Updating a clinical Knowledge-Based Planning prediction model for prostate radiotherapy

Scaggion, A.; Fusella, M.; Cavinato, Samuele; Dusi, Francesca; Khouzai, Badr El; Germani, Alessandra; Pivato, N.; Rossato, M.A.; Roggio, A.; Scott, Anthony; Sepulcri, Matteo; Zandonà, R.; Paiusco, M.

doi:10.1016/j.ejmp.2023.102542

Cited by 10 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these simple models require a substantial prior workload to determine the most relevant correlations between the predictive volumes and dosimetric indicators for each OAR. In addition, regular updates of these guide values are recommended ( 23 ) to improve the overall quality of treatment plans and adapt to possible changes in practice.…”

Section: Discussionmentioning

confidence: 99%

Enhancing dosimetric practices through knowledge-based predictive models: a case study on VMAT prostate irradiation

Hadj Henni,

Arhoun,

Boussetta

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

IntroductionAcquisition of dosimetric knowledge by radiation therapy planners is a protracted and complex process. This study delves into the impact of empirical predictive models based on the knowledge-based planning (KBP) methodology, aimed at detecting suboptimal results and homogenizing and improving existing practices for prostate cancer. Moreover, the dosimetric effect of implementing these models into routine clinical practice was also assessed.Materials and methodsBased on the KBP method, we analyzed 25 prostate treatment plans performed using VMAT by expert operators, aiming to correlate dose indicators with patient geometry. The DavgCav(Gy), V45GyCav(cc), and V15GyCav(cc) of the peritoneal cavity and the V60Gy(%) and V70Gy(%) of the rectum and bladder were linked to geometric characteristics such as the distance from the planning target volume (PTV) to the organs at risk (OAR), the volume of the OAR, or the overlap between the PTV and the OAR. In the second phase, the KBP was used in routine clinical practice in a prospective cohort of 25 patients and compared with the 41 patient plans calculated before implementing the tool.ResultsUsing linear regression, we identified strong geometric predictive factors for the peritoneal cavity, rectum, and bladder (R2 > 0.8), with an average prescribed dose of 97.8%, covering 95% of the target volume. The use of the model led to a significant dose reduction (Δ) for all evaluated OARs. This trend was most notable for ΔV15GyCav=−171.5 cc (p=0.003). Significant reductions were also obtained in average doses to the rectum and bladder, ΔDavgRect= −2.3 Gy (p=0.040), and ΔDavgVess= −3.3 Gy (p=0.039) respectively. Based on this model, we reduced the number of plans with OAR constraints above the clinical recommendations from 19% to 8%.ConclusionsThe KBP methodology established a robust and personalized predictive model for dose estimation to organs at risk in prostate cancer. Implementing the model resulted in improved sparing of these organs. Notably, it yields a solid foundation for harmonizing dosimetric practices, alerting us to suboptimal results, and improving our knowledge.

show abstract

Section: Discussionmentioning

confidence: 99%

Enhancing dosimetric practices through knowledge-based predictive models: a case study on VMAT prostate irradiation

Hadj Henni,

Arhoun,

Boussetta

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…The DVH model was estimated based on the geometrical and dosimetric correlations extracted from a manual plan (MP). The statistics of the KBP model were verified by assessing the goodness of statistical fitting, regression coefficient of determination (R 2 ), chi-square values ( X 2 ), and goodness of statistical estimation by the mean square error (MSE), demonstrating its efficiency in estimating the original DVH in a training plan [10] , [15] . An R 2 approaching 1 signifies a robust regression model, and an X 2 nearing zero indicates a strong fit.…”

Section: Methodsmentioning

confidence: 99%

Effectiveness of multi-criteria optimization in combination with knowledge-based modeling in radiotherapy of left-sided breast including regional nodes

Oonsiri,

Kingkaew,

Vimolnoch

et al. 2024

Physics and Imaging in Radiation Oncology

View full text Add to dashboard Cite

Knowledge-based plan optimization for prostate SBRT delivered with CyberKnife according to RTOG0938 protocol

et al. 2023

View full text Add to dashboard Cite

Updating a clinical Knowledge-Based Planning prediction model for prostate radiotherapy

Cited by 10 publications

References 49 publications

Enhancing dosimetric practices through knowledge-based predictive models: a case study on VMAT prostate irradiation

Enhancing dosimetric practices through knowledge-based predictive models: a case study on VMAT prostate irradiation

Effectiveness of multi-criteria optimization in combination with knowledge-based modeling in radiotherapy of left-sided breast including regional nodes

Knowledge-based plan optimization for prostate SBRT delivered with CyberKnife according to RTOG0938 protocol

Contact Info

Product

Resources

About