Three-dimensional analysis of pulmonary nodules: variability of semiautomated volume measurements between different versions of the same software

Rinaldi, Maria Francesca; Bartalena, Tommaso; Braccaioli, Lucia; Sverzellati, Nicola; Mattioli, Sandro; Rimondi, Eugenio; Rossi, Gabriele; Zompatori, Maurizio; Battista, Giuseppe; Canini, Romeo

doi:10.1007/s11547-010-0511-6

Cited by 8 publications

(10 citation statements)

References 21 publications

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“…Manual correction it is expected to act on these biases [55,115]. Differences in volume estimation have been reported when using different software and different algorithms of correction of partial volume effect artefacts [57,67,[116][117][118]. Therefore, it is advisable to perform nodule follow-up using the same scanner, technique and software package.…”

Section: Factors Influencing Nodule Measurement Variationsmentioning

confidence: 99%

Lung nodules: size still matters

et al. 2017

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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.

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Section: Factors Influencing Nodule Measurement Variationsmentioning

confidence: 99%

Lung nodules: size still matters

et al. 2017

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“…Factors considered in these studies include nodule characteristics such as size [147, 204, 269, 303, 329, 330], shape [269, 330], and appearance types of solidness [273]; pulmonary deformation due to cardiovascular motion [331] and inspiration [269]; CT reconstruction parameters such as slice thickness [164, 204, 304, 329, 330, 332], slice intervals [164, 204, 304, 332], field of views [164, 204, 304, 332], algorithm types [146, 164, 332], kernel types [273, 304], tube current time [146, 164], and dosage settings [271]; CT scanner vendors [303]; segmentation performance due to choices of threshold parameters in a segmentation algorithm [204, 248], segmentation algorithms [146, 248, 302, 333], segmentation algorithms in the same segmentation software package [272], segmentation software packages [274], and versions of a segmentation software package [270]; intra- and interobserver variations by using a commercial software package for semi-automatic nodule segmentation and volumetry [165, 202, 269]; levels of observer's experience and training [302]. …”

Section: Diagnosis Of Lung Nodulesmentioning

confidence: 99%

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

El-Baz

Beache

Gimel’farb

et al. 2013

International Journal of Biomedical Imaging

184

106

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This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

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“…When nodule size is determined volumetrically, the same software version and segmentation algorithm is recommended at follow-up examinations to avoid measurement inaccuracy [ 51 , 52 , 56 ] .…”

Section: Ldct: Nodule Measurementmentioning

confidence: 99%

Low-dose CT: technique, reading methods and image interpretation

2012

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The National Lung Cancer Screening Trial has recently demonstrated that screening of high-risk populations with the use of low-dose computed tomography (LDCT) reduces lung cancer mortality[1]. Based on this encouraging result, the National Comprehensive Cancer Network guidelines recommended LDCT for selected patients at high risk of lung cancer[2]. This suggests that an increasing number of CT screening examinations will be performed. The LDCT technique is relatively simple but some CT parameters are important and should be accurately defined in order to achieve good diagnostic quality and minimize the delivered dose. In addition, LDCT examinations are not as easy to read as they may initially appear; different approaches and tools are available for nodule detection and measurement. Moreover, the management of positive results can be a complex process and can differ significantly from routine clinical practice. Therefore this paper deals with the LDCT technique, reading methods and interpretation in lung cancer screening, particularly for those radiologists who have little experience of the technique.

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Three-dimensional analysis of pulmonary nodules: variability of semiautomated volume measurements between different versions of the same software

Cited by 8 publications

References 21 publications

Lung nodules: size still matters

Lung nodules: size still matters

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

Low-dose CT: technique, reading methods and image interpretation

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