EP-4736113-A1 - AUTOMATIC GENERATION OF A REGION OF INTEREST IN A MEDICAL IMAGE FOR TUMOR TREATING FIELDS TREATMENT PLANNING

EP4736113A1EP 4736113 A1EP4736113 A1EP 4736113A1EP-4736113-A1

Abstract

A computer-implemented method for treatment planning for administering tumor treating fields to a subject, the method comprising: presenting on a display a slice through a medical image of the subject, wherein the medical image comprises voxels; and determining a segmentation of a minimum number of voxels of at least one tissue type in the slice of the medical image; receiving a user selection to automatically generate a region of interest (ROI) in the medical image for application of tumor treating fields to the subject; and automatically generating the region of interest in the medical image for application of tumor treating fields to the subject based on the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image.

Inventors

ZIGELMAN, GIL
URMAN, Noa

Assignees

Novocure GmbH

Dates

Publication Date: 20260506
Application Date: 20240628

Claims (15)

1. A computer-implemented method for treatment planning for administering tumor treating fields to a subject, the method comprising: presenting on a display a slice through a medical image of the subject, wherein the medical image comprises voxels; and determining a segmentation of a minimum number of voxels of at least one tissue type in the slice of the medical image; receiving a user selection to automatically generate a region of interest (RO I) in the medical image for application of tumor treating fields to the subject; and automatically generating the region of interest in the medical image for application of tumor treating fields to the subject based on the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image.
2. The method of claim 1, wherein determining the segmentation of a minimum number of voxels of at least one tissue type in the slice of the medical image comprises: determining a segmentation of a minimum number of voxels of a tumor in the medical image.
3. The method of claim 1, wherein determining the segmentation of a minimum number of voxels of at least one tissue type in the slice of the medical image comprises: determining a segmentation of a minimum number of voxels of a gross tumor volume in the medical image.
4. The method of claim 1, wherein determining the segmentation of a minimum number of voxels of at least one tissue type in the slice of the medical image comprises at least one of: determining a segmentation of a minimum number of voxels of a resection cavity in the medical image; determining a segmentation of a minimum number of voxels of a necrotic area in the medical image; or determining a segmentation of a minimum number of voxels of an enhancing tumor in the medical image.
5. The method of claim 1, wherein automatically generating the region of interest in the medical image for application of tumor treating fields to the subject comprises: adding a proximal boundary zone (PBZ) to a gross tumor volume (GTV) to obtain a clinical tumor volume (CTV) in the medical image for application of tumor treating fields to the subject.
6. The method of claim 1, wherein automatically generating the region of interest in the medical image for application of tumor treating fields to the subject comprises: determining a segmented area of a tissue type in the medical image based on the segmentation of the minimum number of voxels of at least one tissue type in the slice in the medical image; determining a proximal boundary zone (PBZ) for the segmented area of the medical image; and adding the PBZ to the segmented area of the medical image to obtain the region of interest in the medical image for application of tumor treating fields to the subject.
7. The method of claim 6, wherein the segmented area is a tumor, a gross tumor volume, a resection cavity, a necrotic area, an enhancing tumor, or a non-enhancing tumor.
8. The method of claim 1, wherein automatically generating the region of interest in the medical image for application of tumor treating fields to the subject comprises: determining at least two segmented areas of different tissue types in the medical image based on the segmentation of the minimum number of voxels of at least one tissue type in the slice in the medical image; determining a margin for each segmented area in the medical image; adding the margin to each segmented area of the medical image to obtain expanded segmented areas; and combining the expanded segmented areas to obtain the region of interest in the medical image for application of tumor treating fields to the subject.
9. The method of claim 1, wherein automatically generating the region of interest in the medical image for application of tumor treating fields to the subject comprises: determining a proximal boundary zone (PBZ) for expanding a segmented area in the medical image to obtain an expanded segmented area in the medical image.
10. The method of claim 1, wherein automatically generating the region of interest in the medical image for application of tumor treating fields to the subject comprises: providing for display a user-adjustable value for a proximal boundary zone (PBZ) for expanding a segmented area in the medical image to obtain an expanded segmented area in the medical image.
11. The method of claim 1, wherein if the minimum number of voxels of the medical image for segmentation is not satisfied, a warning is provided to a user.
12. The method of claim 1, further comprising: generating a plurality of transducer layouts for application of tumor treating fields to the subject based on the automatically generated region of interest in the medical image.
13. The method of claim 1, further comprising: creating a three-dimensional model of the subject based on the medical image, the three-dimensional model of the subject including the automatically generated region of interest; generating a plurality of transducer layouts for application of tumor treating fields to the subject based on the three-dimensional model of the subject; selecting at least two of the plurality of transducer layouts as recommended transducer layouts; presenting the recommended transducer layouts; receiving a user selection of at least one recommended transducer layout; and providing a report for the at least one selected recommended transducer layout.
14. A non-transitory processor readable medium containing a set of instructions thereon for treatment planning for administering tumor treating fields to a subject, wherein when executed by a processor, the instructions cause the processor to perform a method comprising: presenting on a display a slice through a medical image of the subject, wherein the medical image comprises voxels; and determining a segmentation of a minimum number of voxels of at least one tissue type in the slice of the medical image; receiving a user selection to automatically generate a region of interest (RO I) in the medical image for application of tumor treating fields to the subject; and automatically generating the region of interest in the medical image for application of tumor treating fields to the subject based on the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image.
15. An apparatus for treatment planning for administering tumor treating fields to a subject, the apparatus comprising: one or more processors; and memory accessible by the one or more processors, the memory storing instructions that when executed by the one or more processors, cause the apparatus to perform a method comprising: presenting on a display a slice through a medical image of the subject, wherein the medical image comprises voxels; and determining a segmentation of a minimum number of voxels of at least one tissue type in the slice of the medical image; receiving a user selection to automatically generate a region of interest (RO I) in the medical image for application of tumor treating fields to the subject; and automatically generating the region of interest in the medical image for application of tumor treating fields to the subject based on the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image.

Description

AUTOMATIC GENERATION OF A REGION OF INTEREST IN A MEDICAL IMAGE FOR TUMOR TREATING FIELDS TREATMENT PLANNING CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority to U.S. Provisional Application No. 63/609,202 filed December 12, 2023 and U.S. Provisional Application No. 63/524,470 filed June 30, 2023, the contents of each of which are incorporated by reference herein in their entirety. This application is related to U.S. Patent Application No. 18/750,582 filed June 21, 2024, U.S. Patent Application No. 18/675,714 filed May 28, 2024, and U.S. Provisional Application No. 63/523,853 filed June 28, 2023, the contents of each of which are incorporated by reference herein in their entirety. BACKGROUND [0002] Tumor treating fields (TTFields) are low intensity alternating electric fields within the intermediate frequency range (for example, 50 kHz to 1 MHz), which may be used to treat tumors as described in U.S. Patent No. 7,565,205. In current commercial systems, TTFields are induced non-invasively into a region of interest by electrode assemblies (e.g., arrays of capacitively coupled electrodes, also called electrode arrays, transducer arrays or simply “transducers”) placed on the patient’s body and applying alternating current (AC) voltages between the transducers. Conventionally, a first pair of transducers and a second pair of transducers are placed on the subject’s body. AC voltage is applied between the first pair of transducers for a first interval of time to generate an electric field with field lines generally running in the front-back direction. Then, AC voltage is applied at the same frequency between the second pair of transducers for a second interval of time to generate an electric field with field lines generally running in the right-left direction. The system then repeats this two-step sequence throughout the treatment. BRIEF DESCRIPTION OF THE DRAWINGS [0003] FIG. 1 is a flowchart depicting an example computer-implemented method for treatment planning for administering TTFields to a subject. [0004] FIG. 2 is a flowchart depicting an example computer-implemented method for automatic generation of a ROI in a medical image of a subject. [0005] FIG. 3 is a flowchart depicting an example computer-implemented method for generating transducer layouts for application of TTFields to a subject. [0006] FIG. 4 depicts an example user interface of an application for manual segmentation of a medical image of a subject. [0007] FIG. 5 depicts an example user interface of an application for automatic generation of a region of interest in a medical image of a subject. [0008] FIG. 6 depicts an example user interface of a warning for automatic generation of a region of interest in a medical image of a subject. [0009] FIGS. 7A, 7B, and 7C depict an example medical image of a subject at various stages of having a ROI automatically generated in the medical image. [0010] FIG. 8 depicts an example system to apply alternating electric fields to a subject. [0011] FIG. 9 depicts an example placement of transducers on a subject’s head. [0012] FIG. 10 depicts an example computer apparatus according to one or more embodiments described herein60. [0013] Various embodiments are described in detail below with reference to the accompanying drawings, where like reference numerals represent like elements. DESCRIPTION OF EMBODIMENTS [0014] This application describes exemplary techniques for treatment planning for administering TTFields to a subject. [0015] When treatment planning for administering TTFields to a subject, a region of interest (RO I) in the subject may need to be identified for determining a dosage of TTFields to be administered to the subject. Typically, identifying the ROI in a subject is performed manually by a user reviewing a medical image of the subject, where the medical image of the subject may have numerous slices and even more numerous voxels. As an example, the user may review each slice of the medical image of the subject and manually identify the ROI in the each slice. The ROI for the medical image may be the combination of the ROI’s of each slice. This manual procedure requires a significant amount of time by the user, resulting in increased costs. Additionally, this manual procedure may cause delays in the TTFields treatment planning while a user is scheduled to review and manually process the medical image of the subject. Moreover, if a subject has several medical images to be used in the TTFields treatment planning, each medical image may need to be reviewed, and the ROI may need to be manually identified in each medical image by a user, which may cause even more time expended by the user, more costs associated therewith, and more delays in the TTFields treatment planning. [0016] One or more embodiments described herein provide a technical solution to address this technical problem of identifying a ROI in a subject for determining a dosage of TTFields to be administered to t