Literature DB >> 17675181

Usefulness of circumference difference for estimating the likelihood of malignancy in small solitary pulmonary nodules on CT.

Hajime Saito1, Yoshihiro Minamiya, Hideki Kawai, Taku Nakagawa, Manabu Ito, Yukiko Hosono, Satoru Motoyama, Manabu Hashimoto, Koichi Ishiyama, Jun-Ichi Ogawa.   

Abstract

OBJECTIVE: The presence of a small solitary pulmonary nodule (SSPN) is a common finding on chest computed tomography (CT); however, preoperative diagnosis of SSPN is often difficult. We measured the extent of ground-glass opacity (GGO) and our own original method of circumference difference (CD) as an additional approach in classifying SSPN revealed on CT, and evaluated the likelihood of malignancy.
METHOD: In total, 214 single SSPN with diameter <15mm were studied. All SSPN were histologically examined with surgical resection; preoperative CT findings and pathological diagnosis were evaluated retrospectively. The extent of the ratio of GGO and CD was evaluated using NIH image, where CD is defined as the ratio of the nodule margin distance to the circumference of the predicted circle with an area equal to that of the nodule.
RESULTS: The thresholds for differentiating SSPN are 70% of GGO and 68% of the CD ratio. Differentiation of malignancy from benign tumor using our algorithm had sensitivity of 96.6%, specificity of 86.1%, and positive predictive value of 94.1%.
CONCLUSION: Combined with other malignant likelihood parameters such as extent of GGO, CD ratio is a useful additional factor for estimating the likelihood of malignancy of SSPN on CT.

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Year:  2007        PMID: 17675181     DOI: 10.1016/j.lungcan.2007.06.018

Source DB:  PubMed          Journal:  Lung Cancer        ISSN: 0169-5002            Impact factor:   5.705


  2 in total

1.  Texture feature analysis for computer-aided diagnosis on pulmonary nodules.

Authors:  Fangfang Han; Huafeng Wang; Guopeng Zhang; Hao Han; Bowen Song; Lihong Li; William Moore; Hongbing Lu; Hong Zhao; Zhengrong Liang
Journal:  J Digit Imaging       Date:  2015-02       Impact factor: 4.056

2.  Predicting Unnecessary Nodule Biopsies from a Small, Unbalanced, and Pathologically Proven Dataset by Transfer Learning.

Authors:  Fangfang Han; Linkai Yan; Junxin Chen; Yueyang Teng; Shuo Chen; Shouliang Qi; Wei Qian; Jie Yang; William Moore; Shu Zhang; Zhengrong Liang
Journal:  J Digit Imaging       Date:  2020-06       Impact factor: 4.056

  2 in total

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