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

Scholten, Ernst Th.; Jacobs, Colin; Ginneken, Bram van; Riel, Sarah van; Vliegenthart, Rozemarijn; Oudkerk, Matthijs; Koning, Harry J. de; Horeweg, Nanda; Prokop, Mathias; Gietema, Hester A.; Mali, Willem P.Th.M.; Jong, Pim A. de

doi:10.1007/s00330-014-3427-z

Cited by 56 publications

(51 citation statements)

References 24 publications

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“…Thanks to the development of specific software, volumetric measurement of SSNs has become accurate over the years with a successful segmentation of up to 97% of the nodules [75,[78][79][80]. Similar results have been reported in the detection and segmentation of PSNs and, interestingly, a quantification of the solid component was related to pathological prognostic factors, such as lymphatic, vascular and pleural invasion [75,81,82].…”

Section: Factors Influencing Nodule Measurement Variationssupporting

confidence: 64%

“…Notably, the latter is due to a better capability of detecting the appearance or progression of a solid component in SSNs [131]. The usefulness of the system has been proven afterwards by other experimental studies [78,81,132] and used in the discrimination of histological subtypes in adenocarcinoma [133]. As for volumetric measurement, an existing interscan variability has been described for nodule mass assessment, and an increase in nodule mass of 30% has been regarded as a significant growth [134].…”

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confidence: 99%

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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 Variationssupporting

confidence: 64%

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confidence: 99%

Lung nodules: size still matters

et al. 2017

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“…Third, we investigated solid and subsolid nodules and did not discriminate further between semi-solid and groundglass nodules. One of the most important questions related to detection sensitivity for these nodules is agreement regarding the presence or absence of a solid component, with disagreement reaching as much as 12% in conventional low-dose CT. 26 Fourth, measurements of attenuation and size have not been performed. Increased variation of diameters and mass is a real concern when decreasing dose levels to the levels attempted.…”

Section: Discussionmentioning

confidence: 99%

Ultralow-dose CT with tin filtration for detection of solid and sub solid pulmonary nodules: a phantom study

Martini

Higashigaito

Barth

et al. 2015

BJR

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Objectives: To investigate the diagnostic performance of advanced modelled iterative reconstruction (ADMIRE) to filtered back projection (FBP) when using an ultralow-dose protocol for the detection of solid and subsolid pulmonary nodules. Methods: Single-energy CT was performed at 100 kVp with tin filtration in an anthropomorphic chest phantom with solid and subsolid pulmonary nodules (2-10 mm, attenuation, 20 to 2800 HU at 120 kVp). The mean volume CT dose index (CTDI vol ) of the standard chest protocol was 2.2 mGy. Subsequent scans were obtained at 1/8 (0.28 mGy), 1/20 (0.10 mGy) and 1/70 (0.03 mGy) dose levels by lowering tube voltage and tube current. Images were reconstructed with FBP and ADMIRE. One reader measured image noise; two readers determined image quality and assessed nodule localization.Results: Image noise was significantly reduced using ADMIRE compared with FBP (ADMIRE at a strength level of 5 : 70.4% for 1/20; 71.6% for 1/8; p , 0.001). Interobserver agreement for image quality was excellent (k 5 0.88). Image quality was considered diagnostic for all images at 1/20 dose using ADMIRE. Sensitivity of nodule detection was 97.1% (100% for solid, 93.8% for subsolid nodules) at 1/20 dose and 100% for both nodule entities at 1/8 dose using ADMIRE 5. Images obtained with 1/70 dose had moderate sensitivity (overall 85.7%; solid 95%; subsolid 73.3%). Conclusion: Our study suggests that with a combination of tin filtration and ADMIRE, the CTDI vol of chest CT can be lowered considerably, while sensitivity for nodule detection remains high. For solid nodules, CTDI vol was 0.10 mGy, while subsolid nodules required a slightly higher CTDI vol of 0.28 mGy. Advances in knowledge: Detection of subsolid nodules is feasible with ultralow-dose protocols.

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“…In this study, however, we focus more on solid NSCLC, even though some cases of reduced attenuation like cavitation are still evaluated. Other types of reduced attenuation such as ground glass opacity (GGO) and reticulation are particularly avoided, since earlier studies demonstrated high errors of computer‐assisted segmentation when evaluating sub‐solid nodules, especially the GGO case . The gold standard for these cases would be manual delineations by an expert radiologist, though we do believe a sophisticated algorithm like deep learning which has recently gained tremendous interest from the medical imaging community, could provide a solution.…”

Section: Introductionmentioning

confidence: 99%

Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

Saad

Lee

Choi

2019

Int J Imaging Syst Tech

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Quantitative reasoning in medical decision science relies on the delineation of pathological objects. For example, evidence‐based clinical decisions regarding lung diseases require the segmentation of nodules, tumors, or cancers. Non‐small cell lung cancer (NSCLC) tends to be large sized, irregularly shaped, and grows against surrounding structures imposing challenges in the segmentation, even for expert clinicians. An automated delineation tool based on spatial analysis was developed and studied on 25 sets of computed tomography scans of NSCLC. Manual and automated delineations were compared, and the proposed method exhibited robustness in terms of the tumor size (5.32–18.24 mm), shape (spherical or irregular), contouring (lobulated, spiculated, or cavitated), localization (solitary, pleural, mediastinal, endobronchial, or tagging), and laterality (left or right lobe) with accuracy between 80% and 99%. Small discrepancies observed between the manual and automated delineations may arise from the variability in the practitioners' definitions of region of interest or imaging artifacts that reduced the tissue resolution.

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

Cited by 56 publications

References 24 publications

Lung nodules: size still matters

Lung nodules: size still matters

Ultralow-dose CT with tin filtration for detection of solid and sub solid pulmonary nodules: a phantom study

Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

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