scholarly journals Knowledge‐based planning for intensity‐modulated radiation therapy: A review of data‐driven approaches

2019 ◽  
Vol 46 (6) ◽  
pp. 2760-2775 ◽  
Author(s):  
Yaorong Ge ◽  
Q. Jackie Wu
Keyword(s):  
Radiation Therapy ◽  
Intensity Modulated Radiation Therapy ◽  
Data Driven ◽  
Intensity Modulated ◽  
Knowledge Based ◽  
Knowledge Based Planning

scholarly journals Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma

2021 ◽  
Vol 10 ◽  
Author(s):  
Jiang Hu ◽  
Boji Liu ◽  
Weihao Xie ◽  
Jinhan Zhu ◽  
Xiaoli Yu ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Nasopharyngeal Carcinoma ◽  
Prediction Model ◽  
Intensity Modulated Radiation Therapy ◽  
Optic Chiasm ◽  
Conformity Index ◽  
Plan Quality ◽  
Target Dose ◽  
Knowledge Based ◽  
Knowledge Based Planning

Background and purposeTo validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved.Methods and materialsA knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated.ResultsWith comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable.ConclusionThe automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.

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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