Dosimetric features-driven machine learning model for DVHs/dose prediction
View Patent ↗A treatment planning prediction method to predict a Dose-Volume Histogram (DVH) or Dose Distribution (DD) for patient data using a machine-learning computer framework is provided with the key inclusion of a Planning Target Volume (PTV) only treatment plan in the framework. A dosimetric parameter is used as an additional parameter to the framework and which is obtained from a prediction of the PTV-only treatment plan. The method outputs a Dose-Volume Histogram and/or a Dose Distribution for the patient including the prediction of the PTV-only treatment plan. The method alleviates the complicated process of quantifying anatomical features and harnesses directly the inherent correlation between the PTV-only plan and the clinical plan in the dose domain. The method provides a more robust and efficient solution to the important DVHs prediction problem in treatment planning and plan quality assurance.
1. A treatment planning prediction method, comprising:
(a) training a machine deep learning algorithm operable by a computer wherein the machine deep learning algorithm is trained to correlate (i) Dose-Volume Histograms (DVHs) or Dose Distributions (DDs) from Planning Target Volume (PTV) only treatment plans with (ii) DVHs and DDs of treatment plans for patients, wherein the PTV only treatment plans are defined as treatment plans where dose constraints to Organs-At-Risk (OARs) have been removed and have PTV constrains defined for dose homogeneity and dose conformity;
(b) the computer receiving dosimetric parameters, defined as DVHs or DDs from a PTV only treatment plan for a new patient, and the computer inputting the dosimetric parameters into the machine deep learning algorithm;
(c) the computer processing the inputted dosimetric parameters by the machine deep learning algorithm; and
(d) the computer generating an output from the machine deep learning algorithm, wherein the output are DVHs or DDs for the new patient, wherein the output is characterized by containing OARs constraints for the new patient as well as PTV constrains defined for dose homogeneity and dose conformity for the PTV only treatment plan for the new patient.