PO-1887 Auto-segmentation and knowledge-based planning for radiotherapeutic palliation of spine metastases

2021 ◽  
Vol 161 ◽  
pp. S1607-S1608
Author(s):  
E. Jones ◽  
C. Napoleone-Filho ◽  
C. Sisodia ◽  
M. Couper ◽  
V. Harris ◽  
...  
Keyword(s):  
Spine Metastases ◽  
Knowledge Based ◽  
Knowledge Based Planning

Knowledge-Based Planning

1993 ◽  
Author(s):  
Drew McDermott ◽  
Gregory Hager
Keyword(s):  
Knowledge Based ◽  
Knowledge Based Planning

scholarly journals OpenKBP: The open‐access knowledge‐based planning grand challenge and dataset

2021 ◽  
Author(s):  
Aaron Babier ◽  
Binghao Zhang ◽  
Rafid Mahmood ◽  
Kevin L. Moore ◽  
Thomas G. Purdie ◽  
...  
Keyword(s):  
Open Access ◽  
Grand Challenge ◽  
Knowledge Based ◽  
Knowledge Based Planning

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.

scholarly journals Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study

Cancers ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 420 ◽  
Author(s):  
Alexander Delaney ◽  
Lei Dong ◽  
Anthony Mascia ◽  
Wei Zou ◽  
Yongbin Zhang ◽  
...  
Keyword(s):  
Target Volume ◽  
Single Center ◽  
Intensity Modulated Proton Therapy ◽  
Intensity Modulated ◽  
Knowledge Based ◽  
Model Library ◽  
Beam Angle Optimization ◽  
Knowledge Based Planning ◽  
Automated Knowledge ◽  
Comparable Quality

Background: Radiotherapy treatment planning is increasingly automated and knowledge-based planning has been shown to match and sometimes improve upon manual clinical plans, with increased consistency and efficiency. In this study, we benchmarked a novel prototype knowledge-based intensity-modulated proton therapy (IMPT) planning solution, against three international proton centers. Methods: A model library was constructed, comprising 50 head and neck cancer (HNC) manual IMPT plans from a single center. Three external-centers each provided seven manual benchmark IMPT plans. A knowledge-based plan (KBP) using a standard beam arrangement for each patient was compared with the benchmark plan on the basis of planning target volume (PTV) coverage and homogeneity and mean organ-at-risk (OAR) dose. Results: PTV coverage and homogeneity of KBPs and benchmark plans were comparable. KBP mean OAR dose was lower in 32/54, 45/48 and 38/53 OARs from center-A, -B and -C, with 23/32, 38/45 and 23/38 being >2 Gy improvements, respectively. In isolated cases the standard beam arrangement or an OAR not being included in the model or being contoured differently, led to higher individual KBP OAR doses. Generating a KBP typically required <10 min. Conclusions: A knowledge-based IMPT planning solution using a single-center model could efficiently generate plans of comparable quality to manual HNC IMPT plans from centers with differing planning aims. Occasional higher KBP OAR doses highlight the need for beam angle optimization and manual review of KBPs. The solution furthermore demonstrated the potential for robust optimization.

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&lt;0.001), optic nerve (p=0.021), and temporal lobe (p&lt;0.001), but failed to spare the spinal cord (p&lt;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 Functional-guided radiotherapy using knowledge-based planning

2018 ◽  
Vol 129 (3) ◽  
pp. 494-498 ◽  
Author(s):  
Austin M. Faught ◽  
Lindsey Olsen ◽  
Leah Schubert ◽  
Chad Rusthoven ◽  
Edward Castillo ◽  
...  
Keyword(s):  
Knowledge Based ◽  
Knowledge Based Planning
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