Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features

Ma, Wenjuan; Zhao, Yingxin; Ji, Yu; Guo, Xinpeng; Jian, Xiqi; Liu, Peifang; Wu, Shandong

doi:10.1016/j.acra.2018.01.023

Cited by 96 publications

(62 citation statements)

References 21 publications

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“…Quantitative features of radiomics can distinguish between TNBC and non-TNBC in mammograms, as has been shown in some studies (19,20). Recently, the relationship between mammographic radiomic features and molecular subtypes of breast cancer was evaluated, which showed that quantitative radiomics imaging features were associated with breast cancer subtypes (21). However, no studies have explored the relationship between TIL levels and the characteristics of mammograms of TNBC patients.…”

Section: Introductionmentioning

confidence: 93%

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer

Meng

Huang

et al. 2020

Front. Oncol.

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Objectives: Tumor-infiltrating lymphocytes (TILs) have been identified as a significant prognostic indicator of response to neoadjuvant therapy and immunotherapy for triple-negative breast cancer (TNBC) patients. Herein, we aim to assess the association between TIL levels and mammographic features in TNBC patients. Methods: Forty-three patients with surgically proven TNBC who underwent preoperative mammography from January 2018 to December 2018 were recruited. Pyradiomics software was used to extract 204 quantitative radiomics features, including morphologic, grayscale, and textural features, from the segmented lesion areas. The correlation between radiological characteristics and TIL levels was evaluated by screening the most statistically significant radiological features using Mann-Whitney U-test and Pearson correlation coefficient. The patients were divided into two groups based on tumor TIL levels: patients with TIL levels <50% and those with TIL levels ≥50%. The correlation between TIL levels and clinicopathological characteristics was assessed using the chi-square test or Fisher's exact test. Mann-Whitney U-test and Pearson correlation coefficient were used to analyze the statistical significance and Pearson correlation coefficient of clinical pathological features, age, and radiological features. Conclusion: Mammography features not only distinguish high and low TIL levels in TNBC patients but also can act as imaging biomarkers to enhance diagnosis and the response of patients to neoadjuvant therapies and immunotherapies.

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Section: Introductionmentioning

confidence: 93%

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer

Meng

Huang

et al. 2020

Front. Oncol.

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“…Medical image pattern recognition is another attractive field of research for image processing and machine-based prediction neural networks [29]. CNN architecture and image processing with texture feature parameters have been used in the classification of masses and normal tissue on mammograms with 90% accuracy [30, 31]. Detection of lung nodules, classification of radiograms and bone fractures has attracted many research attractions [32, 33].…”

Section: Discussionmentioning

confidence: 99%

The First Implication of Image Processing Techniques on Influenza A Virus Sub-Typing Based on HA/NA Protein Sequences, using Convolutional Deep Neural Network

Ahsan¹,

Ebrahimi²

2018

Preprint

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Increase in influenza A virus host range throughout its evolution has given rise to major concerns worldwide. Although the increasing host range mechanism of the virus is largely unknown; persistent genetic mutations have been blamed as a key factor in the re-organization of the host response and the host range. To uncover the underlying core bases of the two important antigenic proteins of influenza virus (HA and NA), functional data mining and image processing analysis of over 8000 protein sequences of different HA and NA subtypes were performed. Each amino acid sequence in HA or NA proteins sat as a feature or variable and two polynomial datasets were created and subjected into conventional prediction models.

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“…[104][105][106][107] Besides the prediction of tumour malignancy, several radiomics studies examined the prediction of breast cancer molecular subtypes with the aim of leaving out liquid biopsies in the future. [108][109][110][111] Lymph node metastasis identification is an important prognostic factor and often determines treatment. In all clinically node negative patients, a sentinel lymph node procedure is the basis of the axillary treatment.…”

Section: Breastmentioning

confidence: 99%

Radiomics: from qualitative to quantitative imaging

Rogers

Seetha

Lieverse

et al. 2020

The British Journal of Radiology

209

130

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Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

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Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features

Abstract: Our study showed that quantitative radiomic imaging features of breast tumor extracted from digital mammograms are associated with breast cancer subtypes. Future larger studies are needed to further evaluate the findings.

Cited by 96 publications

References 21 publications

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer

The First Implication of Image Processing Techniques on Influenza A Virus Sub-Typing Based on HA/NA Protein Sequences, using Convolutional Deep Neural Network

Radiomics: from qualitative to quantitative imaging

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