Low-dose CT scan screening for lung cancer: comparison of images and radiation doses between low-dose CT and follow-up standard diagnostic CT

Patient-specific Effective Dose Estimation from Dose-Length Product in Lung Computed Tomography Using Monte Carlo Simulation

Background

Computed tomography (CT) imaging has a large portion in the dose of patients from radiological procedures; therefore, accurate calculation of radiation risk estimation in this modality is inevitable. In this study, a method for determining the patient-specific effective dose using the dose-length product (DLP) index in lung CT scan using Monte Carlo (MC) simulation is introduced.

Methods

EGSnrc/BEAMnrc MC code was used to simulate a CT scanner. The DOSxyznrc simulation code was used to simulate a specific voxelized phantom from the patient's lungs and irradiate it according to X-ray parameter of routing lung CT scan, and dose delivered to thorax organs was calculated. Three types of phantoms were simulated according to three different body habits (slim, standard, and fat patients) in two groups of men and women. A factor was used to convert the relative dose per particle in MC code to the absolute dose. The dose was calculated in all lung organs, and the effective dose was calculated for all three groups of patient body habits. DLP index and volume CT dose index (CTDIvol) were extracted from the patient's dose report in the CT scanner. The DLP to effective dose conversion factor (k-factor) for patients with different body habitus was calculated.

Results

Lung radiation dose in slim, standard, and fat patients in men was 0.164, 0.103, and 0.078 mGy/mAs and in women was 0.164, 0.105, and 0.079 mGy/mAs, respectively. The k-factor in the group of slim patients, especially in women, was higher than in other groups.

Conclusions

CT scan dose indexes for slim patients are reported to be underestimated in studies. The dose report in CT scan systems should be modified in proportion to the patient's body habitus, to accurately estimate the radiation risk.

Favorable Mortality-to-Incidence Ratio Trends of Lung Cancer in Countries with High Computed Tomography Density

Background and Objectives: The prognoses of lung cancer deteriorate dramatically as the cancer progresses through its stages. Therefore, early screening using techniques such as low-dose computed tomography (LDCT) is critical. However, the epidemiology of the association between the popularization of CT and the prognosis for lung cancer is not known. Materials and Methods: Data were obtained from GLOBOCAN and the health data and statistics of the World Health Organization. Mortality-to-incidence ratios (MIRs) and the changes in MIR over time (δMIR; calculated as the difference between MIRs in 2018 and 2012) were used to evaluate the correlation with CT density disparities via Spearman's rank correlation coefficient. Results: Countries with zero CT density presented a relatively low incidence crude rate and a relatively high MIR in 2018 and a negative δMIR. Conversely, countries with a CT density over 30 had a positive δMIR. The CT density was significantly associated with the HDI score and MIR in 2018, whereas it demonstrated no association with MIR in 2012. The CT density and δMIR also showed a significant linear correlation. Conclusions: CT density was significantly associated with lung cancer MIR in 2018 and with δMIR, indicating favorable clinical outcomes in countries in which CT has become popularized.

Lung Cancer Screening: Development and Replication of a Decentralized Program to Increase Access

BACKGROUND

Throughout its evolution, lung cancer screening has remained an evidence-based tool to detect earlier-stage disease and improve survival. Many lung cancer screening programs are planned and developed in one central location, which limits patient access.

OBJECTIVES

The purpose was to develop necessary and complex decentralized program components in affiliation with a large cancer care delivery system and a regional community hospital network in northeast Florida.

METHODS

A program was pilot tested among five geographically diverse primary care offices for three years. The role of oncology nursing was crucial to achieve quality and efficacy in program development, regulatory compliance, and screening outcomes.

FINDINGS

The program resulted in an increase in lung cancer screenings within the large healthcare network. The percentage of early-stage lung cancers identified increased, which led to improved patient outcomes and survival.

Exosomal microRNAs in non-small cell lung cancer

Lung cancer is one of the highest incidence cancer types worldwide and one with the lowest 5-year survival rate of all cancer types. Despite recent insights into lung cancer pathobiology, including novel biomarker-targeted therapies and immunotherapies, most of lung patients are diagnosed at late stages with limited and ineffective treatments. Therefore, more approaches are needed to eradicate lung cancer. In the last years, small extracellular vesicles (EVs) secreted by tumor cells have been gaining relevance. These intercellular signal mediators, called exosomes, contain a huge range of biological elements, including lipids, nucleic acids and miRNAs, among others, that carry relevant information. The role of exosomes in cancer progression is dependent on cancer type, molecular characteristics and stage. MicroRNAs molecules are a big part of the content of exosomes cargo and probably the most studied ones. Due to the regulatory role in gene expression, miRNAs may provide information of the molecular characteristics of the tumor and be also able to reprogram distant target cells. Exosomal miRNAs can modulate different biological processes in cancer such as growth, progression, invasion, angiogenesis, metastasis and drug resistance; playing a critical role in modifying the microenvironment of non-small cell lung cancer (NSCLC). Therefore, they can act by regulating tumor resistance and also be useful to monitoring the response/relapse to targeted therapies. In this work, we summarize the relevant advances on the potential role of exosomal miRNAs in NSCLC pathobiogenesis, highlighting the clinical utility of exosomal microRNAs as biomarkers for the NSCLC diagnosis, prognosis, drug resistance and therapeutic strategies.

A review on chest CT scanning parameters implemented in COVID-19 patients: bringing low-dose CT protocols into play

Background

This study aims to review chest computed tomography (CT) scanning parameters which are utilized to evaluate patients for COVID-19-induced pneumonia. Also, some of radiation dose reduction techniques in CT would be mentioned, because using these techniques or low-dose protocol can decrease the radiation burden on the population.

Main body

Chest CT scan can play a key diagnostic role in COVID-19 patients. Additionally, it can be useful to monitor imaging changes during treatment. However, CT scan overuse during the COVID-19 pandemic raises concerns about radiation-induced adverse effects, both in patients and healthcare workers.

Conclusion

By evaluating the CT scanning parameters used in several studies, one can find the necessity for optimizing these parameters. It has been found that chest CT scan taken using low-dose CT protocol is a reliable diagnostic tool to detect COVID-19 pneumonia in daily practice. Moreover, the low-dose chest CT protocol results in a remarkable reduction (up to 89%) in the radiation dose compared to the standard-dose protocol, not lowering diagnostic accuracy of COVID-19-induced pneumonia in CT images. Therefore, its employment in the era of the COVID-19 pandemic is highly recommended.

A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction

Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.

Predictive Nomograms for Synchronous Liver and Lung Metastasis in Colon Cancer

BACKGROUND

The risk of distant metastasis may be estimated using predictive nomograms. The purpose of this study is to develop nomograms that may assess the risk of synchronous metastasis in patients with colon cancer.

METHODS

A retrospective analysis of the Surveillance Epidemiology and End Results database between 2010 and 2014. Logistic regression was performed to identify factors associated with synchronous liver and lung metastasis.

RESULTS

Overall, 117,934 patients with colon cancer (59,076 [50.1%] males, mean age 68.3 ± 13.7 years) were included, of which 16,135 (13.7%) had liver metastasis and 4601 (3.9%) had lung metastasis at diagnosis. Age, sex, race, tumor location, tumor grade, CEA levels, perineural invasion, and T and N stage were associated with the presence of liver metastasis. Age, sex, race, tumor location, tumor grade, CEA levels, perineural invasion, T stage, N stage, and presence of liver metastasis were associated with the presence of lung metastasis. These variables were used to construct predictive nomograms. The c-indexes for both predictive models were 0.97.

CONCLUSIONS

In this study, we constructed predictive nomograms for the presence of synchronous liver and lung metastasis in patients with colon cancer that may be used to quantitatively assess the risk of synchronous metastatic disease.

Clinical value of a new generation adaptive statistical iterative reconstruction (ASIR-V) in the diagnosis of pulmonary nodule in low-dose chest CT

OBJECTIVE

To evaluate the clinical value of low-dose chest CT combined with the new generation adaptive statistical iterative reconstruction (ASIR-V) algorithm in the diagnosis of pulmonary nodule.

METHODS

30 patients with pulmonary nodules underwent chest CT using Revolution CT. The patients were first scanned with standard-dose at a noise index (NI) of 14, and the images were reconstructed with filtered back projection (FBP) algorithm. If pulmonary nodules were found, a low-dose targeted scan, with NI of 24, was performed localized on the nodules, and the images were reconstructed with 60% ASIR-V. The detection rate of pulmonary nodules in the two scanning modes was recorded. The size of nodules, CT value and standard deviation of nodules were measured. The signal-to-noise ratio and contrast-to-noise ratio were also calculated. Two experienced radiologists used a 5-point method to score the image quality. The volumetric CT dose index, and dose-length product were recorded and the effective dose (ED) was calculated of the two scanning modes.

RESULTS

Volumetric CT dose index (ED) of the standard-dose scan covering the entire lungs was 7.29 ± 2.38 mGy (3.52 ± 1.09 mSv), and that of low-dose targeted scan was 2.56 ± 1.87 mGy (0.51 ± 0.32 mSv). However, the ED of the virtual low-dose scan for the entire lungs was 1.44 ± 0.15 mSv, which would mean a dose reduction of 59.1% compared with the standard-dose scan. 85 of the 87 pulmonary nodules were detected in the low-dose targeted scan, with 2 of the ground-glass density nodules with size less than 1 cm missed, resulting in 97.7% overall detection rate. There was no difference between the low-dose ASIR-V images and standard-dose FBP images for the size (1.49 ± 0.74 cm vs 1.48 ± 0.75 cm), CT value [33.02 ± 1.95 Hounsfield unit (HU) vs 34.6 ± 3.07 HU], standard deviation (27.64 ± 14.42 HU vs 30.38 ± 20.04 HU), signal-to-noise ratio (1.44 ± 0.88 vs 1.43 ± 1.31) and contrast-to-noise ratio (38.95 ± 18.43 vs 38.23 ± 14.99) of nodules (all p > 0.05). There was no difference in the subjective scores between the two scanning modes.

CONCLUSION

The low-dose CT scan combined with ASIR-V algorithm is of comparable value in the detection and the display of pulmonary nodules when compared with the FBP images obtained by standard-dose scan.

ADVANCES IN KNOWLEDGE

This is a clinical study to evaluate the clinical value of pulmonary nodules using ASIR-V algorithm in the same patients in the low-dose chest CT scans. It suggests that ASIR-V provides similar image quality and detection rate for pulmonary nodules at much reduced radiation dose.

A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images

Markov random field (MRF) has been widely used to incorporate a priori knowledge as penalty or regularizer to preserve edge sharpness while smoothing the region enclosed by the edge for pieces-wise smooth image reconstruction. In our earlier study, we proposed a type of MRF reconstruction method for low-dose CT (LdCT) scans using tissue-specific textures extracted from the same patient's previous full-dose CT (FdCT) scans as prior knowledge. It showed advantages in clinical applications. This paper aims to remove the constraint of using previous data of the same patient. We investigated the feasibility of extracting the tissue-specific MRF textures from an FdCT database to reconstruct a LdCT image of another patient. This feasibility study was carried out by experiments designed as follows. We constructed a tissue-specific MRF-texture database from 3990 FdCT scan slices of 133 patients who were scheduled for lung nodule biopsy. Each patient had one FdCT scan (120 kVp/100 mAs) and one LdCT scan (120 kVp/20 mAs) prior to biopsy procedure. When reconstructing the LdCT image of one patient among the 133 patients, we ranked the closeness of the MRF-textures from the other 132 patients saved in the database and used them as the a prior knowledge. Then, we evaluated the reconstructed image quality using Haralick texture measures. For any patient within our database, we found more than eighteen patients' FdCT MRF texures can be used without noticeably changing the Haralick texture measures on the lung nodules (to be biopsied). These experimental outcomes indicate it is promising that a sizable FdCT texture database could be used to enhance Bayesian reconstructions of any incoming LdCT scans.

Automatic current selection with iterative reconstruction reduces effective dose to less than 1 mSv in low-dose chest computed tomography in persons with normal BMI

Most of the recent studies have used fixed tube current while few investigators use automatic current selection (ACS) with iterative reconstruction (IR) techniques to reduce effective dose (ED) to < 1 mSv in low-dose chest computed tomography (LDCCT). We investigated whether image quality of lungs as produced by a fixed tube current (FTC) of 35 mAs can be maintained with ED < 1 mSv produced by ACS with IR techniques in LDCCT. A total of 32 participants were included. The LDCCT was performed by a FTC 35 mAs (with a kilovoltage peak of 120 kVp) in 16 participants (Group A), and by a DoseRight ACS in 16 participants (Group B). Their images were improved by IR technique. The ED was estimated by multiplying the individual dose length product (DLP) by the dose conversion factor. The image quality was assessed by the CT number, noise levels, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the regions of interest in the apex, upper lobe, and lower lobe of lung regions in the CT images. A t-test was used to evaluate the LDCCT image quality between the groups. The ED was significantly 49.2% lower in Group B than in Group A (0.71 ± 0.05 mSv vs 1.40 ± 0.02 mSv, P < .001). However, noise level, SNR, and CNR were not significantly different between Groups A and B, indicating the image quality was similar between two groups, or our setting parameters for DoseRight ACS with IR technique can achieve the image quality as good as obtained on the FTC 35 mAs with IR techniques. Our results suggest that the DoseRight ACS with IR technique reduces ED to lower than 1 mSv (averagely 0.71 mSv) yet maintains an image quality as good as produced by FTC 35 mAs with IR technique in normal BMI persons. The ACS setup thus is more preferable than the FTC to achieve the ALARA (as low as reasonably achievable) principle.

Predictive Nomograms for Synchronous Distant Metastasis in Rectal Cancer

BACKGROUND

Nomograms may be used to quantitatively assess the probability of synchronous distant metastasis. The purpose of this study is to develop predictive nomograms for the presence of synchronous distant metastasis in patients with rectal cancer.

METHODS

A retrospective analysis of the Surveillance Epidemiology and End Results database was performed for cases diagnosed between 2010 and 2014.

RESULTS

Overall, 46,785 patients with rectal cancer (27,773 [59.4%] males, mean age 63.9 ± 13.7 years) were identified, of which 6192 (13.2%) had liver metastasis, 2767 (5.9%) had lung metastasis, and 601 (1.3%) had bone metastasis. Age, sex, race, tumor location, tumor grade, primary tumor size, CEA levels, perineural invasion, T stage, N stage, and liver and lung metastasis were found to be associated with the presence of synchronous distant metastasis and were included in the predictive models. The c-indexes of these models were 0.99 for liver metastasis, 0.99 for lung metastasis, and 1 for bone metastasis.

CONCLUSIONS

Predictive nomograms for the presence of synchronous liver, lung, and bone metastasis were developed and may be used to predict the probability of distant disease in rectal cancer patients.

Diagnostic Imaging and Newer Modalities for Thoracic Diseases: PET/Computed Tomographic Imaging and Endobronchial Ultrasound for Staging and Its Implication for Lung Cancer

Modalities to detect and characterize lung cancer are generally divided into those that are invasive [endobronchial ultrasound (EBUS), esophageal ultrasound (EUS), and electromagnetic navigational bronchoscopy (ENMB)] versus noninvasive [chest radiography (CXR), computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI)]. This chapter describes these modalities, the literature supporting their use, and delineates what tests to use to best evaluate the patient with lung cancer.

Diagnostic Imaging and Newer Modalities for Thoracic Diseases: PET/Computed Tomographic Imaging and Endobronchial Ultrasound for Staging and Its Implication for Lung Cancer

Modalities to detect and characterize lung cancer are generally divided into those that are invasive [endobronchial ultrasound (EBUS), esophageal ultrasound (EUS), and electromagnetic navigational bronchoscopy (ENMB)] versus noninvasive [chest radiography (CXR), computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI)]. This chapter describes these modalities, the literature supporting their use, and delineates what tests to use to best evaluate the patient with lung cancer.

Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans

In this paper, we investigate the automatic detection of white and brown adipose tissues using Positron Emission Tomography/Computed Tomography (PET/CT) scans, and develop methods for the quantification of these tissues at the whole-body and body-region levels. We propose a patient-specific automatic adiposity analysis system with two modules. In the first module, we detect white adipose tissue (WAT) and its two sub-types from CT scans: Visceral Adipose Tissue (VAT) and Subcutaneous Adipose Tissue (SAT). This process relies conventionally on manual or semi-automated segmentation, leading to inefficient solutions. Our novel framework addresses this challenge by proposing an unsupervised learning method to separate VAT from SAT in the abdominal region for the clinical quantification of central obesity. This step is followed by a context driven label fusion algorithm through sparse 3D Conditional Random Fields (CRF) for volumetric adiposity analysis. In the second module, we automatically detect, segment, and quantify brown adipose tissue (BAT) using PET scans because unlike WAT, BAT is metabolically active. After identifying BAT regions using PET, we perform a co-segmentation procedure utilizing asymmetric complementary information from PET and CT. Finally, we present a new probabilistic distance metric for differentiating BAT from non-BAT regions. Both modules are integrated via an automatic body-region detection unit based on one-shot learning. Experimental evaluations conducted on 151 PET/CT scans achieve state-of-the-art performances in both central obesity as well as brown adiposity quantification.

COMPARISON OF WIRELESS DETECTORS FOR DIGITAL RADIOGRAPHY SYSTEMS: IMAGE QUALITY AND DOSE

The purpose of this study was to compare dose and image quality of wireless detectors for digital chest radiography. Entrance dose at both the detector (EDD) and phantom (EPD) and image quality were measured for wireless detectors of seven different vendors. Both the local clinical protocols and a reference protocol were evaluated. In addition, effective dose was calculated. Main differences in clinical protocols involved tube voltage, tube current, the use of a small or large focus and the use of additional filtration. For the clinical protocols, large differences in EDD (1.4-11.8 µGy), EPD (13.9-80.2 µGy) and image quality (IQFinv: 1.4-4.1) were observed. Effective dose was <0.04 mSv for all protocols. Large differences in performance were observed between the seven different systems. Although effective dose is low, further improvement of imaging technology and acquisition protocols is warranted for optimisation of digital chest radiography.

The accuracy of ultrashort echo time MRI sequences for medical additive manufacturing

OBJECTIVES

Additively manufactured bone models, implants and drill guides are becoming increasingly popular amongst maxillofacial surgeons and dentists. To date, such constructs are commonly manufactured using CT technology that induces ionizing radiation. Recently, ultrashort echo time (UTE) MRI sequences have been developed that allow radiation-free imaging of facial bones. The aim of the present study was to assess the feasibility of UTE MRI sequences for medical additive manufacturing (AM).

METHODS

Three morphologically different dry human mandibles were scanned using a CT and MRI scanner. Additionally, optical scans of all three mandibles were made to acquire a "gold standard". All CT and MRI scans were converted into Standard Tessellation Language (STL) models and geometrically compared with the gold standard. To quantify the accuracy of the AM process, the CT, MRI and gold-standard STL models of one of the mandibles were additively manufactured, optically scanned and compared with the original gold-standard STL model.

RESULTS

Geometric differences between all three CT-derived STL models and the gold standard were <1.0 mm. All three MRI-derived STL models generally presented deviations <1.5 mm in the symphyseal and mandibular area. The AM process introduced minor deviations of <0.5 mm.

CONCLUSIONS

This study demonstrates that MRI using UTE sequences is a feasible alternative to CT in generating STL models of the mandible and would therefore be suitable for surgical planning and AM. Further in vivo studies are necessary to assess the usability of UTE MRI sequences in clinical settings.