The role of imaging for interstitial lung disease (ILD) is of paramount importance. With the growth of high resolution chest computed tomography (HRCT) imaging techniques, we are able to visualize nuances between individual ILDs more critically. HRCT is an essential component of an initial ILD evaluation and also has become part of the armamentarium of tools used for routine management of these patients. The technology of HRCT scans has evolved over the years, most recently with the advent of quantitative HRCT (qCT). The technology employs texture-based classification, which identifies and quantifies different radiographic findings. The arrival of qCT scanning has been slowly emerging as a new player in the ILD world. What exactly is qCT, and what role can, and will it serve for our ILD patients?
Quantitative CT scanning has been introduced since the 1980s, but only within the last 15 years has its use for ILD taken form. Human interpretation of CTs is fraught with subjectivity, based on the interpreting radiologist’s training, experience, and individual visual perception of images. This can result in significant variability in radiographic interpretations and, ultimately, affects a patient’s diagnosis, disease monitoring, treatment, and prognosis. Semiquantitative visual scoring by radiologists is highly variable, especially in areas with limited availability of chest radiologists. qCT employs an automated histogram signature technique that utilizes density and texture-based analysis of the lung parenchyma. Utilizing machine learning from pathologically confirmed datasets, computer programs were trained with specialized thoracic radiologists to distinguish some commonly found radiographic abnormalities into four major groups: ground glass, reticular, honeycombing, and emphysema. In addition, these categories are quantified and spatially depicted on an analysis (Bartholmai, et al.). Various computer programs have been built to streamline the process and expedite the interpretation of an individual’s HRCT scan. The more commonly familiar program, CALIPER (Computer-Aided Lung Informatics for Pathology Evaluation and Ratings), has been used in multiple research studies of qCT in ILD and IPF. Each patient’s CT scan is uploaded to the program, and a breakdown of the patient’s lungs into each category is presented. Not only is each abnormality quantified and precisely defined, it is also color-coded by segments to help with visual interpretation by the physician.
The benefit of qCT lies not only in the automated, objective evaluation of interstitial lung disease, but also in its possible use in prognostication and mortality prediction. Neither use has been fully validated as of yet. However, growing evidence shows a promising role in both realms. Thus far, there have been some studies correlating PFT data with qCT findings. A follow-up study of the Scleroderma Lung Study II examined qCT changes over 24 months and correlated those findings with PFTs and patient-reported outcomes. Patients in this study were either treated with cyclophosphamide (CYC) for 1 year/placebo 1 year vs mycophenolate mofetil (MMF) for 2 years. A large portion of patients receiving CYC or MMF had a significant correlation between improved or stable qCT scores and their FVC and TLC. Neither CYC nor MMF was superior in qCT scores, aligning with the findings of the study, which showed noninferiority of MMF compared with CYC (Goldin, et al.. Interestingly, the improvement of ground glass is often viewed by physicians as positive, since this finding is typically thought of as active inflammation. However, if qCT determines that the fibrosis score actually increases over time, despite an improvement in ground glass, this may more accurately reflect the development of subtle fibrosis that is not easily appreciated by the human eye (Goldin, et al. ). In this context, it is feasible that parenchymal changes occur prior to deterioration on PFTs. Diffusing capacity for carbon monoxide (DLCO) correlates largely with the extent of lung involvement on qCT, but DLCO is not a specific biomarker in predicting severity of ILD (ie, because pHTN or anemia can confound DLCO). Forced vital capacity (FVC) in certain diseases may also confound CT correlation (ie, muscle weakness or extrathoracic restriction from skin disease in systemic sclerosis). The usefulness of PFT data as a clinical endpoint in research studies may be replaced by qCTs more consistent and precise detection of disease modification.
IPF has been an interesting area of exploration for the role of qCT in disease monitoring and possible prognostication. It is known that the presence of honeycombing on HRCT is associated with increased mortality. Patients with a progressive fibrotic ILD have similar mortality rates to those with IPF (Adegunsoye, et al.). The ability to correlate radiographic findings with mortality could potentially become an important marker of clinical deterioration, especially in those patients who are unable to perform PFTs. In addition, it can also be beneficial in those with co-existent emphysema, since PFTs may be confounded by this overlap. Nakagawa and colleagues proposed a computer-aided method for qCT analysis of honeycombing in patients with IPF. The algorithm for the qCT analysis also has specific parameters to exclude emphysematous lesions on imaging. The %honeycomb area (HA) was correlated with a composite physiologic index derived from PFTs (calculated from FEV1, FVC and DLCO). This tool can accurately quantify the percentage of honeycombing and aid in monitoring IPF. Using this protocol, Nakagawa was able to demonstrate a significant correlation with 3-year mortality, with a marked difference found when using a cutoff value of 4.8% (Nakagawa, et al. ). Furthermore, patient survival in IPF has been compared against the CALIPER program and PFTs. Mortality for patients was significantly associated with pulmonary vessel volume (PVV), an innovative tool that quantified the volume of the pulmonary artery and veins, which may become a new parameter used for disease monitoring. Using qCT in addition to PFTs provides more tangible evidence to help monitor patients with IPF, guide treatment decisions, and plan for transplant or palliative care. The growing use of PVV in qCT has yet to be fully elucidated, but it does have a promising role (Jacob, et al. Eur Respir J. 2017;49. ).
Despite the positive outlook for qCT, there are major issues that limit its widespread use. During the image acquisition process, there is a lack of consistency and quality control, stemming from multiple different manufacturers of CT scan machines, reconstitution methods, radiation doses, and noise or inspiratory efforts of patients. The Radiologic Society of North America (RSNA) is attempting to fix this issue by creating a standardized protocol for collecting images used for qCT (Castillo-Saldana, et al. . In order to move forward with adaptation of qCT, a standardized approach and handling of images needs to be created.
Quantitative CT is an exciting new prospect for the care of patients with ILD. As these patients, and their management, becomes more complex, expanding the toolbox for physicians is much needed. It will be fascinating to see how the role of qCT takes shape over the coming years.
Dr. D’Annunzio is with Westmed Medical Group, Rye, N.Y.; Dr. Nayar is a Pulmonary/Critical Care Fellow at NYU School of Medicine; and Dr. Patel is with Columbia University Medical Center.