Evaluation of microcalcification contrast in clinical images for digital mammography and synthetic mammography

Baldelli, Paola; Cardarelli, P.; Flanagan, Fidelma; Maguire, Susan; Phelan, Niamh; Tomasi, Santino Ottavio; Taibi, Angelo

doi:10.1016/j.ejrad.2021.109751

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Our results showed that when used alone, AI CAD SM could yield better diagnostic performance than to that of DM, although previous studies reported SM to be non-inferior to DM [ 6 – 8 ]. These results agreed with two studies [ 9 , 10 ], even though this study was conducted using a large-scale study design. This study also showed that the latest version of the image processing algorithm used to generate the AI CAD SM might attain the level required by radiologists.…”

Section: Discussionsupporting

confidence: 92%

“…Also, several studies have successfully shown that SM alone as non-inferior to DM alone in a diagnostic setting [6][7][8]. Recently, two studies reported that SM was superior to DM on calcification lesions [9,10]. However, it remains unclear whether DM can be replaced with SM in both screening and diagnostic settings due to the lower spatial and contrast resolution in the latter, especially for detecting ductal carcinoma in situ (DCIS) with calcification alone [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence computer-aided detection enhances synthesized mammograms: comparison with original digital mammograms alone and in combination with tomosynthesis images in an experimental setting

et al. 2022

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Background It remains unclear whether original full-field digital mammograms (DMs) can be replaced with synthesized mammograms in both screening and diagnostic settings. To compare reader performance of artificial intelligence computer-aided detection synthesized mammograms (AI CAD SMs) with that of DM alone or in combination with digital breast tomosynthesis (DBT) images in an experimental setting. Methods We compared the performance of multireader (n = 4) and reading multicase (n = 388), in 84 cancers, 83 biopsy-proven benign lesions, and 221 normal or benign cases with negative results after 1-year follow-up. Each reading was independently interpreted with four reading modes: DM, AI CAD SM, DM + DBT, and AI CAD SM + DBT. The accuracy of probability of malignancy (POM) and five-category ratings were evaluated using areas under the receiver operating characteristic curve (AUC) in the random-reader analysis. Results The mean AUC values based on POM for DM, AI CAD SM, DM + DBT, and AI CAD SM + DBT were 0.871, 0.902, 0.895, and 0.909, respectively. The mean AUC of AI CAD SM was significantly higher (P = 0.002) than that of DM. For calcification lesions, the sensitivity of SM and DM did not differ significantly (P = 0.204). The mean AUC for AI CAD SM + DBT was higher than that of DM + DBT (P = 0.082). ROC curves based on the five-category ratings showed similar proximity of the overall performance levels. Conclusions AI CAD SM alone was superior to DM alone. Also, AI CAD SM + DBT was superior to DM + DBT but not statistically significant.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence computer-aided detection enhances synthesized mammograms: comparison with original digital mammograms alone and in combination with tomosynthesis images in an experimental setting

et al. 2022

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“…2) 22 . Baldelli et al 23 , en un trabajo retrospectivo, describen que las microcalcificaciones, independientemente de su tamaño, presentan mejor contraste en mamas de menor densidad en la MS. De todas maneras, una vez detectadas las microcalcificaciones, siempre es necesario obtener magnificaciones para su adecuada caracterización. También, al detectar microcalcificaciones en MS es indispensable revisar la TS, ya que esta podría resaltarlas.…”

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Synthetized mammography: questions and answers

Darras,

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2024

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El estudio radiológico de la mama ha ido evolucionando tecnológicamente, desde las mamografías analógicas, pasando por la mamografía digital indirecta, hasta las mamografías digitales de campo completo (M2D). Uno de los últimos avances ha sido la tomosíntesis (TS), una técnica que genera una mamografía casi tridimensional que disminuye la superposición de tejido fibroglandular y ha demostrado un aumento en la detección de cánceres, por lo que se está convirtiendo en el estándar en programas de cribado y diagnóstico a nivel mundial. Dado que inicialmente la FDA aprobó su uso junto a la mamografía M2D, se generó un aumento en la radiación recibida por la paciente, aunque siempre debajo del límite establecido de 3 mGy por exposición. Para resolver este problema se desarrolla la mamografía sintetizada (MS), en la que se obtiene una mamografía a partir de la TS, eliminando la necesidad de realizar una M2D y disminuyendo la radiación a aproximadamente la mitad. El objetivo del presente artículo de revisión es repasar las características técnicas de la MS y mostrar la evidencia publicada, respondiendo preguntas frecuentes.

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“…In the recent years, deep learning-based artificial intelligence (AI) systems have been quickly evolving in the field of breast imaging and surpassing the performance and clinical value of traditional computer-aided detection (CAD) for mammography [5]. In addition, the use of an AI CAD-enhanced SM enabled radiologists to review the DBT 23.5% at a faster rate, without altering the interpretation performance, when compared to that without CAD [6]. Previous studies have reported that the use of AI CAD SM can automatically enhance suspicious findings in the DBT data, thereby improving the quality of the SM [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Comparisons between artificial intelligence computer-aided detection synthesized mammograms and digital mammograms when used alone and in combination with tomosynthesis images in a virtual screening setting

et al. 2022

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Purpose To compare the reader performance of artificial intelligence computer-aided detection synthesized mammograms (AI CAD SM) with that of digital mammograms (DM) when used alone or in combination with digital breast tomosynthesis (DBT) images. Materials and methods This retrospective multireader (n = 4) study compared the reader performances in 388 cases (84 cancer, 83 benign, and 221 normal or benign cases). The overall accuracy of the breast-based assessment was determined by four radiologists using two sequential reading modes: DM followed by DM + DBT; and AI CAD SM followed by AI CAD SM + DBT. Each breast was rated by each reader using five-category ratings, where 3 or higher was considered positive. The area under the receiver-operating characteristic curve (AUC) and reading time were evaluated. Results The mean AUC values for DM, AI CAD SM, DM + DBT, and AI CAD SM + DBT were 0.863, 0.895, 0.886, and 0.902, respectively. The mean AUC of AI CAD SM was significantly higher (P < 0.0001) than that of DM. The mean AUC of AI CAD SM + DBT was higher than that of DM + DBT (P = 0.094). A significant reduction in the reading time was observed after using AI CAD SM + DBT when compared with that after using DM + DBT (P < 0.001). Conclusion AI CAD SM + DBT might prove more effective than DM + DBT in a screening setting because of its lower radiation dose, noninferiority, and shorter reading time compared to DM + DBT.

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Evaluation of microcalcification contrast in clinical images for digital mammography and synthetic mammography

Cited by 6 publications

References 30 publications

Artificial intelligence computer-aided detection enhances synthesized mammograms: comparison with original digital mammograms alone and in combination with tomosynthesis images in an experimental setting

Artificial intelligence computer-aided detection enhances synthesized mammograms: comparison with original digital mammograms alone and in combination with tomosynthesis images in an experimental setting

Synthetized mammography: questions and answers

Comparisons between artificial intelligence computer-aided detection synthesized mammograms and digital mammograms when used alone and in combination with tomosynthesis images in a virtual screening setting

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