METHOD AND SYSTEM FOR IDENTIFYING, ASSESSING, AND MANAGING CANCER DISEASE
Techniques described herein generally relate to identifying, assessing, and managing cancer diseases. Some example methods may include constructing one or more quantitative metrics for the cancer disease in a selected population of other patients retrieved during the scheduling interval, acquiring a first set of numeric biomarker data for the patient before having placed a biomarker in the patient, acquiring a second set of numeric biomarker data for the patient after having placed the biomarker in the patient, identifying one or more nodules from the first set of numeric biomarker data and the second set of numeric biomarker data, wherein each of the one or more nodules is characterized by a mean numeric biomarker difference value derived from the first set of numeric biomarker data and the second set of numeric biomarker data, and predicting quantitative and objective risk for the one or more nodules based on the mean numeric biomarker difference value and at least one of the one or more quantitative metrics.
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The present application claims the benefit of the U.S. Provisional Application No. 61/536,235, filed on Sep. 19, 2011 and having Atty. Docket No. 124-0006-US-PRO. This provisional application, including any appendices or attachments thereof, is hereby incorporated by reference in its entirety.
The present application is related to U.S. patent application Ser. No. ______, Atty. Docket No. 124-0013-US-REG, entitled “METHOD AND SYSTEM FOR IDENTIFYING, ASSESSING, AND MANAGING CANCER GROWTH RATES AND POTENTIAL METASTASIS,” which is concurrently filed and incorporated by reference herein in its entirety.
BACKGROUNDAdvances in imaging techniques have lead to early detection of tumors but have had small (approximately 15-30%) impact on those malignant tumor types currently responsible for most patient mortality including lung and breast cancer. Existing techniques fail to provide quantitative and objective metrics to predict which suspect detected nodules would be found malignant if biopsied. For example, standard mammography method relies heavily on the subjective, experience, and non-quantitative judgment of highly trained mammographic radiologists. Specifically, the detection and diagnosis is based on a radiologist visually reading and interpreting two projection X-ray radiographs in the cranio-caudal (CC) and medial-lateral-oblique (MLO) orientations taken with breast compression. In addition, although some improvements have been made in existing techniques to detect smaller tumors, such improvements tend to worsen the problem of over-diagnosis, causing more harm than good. For example, by focusing on detecting the smaller tumors, more false positives (e.g., benign nodules) may also be detected, leading to more resources spent (e.g., performing additional testing or surgery) and potentially more penalties introduced (e.g., permanent loss of lung capacity due to the surgery).
The technical details set forth below enable a person skilled in the art to implement at least some embodiments of the present disclosure to identify, assess, and manage cancer diseases. In this disclosure, the term “lesion” and “nodule” are used interchangeably. Also, the term “biomarker” generally refers to a characteristic that is objectively measured and evaluated as a medical indicator of normal biologic processes, pathogenic processes, or responses to a therapeutic intervention. It should be noted a single specific biomarker's medical accuracy, precision and usefulness can be increased when used in conjunction with other biomarkers that characterize complimentary characteristics including other more biological or biochemical biomarkers such as proteins, metabolites, genomics, and others.
In block 102 (prepare one or more quantitative metrics for a cancer disease in a selected population of patients), relevant data for a selected population of patients for a cancer disease is collected and analyzed, so that one or more quantitative metrics for the selected population of patients may be constructed. Some examples of the relevant data may include, without limitation, numeric biomarker data associated with suspect nodules in the selected population of patients that may be acquired with or without having injected one or more biomarkers into the patients. In addition, the numeric biomarker data may be for various different anatomical sites of the patients, such as, their lungs, breasts, and others. Some example numeric biomarker data may include, without limitation, computed tomography (CT) Hounsfield Unit (HU) values for a contrast agent (e.g., iodine).
With the collected relevant data, some example quantitative metrics, such as, without limitations, sensitivity, specificity, true positive fraction (TPF), false positive fraction (FPF), Receiver Operator Characteristic (ROC) representations, positive predictive value, false negative fraction (FNF), accuracy, prevalence, and others may be constructed. Some of these quantitative metrics correspond to the following equations:
TPF (or sensitivity)=fraction of all malignancies (TP+FN) correctly diagnosed=TP/(TP+FN),
where TP corresponds to true positives, and FN corresponds to false negatives;
FPF=fraction of all benign (TN+FP) incorrectly diagnosed=FP/(TN+FP), where TN corresponds to true negatives, and FP corresponds to false positives;
Specificity=fraction of all benign (TN+FP) correctly diagnosed=TN/(TN+FP)
Positive Predictive Value=fraction of positives (TP+FP) that are true=TP/(TP+FP)
FNF=fraction of all negatives (FN+TN) that are actually malignant=FN/(FN+TN)
Accuracy=correct diagnoses (TP+TN) divided by total number of nodules=(TP+TN)/(TN+FN+TP+FP)
Prevalence=fraction of all nodules that were malignant=(TP+FN)/(TN+FN+TP+FP)
Subsequent paragraphs and figures will further detail and illustrate the construction of some of these quantitative metrics.
In block 104 (acquire contrast enhanced numeric biomarker data), according to one embodiment of the present disclosure, a first set of numeric biomarker data associated with a patient being evaluated may be acquired before the injection of the biomarker into the patient (e.g., a contrast agent such as iodine), and a second set of numeric biomarker data may be acquired after the injection of the biomarker into the patient. In an alternative embodiment, one set of numeric biomarker data may be acquired after the injection of the biomarker to reduce or eliminate the X-ray exposure dose. The biomarker's values are physically generated by the concentration gradient of the contrast medium and its physical-chemistry-diffusion through the porous membranes of the angiogenesis capillaries in cancer that changes the physical properties of the region (e.g. linear X-ray attenuation coefficients, mass densities, etc.) that are related to metrics descriptive of solid malignant tumors larger than approximately 1 mm in diameter.
In block 106 (determine HU related information from acquired numeric biomarker data), the acquired numeric biomarker data is further processed to determine HU related information (e.g., a mean HU value, a mean HU enhancement difference value, and others). The different approaches are discussed in subsequent paragraphs.
In block 108 (assess cancer risk based on HU related information and at least one of the one or more quantitative metrics), the HU related information may be utilized (e.g., the difference between the mean HU of an identified nodule with the injected biomarker and the mean HU of the same identified nodule without the injected biomarker) to help quantify cancer risk. Additional details associated with cancer risk assessment are elaborated further in subsequent paragraphs.
To illustrate, suppose the ΔHUthreshold value is −6. As shown in
In one implementation, a Gaussian approximation 400 of the histogram data of
Similar to
Similar to
Similar to
In one embodiment, one set, as opposed to two sets, of CT data set with iodine contrast is acquired, eliminating the need for the “without iodine” data set and resulting in the reduction of the X-ray exposure dose by approximately half. Rather than relying on absolute HU values, which may be difficult to calibrate, the mean HU values of adipose tissue surrounding the nodule may be used as a reference to obtain ΔHU values.
In the illustrated embodiment, the control system 2016 includes a processor for executing instructions, such as the executable instructions 1902 shown in
It should be noted that the radiation system 2000 should not be limited to the configuration described above, and that the system can also have other configurations.
While the forgoing is directed to embodiments of the present disclosure, other and further embodiments of the present disclosure may be devised without departing from the basic scope thereof, and the scope thereof may be determined by the claims that follow.
Claims
1. A method of assessing and identifying a cancer disease for a patient, comprising:
- constructing one or more quantitative metrics for the cancer disease in a selected population of other patients;
- acquiring a first set of numeric biomarker data for the patient before having placed a biomarker in the patient;
- acquiring a second set of numeric biomarker data for the patient after having placed the biomarker in the patient;
- identifying one or more nodules from the first set of numeric biomarker data and the second set of numeric biomarker data, wherein each of the one or more nodules is characterized by a mean numeric biomarker difference value derived from the first set of numeric biomarker data and the second set of numeric biomarker data; and
- predicting quantitative and objective risk for the one or more nodules based on the mean numeric biomarker difference value and the quantitative metrics.
2. The method of claim 1, wherein the numeric biomarker data correspond to Computed Tomography (CT) Hounsfield Unit (HU) values for a contrast agent for the patient.
3. The method of claim 1, wherein the constructing one or more quantitative metrics comprising:
- collecting a set of first data points for the cancer disease from the selected population of patients, wherein the set of first data points include one or more mean reference HU enhancement difference values associated with the cancer disease in the selected population of patients.
4. The method of claim 3, wherein the constructing one or more quantitative metrics comprising:
- developing a Received Operator Characteristic (ROC) representation based on the set of first data points for the selected population of patients, wherein each of the set of first data points corresponds to a true positive fraction and a false positive fraction; and
- annotating the ROC representation by placing at least one mean threshold HU enhancement difference value adjacent to at least one second data point out of the set of first data points, wherein the second data point corresponds to a true positive fraction and a false positive fraction derived from a number of true positives and a number of true negatives that are above the at least one mean threshold HU enhancement difference value.
5. The method of claim 2, wherein the identifying one or more nodules comprising:
- calculating a first mean HU value for the one or more nodules based on the first set of CT data;
- calculating a second mean HU value for the one or more nodules based on the second set of CT data; and
- calculating the mean HU enhancement difference value based on the first mean HU value and the second mean HU value.
6. The method of claim 3, further comprising:
- identifying a first subset of benign nodules having angiogenesis, a second subset of benign nodules without angiogenesis, and a third subset of malignant nodules from the set of first data points for the selected population of patients; and
- smoothing the set of first data points with a Gaussian approximation based on the first subset of benign nodules, the second subset of benign nodules, and the third set of malignant nodules.
7. The method of claim 6, further comprising:
- developing a Received Operator Characteristic (ROC) representation based on the set of first data points for the selected population of patients that are smoothed, wherein each of the set of first data points corresponds to a true positive fraction and a false positive fraction; and
- annotating the ROC representation by placing at least one mean threshold HU enhancement difference value adjacent to at least one second data point out of the set of first data points, wherein the second data point corresponds to a true positive fraction and a false positive fraction derived from a number of true positives and a number of true negatives that are above the at least one mean threshold HU enhance difference value.
8. The method of claim 6, wherein the constructing one or more quantitative metrics comprising generating a graph having a specificity curve, a true positive curve, and false positive curve based on based on the set of first data points for the selected population of patients that are smoothed.
9. The method of claim 8, further comprising converting the one or more mean reference HU enhancement difference values into concentration values of the biomarker prior to the constructing one or more quantitative metrics.
Type: Application
Filed: Sep 19, 2012
Publication Date: Mar 21, 2013
Applicant: VARIAN MEDICAL SYSTEMS, INC. (Palo Alto, CA)
Inventor: VARIAN MEDICAL SYSTEMS, INC. (Palo Alto, CA)
Application Number: 13/623,103
International Classification: A61K 49/04 (20060101);