Segmentation-Guided Hybrid Deep Learning for Pulmonary Nodule Detection and Risk Prediction from Multi-Cohort CT Images

To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR). This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location. The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively). At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.

MRI of Pulmonary Nodules: Closing the Gap on CT.

Objective To evaluate the value of computer-aided detection (CAD) system for pulmonary nodule detection using digital chest radiography in lung cancer screening. Methods One hundred consecutive digital chest radiographs from 6280 outpatients for lung cancer screening were independently reviewed by a thoracic radiologist and a computer-aided pulmonary nodule detection system.The radiographs were also reviewed by two experienced thoracic radiologists and the true nodules confirmed by two radiologists with reference to the CT images were marked and stored as a gold standard in the CAD system. The sensitivity and false positive of the radiologist and the CAD system for the detection of nodules on digital chest radiographs were compared. Results Ninety-five and 304 nodules were identified by radiologist and the CAD system, respectively. Of 134 nodules marked as true nodules by experienced radiologists, 82 (61.2%) and 105 (78. 4% ) nodules were identified by the radiologist and the CAD,respectively. The radiologist missed 35 true nodules which were only detected by CAD. The CAD system missed 10 true nodules which were only detected by radiologist. One hundred and twelve (83.6%) nodules were identified by radiologist with the CAD system. One hundred and ninety-nine nodules identified by CAD were false-positive with a rate of 2. 0 ( 199/100 ) per case. Conclusion Combining review of digital radiographs by radiologist with CAD system can improve the detection of pulmonary nodules in lung cancer screening. Key words: Lung neoplasms; Diagnosis,computer-assisted; Radiography

When Will Enough Ever Be Enough?

Mediastinal Lymphadenopathy in Lung Cancer Screening: A Red Flag.

Purpose: We wanted to evaluate the usefulness of low-dose multidetector CT for the detection and follow-up of pulmonary metastatic nodules in patients suffering with malignancy. Materials and Methods: We retrospectively reviewed the conventional chest radiographs and low-dose multidetector CT (low-dose CT) scans of 81 patients who had been under the diagnosis of malignancy. We reviewed the detection of pulmonary nodules and we counted the number of nodules detected by each method. The nodules were confirmed by surgical operation and by the radiologic criteria. The accuracy, sensitivity, specificity and positive and negative predictive values of each method for detecting metastatic nodules were compared with x tests. Results: Low-dose CT depicted more nodules than did chest radiograph, and the indeterminate nodules seen on chest radiograph may be clearly benign on low-dose CT (eg. calcified granulomas or bony lesions). The accuracy of low-dose CT (75.3%) was significantly higher than that of chest radiograph (49.4%) for the detection for metastatic nodules (p

Incidental Lymphadenopathy at CT Lung Cancer Screening.

Optimal threshold in low-dose CT quantification of emphysema

To assess the effect of using a lossy Joint Photographic Experts Group standard for wavelet image compression, JPEG2000, on pulmonary nodule detection at low-dose computed tomography (CT). One hundred sets of lung CT data ("cases") were compressed to 30:1, 20:1, and 10:1 levels by using a wavelet-based JPEG2000 method, resulting in 400 test cases. Each case consisted of nine 1.25-mm sections that had been obtained with 20-40 mAs. Four thoracic radiologists independently interpreted the test case images. Performance was measured by using area under the receiver operating characteristic (ROC) curve (Az) and conventional sensitivity and specificity analyses. There were 51 cases with and 49 without lung nodules. Az values were 0.984, 0.988, 0.972, 0.921, respectively, for original and 10:1, 20:1, and 30:1 compressed images. Az values decreased significantly at 30:1 (P =.014) but not at 10:1 compression, with a trend toward significant decrease at 20:1 (P =.051). Specificity values were unaffected by compression (>98.0% at all compression levels). Sensitivity values were 86.3% (176 of 204 test cases with nodules), 77.9% (159 of 204 cases), 76.5% (156 of 204 cases), and 70.1% (143 of 204 cases), respectively, for original and 10:1, 20:1, and 30:1 compressed images. Results of logistic regression model analysis confirmed the significant effects of compression rate and nodule attenuation, size, and location on sensitivity (P <.05). While no reduction in nodule detection at 10:1 compression levels was demonstrated by using ROC analysis, a significant decrease in sensitivity was identified. Further investigation is needed before widespread use of image compression technology in low-dose chest CT can be recommended.

e22504 Background: In 2021, the U.S. Preventive Services Task Force recommended expanding the population who should undergo routine lung or colorectal cancer screening to include those between 50 and 80, with a 20-pack or more smoking history, and those who are currently smoking or have quit within the last 15 years. According to the Centers for Disease Control, 74.3% of the at-risk population undergo colorectal cancer screening. In contrast, according to the American Lung Association, only 5.8% of the eligible population undergoes lung cancer screening. Methods: This is a retrospective analysis of 158 patients who underwent colorectal cancer screening by colonoscopy between July 2022 and October 2022 at a high-volume hospital in Indiana. Patients were followed up in their primary care office between November 2022 and January 2023 to assess if they had also met the screening criteria for lung cancer with a low dose computed tomography (LDCT) scan. Patients who met the lung screening criteria were interviewed at their subsequent primary care appointment to evaluate their knowledge, attitudes, and compliance with lung cancer screening. Results: Of the 158 participants, 86 (54.4%) met the criteria for lung cancer screening with a LDCT. Only 5 (5.8%) of the eligible subjects underwent LDCT screening. 72 of the 81 patients who were not screened for lung cancer had scheduled follow-ups with their primary care provider during the study time frame. Of these 72 patients, 45 (62.5%) lacked knowledge about LDCT and lung cancer screening, despite meeting the criteria for it. 22 (30.5%) did not think they needed a LDCT, and 5 (6.9%) had no interest in screening despite previous awareness. All 72 patients who had colorectal cancer screening but not lung cancer screening were asked why they underwent the former but not the latter. 68 (94.4%) indicated that colonoscopy was recommended to them and that they knew someone in their families who had it done. Conclusions: Colon cancer screening rates remain high compared to lung cancer screening rates in an at-risk population. A lack of knowledge from patients is the primary reason for not receiving LDCT, despite receiving colon cancer screening.

To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR. A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.28 mSv) and ULDCT (0.17 ± 0.03 mSv) scans. ULDCT images were reconstructed with ASiR-V (ULDCT-ASiR-V) and post-processed using DLIR (ULDCT-DLIR). The quality of the three CT images was analyzed. Three radiologists detected and measured pulmonary nodules on all CT images, with LDCT results serving as references. Nodule conspicuity was assessed using a five-point Likert scale, followed by further statistical analyses. A total of 463 nodules were detected using LDCT. The image noise of ULDCT-DLIR decreased by 60% compared to that of ULDCT-ASiR-V and was lower than that of LDCT (p < 0.001). The subjective image quality scores for ULDCT-DLIR (4.4 [4.1, 4.6]) were also higher than those for ULDCT-ASiR-V (3.6 [3.1, 3.9]) (p < 0.001). The overall nodule detection rates for ULDCT-ASiR-V and ULDCT-DLIR were 82.1% (380/463) and 87.0% (403/463), respectively (p < 0.001). The percentage difference between diameters > 1 mm was 2.9% (ULDCT-ASiR-V vs. LDCT) and 0.5% (ULDCT-DLIR vs. LDCT) (p = 0.009). Scores of nodule imaging sharpness on ULDCT-DLIR (4.0 ± 0.68) were significantly higher than those on ULDCT-ASiR-V (3.2 ± 0.50) (p < 0.001). DLIR-based image domain improves image quality, nodule detection rate, nodule imaging sharpness, and nodule measurement accuracy of ASiR-V on ULDCT. Question Deep learning post-processing is simple and cheap compared with raw data processing, but its performance is not clear on ultralow-dose CT. Findings Deep learning post-processing enhanced image quality and improved the nodule detection rate and accuracy of nodule measurement of ultralow-dose CT. Clinical relevance Deep learning post-processing improves the practicability of ultralow-dose CT and makes it possible for patients with less radiation exposure during lung cancer screening.

Expansion of Guideline-Recommended Lung Cancer Screening Eligibility: Implications for Health Equity of Joint Screening and Cessation Interventions

Lung cancer is the main cause of cancer-related deaths both in Europe and United States, because often it is diagnosed at late stages of the disease, when the survival rate is very low if compared to first asymptomatic stage. Lung cancer screening using annual low-dose Computed Tomography (CT) reduces lung cancer 5-year mortality by about 20% in comparison to annual screening with chest radiography. However, the detection of pulmonary nodules in low-dose chest CT scans is a very difficult task for radiologists, because of the large number (300/500) of slices to be analyzed. In order to support radiologists, researchers have developed Computer aided Detection (CAD) algorithms for the automated detection of pulmonary nodules in chest CT scans. Despite proved benefits of those systems on the radiologists detection sensitivity, the usage of CADs in clinical practice has not spread yet. The main objective of this thesis is to investigate and tackle the issues underlying this inconsistency. In particular, in Chapter 2 we introduce M5L, a fully automated Web and Cloud-based CAD for the automated detection of pulmonary nodules in chest CT scans. This system introduces a new paradigm in clinical practice, by making available CAD systems without requiring to radiologists any additional software and hardware installation. The proposed solution provides an innovative cost-effective approach for clinical structures. In Chapter 3 we present our international challenge aiming at a large-scale validation of state-of-the-art CAD systems. We also investigate and prove how the combination of different CAD systems reaches performances much higher than any best stand-alone system developed so far. Our results open the possibility to introduce in clinical practice very high-performing CAD systems, which miss a tiny fraction of clinically relevant nodules. Finally, we tested the performance of M5L on clinical data-sets. In chapter 4 we present the results of its clinical validation, which prove the positive impact of CAD as second reader in the diagnosis of pulmonary metastases on oncological patients with extra-thoracic cancers. The proposed approaches have the potential to exploit at best the features of different algorithms, developed independently, for any possible clinical application, setting a collaborative environment for algorithm comparison, combination, clinical validation and, if all of the above were successful, clinical practice.

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.7% with 1 false positive per scan and sensitivity of 94.2% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.

PL02.04 Blood MicroRNA and LDCT Reduce Unnecessary LDCT Repeats in Lung Cancer Screening: Results of Prospective BioMILD Trial