Detection of pulmonary nodules by multislice computed tomography: improved detection rate with reduced slice thickness

New insights into imaging of pulmonary metastases from extra-thoracic neoplasms

The lung is one of the most common sites of metastases from extra-thoracic neoplasms. Lung metastases can show heterogeneous imaging appearance, thus mimicking a wide range of lung diseases, from benign lesions to primary lung cancer. The proper interpretation of pulmonary findings is crucial for prognostic assessment and treatment planning, even to avoid unnecessary procedures and patient anxiety. For this purpose, computed tomography (CT) is one of the most used imaging modalities. In the last decades, cancer patients' population has steadily increased and, due to the widespread application of CT for staging and surveillance, the detection of pulmonary nodules has raised, making their characterization and management an urgent and mostly unsolved problem for both radiologists and clinicians. This review will highlight the pathways of dissemination of extra-thoracic tumours to the lungs and the heterogeneous CT imaging appearance of pulmonary metastases, providing useful clues to properly address the diagnosis. Furthermore, we will deal with the promising applications of radiomics in this field. Finally, a focus on the hot-topic of pulmonary nodule management in patients with extra-thoracic neoplasms (ETNs) will be discussed.

The 2023 American Association for Thoracic Surgery (AATS) Expert Consensus Document: Management of subsolid lung nodules

OBJECTIVE

Lung cancers that present as radiographic subsolid nodules represent a subtype with distinct biological behavior and outcomes. The objective of this document is to review the existing literature and report consensus among a group of multidisciplinary experts, providing specific recommendations for the clinical management of subsolid nodules.

METHODS

The American Association for Thoracic Surgery Clinical Practice Standards Committee assembled an international, multidisciplinary expert panel composed of radiologists, pulmonologists, and thoracic surgeons with established expertise in the management of subsolid nodules. A focused literature review was performed with the assistance of a medical librarian. Expert consensus statements were developed with class of recommendation and level of evidence for each of 4 main topics: (1) definitions of subsolid nodules (radiology and pathology), (2) surveillance and diagnosis, (3) surgical interventions, and (4) management of multiple subsolid nodules. Using a modified Delphi method, the statements were evaluated and refined by the entire panel.

RESULTS

Consensus was reached on 17 recommendations. These consensus statements reflect updated insights on subsolid nodule management based on the latest literature and current clinical experience, focusing on the correlation between radiologic findings and pathological classifications, individualized subsolid nodule surveillance and surgical strategies, and multimodality therapies for multiple subsolid lung nodules.

CONCLUSIONS

Despite the complex nature of the decision-making process in the management of subsolid nodules, consensus on several key recommendations was achieved by this American Association for Thoracic Surgery expert panel. These recommendations, based on evidence and a modified Delphi method, provide guidance for thoracic surgeons and other medical professionals who care for patients with subsolid nodules.

Screening for lung cancer using thin-slice low-dose computed tomography in southwestern China: a population-based real-world study

OBJECTIVES

Lung cancer is one of the most common malignant tumors threatening human life and health. At present, low-dose computed tomography (LDCT) screening for the high-risk population to achieve early diagnosis and treatment of lung cancer has become the first choice recommended by many authoritative international medical organizations. To further optimize the lung cancer screening method, we conducted a real-world study of LDCT lung cancer screening in a large sample of a healthy physical examination population, comparing differences in lung nodules and lung cancer detection between thin and thick-slice LDCT scanning.

METHODS

A total of 29 296 subjects who underwent low-dose thick-slice CT scanning (5 mm thickness) from January 2015 to December 2015 and 28 058 subjects who underwent low-dose thin-slice CT scanning (1 mm thickness) from January 2018 to December 2018 in West China Hospital were included. The positive detection rate, detection rate of lung cancer, pathological stage of lung cancer, and mortality rate of lung cancer were analyzed and compared between the two groups.

RESULTS

The positive rate of LDCT screening in the thin-slice scanning group was significantly higher than that in the thick-slice scanning group (20.1% vs. 14.4%, p < 0.001). In addition, the lung cancer detection rate in the thin-slice LDCT screening positive group was significantly higher than that in the thick-slice scanning group (78.0% vs. 52.9%, p < 0.001).

CONCLUSIONS

The screening positive rate of low-dose thin-slice CT scanning is higher and more early-stage lung cancer (IA1 stage) can be detected in the screen-positive group.

LIT-Former: Linking In-Plane and Through-Plane Transformers for Simultaneous CT Image Denoising and Deblurring

This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they focus on 2D images and perform denoising due to low-dose and deblurring for super-resolution separately. Up to date, little work was done for simultaneous in-plane denoising and through-plane deblurring, which is important to obtain high-quality 3D CT images with lower radiation and faster imaging speed. For this task, a straightforward method is to directly train an end-to-end 3D network. However, it demands much more training data and expensive computational costs. Here, we propose to link in-plane and through-plane transformers for simultaneous in-plane denoising and through-plane deblurring, termed as LIT-Former, which can efficiently synergize in-plane and through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both convolution and transformer networks. LIT-Former has two novel designs: efficient multi-head self-attention modules (eMSM) and efficient convolutional feed-forward networks (eCFN). First, eMSM integrates in-plane 2D self-attention and through-plane 1D self-attention to efficiently capture global interactions of 3D self-attention, the core unit of transformer networks. Second, eCFN integrates 2D convolution and 1D convolution to extract local information of 3D convolution in the same fashion. As a result, the proposed LIT-Former synergizes these two sub-tasks, significantly reducing the computational complexity as compared to 3D counterparts and enabling rapid convergence. Extensive experimental results on simulated and clinical datasets demonstrate superior performance over state-of-the-art models. The source code is made available at https://github.com/hao1635/LIT-Former.

The clinical significance of indeterminate pulmonary nodules in patients with primary bone sarcoma: a systematic review

OBJECTIVE

To report the incidence of indeterminate pulmonary nodules (IPN) and the rate of progression of IPNs to metastasis in patients with primary bone cancers. We also aimed to evaluate clinical or radiological parameters that may identify IPNs more likely to progress to metastatic disease and their effect on overall or event-free survival in patients with primary bone sarcoma.

METHODS

A systematic search of the electronic databases Medline, Embase, and Cochrane Library was undertaken for eligible articles on IPNs in patients with primary bone sarcomas, published in the English language from inception of the databases to 2023. The Newcastle-Ottawa Quality Assessment Form for Cohort Studies was utilized to evaluate risk of bias in included studies.

RESULTS

Six studies, involving 1667 patients, were included in this systematic review. Pooled quantitative analysis found the rate of incidence of IPN to be 18.1% (302 out of 1667) and the rate of progression to metastasis to be 45.0% (136 out of 302). Nodule size (more than 5 mm diameter), number (more than or equal to 4), distribution (bilaterally distributed), incomplete calcification, and lobulated margins were associated with an increased likelihood of IPNs progressing to metastasis, however, their impact on overall or event-free survival remains unclear.

CONCLUSION

The risk of IPNs progressing to metastasis in patients with primary bone sarcoma is non-negligible. Large IPNs have a high risk to be an actual metastasis. We suggest that IPNs in these patients be followed up for a minimum of 2 years with CT imaging at 3, 6, and 12 month intervals, particularly for nodules measuring >5 mm in average diameter.

ADVANCES IN KNOWLEDGE

This is the first systematic review on IPNs in patients with primary bone sarcomas only and proposes viable management strategies for such patients.

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data

PURPOSE

To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data.

METHODS

In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland-Altman analysis.

RESULTS

A total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r2 = 0.97), muscle area (r2 = 0.98), liver attenuation (r2 = 0.99), liver volume (r2 = 0.98) and spleen volume (r2 = 0.99), VSR (r2 = 0.98), and aortic calcium (r2 = 0.92); (p < 0.001 for all). Moderate positive correlation was observed for bone attenuation (r2 = 0.35). Bland-Altman analysis showed strong agreement for muscle attenuation, muscle area, liver attenuation, liver volume and spleen volume. Mean percentage differences amongst body composition measures were less than 5% for VSR (4.6%), muscle area (- 0.5%), liver attenuation (0.4%) and liver volume (2.7%) and less than 10% for muscle attenuation (- 5.5%) and spleen volume (5.1%). For aortic calcium, thick slice overestimated for Agatston scores between 0 and 100 and > 400 burden in 3.1% and 0.3% relative to thin slice, respectively, but underestimated scores between 100 and 400.

CONCLUSION

Automated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms.

Survival outcomes and surgical morbidity based on surgical approach to pulmonary metastasectomy in pediatric, adolescent and young adult patients with osteosarcoma

BACKGROUND

Thoracotomy is considered the standard surgical approach for the management of pulmonary metastases in osteosarcoma (OST). Several studies have identified the advantages of a thoracoscopic approach, however, the clinical significance of thoracotomy compared to thoracoscopy is yet to be evaluated in a randomized trial.

AIMS

The primary aim was to determine the survival outcomes in OST patients based on surgical approach for pulmonary metastasectomy (PM) and secondary aim was to assess the post-operative morbidities of OST PM through various surgical approaches.

MATERIALS AND METHODS

We conducted a single institution retrospective study to compare survival outcomes and surgical morbidity according to the surgical approach of the management of pulmonary metastases in patients with OST.

RESULTS

Sixty-one patients with OST underwent PM. Twenty-one patients were metastatic at diagnosis and underwent PM during primary treatment; nine had thoracotomy, six thoracoscopy, and six combined thoracoscopy with thoracotomy (CTT). Forty-three patients with first pulmonary relapse or progression underwent PM; 18 had thoracotomy, 16 thoracoscopy and nine CTT. There was no difference in survival between surgical approaches. There were significantly more postoperative morbidities associated with thoracotomy for initial PM (pain and postoperative chest tube placement), and for PM at first relapse (pneumothoraces, pain, Foley catheter use and prolonged hospitalizations).

CONCLUSION

Our study demonstrates that patients with OST pulmonary metastases have comparable poor outcomes despite varying surgical approaches for PM. There were significantly more postoperative morbidities associated with thoracotomy for PM. Surgical bias and other competing risks could not be assessed given the limitations of a retrospective study and may be addressed in a prospective trial evaluating surgical approach for PM in OST.

Diagnostic performance of artificial intelligence for pediatric pulmonary nodule detection in computed tomography of the chest

PURPOSE

To test the performance of a commercially available adult pulmonary nodule detection artificial intelligence (AI) tool in pediatric CT chests.

METHODS

30 consecutive chest CTs with or without contrast of patients ages 12-18 were included. Images were retrospectively reconstructed at 3 mm and 1 mm slice thickness. AI for detection of lung nodules in adults (Syngo CT Lung Computer Aided Detection (CAD)) was evaluated. 3 mm axial images were retrospectively reviewed by two pediatric radiologists (reference read) who determined the location, type, and size of nodules. Lung CAD results at 3 mm and 1 mm slice thickness were compared to reference read by two other pediatric radiologists. Sensitivity (Sn) and positive predictive value (PPV) were analyzed.

RESULTS

The radiologists identified 109 nodules. At 1 mm, CAD detected 70 nodules; 43 true positive (Sn = 39 %), 26 false positive (PPV = 62 %), and 1 nodule which had not been identified by radiologists. At 3 mm, CAD detected 60 nodules; 28 true positive (Sn = 26 %), 30 false positive (PPV = 48 %) and 2 nodules which had not been identified by radiologists. There were 103 solid nodules (47 measuring < 3 mm) and 6 subsolid nodules (5 measuring < 5 mm). When excluding 52 nodules (solid < 3 mm and subsolid < 5 mm) based on algorithm conditions, the Sn increased to 68 % at 1 mm and 49 % at 3 mm but there was no significant change in the PPV measuring 60 % at 1 mm and 48 % at 3 mm.

CONCLUSION

The adult Lung CAD showed low sensitivity in pediatric patients, but better performance at thinner slice thickness and when smaller nodules were excluded.

Multi-Branch Attention Learning for Bone Age Assessment with Ambiguous Label

Bone age assessment (BAA) is a typical clinical technique for diagnosing endocrine and metabolic diseases in children's development. Existing deep learning-based automatic BAA models are trained on the Radiological Society of North America dataset (RSNA) from Western populations. However, due to the difference in developmental process and BAA standards between Eastern and Western children, these models cannot be applied to bone age prediction in Eastern populations. To address this issue, this paper collects a bone age dataset based on the East Asian populations for model training. Nevertheless, it is laborious and difficult to obtain enough X-ray images with accurate labels. In this paper, we employ ambiguous labels from radiology reports and transform them into Gaussian distribution labels of different amplitudes. Furthermore, we propose multi-branch attention learning with ambiguous labels network (MAAL-Net). MAAL-Net consists of a hand object location module and an attention part extraction module to discover the informative regions of interest (ROIs) based only on image-level labels. Extensive experiments on both the RSNA dataset and the China Bone Age (CNBA) dataset demonstrate that our method achieves competitive results with the state-of-the-arts, and performs on par with experienced physicians in children's BAA tasks.

Pearls and Pitfalls in Lung Cancer CT Screening

Annual LDCT lung cancer screening is recommended by the United States Preventive Services Task Force (USPSTF) for high-risk population based on the results from the National Lung Cancer Screening Trial (NLST) that showed a significant (20%) reduction in lung cancer-specific mortality rate with the use of annual low-dose computed tomography (LDCT) screening. More recently, the benefits of lung cancer screening were confirmed by the Dutch- Belgian NELSON trial in Europe. With the implementation of lung screening in large scale, knowledge of the limitations related to false positive, false negative and other potential pitfalls is essential to avoid misdiagnosis. This review outlines the most common potential pitfalls in the characterization of screen-detected lung nodules that include artifacts in LDCT, benign nodules that mimic lung cancer, and causes of false negative evaluations of lung cancer with LDCT and PET/CT studies. Awareness of the spectrum of potential pitfalls in pulmonary nodule detection and characterization, including equivocal or atypical presentations, is important for avoiding misinterpretation that can alter patient management.

Impact of Bayesian penalized likelihood reconstruction on quantitative and qualitative aspects for pulmonary nodule detection in digital 2-[18F]FDG-PET/CT

To evaluate the impact of block sequential regularized expectation maximization (BSREM) reconstruction on quantitative and qualitative aspects of 2-[¹⁸F]FDG-avid pulmonary nodules compared to conventional ordered subset expectation maximization (OSEM) reconstruction method. Ninety-one patients with 144 2-[¹⁸F]FDG-avid pulmonary nodules (all ≤ 20 mm) undergoing PET/CT for oncological (re-)staging were retrospectively included. Quantitative parameters in BSREM and OSEM (including point spread function modelling) were measured, including maximum standardized uptake value (SUV_max). Nodule conspicuity in BSREM and OSEM images was evaluated by two readers. Wilcoxon matched pairs signed-rank test was used to compare quantitative and qualitative parameters in BSREM and OSEM. Pulmonary nodule SUV_max was significantly higher in BSREM images compared to OSEM images [BSREM 5.4 (1.2–20.7), OSEM 3.6 (0.7–17.4); p = 0.0001]. In a size-based analysis, the relative increase in SUV_max was more pronounced in smaller nodules (≤ 7 mm) as compared to larger nodules (8–10 mm, or > 10 mm). Lesion conspicuity was higher in BSREM than in OSEM (p < 0.0001). BSREM reconstruction results in a significant increase in SUV_max and a significantly improved conspicuity of small 2-[¹⁸F]FDG-avid pulmonary nodules compared to OSEM reconstruction. Digital 2-[¹⁸F]FDG-PET/CT reading may be enhanced with BSREM as small lesion conspicuity is improved.

A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management

Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. ¹⁸F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of ¹⁸F-FDG PET/CT in the field of oncology is well established. Clinical application of ¹⁸F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of ¹⁸F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of ¹⁸F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of ¹⁸F-FDG PET/CT in lung disease management.

A Feature-based Affine Registration Method for Capturing Background Lung Tissue Deformation for Ground Glass Nodule Tracking

Lung nodule tracking assessment relies on cross-sectional measurements of the largest lesion profile depicted in initial and follow-up computed tomography (CT) images. However, apparent changes in nodule size assessed via simple image-based measurements may also be compromised by the effect of the background lung tissue deformation on the GGN between the initial and follow-up images, leading to erroneous conclusions about nodule changes due to disease. To compensate for the lung deformation and enable consistent nodule tracking, here we propose a feature-based affine registration method and study its performance vis-a-vis several other registration methods. We implement and test each registration method using both a lung- and a lesion-centered region of interest on ten patient CT datasets featuring twelve nodules, including both benign and malignant GGO lesions containing pure GGNs, part-solid, or solid nodules. We evaluate each registration method according to the target registration error (TRE) computed across 30 - 50 homologous fiducial landmarks surrounding the lesions and selected by expert radiologists in both the initial and follow-up patient CT images. Our results show that the proposed feature-based affine lesion-centered registration yielded a 1.1 ± 1.2 mm TRE, while a Symmetric Normalization deformable registration yielded a 1.2 ± 1.2 mm TRE, and a least-square fit registration of the 30-50 validation fiducial landmark set yielded a 1.5 ± 1.2 mm TRE. Although the deformable registration yielded a slightly higher registration accuracy than the feature-based affine registration, it is significantly more computationally efficient, eliminates the need for ambiguous segmentation of GGNs featuring ill-defined borders, and reduces the susceptibility of artificial deformations introduced by the deformable registration, which may lead to increased similarity between the registered initial and follow-up images, over-compensating for the background lung tissue deformation, and, in turn, compromising the true disease-induced nodule change assessment. We also assessed the registration qualitatively, by visual inspection of the subtraction images, and conducted a pilot pre-clinical study that showed the proposed feature-based lesion-centered affine registration effectively compensates for the background lung tissue deformation between the initial and follow-up images and also serves as a reliable baseline registration method prior to assessing lung nodule changes due to disease.

Pediatric Pulmonary Nodules: Imaging Guidelines and Recommendations

Incidental pulmonary nodules are not infrequently identified on computed tomography imaging in the pediatric population and can be a challenge in suggesting appropriate follow-up recommendations. An evidence-based and practical imaging approach for diagnosis and appropriate directed management is essential for optimal patient care. This article provides an up-to-date review of the pediatric pulmonary nodule literature and suggests a practical algorithm to manage pulmonary nodules in the pediatric population.

Impact of Vessel Suppressed-CT on Diagnostic Accuracy in Detection of Pulmonary Metastasis and Reading Time

RATIONALE AND OBJECTIVES

To assess if vessel suppression (VS) improves nodule detection rate, interreader agreement, and reduces reading time in oncologic chest computed tomography (CT).

MATERIAL AND METHODS

One-hundred consecutive oncologic patients (65 male; median age 60y) who underwent contrast-enhanced chest CT were retrospectively included. For all exams, additional VS series (ClearRead CT, Riverrain Technologies, Miamisburg) were reconstructed. Two groups of three radiologists each with matched experience were defined. Each group evaluated the SD-CT as well as VS-CT. Each reader marked the presence, size, and position of pulmonary nodules and documented reading time. In addition, for the VS-CT the presence of false positive nodules had to be stated. Cohen's Kappa (k) was used to calculate the interreader-agreement between groups. Reading time was compared using paired t test.

RESULTS

Nodule detection rate was significantly higher in VS-CT compared to the SD-CT (+21%; p <0.001). Interreader-agreement was higher in the VS-CT (k = 0.431, moderate agreement) compared to SD-CT (k = 0.209, fair agreement). Almost all VS-CT series had false positive findings (97-99 out of 100). Average reading time was significantly shorter in the VS-CT compared to the SD-CT (154 ± 134vs. 194 ± 126; 21%, p<0.001).

CONCLUSIONS

Vessel suppression increases nodule detection rate, improves interreader agreement, and reduces reading time in chest CT of oncologic patients. Due to false positive results a consensus reading with the SD-CT is essential.

Interobserver variability in Lung CT Screening Reporting and Data System categorisation in subsolid nodule-enriched lung cancer screening CTs

OBJECTIVES

To assess interobserver agreement in Lung CT Screening Reporting and Data System (Lung-RADS) categorisation in subsolid nodule-enriched low-dose screening CTs.

METHODS

A retrospective review of low-dose screening CT reports from 2013 to 2017 using keyword searches for subsolid nodules identified 54 baseline CT scans. With an additional 108 negative screening CT scans, a total of 162 CT scans were categorised according to the Lung-RADS by two fellowship-trained thoracic radiologists in consensus. We randomly selected 20, 20, 10, and 10 scans from categories 1/2, 3, 4A, and 4B CT scans, respectively, to ensure balanced category representation. Five radiologists classified the 60 CT scans into Lung-RADS categories. The frequencies of concordance and minor and major discordance were calculated, with major discordance defined as at least 6 months of management discrepancy. We used Cohen's κ statistics to analyse reader agreement.

RESULTS

An average of 60.3% (181 of 300) of all cases and 45.0% (90 of 200) of positive screens were correctly categorised. The minor and major discordance rates were 12.3% and 27.3% overall and 18.5% and 36.5% in positive screens, respectively. The concordance rate was significantly higher among experienced thoracic radiologists. Overall, the interobserver agreement was moderate (mean κ, 0.45; 95% confidence interval: 0.40-0.51). The proportion of part-solid risk-dominant nodules was significantly higher in cases with low rates of accurate categorisation.

CONCLUSION

This retrospective study observed variable accuracy and moderate interobserver agreement in radiologist categorisation of subsolid nodules in screening CTs. This inconsistency may affect management recommendations for lung cancer screening.

KEY POINTS

• Diagnostic performance for Lung-RADS categorisation is variable among radiologists with fair to moderate interobserver agreement in subsolid nodule-enriched CT scans. • Experienced thoracic radiologists showed more accurate and consistent Lung-RADS categorisation than radiology residents. • The relative abundance of part-solid nodules was a potential factor related to increased disagreement in Lung-RADS categorisation.

What Effect Do Pulmonary Micronodules Detected at Presentation in Patients with Osteosarcoma Have on 5-Year Overall Survival?

For osteosarcoma, staging criteria, prognosis estimates, and surgical recommendations have not yet changed to reflect increasingly sensitive computed tomography (CT) imaging. However, the frequent identification of micronodules (<5 mm) on presentation leaves clinicians in a difficult position regarding the need to biopsy, resect, or follow the lesions and whether to consider the patient metastatic or non-metastatic. Our objective was to compare the 5-year overall survival rates of patients with osteosarcoma with non-surgically resected lung micronodules on presentation to patients without micronodules to guide community oncologists faced with this common dilemma. We collected data retrospectively on all newly diagnosed osteosarcoma patients, aged less than 50, treated at Rush University Hospital over 25 years without pulmonary nodules >10 mm or pulmonary surgical intervention. Kaplan-Meier curves showed there was no difference in 5-year overall survival in patients with any size nodule <5 mm compared to patients with no nodules. Additionally, our study showed a survival advantage for those who presented with 0 or 1 nodule (90%) compared to ≥2 nodules (53%). Our data suggest surgery may not be necessary for singular nodules <5 mm identified on presentation, and that these patients behave more like "localized" patients than metastatic patients.

Utility of PET/CT in the diagnosis and staging of lung cancer after ecobronchoscopy in mining population

INTRODUCTION

Positron emission tomography (PET) with computerized axial tomography (CT) in a single device is known as PET/CT. It has been widely documented and validated, and it is currently a core part of the diagnosis and staging of lung cancer. However, its reliability has not been analysed in specific populations. The objective of this study is to determine the usefulness of PET/CT in patients exposed to mining activities in which an endobronchial ultrasound (EBUS) has been performed for the diagnosis and/or staging of lung cancer.

PATIENTS AND METHODS

With a prospective and real-time database, all the patients who had undergone an EBUS with suspicion of lung cancer and who had previously undergone a PET/CT were selected. The observation unit was the lymph node and, based on their history of exposure to mining activities, the sample was divided into two categories, group 1: not exposed; and group 2: exposed. In each group, and with the results from anatomical pathology as a dependent variable, logistic models were established to look for independent risk factors for malignancy.

RESULTS

In group 1, lymph nodes larger than 1 cm and PET/CT uptake with maximum standardized uptake value (SUVmax) over 2.5 were independent risk factors for malignancy. However, in group 2 (exposed patients), none of those factors were predictors for malignancy.

DISCUSSION

In the population of individuals with occupational exposure to mining, PET/CT is an imaging technique with diagnostic limitations for lung cancer.

The assessment of consecutive 4D-CT scans during simulation for lung stereotactic body radiation therapy patients

Purpose: To evaluate the breathing amplitude, tumor motion, patient positioning, and treatment volumes among consecutive four-dimensional computed tomography (4D-CT) scans, during the simulation for lung stereotactic body radiation therapy (SBRT).

Material and methods: The variation and shape of the breathing amplitude, patient positioning, and treatment volumes were evaluated for 55 lung cancer patients after consecutive 4D-CT acquisitions, scanned at one-week intervals. The impact of variation in the breathing amplitude on lung tumor motion was determined for 20 patients. The gross tumor volume (GTV) was contoured from a free-breathing CT scan and at ten phases of the respiratory cycle, for both 4D-CTs (440 phases in total).

Results: Breathing amplitude decreased by 3.6 (3.4-4.9) mm, tumor motion by 3.2 (0.4-5.0) mm while breathing period increased by 4 (2-6) s, inter-scan for 20 patients. Intra-scan variation was 4 times greater for the breathing amplitude, 5 times for the breathing period, and 8 times for the breathing cycle, comparing irregular versus regular breathing patterns for 55 patients. Using coaching, the breathing amplitude increases 3 to 8 mm, and the breathing period 2 to 6 s. Differences in the contoured treatment volumes were less than 10% between consecutive scans. Patient positioning remained stable, with a small inter-scan difference of 1.1 (0.6-1.4) mm.

Conclusion: Decreasing the inter-scan breathing amplitude decreases the tumor motion reciprocally. When the breathing amplitude decreases, the breathing period increases at inter- and intra-scan, especially during irregular breathing. Coaching improves respiration, keeping the initial shape of the breathing amplitude. Contoured treatment volumes and patient positioning were reproducible through successive scans.

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation dose, reconstruction kernel, and slice thickness. CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%, and 50% of protocol dose were simulated; reduced-dose and full-dose data were reconstructed using conventional filtered back-projection and iterative-reconstruction kernels at a range of thicknesses (0.6-5.0 mm). Full-dose/B50f kernel reconstructions underwent expert segmentation for reference Region-Of-Interest (ROI) and nodule volume per thickness; each ROI was applied to 40 corresponding images (combinations of 4 doses and 10 kernels). Typical texture analysis metrics (including 5 histogram features, 13 Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-Level Dependence Matrix, and 3 Neighborhood Gray-Tone Difference Matrix) were computed per ROI. Reconstruction conditions resulting in no significant change in volume, density, or texture metrics were identified as "compatible pairs" for a given outcome variable. Our results indicate that as thickness increases, volumetric reproducibility decreases, while reproducibility of histogram- and texture-based features across different acquisition and reconstruction parameters improves. To achieve concomitant reproducibility of volumetric and radiomic results across studies, balanced standardization of the imaging acquisition parameters is required.

Efficacy and Cost-effectiveness of Surgical Biopsy for Histologic Diagnosis of Indeterminate Nodules Suspected for Early Stage Lung Cancer: Comparison with Percutaneous Needle Biopsy

BACKGROUND

Indeterminate pulmonary nodules (IPN) suspected for early stage lung cancer mandate accurate diagnosis. Both percutaneous needle biopsy (PCNB) and surgical biopsy (SB) are valuable options. The present study aimed to compare the efficacy and cost-effectiveness between PCNB and SB for IPN suspected for early stage lung cancer.

METHODS

During January-November 2018, patients who underwent operation for IPN suspected for early stage lung cancer (SB group, n = 245) or operation after PCNB (PCNB group, n = 113) were included. Patient-level cost data were extracted from medical bills from the institution. Propensity score matching was performed between the two groups from a retrospectively-collected database.

RESULTS

Fifteen patients (11.5%) had complications after PCNB; thirteen (11.5%) were not confirmed to have lung cancer through PCNB but underwent operation for IPN. In SB group, 172 (70.2%) and 7 (2.9%) patients underwent wedge resection and segmentectomy for SB, respectively; 66 patients (26.9%) underwent direct lobectomy without SB. After propensity score matching, 58 paired samples were produced. Most patients in PCNB group were admitted twice (n = 55, 94.8%). The average hospital stay was longer in PCNB group (12.9 ± 5.3 vs. 7.3 ± 3.0, P < 0.001). Though the cost of the operation was comparable (USD 12,509 ± 2,909 vs. 12,669 ± 3,334; P = 0.782), the total cost was higher for PCNB group (USD 14,403 ± 3,085 vs. 12,669 ± 3,334; P = 0.006). The average subcategory cost, which increases proportional to hospital stay, was higher in PCNB group, whereas the cost of operation and surgical materials were comparable between the two groups.

CONCLUSION

Lung cancer operation following SB for IPN was associated with lesser cost, shorter hospital stays, and lesser admission time than lung cancer operation after PCNB. The increased cost and longer hospital stay appear largely related to the admission for PCNB.

Comparison between CT and MRI in detection of metastasis of the retroperitoneum in testicular germ cell tumors: a prospective trial

Introduction: To minimize the radiation exposure of mostly young testicular cancer patients, it is essential to find out whether CT could be replaced by magnetic resonance imaging (MRI) in the staging and follow-up of the patients. In this trial, we examined whether abdominal MRI is as effective as computed tomography (CT) in the detection of retroperitoneal metastases of testicular cancer.Material and methods: This prospective study included 50 patients, 46 cases of retroperitoneal metastases and 4 controls without abdominal metastases (mean age 33, 5 years, range 20-65 years). Imaging of the retroperitoneum was performed using CT and 1.5 T MRI with diffusion weighted imaging (DWI). One experienced radiologist re-analyzed all of the examinations without knowledge of clinical information. All metastatic or suspicious lymph nodes were noted and measured two-dimensionally from axial images. Nodal detection and the size of detected nodes on CT and MRI were compared.Results: There was no significant difference in the detection of retroperitoneal metastasis between CT and MRI. The sensitivity of MRI was 0.98. There was no statistically significant difference in the sizes of lymph nodes found in CT and MRI, and even very small lymph nodes could be detected in MRI as well as in CT.Conclusion: MRI with DWI is as good as CT in detection of retroperitoneal lymph node metastases regardless of lymph node size, and it can be used as part of follow-up of testicular cancer patients instead of ionizing radiation producing imaging methods.

Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT

Objectives

This study was conducted in order to compare the effect of field of view (FOV) size on image quality between ultra-high-resolution CT (U-HRCT) and conventional high-resolution CT (HRCT).

Methods

Eleven cadaveric lungs were scanned with U-HRCT and conventional HRCT and reconstructed with five FOVs (40, 80, 160, 240, and 320 mm). Three radiologists evaluated and scored the images. Three image evaluations were performed, comparing the image quality with the five FOVs with respect to the 160-mm FOV. The first evaluation was performed on conventional HRCT images, and the second evaluation on U-HRCT images. Images were scored on normal structure, abnormal findings, and overall image quality. The third evaluation was a comparison of the images obtained with conventional HRCT and U-HRCT, with scoring performed on overall image quality. Quantitative evaluation of noise was performed by setting ROIs.

Results

In conventional HRCT, image quality was improved when the FOV was reduced to 160 mm. In U-HRCT, image quality, except for noise, improved when the FOV was reduced to 80 mm. In the third evaluation, overall image quality was improved in U-HRCT over conventional HRCT at all FOVs. Noise of U-HRCT increased with respect to conventional HRCT when the FOV was reduced from 160 to 40 mm. However, at 240- and 320-mm FOVs, the noise of U-HRCT and conventional HRCT showed no differences.

Conclusions

In conventional HRCT, image quality did not improve when the FOV was reduced below 160 mm. However, in U-HRCT, image quality improved even when the FOV was reduced to 80 mm.

Key Points

• Reducing the size of the field of view to 160 mm improves diagnostic imaging quality in high-resolution CT.

• In ultra-high-resolution CT, improvements in image quality can be obtained by reducing the size of the field of view to 80 mm.

• Ultra-high-resolution CT produces images of higher quality compared with conventional HRCT irrespective of the size of the field of view.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-06704-0) contains supplementary material, which is available to authorized users.

Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance

OBJECTIVES

To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small ¹⁸F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reconstruction affects detection accuracy as compared to ordered subset expectation maximization (OSEM) reconstruction.

METHODS

Fifty-seven patients with 92 ¹⁸F-FDG-avid pulmonary nodules (all ≤ 2 cm) undergoing PET/CT for oncological (re-)staging were retrospectively included and a total of 8824 PET images of the lungs were extracted using OSEM and BSREM reconstruction. Per-slice and per-nodule sensitivity of a deep learning algorithm was assessed, with an expert readout by a radiologist/nuclear medicine physician serving as standard of reference. Receiver-operator characteristic (ROC) curve of OSEM and BSREM were assessed and the areas under the ROC curve (AUC) were compared. A maximum standardized uptake value (SUV_max)-based sensitivity analysis and a size-based sensitivity analysis with subgroups defined by nodule size was performed.

RESULTS

The AUC of the deep learning algorithm for nodule detection using OSEM reconstruction was 0.796 (CI 95%; 0.772-0.869), and 0.848 (CI 95%; 0.828-0.869) using BSREM reconstruction. The AUC was significantly higher for BSREM compared to OSEM (p = 0.001). On a per-slice analysis, sensitivity and specificity were 66.7% and 79.0% for OSEM, and 69.2% and 84.5% for BSREM. On a per-nodule analysis, the overall sensitivity of OSEM was 81.5% compared to 87.0% for BSREM.

CONCLUSIONS

Our results suggest that machine learning algorithms may aid detection of small ¹⁸F-FDG-avid pulmonary nodules in clinical PET/CT. AI performed significantly better on images with BSREM than OSEM.

KEY POINTS

• The diagnostic value of deep learning for detecting small lung nodules (≤ 2 cm) in PET images using BSREM and OSEM reconstruction was assessed. • BSREM yields higher SUV_maxof small pulmonary nodules as compared to OSEM reconstruction. • The use of BSREM translates into a higher detectability of small pulmonary nodules in PET images as assessed with artificial intelligence.

Imaging of Wilms tumor: an update

Wilms tumor is the most common pediatric renal tumor, accounting for approximately 7% of all childhood cancers. Imaging plays an important role in the detection, staging, post-therapy evaluation and surveillance of Wilms tumor. Wilms tumor can be detected during surveillance of a known cancer predisposition or after a child presents with symptoms. In this manuscript we describe an evidence-based approach to the initial evaluation of Wilms tumor using current guidelines from the Children's Oncology Group (COG). We illustrate the COG staging system for pediatric renal tumors and highlight key imaging findings that are critical for surgical management. We also discuss the controversies regarding detection and significance of <5-mm pulmonary nodules at initial staging. And finally, we present some thoughts regarding surveillance of Wilms tumor, where overall survival has now approached 90%.

Toward an Affine Feature-Based Registration Method for Ground Glass Lung Nodule Tracking

Lung nodule progression assessment from medical imaging is a critical biomarker for assessing the course of the disease or the patient's response to therapy. CT images are routinely used to identify the location and size and rack the progression of lung nodules. However, nodule segmentation is challenging and prone to error, due to the irregular nodule boundaries, therefore introducing error in the lung nodule quantification process. Here, we describe the development and evaluation of a feature-based affine image registration framework that enables us to register two time point thoracic CT images as a means to account for the back-ground lung tissue deformation, then use digital subtraction images to assess tumor progression/regression. We have demonstrated this method on twelve de-identified patient datasets and showed that the proposed method yielded a better than 1.5mm registration accuracy vis-à-vis the widely accepted non-rigid image registration techniques. To demonstrate the potential clinical value of our described technique, we conducted a study in which our collaborating clinician was asked to provide an assessment of nodule progression/regression using the digital subtraction images post-registration. This assessment was consistent, yet provided more confidence, than the traditional lung nodule tracking based on visual analysis of the CT images.

Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology

Lung cancer is the leading cause of cancer-related mortality in the United States and worldwide. Early detection of lung cancer is an important opportunity for decreasing mortality. Data support using low-dose computed tomography (LDCT) of the chest to screen select patients who are at high risk for lung cancer. Lung screening is covered under the Affordable Care Act for individuals with high-risk factors. The Centers for Medicare & Medicaid Services (CMS) covers annual screening LDCT for appropriate Medicare beneficiaries at high risk for lung cancer if they also receive counseling and participate in shared decision-making before screening. The complete version of the NCCN Guidelines for Lung Cancer Screening provides recommendations for initial and subsequent LDCT screening and provides more detail about LDCT screening. This manuscript focuses on identifying patients at high risk for lung cancer who are candidates for LDCT of the chest and on evaluating initial screening findings.

Volumetric assessment of solid pulmonary nodules on ultralow-dose CT: a phantom study

Background

To reduce the radiation exposure from chest computed tomography (CT), ultralow-dose CT (ULDCT) protocols performed at sub-millisievert levels were previously tested for the evaluation of pulmonary nodules (PNs). The purpose of our study was to investigate the effect of ULDCT and iterative image reconstruction on volumetric measurements of solid PNs.

Methods

CT datasets of an anthropomorphic chest phantom containing solid microspheres were obtained with a third-generation dual-source CT at standard dose, 1/8th, 1/20th and 1/70th of standard dose [CT volume dose index (CTDI_vol): 0.03-2.03 mGy]. Semi-automated volumetric measurements were performed on CT datasets reconstructed with filtered back projection (FBP) and advanced modelled iterative reconstruction (ADMIRE), at strength level 3 and 5. Absolute percentage error (APE) evaluated measurement accuracy related to the effective volume. Scan repetition differences were evaluated using Bland-Altman analysis. Two-way analysis of variance (ANOVA) assessed influence of different scan parameters on APE. Proportional differences (PDs) tested the effect of dose settings and reconstruction algorithms on volumetric measurements, as compared to the standard protocol (standard dose-FBP).

Results

Bland-Altman analysis revealed small mean interscan differences of APE with narrow limits of agreement (-0.1%±4.3% to -0.3%±3.8%). Dose settings (P<0.001), reconstruction algorithms (P<0.001), nodule diameters (P<0.001) and nodule density (P=0.011) had statistically significant influence on APE. Post-hoc Bonferroni tests showed slightly higher APE when scanning with 1/70th of standard dose [mean difference: 3.4%, 95% confidence interval (CI): 2.5-4.3%; P<0.001], and for image reconstruction with ADMIRE5 (mean difference: 1.8%, 95% CI: 1.0-2.5%; P<0.001). No significant differences for scanning with 1/20th of standard dose (P=0.42), and image reconstruction with ADMIRE3 (P=0.19) were found. Scanning with 1/70th of standard dose and image reconstruction with FBP showed the widest range of PDs (-16.8% to 23.4%) compared to standard dose-FBP.

Conclusions

Our phantom study showed no significant difference between nodule volume measurements on standard dose CT (CTDI_vol: 2 mGy) and ULDCT with 1/20th of standard dose (CTDI_vol: 0.10 mGy).

Management of pulmonary nodules

Pulmonary nodules are frequently detected during clinical practice and require a structured approach in their management in order to identify early lung cancers and avoid harm from over investigation. The article reviews the 2015 British Thoracic Society guidelines for the management of pulmonary nodules and the evidence behind them.

Radiologist performance in the detection of lung cancer using CT

AIM

To measure the level of radiologists' performance in lung cancer detection, and to explore radiologists' performance in cancer specialised and non-specialised centres.

MATERIALS AND METHODS

Thirty radiologists read 60 chest computed tomography (CT) examinations. Thirty cases had surgically or biopsy-proven lung cancer and 30 were cancer-free cases. The cancer cases were validated by four expert radiologists who located the malignant lung nodules. Reader performance was evaluated by calculating sensitivity, location sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). In addition, sensitivity at fixed specificity (0.794) was computed from each reader's estimated ROC curve.

RESULTS

The radiologists had a mean sensitivity of 0.749, sensitivity at fixed specificity of 0.744, location sensitivity of 0.666, specificity of 0.81 and AUC of 0.846. Radiologists in the specialised and non-specialised cancer centres had the following (specialised, non-specialised) pairs of values: sensitivity=(0.80, 0.719); sensitivity for fixed 0.794 specificity=(0.752, 0.740); location sensitivity=(0.712, 0.637); specificity=(0.794, 0.82) and AUC=(0.846, 0.846).

CONCLUSION

The efficacy of radiologists was comparable to other studies. Furthermore, AUC outcomes were similar for specialised and non-specialised cancer centre radiologists, suggesting they have similar discriminatory ability and that the higher sensitivity and lower specificity for specialised-centre radiologists can be attributed to them being less conservative in interpreting case images.

Local complexity metrics to quantify the effect of anatomical noise on detectability of lung nodules in chest CT imaging

The purpose of this study is to (1) develop metrics to characterize the regional anatomical complexity of the lungs, and (2) relate these metrics with lung nodule detection in chest CT. A free-scrolling reader-study with virtually inserted nodules (13 radiologists × 157 total nodules = 2041 responses) is used to characterize human detection performance. Metrics of complexity based on the local density and orientation of distracting vasculature are developed for two-dimensional (2-D) and three-dimensional (3-D) considerations of the image volume. Assessed characteristics included the distribution of 2-D/3-D vessel structures of differing orientation (dubbed "2-D/3-D and dot-like/line-like distractor indices"), contiguity of inserted nodules with local vasculature, mean local gray-level surrounding each nodule, the proportion of lung voxels to total voxels in each section, and 3-D distance of each nodule from the trachea bifurcation. A generalized linear mixed-effects statistical model is used to determine the influence of each these metrics on nodule detectability. In order of decreasing effect size: 3-D line-like distractor index, 2-D line-like distractor index, 2-D dot-like distractor index, local mean gray-level, contiguity with 2-D dots, lung area, and contiguity with 3-D lines all significantly affect detectability ( P < 0.05 ). These data demonstrate that local lung complexity degrades detection of lung nodules.

Effect of Hybrid Kernel and Iterative Reconstruction on Objective and Subjective Analysis of Lung Nodule Calcification in Low-Dose Chest CT

Objective

To evaluate the differences in subjective calcification detection rates and objective calcium volumes in lung nodules according to different reconstruction methods using hybrid kernel (FC13-H) and iterative reconstruction (IR).

Materials and Methods

Overall, 35 patients with small (< 4 mm) calcified pulmonary nodules on chest CT were included. Raw data were reconstructed using filtered back projection (FBP) or IR algorithm (AIDR-3D; Canon Medical Systems Corporation), with three types of reconstruction kernel: conventional lung kernel (FC55), FC13-H and conventional soft tissue kernel (FC13). The calcium volumes of pulmonary nodules were quantified using the modified Agatston scoring method. Two radiologists independently interpreted the role of each nodule calcification on the six types of reconstructed images (FC55/FBP, FC55/AIDR-3D, FC13-H/FBP, FC13-H/AIDR-3D, FC13/FBP, and FC13/AIDR-3D).

Results

Seventy-eight calcified nodules detected on FC55/FBP images were regarded as reference standards. The calcium detection rates of FC55/AIDR-3D, FC13-H/FBP, FC13-H/AIDR-3D, FC13/FBP, and FC13/AIDR-3D protocols were 80.7%, 15.4%, 6.4%, 52.6%, and 28.2%, respectively, and FC13-H/AIDR-3D showed the smallest calcium detection rate. The calcium volume varied significantly with reconstruction protocols and FC13/AIDR-3D showed the smallest calcium volume (0.04 ± 0.22 mm³), followed by FC13-H/AIDR-3D.

Conclusion

Hybrid kernel and IR influence subjective detection and objective measurement of calcium in lung nodules, particularly when both techniques (FC13-H/AIDR-3D) are combined.

Lung cancer screening: nodule identification and characterization

The accurate identification and characterization of small pulmonary nodules at low-dose CT is an essential requirement for the implementation of effective lung cancer screening. Individual reader detection performance is influenced by nodule characteristics and technical CT parameters but can be improved by training, the application of CT techniques, and by computer-aided techniques. However, the evaluation of nodule detection in lung cancer screening trials differs from the assessment of individual readers as it incorporates multiple readers, their inter-observer variability, reporting thresholds, and reflects the program accuracy in identifying lung cancer. Understanding detection and interpretation errors in screening trials aids in the implementation of lung cancer screening in clinical practice. Indeed, as CT screening moves to ever lower radiation doses, radiologists must be cognisant of new technical challenges in nodule assessment. Screen detected lung cancers demonstrate distinct morphological features from incidentally or symptomatically detected lung cancers. Hence characterization of screen detected nodules requires an awareness of emerging concepts in early lung cancer appearances and their impact on radiological assessment and malignancy prediction models. Ultimately many nodules remain indeterminate, but further imaging evaluation can be appropriate with judicious utilization of contrast enhanced CT or MRI techniques or functional evaluation by PET-CT.

Subjective and objective comparisons of image quality between ultra-high-resolution CT and conventional area detector CT in phantoms and cadaveric human lungs

Objectives

To compare the image quality of the lungs between ultra-high-resolution CT (U-HRCT) and conventional area detector CT (AD-CT) images.

Methods

Image data of slit phantoms (0.35, 0.30, and 0.15 mm) and 11 cadaveric human lungs were acquired by both U-HRCT and AD-CT devices. U-HRCT images were obtained with three acquisition modes: normal mode (U-HRCT_N: 896 channels, 0.5 mm × 80 rows; 512 matrix), super-high-resolution mode (U-HRCT_SHR: 1792 channels, 0.25 mm × 160 rows; 1024 matrix), and volume mode (U-HRCT_SHR-VOL: non-helical acquisition with U-HRCT_SHR). AD-CT images were obtained with the same conditions as U-HRCT_N. Three independent observers scored normal anatomical structures (vessels and bronchi), abnormal CT findings (faint nodules, solid nodules, ground-glass opacity, consolidation, emphysema, interlobular septal thickening, intralobular reticular opacities, bronchovascular bundle thickening, bronchiectasis, and honeycombing), noise, artifacts, and overall image quality on a 3-point scale (1 = worst, 2 = equal, 3 = best) compared with U-HRCT_N. Noise values were calculated quantitatively.

Results

U-HRCT could depict a 0.15-mm slit. Both U-HRCT_SHR and U-HRCT_SHR-VOL significantly improved visualization of normal anatomical structures and abnormal CT findings, except for intralobular reticular opacities and reduced artifacts, compared with AD-CT (p < 0.014). Visually, U-HRCT_SHR-VOL has less noise than U-HRCT_SHR and AD-CT (p < 0.00001). Quantitative noise values were significantly higher in the following order: U-HRCT_SHR (mean, 30.41), U-HRCT_SHR-VOL (26.84), AD-CT (16.03), and U-HRCT_N (15.14) (p < 0.0001). U-HRCT_SHR and U-HRCT_SHR-VOL resulted in significantly higher overall image quality than AD-CT and were almost equal to U-HRCT_N (p < 0.0001).

Conclusions

Both U-HRCT_SHR and U-HRCT_SHR-VOL can provide higher image quality than AD-CT, while U-HRCT_SHR-VOL was less noisy than U-HRCT_SHR.

Key Points

• Ultra-high-resolution CT (U-HRCT) can improve spatial resolution.

• U-HRCT can reduce streak and dark band artifacts.

• U-HRCT can provide higher image quality than conventional area detector CT.

• In U-HRCT, the volume mode is less noisy than the super-high-resolution mode.

• U-HRCT may provide more detailed information about the lung anatomy and pathology.

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5491-2) contains supplementary material, which is available to authorized users.

Screening for early stage lung cancer and its correlation with lung nodule detection

Currently, the most effective way of reducing lung cancer mortality is early diagnosis of lung cancer. The National Lung Screening Trial has proved the efficacy of lung cancer screening using low-dose computed tomography to reduce lung cancer mortality. However, many questions remain surrounding lung cancer screening implementation, among which include how to select the optimal risk population, the personalized screening interval based different levels of risk, methods to improve diagnostic discrimination between malignant and benign disease in detected lung nodules, and the roles of biomolecular markers in stratifying risk and in guiding the management of indeterminate nodules. This review concentrates on the latest developments of lung cancer screening and provides an overview of the main unanswered questions on lung nodule detection.

Vessel suppressed chest Computed Tomography for semi-automated volumetric measurements of solid pulmonary nodules

OBJECTIVE

To evaluate whether vessel-suppressed computed tomography (VSCT) can be reliably used for semi-automated volumetric measurements of solid pulmonary nodules, as compared to standard CT (SCT) MATERIAL AND METHODS: Ninety-three SCT were elaborated by dedicated software (ClearRead CT, Riverain Technologies, Miamisburg, OH, USA), that allows subtracting vessels from lung parenchyma. Semi-automated volumetric measurements of 65 solid nodules were compared between SCT and VSCT. The measurements were repeated by two readers. For each solid nodule, volume measured on SCT by Reader 1 and Reader 2 was averaged and the average volume between readers acted as standard of reference value. Concordance between measurements was assessed using Lin's Concordance Correlation Coefficient (CCC). Limits of agreement (LoA) between readers and CT datasets were evaluated.

RESULTS

Standard of reference nodule volume ranged from 13 to 366 mm³. The mean overestimation between readers was 3 mm³ and 2.9 mm³ on SCT and VSCT, respectively. Semi-automated volumetric measurements on VSCT showed substantial agreement with the standard of reference (Lin's CCC = 0.990 for Reader 1; 0.985 for Reader 2). The upper and lower LoA between readers' measurements were (16.3, -22.4 mm³) and (15.5, -21.4 mm³) for SCT and VSCT, respectively.

CONCLUSIONS

VSCT datasets are feasible for the measurements of solid nodules, showing an almost perfect concordance between readers and with measurements on SCT.

Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses

We study the performance of a computer-aided detection (CAD) system for lung nodules in computed tomography (CT) as a function of slice thickness. In addition, we propose and compare three different training methodologies for utilizing nonhomogeneous thickness training data (i.e., composed of cases with different slice thicknesses). These methods are (1) aggregate training using the entire suite of data at their native thickness, (2) homogeneous subset training that uses only the subset of training data that matches each testing case, and (3) resampling all training and testing cases to a common thickness. We believe this study has important implications for how CT is acquired, processed, and stored. We make use of 192 CT cases acquired at a thickness of 1.25 mm and 283 cases at 2.5 mm. These data are from the publicly available Lung Nodule Analysis 2016 dataset. In our study, CAD performance at 2.5 mm is comparable with that at 1.25 mm and is much better than at higher thicknesses. Also, resampling all training and testing cases to 2.5 mm provides the best performance among the three training methods compared in terms of accuracy, memory consumption, and computational time.

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.

Variation in Screening CT-Detected Nodule Volumetry as a Function of Size

OBJECTIVE

The objective of this study is to evaluate measurement variability in volumetric assessment of pulmonary nodules on low-dose CT images with a view toward determining how this variability is influenced by nodule size.

MATERIALS AND METHODS

A large CT screening database was reviewed to identify solid pulmonary nodules that had remained stable in size on the basis of findings from at least three scans obtained over a 2-year period. Two software packages (Lung VCAR and syngo.via) were used to assess the nodule volume on the two most recent CT scans, which were obtained at a slice thickness of 0.625 mm. The percentage of volume change was calculated for each nodule. The SD of the percentage of volume change was determined for nodules in each of the following nodule diameter size categories: less than 4 mm, 4-5 mm, 6-9 mm, and 10 mm or larger. The diameter was the mean of the length and width in the CT image that represented the largest cross-sectional area of the nodule.

RESULTS

The 171 stable nodules that were identified in 117 CT screening participants (median age, 61 years) ranged in size from 2.2 to 18.7 mm. The time between acquisition of the first and last CT images ranged from 3.7 to 17.8 years (median, 11.5 years). For each of the four categories of diameter size (< 4, 4-5, 6-9, and ≥ 10 mm), the SD of the percentage of volume change was 20.4%, 17.7%, 14.6%, and 3.7%, with the use of Lung VCAR, and 59.5%, 24.3%, 9.1%, and 6.2%, with the use of syngo.via, respectively. The SD decreased with increasing nodule diameter, with the use of both software packages.

CONCLUSION

Measurement variability decreased with increasing nodule diameter for both software packages and was different between the two software packages.

The imaging of small pulmonary nodules

Lung cancer is the leading cause of cancer death worldwide. The major goal in lung cancer research is the improvement of long-term survival. Pulmonary nodules have high clinical importance, they may not only prove to be an early manifestation of lung cancer, but decide to choose the right therapy. This review will introduce the development and current situation of several imaging examination methods: computed tomography (CT), positron emission tomography/computed tomography (PET/CT), endobronchial ultrasound (EBUS).

Detecting Pulmonary Nodules in Lung Cancer Patients Using Whole Body FDG PET/CT, High-resolution Lung Reformat of FDG PET/CT, or Diagnostic Breath Hold Chest CT

RATIONALE AND OBJECTIVES

Pulmonary nodules can be missed on the non-breath hold computed tomography (CT) portion of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT), and for this reason prior studies have advocated for routinely performing dedicated breath hold CT of the chest in addition to PET/CT for routine staging of malignancy. We evaluated the rate of pulmonary nodule detection on standard CT images from whole body PET/CT studies (WB-PET/CT), high-resolution lung reconstruction CT images from PET/CT studies (HR-PET/CT), and diagnostic breath hold chest CT (BH-CT).

MATERIALS AND METHODS

A cohort of 25 patients was identified who had a history of lung cancer as well as a PET/CT staging or restaging scan and BH-CT within 30 days of each other. All PET/CTs included a set of CT images using a soft tissue algorithm filter and 3.75- to 5-mm slice thickness, as well as high-resolution reformats with a sharp reconstruction filter and 2-mm slice thickness. The CT images from WB-PET/CT, HR-PET/CT, and BH-CT were reviewed by three radiologists. Significance was analyzed by two-way repeated measures analysis of variance.

RESULTS

There were 2.84 nodules found per patient with WB-PET/CT, 3.85 nodules with HR-PET/CT, and 3.91 nodules with BH-CT. When only nodules less than or equal to 8 mm in size were considered, WB-PET/CT also demonstrated significantly fewer nodules (1.98) compared to the HR-PET/CT (2.94) or a BH-CT (2.86) (P < 0.001). No difference in detection rate was noted between the two higher resolution modalities.

CONCLUSIONS

More pulmonary nodules are detected on the CT portion of PET/CT studies when high-resolution reformatted images are created and reviewed. The ability to detect nodules with the reformatted images was indistinguishable from dedicated BH-CT. Overall, high-resolution reformats of PET/CT images of the lungs can increase the sensitivity for pulmonary nodule detection, approaching that of dedicated BH-CT. These data suggest that if HR-PET/CT reformats are used, additional dedicated BH-CT is unnecessary for routine staging of lung cancer.

Role of Computer Aided Diagnosis (CAD) in the detection of pulmonary nodules on 64 row multi detector computed tomography

AIMS AND OBJECTIVES

To determine the overall performance of an existing CAD algorithm with thin-section computed tomography (CT) in the detection of pulmonary nodules and to evaluate detection sensitivity at a varying range of nodule density, size, and location.

MATERIALS AND METHODS

A cross-sectional prospective study was conducted on 20 patients with 322 suspected nodules who underwent diagnostic chest imaging using 64-row multi-detector CT. The examinations were evaluated on reconstructed images of 1.4 mm thickness and 0.7 mm interval. Detection of pulmonary nodules, initially by a radiologist of 2 years experience (RAD) and later by CAD lung nodule software was assessed. Then, CAD nodule candidates were accepted or rejected accordingly. Detected nodules were classified based on their size, density, and location. The performance of the RAD and CAD system was compared with the gold standard that is true nodules confirmed by consensus of senior RAD and CAD together. The overall sensitivity and false-positive (FP) rate of CAD software was calculated.

OBSERVATIONS AND RESULTS

Of the 322 suspected nodules, 221 were classified as true nodules on the consensus of senior RAD and CAD together. Of the true nodules, the RAD detected 206 (93.2%) and 202 (91.4%) by the CAD. CAD and RAD together picked up more number of nodules than either CAD or RAD alone. Overall sensitivity for nodule detection with the CAD program was 91.4%, and FP detection per patient was 5.5%. The CAD showed comparatively higher sensitivity for nodules of size 4-10 mm (93.4%) and nodules in hilar (100%) and central (96.5%) location when compared to RAD's performance.

CONCLUSION

CAD performance was high in detecting pulmonary nodules including the small size and low-density nodules. CAD even with relatively high FP rate, assists and improves RAD's performance as a second reader, especially for nodules located in the central and hilar region and for small nodules by saving RADs time.

Lung nodule and cancer detection in computed tomography screening

Fundamental to the diagnosis of lung cancer in computed tomography (CT) scans is the detection and interpretation of lung nodules. As the capabilities of CT scanners have advanced, higher levels of spatial resolution reveal tinier lung abnormalities. Not all detected lung nodules should be reported; however, radiologists strive to detect all nodules that might have relevance to cancer diagnosis. Although medium to large lung nodules are detected consistently, interreader agreement and reader sensitivity for lung nodule detection diminish substantially as the nodule size falls below 8 to 10 mm. The difficulty in establishing an absolute reference standard presents a challenge to the reliability of studies performed to evaluate lung nodule detection. In the interest of improving detection performance, investigators are using eye tracking to analyze the effectiveness with which radiologists search CT scans relative to their ability to recognize nodules within their search path in order to determine whether strategies might exist to improve performance across readers. Beyond the viewing of transverse CT reconstructions, image processing techniques such as thin-slab maximum-intensity projections are used to substantially improve reader performance. Finally, the development of computer-aided detection has continued to evolve with the expectation that one day it will serve routinely as a tireless partner to the radiologist to enhance detection performance without significant prolongation of the interpretive process. This review provides an introduction to the current understanding of these varied issues as we enter the era of widespread lung cancer screening.