Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report

Computed tomographic characteristics of interval and post screen carcinomas in lung cancer screening

Objectives To analyse computed tomography (CT) findings of interval and post-screen carcinomas in lung cancer screening. Methods Consecutive interval and post-screen carcinomas from the Dutch-Belgium lung cancer screening trial were included. The prior screening and the diagnostic chest CT were reviewed by two experienced radiologists in consensus with knowledge of the tumour location on the diagnostic CT. Results Sixty-one participants (53 men) were diagnosed with an interval or post-screen carcinoma. Twenty-two (36 %) were in retrospect visible on the prior screening CT. Detection error occurred in 20 cancers and interpretation error in two cancers. Errors involved intrabronchial tumour (n=5), bulla with wall thickening (n=5), lymphadenopathy (n=3), pleural effusion (n=1) and intraparenchymal solid nodules (n=8). These were missed because of a broad pleural attachment (n=4), extensive reticulation surrounding a nodule (n=1) and extensive scarring (n=1). No definite explanation other than human error was found in two cases. None of the interval or post-screen carcinomas involved a subsolid nodule. Conclusions Interval or post-screen carcinomas that were visible in retrospect were mostly due to detection errors of solid nodules, bulla wall thickening or endobronchial lesions. Interval or post-screen carcinomas without explanation other than human errors are rare

Subsolid pulmonary nodule morphology and associated patient characteristics in a routine clinical population

Objectives: To determine the presence and morphology of subsolid pulmonary nodules (SSNs) in a non-screening setting and relate them to clinical and patient characteristics. Methods: A total of 16,890 reports of clinically obtained chest CT (06/2011 to 11/2014, single-centre) were searched describing an SSN. Subjects with a visually confirmed SSN and at least two thin-slice CTs were included. Nodule volumes were measured. Progression was defined as volume increase exceeding the software interscan variation. Nodule morphology, location, and patient characteristics were evaluated. Results: Fifteen transient and 74 persistent SSNs were included (median follow-up 19.6 [8.3–36.8] months). Subjects with an SSN were slightly older than those without (62 vs. 58 years; p = 0.01), but no gender predilection was found. SSNs were mostly located in the upper lobes. Women showed significantly more often persistent lesions than men (94 % vs. 69 %; p = 0.002). Part-solid lesions were larger (1638 vs. 383 mm3; p <0.001) and more often progressive (68 % vs. 38 %; p = 0.02), compared to pure ground-glass nodules. Progressive SSNs were rare under the age of 50 years. Logistic regression analysis did not identify additional nodule parameters of future progression, apart from part-solid nature. Conclusions: This study confirms previously reported characteristics of SSNs and associated factors in a European, routine clinical population. Key Points: • SSNs in women are significantly more often persistent compared to men. • SSN persistence is not associated with age or prior malignancy. • The majority of (persistent) SSNs are located in the upper lung lobes. • A part-solid nature is associated with future nodule growth. • Progressive solitary SSNs are rare under the age of 50 years

Solid, Part-Solid, or Non-Solid? Classification of Pulmonary Nodules in Low-Dose Chest Computed Tomography by a Computer-Aided Diagnosis System

Objectives: The purpose of this study was to develop and validate a computer-aided diagnosis (CAD) tool for automatic classification of pulmonary nodules seen on low-dose computed tomography into solid, part-solid, and non-solid. Materials and Methods: Study lesions were randomly selected from 2 sites participating in the Dutch-Belgian NELSON lung cancer screening trial. On the basis of the annotations made by the screening radiologists, 50 part-solid and 50 non-solid pulmonary nodules with a diameter between 5 and 30 mm were randomly selected from the 2 sites. For each unique nodule, 1 low-dose chest computed tomographic scan was randomly selected, in which the nodule was visible. In addition, 50 solid nodules in the same size range were randomly selected. A completely automatic 3-dimensional segmentation-based classification system was developed, which analyzes the pulmonary nodule, extracting intensity-, texture-, and segmentation-based features to perform a statistical classification. In addition to the nodule classification by the screening radiologists, an independent rating of all nodules by 3 experienced thoracic radiologists was performed. Performance of CAD was evaluated by comparing the agreement between CAD and human experts and among human experts using the Cohen. statistics. Results: Pairwise agreement for the differentiation between solid, part-solid, and non-solid nodules between CAD and each of the human experts had a. range between 0.54 and 0.72. The interobserver agreement among the human experts was in the same range (. range, 0.56-0.81). Conclusions: A novel automated classification tool for pulmonary nodules achieved good agreement with the human experts, yielding. values in the same range as the interobserver agreement. Computer-aided diagnosis may aid radiologists in selecting the appropriate workup for pulmonary nodules

Incidental perifissural nodules on routine chest computed tomography : lung cancer or not?

Objectives: Perifissural nodules (PFNs) are a common finding on chest CT, and are thought to represent non-malignant lesions. However, data outside a lung cancer-screening setting are currently lacking. Methods: In a nested case-control design, out of a total cohort of 16,850 patients ≥ 40 years of age who underwent routine chest CT (2004-2012), 186 eligible subjects with incident lung cancer and 511 controls without were investigated. All non-calcified nodules ≥ 4 mm were semi-automatically annotated. Lung cancer location and subject characteristics were recorded. Results: Cases (56 % male) had a median age of 64 years (IQR 59–70). Controls (60 % male) were slightly younger (p<0.01), median age of 61 years (IQR 51–70). A total of 262/1,278 (21 %) unique non-calcified nodules represented a PFN. None of these were traced to a lung malignancy over a median follow-up of around 4.5 years. PFNs were most often located in the lower lung zones (72 %, p<0.001). Median diameter was 4.6 mm (range: 4.0–8.1), volume 51 mm3 (range: 32–278). Some showed growth rates < 400 days. Conclusions: Our data show that incidental PFNs do not represent lung cancer in a routine care, heterogeneous population. This confirms prior screening-based results. Key Points: • One-fifth of non-calcified nodules represented a perifissural nodule in our non-screening population.• PFNs fairly often show larger size, and can show interval growth.• When morphologically resembling a PFN, nodules are nearly certainly not a malignancy.• The assumed benign aetiology of PFNs seems valid outside the screening setting

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts

Detection and quantification of the solid component in pulmonary subsolid nodules by semiautomatic segmentation

Objective To determine whether semiautomatic volumetric software can differentiate part-solid from nonsolid pulmonary nodules and aid quantification of the solid component. Methods As per reference standard, 115 nodules were differentiated into nonsolid and part-solid by two radiologists; disagreements were adjudicated by a third radiologist. The diameters of solid components were measured manually. Semiautomatic volumetric measurements were used to identify and quantify a possible solid component, using different Hounsfield unit (HU) thresholds. The measurements were compared with the reference standard and manual measurements. Results The reference standard detected a solid component in 86 nodules. Diagnosis of a solid component by semiautomatic software depended on the threshold chosen. A threshold of -300 HU resulted in the detection of a solid component in 75 nodules with good sensitivity (90 %) and specificity (88 %). At a threshold of -130 HU, semiautomatic measurements of the diameter of the solid component (mean 2.4 mm, SD 2.7 mm) were comparable to manual measurements at the mediastinal window setting (mean 2.3 mm, SD 2.5 mm [p=0.63]). Conclusion Semiautomatic segmentation of subsolid nodules could diagnose part-solid nodules and quantify the solid component similar to human observers. Performance depends on the attenuation segmentation thresholds. This method may prove useful in managing subsolid nodules

Visual versus Automated Evaluation of Chest Computed Tomography for the Presence of Chronic Obstructive Pulmonary Disease.

Contains fulltext : 110186.pdf (publisher's version ) (Open Access)BACKGROUND: Incidental CT findings may provide an opportunity for early detection of chronic obstructive pulmonary disease (COPD), which may prove important in CT-based lung cancer screening setting. We aimed to determine the diagnostic performance of human observers to visually evaluate COPD presence on CT images, in comparison to automated evaluation using quantitative CT measures. METHODS: This study was approved by the Dutch Ministry of Health and the institutional review board. All participants provided written informed consent. We studied 266 heavy smokers enrolled in a lung cancer screening trial. All subjects underwent volumetric inspiratory and expiratory chest computed tomography (CT). Pulmonary function testing was used as the reference standard for COPD. We evaluated the diagnostic performance of eight observers and one automated model based on quantitative CT measures. RESULTS: The prevalence of COPD in the study population was 44% (118/266), of whom 62% (73/118) had mild disease. The diagnostic accuracy was 74.1% in the automated evaluation, and ranged between 58.3% and 74.3% for the visual evaluation of CT images. The positive predictive value was 74.3% in the automated evaluation, and ranged between 52.9% and 74.7% for the visual evaluation. Interobserver variation was substantial, even within the subgroup of experienced observers. Agreement within observers yielded kappa values between 0.28 and 0.68, regardless of the level of expertise. The agreement between the observers and the automated CT model showed kappa values of 0.12-0.35. CONCLUSIONS: Visual evaluation of COPD presence on chest CT images provides at best modest accuracy and is associated with substantial interobserver variation. Automated evaluation of COPD subjects using quantitative CT measures appears superior to visual evaluation by human observers

Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images

Subsolid pulmonary nodules occur less often than solid pulmonary nodules, but show a much higher malignancy rate. Therefore, accurate detection of this type of pulmonary nodules is crucial. In this work, a computer-aided detection (CAD) system for subsolid nodules in computed tomography images is presented and evaluated on a large data set from a multi-center lung cancer screening trial. The paper describes the different components of the CAD system and presents experiments to optimize the performance of the proposed CAD system. A rich set of 128 features is defined for subsolid nodule candidates. In addition to previously used intensity, shape and texture features, a novel set of context features is introduced. Experiments show that these features significantly improve the classification performance. Optimization and training of the CAD system is performed on a large training set from one site of a lung cancer screening trial. Performance analysis on an independent test from another site of the trial shows that the proposed system reaches a sensitivity of 80% at an average of only 1.0 false positive detections per scan. A retrospective analysis of the output of the CAD system by an experienced thoracic radiologist shows that the CAD system is able to find subsolid nodules which were not contained in the screening database