A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis

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

Objectives

To construct a noninvasive radiomics model for evaluating the pathological degree and an individualized treatment strategy for patients with the manifestation of ground glass nodules (GGNs) on CT images.

Methods

The retrospective primary cohort investigation included patients with GGNs on CT images who underwent resection between June 2015 and June 2020. The intratumoral regions of interest were segmented semiautomatically, and radiomics features were extracted from the intratumoral and peritumoral regions. After feature selection by ANOVA, Max-Relevance and Min-Redundancy (mRMR) and Least Absolute Shrinkage and Selection Operator (Lasso) regression, a random forest (RF) model was generated. Receiver operating characteristic (ROC) analysis was calculated to evaluate each classification. Shapley additive explanations (SHAP) was applied to interpret the radiomics features.

Results

In this study, 241 patients including atypical adenomatous hyperplasia (AAH) or adenocarcinoma in situ (AIS) (n = 72), minimally invasive adenocarcinoma (MIA) (n = 83) and invasive adenocarcinoma (IAC) (n = 86) were selected for radiomics analysis. Three intratumoral radiomics features and one peritumoral feature were finally identified by the triple RF classifier with an average area under the curve (AUC) of 0.960 (0.963 for AAH/AIS, 0.940 for MIA, 0.978 for IAC) in the training set and 0.944 (0.955 for AAH/AIS, 0.952 for MIA, 0.926 for IAC) in the testing set for evaluation of the GGNs.

Conclusion

The triple classification based on intra- and peritumoral radiomics features derived from the noncontrast CT images had satisfactory performance and may be used as a noninvasive tool for preoperative evaluation of the pure ground-glass nodules and developing of individualized treatment strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00862-x.

Related collections

Most cited references 36

Record: found
Abstract: found
Article: not found

Computational Radiomics System to Decode the Radiographic Phenotype

Chintan Parmar, Ahmed Raafat Hosny, Hugo J.W.L. Aerts … (2017)

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io . With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.

0 comments Cited 1697 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification.

William Travis, Elisabeth Brambilla, Andrew G Nicholson … (2015)

The 2015 World Health Organization (WHO) Classification of Tumors of the Lung, Pleura, Thymus and Heart has just been published with numerous important changes from the 2004 WHO classification. The most significant changes in this edition involve (1) use of immunohistochemistry throughout the classification, (2) a new emphasis on genetic studies, in particular, integration of molecular testing to help personalize treatment strategies for advanced lung cancer patients, (3) a new classification for small biopsies and cytology similar to that proposed in the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (4) a completely different approach to lung adenocarcinoma as proposed by the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (5) restricting the diagnosis of large cell carcinoma only to resected tumors that lack any clear morphologic or immunohistochemical differentiation with reclassification of the remaining former large cell carcinoma subtypes into different categories, (6) reclassifying squamous cell carcinomas into keratinizing, nonkeratinizing, and basaloid subtypes with the nonkeratinizing tumors requiring immunohistochemistry proof of squamous differentiation, (7) grouping of neuroendocrine tumors together in one category, (8) adding NUT carcinoma, (9) changing the term sclerosing hemangioma to sclerosing pneumocytoma, (10) changing the name hamartoma to "pulmonary hamartoma," (11) creating a group of PEComatous tumors that include (a) lymphangioleiomyomatosis, (b) PEComa, benign (with clear cell tumor as a variant) and

0 comments Cited 1275 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Zhenyu Liu, Shuo Wang, Di Dong … (2019)

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

0 comments Cited 277 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Ziyang Yu: 24520200156805@stu.xmu.edu.cn

Chenxi Xu: chenxi.xu0429@gmail.com

Ying Zhang: 13945011724@163.com

Fengying Ji: 15840098023@163.com

Journal

Journal ID (nlm-ta): BMC Med Imaging

Journal ID (iso-abbrev): BMC Med Imaging

Title: BMC Medical Imaging

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2342

Publication date (Electronic): 27 July 2022

Publication date PMC-release: 27 July 2022

Publication date Collection: 2022

Volume: 22

Electronic Location Identifier: 133

Affiliations

[1 ]GRID grid.412596.d, ISNI 0000 0004 1797 9737, Department of Radiology, , The First Affiliated Hospital of Harbin Medical University, ; Harbin, Heilongjiang Province People’s Republic of China

[2 ]GRID grid.12955.3a, ISNI 0000 0001 2264 7233, School of Medicine, , Xiamen University, ; Xiamen, Fujian Province China

Article

Publisher ID: 862

DOI: 10.1186/s12880-022-00862-x

PMC ID: 9327229

PubMed ID: 35896975

SO-VID: bac0fd51-7273-488b-b8eb-4c2b470e64ad

License:

Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 24 March 2022

Date accepted : 21 July 2022

Funding

Funded by: Heilongjiang Provincial Health Commission Scientific Research Project

Award ID: No: 2020-097

Award Recipient : Fengying Ji

Funded by: FundRef http://dx.doi.org/10.13039/501100005046, Natural Science Foundation of Heilongjiang Province;

Award ID: NO：LH2021H051

Award Recipient : Fengying Ji

Funded by: Scientific Research and Innovation Fund of the First Affiliated Hospital of Harbin Medical University

Award ID: NO：2020M05

Award Recipient : Fengying Ji

Custom metadata

ScienceOpen disciplines: Radiology & Imaging

Keywords: lung adenocarcinoma,pulmonary nodules,radiomics,random forest classification,computed tomography

Data availability:

ScienceOpen disciplines: Radiology & Imaging

Keywords: lung adenocarcinoma, pulmonary nodules, radiomics, random forest classification, computed tomography

A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis

Read this article at

Abstract

Objectives

Methods

Results

Conclusion

Supplementary Information

Related collections

Probing cerebral hemodynamics with BOLD fMRI

Most cited references 36

Computational Radiomics System to Decode the Radiographic Phenotype

The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification.

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Author and article information

Contributors

Journal

Affiliations

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 174

Cited by 4

Most referenced authors 639