Computed Tomography Assessment of Response to Therapy: Tumor Volume Change Measurement, Truth Data, and Error

McNitt-Gray, Michael F.; Bidaut, Luc; Armato, Samuel G.; Meyer, Charles R.; Gavrielides, Marios A.; Fenimore, C.P.; McLennan, Geoffrey; Petrick, Nicholas; Zhao, Binsheng; Reeves, Anthony P.; Beichel, Reinhard; Kim, Hyun-Jung Grace; Kinnard, Lisa M.

doi:10.1593/tlo.09226

Cited by 32 publications

(20 citation statements)

References 25 publications

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“…Unlike previous studies [24], [25] that only analyzed the correlation between the tumor volume changes and the radiologist-assigned RECIST ratings, in this study we demonstrated that the quantitative analysis of the volumetric change signals extracted by our B-spline image registration based scheme yielded higher accuracy in predicting actual clinical outcome (i.e., 6-month PFS in the clinical trials) than using the RECIST guideline. Hence, using this or other more reliable non-rigid image registration methods could have a number of advantages.…”

Section: Discussioncontrasting

confidence: 61%

A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration

Tan

Qiu

et al. 2016

IEEE Trans. Med. Imaging

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Although Response Evaluation Criteria in Solid Tumors (RECIST) is the current clinical guideline to assess size change of solid tumors after therapeutic treatment, it has a relatively lower association to the clinical outcome of progression free survival (PFS) of the patients. In this paper, we presented a new approach to assess responses of ovarian cancer patients to new chemotherapy drugs in clinical trials. We first developed and applied a multi-resolution B-spline based deformable image registration method to register two sets of computed tomography (CT) image data acquired pre- and post-treatment. The B-spline difference maps generated from the co-registered CT images highlight the regions related to the volumetric growth or shrinkage of the metastatic tumors, and density changes related to variation of necrosis inside the solid tumors. Using a testing dataset involving 19 ovarian cancer patients, we compared patients’ response to the treatment using the new image registration method and RECIST guideline. The results demonstrated that using the image registration method yielded higher association with the six-month PFS outcomes of the patients than using RECIST. The image registration results also provided a solid foundation of developing new computerized quantitative image feature analysis schemes in the future studies.

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Section: Discussioncontrasting

confidence: 61%

A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration

Tan

Qiu

et al. 2016

IEEE Trans. Med. Imaging

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“…Depending on the source and magnitude of the variability, these may need to be included as part of the assessment of the algorithm's performance. Examples of sources of variability include acquisition settings on input data, scope and nature of reader interaction, and particular lesion characteristic [73]. …”

Section: Process For Establishing the Performance Of Qib Algorithmsmentioning

confidence: 99%

Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons

Obuchowski

Reeves

Huang

et al. 2014

Stat Methods Med Res

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157

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Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the performance of a QIB algorithm, identify limitations in the current statistical literature, and suggest future directions for research.

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“…The whole-body CT image data sets analysed in this study ( Figure 2) were obtained from The Cancer Imaging Archive (https://public. cancerimagingarchive.net/ncia/login.jsf) database [25][26][27][28][29][30][31][32][33]. The images in this database are freely available to browse, download and use for commercial, scientific and educational purposes under the Creative Commons Attribution 3.0 Unported Licence.…”

Section: 2mentioning

confidence: 99%

Biomechanical model for computing deformations for whole‐body image registration: A meshless approach

Mao

Miller

Joldes

et al. 2016

Numer Methods Biomed Eng

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Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2-D models and computing single organ deformations. In this study, 3-D comprehensive patient-specific non-linear biomechanical models implemented using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms are applied to predict a 3-D deformation field for whole-body image registration. Unlike a conventional approach which requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the Fuzzy C-Means (FCM) algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features.

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Computed Tomography Assessment of Response to Therapy: Tumor Volume Change Measurement, Truth Data, and Error

Abstract: The article concludes with a brief discussion of moving from the assessment of measurement variation to the steps necessary to establish the efficacy of a metric as a biomarker for response.

Cited by 32 publications

References 25 publications

A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration

A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration

Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons

Biomechanical model for computing deformations for whole‐body image registration: A meshless approach

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