11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Abstract

          Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance.

          Critical relevance statement

          Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence.

          Key points

          • Factors influencing the volumetry of pulmonary nodules have been extensively investigated.

          • Just 11% of studies test clinical significance (wrongly diagnosing growth).

          • Nodule size interacts with most other influencing factors (especially for smaller nodules).

          • Heterogeneity among studies makes comparison and consolidation of results challenging.

          • Future research should focus on clinical applicability, screening, and updated technology.

          Graphical abstract

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13244-023-01480-z.

          Related collections

          Most cited references181

          • Record: found
          • Abstract: found
          • Article: not found

          Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017.

          The Fleischner Society Guidelines for management of solid nodules were published in 2005, and separate guidelines for subsolid nodules were issued in 2013. Since then, new information has become available; therefore, the guidelines have been revised to reflect current thinking on nodule management. The revised guidelines incorporate several substantive changes that reflect current thinking on the management of small nodules. The minimum threshold size for routine follow-up has been increased, and recommended follow-up intervals are now given as a range rather than as a precise time period to give radiologists, clinicians, and patients greater discretion to accommodate individual risk factors and preferences. The guidelines for solid and subsolid nodules have been combined in one simplified table, and specific recommendations have been included for multiple nodules. These guidelines represent the consensus of the Fleischner Society, and as such, they incorporate the opinions of a multidisciplinary international group of thoracic radiologists, pulmonologists, surgeons, pathologists, and other specialists. Changes from the previous guidelines issued by the Fleischner Society are based on new data and accumulated experience. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on March 13, 2017.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation

            Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening.

              The main challenge in CT screening for lung cancer is the high prevalence of pulmonary nodules and the relatively low incidence of lung cancer. Management protocols use thresholds for nodule size and growth rate to determine which nodules require additional diagnostic procedures, but these should be based on individuals' probabilities of developing lung cancer. In this prespecified analysis, using data from the NELSON CT screening trial, we aimed to quantify how nodule diameter, volume, and volume doubling time affect the probability of developing lung cancer within 2 years of a CT scan, and to propose and evaluate thresholds for management protocols.
                Bookmark

                Author and article information

                Contributors
                ericguedespinto@gmail.com
                Journal
                Insights Imaging
                Insights Imaging
                Insights into Imaging
                Springer Vienna (Vienna )
                1869-4101
                23 September 2023
                23 September 2023
                December 2023
                : 14
                : 152
                Affiliations
                [1 ]GRID grid.7427.6, ISNI 0000 0001 2220 7094, R. Marquês de Ávila E Bolama, , Universidade da Beira Interior Faculdade de Ciências da Saúde, ; 6201-001 Covilhã, Portugal
                [2 ]GRID grid.437500.5, ISNI 0000 0004 0489 5016, Liverpool Heart and Chest Hospital NHS Foundation Trust, ; Thomas Dr, Liverpool, L14 3PE UK
                [3 ]University of Florida, ( https://ror.org/02y3ad647) Gainesville, FL 32611 USA
                [4 ]Faculdade de Medicina, Universidade Federal Do Rio de Janeiro, ( https://ror.org/03490as77) Bloco K - Av. Carlos Chagas Filho, 373 - 2º Andar, Sala 49 - Cidade Universitária da Universidade Federal Do Rio de Janeiro, Rio de Janeiro - RJ, 21044-020 Brasil
                [5 ]Faculdade de Medicina, Universidade Federal Fluminense, ( https://ror.org/02rjhbb08) Av. Marquês Do Paraná, 303 - Centro, Niterói - RJ, 24220-000 Brasil
                [6 ]GRID grid.419319.7, ISNI 0000 0004 0641 2823, Manchester University NHS Foundation Trust, Manchester Royal Infirmary, ; Oxford Rd, Manchester, M13 9WL UK
                Author information
                http://orcid.org/0000-0002-6648-9270
                Article
                1480
                10.1186/s13244-023-01480-z
                10517915
                37741928
                df3be4c6-f8fb-498d-8f5c-53227cd01df1
                © European Society of Radiology (ESR) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 April 2023
                : 8 July 2023
                Categories
                Critical Review
                Custom metadata
                © European Society of Radiology (ESR) 2023

                Radiology & Imaging
                systematic review,screening,cancer,lung cancer,computed tomography,spiral
                Radiology & Imaging
                systematic review, screening, cancer, lung cancer, computed tomography, spiral

                Comments

                Comment on this article