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Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging

Background: This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. Methods: A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct m...

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Autores principales: Jiang, Xu, Liu, Meng-Wen, Zhang, Xue, Dong, Ji-Yan, Miao, Lei, Sun, Zi-Han, Dong, Shu-Shan, Zhang, Li, Yang, Lin, Li, Meng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417025/
https://www.ncbi.nlm.nih.gov/pubmed/37568923
http://dx.doi.org/10.3390/diagnostics13152560
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author Jiang, Xu
Liu, Meng-Wen
Zhang, Xue
Dong, Ji-Yan
Miao, Lei
Sun, Zi-Han
Dong, Shu-Shan
Zhang, Li
Yang, Lin
Li, Meng
author_facet Jiang, Xu
Liu, Meng-Wen
Zhang, Xue
Dong, Ji-Yan
Miao, Lei
Sun, Zi-Han
Dong, Shu-Shan
Zhang, Li
Yang, Lin
Li, Meng
author_sort Jiang, Xu
collection PubMed
description Background: This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. Methods: A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the “py-radiomics” package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT. Results: Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura. Conclusions: Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding.
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spelling pubmed-104170252023-08-12 Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging Jiang, Xu Liu, Meng-Wen Zhang, Xue Dong, Ji-Yan Miao, Lei Sun, Zi-Han Dong, Shu-Shan Zhang, Li Yang, Lin Li, Meng Diagnostics (Basel) Article Background: This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. Methods: A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the “py-radiomics” package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT. Results: Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura. Conclusions: Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding. MDPI 2023-08-01 /pmc/articles/PMC10417025/ /pubmed/37568923 http://dx.doi.org/10.3390/diagnostics13152560 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jiang, Xu
Liu, Meng-Wen
Zhang, Xue
Dong, Ji-Yan
Miao, Lei
Sun, Zi-Han
Dong, Shu-Shan
Zhang, Li
Yang, Lin
Li, Meng
Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging
title Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging
title_full Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging
title_fullStr Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging
title_full_unstemmed Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging
title_short Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging
title_sort observational study of the natural growth history of peripheral small-cell lung cancer on ct imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417025/
https://www.ncbi.nlm.nih.gov/pubmed/37568923
http://dx.doi.org/10.3390/diagnostics13152560
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