Breast Cancer: Pathology, Cytology, and Core Needle Biopsy Methods for Diagnosis

Gong, Yun

doi:10.1007/978-1-4614-1876-4_2

Cited by 6 publications

(2 citation statements)

References 150 publications

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“…According to the latest cancer statistics in Japan, 1 in 11 females develops breast cancer in their lifetime and 1 in 66 dies from breast cancer [1]. With advancement in imaging diagnostic technology and the spread of breast cancer screening, early breast cancer cases that were previously impossible to discover on imaging have recently increased, leading to an increase in cases that are more challenging in breast fine-needle aspiration biopsy (FNAB) [2][3][4][5][6]. In addition, in the WHO Classification of Breast Tumors, 5th Ed., intraductal epithelial proliferative lesions including precancerous breast lesions developing from the terminal duct lobular unit have become a more prominent designation [7].…”

Section: Introductionmentioning

confidence: 99%

Usefulness of Fractal Analysis of Kirsch Edge Images for the Tissue Fragment Inner Structure in Breast FNAB

et al. 2021

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Objective: Recent advances in high-precision mammography and ultrasound screening have led to an increase in the detection of early lesions (ductal carcinoma in situ and small cancers) appearing as microcalcified lesions or microcystic images, and there needs to be an improvement in the accuracy of breast fine-needle aspiration biopsy (FNAB) assessing these lesions. The objective of this study was to investigate whether fractal analysis of Kirsch edge images for the tissue fragment inner structure (FKT) is useful in breast FNAB. FKT measures tissue fragment chromasia of hyperchromatic crowded tissue fragments (HCG), tissue fragment shape unevenness, and tissue fragment inner structure complexity. Study Design Materials: Nineteen epithelial tissue fragments of fibroadenoma (FA) from 7 patients and 52 tissue fragments of invasive breast carcinoma of no special type (IBC-NST) (grade 1–2) from 11 patients were assessed. First, tissue fragments were classified into small (smaller than 60 × 102 μm2), medium, and large (100 × 102 μm2 or larger), and the appearance rate of each size was determined. Second, for FKT, the luminance value of tissue fragment chromasia, the unevenness and fractal value, and the tissue fragment inner structure complexity were determined. In statistical analysis, the Steel-Dwass test, nonlinear discriminant analysis, and receiver operating characteristic analysis were performed, setting the significance level at p < 0.05. Results: “Unevenness of the tissue fragment shape,” “fractal value of the tissue fragment shape,” and “fractal value of the tissue fragment inner structure” were significantly higher in small and large tissue fragments in IBC-NST compared with those in FA. The specificity and sensitivity were the highest (100%) in small tissue fragments in multivariate analysis using 4 variables (“luminance value of tissue fragment chromasia,” “unevenness of tissue fragment shape,” “fractal value of the tissue fragment shape,” and “fractal value of the tissue fragment inner structure”). Conclusion: FKT, which evaluates “tissue fragment darkness,” “tissue fragment shape unevenness,” and “tissue fragment inner structure complexity” focusing on small tissue fragments of HCG in breast FNAB, is useful as a system that assists cytopathological assessment of breast FNAB.

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Section: Introductionmentioning

confidence: 99%

Usefulness of Fractal Analysis of Kirsch Edge Images for the Tissue Fragment Inner Structure in Breast FNAB

et al. 2021

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“…Image analysis in cytology has been studied for years and numerous solutions [36,34,5,31,14] have thus been proposed in the literature. The application of these solutions to histopathology is rather complicated due to the radical dif-ferences between the two imaging modalities and to the highly complex image characteristics.…”

Section: Introductionmentioning

confidence: 99%

Multispectral band selection and spatial characterization: Application to mitosis detection in breast cancer histopathology

Irshad

Gouaillard

Roux

et al. 2014

Computerized Medical Imaging and Graphics

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Breast cancer is the second most frequent cancer. The reference process for breast cancer prognosis is Nottingham grading system. According to this system, mitosis detection is one of the three important criteria required for grading process and quantifying the locality and prognosis of a tumor. Multispectral imaging, as relatively new to the field of histopathology, has the advantage, over traditional RGB imaging, to capture spectrally resolved information at specific frequencies, across the electromagnetic spectrum. This study aims at evaluating the accuracy of mitosis detection on histopathological multispectral images. The proposed framework includes: selection of spectral bands and focal planes, detection of candidate mitotic regions and computation of morphological and multispectral statistical features. A state-of-the-art of the methods for mitosis classification is also provided. This framework has been evaluated on MITOS multispectral dataset and achieved higher detection rate (67.35%) and F-Measure (63.74%) than the best MITOS contest results (Roux et al., 2013). Our results indicate that the selected multispectral bands have more discriminant information than a single spectral band or all spectral bands for mitotic figures, validating the interest of using multispectral images to improve the quality of the diagnostic in histopathology.

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Comparative Analysis of Classification and Detection of Breast Cancer from Histopathology Images Using Deep Neural Network

Malve¹,

Gulhane

2022

Intelligent Computing and Networking

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Breast Cancer: Pathology, Cytology, and Core Needle Biopsy Methods for Diagnosis

Cited by 6 publications

References 150 publications

Usefulness of Fractal Analysis of Kirsch Edge Images for the Tissue Fragment Inner Structure in Breast FNAB

Usefulness of Fractal Analysis of Kirsch Edge Images for the Tissue Fragment Inner Structure in Breast FNAB

Multispectral band selection and spatial characterization: Application to mitosis detection in breast cancer histopathology

Comparative Analysis of Classification and Detection of Breast Cancer from Histopathology Images Using Deep Neural Network

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