NDLI: Clinical evaluation of two AI models for automated breast cancer plan generation

Content Provider	Springer Nature : BioMed Central
Author	Kneepkens, Esther Bakx, Nienke van der Sangen, Maurice Theuws, Jacqueline van der Toorn, Peter-Paul Rijkaart, Dorien van der Leer, Jorien van Nunen, Thérèse Hagelaar, Els Bluemink, Hanneke Hurkmans, Coen
Abstract	Background Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is lacking. This study evaluates two different, deliverable AI plans in terms of their clinical acceptability based on quantitative parameters and qualitative evaluation by four radiation oncologists. Methods For 20 left-sided node-negative breast cancer patients, treated with a prescribed dose of 40.05 Gy, using tangential beam intensity modulated radiotherapy, two model-based treatment plans were evaluated against the corresponding manual plan. The two models used were an in-house developed U-net model and a vendor-developed contextual atlas regression forest model (cARF). Radiation oncologists evaluated the clinical acceptability of each blinded plan and ranked plans according to preference. Furthermore, a comparison with the manual plan was made based on dose volume histogram parameters, clinical evaluation criteria and preparation time. Results The U-net model resulted in a higher average and maximum dose to the PTV (median difference 0.37 Gy and 0.47 Gy respectively) and a slightly higher mean heart dose (MHD) (0.01 Gy). The cARF model led to higher average and maximum doses to the PTV (0.30 and 0.39 Gy respectively) and a slightly higher MHD (0.02 Gy) and mean lung dose (MLD, 0.04 Gy). The maximum MHD/MLD difference was ≤ 0.5 Gy for both AI plans. Regardless of these dose differences, 90–95% of the AI plans were considered clinically acceptable versus 90% of the manual plans. Preferences varied between the radiation oncologists. Plan preparation time was comparable between the U-net model and the manual plan (287 s vs 253 s) while the cARF model took longer (471 s). When only considering user interaction, plan generation time was 121 s for the cARF model and 137 s for the U-net model. Conclusions Two AI models were used to generate deliverable plans for breast cancer patients, in a time-efficient manner, requiring minimal user interaction. Although the AI plans resulted in slightly higher doses overall, radiation oncologists considered 90–95% of the AI plans clinically acceptable.
Related Links	https://ro-journal.biomedcentral.com/counter/pdf/10.1186/s13014-022-01993-9.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s13014-022-01993-9
Journal	Radiation Oncology
Issue Number	1
Volume Number	17
Language	English
Publisher	BioMed Central
Publisher Date	2022-02-05
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Radiotherapy Imaging Radiology Deep learning U-net Breast cancer Dose mimicking cARF Autoplanning
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automated Hypofractionated IMRT treatment planning for early-stage breast Cancer

Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy

Dose-response relationship for breast cancer induction at radiotherapy dose

Dosimetric comparison of two dose expansion methods in intensity modulated radiotherapy for breast cancer

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations

Deep learning method for prediction of patient-specific dose distribution in breast cancer

Diffusion-CSPAM U-Net: A U-Net model integrated hybrid attention mechanism and diffusion model for segmentation of computed tomography images of brain metastases

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer

A deep learning-based dose calculation method for volumetric modulated arc therapy

Clinical evaluation of two AI models for automated breast cancer plan generation

Similar Documents

Automated Hypofractionated IMRT treatment planning for early-stage breast Cancer

Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy

Dose-response relationship for breast cancer induction at radiotherapy dose

Dosimetric comparison of two dose expansion methods in intensity modulated radiotherapy for breast cancer

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations

Deep learning method for prediction of patient-specific dose distribution in breast cancer

Diffusion-CSPAM U-Net: A U-Net model integrated hybrid attention mechanism and diffusion model for segmentation of computed tomography images of brain metastases

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer

A deep learning-based dose calculation method for volumetric modulated arc therapy

Clinical evaluation of two AI models for automated breast cancer plan generation