Chapter 4: Depending on Density

By sdavis | December 12, 2018 | Blog

The Long and Short of It Series

As has been covered in previous chapters, a growing body of evidence suggests that volume-based measurements are more accurate and reproducible than diameter-based measurements. Additionally, changes in volume more accurately reflect true changes in cancer burden and promise to better correlate with outcome. Importantly, three-dimensional (3D) volume measurements pave the way for researchers to leverage other metrics such as tumor density to help advance precision medicine.

Specifically, researchers can draw insights from a variety of analytics made possible through delineation of the 3D space occupied by a tumor, such as averaging the pixel values within that space. In CT scans, this provides a measure of the average x-ray attenuation or tissue “density” within the tumor. Radiologists and oncologists may also be interested in the degree of variation in pixel values, or more sophisticated metrics derived from the pixel values such those that describe a tumor’s “texture”.

Density describes how compact or concentrated something is.

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Density Defined

In CT imaging, “density” normally refers to the radio-opacity resulting from x-ray attenuation. This is a property of both the physical density of the anatomy at a particular location and, if used, the concentration of an intra-venous contrast agent at that location during image acquisition. True physical density can be determined when no contrast agent is used—such as with lung nodule analysis—so only physical density influences attenuation. This allows calculation of nodule mass (physical density x volume), mass doubling time, etc.

For non-contrast CT scans, physical density is calculated using a formula that converts the average Hounsfield Unit (HU) value within the segmented lesion to average density. Density or attenuation in Choi criteria or other therapy response criteria used in clinical trials refers to contrast-enhanced scans and the contrast agent concentration resulting from blood perfusion in the tumor. It is conveyed by reporting the raw Average HU value as a general quantity representing average attenuation within the tumor.


Advancing Precision Medicine

Density measurements are intriguing to researchers because the insights they provide are not available through any other measurement. This additional data helps influence patient management decisions, such as whether to biopsy a lung or breast lesion suspected of malignancy, or whether to carry out salvage surgery after suspected failure of lung radiotherapy.

For these reasons, many predict density will play a key role in the evolution and adoption of precision medicine, which uses information about a person’s genes, proteins and environment to prevent, diagnose and treat disease. Specific to cancer, precision medicine uses specific information about a tumor to help diagnose, determine the course of treatment and evaluate its effectiveness, or to make a prognosis. 1

The role of density in precision medicine is a prediction supported by a body of research that posits density-based measurement helps identify malignancy, treatment response or disease recurrence

For example, one study examined viable tumor volume (VTV), which combines volumetric segmentation capability with density analysis. Researchers concluded that, because it is a more comprehensive approach to assessing tumor burden that quantifies both volume and attenuation, this approach may identify treatment efficacy as early as the first restaging—a process that will become more feasible as automated technology becomes more accessible and is integrated into standard oncology radiologic assessments.

The baseline axial CT on top shows greater enhancement of the lesion. The CT at (below) shows lower attenuation throughout, indicating necrosis. The corresponding histogram of CT densities essentially quantifies presumed necrosis. 2

They wrote: “Using automated methods that take volumetric characterization of attenuation distribution into account should help to assess underlying structural changes. A more extensive examination of the relationships between changes in tumor volume, attenuation, and texture and patient outcomes should promote accelerated software development and practical implementation of volumetric tumor analysis.” 2

Another article reviewed and summarized the applications of quantification methods used for evaluating the treatment response of liver tumors. Researchers concluded that changes in CT attenuation at one month after therapy may predict tumor response.3

Finally, research suggests that changes in the cumulative mean density of consolidative regions and textural analysis have the potential to differentiate between radiation-induced lung injury (RILI) and cancer recurrence as early as nine months after stereotactic ablative radiotherapy (SABR). That compares to 15 months for more traditional 3D volume and RECIST approaches. The findings are particularly noteworthy as increased utilization of SABR will result in ambiguous CT findings becoming a more common clinical problem.

“With further validation of our results on a larger sample size, and more detailed analysis of the features and changes observed throughout the course of SABR follow-up, there is the potential for an earlier detection of recurrence compared with traditional measures. This could potentially allow for earlier salvage of patients with recurrence, and result in fewer investigations for patients exhibiting only benign RILI,” researchers concluded.4

Cumulative appearance measures of the consolidative regions throughout follow-up post-SABR; a) Mean (± 95% CI) CT density, and b) standard deviation (± 95% CI) of CT density. Indicates statistical significance at p ≤ 0.054

Semiautomated Advantages

As many of the studies reviewed for this chapter highlighted, a semi-automated approach makes it easier to get to lesion density. This is the key enabler for density measurements and beyond.

Semi-automated segmentation tools allow rapid computation of a comprehensive set of quantitative measures, including density. They enable evidence-based alerting, such as notifying the physician when a lesion has increased or decreased in volume or density by a pre-set amount, which influences patient care and follow up.

In short, “…Volumetric measurements can be accomplished on readily accessible software with highly reproducible results…As imaging technology advances, so must our associated practices. The utilization of a volumetric method provides a more comprehensive and accurate assessment of tumor size, which may alter clinical decisions. The method boasts promising potential and may have widespread applicability.” 5

Density Changes Reveal Texture

Made possible using the pattern of density changes throughout a tumor, textural analysis holds great promise in revealing underlying biology, malignancy and likely response to therapy. “Texture” refers to the heterogeneity of a tumor as seen on an image, such as a granular or speckled appearance. These patterns reflect how the tumor has evolved due to the genetic diversity of its cells and the different micro-environments (e.g. degree of acidity and oxygenation) they inhabit.

An example of a heterogeneity or texture map showing areas of a lesion that may be more active (shown in red)

Cells become adapted to their diverse surroundings, which results in some being resistant to external attacks such as from chemotherapy. During therapy, further selection of resistant cells occurs, and therapy may ultimately fail when the most well adapted cell populations continue to thrive. Textural analysis can reveal genetic and environmental diversity within a tumor and therefore help in decision making.

There are almost limitless ways of describing texture, such as examining how gray levels are clustered, sharpness of edges, regularity of patterns, etc. Within the field of radiomics, researchers attempt to determine the texture descriptions that are most useful in clinical decision support. 

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  1. National Cancer Institute. Precision Medicine in Cancer Treatment. NCI website. October 3, 2017.
  2. Les Roger Folio, Evrim B. Turkbey, Seth M. Steinberg, Andrea B. Apolo. Viable tumor volume: volume of interest within segmented metastatic lesions, a pilot study of proposed computed tomography response criteria for urothelial cancer. European Journal of Radiology. 2015 Sep; 84(9): 1708–1714.
  3. Fernanda D. Gonzalez-Guindalini, Marcos P. F. Botelho, Carla B. Harmath, et al. Evaluation of Treatment Response and Complications: Assessment of Liver Tumor Response to Therapy: Role of Quantitative Imaging. RSNA RadioGraphics. 2013. Volume 33, Issue 6.
  4. Sarah A. Mattonen, David A. Palma, Cornelis J. A. Haasbeek, Suresh Senan & Aaron D. Ward. Distinguishing radiation fibrosis from tumour recurrence after stereotactic ablative radiotherapy (SABR) for lung cancer: A quantitative analysis of CT density changes. Journal Acta Oncologica. 2013. Volume 52, Issue 5.
  5. Aaron Frenette, Joshua Morrell, * Kirk Bjella, Edward Fogarty, James Beal, and Vijay Chaudhary. Do Diametric Measurements Provide Sufficient and Reliable Tumor Assessment? An Evaluation of Diametric, Areametric, and Volumetric Variability of Lung Lesion Measurements on Computerized Tomography Scans. Journal of Oncology. 2015 May 10.