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.

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 ◽  
Treatment Plan ◽  
Locally Advanced ◽  
Intensity Modulated Radiation Therapy ◽  
Patient Specific ◽  
Intensity Modulated ◽  
Knowledge Based ◽  
Planning Technique

Abstract Background To investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally advanced nasopharyngeal carcinoma (NPC) radiotherapy. Methods 140 NPC patients treated with definitive radiation therapy with the step-and-shoot IMRT techniques were retrospectively selected and consisted of a knowledge library (115 patients) and a test library (25 patients). For each patient in the knowledge library, their overlap volume histogram (OVH), target volume histogram (TVH) and dose objectives were extracted from the patient’s manual treatment plan, and these were used to train a 3 layer neural network (NN) model. The OVH and TVH from the test library were input into the trained model to derive patients’ dose objectives which were subsequently used to generate automated plans (APs) by an in-house developed Perl and HotScripts planning scripts with a single iteration optimization. The corresponding manual plans (MPs) of patients in the test library were manually generated by an experienced medical physicist according to clinical protocols. Plan quality was ranked and dosimetric parameters were compared between the APs and MPs. Results Qualitatively, the APs and MPs had the same rank for the majority of the patients (19 of 25). PTV achieved each given criteria in the majority of the patients (greater than 80%) between both the APs and MPs. For each OAR, the number achieving its criterion in the APs was close to that in the MPs. The APs would improve the treatment delivery efficiency by reducing total plan MUs by ~5% (685.24±58.89 vs. 721.36±63.36, P =0.004). AP also significantly improved planning efficiency by reducing the planning duration to ~17% of the MP (9.73±1.80 min vs. 57.10±6.35 min, P <0.001). Conclusion A robust and effective knowledge-based IMRT treatment planning technique for locally advanced NPC is developed. Patient specific TDV 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 TDV to generate APs with largely shortened planning time with comparable or improved dosimetric qualities compared to our clinic’s manual plans.

scholarly journals Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mingli Wang ◽  
Huikuan Gu ◽  
Jiang Hu ◽  
Jian Liang ◽  
Sisi Xu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Prediction Model ◽  
Volumetric Modulated Arc Therapy ◽  
Model Evolution ◽  
Prediction Ability ◽  
Knowledge Based ◽  
Proposed Model ◽  
Arc Therapy ◽  
Knowledge Based Planning

Abstract Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.

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