CT Densitometry as a Predictor of Pulmonary Function in Lung Cancer Patients

The Emerging Role and Clinical Applications of Morphomics in Diagnostic Imaging

Analytic morphomics refers to the accurate measurement of specific biological markers of human body composition in diagnostic medical imaging. The increasing prevalence of disease processes that alter body composition including obesity, cachexia, and sarcopenia has generated interest in specific targeted measurement of these metrics to possibly prevent or reduce negative health outcomes. Typical morphomic measurements include the area and density of muscle, bone, vascular calcification, visceral fat, and subcutaneous fat on a specific validated axial level in the patient's cross-sectional diagnostic imaging. A distinct advantage of these measurements is that they can be made retrospectively and opportunistically with pre-existing datasets. We provide a narrative review of the current state of art in morphomics, but also consider some potential future directions for this exciting field. Imaging based quantitative assessment of body composition has enormous potential across the breadth and scope of modern clinical practice. From risk stratification to treatment planning, and outcome assessment, all can be enhanced with the use of analytic morphomics. Moreover, it is likely that many new opportunities for personalized medicine will emerge as the field evolves. As radiologists, embracing analytic morphomics will enable us to contribute added value in the care of every patient.

A study of the correlation between total lung volume and the percent of low attenuation volume and PFT indicators in patients with preoperative lung cancer

The objective was to explore the relationships between computed tomography (CT) lung volume parameters and pulmonary function test (PFT) indexes and develop predictive scores to predict PFT indexes in Chinese preoperative patients suspected with lung cancer. Preoperative patients suspected with lung cancer aged 18 years or more and examined by chest CT scan and PET were consecutively recruited from April to August 2020, at Yunnan Cancer Hospital. CT and PET data were selected from medical record. Pearson correlation was used to explore the relationships between CT parameters and PFT indexes. Predictive scores of PFT indexes were developed from unstandardized coefficients of linear regression models of using CT parameters as predictors. The assessments of predictive ability of scores were conducted by receiver operating characteristics curves. A total of 124 preoperative patients suspected with lung cancer participated in this study. Total lung volume significantly correlated with total lung capacity (r = 0.708), residual volume (r = 0.411), forced expiratory volume in one second (FEV1, r = 0.535), forced vital capacity (FVC, r = 0.687), and FEV1/FVC (r = -0.319). Percent of low attenuation volume significantly correlated with total lung capacity (r = 0.200), residual volume (r = 0.215), FEV1 percentage of predictive value (FEV1%, r = -0.204) and FEV1/FVC (r = -0.345). Four predictive scores for FEV1, FEV1%, FEV1/FVC and FVC% were developed. The area under the curve of receiver operating characteristics for FEV1 <2L, FEV1% <80%, FEV1/FVC <80% and FVC% <80% were 0.856, 0.667, 0.749 and 0.715, respectively. A prediction of poor lung function in preoperative patients suspected with lung cancer, using total lung volume and percent of low attenuation volume was possible. The predictive scores should be further evaluated for external validity.

Textural analysis and lung function study: Predicting lung fitness for radiotherapy from a CT scan

OBJECTIVE

This study tested the hypothesis that shows advanced image analysis can differentiate fit and unfit patients for radical radiotherapy from standard radiotherapy planning imaging, when compared to formal lung function tests, FEV1 (forced expiratory volume in 1 s) and TLCO (transfer factor of carbon monoxide).

METHODS

An apical region of interest (ROI) of lung parenchyma was extracted from a standard radiotherapy planning CT scan. Software using a grey level co-occurrence matrix (GLCM) assigned an entropy score to each voxel, based on its similarity to the voxels around it.

RESULTS

Density and entropy scores were compared between a cohort of 29 fit patients (defined as FEV1 and TLCO above 50 % predicted value) and 32 unfit patients (FEV1 or TLCO below 50% predicted). Mean and median density and median entropy were significantly different between fit and unfit patients (p = 0.005, 0.0008 and 0.0418 respectively; two-sided Mann-Whitney test).

CONCLUSION

Density and entropy assessment can differentiate between fit and unfit patients for radical radiotherapy, using standard CT imaging.

ADVANCES IN KNOWLEDGE

This study shows that a novel assessment can generate further data from standard CT imaging. These data could be combined with existing studies to form a multiorgan patient fitness assessment from a single CT scan.

CT Radiomics in Thoracic Oncology: Technique and Clinical Applications

Precision medicine offers better treatment options and improved survival for cancer patients based on individual variability. As the success of precision medicine depends on robust biomarkers, the requirement for improved imaging biomarkers that reflect tumor biology has grown exponentially. Radiomics, the field of study in which high-throughput data are generated and large amounts of advanced quantitative features are extracted from medical images, has shown great potential as a source of quantitative biomarkers in the field of oncology. Radiomics provides quantitative information about the morphology, texture, and intratumoral heterogeneity of the tumor itself as well as features related to pulmonary function. Hence, radiomics data can be used to build descriptive and predictive clinical models that relate imaging characteristics to tumor biology phenotypes. In this review, we describe the workflow of CT radiomics, types of CT radiomics, and its clinical application in thoracic oncology.

Electrical impedance tomography for lung ventilation monitoring of the dog

BACKGROUND

Electrical impedance tomography (EIT) is a radiation free technique which takes advantage of the different electrical conductivities of different tissues. Its main field of application is lung ventilation monitoring. The aim of this prospective study was to evaluate the feasibility of collecting EIT information on a sample of dogs with different thoracic shapes under clinical conditions by connecting an electrode belt without fur clipping.

MATERIAL AND METHODS

Fifteen pulmonary healthy dogs were anaesthetized, positioned in sternal recumbency and ventilated in a pressure-controlled mode at three different positive end-expiratory pressure levels (PEEP) of 0, 5 and 10 cmH₂O for five breaths each, with a peak inspiratory pressure of 15 cmH₂O. The impedance changes were recorded with a commercial EIT device applied around the thorax. Subsequently, the ventilation regime was repeated and a computed tomography scan (CT) of the same thoracic segment was performed for each PEEP level. The tidal volume (V_t) was recorded. For the collection of EIT data the sum of regional impedance changes was recorded. The impedance value of the entire lung (global) was recorded and the ventilated area was quartered into four regions of interest (ROI). In a CT image with the fewest adjacent organs, lung tissue was selected to obtain the mean value of lung radiodensitiy in Hounsfield-Units (HU) for the entire lung and for the four ROIs.

RESULTS

EIT recordings via the electrode belt were possible without clipping. There was a significant correlation for the parameters of aeration as measured by EIT and CT for both the entire ventilated lung and the corresponding ROIs. The increasing PEEP resulted in a proportional increase of the impedance, and there was a negative correlation between EIT and V_t. The better ventilated dorsal ROIs could be identified using both EIT and CT. An intra-assay coefficient of variation showed a good reproducibility for lung ventilation in anaesthetized dogs in the EIT.

DISCUSSION

The results show that EIT is a reliable method for evaluating the ventilation of dogs in a clinical setting. The accuracy of EIT might be improved by using a mesh corresponding to the different thoracic shapes of the dogs.

Quantative computerized tomography assessment of lung density as a predictor of postoperative pulmonary morbidity in patients with lung cancer

BACKGROUND

The aim of this study was to evaluate the pulmonary reserve of the patients via preoperative quantitative computerized tomography (CT) and to determine if these preoperative quantitative measurements could predict the postoperative pulmonary morbidity.

METHODS

Fifty patients with lung cancer who underwent lobectomy/segmentectomy were included in the study. Preoperative quantitative CT scans and pulmonary function tests data were evaluated retrospectively. We compare these measurements with postoperative morbidity.

RESULTS

There were 32 males and 18 females with a mean age of 54.4±13.9 years. Mean total density was -790.6±73.4 HU. The volume of emphysematous lung was (<-900 HU) 885.2±1,378.4 cm(3). Forced expiratory volume in one second (FEV1) (r=-0.494, P=0.02) and diffusion capacity of carbon monoxide (DLCO) (r=-0.643, P<0.001) were found to be correlate with the volume of emphysematous lung. Furthermore FEV1 (r=0.59, P<0.001) and DLCO (r=0.48, P<0.001) were also found to be correlate with mean lung density. Postoperative pulmonary morbidity was significantly higher in patients with lower lung density (P<0.001), larger volume of emphysema (P<0.001) and lower DLCO (P=0.039). A cut-off point of -787.5 HU for lung density showed 86.96% sensitivity and 81.48% specificity for predicting the pulmonary morbidity (kappa =-0.68, P<0.001). Additionally a cut-off point of 5.41% for emphysematous volume showed 84.00% sensitivity and 80.00% specificity for predicting the pulmonary morbidity (kappa =0.64, P<0.001). According to logistic regression analyses emphysematous volume >5.41% (P=0.014) and lung density <-787.5 HU (P=0.009) were independent prognostic factors associated with postoperative pulmonary morbidity.

CONCLUSIONS

In this study, the patients with a lower lung density than -787.5 HU and a higher volume of emphysema than 5.41% were found to be at increased risk for developing postoperative pulmonary morbidity. More stringent precautions should be taken in those patients that were found to be at high risk to avoid pulmonary complications.

Direct assessment of lung function in COPD using CT densitometric measures

To investigate whether lung function in patients with chronic obstructive pulmonary disease (COPD) can be directly predicted using CT densitometric measures and assess the underlying prediction errors as compared with the traditional spirometry-based measures. A total of 600 CT examinations were collected from a COPD study. In addition to the entire lung volume, the extent of emphysema depicted in each CT examination was quantified using density mask analysis (densitometry). The partial least square regression was used for constructing the prediction model, where a repeated random split-sample validation was employed. For each split, we randomly selected 400 CT exams for training (regression) purpose and the remaining 200 exams for assessing performance in prediction of lung function (e.g., FEV1 and FEV1/FVC) and disease severity. The absolute and percentage errors as well as their standard deviations were computed. The averaged percentage errors in prediction of FEV1, FEV1/FVC%, TLC, RV/TLC% and DLco% predicted were 33%, 17%, 9%, 18% and 23%, respectively. When classifying the exams in terms of disease severity grades using the CT measures, 37% of the subjects were correctly classified with no error and 83% of the exams were either correctly classified or classified into immediate neighboring categories. The linear weighted kappa and quadratic weighted kappa were 0.54 (moderate agreement) and 0.72 (substantial agreement), respectively. Despite the existence of certain prediction errors in quantitative assessment of lung function, the CT densitometric measures could be used to relatively reliably classify disease severity grade of COPD patients in terms of GOLD.