Tumor characteristics of ductal carcinoma<i>in situ</i>of breast visualized on [F-18] fluorodeoxyglucose-positron emission tomography/computed tomography: Results from a retrospective study

Fujioka, Tomoyuki; Kubota, Kazunori; Toriihara, Akira; Machida, Youichi; Okazawa, Kaori; Nakagawa, Tsuyoshi; Saida, Yukihisa; Tateishi, Ukihide

doi:10.4329/wjr.v8.i8.743

Cited by 11 publications

(8 citation statements)

References 17 publications

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“…Reportedly, even malignant tumors with low cell density and low aggressiveness tend to have low stiffness. 10,28,29 Therefore, the diagnosis of these tumors may be difficult not only for humans but also for CNNs. To construct CNN models with higher performance, it will be necessary for the models to be trained further using low-stiffness malignant and high-stiffness benign masses as training data.…”

Section: Discussionmentioning

confidence: 99%

Classification of Breast Masses on Ultrasound Shear Wave Elastography using Convolutional Neural Networks

et al. 2020

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We aimed to use deep learning with convolutional neural networks (CNNs) to discriminate images of benign and malignant breast masses on ultrasound shear wave elastography (SWE). We retrospectively gathered 158 images of benign masses and 146 images of malignant masses as training data for SWE. A deep learning model was constructed using several CNN architectures (Xception, InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169, and NASNetMobile) with 50, 100, and 200 epochs. We analyzed SWE images of 38 benign masses and 35 malignant masses as test data. Two radiologists interpreted these test data through a consensus reading using a 5-point visual color assessment (SWEc) and the mean elasticity value (in kPa) (SWEe). Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. The best CNN model (which was DenseNet169 with 100 epochs), SWEc, and SWEe had a sensitivity of 0.857, 0.829, and 0.914 and a specificity of 0.789, 0.737, and 0.763 respectively. The CNNs exhibited a mean AUC of 0.870 (range, 0.844–0.898), and SWEc and SWEe had an AUC of 0.821 and 0.855. The CNNs had an equal or better diagnostic performance compared with radiologist readings. DenseNet169 with 100 epochs, Xception with 50 epochs, and Xception with 100 epochs had a better diagnostic performance compared with SWEc ( P = 0.018–0.037). Deep learning with CNNs exhibited equal or higher AUC compared with radiologists when discriminating benign from malignant breast masses on ultrasound SWE.

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Section: Discussionmentioning

confidence: 99%

Classification of Breast Masses on Ultrasound Shear Wave Elastography using Convolutional Neural Networks

et al. 2020

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“…Tumour cell density appears to be strongly correlated to the detection of DCIS by FDG PET [38]. FDG uptake is higher in DCIS with microinvasion than pure DCIS [36].…”

Section: Role Of Pet/ct Imaging In Breast Cancer Fdg Pet and Naf Pet mentioning

confidence: 91%

“…DCIS usually show low FDG uptake but symptomatic and large DCIS (≥ 20 mm) are often visualized on FDG PET [37,38].…”

Section: Role Of Pet/ct Imaging In Breast Cancer Fdg Pet and Naf Pet mentioning

confidence: 99%

Breast Cancer and PET Imaging

Sarıkaya

2021

Nucl. Med. Rev.

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Breast cancer is the most common malignancy in women and among the most common indications of oncologic positron emission tomography (PET) studies. In this review article, updated anatomical, pathological, and clinical information about breast cancer were provided for Nuclear Medicine physicians to better understand breast cancer and interpret PET images and a review of the literature on the use of PET imaging in breast cancer was summarized.

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“…1 The diagnosis of breast images is conducted comprehensively using a plurality of modalities, such as mammography, ultrasound, MRI, and positron emission tomography. [2][3][4][5] MRI is widely used in routine practice for detecting and diagnosing breast tumor when an abnormality is found by other imagings and when screening high-risk patients or patients with a personal history of breast cancer. [6][7][8] Because dynamic contrast-enhanced (DCE) MRI is currently regarded as a very sensitive imaging tool for breast cancer detection, breast MRI examination mainly consists of DCE-MRI.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Image Quality and Utility of Synthetic MRI in the Breast

et al. 2021

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Purpose: Synthetic MRI reconstructs multiple sequences in a single acquisition. In the present study, we aimed to compare the image quality and utility of synthetic MRI with that of conventional MRI in the breast. Methods:We retrospectively collected the imaging data of 37 women (mean age: 55.1 years; range: 20-78 years) who had undergone both synthetic and conventional MRI of T2-weighted, T1-weighted, and fatsuppressed (FS)-T2-weighted images. Two independent breast radiologists evaluated the overall image quality, anatomical sharpness, contrast between tissues, image homogeneity, and presence of artifacts of synthetic and conventional MRI on a 5-point scale (5 = very good to 1 = very poor). The interobserver agreement between the radiologists was evaluated using weighted kappa.Results: For synthetic MRI, the acquisition time was 3 min 28 s. On the 5-point scale evaluation of overall image quality, although the scores of synthetic FS-T2-weighted images (4.01 ± 0.56) were lower than that of conventional images (4.95 ± 0.23; P < 0.001), the scores of synthetic T1-and T2-weighted images (4.95 ± 0.23 and 4.97 ± 0.16) were comparable with those of conventional images (4.92 ± 0.27 and 4.97 ± 0.16; P = 0.484 and 1.000, respectively). The kappa coefficient of conventional MRI was fair (0.53; P < 0.001), and that of conventional MRI was fair (0.46; P < 0.001). Conclusion:The image quality of synthetic T1-and T2-weighted images was similar to that of conventional images and diagnostically acceptable, whereas the quality of synthetic T2-weighted FS images was inferior to conventional images. Although synthetic MRI images of the breast have the potential to provide efficient image diagnosis, further validation and improvement are required for clinical application.

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Tumor characteristics of ductal carcinomain situof breast visualized on [F-18] fluorodeoxyglucose-positron emission tomography/computed tomography: Results from a retrospective study

Abstract: Although DCIS of breast is generally non-avid tumor, symptomatic and large tumors (≥ 20 mm) tend to be visualized on [F-18] FDG-PET/CT.

Cited by 11 publications

References 17 publications

Classification of Breast Masses on Ultrasound Shear Wave Elastography using Convolutional Neural Networks

Classification of Breast Masses on Ultrasound Shear Wave Elastography using Convolutional Neural Networks

Breast Cancer and PET Imaging

Investigating the Image Quality and Utility of Synthetic MRI in the Breast

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