An Approach Based on Mammographic Imaging and Radiomics for Distinguishing Male Benign and Malignant Lesions: A Preliminary Study

Huang, Yan; Xiao, Qin; Sun, Yiqun; Wang, Zhe; Li, Qin; Wang, He; Gu, Yajia

doi:10.3389/fonc.2020.607235

Cited by 3 publications

(7 citation statements)

References 26 publications

(23 reference statements)

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“…In this study, we developed the radiomics models for diagnosis of breast cancer using DCE-MRI alone, mammography alone, and the combined MRI and mammography. While quite a few studies have reported the radiomics models developed using MRI (23,24,34) or mammography (19)(20)(21)(22), the combined analysis was rarely reported (35). We further investigated the complementary role of MRI and mammography features in diagnostic sensitivity and specificity.…”

Section: Discussionmentioning

confidence: 99%

“…MRI radiomics was shown to provide better discrimination than conventional parameters for the diagnosis of breast cancer (23,24). Mammography radiomics analysis has also been performed in several diagnostic studies (19)(20)(21)(22). However, since the patient cohort is different, the diagnostic accuracy will be highly dependent on the inclusion/exclusion criteria, and not directly comparable among studies.…”

Section: Discussionmentioning

confidence: 99%

“…For characterizing microcalcifications, since the lesion area was not well-defined, the ROI drawing will affect the extracted features, and thus, the diagnostic results. Huang et al (21) applied mammography radiomics for distinguishing male malignant and benign lesions, and reported an AUC of 0.82a very unique study in rarely reported male patients. Another study by Niu et al (22) also analyzed patients showing abnormal lesions on mammography and MRI, close to our patient cohort, but their goal was to evaluate the combined effect of mammography and digital breast tomosynthesis (DBT), as well as the combined effect of DCE and diffusion weighted MRI.…”

Section: Discussionmentioning

confidence: 99%

“…Huang et al. ( 21 ) applied mammography radiomics for distinguishing male malignant and benign lesions, and reported an AUC of 0.82 – a very unique study in rarely reported male patients. Another study by Niu et al.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence (AI) based radiomics study has been widely applied for medical applications. The method allows for high-throughput extraction of quantitative features from radiographic images (18), and it has been shown as a feasible approach for diagnosis of breast cancer using mammography (19)(20)(21)(22) and MRI (23)(24)(25). However, the combined model using different imaging modalities was rarely reported.…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

et al. 2021

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ObjectiveTo build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.Materials and Methods266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146 malignant and 56 benign, and testing dataset had 48 malignant and 18 benign lesions. Fuzzy-C-means clustering algorithm was used to segment the enhanced lesion on subtraction MRI maps. Two radiologists manually outlined the corresponding lesion on mammography by consensus, with the guidance of MRI maximum intensity projection. Features were extracted using PyRadiomics from three DCE-MRI parametric maps, and from the lesion and a 2-cm bandshell margin on mammography. The support vector machine (SVM) was applied for feature selection and model building, using 5 datasets: DCE-MRI, mammography lesion-ROI, mammography margin-ROI, mammography lesion+margin, and all combined.ResultsIn the training dataset evaluated using 10-fold cross-validation, the diagnostic accuracy of the individual model was 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin, and 77.2% for lesion+margin. When all features were combined, the accuracy was improved to 89.6%. By adding mammography features to MRI, the specificity was significantly improved from 69.6% (39/56) to 82.1% (46/56), p<0.01. When the developed models were applied to the independent testing dataset, the accuracy was 78.8% for DCE-MRI and 83.3% for combined MRI+Mammography.ConclusionThe radiomics model built from the combined MRI and mammography has the potential to provide a machine learning-based diagnostic tool and decrease the false positive diagnosis of contrast-enhanced benign lesions on MRI.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

et al. 2021

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Male Breast Cancer Review. A Rare Case of Pure DCIS: Imaging Protocol, Radiomics and Management

Tari¹,

Morelli²,

Guida³

et al. 2021

Diagnostics

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Ductal carcinoma in situ (DCIS) of male breast is a rare lesion, often associated with invasive carcinoma. When the in situ component is present in pure form, histological grade is usually low or intermediate. Imaging is difficult as gynaecomastia is often present and can mask underlying findings. We report a rare case of pure high-grade DCIS in a young male patient, with associated intraductal papilloma and atypical ductal hyperplasia. Digital breast tomosynthesis (DBT) showed an area of architectural distortion at the union of outer quadrants of the left breast without gynaecomastia. Triple assessment suggested performing a nipple-sparing mastectomy, which revealed the presence of a focal area of high-grade DCIS of 2 mm. DCIS, even of high grade, is difficult to detect with mammography and even more rare, especially when associated with other proliferative lesions. DBT with 2D synthetic reconstruction is useful as the imaging step of a triple assessment and it should be performed in both symptomatic and asymptomatic high-risk men to differentiate between malignant and benign lesions. We propose a diagnostic model to early detect breast cancer in men, optimizing resources according to efficiency, effectiveness and economy, and look forward to radiomics as a powerful tool to help radiologists.

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Gender Medicine in Clinical Radiology Practice

Giacobbe

Granata

Trovato

et al. 2023

JPM

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Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.

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An Approach Based on Mammographic Imaging and Radiomics for Distinguishing Male Benign and Malignant Lesions: A Preliminary Study

Cited by 3 publications

References 26 publications

Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

Male Breast Cancer Review. A Rare Case of Pure DCIS: Imaging Protocol, Radiomics and Management

Gender Medicine in Clinical Radiology Practice

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