scholarly journals Robustness of a Pretrained Dose-Volume Histogram Estimation Model on Knowledge-Based Planning of Other Types

2016 ◽  
Vol 96 (2) ◽  
pp. E603-E604
Author(s):  
Y. Zhang ◽  
H. Wu ◽  
F. Jiang ◽  
H. Yue ◽  
J. Deng
Keyword(s):  
Dose Volume Histogram ◽  
Estimation Model ◽  
Knowledge Based ◽  
Dose Volume ◽  
Knowledge Based Planning ◽  
Histogram Estimation

scholarly journals A hybrid automated treatment planning solution for esophageal cancer

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Chifang Ling ◽  
Xu Han ◽  
Peng Zhai ◽  
Hao Xu ◽  
Jiayan Chen ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Estimation Model ◽  
Knowledge Based ◽  
Plan Optimization ◽  
Dose Volume ◽  
Knowledge Based Planning ◽  
Planning Methods ◽  
Planning Strategies

Abstract Objective This study aims to investigate a hybrid automated treatment planning (HAP) solution that combines knowledge-based planning (KBP) and script-based planning for esophageal cancer. Methods In order to fully investigate the advantages of HAP, three planning strategies were implemented in the present study: HAP, KBP, and full manual planning. Each method was applied to 20 patients. For HAP and KBP, the objective functions for plan optimization were generated from a dose–volume histogram (DVH) estimation model, which was based on 70 esophageal patients. Script-based automated planning was used for HAP, while the regular IMRT inverse planning method was used for KBP. For full manual planning, clinical standards were applied to create the plans. Paired t-tests were performed to compare the differences in dose-volume indices among the three planning methods. Results Among the three planning strategies, HAP exhibited the best performance in all dose-volume indices, except for PTV dose homogeneity and lung V5. PTV conformity and spinal cord sparing were significantly improved in HAP (P < 0.001). Compared to KBP, HAP improved all indices, except for lung V5. Furthermore, the OAR sparing and target coverage between HAP and full manual planning were similar. Moreover, HAP had the shortest average planning time (57 min), when compared to KBP (63 min) and full manual planning (118 min). Conclusion HAP is an effective planning strategy for obtaining a high quality treatment plan for esophageal cancer.

scholarly journals Automated contouring and planning pipeline for hippocampal-avoidant whole-brain radiotherapy

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Christine H. Feng ◽  
Mariel Cornell ◽  
Kevin L. Moore ◽  
Roshan Karunamuni ◽  
Tyler M. Seibert
Keyword(s):  
Artificial Intelligence ◽  
Treatment Planning ◽  
Whole Brain Radiotherapy ◽  
Planning System ◽  
Whole Brain ◽  
Knowledge Based ◽  
Brain Radiotherapy ◽  
Manual Intervention ◽  
Dose Volume ◽  
Knowledge Based Planning

Abstract Background Whole-brain radiotherapy (WBRT) remains an important treatment for over 200,000 cancer patients in the United States annually. Hippocampal-avoidant WBRT (HA-WBRT) reduces neurocognitive toxicity compared to standard WBRT, but HA-WBRT contouring and planning are more complex and time-consuming than standard WBRT. We designed and evaluated a workflow using commercially available artificial intelligence tools for automated hippocampal segmentation and treatment planning to efficiently generate clinically acceptable HA-WBRT radiotherapy plans. Methods We retrospectively identified 100 consecutive adult patients treated for brain metastases outside the hippocampal region. Each patient’s T1 post-contrast brain MRI was processed using NeuroQuant, an FDA-approved software that provides segmentations of brain structures in less than 8 min. Automated hippocampal segmentations were reviewed for accuracy, then converted to files compatible with a commercial treatment planning system, where hippocampal avoidance regions and planning target volumes (PTV) were generated. Other organs-at-risk (OARs) were previously contoured per clinical routine. A RapidPlan knowledge-based planning routine was applied for a prescription of 30 Gy in 10 fractions using volumetric modulated arc therapy (VMAT) delivery. Plans were evaluated based on NRG CC001 dose-volume objectives (Brown et al. in J Clin Oncol, 2020). Results Of the 100 cases, 99 (99%) had acceptable automated hippocampi segmentations without manual intervention. Knowledge-based planning was applied to all cases; the median processing time was 9 min 59 s (range 6:53–13:31). All plans met per-protocol dose-volume objectives for PTV per the NRG CC001 protocol. For comparison, only 65.5% of plans on NRG CC001 met PTV goals per protocol, with 26.1% within acceptable variation. In this study, 43 plans (43%) met OAR constraints, and the remaining 57 (57%) were within acceptable variation, compared to 42.5% and 48.3% on NRG CC001, respectively. No plans in this study had unacceptable dose to OARs, compared to 0.8% of manually generated plans from NRG CC001. 8.4% of plans from NRG CC001 were not scored or unable to be evaluated. Conclusions An automated pipeline harnessing the efficiency of commercially available artificial intelligence tools can generate clinically acceptable VMAT HA-WBRT plans with minimal manual intervention. This process could improve clinical efficiency for a treatment established to improve patient outcomes over standard WBRT.

scholarly journals Dose–volume histogram analysis and clinical evaluation of knowledge-based plans with manual objective constraints for pharyngeal cancer

2020 ◽  
Vol 61 (3) ◽  
pp. 499-505 ◽  
Author(s):  
Takuya Uehara ◽  
Hajime Monzen ◽  
Mikoto Tamura ◽  
Kazuki Ishikawa ◽  
Hiroshi Doi ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Volumetric Modulated Arc Therapy ◽  
Intensity Modulated Radiation Therapy ◽  
Target Volume ◽  
Dose Volume Histogram ◽  
Parotid Glands ◽  
Pharyngeal Cancer ◽  
Knowledge Based ◽  
Dose Volume ◽  
Arc Therapy

Abstract The present study aimed to evaluate whether knowledge-based plans (KBP) from a single optimization could be used clinically, and to compare dose–volume histogram (DVH) parameters and plan quality between KBP with (KBPCONST) and without (KBPORIG) manual objective constraints and clinical manual optimized (CMO) plans for pharyngeal cancer. KBPs were produced from a system trained on clinical plans from 55 patients with pharyngeal cancer who had undergone intensity-modulated radiation therapy or volumetric-modulated arc therapy (VMAT). For another 15 patients, DVH parameters of KBPCONST and KBPORIG from a single optimization were compared with CMO plans with respect to the planning target volume (D98%, D50%, D2%), brainstem maximum dose (Dmax), spinal cord Dmax, parotid gland median and mean dose (Dmed and Dmean), monitor units and modulation complexity score for VMAT. The Dmax of spinal cord and brainstem and the Dmed and Dmean of ipsilateral parotid glands were unacceptably high for KBPORIG, although the KBPCONST DVH parameters met our goal for most patients. KBPCONST and CMO plans produced comparable DVH parameters. The monitor units of KBPCONST were significantly lower than those of the CMO plans (P &lt; 0.001). Dose distribution of the KBPCONST was better than or comparable to that of the CMO plans for 13 (87%) of the 15 patients. In conclusion, KBPORIG was found to be clinically unacceptable, while KBPCONST from a single optimization was comparable or superior to CMO plans for most patients with head and neck cancer.

scholarly journals Automated Contouring and Planning for Hippocampal-Avoidant Whole-Brain Radiotherapy

2020 ◽  
Author(s):  
Christine H. Feng ◽  
Mariel Cornell ◽  
Kevin L. Moore ◽  
Roshan Karunamuni ◽  
Tyler M. Seibert
Keyword(s):  
Artificial Intelligence ◽  
Treatment Planning ◽  
Planning System ◽  
Treatment Planning System ◽  
Post Contrast ◽  
Whole Brain ◽  
Knowledge Based ◽  
Manual Intervention ◽  
Dose Volume ◽  
Knowledge Based Planning

AbstractPurposeDesign and evaluate a workflow using commercially available artificial intelligence tools for automated hippocampal segmentation and treatment planning to efficiently generate clinically acceptable hippocampal-avoidant whole brain (HA-WBRT) radiotherapy plans.Methods and MaterialsWe retrospectively identified 100 consecutive adult patients treated for brain metastases outside the hippocampal region. Each patient’s T1 post-contrast brain MRI was processed using FDA-approved software that provides segmentations of brain structures in 5-7 minutes. Automated hippocampal segmentations were reviewed for accuracy and edited manually if necessary, then converted to files compatible with a commercial treatment planning system, where hippocampal avoidance regions and planning target volumes (PTV) were generated. Other organs-at-risk (OARs) were previously contoured per clinical routine. A RapidPlan knowledge-based planning routine was applied for a prescription of 30 Gy in 10 fractions using volumetric modulated arc therapy (VMAT) delivery. Plans were evaluated based on NRG CC001 dose-volume objectives.ResultsOf the 100 cases, 99 (99%) had acceptable automated hippocampi segmentations without manual intervention. Knowledge-based planning was applied to all cases; the median processing time was 9 minutes 59 seconds (range 6:53 – 13:31). All plans met per-protocol dose-volume objectives for PTV per the NRG CC001 protocol. For comparison, only 66.0% of plans on NRG CC001 met PTV goals per protocol, with 26.3% within acceptable variation. In this study, 43 plans (43%) met OAR constraints, and the remaining 57 (57%) were within acceptable variation, compared to 42.9% and 48.6% on NRG CC001, respectively. No plans in this study had unacceptable dose to OARs, compared to 0.8% of manually generated plans from NRG CC001.ConclusionAn automated pipeline harnessing the efficiency of commercially available artificial intelligence tools can generate clinically acceptable VMAT HA-WBRT plans with minimal manual intervention. This process could improve clinical efficiency for a treatment established to improve patient outcomes over standard WBRT.

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