Deep learning for patient-specific quality assurance of volumetric modulated arc therapy: Prediction accuracy and cost-sensitive classification performance.
Phys Med
; 125: 104500, 2024 Sep.
Article
in En
| MEDLINE
| ID: mdl-39191190
ABSTRACT
PURPOSE:
To evaluate a deep learning model's performance in predicting and classifying patient-specific quality assurance (PSQA) results for volumetric modulated arc therapy (VMAT), aiming to streamline PSQA workflows and reduce the onsite measurement workload.METHODS:
A total of 761 VMAT plans were analyzed using 3D-MResNet to process multileaf collimator images and monitor unit data, with the gamma passing rate (GPR) as the output. Thresholds for the predicted GPR (Th-p) and measured GPR (Th-m) were established to aid in PSQA decision-making, using cost curves and error rates to assess classification performance.RESULTS:
The mean absolute errors of the model for the test set were 1.63 % and 2.38 % at 3 %/2 mm and 2 %/2 mm, respectively. For the classification of the PSQA results, Th-m was 88.3 % at 2 %/2 mm and 93.3 % at 3 %/2 mm. The lowest cost-sensitive error rates of 0.0127 and 0.0925 were obtained when Th-p was set as 91.2 % at 2 %/2 mm and 96.4 % at 3 %/2 mm, respectively. Additionally, the 2 %/2 mm classifier also achieved a lower total expected cost of 0.069 compared with 0.110 for the 3 %/2 mm classifier. The deep learning classifier under the 2 %/2 mm gamma criterion had a sensitivity and specificity of 100 % (10/10) and 83.5 % (167/200), respectively, for the test set.CONCLUSIONS:
The developed 3D-MResNet model can accurately predict and classify PSQA results based on VMAT plans. The introduction of a deep learning model into the PSQA workflow has considerable potential for improving the VMAT PSQA process and reducing workloads.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Quality Assurance, Health Care
/
Radiotherapy Planning, Computer-Assisted
/
Radiotherapy, Intensity-Modulated
/
Deep Learning
Limits:
Humans
Language:
En
Journal:
Phys Med
/
Phys. medica (Testo stamp.)
/
Physica medica
Journal subject:
BIOFISICA
/
BIOLOGIA
/
MEDICINA
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
Italy