Quantitative Sonography of Basal Cell Carcinoma

Piotrzkowska-Wróblewska, Hanna; Litniewski, Jerzy; Szymańska, Elżbieta; Nowicki, Andrzej

doi:10.1016/j.ultrasmedbio.2014.11.016

Cited by 13 publications

(11 citation statements)

References 30 publications

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“…Both melanoma and BCC cells demonstrate high sound attenuation of about 20.7 ± 1.4 dB/mm and 22.4 ± 1.3 dB/mm, respectively, compared to healthy tissue. These values are in correlation with data obtained by other ultrasonic techniques . While individual cancer cells demonstrate lower SOS or decreased hardness compared to healthy cells, the averaged values of these parameters for a tumour can be higher than the surrounding healthy tissue .…”

Section: Discussionsupporting

confidence: 86%

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High‐resolution quantitative acoustic microscopy of cutaneous carcinoma and melanoma: Comparison with histology

Youssef

Seviaryna

Shum

et al. 2019

Skin Research and Technology

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Background The increased incidence rate of skin cancers during the last decades is alarming. One of the significant difficulties in the histopathology of skin cancers is appearance variability due to the heterogeneity of diseases or tissue preparation and staining process. This study aims to investigate whether the high‐resolution acoustic microscopy has the potential for identifying and quantitatively classifying skin cancers. Material/Methods Unstained standard formalin‐fixed skin tissue samples were used for ultrasonic examination. The high‐frequency acoustic microscope equipped with the 320 MHz transducer was utilized to visualize skin structure. Fourier transform was performed to calculate the sound speed and attenuation in the tissue. Results The acoustic images demonstrate good concordance with the traditional histology images. All histological features in the tumour were easily identifiable on acoustic images. Each skin cancer type has its combination of ultrasonic properties significantly different from the healthy skin. Conclusions High‐resolution acoustic imaging strengthened with quantitative analysis shows a potential to work as an auxiliary imaging modality assisting pathologists to lean to the particular decision in doubtful cases. The method can also assist surgeon to ensure the complete resection of a tumour.

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Section: Discussionsupporting

confidence: 86%

“…They are typically surrounded by collagen fibres. The progressive cancer stage is represented by reduced stroma in the dermis along with the enlarged tumour nests . These clusters present various histological types of BCC depending on the different configuration of atypical cells.…”

Section: Resultsmentioning

confidence: 99%

High‐resolution quantitative acoustic microscopy of cutaneous carcinoma and melanoma: Comparison with histology

Youssef

Seviaryna

Shum

et al. 2019

Skin Research and Technology

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“…Several studies have performed attenuation estimation in liver [5,6,7,8,9], thyroid [10,11], breast [12,13,14], spleen [15], muscle [16], skin [17,18], cervical and uterine tissues [19,20] and small plaques [21]. A related parameter is the total attenuation that an ultrasonic wave experiences from the surface of the transducer to a region of analysis within a tissue of interest.…”

Section: Studies On Attenuation Coefficient Estimationmentioning

confidence: 99%

Regularized Spectral Log Difference Technique for Ultrasonic Attenuation Imaging

Coila

Lavarello

2018

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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The attenuation coefficient slope (ACS) has the potential to be used for tissue characterization and as a diagnostic ultrasound tool, hence complementing B-mode images. The ACS can be valuable for the estimation of other ultrasound parameters such as the backscatter coefficient. There is a well-known tradeoff between the precision of the estimated ACS values and the data block size used in the spectral-based techniques such as the spectral-log difference (SLD). This tradeoff limits the practical usefulness of the spectral-based attenuation imaging techniques. In this paper, the regularized SLD (RSLD) technique is presented in detail, and evaluated with simulations and experiments with physical phantoms, ex vivo and in vivo. The RSLD technique allowed decreasing estimation variance when using small data block sizes, i.e., fivefold reduction in the standard deviation of percentage error when using data block sizes larger than and more than a tenfold reduction when using data blocks. The precision improvement was obtained without sacrificing estimation accuracy (i.e., estimation bias improved in 70% of the cases by 10% of the ground truth-value on average while degraded in 30% of the cases by 3% of the ground truth-value on average). The improvements in precision allowed for better differentiation of inclusions especially when using small data blocks (i.e., smaller than ) where the contrast-to-noise ratio improved by an order of magnitude on average. The results suggest that the RSLD allows for the reconstruction of attenuation coefficient images with an improved tradeoff between spatial resolution and estimation precision.

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“…For some examples, see [11][12][13]. The current work is an attempt to investigate the relative usefulness of textural and shape markers, as inspired by clinical experience, in differentiating between melanoma, BCC, and benign nevi.…”

Section: Introductionmentioning

confidence: 99%

Automated classification of common skin lesions using bioinspired features

Csabai

Szalai

Gyöngy

2016

2016 IEEE International Ultrasonics Symposium (IUS)

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Abstract-Ultrasound (US) imaging of skin lesions provides information supplementary to dermoscopy and helps in improving diagnostic accuracy. The aim of the current work is to explore the feasibility of using ultrasound image features derived from radiological experience to distinguish between common skin lesions. 5-18 MHz B-mode ultrasound images were acquired of incoming patients. Images containing lesions 1-2 mm thick were selected (N=248), with histology used to diagnose suspicious lesions. 73 melanomas, 130 BCC, and 45 nevi were studied. Following semi-automatic segmentation, a number of relevant features expressing the geometry of the lesion boundary and boundary layer, as well as the image characteristics of the lesion, lesion boundary layer, and post-lesion region were considered. With the exception of lesion echogenicity, all features had an area under the curve (AUC) value of above 0.70. The AdaBoost and Support Vector Machine (SVM) classifiers were then trained and tested using cross-validation of 50 random equal populations of melanomas, BCC, and nevi; each population was then 2-fold (holdout) cross-validated 50 times. When detecting one group against two other groups, the detection of cancerous lesions fared best, with an AUC of at least 0.84 and a specificity of at least 19% at 100% sensitivity for both classifiers. The results demonstrate the potential of clinically useful ultrasound-based automatic differential diagnosis of skin lesions, which could perhaps be attained by better segmentation, having more training data, using several images of the same lesion when performing classification, as well as refinements in the definition of image features.

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Quantitative Sonography of Basal Cell Carcinoma

Cited by 13 publications

References 30 publications

High‐resolution quantitative acoustic microscopy of cutaneous carcinoma and melanoma: Comparison with histology

High‐resolution quantitative acoustic microscopy of cutaneous carcinoma and melanoma: Comparison with histology

Regularized Spectral Log Difference Technique for Ultrasonic Attenuation Imaging

Automated classification of common skin lesions using bioinspired features

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