Quantitative sonographic image analysis for hepatic nodules: a pilot study

Matsumoto, Naoki; Ogawa, Masahiro; Takayasu, K; Hirayama, Masayuki; Miura, Takao; Shiozawa, Katsuhiko; Abe, Masahisa; Nakagawara, Hiroshi; Moriyama, Mitsuhiko; Udagawa, Seiichi

doi:10.1007/s10396-015-0627-3

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…The range of gray values between minimum and maximum was divided into 256 bins. The method of image analysis used in this study was previously described 10 . Interobserver agreement was confirmed in the previous report.…”

Section: Methodssupporting

confidence: 66%

“…To date, several studies of quantitative image analyses for morphologic features of HCC have been published 3,7–15 . Our previous study reported that the circularity and minimal gray value were predictive factors of malignancy to differentiate HCC from hepatic hemangioma, and an image analysis improves the accuracy of diagnosis 10 . In other previous studies using image analyses, HCC was divided into 3 groups according to the shape detected by contrast‐enhanced computed tomography.…”

Section: Discussionmentioning

confidence: 99%

“…These parameters were automatically calculated by the ImageJ software. Definitions of each parameter are described below (Figure 1) 10,28 :

Circularity = 4 π \times \frac{[Area]}{{([], Perimeter)}^{2}} .

Solidity = \frac{[Area]}{[Convex \cdot area]} .

AR = major axis / minor axis .

…”

Section: Methodsmentioning

confidence: 99%

“…Quantitative image analysis has been investigated for decades as an attempt to overcome this flaw 1–6 . Recent advances in imaging software have led to an increased number of hepatic tumor studies using this technique 3,7–15 …”

mentioning

confidence: 99%

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Quantitative Ultrasound Image Analysis Helps in the Differentiation of Hepatocellular Carcinoma (HCC) From Borderline Lesions and Predicting the Histologic Grade of HCC and Microvascular Invasion

Matsumoto

Ogawa

Kaneko

et al. 2020

J of Ultrasound Medicine

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Objectives Quantitative image analysis is one of the methods to overcome the lack of objectivity of ultrasound (US). The aim of this study was to clarify the correlation between the features from a US image analysis and the histologic grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and differentiation of HCC smaller than 2 cm from borderline lesions. Methods We retrospectively analyzed grayscale US images with histopathologic evidence of HCC or a precancerous lesion using ImageJ version 1.47 software (National Institutes of Health, Bethesda, MD). Results A total of 148 nodules were included (borderline lesion, n = 31; early HCC [eHCC], n = 3; well‐differentiated HCC [wHCC], n = 16; moderately differentiated HCC [mHCC], n = 79; and poorly differentiated HCC [pHCC], n = 19). A multivariate analysis selected lower minimum gray values (odds ratio [OR], 0.431; P = .003) and a higher standard deviation (OR, 1.880; P = .019) as predictors of HCC smaller than 2 cm. Median (range) minimum gray values of borderline lesions, eHCC, wHCC, mHCC, and pHCC were 29 (0–103), 7 (0–47), 6 (0–60), 10 (0–53), and 2 (0–38), respectively, and gradually decreased from borderline lesions to pHCC (P < 0.001). The multivariate analysis showed a higher aspect ratio (OR, 2.170; P = .001) and lower minimum gray value (OR, 0.475; P = .043) as predictors of MVI. An anechoic area diagnosed by a subjective evaluation was correlated with the minimum gray value (P < .0001). The proportion of the anechoic area gradually increased from eHCC to pHCC (P = .031). Conclusions In a US image analysis, HCC smaller than 2 cm had features of greater heterogeneity and a lower minimum gray value than borderline lesions. Moderately differentiated HCC was smoother than borderline lesions, and the anechoic area correlated with histologic grading. Microvascular invasion was correlated with a slender shape and a lower minimum gray value.

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Section: Methodssupporting

confidence: 66%

Section: Discussionmentioning

confidence: 99%

“…These parameters were automatically calculated by the ImageJ software. Definitions of each parameter are described below (Figure 1) 10,28 :

Circularity = 4 π \times \frac{[Area]}{{([], Perimeter)}^{2}} .

Solidity = \frac{[Area]}{[Convex \cdot area]} .

AR = major axis / minor axis .

…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Quantitative Ultrasound Image Analysis Helps in the Differentiation of Hepatocellular Carcinoma (HCC) From Borderline Lesions and Predicting the Histologic Grade of HCC and Microvascular Invasion

Matsumoto

Ogawa

Kaneko

et al. 2020

J of Ultrasound Medicine

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“…The subcutaneous tissue, which underlies the dermis and appears as a hypoechoic area compared with the overlying reticular dermis, has already been shown to be thickened in oedema (Caggiati, 2016). Third, echogenicity within the subcutaneous layer was measured using image analysis software (ImageJ; National Institutes of Health) (Matsumoto et al, 2015). Digital Imaging and Communications in Medicine (DICOM) images of the subcutaneous layer were analysed on an offline personal computer equipped with the ImageJ software, and echogenicity was measured as a grey‐scale value ranging from 0 (absolute black) to 255 (absolute white) (pixel intensity, 256 grey levels per pixel).…”

Section: Methodsmentioning

confidence: 99%

Sonographic detection of physiological lower leg oedema in the late afternoon in healthy young women

Shibasaki

Kishino

Yokoyama

et al. 2020

Clin Physio Funct Imaging

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Microfluidic Platform for Generating and Releasing Patient‐Derived Cancer Organoids with Diverse Shapes: Insight into Shape‐Dependent Tumor Growth

Kheiri,

Yakavets,

Cruickshank

et al. 2024

Advanced Materials

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Multicellular spheroids and patient‐derived organoids find many applications in fundamental research, drug discovery, and regenerative medicine. Advances in the understanding and recapitulation of organ functionality and disease development require the generation of complex organoid models, including organoids with diverse morphologies. Microfluidics‐based cell culture platforms enable time‐efficient confined organoid generation. However, the ability to form organoids with different shapes with a subsequent transfer from microfluidic devices to unconstrained environments for studies of morphology‐dependent organoid growth is yet to be demonstrated. Here, a microfluidic platform is introduced that enables high‐fidelity formation and addressable release of breast cancer organoids with diverse shapes. Using this platform, the impact of organoid morphology on their growth in unconstrained biomimetic hydrogel is explored. It is shown that proliferative cancer cells tend to localize in high positive curvature organoid regions, causing their faster growth, while the overall growth pattern of organoids with diverse shapes tends to reduce interfacial tension at the organoid‐hydrogel interface. In addition to the formation of organoids with diverse morphologies, this platform can be integrated into multi‐tissue micro‐physiological systems.

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Quantitative sonographic image analysis for hepatic nodules: a pilot study

Abstract: Quantitative image analysis for ultrasonic images of hepatic nodules may correlate with subjective judgment in predicting malignancy.

Cited by 10 publications

References 16 publications

Quantitative Ultrasound Image Analysis Helps in the Differentiation of Hepatocellular Carcinoma (HCC) From Borderline Lesions and Predicting the Histologic Grade of HCC and Microvascular Invasion

Quantitative Ultrasound Image Analysis Helps in the Differentiation of Hepatocellular Carcinoma (HCC) From Borderline Lesions and Predicting the Histologic Grade of HCC and Microvascular Invasion

Sonographic detection of physiological lower leg oedema in the late afternoon in healthy young women

Microfluidic Platform for Generating and Releasing Patient‐Derived Cancer Organoids with Diverse Shapes: Insight into Shape‐Dependent Tumor Growth

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