US-12620468-B2 - Personalized liver cancer treatment

US12620468B2US 12620468 B2US12620468 B2US 12620468B2US-12620468-B2

Abstract

Systems and methods are provided for collecting and processing data from liver cancer patients that can be used to personalize treatment plans for these patients, including by determining and considering their liver functional reserve LFR. Specifically, systems and methods are provided for using fLV (functional liver volume) and PHM (perfused hepatic mass), before and after treatment, to determine personalized treatment plans and improve overall survival rates.

Inventors

Dipankar Ghosh
John Carl Hoefs

Assignees

HEPATIQ, INC.

Dates

Publication Date: 20260505
Application Date: 20250306

Claims (15)

1 . A system for increasing the overall survival for a liver disease patient, the system comprising: a functional scanner configured to generate functional image data of a liver of the patient; an image storage device in communication with the functional scanner, the image storage device being configured to receive and store the functional image data of the liver of the patient from the functional scanner; a memory configured to store specific computer-executable instructions; and one or more hardware computer processors in communication with the memory and configured to execute the specific computer-executable instructions to at least: receive the functional image data of the liver of the patient from the image storage device; access patient information from a patient information database over a wireless or wire-like connection; obtain parameters of a planned surgery or intervention, wherein the parameters include a margin volume indicative of functioning liver tissue to be eliminated during the planned surgery or intervention; automatically process and analyze the functional image data to determine a functional liver volume, wherein the functional image data comprises a stack of transaxial functional images that includes healthy functional tissue, tumor tissue, and non-functional non-tumor tissue, and wherein processing and analyzing the functional image data comprises generating a combined image from the stack of transaxial functional images and determining a volume represented by the healthy functional tissue excluding the tumor tissue and non-functional non-tumor tissue in the combined image; determine a pre-therapy liver functional reserve of the patient by determining a pre-therapy perfused hepatic mass index and a pre-therapy functional liver volume index; determine a post-therapy liver functional reserve of the patient by determining a post-therapy perfused hepatic mass index and a post-therapy functional liver volume index; determine a virulence of a liver disease of the patient; determine a recommended dose escalation factor by constructing a virulence-reserve matrix comprising the liver disease virulence and pre-therapy and post-therapy liver functional data; and provide a recommended plan for treating liver disease comprising escalation of a treatment dose when the dose escalation factor is greater than one or de-escalation of a treatment dose when the dose escalation factor is less than one.
2 . The system of claim 1 , wherein the pre-therapy liver functional reserve is normalized by patient characteristics comprising one or more of gender, height, weight, age, biomarkers and genetic factors.
3 . The system of claim 1 , wherein the one or more computer hardware processors are further configured to determine a likelihood of post-therapy liver failure based on a comparison between the pre-therapy and post-therapy perfused hepatic mass indices and a comparison between the pre-therapy and post-therapy functional liver volume indices.
4 . The system of claim 3 , wherein the one or more computer hardware processors are further configured to determine a trajectory indicative of health of the patient based on a difference between the post-therapy perfused hepatic mass index and the post-therapy functional liver volume index.
5 . The system of claim 4 , wherein the one or more computer hardware processors are further configured to determine a traversal rate of the trajectory of the patient.
6 . The system of claim 5 , wherein the one or more computer hardware processors are further configured to update the post-therapy liver functional reserve based on the trajectory of the patient and the traversal rate of the trajectory.
7 . The system of claim 1 , wherein the patient receives treatment for the liver disease of the patient based on the dose escalation factor.
8 . The system of claim 1 , wherein the dose escalation factor is greater than one if the virulence is above a predetermined threshold and the post-therapy liver functional reserve is above a predetermined threshold, wherein the dose escalation factor is less than one if the virulence is below a predetermined threshold and the post-therapy liver functional reserve is below a predetermined threshold, wherein the dose escalation factor is one if the virulence is below a predetermined threshold and the post-therapy liver functional reserve is above a predetermined threshold, wherein the dose escalation factor is null if the virulence is above a predetermined threshold and the post-therapy liver functional reserve is below a predetermined threshold.
9 . A system for treating a patient having a liver disease, the system comprising one or more computer hardware processors configured to at least: receive functional image data of the liver of the patient that was generated by a functional scanner; access patient information from a patient information database over a wireless or wire-like connection; automatically process and analyze the functional image data to determine a functional liver volume (fLV), wherein the functional image data comprises a stack of transaxial functional images that includes healthy functional tissue, tumor tissue, and non-functional non-tumor tissue, and wherein processing and analyzing the functional image data comprises-generating a combined image from the stack of transaxial functional images and determining a volume represented by the healthy functional tissue excluding the tumor tissue and non-functional non-tumor tissue in the combined image; determine a liver functional reserve (LFR) of the liver of the patient as a function of a product of at least two independent variables derived from the functional image data, the at least two independent variables comprising a quantitative liver function including a perfused hepatic mass (PHM) and the fLV; and provide a recommended plan for treating liver disease comprising escalating or de-escalating a treatment based on the LFR, wherein the treatment comprises radiation therapy, embolization, ablation, or surgical resection.
10 . The system of claim 9 , wherein the one or more computer hardware processors is further configured to determine a personalized dose value (PDV) based on the LFR and a virulence (V) of the liver disease.
11 . The system of claim 10 , wherein the one or more computer hardware processors is further configured to determine a dose escalation factor (DEF), wherein DEF=PDV/DN, in which DN is a radiation dose absorbed by non-tumor liver tissue of the patient.
12 . The system of claim 9 , wherein the one or more computer hardware processors is further configured to: obtain parameters of a planned surgery or intervention; determine the LFR of the liver of the patient pre-therapy; determine, based on the pre-therapy LFR of the patient and the parameters of the planned surgery or intervention, a post-therapy LFR of the patient; determine a virulence of a liver disease of the patient; and determine, based on the post-therapy LFR of the patient and the virulence of the liver disease of the patient, a recommended dose escalation factor.
13 . A method for increasing the overall survival for a liver cancer patient, the method comprising: automatically processing and analyzing functional image data to determine a functional liver volume (fLV), wherein the functional image data comprises a stack of transaxial functional images that includes healthy functional tissue, tumor tissue, and non-functional non-tumor tissue, and wherein processing and analyzing the functional image data comprises generating a combined image from the stack of transaxial functional images and determining a volume represented by the healthy functional tissue excluding the tumor tissue and non-functional non-tumor tissue in the combined image; determining a liver functional reserve (LFR) of a liver of the patient as a function of at least two independent variables comprising a quantitative liver function including a perfused hepatic mass (PHM) and the fLV; determining a dose escalation factor (DEF), wherein DEF=PDV/DN, in which PDV is a personalized dose value based on the LFR and DN is a radiation dose absorbed by non-tumor liver tissue of the patient; and treating a patient for liver cancer by escalating a radiation-treatment dose when the DEF is greater than 1 or de-escalating the radiation treatment dose when the DEF is less than 1.
14 . The method of claim 13 further comprising determining the PDV based on a virulence (V) of the liver disease.
15 . The method of claim 13 further comprising: receiving an image of the liver of the patient from an image storage device; accessing patient information from a patient information database over a wireless or wire-like connection; obtaining parameters of a planned surgery or intervention; determining the LFR of the liver of the patient pre-therapy, based on the image; determining, based on the pre-therapy LFR of the patient and the parameters of the planned surgery or intervention, a post-therapy LFR of the patient; determining a virulence of a liver disease of the patient; and determining, based on the post-therapy LFR of the patient and the virulence of the liver disease of the patient, the recommended DEF.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63,562,626, filed Mar. 7, 2024, which is hereby incorporated by reference, to the extent that it is not conflicting with the present application. BACKGROUND OF INVENTION 1. Field of the Invention The invention relates generally to technologies and methods for treating liver cancer patients. Specifically, the invention relates to data-driven systems and methods for designing personalized treatment plans for liver cancer patients, to improve their overall survival rates. 2. Description of the Related Art Liver cancer (LC) is a deadly disease. Worldwide there were more than 900,000 new cases of liver cancer in 2020. The American Cancer Society estimates that about 41,000 new liver cancers are diagnosed in the U.S. each year and about 30,000 people die of the disease annually. Symptoms of liver cancer, such as abdominal pain, weight loss, nausea, ascites and jaundice are often not present until the later stages of the disease. For this reason, liver cancer is generally not detected early. Although there are several forms of liver cancer, the most common primary liver cancer is hepatocellular cancer (HCC). Bridge Therapies Liver transplantation (LT) has the greatest cure potential for LC patients. However, there is often a long wait (sometimes years) before a donor liver becomes available. Thus, LC patients are given palliative bridge therapies till a suitable donor liver becomes available. Bridge therapies include stereotactic body radiation therapy (SBRT), Yttrium-90 radioembolization (Y90), radiofrequency ablation (RFA), trans-arterial chemoembolization (TACE) and surgical resections. Response to the bridge therapy may be low and overall survival (OS) even lower due to recurrence of the LC. The number of patients who achieved a significant response after bridge therapy was 61% for TACE, 65% for SBRT, 67% for RFA, and 67% for Y90. Although, this supports the use of these interventions, it leaves open the question of OS. Significant portions of these patients did not survive even a short time after the intervention. Overall Survival Overall survival (OS) has been researched quite extensively. For example, in elderly patients with hepatocellular carcinoma (HCC), hepatectomy (OS 55%) beat ablation (OS 35%). Although the results are impressive for hepatectomy compared to ablation, it is not known why significant portions of both types of patients did not survive. Therefore, there is a need to solve the problems described above by providing a system and method for improving outcomes for liver cancer patients. The aspects or the problems and the associated solutions presented in this section could be or could have been pursued; they are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches presented in this section qualify as prior art merely by virtue of their presence in this section of the application. BRIEF INVENTION SUMMARY This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key aspects or essential aspects of the claimed subject matter. Moreover, this Summary is not intended for use as an aid in determining the scope of the claimed subject matter. In an aspect a system and a method are provided for collecting and processing data from liver cancer patients that can be used to personalize treatment plans for these patients, including by determining and considering their Liver Functional Reserve (LFR). Specifically, in an aspect, a system and a method are provided for using fLV (functional liver volume) and PHM (quantitative liver function), before and after treatment, to determine personalized treatment plans, including a personalized dose value (PDV) or a dose escalation factor (DEF). Thus, an advantage is the increase of the overall survival (OS) rates for the liver cancer patients. Another advantage is more data-driven and thus more control in designing the treatment plans for these patients and in predicting the outcomes of the ensuing treatments. The above aspects or examples and advantages, as well as other aspects or examples and advantages, will become apparent from the ensuing description and accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS For exemplification purposes, and not for limitation purposes, aspects, embodiments or examples of the invention are illustrated in the figures of the accompanying drawings, in which: FIG. 1 is a CT image showing a large liver tumor. FIG. 2 shows a SPECT image of the liver after Y90 therapy. FIG. 3 is a chart showing a simplified Liver Functional Reserve (LFR), according to an aspect. FIG. 4 is a schematic diagram of a liver cancer (LC) treatment. FIG. 5 is a chart showing non-tumor dose limits according to