Variation in Screening CT-Detected Nodule Volumetry as a Function of Size

The Effect of X-ray Dose Photon-Counting Detector Computed Tomography on Nodule Properties in a Lung Cancer Screening Cohort: A Prospective Study

OBJECTIVES

The aim of the study was to evaluate the effect of photon-counting detector (PCD-)CT dose reduction to x-ray equivalent levels on nodule detection, diameter, volume, and density compared to a low-dose reference standard using semiautomated and manual methods.

MATERIALS AND METHODS

Between February and July 2023, 101 prospectively enrolled participants underwent noncontrast same-study low- and chest x-ray-dose CT scans using PCD-CT. Patients who were not referred for lung cancer screening or nodule follow-up, as well as those with nodules smaller than 5 mm in diameter, were excluded. Nodule detection and measurement of nodule diameters and volumes was semiautomatically performed for low- and x-ray-dose scans using computer-aided diagnosis software. Additionally, 2 blinded readers manually measured largest nodule diameters and examined nodule density. Nodules were classified using Lung-RADS v2022. Image quality was assessed with subjective and objective measures.

RESULTS

Mean CTDIvol for x-ray dose scans was 0.11 ± 0.03 mGy, compared to 0.65 ± 0.15 mGy for low-dose images (P < 0.001). One hundred seventy-two nodules larger than 5 mm were detected in 53 of the 101 participants (32 male, 61.6 ± 12.5 years; 21 female, 60.3 ± 12.5 years). The semiautomated method had high overall sensitivity for nodule detection (0.94) on x-ray dose scans, with a higher sensitivity for solid nodules (>0.95) and lower for subsolid nodules (>0.86). Nodules not detected on x-ray dose scans were significantly smaller. Semiautomated measurements underestimated nodule diameter for solid nodules on x-ray dose scans (P = 0.01), but no significant effect for nodule volume was found (P = 0.775). Readers rated nodule density less dense on x-ray dose scans (R1: P < 0.001, R2: P = 0.006). There was no significant difference in nodule diameter for both readers between scan doses (R1: P = 0.141; R2: P = 0.554). There were good to excellent correlations between semiautomated and reader nodule diameters. Agreement and accuracy between low-dose and x-ray dose Lung-RADS classifications across methods were good (Cohens' к = 0.73, 0.62, 0.76 for semiautomated method, R1 and R2; resp. Accuracy: 0.82, 0.78, 0.85). No Lung-RADS classification changes were observed with semiautomated volumetric measurements of nodules.

CONCLUSIONS

Semiautomated nodule detection is highly sensitive in PCD-CT x-ray dose scans. Semiautomated nodule volume measurement is more robust to image quality changes than nodule diameter. Accurate semiautomated and manual nodule measurements are feasible on x-ray dose scans, but nodule density was in tendency underestimated. Nodule classification using Lung-RADS was shown to be accurate on x-ray dose scans.

Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT

PURPOSE

To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR).

METHODS

The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool.

RESULTS

DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65).

CONCLUSION

DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.

Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis

Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.

Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of sensitivity, this exam has a low specificity rate and it is still difficult to separate benign and malignant lesions. The ISBI 2018 Lung Nodule Malignancy Prediction Challenge, developed by a team from the Quantitative Imaging Network of the National Cancer Institute, was focused on the prediction of lung nodule malignancy from two sequential LDCT screening exams using automated (non-manual) algorithms. We curated a cohort of 100 subjects who participated in the National Lung Screening Trial and had established pathological diagnoses. Data from 30 subjects were randomly selected for training and the remaining was used for testing. Participants were evaluated based on the area under the receiver operating characteristic curve (AUC) of nodule-wise malignancy scores generated by their algorithms on the test set. The challenge had 17 participants, with 11 teams submitting reports with method description, mandated by the challenge rules. Participants used quantitative methods, resulting in a reporting test AUC ranging from 0.698 to 0.913. The top five contestants used deep learning approaches, reporting an AUC between 0.87 - 0.91. The team's predictor did not achieve significant differences from each other nor from a volume change estimate (p =.05 with Bonferroni-Holm's correction).

Prospective Study of Low- and Standard-dose Chest CT for Pulmonary Nodule Detection: A Comparison of Image Quality, Size Measurements and Radiation Exposure

OBJECTIVE

To comprehensively and accurately analyze the out-performance of low-dose chest CT (LDCT) vs. standard-dose CT (SDCT).

METHODS

The image quality, size measurements and radiation exposure for LDCT and SDCT protocols were evaluated. A total of 117 patients with extra-thoracic malignancies were prospectively enrolled for non-enhanced CT scanning using LDCT and SDCT protocols. Three experienced radiologists evaluated subjective image quality independently using a 5-point score system. Nodule detection efficiency was compared between LDCT and SDCT based on nodule characteristics (size and volume). Radiation metrics and organ doses were analyzed using Radimetrics.

RESULTS

The images acquired with the LDCT protocol yielded comparable quality to those acquired with the SDCT protocol. The sensitivity of LDCT for the detection of pulmonary nodules (n=650) was lower than that of SDCT (n=660). There was no significant difference in the diameter and volume of pulmonary nodules between LDCT and SDCT (for BMI <22 kg/m², 4.37 vs. 4.46 mm, and 43.66 vs. 46.36 mm³; for BMI ≥22 kg/m², 4.3 vs. 4.41 mm, and 41.66 vs. 44.86 mm³) (P>0.05). The individualized volume CT dose index (CTDI_vol), the size specific dose estimate and effective dose were significantly reduced in the LDCT group compared with the SDCT group (all P<0.0001). This was especially true for dose-sensitive organs such as the lung (for BMI <22 kg/m², 2.62 vs. 12.54 mSV, and for BMI ≥22 kg/m², 1.62 vs. 9.79 mSV) and the breast (for BMI <22 kg/m², 2.52 vs. 10.93 mSV, and for BMI ≥22 kg/m², 1.53 vs. 9.01 mSV) (P<0.0001).

CONCLUSION

These results suggest that with the increases in image noise, LDCT and SDCT exhibited a comparable image quality and sensitivity. The LDCT protocol for chest scans may reduce radiation exposure by about 80% compared to the SDCT protocol.

The radiologist's role in lung cancer screening

Lung cancer is still the deadliest cancer in men and women worldwide. This high mortality is related to diagnosis in advanced stages, when curative treatment is no longer an option. Large randomized controlled trials have shown that lung cancer screening (LCS) with low-dose computed tomography (CT) can detect lung cancers at earlier stages and reduce lung cancer-specific mortality. The recent publication of the significant reduction of cancer-related mortality by 26% in the Dutch-Belgian NELSON LCS trial has increased the likelihood that implementation of LCS in Europe will move forward. Radiologists are important stakeholders in numerous aspects of the LCS pathway. Their role goes beyond nodule detection and nodule management. Being part of a multidisciplinary team, radiologists are key players in numerous aspects of implementation of a high quality LCS program. In this non-systematic review we discuss the multifaceted role of radiologists in LCS.

Surveillance of small, solid pulmonary nodules at digital chest tomosynthesis: data from a cohort of the pilot Swedish CArdioPulmonary bioImage Study (SCAPIS)

Background

Digital tomosynthesis (DTS) might be a low-dose/low-cost alternative to computed tomography (CT).

Purpose

To investigate DTS relative to CT for surveillance of incidental, solid pulmonary nodules.

Material and Methods

Recruited from a population study, 106 participants with indeterminate solid pulmonary nodules on CT underwent surveillance with concurrently performed CT and DTS. Nodule size on DTS was assessed by manual diameter measurements and semi-automatic nodule segmentations were independently performed on CT. Measurement agreement was analyzed according to Bland–Altman with 95% limits of agreement (LoA). Detection of nodule volume change > 25% by DTS in comparison to CT was evaluated with receiver operating characteristics (ROC).

Results

A total of 81 nodules (76%) were assessed as measurable on DTS by two independent observers. Inter- and intra-observer LoA regarding change in average diameter were ± 2 mm. Calculation of relative volume change on DTS resulted in wide inter- and intra-observer LoA in the order of ± 100% and ± 50%. Comparing relative volume change between DTS and CT resulted in LoA of –58% to 67%. The area under the ROC curve regarding the ability of DTS to detect volumetric changes > 25% on CT was 0.58 (95% confidence interval [CI] = 0.40–0.76) and 0.50 (95% CI = 0.35–0.66) for the two observers.

Conclusion

The results of the present study show that measurement variability limits the agreement between DTS and CT regarding nodule size change for small solid nodules.

Implementation of low-dose CT screening in two different health care systems: Mount Sinai Healthcare System and Phoenix VA Health Care System

Implementation of lung screening (LS) programs is challenging even among health care organizations that have the motivation, the resources, and more importantly, the goal of providing for life-saving early detection, diagnosis, and treatment of lung cancer. We provide a case study of LS implementation in different healthcare systems, at the Mount Sinai Healthcare System (MSHS) in New York City, and at the Phoenix Veterans Affairs Health Care System (PVAHCS) in Phoenix, Arizona. This will illustrate the commonalities and differences of the LS implementation process in two very different health care systems in very different parts of the United States. Underlying the successful implementation of these LS programs was the use of a comprehensive management system, the Early Lung Cancer Action Program (ELCAP) Management SystemTM. The collaboration between MSHS and PVAHCS over the past decade led to the ELCAP Management SystemTM being gifted by the Early Diagnosis and Treatment Research Foundation to the PVAHCS, to develop a "VA-ELCAP" version. While there remain challenges and opportunities to continue improving LS and its implementation, there is an increasing realization that most patients who are diagnosed with lung cancer as a result of annual LS can be cured, and that of all the possible risks associated with LS, the greater risk of all is for heavy cigarette smokers not to be screened. We identified 10 critical components in implementing a LS program. We provided the details of each of these components for the two healthcare systems. Most importantly, is that continual re-evaluation of the screening program is needed based on the ongoing quality assurance program and database of the actual screenings. At minimum, there should be an annual review and updating. As early diagnosis of lung cancer must be followed by optimal treatment to be effective, treatment advances for small, early lung cancers diagnosed as a result of screening also need to be assessed and incorporated into the entire screening and treatment program.

Accuracy of Pulmonary Nodule Volumetry Using Noise-Optimized Virtual Monoenergetic Image and Nonlinear Blending Image Algorithms in Dual-Energy Computed Tomography: A Phantom Study

OBJECTIVE

The aim of the study was to assess accuracy of pulmonary nodule volumetry using noise-optimized virtual monoenergetic image (VMI+) and nonlinear blending image (NBI) algorithms in dual-energy computed tomography (DECT).

METHODS

An anthropomorphic chest phantom with 10 simulated nodules (5 solid nodules and 5 ground-glass opacities) was scanned using DECT80/Sn140kV, DECT100/Sn140kV, and single-energy CT (SECT120kV/200mAs), respectively. The dual-energy images were reconstructed using VMI+ (70 keV) and NBI algorithms. The contrast-to-noise ratio and absolute percentage error (APE) of nodule volume were measured to assess image quality and accuracy of nodule volumetry. The radiation dose was also estimated.

RESULTS

The contrast-to-noise ratio of SECT120kV/200mAs was significantly higher than that of NBI80/Sn140kV and VMI+80/Sn140kV (both corrected P < 0.05), whereas there were no significant differences between NBI100/sn140kV and SECT120kV/200mAs and between VMI+100/sn140kV and SECT120kV/200mAs (both corrected P > 0.05). The APE of SECT120kV/200mAs was significantly lower than that of NBI80/Sn140kV and VMI+80/Sn140kV in both types of nodules (all corrected P < 0.05), whereas there were no significant differences between VMI+100/sn140kV and SECT120kV/200mAs in solid nodules and between NBI100/Sn140kV and SECT120kV/200mAs in ground-glass opacities (both corrected P > 0.05). The radiation dose of DECT100/Sn140kV and DECT80/Sn140kV were significantly lower than that of SECT120kV/200mAs (both corrected P < 0.05).

CONCLUSIONS

The DECT100/sn140kV can ensure image quality and nodule volumetry accuracy with lower radiation dose compared with SECT120kV/200mAs. Specifically, the VMI+ algorithm could be used in solid nodules and NBI algorithm in ground-glass opacities.

Measurement accuracy of lung nodule volumetry in a phantom study: Effect of axial-volume scan and iterative reconstruction algorithm

An axial-volume scan with adaptive statistical iterative reconstruction-V (ASIR-V) is newly developed. Our goal was to identify the influence of axial-volume scan and ASIR-V on accuracy of automated nodule volumetry.An "adult' chest phantom containing various nodules was scanned using both helical and axial-volume modes at different dose settings using 256-slice CT. All CT scans were reconstructed using 30% and 50% blending of ASIR-V and filtered back projection. Automated nodule volumetry was performed using commercial software. The image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were measured.The axial-volume scan reduced radiation dose by 19.7% compared with helical scan at all radiation dose settings without affecting the accuracy of nodule volumetric measurement (P = .375). Image noise, CNR, and SNR were not significantly different between two scan modes (all, P > .05).The use of axial-volume scan with ASIR-V achieved effective radiation dose reduction while preserving the accuracy of nodule volumetry.

Volumetric assessment of solid pulmonary nodules on ultralow-dose CT: a phantom study

Background

To reduce the radiation exposure from chest computed tomography (CT), ultralow-dose CT (ULDCT) protocols performed at sub-millisievert levels were previously tested for the evaluation of pulmonary nodules (PNs). The purpose of our study was to investigate the effect of ULDCT and iterative image reconstruction on volumetric measurements of solid PNs.

Methods

CT datasets of an anthropomorphic chest phantom containing solid microspheres were obtained with a third-generation dual-source CT at standard dose, 1/8th, 1/20th and 1/70th of standard dose [CT volume dose index (CTDI_vol): 0.03-2.03 mGy]. Semi-automated volumetric measurements were performed on CT datasets reconstructed with filtered back projection (FBP) and advanced modelled iterative reconstruction (ADMIRE), at strength level 3 and 5. Absolute percentage error (APE) evaluated measurement accuracy related to the effective volume. Scan repetition differences were evaluated using Bland-Altman analysis. Two-way analysis of variance (ANOVA) assessed influence of different scan parameters on APE. Proportional differences (PDs) tested the effect of dose settings and reconstruction algorithms on volumetric measurements, as compared to the standard protocol (standard dose-FBP).

Results

Bland-Altman analysis revealed small mean interscan differences of APE with narrow limits of agreement (-0.1%±4.3% to -0.3%±3.8%). Dose settings (P<0.001), reconstruction algorithms (P<0.001), nodule diameters (P<0.001) and nodule density (P=0.011) had statistically significant influence on APE. Post-hoc Bonferroni tests showed slightly higher APE when scanning with 1/70th of standard dose [mean difference: 3.4%, 95% confidence interval (CI): 2.5-4.3%; P<0.001], and for image reconstruction with ADMIRE5 (mean difference: 1.8%, 95% CI: 1.0-2.5%; P<0.001). No significant differences for scanning with 1/20th of standard dose (P=0.42), and image reconstruction with ADMIRE3 (P=0.19) were found. Scanning with 1/70th of standard dose and image reconstruction with FBP showed the widest range of PDs (-16.8% to 23.4%) compared to standard dose-FBP.

Conclusions

Our phantom study showed no significant difference between nodule volume measurements on standard dose CT (CTDI_vol: 2 mGy) and ULDCT with 1/20th of standard dose (CTDI_vol: 0.10 mGy).

Ultralow dose CT for follow-up of solid pulmonary nodules: A pilot single-center study using Bland-Altman analysis

Solid pulmonary nodules are a common finding requiring serial computed tomography (CT) imaging. We sought to explore the detection and measurement accuracy of an ultralow-dose CT (ULDCT) protocol compared with our standard low-dose CT (LDCT) nodule follow-up protocol.In this pragmatic single-center pilot prospective cohort study, patients scheduled for clinically indicated CT surveillance of 1 or more known solid pulmonary nodules >2 mm underwent ULDCT immediately after routine LDCT. The Bland-Altman 95% limits of agreement for diameter and volumetry were calculated.In all, 57 patients underwent 60 imaging episodes, with 170 evaluable nodules. ULDCT detected all known solid pulmonary nodules >2 mm. Bland-Altman analyses demonstrated clinically agreement for both nodule diameter and volume, both of which fell within prespecified limits.This single-center pilot study suggests that ULDCT may be of use in surveillance of known solid pulmonary nodules >2 mm.

In vivo growth of 60 non-screening detected lung cancers: a computed tomography study

Current pulmonary nodule management guidelines are based on nodule volume doubling time, which assumes exponential growth behaviour. However, this is a theory that has never been validated in vivo in the routine-care target population. This study evaluates growth patterns of untreated solid and subsolid lung cancers of various histologies in a non-screening setting.Growth behaviour of pathology-proven lung cancers from two academic centres that were imaged at least three times before diagnosis (n=60) was analysed using dedicated software. Random-intercept random-slope mixed-models analysis was applied to test which growth pattern most accurately described lung cancer growth. Individual growth curves were plotted per pathology subgroup and nodule type.We confirmed that growth in both subsolid and solid lung cancers is best explained by an exponential model. However, subsolid lesions generally progress slower than solid ones. Baseline lesion volume was not related to growth, indicating that smaller lesions do not grow slower compared to larger ones.By showing that lung cancer conforms to exponential growth we provide the first experimental basis in the routine-care setting for the assumption made in volume doubling time analysis.

Semi-automated pulmonary nodule interval segmentation using the NLST data

PURPOSE

To study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict nodule malignancy.

METHODS

We obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi-automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of <8 mm and those with longest diameter ≥ 8 mm.

RESULTS

We find that segmentation of the nodules shows substantial variability across algorithms, with the CCC ranging from 0.56 to 0.95 for change in volume (percent change in volume range was [0.15 to 0.86]) across the nodules. When examining nodules based on their longest diameter, we find the CCC had higher values for large nodules with a range of [0.54 to 0.93] among the algorithms, while percent change in volume was [0.3 to 0.95]. Compared to that of smaller nodules which had a range of [-0.0038 to 0.69] and percent change in volume was [-0.039 to 0.92]. The malignancy prediction results showed fairly consistent results across the institutions, the AUC using change in volume ranged from 0.65 to 0.89 (Percent change in volume was 0.64 to 0.86) for entire nodule range. Prediction improves for large nodule range (≥ 8 mm) with AUC range 0.75 to 0.90 (percent change in volume was 0.74 to 0.92). Compared to smaller nodule range (<8 mm) with AUC range 0.57 to 0.78 (percent change in volume was 0.59 to 0.77).

CONCLUSIONS

We find there is a fairly high concordance in the size measurements for larger nodules (≥8 mm) than the lower sizes (<8 mm) across algorithms. We find the change in nodule volume (absolute and percent change) were consistent predictors of malignancy across institutions, despite using different segmentation algorithms. Using volume change estimates without corrections shows slightly lower predictability (for two teams).

Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications

The frenetic development of imaging technology-both hardware and software-provides exceptional potential for investigation of the lung. In the last two decades, CT was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative CT (QCT). Hundreds of thousands of CTs have been analysed for characterization of focal and diffuse disease of the lung. Several QCT metrics were developed and tested against clinical, functional and prognostic descriptors. Computer-aided detection of nodules, textural analysis of focal lesions, densitometric analysis and airway segmentation in obstructive pulmonary disease and textural analysis in interstitial lung disease are the major chapters of this discipline. The validation of QCT metrics for specific clinical and investigational needs prompted the translation of such metrics from research field to patient care. The present review summarizes the state of the art of QCT in both focal and diffuse lung disease, including a dedicated discussion about application of QCT metrics as parameters for clinical care and outcomes in clinical trials.