Method and apparatus that includes generating a clinical target volume for therapeutic radiation
Image information regarding a particular patient is provided, which image information includes, at least in part, a tumor to be irradiated. These teachings can also include providing non-image clinical information that corresponds to the particular patient. A control circuit accesses the foregoing image information and non-image clinical information and automatically generates a clinical target volume that is larger than the tumor as a function of both the image information and the non-image clinical information. The control circuit can then generate a corresponding radiation treatment plan based upon that clinical target volume, which plan can be utilized to irradiate the clinical target volume.
1. A method to facilitate irradiating a particular patient with therapeutic radiation, the method comprising:
providing image information regarding the particular patient, which image information includes, at least in part, a tumor to be irradiated;
providing non-image clinical information;
by a control circuit:
accessing the image information and the non-image clinical information and automatically generating a clinical target volume that is larger than the tumor as a function of both the image information and the non-image clinical information, wherein the non-image clinical information comprises at least one of tumor type, tumor location, and tumor grading;
generating a radiation treatment plan to irradiate the clinical target volume.
2. The method of claim 1 further comprising:
by the control circuit:
generating a gross tumor volume from the image information;
and wherein automatically generating the clinical target volume that is larger than the tumor as a function of both the image information and the non-image clinical information comprises automatically generating the clinical target volume that is larger than the tumor as a function of both the gross tumor volume and the non-image clinical information.
3. The method of claim 1 wherein the non-image clinical information comprises at least one of a logical component and at least one machine-learning model.
4. The method of claim 3 wherein the non-image clinical information comprises both of the logical component and the at least one machine-learning model.
5. The method of claim 4 wherein the non-image clinical information comprises each of the logical component and at least two machine-learning models.
6. The method of claim 3 wherein the at least one machine-learning model predicts tumor laterality.
7. The method of claim 3 wherein the at least one machine-learning model comprises a segmentation model for at least one subclinical region.
8. An apparatus to facilitate irradiating a particular patient with therapeutic radiation, the apparatus comprising:
a memory having stored therein:
image information regarding the particular patient, which image information includes, at least in part, a tumor to be irradiated;
non-image clinical information, wherein the non-image clinical information comprises at least one of tumor type, tumor location, and tumor grading;
a control circuit operably coupled to the memory and configured to:
access the image information and the non-image clinical information and automatically generate a clinical target volume that is larger than the tumor as a function of both the image information and the non-image clinical information;
generate a radiation treatment plan to irradiate the clinical target volume.
9. The apparatus of claim 8 wherein the control circuit is further configured to:
generate a gross tumor volume from the image information;
and wherein the control circuit is configured to automatically generate the clinical target volume that is larger than the tumor as a function of both the image information and the non-image clinical information by automatically generating the clinical target volume that is larger than the tumor as a function of both the gross tumor volume and the non-image clinical information.
10. The apparatus of claim 8 wherein the non-image clinical information comprises at least one of a logical component and at least one machine-learning model.
11. The apparatus of claim 10 wherein the non-image clinical information comprises both of the logical component and the at least one machine-learning model.
12. The apparatus of claim 11 wherein the non-image clinical information comprises each of the logical component and at least two machine-learning models.
13. The apparatus of claim 10 wherein the at least one machine-learning model predicts tumor laterality.
14. The apparatus of claim 10 wherein the at least one machine-learning model comprises a segmentation model for at least one subclinical region.