason Jack Doherty Sarah Robinson Meagan de la Bastide Jack Miskell Ruth McLauchlan |
| author_sort | Josh Mason |
| collection | DOAJ |
| description | For 18 months following clinical introduction of deep-learning auto-segmentation (DLAS), an audit of organ at risk (OAR) contour editing was performed, including 1255 patients from a single institution and the majority of tumour sites. Mean surface-Dice similarity coefficient increased from 0.87 to 0.97, the number of unedited OARs increased from 21.5 % to 40 %. The audit identified changes in editing corresponding to vendor model changes, adaption of local contouring practice and reduced editing in areas of no clinical significance. The audit allowed assessment of the level and frequency of editing and identification of outlier cases. |
| format | Article |
| id | doaj-art-7dc607102a94478ba4f1c2c46a755773 |
| institution | Kabale University |
| issn | 2405-6316 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj-art-7dc607102a94478ba4f1c2c46a7557732025-02-03T04:16:47ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162025-01-0133100716Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapyJosh Mason0Jack Doherty1Sarah Robinson2Meagan de la Bastide3Jack Miskell4Ruth McLauchlan5Corresponding author.; Department of Radiobiology and Radiation Physics, Imperial College Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Road W6 8RF London, UKDepartment of Radiobiology and Radiation Physics, Imperial College Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Road W6 8RF London, UKDepartment of Radiobiology and Radiation Physics, Imperial College Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Road W6 8RF London, UKDepartment of Radiobiology and Radiation Physics, Imperial College Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Road W6 8RF London, UKDepartment of Radiobiology and Radiation Physics, Imperial College Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Road W6 8RF London, UKDepartment of Radiobiology and Radiation Physics, Imperial College Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Road W6 8RF London, UKFor 18 months following clinical introduction of deep-learning auto-segmentation (DLAS), an audit of organ at risk (OAR) contour editing was performed, including 1255 patients from a single institution and the majority of tumour sites. Mean surface-Dice similarity coefficient increased from 0.87 to 0.97, the number of unedited OARs increased from 21.5 % to 40 %. The audit identified changes in editing corresponding to vendor model changes, adaption of local contouring practice and reduced editing in areas of no clinical significance. The audit allowed assessment of the level and frequency of editing and identification of outlier cases.http://www.sciencedirect.com/science/article/pii/S2405631625000211Clinical auditDeep learning auto-segmentation |
| spellingShingle | Josh Mason Jack Doherty Sarah Robinson Meagan de la Bastide Jack Miskell Ruth McLauchlan Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy Physics and Imaging in Radiation Oncology Clinical audit Deep learning auto-segmentation |
| title | Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy |
| title_full | Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy |
| title_fullStr | Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy |
| title_full_unstemmed | Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy |
| title_short | Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy |
| title_sort | auditing the clinical usage of deep learning based organ at risk auto segmentation in radiotherapy |
| topic | Clinical audit Deep learning auto-segmentation |
| url | http://www.sciencedirect.com/science/article/pii/S2405631625000211 |
| work_keys_str_mv | AT joshmason auditingtheclinicalusageofdeeplearningbasedorganatriskautosegmentationinradiotherapy AT jackdoherty auditingtheclinicalusageofdeeplearningbasedorganatriskautosegmentationinradiotherapy AT sarahrobinson auditingtheclinicalusageofdeeplearningbasedorganatriskautosegmentationinradiotherapy AT meagandelabastide auditingtheclinicalusageofdeeplearningbasedorganatriskautosegmentationinradiotherapy AT jackmiskell auditingtheclinicalusageofdeeplearningbasedorganatriskautosegmentationinradiotherapy AT ruthmclauchlan auditingtheclinicalusageofdeeplearningbasedorganatriskautosegmentationinradiotherapy |