Growth pattern analysis of murine lung neoplasms by advanced semi-automated quantification of micro-CT images.
Growth pattern analysis of murine lung neoplasms by advanced semi-automated quantification of micro-CT images.
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DOI：
10.1371/journal.pone.0083806
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Reeves AP
中科院分区：
文献类型：
--
作者：
Li M;Jirapatnakul A;Biancardi A;Riccio ML;Weiss RS;Reeves AP
Computed tomography (CT) is a non-invasive imaging modality used to monitor human lung cancers. Typically, tumor volumes are calculated using manual or semi-automated methods that require substantial user input, and an exponential growth model is used to predict tumor growth. However, these measurement methodologies are time-consuming and can lack consistency. In addition, the availability of datasets with sequential images of the same tumor that are needed to characterize in vivo growth patterns for human lung cancers is limited due to treatment interventions and radiation exposure associated with multiple scans. In this paper, we performed micro-CT imaging of mouse lung cancers induced by overexpression of ribonucleotide reductase, a key enzyme in nucleotide biosynthesis, and developed an advanced semi-automated algorithm for efficient and accurate tumor volume measurement. Tumor volumes determined by the algorithm were first validated by comparison with results from manual methods for volume determination as well as direct physical measurements. A longitudinal study was then performed to investigate in vivo murine lung tumor growth patterns. Individual mice were imaged at least three times, with at least three weeks between scans. The tumors analyzed exhibited an exponential growth pattern, with an average doubling time of 57.08 days. The accuracy of the algorithm in the longitudinal study was also confirmed by comparing its output with manual measurements. These results suggest an exponential growth model for lung neoplasms and establish a new advanced semi-automated algorithm to measure lung tumor volume in mice that can aid efforts to improve lung cancer diagnosis and the evaluation of therapeutic responses.
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影响因子：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
DOI：
10.1093/jnci/91.6.523
发表时间：
1999-03-17
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
James, K;Eisenhauer, E;Therasse, P
通讯作者：
Therasse, P
影响因子：
3.5
作者：
Artaechevarria, X.;Perez-Martin, D.;Munoz-Barrutia, A.
通讯作者：
Munoz-Barrutia, A.
影响因子：
3.3
作者：
Ford, N. L.;Martin, E. L.;Holdsworth, D. W.
通讯作者：
Holdsworth, D. W.
影响因子：
5.7
作者：
Fushiki, Hiroshi;Kanoh-Azuma, Tomoko;Hayakawa, Yoshihiro
通讯作者：
Hayakawa, Yoshihiro