Lung nodule and cancer detection in computed tomography screening

Hierarchical classification of early microscopic lung nodule based on cascade network

Purpose

Early-stage lung cancer is typically characterized clinically by the presence of isolated lung nodules. Thousands of cases are examined each year, and one case usually contains numerous lung CT slices. Detecting and classifying early microscopic lung nodules is demanding due to their diminutive dimensions and restricted characterization capabilities. Therefore, a lung nodule classification model that performs well and is sensitive to microscopic lung nodules is needed to accurately classify lung nodules.

Methods

This paper uses the Resnet34 network as a basic classification model. A new cascade lung nodule classification method is proposed to classify lung nodules into 6 classes instead of the traditional 2 or 4 classes. It can effectively classify six different nodule types including ground-glass and solid nodules, benign and malignant nodules, and nodules with predominantly ground-glass or solid components.

Results

In this paper, the traditional multi-classification method and the cascade classification method proposed in this paper were tested using real lung nodule data collected in the clinic. The test results demonstrate that the cascade classification method in this study achieves an accuracy of 80.04% , outperforming the conventional multi-classification approach.

Conclusions

Different from the existing methods for categorizing the benign and malignant nature of lung nodules, the approach presented in this paper can classify lung nodules into 6 categories more accurately. At the same time, This paper proposes a rapid, precise, and dependable approach for classifying six distinct categories of lung nodules, which increases the accuracy categorization compared with the traditional multivariate categorization method.

Improving image quality of sparse-view lung tumor CT images with U-Net

BACKGROUND

We aimed to improve the image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determine the best tradeoff between number of views, IQ, and diagnostic confidence.

METHODS

CT images from 41 subjects aged 62.8 ± 10.6 years (mean ± standard deviation, 23 men), 34 with lung metastasis, 7 healthy, were retrospectively selected (2016-2018) and forward projected onto 2,048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, 7 healthy) for a single-blinded multireader study. These slices, for all levels of subsampling, with and without U-Net postprocessing, were presented to three readers. IQ and diagnostic confidence were ranked using predefined scales. Subjective nodule segmentation was evaluated using sensitivity and Dice similarity coefficient (DSC); clustered Wilcoxon signed-rank test was used.

RESULTS

The 64-projection sparse-view images resulted in 0.89 sensitivity and 0.81 DSC, while their counterparts, postprocessed with the U-Net, had improved metrics (0.94 sensitivity and 0.85 DSC) (p = 0.400). Fewer views led to insufficient IQ for diagnosis. For increased views, no substantial discrepancies were noted between sparse-view and postprocessed images.

CONCLUSIONS

Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level.

RELEVANCE STATEMENT

Our reader study demonstrates the benefit of U-Net postprocessing for regular CT screenings of patients with lung metastasis to increase the IQ and diagnostic confidence while reducing the dose.

KEY POINTS

• Sparse-projection-view streak artifacts reduce the quality and usability of sparse-view CT images. • U-Net-based postprocessing removes sparse-view artifacts while maintaining diagnostically accurate IQ. • Postprocessed sparse-view CTs drastically increase radiologists' confidence in diagnosing lung metastasis.

The Impact of Expectation Management and Model Transparency on Radiologists' Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study

BACKGROUND

Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems.

OBJECTIVE

We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists' trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans.

METHODS

In total, 20 radiologists from 7 Dutch medical centers performed lung nodule assessment on CT scans under different conditions in a simulated use study as part of a 2×2 repeated-measures quasi-experimental design. Two types of AI onboarding tutorials (reflective vs informative) and 2 levels of AI output (black box vs explainable) were designed. The radiologists first received an onboarding tutorial that was either informative or reflective. Subsequently, each radiologist assessed 7 CT scans, first without AI recommendations. AI recommendations were shown to the radiologist, and they could adjust their initial assessment. Half of the participants received the recommendations via black box AI output and half received explainable AI output. Mental model and psychological trust were measured before onboarding, after onboarding, and after assessing the 7 CT scans. We recorded whether radiologists changed their assessment on found nodules, malignancy prediction, and follow-up advice for each CT assessment. In addition, we analyzed whether radiologists' trust in their assessments had changed based on the AI recommendations.

RESULTS

Both variations of onboarding tutorials resulted in a significantly improved mental model of the AI-CAD system (informative P=.01 and reflective P=.01). After using AI-CAD, psychological trust significantly decreased for the group with explainable AI output (P=.02). On the basis of the AI recommendations, radiologists changed the number of reported nodules in 27 of 140 assessments, malignancy prediction in 32 of 140 assessments, and follow-up advice in 12 of 140 assessments. The changes were mostly an increased number of reported nodules, a higher estimated probability of malignancy, and earlier follow-up. The radiologists' confidence in their found nodules changed in 82 of 140 assessments, in their estimated probability of malignancy in 50 of 140 assessments, and in their follow-up advice in 28 of 140 assessments. These changes were predominantly increases in confidence. The number of changed assessments and radiologists' confidence did not significantly differ between the groups that received different onboarding tutorials and AI outputs.

CONCLUSIONS

Onboarding tutorials help radiologists gain a better understanding of AI-CAD and facilitate the formation of a correct mental model. If AI explanations do not consistently substantiate the probability of malignancy across patient cases, radiologists' trust in the AI-CAD system can be impaired. Radiologists' confidence in their assessments was improved by using the AI recommendations.

Harnessing the power of Microscale AcoustoFluidics: A perspective based on BAW cancer diagnostics

Cancer directly affects one in every three people, and mortality rates strongly correlate with the stage at which diagnosis occurs. Each of the multitude of methods used in cancer diagnostics has its own set of advantages and disadvantages. Two common drawbacks are a limited information value of image based diagnostic methods and high invasiveness when opting for methods that provide greater insight. Microfluidics offers a promising avenue for isolating circulating tumor cells from blood samples, offering high informational value at predetermined time intervals while being minimally invasive. Microscale AcoustoFluidics, an active method capable of manipulating objects within a fluid, has shown its potential use for the isolation and measurement of circulating tumor cells, but its full potential has yet to be harnessed. Extensive research has focused on isolating single cells, although the significance of clusters should not be overlooked and requires attention within the field. Moreover, there is room for improvement by designing smaller and automated devices to enhance user-friendliness and efficiency as illustrated by the use of bulk acoustic wave devices in cancer diagnostics. This next generation of setups and devices could minimize streaming forces and thereby enable the manipulation of smaller objects, thus aiding in the implementation of personalized oncology for the next generation of cancer treatments.

Detection of early (T1) lung cancers and lepidic adenocarcinomas in sputum and bronchial cytology

BACKGROUND

The lung is an extensively epithelialized organ, producing ample exfoliated material for sputum and bronchial cytology. In view of the updates in the World Health Organization classification of early (T1/≤ 3 cm) lung cancer with respect to adenocarcinomas with lepidic pattern, this study retrospectively reviews sputum and bronchial cytology paired with resection-confirmed lung cancers.

METHODS

A computerized search for all lung resection specimens of carcinomas over a 20-year period was performed. Cytologic diagnoses of corresponding sputum and bronchial cytology were classified into five-tiered categories (C1-insufficient/inadequate, C2-benign, C3-atypia, C4-suspicious and C5-malignant). Reports and slides of the resection specimen were reviewed for reclassification of T1 cancers.

RESULTS

Totally 472 and 383 sputum and bronchial cytology specimens respectively were included. Sensitivity for T1 lesions on sputum cytology were 10.6 %, 2.1 % and 0.5 % at cutoffs of atypia/C3, suspicious/C4 and malignant/C5 categories, lower than bronchial cytology (35.1 %, 15.5 %, 8.1 %; p < 0.001). T1 lesions correlated with lower detection rates, whereas squamous cell carcinoma histology, larger size and bronchial invasion were associated with increased detection rates in sputum and bronchial cytology (p < 0.050). Detection rates for abrasive bronchial cytology (brushing) were overall higher (p = 0.018- < 0.001), but on subgroup comparison, non-abrasive (aspiration, lavage and washing) cytology demonstrated favorable trends (p = 0.063-0.088) in detecting T1 lesions. Adenocarcinomas with lepidic pattern had lower suspicious/C4 (p = 0.040) or above and malignant/C5 (p = 0.019), but not atypia/C3 or above (p = 0.517) rates.

CONCLUSIONS

Most adenocarcinomas with lepidic pattern are only diagnosed as atypia/C3 on cytology. With its modest sensitivity, interpretation of negative and indeterminate cytology results mandates caution.

On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans

Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.

ACR Appropriateness Criteria® Incidentally Detected Indeterminate Pulmonary Nodule

Incidental pulmonary nodules are common. Although the majority are benign, most are indeterminate for malignancy when first encountered making their management challenging. CT remains the primary imaging modality to first characterize and follow-up incidental lung nodules. This document reviews available literature on various imaging modalities and summarizes management of indeterminate pulmonary nodules detected incidentally. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans

BACKGROUND

Outside a screening program, early-stage lung cancer is generally diagnosed after the detection of incidental nodules in clinically ordered chest CT scans. Despite the advances in artificial intelligence (AI) systems for lung cancer detection, clinical validation of these systems is lacking in a non-screening setting.

METHOD

We developed a deep learning-based AI system and assessed its performance for the detection of actionable benign nodules (requiring follow-up), small lung cancers, and pulmonary metastases in CT scans acquired in two Dutch hospitals (internal and external validation). A panel of five thoracic radiologists labeled all nodules, and two additional radiologists verified the nodule malignancy status and searched for any missed cancers using data from the national Netherlands Cancer Registry. The detection performance was evaluated by measuring the sensitivity at predefined false positive rates on a free receiver operating characteristic curve and was compared with the panel of radiologists.

RESULTS

On the external test set (100 scans from 100 patients), the sensitivity of the AI system for detecting benign nodules, primary lung cancers, and metastases is respectively 94.3% (82/87, 95% CI: 88.1-98.8%), 96.9% (31/32, 95% CI: 91.7-100%), and 92.0% (104/113, 95% CI: 88.5-95.5%) at a clinically acceptable operating point of 1 false positive per scan (FP/s). These sensitivities are comparable to or higher than the radiologists, albeit with a slightly higher FP/s (average difference of 0.6).

CONCLUSIONS

The AI system reliably detects benign and malignant pulmonary nodules in clinically indicated CT scans and can potentially assist radiologists in this setting.

Accurate and non-invasive localization of multi-focal ground-glass opacities via electromagnetic navigation bronchoscopy assisting video-assisted thoracoscopic surgery: a single-center study

Background

Accurate localization of multi-focal ground-glass opacities (GGOs) is crucial for successful video-assisted thoracoscopic surgery (VATS). Electromagnetic navigation bronchoscopy (ENB) provides a minimally invasive and dependable approach for precise localization. This study assessed the accuracy and safety of ENB-guided localization in cases involving multi-focal GGOs.

Methods

This retrospective study presents a single-center investigation into ENB-guided localization, utilizing methylene blue, for multi-focal GGOs assisting VATS. Clinical, surgical, and pathological data were collected from patients who underwent ENB-guided localization between 23 December 2019 and 31 August 2022.

Results

The study examined 57 patients with multi-focal GGOs who underwent ENB-guided localization and VATS. A total of 150 GGOs were treated, with ENB-guided localization taking a median time of 65 min. Following localization, all patients proceeded to VATS, with a median duration of 170 min. The median lesion size measured 7.8 mm, with a 5-mm distance between GGO and pleura or fissure. When the distance between GGO and pleura/fissure exceeded 1 cm, an additional location point was introduced below the pleura or fissure based on GGO location. No complications related to localization were observed. The overall malignancy rate stood at 66%. Location precision was confirmed by measuring the marker-to-GGO lesion distance, resulting in a 94% (141/150) accuracy rate for GGO localization.

Conclusion

ENB-guided methylene blue injection is a safe and precise method to treat multi-focal GGOs, potentially minimizing operation time and simplifying lesion detection.

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

Detection of solid and subsolid pulmonary nodules with lung MRI: performance of UTE, T1 gradient-echo, and single-shot T2 fast spin echo

BACKGROUND

Although MRI is a radiation-free imaging modality, it has historically been limited in lung imaging due to inherent technical restrictions. The aim of this study is to explore the performance of lung MRI in detecting solid and subsolid pulmonary nodules using T1 gradient-echo (GRE) (VIBE, Volumetric interpolated breath-hold examination), ultrashort time echo (UTE) and T2 Fast Spin Echo (HASTE, Half fourier Single-shot Turbo spin-Echo).

METHODS

Patients underwent a lung MRI in a 3 T scanner as part of a prospective research project. A baseline Chest CT was obtained as part of their standard of care. Nodules were identified and measured on the baseline CT and categorized according to their density (solid and subsolid) and size (> 4 mm/ ≤ 4 mm). Nodules seen on the baseline CT were classified as present or absent on the different MRI sequences by two thoracic radiologists independently. Interobserver agreement was determined using the simple Kappa coefficient. Paired differences were compared using nonparametric Mann-Whitney U tests. The McNemar test was used to evaluate paired differences in nodule detection between MRI sequences.

RESULTS

Thirty-six patients were prospectively enrolled. One hundred forty-nine nodules (100 solid/49 subsolid) with mean size 10.8 mm (SD = 9.4) were included in the analysis. There was substantial interobserver agreement (k = 0.7, p = 0.05). Detection for all nodules, solid and subsolid nodules was respectively; UTE: 71.8%/71.0%/73.5%; VIBE: 61.6%/65%/55.1%; HASTE 72.4%/72.2%/72.7%. Detection rate was higher for nodules > 4 mm in all groups: UTE 90.2%/93.4%/85.4%, VIBE 78.4%/88.5%/63.4%, HASTE 89.4%/93.8%/83.8%. Detection of lesions ≤4 mm was low for all sequences. UTE and HASTE performed significantly better than VIBE for detection of all nodules and subsolid nodules (diff = 18.4 and 17.6%, p = < 0.01 and p = 0.03, respectively). There was no significant difference between UTE and HASTE. There were no significant differences amongst MRI sequences for solid nodules.

CONCLUSIONS

Lung MRI shows adequate performance for the detection of solid and subsolid pulmonary nodules larger than 4 mm and can serve as a promising radiation-free alternative to CT.

Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI)

This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses the current AI scientific evidence in thoracic imaging, its potential clinical utility, implementation and costs, training requirements and validation, its' effect on the training of new radiologists, post-implementation issues, and medico-legal and ethical issues. All these issues have to be addressed and overcome, for AI to become implemented clinically in thoracic radiology. KEY POINTS: • Assessing the datasets used for training and validation of the AI system is essential. • A departmental strategy and business plan which includes continuing quality assurance of AI system and a sustainable financial plan is important for successful implementation. • Awareness of the negative effect on training of new radiologists is vital.

Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial

Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4−5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.

Preoperative computed tomography-guided localization for multiple lung nodules: a Meta-analysis

PURPOSE

Approximately 20% of patients with lung nodules (LNs) have multiple LNs (MLNs). This meta-analysis was performed to assess the safety and efficacy of computed tomography (CT)-guided localization of MLNs in comparison with those of single LN (SLN) localization.

MATERIAL AND METHODS

The PubMed, Embase, and Cochrane Library were searched to collect relevant articles published till February 2022. The meta-analysis was performed using the RevMan v5.3.

RESULTS

In total, seven studies met the inclusion criteria for this meta-analysis. No significant difference was observed between patients with MLNs and SLN in terms of pooled successful localization rate based on LNs (p = 0.64) and patients (p = 0.06). The pooled duration of localization was significantly shorter and the pooled pneumothorax and lung hemorrhage rates were significantly lower in the SLN group than in the MLNs group (p < 0.00001 for all). The pooled duration of hospital stay was comparable between the MLNs and SLN groups (p = 0.96). Significant heterogeneity was observed in the endpoints of duration of localization (I² = 75%) and pneumothorax (I² = 53%).

CONCLUSIONS

CT-guided simultaneous MLN localization is clinically safe and effective, despite requiring a longer procedural time and having higher incidence of pneumothorax and lung hemorrhage than SLN localization.

Factors related to the diagnosis of lung cancer by transbronchial biopsy with endobronchial ultrasonography and a guide sheath

BACKGROUND

Transbronchial biopsy (TBB) with endobronchial ultrasonography and a guide sheath (EBUS-GS) is an effective examination tool for the diagnosis of lung cancer. Factors related to making the diagnosis are still not fully understood.

METHODS

A total of 367 patients who underwent EBUS-GS and were diagnosed with lung cancer in Saga University Hospital were investigated retrospectively. Clinical characteristics were compared between 244 patients who were diagnosed with lung cancer and 123 patients who were not diagnosed by TBB with EBUS-GS but were diagnosed by other examinations.

RESULTS

Size of target lesion, rate of patients with target lesion size ≥20 mm, presence of the bronchus sign, and detection by EBUS imaging were significantly associated with making the diagnosis (all p < 0.01). In patients whose lesion was detected by EBUS imaging, patients with positive findings within the lesion were significantly more often diagnosed by TBB with EBUS-GS than those with positive findings adjacent to the lesion (p < 0.01). The odds ratio (OR) of patients whose lesion was detected by EBUS imaging (OR [95% confidence interval] 14.5 [8.0-26.4]) tended to be higher compared to the ORs of size of lesion ≥20 mm (3.9 [2.2-6.8]) and the bronchus sign (7.5 [4.6-12.2]).

CONCLUSION

Targeted lesion diameter ≥20 mm, bronchus sign, and detection by EBUS imaging, especially within the lesion, are important factors for the diagnosis of lung cancer by TBB with EBUS-GS.

Lung Cancer Screening

Lung cancer is a leading cause of cancer death in the United States and globally with the majority of lung cancer cases attributable to cigarette smoking. Given the high societal and personal cost of a diagnosis of lung cancer including that most cases of lung cancer when diagnosed are found at a late stage, work over the past 40 years has aimed to detect lung cancer earlier when curative treatment is possible. Screening trials using chest radiography and sputum failed to show a reduction in lung cancer mortality however multiple studies using low dose CT have shown the ability to detect lung cancer early and a survival benefit to those screened. This review will discuss the history of lung cancer screening, current recommendations and screening guidelines, and implementation and components of a lung cancer screening program.

Computer-Aided Diagnosis of Pulmonary Nodules in Rheumatoid Arthritis

(1) Background: Rheumatoid arthritis (RA) is considered a systemic inflammatory pathology characterized by symmetric polyarthritis associated with extra-articular manifestations, such as lung disease. The purpose of the present study is to use CAD in the detection of rheumatoid pulmonary nodules. In addition, we aim to identify the characteristics and associations between clinical, laboratory and imaging data in patients with rheumatoid arthritis and lung nodules. (2) Methods: The study included a number of 42 patients diagnosed with rheumatoid arthritis according to the 2010 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) criteria, examined from January 2017 to November 2022 in the Departments of Rheumatology and Radiology and Medical Imaging of the University of Medicine and Pharmacy of Craiova. Medical records were reviewed. A retrospective blinded review of CT for biopsy-proven pulmonary nodules in RA using Veolity LungCAD software was performed (MeVis Medical Solutions AG, Bremen, Germany). Imaging was also reviewed by a senior radiologist. (3) Results: The interobserver agreement proved to be moderate (κ = 0.478) for the overall examined cases. CAD interpretation resulted in false positive results in the case of 12 lung nodules, whereas false negative results were reported in the case of 8 lung nodules. The mean time it took for the detection of lung nodules using CAD was 4.2 min per patient, whereas the detection of lung nodules by the radiologist was 8.1 min per patient. This resulted in a faster interpretation of lung CT scans, almost reducing the detection time by half (p < 0.001). (4) Conclusions: The CAD software is useful in identifying lung nodules, in shortening the interpretation time of the CT examination and also in aiding the radiologist in better assessing all the pulmonary lung nodules. However, the CAD software cannot replace the human eye yet due to the relative high rate of false positive and false negative results.

Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications

Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated for detecting and characterizing lung nodules and for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiography or vessel suppression on CT) and for noninterpretive aspects of reporting and workflow, including management of nodule follow-up. Despite growing interest in and rapid development of AI tools and FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns about generalizability, regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of AI tools for clinical use and demonstration of benefit in terms of patient-oriented outcomes also are needed. This article provides an overview of potential applications of AI tools in the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in clinical implementation of such tools.

Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey

Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients' comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks.

Radiopaque Recreations of Lung Pathologies From Clinical Computed Tomography Images Using Potassium Iodide Inkjet 3-dimensional Printing: Proof of Concept

PURPOSE

The purpose of this study was to develop a 3-dimensional (3D) printing method to create computed tomography (CT) realistic phantoms of lung cancer nodules and lung parenchymal disease from clinical CT images.

MATERIALS AND METHODS

Low-density paper was used as substrate material for inkjet printing with potassium iodide solution to reproduce phantoms that mimic the CT attenuation of lung parenchyma. The relationship between grayscale values and the corresponding CT numbers of prints was first established through the derivation of exponential fitted equation from scanning data. Next, chest CTs from patients with early-stage lung cancer and coronavirus disease 2019 (COVID-19) pneumonia were chosen for 3D printing. CT images of original lung nodule and the 3D-printed nodule phantom were compared based on pixel-to-pixel correlation and radiomic features.

RESULTS

CT images of part-solid lung cancer and 3D-printed nodule phantom showed both high visual similarity and quantitative correlation. R2 values from linear regressions of pixel-to-pixel correlations between 5 sets of patient and 3D-printed image pairs were 0.92, 0.94, 0.86, 0.85, and 0.83, respectively. Comparison of radiomic measures between clinical CT and printed models demonstrated 6.1% median difference, with 25th and 75th percentile range at 2.4% and 15.2% absolute difference, respectively. The densities and parenchymal morphologies from COVID-19 pneumonia CT images were well reproduced in the 3D-printed phantom scans.

CONCLUSION

The 3D printing method presented in this work facilitates creation of CT-realistic reproductions of lung cancer and parenchymal disease from individual patient scans with microbiological and pathology confirmation.

Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease: A Noninferiority Study

OBJECTIVES

The aim of the study is to investigate the performance of artificial intelligence (AI) convolutional neural networks (CNN) in detecting lung nodules on chest computed tomography of patients with complex lung disease, and demonstrate its noninferiority when compared against an experienced radiologist through clinically relevant assessments.

METHODS

A CNN prototype was used to retrospectively evaluate 103 complex lung disease cases and 40 control cases without reported nodules. Computed tomography scans were blindly evaluated by an expert thoracic radiologist; a month after initial analyses, 20 positive cases were re-evaluated with the assistance of AI. For clinically relevant applications: (1) AI was asked to classify each patient into nodules present or absent and (2) AI results were compared against standard radiology reports. Standard statistics were performed to determine detection performance.

RESULTS

AI was, on average, 27 seconds faster than the expert and detected 8.4% of nodules that would have been missed. AI had a sensitivity of 67.7%, similar to an accuracy reported for experienced radiologists. AI correctly classified each patient (nodules present/absent) with a sensitivity of 96.1%. When matched against radiology reports, AI performed with a sensitivity of 89.4%. Control group assessment demonstrated an overall specificity of 82.5%. When aided by AI, the expert decreased the average assessment time per case from 2:44 minutes to 35.7 seconds, while reporting an overall increase in confidence.

CONCLUSION

In a group of patients with complex lung disease, the sensitivity of AI is similar to an experienced radiologist and the tool helps detect previously missed nodules. AI also helps experts analyze for lung nodules faster and more confidently, a feature that is beneficial to patients and favorable to hospitals due to increased patient load and need for shorter turnaround times.

Recurrence dynamics after curative surgery in patients with invasive mucinous adenocarcinoma of the lung

Background

We investigated the patterns and timing of recurrence and death as well as prognostic factors based on clinicopathological and radiological factors in patients who underwent surgical treatment for invasive mucinous adenocarcinoma (IMA).

Methods

We reviewed clinicopathological findings including spread through air spaces (STAS) and CT findings of IMA such as morphology, solidity, margin, well-defined heterogeneous ground-glass opacity, CT angiogram, and air bronchogram signs from 121 consecutive patients who underwent surgical resection. Prognostic factors for disease-free survival (DFS) and overall survival (OS) were identified. Hazard rate analyses were performed for the survival dynamics.

Results

T stage (hazard ratio [HR] = 4.102, p = 0.03), N stage (N2 vs. N0, HR = 7.653, p < 0.001), and consolidative CT morphology (HR = 3.556, p = 0.008) remained independent predictors for DFS. Age (HR = 1.110, p = 0.002), smoking (HR = 12.893, p < 0.001), T stage (HR = 13.005, p = 0.006), N stage (N2 vs. N0, HR = 7.653, p = 0.004), STAS (HR = 7.463, p = 0.008), and consolidative CT morphology (HR = 6.779, p = 0.007) remained independent predictors for OS. Consolidative morphology, higher T and N stage, and presence of STAS revealed initial sharp peaks after steep decline of the hazard rate curves for recurrence or death in follow-up period.

Conclusions

Consolidative morphology, higher T and N stage, smoking, and STAS were indicators of significantly greater risk of early recurrence or death in patients with IMA. Thus, these findings could be incorporated into future surveillance strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01208-5.

Tumor burden of lung metastases at initial staging in breast cancer patients detected by artificial intelligence as a prognostic tool for precision medicine

Background

Determination of the total number and size of all pulmonary metastases on chest CT is time-consuming and as such has been understudied as an independent metric for disease assessment. A novel artificial intelligence (AI) model may allow for automated detection, size determination, and quantification of the number of pulmonary metastases on chest CT.

Objective

To investigate the utility of a novel AI program applied to initial staging chest CT in breast cancer patients in risk assessment of mortality and survival.

Methods

Retrospective imaging data from a cohort of 226 subjects with breast cancer was assessed by the novel AI program and the results validated by blinded readers. Mean clinical follow-up was 2.5 years for outcomes including cancer-related death and development of extrapulmonary metastatic disease. AI measurements including total number of pulmonary metastases and maximum nodule size were assessed by Cox-proportional hazard modeling and adjusted survival.

Results

752 lung nodules were identified by the AI program, 689 of which were identified in 168 subjects having confirmed lung metastases (Lmet+) and 63 were identified in 58 subjects without confirmed lung metastases (Lmet-). When compared to the reader assessment, AI had a per-patient sensitivity, specificity, PPV and NPV of 0.952, 0.639, 0.878, and 0.830. Mortality in the Lmet + group was four times greater compared to the Lmet-group (p = 0.002). In a multivariate analysis, total lung nodule count by AI had a high correlation with overall mortality (OR 1.11 (range 1.07–1.15), p < 0.001) with an AUC of 0.811 (R2 = 0.226, p < 0.0001). When total lung nodule count and maximum nodule diameter were combined there was an AUC of 0.826 (R2 = 0.243, p < 0.001).

Conclusion

Automated AI-based detection of lung metastases in breast cancer patients at initial staging chest CT performed well at identifying pulmonary metastases and demonstrated strong correlation between the total number and maximum size of lung metastases with future mortality.

Clinical impact

As a component of precision medicine, AI-based measurements at the time of initial staging may improve prediction of which breast cancer patients will have negative future outcomes.

•

Automated detection software can quantify lung metastases on initial staging chest CT in breast cancer patients.

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AI-detected lung metastases number and max diameter on CT at initial cancer staging were strong predictors of mortality.

•

AI detection and segmentation tool contributes to accurate individualized prognostication in breast cancer patients.

Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT images

Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and is often diagnosed by incidental findings on computed tomography. Automated pulmonary nodule detection is an essential part of computer-aided diagnosis, which is still facing great challenges and difficulties to quickly and accurately locate the exact nodules' positions. This paper proposes a dual skip connection upsampling strategy based on Dual Path network in a U-Net structure generating multiscale feature maps, which aims to minimize the ratio of false positives and maximize the sensitivity for lesion detection of nodules. The results show that our new upsampling strategy improves the performance by having 85.3% sensitivity at 4 FROC per image compared to 84.2% for the regular upsampling strategy or 81.2% for VGG16-based Faster-R-CNN.

A systematic review and meta-analysis of diagnostic performance and physicians' perceptions of artificial intelligence (AI)-assisted CT diagnostic technology for the classification of pulmonary nodules

Background

Lung cancer was the second most commonly diagnosed cancer and the leading cause of cancer death in 2020. Although artificial intelligence (AI)-assisted diagnostic technologies have shown promise and has been used in clinical practice in recent years, no products related to AI-assisted CT diagnostic technologies for the classification of pulmonary nodules have been approved by the National Medical Products Administration in China. The objective of this article was to systematically review the diagnostic performance of AI-assisted CT diagnostic technology for the classification of pulmonary nodules as benign or malignant and to analyze physicians’ perceptions of this technology in China.

Methods

All relevant studies from 6 literature databases were searched and screened according to the inclusion and exclusion criteria. Data were extracted and the study quality was assessed by two reviewers. The study heterogeneity and publication bias were estimated. A questionnaire survey on the perceptions of physicians was conducted in 9 public tertiary hospitals in China. A meta-analysis, meta-regression and univariate logistic model were used in the systematic review and to explore the association of physicians’ perceptions with their rate of support for the clinical application of the technology.

Results

Twenty-seven studies with 5,727 pulmonary nodules were finally included in the meta-analysis. We found that the quality of the included studies was generally acceptable and that the pooled sensitivity and specificity of AI-assisted CT diagnostic technology for the classification of pulmonary nodules as benign or malignant were 0.90 and 0.89, respectively. The pooled diagnostic odds ratio (DOR) was 70.33. The majority of the surveyed physicians in China perceived “reduced workload for radiologists” and “improved diagnostic efficiency” as the important benefits of this technology. In addition, diagnostic accuracy (including misdiagnosis) and practical experience were significantly associated with whether physicians supported its clinical application.

Conclusions

In the context of lung cancer diagnosis, AI-assisted CT diagnostic technology for the classification of pulmonary nodules as benign or malignant has good diagnostic performance, but its specificity needs to be improved.

Characteristics of expert search behavior in volumetric medical image interpretation

Purpose: Experienced radiologists have enhanced global processing ability relative to novices, allowing experts to rapidly detect medical abnormalities without performing an exhaustive search. However, evidence for global processing models is primarily limited to two-dimensional image interpretation, and it is unclear whether these findings generalize to volumetric images, which are widely used in clinical practice. We examined whether radiologists searching volumetric images use methods consistent with global processing models of expertise. In addition, we investigated whether search strategy (scanning/drilling) differs with experience level. Approach: Fifty radiologists with a wide range of experience evaluated chest computed-tomography scans for lung nodules while their eye movements and scrolling behaviors were tracked. Multiple linear regressions were used to determine: (1) how search behaviors differed with years of experience and the number of chest CTs evaluated per week and (2) which search behaviors predicted better performance. Results: Contrary to global processing models based on 2D images, experience was unrelated to measures of global processing (saccadic amplitude, coverage, time to first fixation, search time, and depth passes) in this task. Drilling behavior was associated with better accuracy than scanning behavior when controlling for observer experience. Greater image coverage was a strong predictor of task accuracy. Conclusions: Global processing ability may play a relatively small role in volumetric image interpretation, where global scene statistics are not available to radiologists in a single glance. Rather, in volumetric images, it may be more important to engage in search strategies that support a more thorough search of the image.

Prospective Study of Spatial Distribution of Missed Lung Nodules by Readers in CT Lung Screening Using Computer-assisted Detection

PURPOSE

To evaluate the spatial patterns of missed lung nodules in a real-life routine screening environment.

MATERIALS AND METHODS

In a screening institute, 4,822 consecutive adults underwent chest CT, and each image set was independently interpreted by two radiologists in three steps: (1) independently interpreted without computer-assisted detection (CAD) software, (2) independently referred to the CAD results, (3) determined by the consensus of the two radiologists. The locations of nodules and the detection performance data were semi-automatically collected using a CAD server integrated into the reporting system. Fisher's exact test was employed for evaluating findings in different lung divisions. Probability maps were drawn to illustrate the spatial distribution of radiologists' missed nodules.

RESULTS

Radiologists significantly tended to miss lung nodules in the bilateral hilar divisions (p < 0.01). Some radiologists had their own spatial pattern of missed lung nodules.

CONCLUSION

Radiologists tend to miss lung nodules present in the hilar regions significantly more often than in the rest of the lung.

Screen-detected solid nodules: from detection of nodule to structured reporting

Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.

Artificial Intelligence and its future potential in lung cancer screening

Artificial intelligence (AI) simulates intelligent behavior as well as critical thinking comparable to a human being and can be used to analyze and interpret complex medical data. The application of AI in imaging diagnostics reduces the burden of radiologists and increases the sensitivity of lung cancer screening so that the morbidity and mortality associated with lung cancer can be decreased. In this article, we have tried to evaluate the role of artificial intelligence in lung cancer screening, as well as the future potential and efficiency of AI in the classification of nodules. The relevant studies between 2010-2020 were selected from the PubMed database after excluding animal studies and were analyzed for the contribution of AI. Techniques such as deep learning and machine learning allow automatic characterization and classification of nodules with high precision and promise an advanced lung cancer screening method in the future. Even though several combination models with high performance have been proposed, an effectively validated model for routine use still needs to be improvised. Combining the performance of artificial intelligence with a radiologist's expertise offers a successful outcome with higher accuracy. Thus, we can conclude that higher sensitivity, specificity, and accuracy of lung cancer screening and classification of nodules is possible through the integration of artificial intelligence and radiology. The validation of models and further research is to be carried out to determine the feasibility of this integration.

PET/MRI assessment of lung nodules in primary abdominal malignancies: sensitivity and outcome analysis

PURPOSE

To evaluate PET/MR lung nodule detection compared to PET/CT or CT, to determine growth of nodules missed by PET/MR, and to investigate the impact of missed nodules on clinical management in primary abdominal malignancies.

METHODS

This retrospective IRB-approved study included [18F]-FDG PET/MR in 126 patients. All had standard of care chest imaging (SCI) with diagnostic chest CT or PET/CT within 6 weeks of PET/MR that served as standard of reference. Two radiologists assessed lung nodules (size, location, consistency, position, and [18F]-FDG avidity) on SCI and PET/MR. A side-by-side analysis of nodules on SCI and PET/MR was performed. The nodules missed on PET/MR were assessed on follow-up SCI to ascertain their growth (≥ 2 mm); their impact on management was also investigated.

RESULTS

A total of 505 nodules (mean 4 mm, range 1-23 mm) were detected by SCI in 89/126 patients (66M:60F, mean age 60 years). PET/MR detected 61 nodules for a sensitivity of 28.1% for patient and 12.1% for nodule, with higher sensitivity for > 7 mm nodules (< 30% and > 70% respectively, p < 0.05). 75/337 (22.3%) of the nodules missed on PET/MR (follow-up mean 736 days) demonstrated growth. In patients positive for nodules at SCI and negative at PET/MR, missed nodules did not influence patients' management.

CONCLUSIONS

Sensitivity of lung nodule detection on PET/MR is affected by nodule size and is lower than SCI. 22.3% of missed nodules increased on follow-up likely representing metastases. Although this did not impact clinical management in study group with primary abdominal malignancy, largely composed of extra-thoracic advanced stage cancers, with possible different implications in patients without extra-thoracic spread.

Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems

The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors' diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image. The complete boundary of a pulmonary nodule is extracted by the improved Snake model, which can take full advantage of the parallel calculation of the Spike Neural P Systems to build a new neural network structure. In this paper, our experiments show that the proposed algorithm can accurately extract the boundary of a pulmonary nodule and effectively improve the recognition rate of the spiculation sign.

Comparison of computed tomographic imaging-guided hook wire localization and electromagnetic navigation bronchoscope localization in the resection of pulmonary nodules: a retrospective cohort study

The resection of nodules by thoracoscopic surgery is difficult because the nodules may be hard to identify. Preoperative localization of pulmonary nodules is widely used in the clinic. In this study, we retrospectively compared CT-guided hook wire localization and electromagnetic navigation bronchoscopy (ENB) localization of small pulmonary nodules before resection. Patients who underwent localization with CT-guided hook wire or ENB followed by video-assisted thoracoscopic surgery (VATS) at Qilu Hospital of Shandong University between January 2016 and December 2019 were retrospectively included. Clinical parameters, complication and failure rate, and localization time were compared between two groups. A total of 157 patients underwent the localization procedure successfully. Pulmonary nodules were localized by CT-guided hook wire in 105 patients and by ENB in 52 patients. The nodule size in ENB group was smaller than that in CT-guided localization group (P < 0.001). Both CT-guided localization and ENB localization were well tolerated in all patients, while ENB localization leaded to less complications (P = 0.0058). In CT-guided localization group, 6 patients failed to be located while none failed in ENB group (P = 0.079). The procedure time was 15.15 ± 3.70 min for CT-guided localization and 21.29 ± 4.00 min for ENB localization (P < 0.001). CT-guided localization is simple and feasible for uncertain pulmonary nodules before surgery. ENB localization could identify small lung nodules with high accuracy and achieve lower incidence of complications.

Lung cancer screening by low-dose computed tomography: a cost-effectiveness analysis of alternative programmes in the UK using a newly developed natural history-based economic model

BACKGROUND

A systematic review of economic evaluations for lung cancer identified no economic models of the UK setting based on disease natural history. We first sought to develop a new model of natural history for population screening, then sought to explore the cost-effectiveness of multiple alternative potential programmes.

METHODS

An individual patient model (ENaBL) was constructed in MS Excel® and calibrated against data from the US National Lung Screening Trial. Costs were taken from the UK Lung Cancer Screening Trial and took the perspective of the NHS and PSS. Simulants were current or former smokers aged between 55 and 80 years and so at a higher risk of lung cancer relative to the general population. Subgroups were defined by further restricting age and risk of lung cancer as predicted by patient self-questionnaire. Programme designs were single, triple, annual and biennial arrangements of LDCT screens, thereby examining number and interval length. Forty-eight distinct screening strategies were compared to the current practice of no screening. The primary outcome was incremental cost-effectiveness of strategies (additional cost per QALY gained).

RESULTS

LDCT screening is predicted to bring forward the stage distribution at diagnosis and reduce lung cancer mortality, with decreases versus no screening ranging from 4.2 to 7.7% depending on screen frequency. Overall healthcare costs are predicted to increase; treatment cost savings from earlier detection are outweighed by the costs of over-diagnosis. Single-screen programmes for people 55-75 or 60-75 years with ≥ 3% predicted lung cancer risk may be cost-effective at the £30,000 per QALY threshold (respective ICERs of £28,784 and £28,169 per QALY gained). Annual and biennial screening programmes were not predicted to be cost-effective at any cost-effectiveness threshold.

LIMITATIONS

LDCT performance was unaffected by lung cancer type, stage or location and the impact of a national screening programme of smoking behaviour was not included.

CONCLUSION

Lung cancer screening may not be cost-effective at the threshold of £20,000 per QALY commonly used in the UK but may be cost-effective at the higher threshold of £30,000 per QALY.

Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules

Lung cancer remains the leading cause of cancer related death world-wide despite advances in treatment. This largely relates to the fact that many of these patients already have advanced diseases at the time of initial diagnosis. As most lung cancers present as nodules initially, an accurate classification of pulmonary nodules as early lung cancers is critical to reducing lung cancer morbidity and mortality. There have been significant recent advances in artificial intelligence (AI) for lung nodule evaluation. Deep learning (DL) and convolutional neural networks (CNNs) have shown promising results in pulmonary nodule detection and have also excelled in segmentation and classification of pulmonary nodules. This review aims to provide an overview of progress that has been made in AI recently for pulmonary nodule detection and characterization with the ultimate goal of lung cancer prediction and classification while outlining some of the pitfalls and challenges that remain to bring such advancements to routine clinical use.

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.

Spread through air spaces (STAS) in invasive mucinous adenocarcinoma of the lung: Incidence, prognostic impact, and prediction based on clinicoradiologic factors

Background

Spread through air spaces (STAS) has recently been demonstrated to exhibit a negative impact on lung adenocarcinoma prognosis. However, most of these studies investigated STAS in nonmucinous adenocarcinoma. Here, we investigated the incidence of STAS in invasive mucinous adenocarcinoma (IMA) of the lung and evaluated whether tumor STAS was a risk factor of disease recurrence in IMA. We also examined clinicoradiologic factors in patients with IMA harboring STAS.

Methods

We reviewed pathologic specimens and imaging characteristics of primary tumors from 132 consecutive patients who underwent surgical resection for IMA to evaluate STAS. Patients with and without STAS were compared with respect to clinical characteristics as well as computed tomography (CT) imaging using logistic regression. The relationships between all variables including STAS and survival were analyzed.

Results

Among a total of 132 patients, full pathologic specimens were available for 119 patients, and STAS was observed in 86 (72.3%). IMA patients with STAS were significantly associated with older age, presence of lobulated and spiculated margins on CT scan (P = 0.009, P = 0.006, and P = 0.027). In multivariate analysis for overall survival (OS), STAS was a borderline independent poor prognostic predictor (P = 0.028). Older age, history of smoking, higher T stage, presence of lymph node metastasis, and consolidative morphologic type remained independent predictors for OS.

Conclusions

STAS was associated with reduced OS and was a borderline independent poor prognostic factor in IMA. IMA with STAS was associated with older age and presence of lobulated and spiculated margins on CT scan.

Key points

Significant findings of the study

Compared with other subtypes, IMA shows a higher incidence of STAS, which is an independent poor prognostic predictor even in IMA. Lobulated and spiculated margins on CT are associated with STAS.

What this study adds

Considering that STAS can carry the potential for aerogenous metastasis, predicting STAS using preoperative surrogate CT imaging is desirable to avoid limited resection.

Automated Lung Cancer Detection Using Artificial Intelligence (AI) Deep Convolutional Neural Networks: A Narrative Literature Review

Lung cancer is the number one cause of cancer-related deaths in the United States as well as worldwide. Radiologists and physicians experience heavy daily workloads, thus are at high risk for burn-out. To alleviate this burden, this narrative literature review compares the performance of four different artificial intelligence (AI) models in lung nodule cancer detection, as well as their performance to physicians/radiologists reading accuracy.

A total of 648 articles were selected by two experienced physicians with over 10 years of experience in the fields of pulmonary critical care, and hospital medicine. The data bases used to search and select the articles are PubMed/MEDLINE, EMBASE, Cochrane library, Google Scholar, Web of science, IEEEXplore, and DBLP. The articles selected range from the years between 2008 and 2019. Four out of 648 articles were selected using the following inclusion criteria: 1) 18-65 years old, 2) CT chest scans, 2) lung nodule, 3) lung cancer, 3) deep learning, 4) ensemble and 5) classic methods. The exclusion criteria used in this narrative review include: 1) age greater than 65 years old, 2) positron emission tomography (PET) hybrid scans, 3) chest X-ray (CXR) and 4) genomics. The model performance outcomes metrics are measured and evaluated in sensitivity, specificity, accuracy, receiver operator characteristic (ROC) curve, and the area under the curve (AUC).

This hybrid deep-learning model is a state-of-the-art architecture, with high-performance accuracy and low false-positive results. Future studies, comparing each model accuracy at depth is key. Automated physician-assist systems as this model in this review article help preserve a quality doctor-patient relationship.

Low-dose chest CT and the impact on nodule visibility

INTRODUCTION

The need to continually optimise CT protocols is essential to ensure the lowest possible radiation dose for the clinical task and individual patient. The aim of this study was to explore the effect of reducing effective mAs on nodule detection and radiation dose across six scanners.

METHODS

An anthropomorphic chest phantom was scanned using a low-dose chest CT protocol, with the effective mAs lowered to the lowest permissible level. All other acquisition parameters remained consistent. Images were evaluated by five radiologists to determine their sensitivity in detecting six simulated nodules within the phantom. Image noise was calculated together with DLP.

RESULTS

The lowest possible mAs achievable ranged from 7 to 19 mAs. The two highest mAs setting (17 mAs + 19 mAs) had kV modulation enabled (100 kV instead of 120 kV) which consequently resulted in a higher nodule detection rate. Overall nodule detection averaged at 91% (range 80-97%). Out of a possible 180 nodules, 16 were missed, with 12 of those 16 being the same nodule. Noise was double for the Somatom Sensation scanner when compared to the others; however, this scanner did not have iterative reconstruction and it was installed over 10 years ago. There was a strong correlation between image noise and scanner age.

CONCLUSION

This study highlighted that nodules can be detected at very low effective mAs (<20 mAs) but only when other acquisition parameters are optimised i.e. iterative reconstruction and kV modulation. Nodule detection rates were affected by nodule location and image noise.

IMPLICATIONS FOR PRACTICE

This study consolidates previous findings on how to successfully optimise low-dose chest CT. It also highlights the difficulty with standardisation owing to factors such as scanner age and different vendor attributes.

Clinical and Radiologic Features Together Better Predict Lung Nodule Malignancy in Patients with Soft-Tissue Sarcoma

We test the hypothesis that a model including clinical and computed tomography (CT) features may allow discrimination between benign and malignant lung nodules in patients with soft-tissue sarcoma (STS). Seventy-one patients with STS undergoing their first lung metastasectomy were examined. The performance of multiple logistic regression models including CT features alone, clinical features alone, and combined features, was tested to evaluate the best model in discriminating malignant from benign nodules. The likelihood of malignancy increased by more than 11, 2, 6 and 7 fold, respectively, when histological synovial sarcoma sub-type was associated with the following CT nodule features: size ≥ 5.6 mm, well defined margins, increased size from baseline CT, and new onset at preoperative CT. Likewise, in the case of grade III primary tumor, the odds ratio (OR) increased by more than 17 times when the diameter of pulmonary nodules (PNs) was >5.6 mm, more than 13 times with well-defined margins, more than 7 times with PNs increased from baseline CT, and more than 20 times when there were new-onset nodules. Finally, when CT nodule was ≥5.6 in size, it had well-defined margins, it increased in size from baseline CT, and when new onset nodules at preoperative CT were concomitant to residual primary tumor R2, the risk of malignancy increased by more than 10, 6, 25 and 28 times, respectively. The combination of clinical and CT features has the highest predictive value for detecting the malignancy of pulmonary nodules in patients with soft tissue sarcoma, allowing early detection of nodule malignancy and treatment options.

Diagnostic error in the emergency department: learning from national patient safety incident report analysis

BACKGROUND

Diagnostic error occurs more frequently in the emergency department than in regular in-patient hospital care. We sought to characterise the nature of reported diagnostic error in hospital emergency departments in England and Wales from 2013 to 2015 and to identify the priority areas for intervention to reduce their occurrence.

METHODS

A cross-sectional mixed-methods design using an exploratory descriptive analysis and thematic analysis of patient safety incident reports. Primary data were extracted from a national database of patient safety incidents. Reports were filtered for emergency department settings, diagnostic error (as classified by the reporter), from 2013 to 2015. These were analysed for the chain of events, contributory factors and harm outcomes.

RESULTS

There were 2288 cases of confirmed diagnostic error: 1973 (86%) delayed and 315 (14%) wrong diagnoses. One in seven incidents were reported to have severe harm or death. Fractures were the most common condition (44%), with cervical-spine and neck of femur the most frequent types. Other common conditions included myocardial infarctions (7%) and intracranial bleeds (6%). Incidents involving both delayed and wrong diagnoses were associated with insufficient assessment, misinterpretation of diagnostic investigations and failure to order investigations. Contributory factors were predominantly human factors, including staff mistakes, healthcare professionals' inadequate skillset or knowledge and not following protocols.

CONCLUSIONS

Systems modifications are needed that provide clinicians with better support in performing patient assessment and investigation interpretation. Interventions to reduce diagnostic error need to be evaluated in the emergency department setting, and could include standardised checklists, structured reporting and technological investigation improvements.

What do we know about volumetric medical image interpretation?: a review of the basic science and medical image perception literatures

Interpretation of volumetric medical images represents a rapidly growing proportion of the workload in radiology. However, relatively little is known about the strategies that best guide search behavior when looking for abnormalities in volumetric images. Although there is extensive literature on two-dimensional medical image perception, it is an open question whether the conclusions drawn from these images can be generalized to volumetric images. Importantly, volumetric images have distinct characteristics (e.g., scrolling through depth, smooth-pursuit eye-movements, motion onset cues, etc.) that should be considered in future research. In this manuscript, we will review the literature on medical image perception and discuss relevant findings from basic science that can be used to generate predictions about expertise in volumetric image interpretation. By better understanding search through volumetric images, we may be able to identify common sources of error, characterize the optimal strategies for searching through depth, or develop new training and assessment techniques for radiology residents.

Segmentation and suppression of pulmonary vessels in low-dose chest CT scans

PURPOSE

The suppression of pulmonary vessels in chest computed tomography (CT) images can enhance the conspicuity of lung nodules, thereby improving the detection rate of early lung cancer. This study aimed to develop two key techniques in vessel suppression, that is, segmentation and removal of pulmonary vessels while preserving the nodules.

METHODS

Pulmonary vessel segmentation and removal methods in CT images were developed. The vessel segmentation method used a framework of two cascaded convolutional neural networks (CNNs). A bi-class segmentation network was utilized in the first step to extract high-intensity structures, including both vessels and nonvascular tissues such as nodules. A tri-class segmentation network was employed in the second step to distinguish the vessels from nonvascular tissues (mainly nodules) and the lung parenchyma. In the vessel removal method, the voxels in the segmented vessels were replaced with randomly selected voxels from the surrounding lung parenchyma. The dataset in this study comprised 50 three-dimensional (3D) low-dose chest CT images. The labels for vessel and nodule segmentation were annotated with a semi automatic approach. The two cascaded networks for pulmonary vessel segmentation were trained with CT images of 40 cases and tested with CT images of ten cases. Pulmonary vessels were removed from the ten testing scans based on the predicted segmentation results. In addition to qualitative evaluation to the effects of segmentation and removal, the segmentation results were quantitatively evaluated using Dice coefficient (DICE), Jaccard index (JAC), and volumetric similarity (VS) and the removal results were evaluated using contrast-to-noise ratio (CNR).

RESULTS

In the first step of vessel segmentation, the mean DICE, JAC, and VS for high-intensity tissues, including both vessels and nodules, were 0.943, 0.893, and 0.991, respectively. In the second step, all the nodules were separated from the vessels, and the mean DICE, JAC, and VS for the vessels were 0.941, 0.890, and 0.991, respectively. After vessel removal, the mean CNR for nodules was improved from 4.23 (6.26 dB) to 6.95 (8.42 dB).

CONCLUSIONS

Quantitative and qualitative evaluations demonstrated that the proposed method achieved a high accuracy for pulmonary vessel segmentation and a good effect on pulmonary vessel suppression.

Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications

Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main application of these techniques has been the detection and classification of pulmonary nodules. In addition, there have been other less intensely researched applications, such as the diagnosis of interstitial lung disease, chronic obstructive pulmonary disease, and the detection of pulmonary emboli. Despite extensive literature on the use of convolutional neural networks in thoracic imaging over the last few decades, we are yet to see these systems in use in clinical practice. The article reviews current state-of-the-art applications of AI and in detection, classification, and follow-up of pulmonary nodules and how deep-learning techniques might influence these going forward. Finally, we postulate the impact of these advancements on the role of radiologists and the importance of radiologists in the development and evaluation of these techniques.

Integration of Chest CT CAD into the Clinical Workflow and Impact on Radiologist Efficiency

RATIONALE AND OBJECTIVES

The purpose of this paper is to describe the integration of a commercial chest CT computer-aided detection (CAD) system into the clinical radiology reporting workflow and perform an initial investigation of its impact on radiologist efficiency. It seeks to complement research into CAD sensitivity and specificity of stand-alone systems, by focusing on report generation time when the CAD is integrated into the clinical workflow.

MATERIALS AND METHODS

A commercial chest CT CAD software that provides automated detection and measurement of lung nodules, ascending and descending aorta, and pleural effusion was integrated with a commercial radiology report dictation application. The CAD system automatically prepopulated a radiology report template, thus offering the potential for increased efficiency. The integrated system was evaluated using 40 scans from a publicly available lung nodule database. Each scan was read using two methods: (1) without CAD analytics, i.e., manually populated report with measurements using electronic calipers, and (2) with CAD analytics to prepopulate the report for reader review and editing. Three radiologists participated as readers in this study.

RESULTS

CAD assistance reduced reading times by 7%-44%, relative to the conventional manual method, for the three radiologists from opening of the case to signing of the final report.

CONCLUSION

This study provides an investigation of the impact of CAD and measurement on chest CTs within a clinical reporting workflow. Prepopulation of a report with automated nodule and aorta measurements yielded substantial time savings relative to manual measurement and entry.