Effectiveness of a single low-dose computed tomography screening for lung cancer: A population-based perspective cohort study in China.

The purpose of this perspective cohort study was to evaluate the effectiveness of low-dose computed tomography (LDCT) screening for lung cancer in China. This study was conducted under the China Urban Cancer Screening Program (CanSPUC). The analysis was based on participants aged 40 to 74 years from 2012 to 2019. A total of 255 569 eligible participants were recruited in the study. Among the 58 136 participants at high risk of lung cancer, 20 346 (35.00%) had a single LDCT scan (defined as the screened group) and 37 790 (65.00%) not (defined as the non-screened group). Overall, 1162 participants were diagnosed with lung cancer at median follow-up time of 5.25 years. The screened group had the highest cumulative incidence of lung cancer and the non-screened group had the highest cumulative lung cancer mortality and all-cause cumulative mortality. We performed inverse probability weighting (IPW) to account for potential imbalances, and Cox proportional hazards model to estimate the weighted association between mortality and LDCT scans. After IPW adjusted with baseline characteristics, the lung cancer incidence density was significantly increased (37.0% increase) (HR1.37 [95%CI 1.12-1.69]), lung cancer mortality was decreased (31.0% decrease) (HR0.69 [95%CI 0.49-0.97]), and the all-cause mortality was significantly decreased (23.0% lower) (HR0.77 [95% CI 0.68-0.87]) in the screened group. In summary, a single LDCT for lung cancer screening will reduce the mortality of lung cancer and all-cause mortality in China.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Provider engagement and outreach.

The American Cancer Society National Lung Cancer Roundtable strategic plan for provider engagement and outreach addresses barriers to the uptake of lung cancer screening, including lack of provider awareness and guideline knowledge about screening, concerns about potential harms from false-positive examinations, lack of time to implement workflows within busy primary care practices, insufficient infrastructure and administrative support to manage a screening program and patient follow-up, and implicit bias based on sex, race/ethnicity, social class, and smoking status. Strategies to facilitate screening include educational programming, clinical reminder systems within the electronic medical record, decision support aids, and tools to track nodules that can be implemented across a diversity of practices and health care organizational structures. PLAIN LANGUAGE SUMMARY: The American Cancer Society National Lung Cancer Roundtable strategic plan to reduce deaths from lung cancer includes strategies designed to support health care professionals, to better understand lung cancer screening, and to support adults who are eligible for lung cancer screening by providing counseling, referral, and follow-up.

Ultra-low-dose vs. standard-of-care-dose CT of the chest in patients with post-COVID-19 conditions-a prospective intra-patient multi-reader study.

OBJECTIVES: To conduct an intrapatient comparison of ultra-low-dose computed tomography (ULDCT) and standard-of-care-dose CT (SDCT) of the chest in terms of the diagnostic accuracy of ULDCT and intrareader agreement in patients with post-COVID conditions. METHODS: We prospectively included 153 consecutive patients with post-COVID-19 conditions. All participants received an SDCT and an additional ULDCT scan of the chest. SDCTs were performed with standard imaging parameters and ULDCTs at a fixed tube voltage of 100 kVp (with tin filtration), 50 ref. mAs (dose modulation active), and iterative reconstruction algorithm level 5 of 5. All CT scans were separately evaluated by four radiologists for the presence of lung changes and their consistency with post-COVID lung abnormalities. Radiation dose parameters and the sensitivity, specificity, and accuracy of ULDCT were calculated. RESULTS: Of the 153 included patients (mean age 47.4 ± 15.3 years; 48.4% women), 45 (29.4%) showed post-COVID lung abnormalities. In those 45 patients, the most frequently detected CT patterns were ground-glass opacities (100.0%), reticulations (43.5%), and parenchymal bands (37.0%). The accuracy, sensitivity, and specificity of ULDCT compared to SDCT for the detection of post-COVID lung abnormalities were 92.6, 87.2, and 94.9%, respectively. The median total dose length product (DLP) of ULDCTs was less than one-tenth of the radiation dose of our SDCTs (12.6 mGy*cm [9.9; 15.5] vs. 132.1 mGy*cm [103.9; 160.2]; p < 0.001). CONCLUSION: ULDCT of the chest offers high accuracy in the detection of post-COVID lung abnormalities compared to an SDCT scan at less than one-tenth the radiation dose, corresponding to only twice the dose of a standard chest radiograph in two views. CLINICAL RELEVANCE STATEMENT: Ultra-low-dose CT of the chest may provide a favorable, radiation-saving alternative to standard-dose CT in the long-term follow-up of the large patient cohort of post-COVID-19 patients.

Photon-Counting Detector CT With Denoising for Imaging of the Osseous Pelvis at Low Radiation Doses: A Phantom Study.

BACKGROUND. Photon-counting detector (PCD) CT may allow lower radiation doses than used for conventional energy-integrating detector (EID) CT, with preserved image quality. OBJECTIVE. The purpose of this study was to compare PCD CT and EID CT, reconstructed with and without a denoising tool, in terms of image quality of the osseous pelvis in a phantom, with attention to low radiation doses. METHODS. A pelvic phantom comprising human bones in acrylic material mimicking soft tissue underwent PCD CT and EID CT at various tube potentials and radiation doses ranging from 0.05 to 5.00 mGy. Additional denoised reconstructions were generated using a commercial tool. Noise was measured in the acrylic material. Two readers performed independent qualitative assessments that entailed determining the denoised EID CT reconstruction with the lowest acceptable dose and then comparing this reference reconstruction with PCD CT reconstructions without and with denoising, using subjective Likert scales. RESULTS. Noise was lower for PCD CT than for EID CT. For instance, at 0.05 mGy and 100 kV with tin filter, noise was 38.4 HU for PCD CT versus 48.8 HU for EID CT. Denoising further reduced noise; for example, for PCD CT at 100 kV with tin filter at 0.25 mGy, noise was 19.9 HU without denoising versus 9.7 HU with denoising. For both readers, lowest acceptable dose for EID CT was 0.10 mGy (total score, 11 of 15 for both readers). Both readers somewhat agreed that PCD CT without denoising at 0.10 mGy (reflecting reference reconstruction dose) was relatively better than the reference reconstruction in terms of osseous structures, artifacts, and image quality. Both readers also somewhat agreed that denoised PCD CT reconstructions at 0.10 mGy and 0.05 mGy (reflecting matched and lower doses, respectively, with respect to reference reconstruction dose) were relatively better than the reference reconstruction for the image quality measures. CONCLUSION. PCD CT showed better-quality images than EID CT when performed at the lowest acceptable radiation dose for EID CT. PCD CT with denoising yielded better-quality images at a dose lower than lowest acceptable dose for EID CT. CLINICAL IMPACT. PCD CT with denoising could facilitate lower radiation doses for pelvic imaging.

Examination of Firefighting as an Occupational Exposure Criteria for Lung Cancer Screening.

PURPOSE: Firefighting is known to be carcinogenic to humans. However, current lung cancer screening guidelines do not account for occupational exposure. We hypothesize that firefighting is an independent risk factor associated with the development of high-risk lung nodules on low-dose CT (LDCT). METHODS: Members of a firefighter's union underwent LDCT at a single institution between April 2022 and June 2023 within a lung cancer screening program. Results were interpreted by designated chest radiologists and reported using the Lung-RADS scoring system. Demographic and radiographic data were recorded, and summary statistics are reported. RESULTS: 1347 individuals underwent lung cancer screening, with a median age of 51 years (IQR 42-58), including 56 (4.2%) females. Overall, 899 (66.7%) were never smokers, 345 (25.6%) were former smokers, and 103 (7.7%) were current smokers. There were 41 firefighters (3.0%) who had high-risk (Lung-RADS 3 or 4) nodules requiring intervention or surveillance, of which 21 (1.5%) were Lung-RADS 3 and 20 (1.5%) that were Lung-RADS 4. Of the firefighters with high-risk nodules, only 6 (14.6%) were eligible for LDCT based on current screening guidelines. There were 7 high-risk nodules (0.5%) that required procedural intervention, 6 (85.7%) of which were from the non-screening eligible cohort. There were also 20 never-smoking firefighters (57.1%) with high-risk nodules that were non-screening eligible. CONCLUSION: Firefighting, even in the absence of smoking history, may be associated with the development of high-risk lung nodules on LDCT. Carefully selected occupational exposures should be considered in the development of future lung cancer screening guidelines.

Preoperative Planning for Shoulder Arthroplasty is Feasible with Computed Tomography at Lower-Than-Conventional Radiation Doses.

INTRODUCTION: Computed tomography (CT) offers a detailed assessment of the shoulder for preoperative shoulder arthroplasty planning; however, this technique exposes the patient to ionizing radiation. The purpose of this study was to prospectively evaluate the practicality of reducing the CT radiation dose compared to conventional dose levels for manual and preoperative planning software measurements for shoulder arthroplasty. METHODS: A total of 10 shoulder CT examinations were performed for preoperative planning purposes on a dual x-ray source CT scanner. A specialized dose-split scan technique was utilized to reconstruct CT images corresponding to 100%, 70%, and 30% radiation dose relative to our institution's standard of care imaging protocol. Glenoid version, inclination, and humeral head subluxation were measured manually by three authors and by commercially available software platforms. These measurements were analyzed for agreement between the 100%, 70%, and 30% dose levels for each patient. Tolerances of 5° of glenoid version, 5° of glenoid inclination, and 10% humeral head subluxation were used as equivalent for preoperative planning. RESULTS: Automated measurements of 70% dose images were within 5° of version, 5° of inclination, and 10% subluxation in 95.0% of cases. Manual measurements of 70% RD images were within 5° of version for 90.0% of cases, 5° of inclination in 86.7% of cases, and 10% subluxation in 100% of cases. Automated measurements from the 30% dose images were within 5° of version, 5° of inclination, and 10% subluxation for 100% of cases. Manual measurements from the 30% dose images were within 5° of version for 86.7% of cases, 5° of inclination in 76.7% of cases, and 10% subluxation in 100% of cases. The mean absolute difference in software measurement of glenoid version (p = 0.96), glenoid inclination (p = 0.64), or humeral head subluxation (p = 0.09) or in aggregated manual mean absolute difference of version (p = 0.22), inclination (p = 0.31), or humeral head subluxation (p = 0.56) was not significant. Good to excellent reliability was determined by interclass correlation coefficients among the manual observers and automatic software platforms for measurements at all doses (P<0.001) CONCLUSIONS: The results indicate that both preoperative planning software platforms and human observers produced similar measurements of glenoid version, inclination, and humeral head subluxation from reduced-dose images compared to standard of care doses. By implementing reduced dose techniques in preoperative shoulder CT, the potential risks associated with radiation exposure could be reduced for patients undergoing shoulder arthroplasty.

Chest CT at X-Ray Dose Using a Noise-Mitigating Weighted Projection: The Thoracic Tomogram. Diagnostic Performance for Pneumonia Detection in Hemato-Oncology Patients.

PURPOSE: To compare the diagnostic performance of a thick-slab reconstruction obtained from an ultra-low-dose CT (termed thoracic tomogram) with standard-of-care low-dose CT (SOC-CT) for rapid interpretation and detection of pneumonia in hemato-oncology patients. METHODS: Hemato-oncology patients with a working diagnosis of pneumonia underwent an SOC-CT followed by an ultra-low-dose CT, from which the thoracic tomogram (TT) was reconstructed. Three radiologists evaluated the TT and SOC-CT in the following categories: (I) infectious/inflammatory opacities, (II) small airways infectious/inflammatory changes, (III) atelectasis, (IV) pleural effusions, and (V) interstitial abnormalities. The TT interpretation time and radiation dose were recorded. Sensitivity, specificity, diagnostic accuracy, ROC, and AUC were calculated with the corresponding power analyses. The agreement between TT and SOC-CT was calculated by Correlation Coefficient for Repeated Measures (CCRM), and the Shrout-Fleiss intra-class correlations test was used to calculate interrater agreement. RESULTS: Forty-seven patients (mean age 58.7 ± 14.9 years; 29 male) were prospectively enrolled. Sensitivity, specificity, accuracy, AUC, and Power for categories I/II/III/IV/V were: 94.9/99/97.9/0.971/100, 78/91.2/86.5/0.906/100, 88.6/100/97.2/0.941/100, 100/99.2/99.3/0.995/100, and 47.6/100/92.2/0.746/87.3. CCRM between TT and SOC-CT for the same categories were .97/.81/.92/.96/.62 with an interobserver agreement of .93/.88/.82/.96/.61. Mean interpretation time was 18.6 ± 5.4 seconds. The average effective radiation dose of TT was similar to a frontal and lateral chest X-ray (0.27 ± 0.08 vs 1.46 ± 0.64 mSv for SOC-CT; P < .01). CONCLUSION: Thoracic tomograms provide comparable diagnostic information to SOC-CT for the detection of pneumonia in immunocompromised patients at one-fifth of the radiation dose with high interobserver agreement.

APNet: Adaptive projection network for medical image denoising.

BACKGROUND: In clinical medicine, low-dose radiographic image noise reduces the quality of the detected image features and may have a negative impact on disease diagnosis. OBJECTIVE: In this study, Adaptive Projection Network (APNet) is proposed to reduce noise from low-dose medical images. METHODS: APNet is developed based on an architecture of the U-shaped network to capture multi-scale data and achieve end-to-end image denoising. To adaptively calibrate important features during information transmission, a residual block of the dual attention method throughout the encoding and decoding phases is integrated. A non-local attention module to separate the noise and texture of the image details by using image adaptive projection during the feature fusion. RESULTS: To verify the effectiveness of APNet, experiments on lung CT images with synthetic noise are performed, and the results demonstrate that the proposed approach outperforms recent methods in both quantitative index and visual quality. In addition, the denoising experiment on the dental CT image is also carried out and it verifies that the network has a certain generalization. CONCLUSIONS: The proposed APNet is an effective method that can reduce image noise and preserve the required image details in low-dose radiographic images.

Diagnostic accuracy of low dose CT-Scan abdomen in patients with clinical features of acute appendicitis.

Background & Objective: Acute appendicitis is one of the commonest causes of acute abdominal pain presenting to emergency department (ED) and Computerized Tomography scan (CT) is considered gold standard for its diagnosis. Internationally Low Dose Computerized Tomography scan (LDCT) in emergency department is recommended as a beneficial tool to diagnose acute appendicitis with less exposure to radiation and reduction in the rate of negative laparotomy. Local trials are needed to determine the diagnostic accuracy of LDCT as the first line imaging test for acute appendicitis. Our objective was to determine the diagnostic accuracy of LDCT as the first line imaging test for acute appendicitis. Methods: An observational study was conducted over a sample of 147 patients presented with suspected acute appendicitis to the emergency department of Ziauddin University Hospital, Karachi from November 2018 till May 2019. Non-probability consecutive technique used. Aged ≥ 16 years presented in emergency department with the history (symptoms) and physical examination (Signs) suspecting acute appendicitis were included. Patients with contraindications to CT scan e.g. pregnant women. Patients with signs of Acute Peritonitis requiring immediate surgery. CT scan refused by the patient or patient's attendant were excluded. Histopathology was the gold standard in diagnosing acute appendicitis. The data was analyzed using open epi sample size calculator. Results: One hundred forty six patients had positive findings on LDCT for acute appendicitis (99.3%) whereas only one patient had negative findings (0.7%). The sensitivity and specificity of LDCT for the detection of acute appendicitis were estimated as 96.45% and 16.67% by taking histopathology as gold standard. Negative predictive value (NPV) and positive predictive value (PPV) were estimated as 16.67% and 96.45% respectively. The overall accuracy of LDCT was 93.88%. Conclusion: Our study showed that for diagnosing acute appendicitis, LDCT is harmless, fast and economical imaging modality and has diagnostic accuracy with decrease in radiation dose.

Promoting lung cancer screening of high-risk patients by primary care providers.

BACKGROUND: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) of the chest of eligible patients remains low. Accordingly, augmentation of appropriate LCS referrals by primary care providers (PCPs) was sought. METHODS: The quality improvement (QI) project was performed between April 2021 and June 2022. It incorporated patient education, shared decision-making (SDM) with PCPs, and tracking of initial LDCT completion. In each case, lag time (LT) to LCS and pack-years (PYs) were calculated from initial LCS eligibility. The cohort's scores were compared to national scores. Patient zip codes were used to create a geographic map of our cohort for comparison with public health data. RESULTS: An immediate and sustained increase in weekly LCS referrals from PCPs was recorded. Of 337 initial referrals, 95% were men, consisting of 66.2% Black, 28.4% White, and 5.4% other. Mean PY was less for minorities (45.3 vs. 37.3 years; p = .0002) but mean LT was greater for Whites (7.9 vs. 6.2 years; p = .03). Twenty-five percent of veterans failed to report to their scheduled screening, and two declined referrals. Notably, most no-show patients lived in transit deserts. Furthermore, Lung-RADS scores 4B/4X were more than double the expected prevalence (p = .008). CONCLUSIONS: The PCPs in this study successfully augmented LCS referrals. A substantial proportion of these patients were no-shows, and our data suggest complex racial and socioeconomic factors as contributing variables. In addition, a higher-than-expected number of initial Lung-RADS scores 4B/4X were reported. A large, multisite QI project is warranted to address overcoming potential transportation barriers in high-risk patient populations.

Sequential low-dose CT thorax scans to determine invasive pulmonary fungal infection incidence after allogeneic hematopoietic cell transplantation.

Invasive fungal disease (IFD) during neutropenia goes along with a high mortality for patients after allogeneic hematopoietic cell transplantation (alloHCT). Low-dose computed tomography (CT) thorax shows good sensitivity for the diagnosis of IFD with low radiation exposure. The aim of our study was to evaluate sequential CT thorax scans at two time points as a new reliable method to detect IFD during neutropenia after alloHCT. We performed a retrospective single-center observational study in 265/354 screened patients admitted for alloHCT from June 2015 to August 2019. All were examined by a low-dose CT thorax scan at admission (CT t0) and after stable neutrophil recovery (CT t1) to determine the incidences of IFD. Furthermore, antifungal prophylaxis medications were recorded and cohorts were analyzed for statistical differences in IFD incidence using the sequential CT scans. In addition, IFD cases were classified according to EORTC 2008. At CT t0 in 9.6% of the patients, an IFD was detected and antifungal therapy initiated. The cumulative incidence of IFD in CT t1 in our department was 14%. The use of Aspergillus-effective prophylaxis through voriconazole or posaconazole decreased CT thorax t1 suggesting IFD is statistically significant compared to prophylaxis with fluconazole (5.6% asp-azol group vs 16.3% fluconazole group, p = 0.048). In 86%, CT t1 was negative for IFD. Low-dose sequential CT thorax scans are a valuable tool to detect pulmonary IFDs and guide antifungal prophylaxis and therapies. Furthermore, a negative CT t1 scan shows a benefit by allowing discontinuation of antifungal medication sparing patients from drug interactions and side effects.

Low-dose coronary artery calcium scoring compared to the standard protocol.

BACKGROUND: We aimed to compare coronary artery calcium scoring (CACS) with computed tomography (CT) with 80 and 120 kVp in a large patient population and to establish whether there is a difference in risk classification between the two scores. METHODS: Patients with suspected CAD undergoing MPS were included. All underwent standard CACS assessment with 120-kVp tube voltage and with 80 kVp. Two datasets (low-dose and standard) were generated and compared. Risk classes (0 to 25, 25 to 50, 50 to 75, 75 to 90, and > 90%) were recorded. RESULTS: 1511 patients were included (793 males, age 69 ± 9.1 years). There was a very good correlation between scores calculated with 120 and 80 kVp (R = 0.94, R2 = 0.88, P < .001), with Bland-Altman limits of agreement of - 563.5 to 871.9 and a bias of - 154.2. The proportion of patients assigned to the < 25% percentile class (P = .03) and with CACS = 0 differed between the two protocols (n = 264 vs 437, P < .001). CONCLUSION: In a large patient population, despite a good correlation between CACS calculated with standard and low-dose CT, there is a systematic underestimation of CACS with the low-dose protocol. This may have an impact especially on the prognostic value of the calcium score, and the established "power of zero" may no longer be warranted if CACS is assessed with low-dose CT.

U.S. Newspaper Coverage of Lung Cancer Screening From 2010 to 2022.

BACKGROUND. Newspapers are an important source of information for the public about low-dose CT (LDCT) lung cancer screening (LCS) and may influence public perception and knowledge of this important cancer screening service. OBJECTIVE. The purpose of this article was to evaluate the volume, content, and other characteristics of articles pertaining to LCS that have been published in U.S. newspapers. METHODS. The ProQuest U.S. Newsstream database was searched for U.S. newspaper articles referring to LCS published between January 1, 2010 (the year of publication of the National Lung Screening Trial results), and March 28, 2022. Search terms included "lung cancer screening(s)," "lung screening(s)," "low dose screening(s)," and "LDCT." Search results were reviewed to identify those articles mentioning LCS. Characteristics of included articles and originating newspapers were extracted. Articles were divided among nine readers, who independently assessed article sentiment regarding LCS and additional article content using a standardized form. RESULTS. The final analysis included 859 articles, comprising 816 nonsyndicated articles published in a single newspaper and 43 syndicated articles published in multiple newspapers. Sentiment regarding LCS was positive in 76% (651/859) of articles, neutral in 21% (184/859), and negative in 3% (24/859). Frequency of positive sentiment was lowest (61%) for articles published from 2010 to 2012; frequency of negative sentiment was highest (8%) for articles published in newspapers in the highest quartile for weekly circulation. LCS enrollment criteria were mentioned in 52% of articles, smoking cessation programs in 28%, need for annual CT in 27%, and shared decision-making in 4%. Cost or insurance coverage for LCS was mentioned in 33% in articles. A total of 64% of articles mentioned at least one benefit of LCS (most commonly early detection or possible cure of lung cancer), and 23% mentioned at least one harm (most commonly false-positives). A total of 9% of articles interviewed or mentioned a radiologist. CONCLUSION. The sentiment of U.S. newspaper articles covering LCS from 2010 to 2022 was overall positive. However, certain key elements of LCS were infrequently mentioned. CLINICAL IMPACT. The findings highlight areas for potential improvement of LCS media coverage; radiologists have an opportunity to take a more active role in this coverage.

Axial Spondyloarthritis and Diagnostic Challenges: Over-diagnosis, Misdiagnosis, and Under-diagnosis.

PURPOSE OF REVIEW: This article aims to review the challenges in axial spondyloarthritis diagnosis and identify the possible contributing factors. RECENT FINDINGS: The inability to reach an accurate diagnosis in a timely fashion can lead to treatment delays and worse disease outcomes. The lack of validated diagnostic criteria and the misuse of the currently available classification criteria could be contributing. There is also significant inter-reader variability in interpreting images, and the radiologic definitions of axial spondyloarthritis continue to be re-defined to improve their positive predictive value. The role of inflammatory back pain features, serologic biomarkers, genetics, and their diagnostic contribution to axial spondyloarthritis continues to be investigated. There is still a significant amount of delay in the diagnosis of axial spondyloarthritis. Appreciating the factors that contribute to this delay is of utmost importance to close the gap. It is similarly important to recognize other conditions that may present with symptoms that mimic axial spondyloarthritis so that misdiagnosis and wrong treatment can be avoided.

Low-dose CT imaging via cascaded ResUnet with spectrum loss.

The suppression of artifact noise in computed tomography (CT) with a low-dose scan protocol is challenging. Conventional statistical iterative algorithms can improve reconstruction but cannot substantially eliminate large streaks and strong noise elements. In this paper, we present a 3D cascaded ResUnet neural network (Ca-ResUnet) strategy with modified noise power spectrum loss for reducing artifact noise in low-dose CT imaging. The imaging workflow consists of four components. The first component is filtered backprojection (FBP) reconstruction via a domain transformation module for suppressing artifact noise. The second is a ResUnet neural network that operates on the CT image. The third is an image compensation module that compensates for the loss of tiny structures, and the last is a second ResUnet neural network with modified spectrum loss for fine-tuning the reconstructed image. Verification results based on American Association of Physicists in Medicine (AAPM) and United Image Healthcare (UIH) datasets confirm that the proposed strategy significantly reduces serious artifact noise while retaining desired structures.

Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans.

PURPOSE: Kidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its automation are promising but mostly require contrast-enhanced computed tomography (CT) scans. This study aimed at establishing an automated estimation of kidney volume in non-contrast, low-dose CT scans of patients with suspected urolithiasis. METHODS: The kidney segmentation process was automated with 2D Convolutional Neural Network (CNN) models trained on manually segmented 2D transverse images extracted from low-dose, unenhanced CT scans of 210 patients. The models' segmentation accuracy was assessed using Dice Similarity Coefficient (DSC), for the overlap with manually-generated masks on a set of images not used in the training. Next, the models were applied to 22 previously unseen cases to segment kidney regions. The volume of each kidney was calculated from the product of voxel number and their volume in each segmented mask. Kidney volume results were then validated against results semi-automatically obtained by radiologists. RESULTS: The CNN-enabled kidney volume estimation took a mean of 32 s for both kidneys in a CT scan with an average of 1026 slices. The DSC was 0.91 and 0.86 and for left and right kidneys, respectively. Inter-rater variability had consistencies of ICC = 0.89 (right), 0.92 (left), and absolute agreements of ICC = 0.89 (right), 0.93 (left) between the CNN-enabled and semi-automated volume estimations. CONCLUSION: In our work, we demonstrated that CNN-enabled kidney volume estimation is feasible and highly reproducible in low-dose, non-enhanced CT scans. Automatic segmentation can thereby quantitatively enhance radiological reports.

Feasibility analysis of low-dose CT with asynchronous quantitative computed tomography to assess vBMD.

BACKGROUND: To explore the feasibility of low-dose computed tomography (LDCT) with asynchronous quantitative computed tomography (asynchronous QCT) for assessing the volumetric bone mineral density (vBMD). METHODS: 416 women patients, categorized into 4 groups, were included and underwent chest CT examinations combined with asynchronous QCT, and CT scanning dose protocols (LDCT or CDCT) were self-determined by the participants. Radiation dose estimations were retrieved from patient protocols, including volume CT dose index (CTDIvol) and dose-length-product (DLP), and then calculated effective dose (ED). Delimiting ED by 1.0 mSv, chest CT examinations were categorized into 2 groups, LDCT group and CDCT group. vBMD of T12-L2 was obtained by transferring the LDCT and CDCT images to the QCT workstation, without extra radiation. RESULTS: There was no difference of vBMD among 4 age groups in LDCT group (P = 0.965), and no difference in CDCT group (P = 0.988). In LDCT group and CDCT group, vBMD was not correlated to mAs, CTDIvol and DLP (P > 0.05), respectively. Between LDCT group and CDCT group, there was no difference of vBMD (P ≥ 0.480), while differences of mAs, CTDIvol and DLP. CONCLUSION: There was no difference of vBMD between LDCT group and CDCT group and vBMD was not correlated to mAs. While screening for diseases such as lung cancer and mediastinal lesions, LDCT combined with asynchronous QCT can be also used to assess vBMD simultaneously with no extra imaging equipment, patient visit time, radiation dose and no additional economic cost.

Assessing, updating and utilising primary care smoking records for lung cancer screening.

BACKGROUND: Lung cancer screening with low-dose computed tomography for high-risk populations is being implemented in the UK. However, inclusive identification and invitation of the high-risk population is a major challenge for equitable lung screening implementation. Primary care electronic health records (EHRs) can be used to identify lung screening-eligible individuals based on age and smoking history, but the quality of EHR smoking data is limited. This study piloted a novel strategy for ascertaining smoking status in primary care and tested EHR search combinations to identify those potentially eligible for lung cancer screening. METHODS: Seven primary care General Practices in South Wales, UK were included. Practice-level data on missing tobacco codes in EHRs were obtained. To update patient EHRs with no tobacco code, we developed and tested an algorithm that sent a text message request to patients via their GP practice to update their smoking status. The patient's response automatically updated their EHR with the relevant tobacco code. Four search strategies using different combinations of tobacco codes for the age range 55-74+ 364 were tested to estimate the likely impact on the potential lung screening-eligible population in Wales. Search strategies included: BROAD (wide range of ever smoking codes); VOLUME (wide range of ever-smoking codes excluding "trivial" former smoking); FOCUSED (cigarette-related tobacco codes only), and RECENT (current smoking within the last 20 years). RESULTS: Tobacco codes were not recorded for 3.3% of patients (n = 724/21,956). Of those with no tobacco code and a validated mobile telephone number (n = 333), 55% (n = 183) responded via text message with their smoking status. Of the 183 patients who responded, 43.2% (n = 79) had a history of smoking and were potentially eligible for lung cancer screening. Applying the BROAD search strategy was projected to result in an additional 148,522 patients eligible to receive an invitation for lung cancer screening when compared to the RECENT strategy. CONCLUSION: An automated text message system could be used to improve the completeness of primary care EHR smoking data in preparation for rolling out a national lung cancer screening programme. Varying the search strategy for tobacco codes may have profound implications for the size of the population eligible for lung-screening invitation.

A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT.

BACKGROUND: Whole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional deep learning) algorithm to detect lytic lesions on CTs could improve the value of these CTs for myeloma imaging. Our objectives were to develop a DL algorithm and determine its performance at detecting lytic lesions of multiple myeloma. METHODS: Axial slices (2-mm section thickness) from whole-body low-dose CT scans of subjects with biochemically confirmed plasma cell dyscrasias were included in the study. Data were split into train and test sets at the patient level targeting a 90%/10% split. Two musculoskeletal radiologists annotated lytic lesions on the images with bounding boxes. Subsequently, we developed a two-step deep learning model comprising bone segmentation followed by lesion detection. Unet and "You Look Only Once" (YOLO) models were used as bone segmentation and lesion detection algorithms, respectively. Diagnostic performance was determined using the area under the receiver operating characteristic curve (AUROC). RESULTS: Forty whole-body low-dose CTs from 40 subjects yielded 2193 image slices. A total of 5640 lytic lesions were annotated. The two-step model achieved a sensitivity of 91.6% and a specificity of 84.6%. Lesion detection AUROC was 90.4%. CONCLUSION: We developed a deep learning model that detects lytic bone lesions of multiple myeloma on whole-body low-dose CTs with high performance. External validation is required prior to widespread adoption in clinical practice.

A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT.

Most penalized maximum likelihood methods for tomographic image reconstruction based on Bayes' law include a freely adjustable hyperparameter to balance the data fidelity term and the prior/penalty term for a specific noise-resolution tradeoff. The hyperparameter is determined empirically via a trial-and-error fashion in many applications, which then selects the optimal result from multiple iterative reconstructions. These penalized methods are not only time-consuming by their iterative nature, but also require manual adjustment. This study aims to investigate a theory-based strategy for Bayesian image reconstruction without a freely adjustable hyperparameter, to substantially save time and computational resources. The Bayesian image reconstruction problem is formulated by two probability density functions (PDFs), one for the data fidelity term and the other for the prior term. When formulating these PDFs, we introduce two parameters. While these two parameters ensure the PDFs completely describe the data and prior terms, they cannot be determined by the acquired data; thus, they are called complete but unobservable parameters. Estimating these two parameters becomes possible under the conditional expectation and maximization for the image reconstruction, given the acquired data and the PDFs. This leads to an iterative algorithm, which jointly estimates the two parameters and computes the to-be reconstructed image by maximizing a posteriori probability, denoted as joint-parameter-Bayes. In addition to the theoretical formulation, comprehensive simulation experiments are performed to analyze the stopping criterion of the iterative joint-parameter-Bayes method. Finally, given the data, an optimal reconstruction is obtained without any freely adjustable hyperparameter by satisfying the PDF condition for both the data likelihood and the prior probability, and by satisfying the stopping criterion. Moreover, the stability of joint-parameter-Bayes is investigated through factors such as initialization, the PDF specification, and renormalization in an iterative manner. Both phantom simulation and clinical patient data results show that joint-parameter-Bayes can provide comparable reconstructed image quality compared to the conventional methods, but with much less reconstruction time. To see the response of the algorithm to different types of noise, three common noise models are introduced to the simulation data, including white Gaussian noise to post-log sinogram data, Poisson-like signal-dependent noise to post-log sinogram data and Poisson noise to the pre-log transmission data. The experimental outcomes of the white Gaussian noise reveal that the two parameters estimated by the joint-parameter-Bayes method agree well with simulations. It is observed that the parameter introduced to satisfy the prior's PDF is more sensitive to stopping the iteration process for all three noise models. A stability investigation showed that the initial image by filtered back projection is very robust. Clinical patient data demonstrated the effectiveness of the proposed joint-parameter-Bayes and stopping criterion.