Literature DB >> 26576975

Quantitative sonographic image analysis for hepatic nodules: a pilot study.

Naoki Matsumoto1, Masahiro Ogawa2, Kentaro Takayasu2, Midori Hirayama2, Takao Miura2, Katsuhiko Shiozawa2, Masahisa Abe2, Hiroshi Nakagawara2, Mitsuhiko Moriyama2, Seiichi Udagawa3.   

Abstract

PURPOSE: The aim of this study was to investigate the feasibility of quantitative image analysis to differentiate hepatic nodules on gray-scale sonographic images.
METHODS: We retrospectively evaluated 35 nodules from 31 patients with hepatocellular carcinoma (HCC), 60 nodules from 58 patients with liver hemangioma, and 22 nodules from 22 patients with liver metastasis. Gray-scale sonographic images were evaluated with subjective judgment and image analysis using ImageJ software. Reviewers classified the shape of nodules as irregular or round, and the surface of nodules as rough or smooth.
RESULTS: Circularity values were lower in the irregular group than in the round group (median 0.823, 0.892; range 0.641-0.915, 0.784-0.932, respectively; P = 3.21 × 10(-10)). Solidity values were lower in the rough group than in the smooth group (median 0.957, 0.968; range 0.894-0.986, 0.933-0.988, respectively; P = 1.53 × 10(-4)). The HCC group had higher circularity and solidity values than the hemangioma group. The HCC and liver metastasis groups had lower median, mean, modal, and minimum gray values than the hemangioma group. Multivariate analysis showed circularity [standardized odds ratio (OR), 2.077; 95 % confidential interval (CI) = 1.295-3.331; P = 0.002] and minimum gray value (OR 0.482; 95 % CI = 0.956-0.990; P = 0.001) as factors predictive of malignancy. The combination of subjective judgment and image analysis provided 58.3 % sensitivity and 89.5 % specificity with AUC = 0.739, representing an improvement over subjective judgment alone (68.4 % sensitivity, 75.0 % specificity, AUC = 0.701) (P = 0.008).
CONCLUSION: Quantitative image analysis for ultrasonic images of hepatic nodules may correlate with subjective judgment in predicting malignancy.

Entities:  

Keywords:  Hepatocellular carcinoma; Liver hemangioma; Liver metastasis; Quantitative image analysis; Ultrasonography

Mesh:

Year:  2015        PMID: 26576975     DOI: 10.1007/s10396-015-0627-3

Source DB:  PubMed          Journal:  J Med Ultrason (2001)        ISSN: 1346-4523            Impact factor:   1.314


  18 in total

1.  Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images.

Authors:  Hiroyuki Yoshida; David D Casalino; Bilgin Keserci; Abdulhakim Coskun; Omer Ozturk; Ahmet Savranlar
Journal:  Phys Med Biol       Date:  2003-11-21       Impact factor: 3.609

2.  Sonographic characterization of 271 hepatic hemangiomas with typical appearance on CT imaging.

Authors:  Hiroaki Ito; Fumio Tsujimoto; Yasuo Nakajima; Gaku Igarashi; Takanori Okamura; Masaru Sakurai; Sachihiko Nobuoka; Takehito Otsubo
Journal:  J Med Ultrason (2001)       Date:  2012-01-12       Impact factor: 1.314

3.  Usefulness of arrival time parametric imaging in evaluating the degree of liver disease progression in chronic hepatitis C infection.

Authors:  Noritaka Wakui; Ryuji Takayama; Takenori Kanekawa; Mioe Ichimori; Takafumi Otsuka; Mie Shinohara; Koji Ishii; Naohisa Kamiyama; Yasukiyo Sumino
Journal:  J Ultrasound Med       Date:  2012-03       Impact factor: 2.153

4.  Characterization of primary and secondary malignant liver lesions from B-mode ultrasound.

Authors:  Jitendra Virmani; Vinod Kumar; Naveen Kalra; Niranjan Khandelwal
Journal:  J Digit Imaging       Date:  2013-12       Impact factor: 4.056

5.  Studies on tissue characterization by texture analysis with co-occurrence matrix method using ultrasonography and CT imaging.

Authors:  Yi Wang; Kouichi Itoh; Nobuyuki Taniguchi; Hisao Toei; Fukiko Kawai; Michiru Nakamura; Kiyoka Omoto; Kyoko Yokota; Tomoko Ono
Journal:  J Med Ultrason (2001)       Date:  2002-12       Impact factor: 1.314

6.  A pilot approach for quantitative assessment of liver fibrosis using ultrasound: preliminary results in 79 cases.

Authors:  Hiroyuki Yamada; Masaaki Ebara; Tadashi Yamaguchi; Shinichirou Okabe; Hiroyuki Fukuda; Masaharu Yoshikawa; Takashi Kishimoto; Hisahiro Matsubara; Hiroyuki Hachiya; Hiroshi Ishikura; Hiromitsu Saisho
Journal:  J Hepatol       Date:  2005-09-15       Impact factor: 25.083

7.  Texture analysis of ultrasound B-mode images by segmentation.

Authors:  G E Mailloux; M Bertrand; R Stampfler; S Ethier
Journal:  Ultrason Imaging       Date:  1984-07       Impact factor: 1.578

8.  Tumor boundary detection in ultrasound imagery using multi-scale generalized gradient vector flow.

Authors:  Yi Le; Xianze Xu; Li Zha; Wencheng Zhao; Yanyan Zhu
Journal:  J Med Ultrason (2001)       Date:  2014-08-05       Impact factor: 1.314

9.  Ultrasonographic patterns in hepatic hemangiomas.

Authors:  P Mirk; L Rubaltelli; M Bazzocchi; P Busilacchi; F Candiani; F Ferrari; G Giuseppetti; G Maresca; G Rizzatto; L Volterrani; F Zappasodi
Journal:  J Clin Ultrasound       Date:  1982-10       Impact factor: 0.910

10.  Evaluation of quantitative contrast harmonic imaging to assess malignancy of liver tumors: a prospective controlled two-center study.

Authors:  E M Jung; D A Clevert; A G Schreyer; S Schmitt; J Rennert; R Kubale; S Feuerbach; F Jung
Journal:  World J Gastroenterol       Date:  2007-12-21       Impact factor: 5.742

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  1 in total

1.  Pediatric Corneal Structural Development During Childhood Characterized by Ultrasound Biomicroscopy.

Authors:  Snehaa Maripudi; Julia Byrd; Azam Qureshi; Gianna Stoleru; Moran Roni Levin; Osamah J Saeedi; Wuqaas Munir; Marlet Bazemore; Bethany Karwoski; Camilo Martinez; Mohamad S Jaafar; William P Madigan; Janet Leath Alexander
Journal:  J Pediatr Ophthalmol Strabismus       Date:  2020-07-01       Impact factor: 1.402

  1 in total

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