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. Author manuscript; available in PMC: 2023 Aug 1.
Published in final edited form as: Lung. 2022 Jun 25;200(4):447–455. doi: 10.1007/s00408-022-00550-1

Quantitative computed tomography: what clinical questions can it answer in chronic lung disease?

Marcelo Cardoso Barros 1, Stephan Altmayer 2, Alysson Roncally Carvalho 3, Rosana Rodrigues 3, Matheus Zanon 1, Tan-Lucien Mohammed 4, Pratik Patel 4, Al-Ani Mohammad 4, Borna Mehrad 4, Jose Miguel Chatkin 1, Bruno Hochhegger 4
PMCID: PMC9378468  NIHMSID: NIHMS1825756  PMID: 35751660

Abstract

Quantitative computed tomography (QCT) has recently gained an important role in the functional assessment of chronic lung disease. Its capacity in diagnostic, staging, and prognostic evaluation in this setting is similar to that of traditional pulmonary function testing. Furthermore, it can demonstrate lung injury before the alteration of pulmonary function test parameters, and it enables the classification of disease phenotypes, contributing to the customisation of therapy and performance of comparative studies without the intra- and inter-observer variation that occurs with qualitative analysis. In this review, we address technical issues with QCT analysis and demonstrate the ability of this modality to answer clinical questions encountered in daily practice in the management of patients with chronic lung disease.

Keywords: Quantitative CT, Functional CT, Chronic Lung Disease

1. Introduction

Quantitative computed tomography (QCT) has important roles in the diagnosis and phenotyping of numerous chronic lung diseases, including chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD) [1–3]. QCT imaging is a non-invasive means of directly visualizing, characterizing, and quantifying anatomical structures, providing insight into the pathophysiological processes of pulmonary diseases. There are a number of quantitative analysis techniques to evaluate diffuse lung diseases, including threshold-based and density-based measures, histogram-based statistical analysis, structural analysis (pulmonary vessel volume, airway geometry, etc), texture analysis, or a combination of these features coupled with artificial intelligence for segmentation and classification [1, 3].

Threshold analysis is based on the quantification of pixels within a range of lung attenuation in Hounsfield Units (HU). The normal lung attenuation is often defined within the range of −950 to −700 HU and the percentage of lung within this range is known as the normal lung index (NLI) [4–6]. Conversely, the measurement of the volume of pixels below the threshold of −950 HU allows the quantification of areas of emphysema (emphysema index), while the areas of higher attenuation within −700 and −250 HU are associated with regions of fibrosis (Figure 1). In threshold analysis, density curves are created containing the percentage of pixels within each category of lung attenuation (low, normal, high), and first-order statistical parameters (e.g., mean attenuation, skewness, kurtosis) are used to distinguish the shape of the curves of index patients and normal individuals. However, the limitation of this method is its ability to differentiate lung findings with similar CT attenuation, such as emphysema from honeycombing and cystic lesions, or areas of reticulation from ground-glass opacities and consolidation.

Figure 1.

Figure 1

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(A, B) Coronal CT scan in lung window and QCT of a patient with COPD with median lung attenuation of −956 HU and emphysema index of (−1024 to −950 HU, red color) of 46.5%. (C, D) Coronal CT scan with QCT in a patient with a healthy patient with median lung attenuation of −882 HU and a normal lung index of 90.2%. Note the significant peak (kurtosis) and the asymmetry of the curve, which is normally skewed to the right. (E, F) Coronal CT and QCT analysis of a patient with severe IPF demonstrated significantly low median lung attenuation of −611 HU, decreased kurtosis (less peaked) and skewed (more symmetric towards the higher values), and a percentage of high attenuation area (−700 to −250 HU, blue color) of 44%.

Texture-based analysis, on the other hand, extracts different imaging-derived morphological features using second-order statistics, such as gray-level co-occurrence matrix and run-length matrix, considering the spatial relationship between pixels/voxels and their local context [1, 3]. These textural data are often used with a machine learning algorithm for the segmentation, classification, and prediction of the patterns of lung disease. Two well-known machine learning algorithms for CT are the Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER), developed at Mayo Clinic for texture-based quantification of lung into five lung parenchymal patterns (Figure 2), and the data-driven textural analysis (DTA) from the National Jewish Hospital group, which is capable to quantify the extent of lung fibrosis [7, 8].

Figure 2.

Figure 2.

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Texture-based analysis with CALIPER in a patient with severe fibrotic ILD. Voxels are classified into 5 categories – normal, hyperlucent, ground glass, reticular, honeycombing – and the percentage of lung compromise is demonstrated with respective anatomical location. Total area of hyperlucent areas was 3%, ground-glass 13%, reticulation 11%, and honeycombing 2%. The pulmonary vessel volume was 143 cm3. Courtesy of Imbio (Minneapolis, MN).

Both the density- and texture-based techniques enable objective and reproducible disease quantification to assist in the diagnosis and longitudinal management for the assessment of disease progression or treatment response [9–11]. Compared to pulmonary function tests (PFT) that are able to detect physiological impairment in diffuse lung disease, QCT has the advantage of providing data on morphologic changes in the lung parenchyma along with their spatial distribution, even in early disease when PFT could not be sensitive enough to detect initial abnormalities [1]. Thus, QCT can be a valuable complementary method to PFT in the evaluation of patients with chronic lung disease.

Despite the technological advances in recent years, incorporation of QCT analyses into daily practice did not grow proportionally [3]. One of the reasons behind this might be the lack of knowledge about the QCT technique and the potential it can provide to assist in real-world clinical practice. The aim of this study is to review state-of-the-art QCT applications for chronic lung diseases, with the provision of details on technical requirements and limitations.

2. Technical settings

Accurate technical settings are essential to ensure QCT precision and repeatability. Differences among devices and variations in technical parameters can critically impact image acquisition and quantification, leading to the formulation of erroneous answers to clinical questions. For these reasons, the Sub-Populations and Intermediate Outcome Measures in COPD Study (SPIROMICS) group implemented a standardized protocol to guarantee appropriate imaging quality for the least radiation dose [12]. The main points for quality control in a QCT protocol are summarised in Table 1.

TABLE 1.

Recommendations for QCT quality control

Scanner calibration • Daily calibration with air, water and dedicated phantoms designed to evaluate the accuracy of the reproduction of CT values for the lung parenchyma.
Radiation dose • The X-ray tube current-time product should be kept constant on follow-up CT examinations.
• Adjusting CTDIvol (mGy) for BMI (i.e., <20, 20–30, >30), is recommended
Slices • Use of a volumetric CT whole-lung scan.
• Use of the same X-ray tube collimation and section thickness.
• Slice thickness of 0.75 mm and interval of 0.5 mm are recommended. The section thickness should be kept constant on follow-up CT examinations.
Reconstruction filter • Use of smooth, soft-tissue or standard filters for densitometry.
Respiratory volume • The patient should be trained to accurately perform the respiratory manoeuvres needed to reach the desired lung volume.
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Note. QCT, quantitative computed tomography; BMI, body mass index; CT, computed tomography; CTDIvol, volumetric computed tomography dose index.

The standardized protocol is a reliable reference point; however, as scanner technologies evolve, the field has been consistently improving image quality with further reductions in radiation doses [1, 12]. Ongoing CT technical advances, such as tube current modulation and iterative reconstruction algorithm, allowed low- and ultra-low-dose CT protocols to deliver image quality similar to a standard protocol for quantitative measurement of emphysema and airway measurements [13–15]. For instance, Chen et al. demonstrated that quantitative low-dose CT can be a reliable tool to differentiate between COPD and asthma in the outpatient setting using QCT [16].

In addition to the technical parameters outlined in Table 1 for an adequate QCT protocol, contrast media should be avoided, as it can increase the pulmonary parenchymal density and influence the quantitative analyses [17].

3. Clinical questions

A growing body of evidence suggests that QCT is an important clinical tool for the accurate and reproducible detection and prognosis of lung disease. Thoracic QCT is now considered by many to be an indispensable technology for longitudinal analyses and intervention trials [1, 18]. Many QCT imaging measurements are available to respiratory physicians for the answering of important questions.

3.1. Does the patient have smoking-related lung disease?

Chronic obstructive pulmonary disease (COPD) is the most common of smoke-related lung disease, characterized by irreversible progressive airflow limitation [19]. COPD can be further categorized into emphysema-predominant, airway-predominant, and mixed phenotypes on imaging [20]. QCT can quantify the areas of emphysema, the degree of airway remodeling, and the percentage of air trapping on expiratory imaging.

The emphysema index is defined as the percentage of lung with attenuation below −950 HU with higher percentages associated with more extensive disease [21]. The degree of air trapping is calculated based on the percentage of pixels with an attenuation of less than −856 HU on expiratory imaging, given that areas of normal lung are expected to increase its attenuation below this normal level on full expiration [22]. However, there is a caveat in this threshold analysis as areas of emphysema would fall within the attenuation range of air trapping on imaging. To overcome this limitation, parametric response mapping (PRM) was designed to coregister inspiratory and expiratory CT voxels to distinguish the areas of normal lung that retained an attenuation lower than −856 HU on expiration from areas of emphysema, which are abnormally lucent in both phases [11]. Other parameters such as the 15th percentile (Perc15), which represents the threshold HU point below which 15% of the voxels are distributed, and the NLI have been used to quantify emphysema (Figure 1A) [21].

Segmentation of airway thickness, which is a surrogate of bronchial wall inflammation in bronchitis, is also possible with QCT and has shown to correlate with signs of bronchitis measured using histology [23]. One commonly used parameter is the Pi10, which is the square root of the wall area of a hypothetical airway with internal perimeter of 10mm[24]. Another tool to assess airway disease is CT total airway count which showed that subjects with reduced CT total airway count had a two-times higher risk for developing COPD, and reduced TAC was significantly associated with accelerated longitudinal FEV1/FVC decline [25].

The severity of several of the QCT indexes, such as Perc15, emphysema index, PRM, and airway thickness parameters have shown to be correlated to low baseline lung function by spirometry and greater rates of lung function decline in the follow-up [23, 24, 26, 27]. Therefore, QCT may be used as an additional tool in the evaluation of severity of lung disease in COPD, especially in those patients that are not well fit to perform spirometry, such as in patients who are not able to cooperate with the spirometry due to neurological disease or severe dyspnea [5, 24]. In addition, Regan et al. demonstrated that smokers with no evidence of airway obstruction on spirometry had significant emphysema and airway disease on QCT (Figure 3) [28]. This can be particularly useful as strong evidence of smoking-related lung injury when counseling smokers to quit when spirometry is normal.

Figure 3.

Figure 3

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55-year-old smoker with normal lung function on spirometry. (A) Coronal CT scan of the chest demonstrated predominantly upper lobe centrilobular emphysema. (B) Quantitative CT analysis showed the low-attenuation areas (threshold < −950 HU) in red and an emphysema index of 10%.

3.2. Is the smoking-related lung disease progressing?

Despite being a heterogeneous group of diseases, changes in the lung parenchyma and airway remodelling can be objectively quantified for the assessment of disease progression [23]. The percentage increase in areas of low attenuation, increase in air trapping, or airway thickness with its regional distribution of lesions can be easily obtained and compared with findings from previous exams (Figure 4).

Figure 4.

Figure 4

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Quantitative CT in the follow-up of a patient with emphysema. (A) Baseline CT demonstrated an emphysema index of 5% (threshold of −950 HU). (B) Follow-up CT performed 3 years later revaled an increase of the emphysema index to 15%.

QCT has been particularly valuable in our understanding of the relationship of between emphysema progression and mortality in large observational studies of smokers with COPD such as the COPDGene and the ECLIPSE cohorts. Ash et al. demonstrated that the rate of emphysema progression measured by QCT over 3–5 years of follow-up of 6692 patients from those cohorts was associated with high all-cause and respiratory mortality [29]. Also, QCT is capable of identifying even short-term progression (3 months) of severe emphysema as demonstrated by Konietzke et al. while spirometry variables remained stable [30]. These measurements on follow-up may serve as an objective parameter for clinicians when counseling smokers about the progression of their disease and its worse outcome.

3.3. Does the patient have interstitial lung disease?

The key precursor events that lead to lung fibrosis and increase lung density that could be detected by QCT are related to recurrent alveolar epithelial cell injury, lung inflammation and dysregulated extracellular matrix remodeling [31]. Normal lung attenuation follows an asymmetric distribution with a tail to the right (i.e., right-skewed). It is highly peaked (high kurtosis) with a median attenuation of −850 HU and mean attenuation around −800 HU highly skewed towards more positive values (Figure 1B) [3–5]. Using a threshold of −700 HU (higher thresholds of up to −600 HU have been proposed), it is possible to quantify the percentage of areas with high attenuation and define the extent of fibrotic involvement [6, 32, 33].

These areas of high attenuation are thought to behave as biomarkers of fibrosis and are related to the first subclinical biological changes in the lung parenchyma that lead to ILD as proposed in the analysis of the Multi-Ethnic Study of Atherosclerosis (MESA) [33]. Moreover, with replacement of normal lung by fibrotic lung, the shape of the curve becomes less peaked (lower kurtosis) and less asymmetric (lower skewness) as the frequency of pixels below the HAA (e.g., −700 HU) increases (Figure 1C) [3, 5]. Our group demonstrated a group of 114 patients with ILD had significantly lower mean lung attenuation (−737 vs. −813 HU, <0.001), skewness (2.01 vs. 3.67), and kurtosis (4.82 vs. 17.43) compared to 20 healthy controls [5]. All these parameters have shown moderate to high correlation with forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), and severity of symptoms in patients with ILD, particularly in patients with idiopathic pulmonary fibrosis (IPF) [5, 31, 34].

As previously discussed, the main limitation of the threshold and density methods is their inability to discriminate areas of reticulation from ground-glass opacities only or consolidation. Also, the presence of honeycombing, which is the hallmark of end-stage fibrosis, will appear as areas of lower attenuation. For this reason, texture-based analysis has emerged as a technique capable of overcoming these difficulties, and CALIPER is perhaps the benchmark in the field and has shown to correlate with spirometry and clinical markers (Figure 2)[35].

3.4. Is the interstitial lung disease progressing?

ILD progression or positive treatment response may not be obvious for radiologists conducting visual tomographic analysis, and interobserver variation may affect such assessments, which provide essential information for adequate clinical management [1]. In the same context, the ability to obtain accurate, quantitative information about disease severity and extent, or the identification of a characteristic pattern of parenchymal involvement that can be predictive of mortality, is of great importance, as it guides screening protocols for transplantation or the identification of the need for other therapies [36].

Kim et al. showed that early (within 6 months of follow-up) structural changes observed on QCT examinations performed for fibrosis assessment (>4%) were missed on visual CT assessment, with which progression was identified only at 12 months [37]. Best et al followed up 167 patients with IPF with QCT at baseline and 12-month follow-up using histogram-derived parameters, such as skewness and kurtosis, and demonstrated worsening numbers on follow-up and its association with mortality prediction [38]. Several texture-based parameters have shown promising results in the literature to measure the progression of the disease. The use of DTA score on follow-up of patients with IPF has also shown to be directly related to worsening pulmonary function [7]. CALIPER-derived parameters such as percentage involvement of reticulation, ground-glass, and honeycombing are also accurate and reproducible biomarker to measure progression of disease in patients with IPF and correlates with mortality [9]. All these tools could be beneficial for screening patients at increased risk for disease progression who would benefit from a more aggressive treatment approach.

3.5. What are the differential diagnoses for patients with chronic lung disease?

QCT is capable of evaluating several density intervals and thus simultaneously detecting the extent and distribution of fibrosis and emphysema areas, as well as assessing airway disease, enabling the clinician to establish, phenotyping the condition (e.g., COPD), or have more confidence in their diagnosis when correlating with clinical history [1, 31]. Figure 5 shows a case of combined pulmonary fibrosis with emphysema, which has challenging diagnostic criteria and unique clinical history, that QTC increased the confidence in the diagnosis. QCT is also valuable in establishing the phenotype of patients with COPD [11, 12].

Figure 5.

Figure 5

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(A) Axial and (B) coronal CT in lung window showed lower lobe predominant fibrosis with honeycombing, and upper lobe emphysema in a patient with combined pulmonary fibrosis with emphysema. (C) Axial and (D) Coronal QCT revealed the areas high attenuation (−700 to −250 HU; 18.3%; blue) in the base and low attenuation (lower than −950 HU; 6.8%; red) in the upper lobes (E) Density histogram demonstrating lower peak (kurtosis) and skewness due to the larger areas of lower (red) and higher (blue) attenuation.

However, the most promising tool for differential diagnosis are the modalities that use texture-based analysis, such as CALIPER (Figure 2). By quantifying the areas of ground-glass, reticular pattern, honeycombing, and emphysema, it can be valuable not only in difficult cases of established patterns of lung disease, such as probable usual interstitial pneumonia vs. nonspecific interstitial pneumonia, but also in stratification of patients with unclassifiable ILD [39, 40].

3.6. What is the patient’s prognosis?

Despite sharing similar symptoms and clinical features, diffuse lung diseases form a heterogeneous group of pathologically distinct processes with markedly different prognoses and treatment options. The quantification of diagnosed abnormalities allows staging and definition of phenotypes that, associated with clinical manifestations, allow the identification of patients with greater chances of unfavorable outcomes [3, 25, 30, 31, 36].

Several studies have shown that QCT-derived parameters, such as the extent of emphysema and increased airway thickness, have shown to be predictive of worse clinical outcomes and mortality in patients with COPD [5, 41–43]. The NLI, which can be used for quantification of both ILD and COPD severity, has also been shown to predict mortality in patients with COPD and ILD [5]. The progression of disease measured in follow-up QCT has also been validated as a predictor of mortality in large cohorts derived from COPDGene and ECLIPSE study [29]. The use of QCT parameters in ILD has also shown that it is capable of predicting patients that will have worse outcomes [38, 44].

An increase in interstitial abnormalities depicted by CALIPER on follow-up was shown to predict survival in patients with IPF and unclassifiable ILD [9, 40]. Jacob et al. compared to use of CALIPER with traditional visual assessment of CT features to predict mortality in 283 patients with IPF and showed that CALIPER-derived parameters, especially pulmonary vessel volume, outperformed traditional visual assessment [10].

4. Limitations and future directions

One of the main reasons that limit the practical clinical application of the QCT is the lack of use of standardized protocols, as in traditional pulmonary function tests. It is known that different models of the CT scanner, and parameters such as section thickness, radiation dose, filter, reconstruction algorithm and post-processing tools can influence the assessment of emphysema and fibrosis [1, 24]. The degree of inspiration and expiration itself can significantly alter lung densities in the same patient [45]. All these variables can compromise the accuracy and comparability of QCT results across different times for each patient as well as the reproducibility of multicentric studies [1]. Despite all these limitations, new horizons arise with the incorporation of artificial intelligence in medical imaging and other machine learning techniques in QCT analysis. These technologies have the potential to improve the accuracy and interobserver agreement for QCT radiological findings in patients with chronic lung diseases, paving the way for the incorporation of their use in daily clinical practice [1, 46].

5. Conclusion

A large body of data supports the value of QCT in pulmonary medicine, and QCT tools have been brought rapidly into clinical practice. QCT is less operator-dependent and faster than the use of visually derived semi-quantitative scores. It enables complete assessment of the extent and severity of diffuse pulmonary structural abnormalities implying decreased or increased pulmonary density. Given the level of detail achieved, QCT analysis enables the determination of the contributions of heterogeneous determinants of lung disease. This characteristic appears to be particularly useful for clinical trials and, potentially, patient selection for personalized treatment.

Acknowledgement:

We thank Imbio (Minneapolis, MN) for providing us with a sample report of their software for this review.

Abbreviations list:

CALIPER

Computer-Aided Lung Informatics for Pathology Evaluation and Rating

COPD

Chronic obstructive pulmonary disease

CT

Computed tomography

DTA

data-driven textural analysis

FEV1

Forced expiratory volume in the first second

FVC

forced vital capacity

HAAs

high-attenuation areas

HU

Hounsfield units

ILD

Interstitial lung disease

MESA

Multi-Ethnic Study of Atherosclerosis

NLI

normal lung index

PFT

pulmonary function test

PRM

parametric response mapping

QCT

Quantitative computed tomography

SPIROMICS

Sub-Populations and Intermediate Outcome Measures in COPD Study

Footnotes

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Statements & Declarations

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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