Clinical iterative model development improves knowledge-based plan quality for high-risk prostate cancer with four integrated dose levels

Hundvin, J.A.; Fjellanger, Kristine; Pettersen, Helge Egil Seime; Nygaard, Britt; Revheim, Kari; Sulen, Turid Husevåg; Ekanger, Christian; Hysing, L.B.

doi:10.1080/0284186x.2020.1828619

Cited by 9 publications

(12 citation statements)

References 31 publications

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“…In this study, we validated the feasibility and efficiency of a KBP method based on estimated DVHs with special efforts to improve the success rate of auto-planning for NPC treatment. As the database quality might have a direct impact on the prediction results ( 26 ), only high quality prior plans with definite curative effects were enrolled. Also, a refinement process was applied here for the primary model to enhance its predictive ability as recommended by several authors ( 18 , 19 , 27 ).…”

Section: Discussionmentioning

confidence: 99%

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

Liu

Xie

et al. 2021

Front. Oncol.

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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.

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Section: Discussionmentioning

confidence: 99%

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

Liu

Xie

et al. 2021

Front. Oncol.

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“…28 In our study, the dose-volume parameters were significantly improved by the updated RP models from the first to second or third models in an open-loop validation, which indicates that over-fitting can be avoided. Each updated model in our study had sufficient stability and robustness like some studies adopted the open-loop validation, 6,11,31 because the quality of the plans generated for new patients (which strongly depends on the quality and robustness of the plans in the library) was superior to the quality of the clinical manual plans. 6 The proportion of the plans with a high Cook's distance value in the regression scatterplots can indicate large variations in dose prediction with the RP model, leading to large variability in the RP quality.…”

Section: Discussionmentioning

confidence: 95%

“…[2][3][4][5] RapidPlan (RP) which is integrated in the Eclipse treatment planning system (TPS) (Varian Medical Systems, Palo Alto, CA, USA) is a specific knowledge-based planning (KBP) solution that can reduce the variation of dose-volume parameters and ensure planning consistency between planners and institutions. [6][7][8][9][10][11] Many studies reported that RP using a single optimization of dose-volume parameters and dose distributions can produce IMRT/VMAT plans superior or comparable to clinically accepted plans for various treatment sites. [12][13][14][15][16][17][18][19][20][21][22][23][24][25] The RP model performance, such as sparing of OARs, depends on the training plans included in the model library; therefore, the registration of better plans is useful to update the model and enhance its performance.…”

Section: Introductionmentioning

confidence: 99%

“…However, coverage of the target and sparing of the organs at risks (OARs) with inverse planning depends on the planner's or institution's experience and protocol compliance, and can therefore compromise the gains of high‐precision radiotherapy 2–5 . RapidPlan (RP) which is integrated in the Eclipse treatment planning system (TPS) (Varian Medical Systems, Palo Alto, CA, USA) is a specific knowledge‐based planning (KBP) solution that can reduce the variation of dose–volume parameters and ensure planning consistency between planners and institutions 6‐11 12‐25 …”

Section: Introductionmentioning

confidence: 99%

“… 2 , 3 , 4 , 5 RapidPlan (RP) which is integrated in the Eclipse treatment planning system (TPS) (Varian Medical Systems, Palo Alto, CA, USA) is a specific knowledge‐based planning (KBP) solution that can reduce the variation of dose–volume parameters and ensure planning consistency between planners and institutions. 6 , 7 , 8 , 9 , 10 , 11 Many studies reported that RP using a single optimization of dose–volume parameters and dose distributions can produce IMRT/VMAT plans superior or comparable to clinically accepted plans for various treatment sites. 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25…”

Section: Introductionmentioning

confidence: 99%

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An updating approach for knowledge‐based planning models to improve plan quality and variability in volumetric‐modulated arc therapy for prostate cancer

Nakamura

Okuhata

Tamura

et al. 2021

J Applied Clin Med Phys

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The purpose of this study was to compare the dose-volume parameters and regression scatter plots of the iteratively improved RapidPlan (RP) models, specific knowledge-based planning (KBP) models, in volumetric-modulated arc therapy (VMAT) for prostate cancer over three periods.Methods: A RP1 model was created from 47 clinical intensity-modulated radiation therapy (IMRT)/VMAT plans. A RP2 model was created to exceed dosimetric goals which set as the mean values +1SD of the dose-volume parameters of RP1 (50 consecutive new clinical VMAT plans). A RP3 model was created with more strict dose constraints for organs at risks (OARs) than RP1 and RP2 models (50 consecutive anew clinical VMAT plans). Each RP model was validated against 30 validation plans (RP1, RP2, and RP3) that were not used for model configuration, and the dose-volume parameters were compared. The Cook's distances of regression scatterplots of each model were also evaluated.Results: Significant differences (p < 0.05) between RP1 and RP2 were found in D mean (101.5% vs. 101.9%), homogeneity index (3.90 vs. 4.44), 95% isodose conformity index (1.22 vs. 1.20) for the target, V 40Gy (47.3% vs. 45.7%), V 60Gy (27.9% vs. 27.1%), V 70Gy (16.4% vs. 15.2%), and V 78Gy (0.4% vs. 0.2%) for the rectal wall, and V 40Gy (43.8% vs. 41.8%) and V 70Gy (21.3% vs. 20.5%) for the bladder wall, whereas only V 70Gy (15.2% vs. 15.8%) of the rectal wall differed significantly between RP2 and RP3. The proportions of cases with a Cook's distance of <1.0 (RP1, RP2, and RP3 models) were 55%, 78%, and 84% for the rectal wall, and 77%, 68%, and 76% for the bladder wall, respectively. Conclusions:The iteratively improved RP models, reflecting the clear dosimetric goals based on the RP feedback (dose-volume parameters) and more strict dose constraints for the OARs, generated superior dose-volume parameters How to cite this article: Nakamura K, Okuhata K, Tamura M, et al. An updating approach for knowledge-based planning models to improve plan quality and variability in volumetricmodulated arc therapy for prostate cancer.

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Updating approach for lexicographic optimization-based planning to improve cervical cancer plan quality

Caricato,

Trivellato,

Pellegrini

et al. 2023

Discov Onc

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Background To investigate the capability of a not-yet commercially available fully automated lexicographic optimization (LO) planning algorithm, called mCycle (Elekta AB, Stockholm, Sweden), to further improve the plan quality of an already-validated Wish List (WL) pushing on the organs-at-risk (OAR) sparing without compromising target coverage and plan delivery accuracy. Material and Methods Twenty-four mono-institutional consecutive cervical cancer Volumetric-Modulated Arc Therapy (VMAT) plans delivered between November 2019 and April 2022 (50 Gy/25 fractions) have been retrospectively selected. In mCycle the LO planning algorithm was combined with the a-priori multi-criterial optimization (MCO). Two versions of WL have been defined to reproduce manual plans (WL01), and to improve the OAR sparing without affecting minimum target coverage and plan delivery accuracy (WL02). Robust WLs have been tuned using a subset of 4 randomly selected patients. The remaining plans have been automatically re-planned by using the designed WLs. Manual plans (MP) and mCycle plans (mCP01 and mCP02) were compared in terms of dose distributions, complexity, delivery accuracy, and clinical acceptability. Two senior physicians independently performed a blind clinical evaluation, ranking the three competing plans. Furthermore, a previous defined global quality index has been used to gather into a single score the plan quality evaluation. Results The WL tweaking requests 5 and 3 working days for the WL01 and the WL02, respectively. The re-planning took in both cases 3 working days. mCP01 best performed in terms of target coverage (PTV V95% (%): MP 98.0 [95.6–99.3], mCP01 99.2 [89.7–99.9], mCP02 96.9 [89.4–99.5]), while mCP02 showed a large OAR sparing improvement, especially in the rectum parameters (e.g., Rectum D50% (Gy): MP 41.7 [30.2–47.0], mCP01 40.3 [31.4–45.8], mCP02 32.6 [26.9–42.6]). An increase in plan complexity has been registered in mCPs without affecting plan delivery accuracy. In the blind comparisons, all automated plans were considered clinically acceptable, and mCPs were preferred over MP in 90% of cases. Globally, automated plans registered a plan quality score at least comparable to MP. Conclusions This study showed the flexibility of the Lexicographic approach in creating more demanding Wish Lists able to potentially minimize toxicities in RT plans.

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Clinical iterative model development improves knowledge-based plan quality for high-risk prostate cancer with four integrated dose levels

Cited by 9 publications

References 31 publications

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

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

An updating approach for knowledge‐based planning models to improve plan quality and variability in volumetric‐modulated arc therapy for prostate cancer

Updating approach for lexicographic optimization-based planning to improve cervical cancer plan quality

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