Computer-aided detection of lung nodules: A review

We present an in-depth review and analysis of salient methods for computer-aided detection of lung nodules. We evaluate the current methods for detecting lung nodules using literature searches with selection criteria based on validation dataset types, nodule sizes, numbers of cases, types of nodules, extracted features in traditional feature-based classifiers, sensitivity, and false positives (FP)/scans. Our review shows that current detection systems are often optimized for particular datasets and can detect only one or two types of nodules. We conclude that, in addition to achieving high sensitivity and reduced FP/scans, strategies for detecting lung nodules must detect a variety of nodules with high precision to improve the performances of the radiologists. To the best of our knowledge, ours is the first review of the effectiveness of feature extraction using traditional feature-based classifiers. Moreover, we discuss deep-learning methods in detail and conclude that features must be appropriately selected to improve the overall accuracy of the system. We present an analysis of current schemes and highlight constraints and future research areas

Translating potential improvement in the precision and accuracy of lung nodule measurements on computed tomography scans by software derived from artificial intelligence into impact on clinical practice:a simulation study

Objectives Accurate measurement of lung nodules is pivotal to lung cancer detection and management. Nodule size forms the main basis of risk categorisation in existing guidelines. However, measurements can be highly variable between manual readers. This paper explores the impact of potentially improved nodule size measurement assisted by generic artificial intelligence (AI)-derived software on clinical management compared with manual measurement. Methods The simulation study created a baseline cohort of people with lung nodules, guided by nodule size distributions reported in the literature. Precision and accuracy were simulated to emulate measurement of nodule size by radiologists with and without the assistance of AI-derived software and by the software alone. Nodule growth was modelled over a 4-year time frame, allowing evaluation of management strategies based on existing clinical guidelines. Results Measurement assisted by AI-derived software increased cancer detection compared to an unassisted radiologist for a combined solid and sub-solid nodule population (62.5% vs 61.4%). AI-assisted measurement also correctly identified more benign nodules (95.8% vs 95.4%), however it was associated with over an additional month of surveillance on average (5.12 vs 3.95 months). On average, with AI assistance people with cancer are diagnosed faster, and people without cancer are monitored longer. Conclusions In this simulation, the potential benefits of improved accuracy and precision associated with AI-based diameter measurement is associated with additional monitoring of non-cancerous nodules. AI may offer additional benefits not captured in this simulation, and it is important to generate data supporting these, and adjust guidelines as necessary. Advances in Knowledge This paper shows the effects of greater measurement accuracy associated with AI assistance compared with unassisted measurement

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems

Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements using Radiomics

Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics. © 2019 Wolters Kluwer Health, Inc. All rights reserved

Lung cancer LDCT screening and mortality reduction — evidence, pitfalls and future perspectives

In the past decade, the introduction of molecularly targeted agents and immune-checkpoint inhibitors has led to improved survival outcomes for patients with advanced-stage lung cancer; however, this disease remains the leading cause of cancer-related mortality worldwide. Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening in high-risk populations - the US National Lung Screening Trial (NLST) and NELSON - have provided evidence of a statistically significant mortality reduction in patients. LDCT-based screening programmes for individuals at a high risk of lung cancer have already been implemented in the USA. Furthermore, implementation programmes are currently underway in the UK following the success of the UK Lung Cancer Screening (UKLS) trial, which included the Liverpool Health Lung Project, Manchester Lung Health Check, the Lung Screen Uptake Trial, the West London Lung Cancer Screening pilot and the Yorkshire Lung Screening trial. In this Review, we focus on the current evidence on LDCT-based lung cancer screening and discuss the clinical developments in high-risk populations worldwide; additionally, we address aspects such as cost-effectiveness. We present a framework to define the scope of future implementation research on lung cancer screening programmes referred to as Screening Planning and Implementation RAtionale for Lung cancer (SPIRAL)

Computer-Aided Detection (CAD) Deteksi Nodul Paru-Paru dari Computed Tomography (CT)

Nodul paru merupakan pertumbuhan jaringan abnormal pada paru yang digunakan sebagai diagnosis dini kanker paru. Kanker paru-paru adalah kanker yang paling banyak ditemukan dan mematikan di dunia. Umumnya, deteksi pertama nodul paru diperoleh dari citra CT yang didiagnosis secara visual oleh ahli radiologi. Artinya subjektivitas individu radiologis berpengaruh dalam citra diagnosis tersebut. Untuk membantu ahli radiologi dalam mendeteksi dan mengevaluasi nodul paru pada citra CT secara otomatis, penelitian ini telah mengembangkan sistem Computer-Aided Detection (CAD). Sistem CAD menggunakan metode segmentasi Otsu, dengan ekstraksi fitur Gray Level Co-occurrence Matrix (GLCM) sebagai input untuk klasifikasi nodul. Algoritma Random Forest digunakan untuk membedakan antara normal dan abnormal pada citra CT, khususnya citra dengan kelainan nodul paru. Evaluasi estimasi keberadaan nodul paru pada sistem dilakukan menggunakan Receiver Operating Characteristic (ROC) dengan sensitivitas 95%.Kata Kunci: CAD, CT dada, Deteksi nodul paru, Random Fores

Deep convolutional neural networks for multi-planar lung nodule detection: improvement in small nodule identification

Objective: In clinical practice, small lung nodules can be easily overlooked by radiologists. The paper aims to provide an efficient and accurate detection system for small lung nodules while keeping good performance for large nodules. Methods: We propose a multi-planar detection system using convolutional neural networks. The 2-D convolutional neural network model, U-net++, was trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are combined. For false positive reduction, we apply 3-D multi-scale dense convolutional neural networks to efficiently remove false positive candidates. We use the public LIDC-IDRI dataset which includes 888 CT scans with 1186 nodules annotated by four radiologists. Results: After ten-fold cross-validation, our proposed system achieves a sensitivity of 94.2% with 1.0 false positive/scan and a sensitivity of 96.0% with 2.0 false positives/scan. Although it is difficult to detect small nodules (i.e. < 6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall false positive rate of 1.0 (2.0) false positives/scan. At the nodule candidate detection stage, results show that a multi-planar method is capable to detect more nodules compared to using a single plane. Conclusion: Our approach achieves good performance not only for small nodules, but also for large lesions on this dataset. This demonstrates the effectiveness and efficiency of our developed CAD system for lung nodule detection. Significance: The proposed system could provide support for radiologists on early detection of lung cancer

Deep convolutional neural networks for multi-planar lung nodule detection:improvement in small nodule identification

Objective: In clinical practice, small lung nodules can be easily overlooked by radiologists. The paper aims to provide an efficient and accurate detection system for small lung nodules while keeping good performance for large nodules. Methods: We propose a multi-planar detection system using convolutional neural networks. The 2-D convolutional neural network model, U-net++, was trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are combined. For false positive reduction, we apply 3-D multi-scale dense convolutional neural networks to efficiently remove false positive candidates. We use the public LIDC-IDRI dataset which includes 888 CT scans with 1186 nodules annotated by four radiologists. Results: After ten-fold cross-validation, our proposed system achieves a sensitivity of 94.2% with 1.0 false positive/scan and a sensitivity of 96.0% with 2.0 false positives/scan. Although it is difficult to detect small nodules (i.e. &lt; 6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall false positive rate of 1.0 (2.0) false positives/scan. At the nodule candidate detection stage, results show that a multi-planar method is capable to detect more nodules compared to using a single plane. Conclusion: Our approach achieves good performance not only for small nodules, but also for large lesions on this dataset. This demonstrates the effectiveness and efficiency of our developed CAD system for lung nodule detection. Significance: The proposed system could provide support for radiologists on early detection of lung cancer

Implementing streamlined radiology reporting and clinical results management in low-dose CT screening for lung cancer

Lung cancer kills more people in the UK than any other cancer. Mortality rates are poor, with fewer than 10% of people alive 10 years after diagnosis. Lung Cancer Screening (LCS) with low-dose CT (LDCT) is effective at reducing lung cancer mortality when employed in at-risk populations; because of this, in the US, LCS has been implemented as a national programme. The UK does not currently screen for lung cancer, but in 2019 NHS England announced a pilot scheme to implement lung health checks (LHC) in areas with the poorest lung cancer outcomes. Despite these advances in LCS in the UK, there are outstanding questions about how LCS could be implemented safely and effectively, which this thesis, based on experience and data from the SUMMIT Study, aims to investigate. To provide screening safely, implementation of any study or programme must focus on maintaining a favourable cost to benefit ratio. This is particularly true in LCS where high false positive and overdiagnosis rates, as well as considerable levels of incidental findings, lead to possible psychological stress, needless investigations and interventions, making provision challenging to both screenees and healthcare providers. The SUMMIT Study investigates how to deliver evidence-based LCS in a large population (25,000), and this thesis in particular focusses on how LCS can be streamlined through proformatisation of radiological data collection, clinical actioning of results and standardised communication with general practitioners (GPs) and participants. This thesis explains the approach to managing pulmonary nodules and incidental findings detected at LDCT in SUMMIT, and how these findings are collected, triaged, and communicated in a way that is both efficient and safe. Early data from SUMMIT is presented to understand how evidence-based proformas may enable streamlined clinical management, data collection and results communications, while decreasing the burden on healthcare professionals and participants alike