Automated detection of lung nodules in low-dose computed tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness (~300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very high (75% range) even at 1-6 FP/scanComment: 4 pages, 2 figures: Proceedings of the Computer Assisted Radiology and Surgery, 21th International Congress and Exhibition, Berlin, Volume 2, Supplement 1, June 2007, pp 357-35

Lung nodules: size still matters

The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules

Computer-aided detection of pulmonary nodules in low-dose CT

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical CT images with 1.25 mm slice thickness is being developed in the framework of the INFN-supported MAGIC-5 Italian project. The basic modules of our lung-CAD system, a dot enhancement filter for nodule candidate selection and a voxel-based neural classifier for false-positive finding reduction, are described. Preliminary results obtained on the so-far collected database of lung CT scans are discussed.Comment: 3 pages, 4 figures; Proceedings of the CompIMAGE - International Symposium on Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications, 20-21 Oct. 2006, Coimbra, Portuga

Comparison of National Comprehensive Cancer Network and European Position Statement protocols for nodule management in low-dose computed tomography lung cancer screening in a general Chinese population

BACKGROUND: Low-dose computed tomography (LDCT) lung cancer screening often refers individuals to unnecessary examinations. This study aims to compare the European Position Statement (EUPS) and National Comprehensive Cancer Network (NCCN) protocols in management of participants at baseline screening round. METHODS: LDCT lung cancer screening was prospectively performed in a Chinese asymptomatic population aged 40–74 years. A total of 1,000 consecutive baseline LDCT scans were read twice independently. All screen-detected lung nodules by the first reader were included. The first reader manually measured the diameter of lung nodules (NCCN protocol), and the second reader semi-automatically measured the volume and diameter (EUPS volume and diameter protocols). The protocols were used to classify the participants into three management groups: next screening round, short-term repeat LDCT scan and referral to a pulmonologist. Groups were compared using Wilcoxon test for paired samples. Number of lung cancers by protocols was provided. RESULTS: Of the 1,000 participants (61.4±6.7 years old), 168 lung nodules in 124 participants were visually detected and manually measured in the first reading, and re-measured semi-automatically. Applying the NCCN protocol, EUPS volume and diameter protocol, the proportion of referrals among all participants was 0.6%, 1.9%, and 1.4%, respectively. The proportion of short-term repeat scans was 4.5%, 9.7% and 4.5%, respectively. Among the 10 lung cancer patients, one would have been diagnosed earlier if the EUPS volume protocol would have been followed. CONCLUSIONS: In a first round screening in a Chinese general population, the lower threshold for referral in the EUPS protocol as compared to the NCCN protocol, leads to more referrals to a pulmonologist, with the potential of earlier cancer diagnosis. The EUPS volume protocol recommends fewer participants to short-term repeat LDCT scan than the EUPS diameter protocol. Follow-up studies should show the impact of both protocols on (interval) cancer diagnosis