Computer-Assisted Quantitative Evaluation of Therapeutic Responses for Lymphoma Using Serial PET/CT Imaging

Rationale and Objectives

Molecular imaging modalities such as positron emission tomography (PET)/computed tomography (CT) have emerged as an essential diagnostic tool for monitoring treatment response in lymphoma patients. However, quantitative assessment of treatment outcomes from serial scans is often difficult, laborious, and time consuming. Automatic quantization of longitudinal PET/CT scans provides more efficient and comprehensive quantitative evaluation of cancer therapeutic responses. This study develops and validates a Longitudinal Image Navigation and Analysis (LINA) system for this quantitative imaging application.

Materials and Methods

LINA is designed to automatically construct longitudinal correspondence along serial images of individual patients for changes in tumor volume and metabolic activity via regions of interest (ROI) segmented from a given time point image and propagated into the space of all follow-up PET/CT images. We applied LINA retrospectively to nine lymphoma patients enrolled in an immunotherapy clinical trial conducted at the Center for Cell and Gene Therapy, Baylor College of Medicine. This methodology was compared to the readout by a diagnostic radiologist, who manually measured the ROI metabolic activity as defined by the maximal standardized uptake value (SUVmax).

Results

Quantitative results showed that the measured SUVs obtained from automatic mapping are as accurate as semiautomatic segmentation and consistent with clinical examination findings. The average of relative squared differences of SUVmax between automatic and semiautomatic segmentation was found to be 0.02.

Conclusions

These data support a role for LINA in facilitating quantitative analysis of serial PET/CT images to efficiently assess cancer treatment responses in a comprehensive and intuitive software platform.

Lymphoma is a hematologic malignancy of lymphocyte origin, accounting for approximately 5% of all cancers in the United States . This diverse group of diseases is broadly classified as Hodgkin disease or non-Hodgkin’s lymphoma with several subclassification schemes to describe various cellular, genetic, and clinical subtypes . Treatment for lymphoma is dependent on its type and stage, as well as the age and general clinical status of the patient. For most early-stage lymphomas, the standard first-line treatment for lymphoma includes chemotherapy with or without radiation therapy . Immunotherapy, radioimmunotherapy, and hematopoietic stem-cell transplantation have added to the therapeutic armamentarium especially for patients with aggressive, recurrent, or advanced disease. Accurate monitoring of patients undergoing any form of treatment is critical for evaluating response and disease recurrence. However, the diagnostic gold standard, tissue biopsy, is both invasive and logistically difficult to perform on all patients at various time intervals.

Currently, treatment responses are assessed by a canonical approach that integrates clinical examination, laboratory findings, and imaging data. Positron emission tomography (PET)/computed tomography (CT) combining metabolic information using the positron-emitting sugar analog, [F-18]-fluorodeoxyglucose (FDG), with morphologic changes in tumor size , has been well studied in the evaluation of treatment response and predictive value in patients with lymphoma . Combined modality PET/CT has been shown to improve the diagnostic accuracy over either modality alone, particularly for lymphoma . Relative FDG uptake in lesions of active high-grade lymphomas are typically high, thus allowing for quantitative assessment of disease status . Despite the growing clinical imaging knowledgebase, interpretation of PET/CT studies is largely dependent on imprecise criteria for measuring mass lesions and relative tumor metabolism, both of which are manually acquired. Moreover, multiple PET/CT scans obtained over time generate massive amounts of data, most of which are analyzed manually adding significant inefficiency and risk for errors in the interpretation process. Clinical investigators are thus hampered by the absence of validated quantitative methods for evaluating therapy response in an efficient and robust format.

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Materials and methods

Data

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Patients

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Computer-Assisted Quantitative Analysis

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Co-registration of PET and CT images

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Segmentation of ROI

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Determining Longitudinal Deformation of serial CT Images

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Et=∑v∈Ω{|e[It(ft(Rt(v)))]−e[I0(v)]|2+∑i=−1,1|e[It(ft(Rt(v)))]−e[It+i(ft+i(Rt+i(v)))]|2}, E

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∑

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−

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where e[] e

[

] is the operator for calculating the features around each voxel of the image, and Ω Ω is the image domain. In this work, the feature vector for each voxel consisted of the image intensity, gradient magnitude, and the fuzzy membership functions obtained by performing a four-dimensional (4D) fuzzy c-mean algorithm on the images (assuming three tissue types: bone, low-intensity and high-intensity tissues) (ie, e[v]=[I(v),|∇I(v)|,μv,1,μv,2,μv,3]) e

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) . Because the cubic B-Spline was used to model the deformation field, the continuity and smoothness was guaranteed, and the smoothness regularization term of the deformation field was omitted. Further, we used a topological regularization step to ensure that the Jacobian determinants of the deformations fields were positive. Thus the topology of the deformation field did not change from one image onto the subsequent images. The serial image registration algorithm then iteratively refines the deformation field ft f

t of each time point image by minimizing the energy function in Equation 1 and performs 4D clustering of the image series until convergence. Notice that in the first iteration, since the registration results for neighboring images were not available, only the first term of Equation 1 was used, which is essentially a pairwise FFD .

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Automatic ROI Delineation and Quantitative Indexing

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SUV(v)=Radioactive concentration at v(MBq/g)Injected dose(MBq)/Patient body weight(g). S

(

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Radioactive concentration at v

(

MBq

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Injected dose

(

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Patient body weight

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QI(rt)=SUVmaxSUVmean=maxv∈rt{SUV(v)}mean(SUV(livert)). Q

(

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max

mean

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Results

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Quantitative Evaluation of the Algorithm

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Registration Error=1N|Ω|∑i=1,…,N∑v∈Ω∣∣fi,v−f∗i,v∣∣, Registration Error

∑

…

∑

∈

−

∗

where N=10 N

10 is the number of testing images, and |Ω| |

| represents the number of voxels in the template image domain. fi,v f

v is the resultant deformation at voxel v v for testing image i i , and f∗i,v f

∗ is the corresponding ground truth from the simulated deformation fields.

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error=∑i∈N(SUVmax(ri)−SUVmax(rˆi))2(SUVmax(ri))2/N, e

∑

∈

(

max

(

)

−

max

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max

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)

where ri r

i and rˆi r

i denote the segmentation results using the semiautomatic/manual segmentation and the proposed automatic mapping for the i th, respectively. N N is the total number of ROIs under evaluation. For all the ROIs of the nine patients we studied, this average normalized squared difference is 0.02. Therefore, the computer-assisted quantitative analysis method obtains similar results with the “gold standard.”

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Segmentation and Visualization of Longitudinal ROI Mapping

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Conclusion

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Computer-Assisted Quantitative Evaluation of Therapeutic Responses for Lymphoma Using Serial PET/CT Imaging

Rationale and Objectives

Materials and Methods

Results

Conclusions

Materials and methods

Data

Patients

Computer-Assisted Quantitative Analysis

Co-registration of PET and CT images

Segmentation of ROI

Determining Longitudinal Deformation of serial CT Images

Automatic ROI Delineation and Quantitative Indexing

Results

Quantitative Evaluation of the Algorithm

Segmentation and Visualization of Longitudinal ROI Mapping

Conclusion

References

Further Reading

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