scholarly journals Prognostic analysis of patients with locally advanced nasopharyngeal carcinoma following intensity modulated radiation therapy

2018 ◽  
Author(s):  
Yajie Zhao ◽  
Lin Shen ◽  
Xinqiong Huang ◽  
Yuxiang He ◽  
Jun Fu ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Nasopharyngeal Carcinoma ◽  
Locally Advanced ◽  
Intensity Modulated Radiation Therapy ◽  
Intensity Modulated ◽  
Prognostic Analysis

scholarly journals A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy

2020 ◽  
Author(s):  
Penggang Bai ◽  
Xing Weng ◽  
Kerun Quan ◽  
Jihong Chen ◽  
Yitao Dai ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Nasopharyngeal Carcinoma ◽  
Treatment Planning ◽  
Locally Advanced ◽  
Intensity Modulated Radiation Therapy ◽  
Target Volume ◽  
Planning System ◽  
Intensity Modulated ◽  
Knowledge Based ◽  
Planning Technique

Abstract BackgroundTo investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally advanced nasopharyngeal carcinoma (NPC) radiotherapy.Methods140 NPC patients treated with definitive radiation therapy with the step-and-shoot IMRT techniques were retrospectively selected and separated into a knowledge library (n=115) and a test library (n=25). For each patient in the knowledge library, the overlap volume histogram (OVH), target volume histogram (TVH) and dose objectives were extracted from the manually generated plan. 5-fold cross validation was performed to divide the patients in the knowledge library into 5 groups before validating one group by using the other 4 groups to train each neural network (NN) machine learning models. For patients in the test library, their OVH and TVH were then used by the trained models to predict a corresponding set of mean dose objectives, which were subsequently used to generate automated plans (APs) in Pinnacle planning system via an in-house developed automated scripting system. All APs were obtained after a single step of optimization. Manual plans (MPs) for the test patients were generated by an experienced medical physicist strictly following the established clinical protocols. The qualities of the APs and MPs were evaluated by an attending radiation oncologist. The dosimetric parameters for planning target volume (PTV) coverage and the organs-at-risk (OAR) sparing were also quantitatively measured and compared using Mann-Whitney U test and Bonferroni correction.ResultsAPs and MPs had the same rating for more than 80% of the patients (19 out of 25) in the test group. Both AP and MP achieved PTV coverage criteria for no less than 80% of the patients. For each OAR, the number of APs achieving its criterion was similar to that in the MPs. The AP approach improved planning efficiency by greatly reducing the planning duration to about 17% of the MP (9.85±1.13 min vs. 57.10±6.35 min).ConclusionA robust and effective knowledge-based IMRT treatment planning technique for locally advanced NPC is developed. Patient specific dose objectives can be predicted by trained NN models based on the individual’s OVH and clinical TVH goals. The automated planning scripts can use these dose objectives to efficiently generate APs with largely shortened planning time. These APs had comparable dosimetric qualities when compared to our clinic’s manual plans.

scholarly journals A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy

2020 ◽  
Author(s):  
Penggang Bai ◽  
Xing Weng ◽  
Kerun Quan ◽  
Jihong Chen ◽  
Yitao Dai ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Nasopharyngeal Carcinoma ◽  
Treatment Planning ◽  
Locally Advanced ◽  
Intensity Modulated Radiation Therapy ◽  
Target Volume ◽  
Planning System ◽  
Intensity Modulated ◽  
Knowledge Based ◽  
Planning Technique

Abstract BackgroundTo investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally advanced nasopharyngeal carcinoma (NPC) radiotherapy.Methods140 NPC patients treated with definitive radiation therapy with the step-and-shoot IMRT techniques were retrospectively selected and seperated into a knowledge library (n=115) and a test library (n=25). For each case, in the knowledge library, the patient’s overlap volume histogram (OVH), target volume histogram (TVH) and dose objectives were extracted from the manually generated plan to train a 3-layer neural network (NN) machine learning model. For patients in the test library, their OVH and TVH were then used by the trained model to predict a corresponding set of dose objectives, which were subsequently used to generate automated plans (APs) in Pinnacle planning system via an in-house developed automated scripting system. All APs were obtained after a single step of optimization. Manual plans (MPs) of the same test patients were generated by an experienced medical physicist strictly following the established clinical protocols. The qualities of the APs and MPs were evaluated by an attending radiation oncologist. The dosimetric parameters for planning target volume (PTV) coverage and the organs-at-risk (OAR) sparing were also quantitatively measured and compared.ResultsAPs and MPs had the same rating for more than 80% of the patients (19 out of 25) in the test group. For greater than 80% of the patients, both AP and MP achieved PTV coverage criteria. For each OAR, the number of APs achieving its criterion was similar to that in the MPs. The AP approach significantly improved planning efficiency by reducing the planning duration to about 17% of the MP (9.73±1.80 min vs. 57.10±6.35 min, P<0.001). ConclusionA robust and effective knowledge-based IMRT treatment planning technique for locally advanced NPC is developed. Patient specific dose objectives can be predicted by a trained NN model based on the individual’s OVH and clinical TVH goals. The automated planning scripts can use these dose objectives to efficiently generate APs with largely shortened planning time. These APs had comparable dosimetric qualities when compared to our clinic’s manual plans.

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