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Ocaña-Tienda B, Eroles-Simó A, Pérez-Beteta J, Arana E, Pérez-García VM. Growth dynamics of lung nodules: implications for classification in lung cancer screening. Cancer Imaging 2024; 24:113. [PMID: 39187900 PMCID: PMC11346294 DOI: 10.1186/s40644-024-00755-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 08/07/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Lung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant. METHODS Data were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods. RESULTS Most lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year). CONCLUSIONS In our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.
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Affiliation(s)
- Beatriz Ocaña-Tienda
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain.
| | - Alba Eroles-Simó
- Instituto de Instrumentación para la Imagen Molecular (i3M), Universitat Politécnica de València, Consejo Superior de Investigaciones Científicas (CSIC), València, Spain
| | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Estanislao Arana
- Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain
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Guedes Pinto E, Penha D, Ravara S, Monaghan C, Hochhegger B, Marchiori E, Taborda-Barata L, Irion K. Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis. Insights Imaging 2023; 14:152. [PMID: 37741928 PMCID: PMC10517915 DOI: 10.1186/s13244-023-01480-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/08/2023] [Indexed: 09/25/2023] Open
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.
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Affiliation(s)
- Erique Guedes Pinto
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal.
| | - Diana Penha
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Thomas Dr, Liverpool, L14 3PE, UK
| | - Sofia Ravara
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
| | - Colin Monaghan
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Thomas Dr, Liverpool, L14 3PE, UK
| | | | - Edson Marchiori
- Faculdade de Medicina, Universidade Federal Do Rio de Janeiro, 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
- Faculdade de Medicina, Universidade Federal Fluminense, Av. Marquês Do Paraná, 303 - Centro, Niterói - RJ, 24220-000, Brasil
| | - Luís Taborda-Barata
- R. Marquês de Ávila E Bolama, Universidade da Beira Interior Faculdade de Ciências da Saúde, 6201-001, Covilhã, Portugal
| | - Klaus Irion
- Manchester University NHS Foundation Trust, Manchester Royal Infirmary, Oxford Rd, Manchester, M13 9WL, UK
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Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI). Eur Radiol 2023:10.1007/s00330-023-09409-2. [PMID: 36729173 PMCID: PMC9892666 DOI: 10.1007/s00330-023-09409-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 11/29/2022] [Accepted: 12/27/2022] [Indexed: 02/03/2023]
Abstract
This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses the current AI scientific evidence in thoracic imaging, its potential clinical utility, implementation and costs, training requirements and validation, its' effect on the training of new radiologists, post-implementation issues, and medico-legal and ethical issues. All these issues have to be addressed and overcome, for AI to become implemented clinically in thoracic radiology. KEY POINTS: • Assessing the datasets used for training and validation of the AI system is essential. • A departmental strategy and business plan which includes continuing quality assurance of AI system and a sustainable financial plan is important for successful implementation. • Awareness of the negative effect on training of new radiologists is vital.
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Odehnalová E, Valíková L, Caluori G, Kulík T, Římalová V, Jadczyk T, Dražanová E, Pavlova I, Pešl M, Kubeš V, Stárek Z. Comparison of gross pathology inspection and 9.4 T magnetic resonance imaging in the evaluation of radiofrequency ablation lesions in the left ventricle of the swine heart. Front Physiol 2022; 13:834328. [PMID: 36338496 PMCID: PMC9626654 DOI: 10.3389/fphys.2022.834328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 10/03/2022] [Indexed: 11/15/2022] Open
Abstract
Aims: Gross pathology inspection (patho) is the gold standard for the morphological evaluation of focal myocardial pathology. Examination with 9.4 T magnetic resonance imaging (MRI) is a new method for very accurate display of myocardial pathology. The aim of this study was to demonstrate that lesions can be measured on high-resolution MRI images with the same accuracy as on pathological sections and compare these two methods for the evaluation of radiofrequency (RF) ablation lesion dimensions in swine heart tissue during animal experiment. Methods: Ten pigs underwent radiofrequency ablations in the left ventricle during animal experiment. After animal euthanasia, hearts were explanted, flushed with ice-cold cardioplegic solution to relax the whole myocardium, fixed in 10% formaldehyde and scanned with a 9.4 T magnetic resonance system. Anatomical images were processed using ImageJ software. Subsequently, the hearts were sliced, slices were photographed and measured in ImageJ software. Different dimensions and volumes were compared. Results: The results of both methods were statistically compared. Depth by MRI was 8.771 ± 2.595 mm and by patho 9.008 ± 2.823 mm; p = 0.198. Width was 10.802 ± 2.724 mm by MRI and 11.125 ± 2.801 mm by patho; p = 0.049. Estuary was 2.006 ± 0.867 mm by MRI and 2.001 ± 0.872 mm by patho; p = 0.953. The depth at the maximum diameter was 4.734 ± 1.532 mm on MRI and 4.783 ± 1.648 mm from the patho; p = 0.858. The volumes of the lesions calculated using a formula were 315.973 ± 257.673 mm3 for MRI and 355.726 ± 255.860 mm3 for patho; p = 0.104. Volume directly measured from MRI with the “point-by-point” method was 671.702 ± 362.299 mm3. Conclusion: Measurements obtained from gross pathology inspection and MRI are fully comparable. The advantage of MRI is that it is a non-destructive method enabling repeated measurements in all possible anatomical projections.
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Affiliation(s)
- Eva Odehnalová
- Interventional Cardiac Electrophysiology Group, International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech
| | - Lucia Valíková
- Interventional Cardiac Electrophysiology Group, International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech
| | - Guido Caluori
- Interventional Cardiac Electrophysiology Group, International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech
- Nanotechnology, CEITEC Masaryk University, Brno, Czech
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac, France
- University Bordeaux, INSERM, Cardiothoracic Research Center of Bordeaux, Pessac, France
| | - Tomáš Kulík
- Interventional Cardiac Electrophysiology Group, International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech
- 1st Department of Internal Medicine—Cardioangiology, St. Anne’s University Hospital Brno, Brno, Czech
| | - Veronika Římalová
- Biostatistics, International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech
| | - Tomasz Jadczyk
- Interventional Cardiac Electrophysiology Group, International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech
- Division of Cardiology and Structural Heart Diseases, Medical University of Silesia, Katowice, Poland
| | - Eva Dražanová
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech
| | - Iveta Pavlova
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech
| | - Martin Pešl
- Interventional Cardiac Electrophysiology Group, International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech
- Nanotechnology, CEITEC Masaryk University, Brno, Czech
- Department of Biology, Faculty of Medicine Masaryk University Brno, Brno, Czech
| | - Václav Kubeš
- Department of Pathology, University Hospital Brno, Brno, Czech
| | - Zdeněk Stárek
- Interventional Cardiac Electrophysiology Group, International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech
- 1st Department of Internal Medicine—Cardioangiology, St. Anne’s University Hospital Brno, Brno, Czech
- *Correspondence: Zdeněk Stárek,
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Intra- and Inter-Reader Variations in Lung Nodule Measurements: Influences of Nodule Size, Location, and Observers. Diagnostics (Basel) 2022; 12:diagnostics12102319. [PMID: 36292008 PMCID: PMC9600531 DOI: 10.3390/diagnostics12102319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/21/2022] [Accepted: 09/21/2022] [Indexed: 11/17/2022] Open
Abstract
(1) Background: Accurate measurement of lung-nodule size is necessary, but whether a three-dimensional volume measurement is better or more reliable than the one-dimensional method is still unclear. This study aimed to investigate the intra- and inter-reader variations according to nodule type, size, three-dimensional volume measurements, and one-dimensional linear measurements. (2) Methods: This retrospective study included computed tomography (CT) examinations of lung nodules and volume measurements performed from October to December 2016. Two radiologists independently performed all measurements. Intra-class correlation coefficients (ICC) and Bland-Altman plots were used for analysis. (3) Results: The overall variability in the calculated volume was larger than when using the semiautomatic volume measurement. Nodules <6 mm tended to have larger variability than nodules ≥6 mm in both one-dimensional and calculated volume measurements. The isolated type showed smaller variability in both intra- and inter-reader comparisons. The juxta-vascular type showed the largest variability in both one-dimensional and calculated volume measurements. The variability was decreased when using the 3D volume semiautomated software. (4) Conclusions: The present study suggests that 3D semiautomatic volume measurements showed lower variability than the calculated volume measurement. Nodule size and location influence measurement variability. The intra- and inter-reader variabilities in nodule volume measurement were considerable.
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Wang L, Tan J, Ge Y, Tao X, Cui Z, Fei Z, Lu J, Zhang H, Pan Z. Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software. Acta Radiol 2021; 62:291-301. [PMID: 32517533 DOI: 10.1177/0284185120922822] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Good feature reproducibility enhances model reliability. The manual segmentation of gastric cancer with liver metastasis (GCLM) can be time-consuming and unstable. PURPOSE To assess the value of a semi-automatic segmentation tool in improving the reproducibility of the radiomic features of GCLM. MATERIAL AND METHODS Patients who underwent dual-source computed tomography were retrospectively reviewed. As an intra-observer analysis, one radiologist segmented metastatic liver lesions manually and semi-automatically twice. Another radiologist re-segmented the lesions once as an inter-observer analysis. A total of 1691 features were extracted. Spearman rank correlation was used for feature reproducibility analysis. The times for manual and semi-automatic segmentation were recorded and analyzed. RESULTS Seventy-two patients with 168 lesions were included. Most of the GCLM radiomic features became more reliable with the tool than the manual method. For the intra-observer feature reproducibility analysis of manual and semi-automatic segmentation, the rates of features with good reliability were 45.5% and 62.3% (P < 0.02), respectively; for the inter-observer analysis, the rates were 29.3% and 46.0% (P < 0.05), respectively. For feature types, the semi-automatic method increased reliability in 6/7 types in the intra-observer analysis and 5/7 types in the inter-observer analysis. For image types, the reliability of the square and exponential types was significantly increased. The mean time of semi-automatic segmentation was significantly shorter than that of the manual method (P < 0.05). CONCLUSION The application of semi-automated software increased feature reliability in the intra- and inter-observer analyses. The semi-automatic process took less time than the manual process.
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Affiliation(s)
- Lan Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Jingwen Tan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | | | | | - Zheng Cui
- Siemens Shanghai Medical Equipment Ltd., Shanghai, PR China
| | - Zhenyu Fei
- Siemens Shanghai Medical Equipment Ltd., Shanghai, PR China
| | - Jing Lu
- Siemens Shanghai Medical Equipment Ltd., Shanghai, PR China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Zilai Pan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
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Dmitriev K, Marino J, Baker K, Kaufman AE. Visual Analytics of a Computer-Aided Diagnosis System for Pancreatic Lesions. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021; 27:2174-2185. [PMID: 31613771 DOI: 10.1109/tvcg.2019.2947037] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Machine learning is a powerful and effective tool for medical image analysis to perform computer-aided diagnosis (CAD). Having great potential in improving the accuracy of a diagnosis, CAD systems are often analyzed in terms of the final accuracy, leading to a limited understanding of the internal decision process, impossibility to gain insights, and ultimately to skepticism from clinicians. We present a visual analytics approach to uncover the decision-making process of a CAD system for classifying pancreatic cystic lesions. This CAD algorithm consists of two distinct components: random forest (RF), which classifies a set of predefined features, including demographic features, and a convolutional neural network (CNN), which analyzes radiological (imaging) features of the lesions. We study the class probabilities generated by the RF and the semantical meaning of the features learned by the CNN. We also use an eye tracker to better understand which radiological features are particularly useful for a radiologist to make a diagnosis and to quantitatively compare with the features that lead the CNN to its final classification decision. Additionally, we evaluate the effects and benefits of supplying the CAD system with a case-based visual aid in a second-reader setting.
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Weber NM, Koo CW, Yu L, Bartholmai BJ, Halaweish AF, McCollough CH, Fletcher JG. Breathe New Life Into Your Chest CT Exams: Using Advanced Acquisition and Postprocessing Techniques. Curr Probl Diagn Radiol 2019; 48:152-160. [DOI: 10.1067/j.cpradiol.2018.10.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 10/06/2018] [Accepted: 10/16/2018] [Indexed: 11/22/2022]
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Larici AR, Farchione A, Franchi P, Ciliberto M, Cicchetti G, Calandriello L, del Ciello A, Bonomo L. Lung nodules: size still matters. Eur Respir Rev 2017; 26:26/146/170025. [DOI: 10.1183/16000617.0025-2017] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 10/28/2017] [Indexed: 12/18/2022] Open
Abstract
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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Moghbel M, Mashohor S, Mahmud R, Saripan MIB. Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography. Artif Intell Rev 2017. [DOI: 10.1007/s10462-017-9550-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Moghbel M, Mashohor S, Mahmud R, Saripan MIB. Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring. EXCLI JOURNAL 2016; 15:406-23. [PMID: 27540353 PMCID: PMC4983804 DOI: 10.17179/excli2016-402] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Accepted: 06/22/2016] [Indexed: 12/25/2022]
Abstract
Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset.
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Affiliation(s)
- Mehrdad Moghbel
- Dept. of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - Syamsiah Mashohor
- Dept. of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - Rozi Mahmud
- Cancer Resource & Education Center, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - M Iqbal Bin Saripan
- Dept. of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
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Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation. Med Image Anal 2016; 30:120-129. [DOI: 10.1016/j.media.2015.07.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 03/29/2015] [Accepted: 07/11/2015] [Indexed: 12/19/2022]
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13
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Hughes KJ, Laidlaw EH, Reed SM, Keen J, Abbott JB, Trevail T, Hammond G, Parkin TDH, Love S. Repeatability and intra- and inter-observer agreement of cervical vertebral sagittal diameter ratios in horses with neurological disease. J Vet Intern Med 2016; 28:1860-70. [PMID: 25410955 PMCID: PMC4895627 DOI: 10.1111/jvim.12431] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Revised: 04/22/2014] [Accepted: 07/09/2014] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Sagittal ratio values (SRVs) of cervical vertebrae are used for ante-mortem diagnosis of cervical vertebral stenotic myelopathy, but intraobserver and interobserver variability in measurement may influence radiographic interpretation of vertebral stenosis in horses with neurological disease. OBJECTIVES To determine intraobserver repeatability in SRVs, intra- and interobserver agreement in SRVs and whether or not agreement was influenced by animal age. ANIMALS Forty-two horses (>1 year old) with neurological disease from which laterolateral computed radiographic images of C2-C7 were obtained. METHODS Four observers made measurements from C2 to C7 for each horse and interobserver agreement for intra- and intervertebral SRVs was determined using Bland-Altman analysis (acceptable agreement: limits of agreement [LOA] ≤ 0.05) on all horses and those ≤3 (n = 25) and >3 (n = 17) years old. Each observer also made repeated measurements for 10 horses and intraobserver repeatability and agreement were determined. RESULTS Adequate intraobserver repeatability was achieved for 6 sites. Within observers, paired measurements had a median difference ≤5.7%, but a large range in differences often occurred, most frequently at intervertebral sites. For C5, C6, C7, and C3-4, LOA ≤ 0.05 were achieved by at least 1 observer. With the exception of C5 for 1 pair, LOA were >0.05 for interobserver agreement, regardless of animal age. LOA were largest at intervertebral sites. CONCLUSIONS AND CLINICAL IMPORTANCE Within and between observers, measurement error may limit the diagnostic accuracy of SRVs and result in discrepancies of diagnosis and treatment and warrants consideration when used clinically in horses with neurological disease.
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Affiliation(s)
- K J Hughes
- Weipers Centre for Equine Welfare, School of Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK; School of Animal and Veterinary Sciences, Charles Sturt University, Wagga Wagga, NSW, Australia
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Advanced imaging tools in pulmonary nodule detection and surveillance. Clin Imaging 2016; 40:296-301. [PMID: 26916752 DOI: 10.1016/j.clinimag.2016.01.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2015] [Revised: 01/27/2016] [Accepted: 01/29/2016] [Indexed: 11/23/2022]
Abstract
Lung cancer is a leading cause of death worldwide. The National Lung Screening Trial has demonstrated that lung cancer screening can reduce lung cancer specific and all cause mortality. With approval of national coverage for lung cancer screening, it is expected that an increase in exams related to pulmonary nodule detection and surveillance will ensue. Advanced imaging technologies for nodule detection and surveillance will be more important than ever. While computed tomography (CT) remains the modality of choice, other emerging modalities such as magnetic resonance imaging provides viable alternatives to CT.
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Zhao YR, Ooijen PMAV, Dorrius MD, Heuvelmans M, Bock GHD, Vliegenthart R, Oudkerk M. Comparison of three software systems for semi-automatic volumetry of pulmonary nodules on baseline and follow-up CT examinations. Acta Radiol 2014; 55:691-8. [PMID: 24132766 DOI: 10.1177/0284185113508177] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Early diagnosis of lung cancer in a treatable stage is the main purpose of lung cancer screening by computed tomography (CT). Accurate three-dimensional size and growth measurements are essential to assess the risk of malignancy. Nodule volumes can be calculated by using semi-automated volumetric software. Systematic differences in volume measurements between packages could influence nodule categorization and management decisions. PURPOSE To compare volumetric measurements of solid pulmonary nodules on baseline and follow-up CT scans as well as the volume doubling time (VDT) for three software packages. MATERIAL AND METHODS From a Lung Cancer Screening study (NELSON), 50 participants were randomly selected from the baseline round. The study population comprised participants with at least one pulmonary nodule at the baseline and consecutive CT examination. The volume of each nodule was determined for both time points using three semi-automated software packages (P1, P2, and P3). Manual modification was performed when automated assessment was visually inaccurate. VDT was calculated to evaluate nodule growth. Volume, VDT, and nodule management were compared for the three software packages, using P1 as the reference standard. RESULTS In 25 participants, 147 nodules were present on both examinations (volume: 12.0-436.6 mm(3)). Initial segmentation at baseline was evaluated to be satisfactory in 93.9% of nodules for P1, 84.4 % for P2, and 88.4% for P3. Significant difference was found in measured volume between P1 and the other two packages (P < 0.001). P2 overestimated the volume by 38 ± 24%, and P3 by 50 ± 22%. At baseline, there was consensus on nodule size categorization in 80% for P1&P2 and 74% for P1&P3. At follow-up, consensus on VDT categorization was present in 47% for P1&P2 and 44% for P1&P3. CONCLUSION Software packages for lung nodule evaluation yield significant differences in volumetric measurements and VDT. This variation affects the classification of lung nodules, especially in follow-up examinations.
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Affiliation(s)
- Ying Ru Zhao
- Center for Medical Imaging, North East Netherlands, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Peter MA van Ooijen
- Center for Medical Imaging, North East Netherlands, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Monique D Dorrius
- Center for Medical Imaging, North East Netherlands, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marjolein Heuvelmans
- Center for Medical Imaging, North East Netherlands, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Geertruida H de Bock
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Rozemarijn Vliegenthart
- Center for Medical Imaging, North East Netherlands, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Matthijs Oudkerk
- Center for Medical Imaging, North East Netherlands, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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Huang EP, Wang XF, Choudhury KR, McShane LM, Gönen M, Ye J, Buckler AJ, Kinahan PE, Reeves AP, Jackson EF, Guimaraes AR, Zahlmann G. Meta-analysis of the technical performance of an imaging procedure: guidelines and statistical methodology. Stat Methods Med Res 2014; 24:141-74. [PMID: 24872353 DOI: 10.1177/0962280214537394] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Medical imaging serves many roles in patient care and the drug approval process, including assessing treatment response and guiding treatment decisions. These roles often involve a quantitative imaging biomarker, an objectively measured characteristic of the underlying anatomic structure or biochemical process derived from medical images. Before a quantitative imaging biomarker is accepted for use in such roles, the imaging procedure to acquire it must undergo evaluation of its technical performance, which entails assessment of performance metrics such as repeatability and reproducibility of the quantitative imaging biomarker. Ideally, this evaluation will involve quantitative summaries of results from multiple studies to overcome limitations due to the typically small sample sizes of technical performance studies and/or to include a broader range of clinical settings and patient populations. This paper is a review of meta-analysis procedures for such an evaluation, including identification of suitable studies, statistical methodology to evaluate and summarize the performance metrics, and complete and transparent reporting of the results. This review addresses challenges typical of meta-analyses of technical performance, particularly small study sizes, which often causes violations of assumptions underlying standard meta-analysis techniques. Alternative approaches to address these difficulties are also presented; simulation studies indicate that they outperform standard techniques when some studies are small. The meta-analysis procedures presented are also applied to actual [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) test-retest repeatability data for illustrative purposes.
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Affiliation(s)
- Erich P Huang
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Xiao-Feng Wang
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Kingshuk Roy Choudhury
- Department of Biostatistics and Bioinformatics/Department of Radiology, Duke University Medical School, Durham, NC, USA
| | - Lisa M McShane
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Mithat Gönen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jingjing Ye
- Division of Biostatistics, Center of Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA
| | | | - Paul E Kinahan
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Anthony P Reeves
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Edward F Jackson
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA
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Heckel F, Meine H, Moltz JH, Kuhnigk JM, Heverhagen JT, Kiessling A, Buerke B, Hahn HK. Segmentation-based partial volume correction for volume estimation of solid lesions in CT. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:462-480. [PMID: 24184707 DOI: 10.1109/tmi.2013.2287374] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
In oncological chemotherapy monitoring, the change of a tumor's size is an important criterion for assessing cancer therapeutics. Measuring the volume of a tumor requires its delineation in 3-D. This is called segmentation, which is an intensively studied problem in medical image processing. However, simply counting the voxels within a binary segmentation result can lead to significant differences in the volume, if the lesion has been segmented slightly differently by various segmentation procedures or in different scans, for example due to the limited spatial resolution of computed tomography (CT) or partial volume effects. This variability limits the sensitivity of size measurements and thus of therapy response assessments and it can even lead to misclassifications. We present a fast, generic algorithm for measuring the volume of solid, compact tumors in CT that considers partial volume effects at the border of a given segmentation result. The algorithm is an extension of the segmentation-based partial volume analysis proposed by Kuhnigk for the volumetry of solid lung lesions , such that it can be applied to inhomogeneous lesions and lesions with inhomogeneous surroundings. Our generalized segmentation-based partial volume correction is based on a spatial subdivision of the segmentation result, from which the fraction of tumor for each voxel is computed. It has been evaluated on phantom data, 1516 lesion segmentation pairs (lung nodules, liver metastases and lymph nodes) as well as 1851 lung nodules from the LIDC-IDRI database. The evaluations of our algorithm show a more accurate estimation of the real volume and its ability to reduce inter- and intra-observer variability significantly for each entity. Overall, the variability (interquartile range) for phantom data is reduced by 49% ( p ≪ 0.001) and the variability between different readers is reduced by 28% ( p ≪ 0.001). The average computation time is 0.2 s.
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Schaefer A, Kim YJ, Kremp S, Mai S, Fleckenstein J, Bohnenberger H, Schäfers HJ, Kuhnigk JM, Bohle RM, Rübe C, Kirsch CM, Grgic A. PET-based delineation of tumour volumes in lung cancer: comparison with pathological findings. Eur J Nucl Med Mol Imaging 2013; 40:1233-44. [PMID: 23632957 DOI: 10.1007/s00259-013-2407-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Accepted: 03/21/2013] [Indexed: 11/27/2022]
Abstract
PURPOSE The objective of the study was to validate an adaptive, contrast-oriented thresholding algorithm (COA) for tumour delineation in (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) for non-small cell lung cancer (NSCLC) in comparison with pathological findings. The impact of tumour localization, tumour size and uptake heterogeneity on PET delineation results was also investigated. METHODS PET tumour delineation by COA was compared with both CT delineation and pathological findings in 15 patients to investigate its validity. Correlations between anatomical volume, metabolic volume and the pathology reference as well as between the corresponding maximal diameters were determined. Differences between PET delineations and pathological results were investigated with respect to tumour localization and uptake heterogeneity. RESULTS The delineated volumes and maximal diameters measured on PET and CT images significantly correlated with the pathology reference (both r > 0.95, p < 0.0001). Both PET and CT contours resulted in overestimation of the pathological volume (PET 32.5 ± 26.5%, CT 46.6 ± 27.4%). CT volumes were larger than those delineated on PET images (CT 60.6 ± 86.3 ml, PET 48.3 ± 61.7 ml). Maximal tumour diameters were similar for PET and CT (51.4 ± 19.8 mm for CT versus 53.4 ± 19.1 mm for PET), slightly overestimating the pathological reference (mean difference CT 4.3 ± 3.2 mm, PET 6.2 ± 5.1 mm). PET volumes of lung tumours located in the lower lobe were significantly different from those determined from pathology (p = 0.037), whereas no significant differences were observed for tumours located in the upper lobe (p = 0.066). Only minor correlation was found between pathological tumour size and PET heterogeneity (r = -0.24). CONCLUSION PET tumour delineation by COA showed a good correlation with pathological findings. Tumour localization had an influence on PET delineation results. The impact of tracer uptake heterogeneity on PET delineation should be considered carefully and individually in each patient. Altogether, PET tumour delineation by COA for NSCLC patients is feasible and reliable with the potential for routine clinical application.
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Affiliation(s)
- Andrea Schaefer
- Department of Nuclear Medicine, Saarland University Medical Center, 66421 Homburg, Germany.
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Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Int J Biomed Imaging 2013; 2013:942353. [PMID: 23431282 PMCID: PMC3570946 DOI: 10.1155/2013/942353] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2012] [Accepted: 11/20/2012] [Indexed: 11/24/2022] Open
Abstract
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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Measurement of tumor volumes improves RECIST-based response assessments in advanced lung cancer. Transl Oncol 2012; 5:19-25. [PMID: 22348172 DOI: 10.1593/tlo.11232] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2011] [Revised: 10/25/2011] [Accepted: 10/25/2011] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE This study was designed to characterize the reproducibility of measurement for tumor volumes and their longest tumor diameters (LDs) and estimate the potential impact of using changes in tumor volumes instead of LDs as the basis for response assessments. METHODS We studied patients with advanced lung cancer who have been observed longitudinally with x-ray computed tomography in a multinational trial. A total of 71 time points from 10 patients with 13 morphologically complex target lesions were analyzed. A total of 6461 volume measurements and their corresponding LDs were made by seven independent teams using their own work flows and image analysis tools. Interteam agreement and overall interrater concurrence were characterized. RESULTS Interteam agreement between volume measurements was better than between LD measurements (ı = 0.945 vs 0.734, P = .005). The variability in determining the nadir was lower for volumes than for LDs (P = .005). Use of standard thresholds for the RECIST-based method and use of experimentally determined cutoffs for categorizing responses showed that volume measurements had a significantly greater sensitivity for detecting partial responses and disease progression. Earlier detection of progression would have led to earlier changes in patient management in most cases. CONCLUSIONS Our findings indicate that measurement of changes in tumor volumes is adequately reproducible. Using tumor volumes as the basis for response assessments could have a positive impact on both patient management and clinical trials. More authoritative work to qualify or discard changes in volume as the basis for response assessments should proceed.
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Comparison of semiautomated and manual measurements for simulated hypo- and hyper-attenuating hepatic tumors on MDCT: effect of slice thickness and reconstruction increment on their accuracy. Acad Radiol 2011; 18:626-33. [PMID: 21393028 DOI: 10.1016/j.acra.2010.12.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2010] [Revised: 12/23/2010] [Accepted: 12/28/2010] [Indexed: 11/24/2022]
Abstract
RATIONALE AND OBJECTIVES The aims of this study were to compare accuracy between semiautomated and manual measurements of the longest diameter and volume of simulated hepatic tumors in phantoms and to evaluate the effects of slice thickness (ST) and reconstruction increment (RI) on accuracy. MATERIALS AND METHODS Liver phantoms with 45 hypoattenuating and 45 hyperattenuating lesions of different sizes (diameter, 13.3-50.7 mm; volume, 0.4-54.0 mm(3)) and shapes (spherical or elliptical) were scanned using a 64-row multidetector computed tomographic scanner. Images were reconstructed with ST and RI settings of 0.75 and 0.7 mm, 1.0 and 0.7 mm, 1.5 and 1.0 mm, 3.0 and 2.0 mm, 3.0 and 3.0 mm, and 5.0 and 5.0 mm. The longest diameter and volume of each lesion were measured both manually and semiautomatically. To assess accuracy, measurements were compared to reference values by calculating absolute percentage error. Comparisons of absolute percentage error between methods and between ST and RI settings were performed using paired t tests. The degree of correlation between each measurement and a reference value was also assessed. RESULTS The semiautomated method showed significantly higher accuracy than the manual method in volume for most ST and RI settings (0.75 and 0.7 mm, 1.0 and 0.7 mm, and 1.5 and 1.0 mm in hypoattenuating lesions and all settings in hyperattenuating lesions; P < .05) and showed similar accuracy in diameter for all ST and RI settings regardless of lesion attenuation (P > .05). Semiautomated measurements also demonstrated higher correlation with reference values than the manual method for both diameter and volume. The absolute percentage error tended to be increased as ST and RI increased for both methods, and acceptable maximum ST and RI in semiautomated method were 1.5 and 1.0 mm. CONCLUSIONS Semiautomated computed tomographic measurement showed higher accuracy and correlation than the manual method in measuring the diameter and volume of hepatic lesions. The accuracy of both methods was highly dependent on z-axis resolution.
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Sieren JC, Ohno Y, Koyama H, Sugimura K, McLennan G. Recent technological and application developments in computed tomography and magnetic resonance imaging for improved pulmonary nodule detection and lung cancer staging. J Magn Reson Imaging 2011; 32:1353-69. [PMID: 21105140 DOI: 10.1002/jmri.22383] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
This review compares the emerging technologies and approaches in the application of magnetic resonance (MR) and computed tomography (CT) imaging for the assessment of pulmonary nodules and staging of malignant findings. Included in this review is a brief definition of pulmonary nodules and an introduction to the challenges faced. We have highlighted the current status of both MR and CT for the early detection of lung nodules. Developments are detailed in this review for the management of pulmonary nodules using advanced imaging, including: dynamic imaging studies, dual energy CT, computer aided detection and diagnosis, and imaging assisted nodule biopsy approaches which have improved lung nodule detection and diagnosis rates. Recent advancements linking in vivo imaging to corresponding histological pathology are also highlighted. In vivo imaging plays a pivotal role in the clinical staging of pulmonary nodules through TNM assessment. While CT and positron emission tomography (PET)/CT are currently the most commonly clinically employed modalities for pulmonary nodule staging, studies are presented that highlight the augmentative potential of MR.
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Affiliation(s)
- Jessica C Sieren
- Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA.
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Niehues SM, Unger JK, Malinowski M, Neymeyer J, Hamm B, Stockmann M. Liver volume measurement: reason of the difference between in vivo CT-volumetry and intraoperative ex vivo determination and how to cope it. Eur J Med Res 2010; 15:345-50. [PMID: 20947471 PMCID: PMC3458704 DOI: 10.1186/2047-783x-15-8-345] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Purpose Volumetric assessment of the liver regularly yields discrepant results between pre- and intraoperatively determined volumes. Nevertheless, the main factor responsible for this discrepancy remains still unclear. The aim of this study was to systematically determine the difference between in vivo CT-volumetry and ex vivo volumetry in a pig animal model. Material and Methods Eleven pigs were studied. Liver density assessment, CT-volumetry and water displacement volumetry was performed after surgical removal of the complete liver. Known possible errors of volume determination like resection or segmentation borders were eliminated in this model. Regression analysis was performed and differences between CT-volumetry and water displacement determined. Results Median liver density was 1.07 g/ml. Regression analysis showed a high correlation of r2 = 0.985 between CT-volumetry and water displacement. CTvolumetry was found to be 13% higher than water displacement volumetry (p < 0.0001). Conclusion In this study the only relevant factor leading to the difference between in vivo CT-volumetry and ex vivo water displacement volumetry seems to be blood perfusion of the liver. The systematic difference of 13 percent has to be taken in account when dealing with those measures.
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Affiliation(s)
- Stefan M Niehues
- Klinik für Strahlenheilkunde, Charite Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.
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Mozley P, Schwartz L, Bendtsen C, Zhao B, Petrick N, Buckler A. Change in lung tumor volume as a biomarker of treatment response: a critical review of the evidence. Ann Oncol 2010; 21:1751-1755. [DOI: 10.1093/annonc/mdq051] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Sensakovic WF, Starkey A, Roberts R, Straus C, Caligiuri P, Kocherginsky M, Armato SG. The influence of initial outlines on manual segmentation. Med Phys 2010; 37:2153-8. [PMID: 20527549 DOI: 10.1118/1.3392287] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Initial outlines are often presented as an aid to reduce the time-cost associated with manual segmentation and measurement of structures in medical images. This study evaluated the influence of initial outlines on manual segmentation intraobserver and interobserver precision. METHODS Three observers independently outlined all pleural mesothelioma tumors present in five computed tomography (CT) sections in each of 30 patient scans. After a lapse of time, each observer was presented with the same series of CT sections with the outlines of each observer superimposed as initial outlines. Each observer created altered outlines by altering the initial outlines to reflect their perception of the tumor boundary. Altered outlines were compared to original outlines using the Jaccard similarity coefficient (J). Intraobserver and interobserver precision of observer outlines were calculated by applying linear mixed effects analysis of variance models to the J values. The percent of minor alterations (alterations that resulted in only slight changes in the initial outline) was also recorded. RESULTS The average J value between pairs of observer original outlines was 0.371. The average J value between pairs of observer outlines when altered from an identical initial outline was 0.796, indicating increased interobserver precision. The average difference between J values of an observer's segmentation created by altering their own initial outline and when altering a different observer's initial outline was 0.476, indicating initial outlines strongly influence intraobserver precision. Observers made minor alterations on 74.5% of initial outlines with which they were presented. CONCLUSIONS Intraobserver and interobserver precision were strongly dependent on the initial outline. These effects are likely due to the tendency of observers to make only minor corrections to initial outlines. This finding could impact observer study design, tumor growth assessment, computer-aided diagnosis system validation, and radiation therapy target volume definition when initial outlines are used as an observer aid.
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Affiliation(s)
- William F Sensakovic
- Department of Radiology, The University of Chicago, 5841 S. Maryland Ave., Chicago, Illinois 60637, USA.
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Buerke B, Puesken M, Beyer F, Gerss J, Weckesser M, Seifarth H, Heindel W, Wessling J. Semiautomatic Lymph Node Segmentation in Multislice Computed Tomography. Invest Radiol 2010; 45:82-8. [DOI: 10.1097/rli.0b013e3181c443e1] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Honda O, Kawai M, Gyobu T, Kawata Y, Johkoh T, Sekiguchi J, Tomiyama N, Yoshida S, Sumikawa H, Inoue A, Yanagawa M, Daimon T, Nakamura H. Reproducibility of temporal volume change in CT of lung cancer: comparison of computer software and manual assessment. Br J Radiol 2009; 82:742-7. [PMID: 19332515 DOI: 10.1259/bjr/67746844] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The purpose of this study was to investigate the reproducibility of volumetric software evaluation and manual evaluation of tumour growth. Three observers manually evaluated whether tumour volume was increasing, if it was unchanged, or if it had decreased in size in 2 serial CT examinations of 45 solid lung cancers. The tumour volumes were calculated 3 times using volumetric software and were evaluated using the same classifications as for manual evaluation. Both data sets were divided into three groups: growth or reduction with consistency among all three evaluations (group A), growth or reduction with consistency between only two evaluations (group B), and others (group C). The volume variation and relative volume variation were calculated from the median volumes measured by volumetric software. Although all 45 tumours were categorised in group A by volumetric software, only 21 tumours were categorised in group A by manual assessment. The relative volume variation of the manual assessment was 88.5 +/- 76.5%, 20.8 +/- 28.3% and 12.9 +/- 12.8% in group A, B and C, respectively. Significant differences were found between groups A and B (p<0.01) and between groups A and C (p<0.001). Inconsistency is often seen in manual assessment; in contrast, evaluation using volumetric software has good reproducibility, even when the relative change in tumour volume is small.
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Affiliation(s)
- O Honda
- Department of radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-City, Osaka, 565-0871, Japan.
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Rkein AM, Harrigal C, Friedman AC, Persky D, Krupinski E. Comparison of the accuracy of CT volume calculated by circumscription to prolate ellipsoid volume (bidimensional measurement multiplied by coronal long axis). Acad Radiol 2009; 16:181-6. [PMID: 19124103 DOI: 10.1016/j.acra.2008.08.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2008] [Revised: 07/26/2008] [Accepted: 08/07/2008] [Indexed: 12/21/2022]
Abstract
RATIONALE AND OBJECTIVES Tumor volume is one of the most important factors in evaluating the response to treatment of patients with cancer. The objective of this study was to compare computed tomographic (CT) volume calculation using a semiautomated circumscribing tracing tool (manual circumscription [MC]) to prolate ellipsoid volume calculation (PEVC; bidimensional measurement multiplied by coronal long axis) and determine which was more accurate and consistent. MATERIALS AND METHODS The study included six patients with nine neoplasms, six phantoms, and two radiologists. The neoplasms and phantoms of varying sizes and shapes were imaged using multidetector CT scanners, with slice thicknesses ranging from 0.5 to 3 mm. Measurements were performed using a TeraRecon 3D workstation. Each lesion and phantom was manually circumscribed, and its three dimensions were measured. The measurements were repeated 2 weeks later. RESULTS MC of the phantoms deviated from their true volumes by an average of 3.0 +/- 1%, whereas PEVC deviated by 10.1 +/- 3.99%. MC interobserver readings varied by 1.2 +/- 0.6% and PEVC by 4.8 +/- 3.3%. MC intraobserver readings varied by 1.95 +/- 1.75% and PEVC by 2.5 +/- 1.55%. Patient tumor volume predicted by MC and PEVC varied greatly; MC interobserver readings differed by 3.3 +/- 2.1% and PEVC by 20.1 +/- 10.6%. MC intraobserver readings varied by 2.5 +/- 1.9% and PEVC by 5.5 +/- 3.2%. Variability was greater for complex shapes than for simple shapes. Bidimensional analysis demonstrated an interobserver difference of 12.1 +/- 8.7% and an intraobserver difference of 5.05 +/- 3.3%. These results demonstrate large interobserver and intraobserver variability. Variability was greater for complex shapes than for simple shapes. CONCLUSION MC of neoplasms provided more accurate and consistent volume predictions than PEVC. More complicated shapes demonstrated the superiority of MC over PEVC.
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Affiliation(s)
- Ali M Rkein
- Departments of Radiology, University of Arizona College of Medicine, Tucson, AZ 85724-5067, USA
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Rominger MB, Fournell D, Nadar BT, Behrens SNM, Figiel JH, Keil B, Heverhagen JT. Accuracy of MRI volume measurements of breast lesions: comparison between automated, semiautomated and manual assessment. Eur Radiol 2009; 19:1097-107. [PMID: 19159935 DOI: 10.1007/s00330-008-1243-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2008] [Revised: 10/08/2008] [Accepted: 10/12/2008] [Indexed: 11/25/2022]
Affiliation(s)
- Marga B Rominger
- Department of Radiology, Philipps University, Baldingerstrasse, 35033 Marburg, Germany.
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Hein PA, Romano VC, Rogalla P, Klessen C, Lembcke A, Bornemann L, Dicken V, Hamm B, Bauknecht HC. Variability of semiautomated lung nodule volumetry on ultralow-dose CT: comparison with nodule volumetry on standard-dose CT. J Digit Imaging 2008; 23:8-17. [PMID: 18773240 DOI: 10.1007/s10278-008-9157-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2008] [Revised: 06/23/2008] [Accepted: 07/28/2008] [Indexed: 11/28/2022] Open
Abstract
The study investigates the effect of a substantial dose reduction on the variability of lung nodule volume measurements by assessing and comparing nodule volumes using a dedicated semiautomated segmentation software on ultralow-dose computed tomography (ULD-CT) and standard-dose computed tomography (SD-CT) data. In 20 patients, thin-slice chest CT datasets (1 mm slice thickness; 20% reconstruction overlap) were acquired at ultralow-dose (120 kV, 5 mAs) and at standard-dose (120 kV, 75 mAs), respectively, and analyzed using the segmentation software OncoTREAT (MeVis, Bremen, Germany; version 1.3). Interobserver variability of volume measurements of 202 solid pulmonary nodules (mean diameter 11 mm, range 3.2-44.5 mm) was calculated for SD-CT and ULD-CT. With respect to interobserver variability, the 95% confidence interval for the relative differences in nodule volume in the intrascan analysis was measured with -9.7% to 8.3% (mean difference -0.7%) for SD-CT and with -12.6% to 12.4% (mean difference -0.2%) for ULD-CT. In the interscan analysis, the 95% confidence intervals for the differences in nodule volume ranged with -25.1% to -23.4% and 26.2% to 28.9% (mean difference 1.4% to 2.1%) dependent on the combination of readers and scans. Intrascan interobserver variability of volume measurements was comparable for ULD-CT and SD-CT data. The calculated variability of volume measurements in the interscan analysis was similar to the data reported in the literature for CT data acquired with equal radiation dose. Thus, the evaluated segmentation software provides nodule volumetry that appears to be independent of the dose level with which the CT source dataset is acquired.
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Affiliation(s)
- Patrick A Hein
- Department of Radiology, Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
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