Radiomics-based Prediction Model of Tumor Spread Through Air Space in Lung Adenocarcinoma
NCT ID: NCT04893200
Last Updated: 2021-09-05
Study Results
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Basic Information
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COMPLETED
150 participants
OBSERVATIONAL
2020-02-01
2021-06-01
Brief Summary
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Radiological and clinical data from 100 consecutive patients with resected lung adenocarcinoma were retrospectively collected for the training section. As in common clinical practice, preoperative CT images were acquired independently by different physicians and from different hospitals. Therefore, our dataset presents high variance in model and manufacture of scanner, acquisition and reconstruction protocol, endovenous contrast phase and pixel size. To test the effect of normalization in highly varying data, preoperative CT images and tumor region of interest were preprocessed with four different pipelines. Features were extracted using pyradiomics and selected considering both separation power and robustness within pipelines. After that, a radiomics-based prediction model of STAS were created using the most significant associated features. This model were than validated in a group of 50 patients prospectively enrolled as external validation group to test its efficacy in STAS prediction.
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Detailed Description
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Conditions
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Study Design
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CASE_ONLY
PROSPECTIVE
Study Groups
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Lung adenocarcinoma
Imaging from patients with surgically treated lung adenocarcinoma were collected and processed for the construction of the radiomics-based prediction model
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* Available preoperative CT images
* Age older than 18 years
Exclusion Criteria
* Induction radio or chemotherapy
* Incomplete surgical resection
18 Years
ALL
No
Sponsors
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University of Roma La Sapienza
OTHER
Responsible Party
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Marco Anile
Principal Investigator
Principal Investigators
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Marco Anile, MD
Role: PRINCIPAL_INVESTIGATOR
La Sapienza Università di Roma
Locations
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Dipartimento di chirurgia Generale e Specialistica "Paride Stefanini"
Roma, , Italy
Countries
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References
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Jiang C, Luo Y, Yuan J, You S, Chen Z, Wu M, Wang G, Gong J. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma. Eur Radiol. 2020 Jul;30(7):4050-4057. doi: 10.1007/s00330-020-06694-z. Epub 2020 Feb 28.
Chen D, She Y, Wang T, Xie H, Li J, Jiang G, Chen Y, Zhang L, Xie D, Chen C. Radiomics-based prediction for tumour spread through air spaces in stage I lung adenocarcinoma using machine learning. Eur J Cardiothorac Surg. 2020 Jul 1;58(1):51-58. doi: 10.1093/ejcts/ezaa011.
Zhuo Y, Feng M, Yang S, Zhou L, Ge D, Lu S, Liu L, Shan F, Zhang Z. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma. Transl Oncol. 2020 Oct;13(10):100820. doi: 10.1016/j.tranon.2020.100820. Epub 2020 Jul 1.
Bassi M, Russomando A, Vannucci J, Ciardiello A, Dolciami M, Ricci P, Pernazza A, D'Amati G, Mancini Terracciano C, Faccini R, Mantovani S, Venuta F, Voena C, Anile M. Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset. Transl Lung Cancer Res. 2022 Apr;11(4):560-571. doi: 10.21037/tlcr-21-895.
Other Identifiers
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RADIOMICS
Identifier Type: -
Identifier Source: org_study_id
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