ASO Visual Abstract: Is the 21-Gene Recurrence Score on Core Needle Biopsy Equivalent to Surgical Specimen in Early-Stage Breast Cancer?

Orozco, Javier I. J.; Chang, Shu-Ching; Matsuba, Chikako; Ensenyat-Mendez, Miquel; Grunkemeier, Gary L.; Marzese, Diego M.; Grumley, Janie

doi:10.1245/s10434-021-10589-8

Cited by 2 publications

(1 citation statement)

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“…4 Overall, we found a high correlation in gene expression levels between the core needle biopsy and the surgical specimen, with only minor differences (median difference in gene expressions of the 21-gene approximated zero), demonstrating that both sample types provide similar information. 4 Furthermore, the concordance rate of the microarray-based 21-gene recurrence score categories was at least 82%, which is similar to the observed concordance rate of anatomopathological evaluation of hormone receptors and HER2 in paired core needle biopsy and surgical specimens. 5 This provides additional information on the similarities between the diagnostic and surgical tissue samples.…”

Section: Presentmentioning

confidence: 56%

ASO Author Reflections: Molecular Testing in Breast Cancer: Is Core Biopsy Equivalent to Surgical Specimen?

Orozco

Grumley

2021

Ann Surg Oncol

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Section: Presentmentioning

confidence: 56%

ASO Author Reflections: Molecular Testing in Breast Cancer: Is Core Biopsy Equivalent to Surgical Specimen?

Orozco

Grumley

2021

Ann Surg Oncol

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Improved Mask R-CNN Segmentation and Bayesian Interactive Adaboost CNN Classification for Breast Cancer Detection on Bach Dataset

Malarvizhi

Nagappan

2022

IJEER

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Breast cancer is considered as the predominant type of cancer that affects more than ten percentage of the worldwide female population. Though microscopic evaluation remains to be a significant method for diagnosing, time and cost complexity seeks alternative and effective computer aided design for rapid and more accurate detection of the disease. As DL (Deep Learning) possess a significant contribution in accomplishing machine automation, this study intends to resolve existing problems with regard to lack of accuracy by proposing DL based algorithms. The study proposes Improved-Mask R CNN (I-MRCNN) method for segmentation. In this process, RPN (Region Proposal Network), predicts the objectless scores and object bound at every position. Here, (RoI Align) Region of interest Align is used for feature extraction as it is capable of resolving the conventional RoI pooling issues by attaining high accuracy for small objects and also eliminates quantization issues. Further, classification is performed using the proposed Bayesian Interactive Adaboost CNN classifier (B-IAB- CNN) that integrates the advantages of CNN, Bayesian and Adaboost classifier. The advantages of the three classifier enable optimum classification of the input Bach dataset that is confirmed through the results of performance analysis of the proposed system. Outcomes reveal that, average accuracy for segmentation is 96.32%, while, the classification accuracy is exposed to be 96%. As Timely prediction is significant, high prediction rate of the proposed system will assist the medical practitioners to detect breast cancer quickly which is the important practical implication from this study for diagnosing breast cancer.

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ASO Visual Abstract: Is the 21-Gene Recurrence Score on Core Needle Biopsy Equivalent to Surgical Specimen in Early-Stage Breast Cancer?

Cited by 2 publications

References 0 publications

ASO Author Reflections: Molecular Testing in Breast Cancer: Is Core Biopsy Equivalent to Surgical Specimen?

ASO Author Reflections: Molecular Testing in Breast Cancer: Is Core Biopsy Equivalent to Surgical Specimen?

Improved Mask R-CNN Segmentation and Bayesian Interactive Adaboost CNN Classification for Breast Cancer Detection on Bach Dataset

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