Comparison of the sensitivity of mammography, ultrasound, magnetic resonance imaging and combinations of these imaging modalities for the detection of small (≤2 cm) breast cancer

Chen, Hailong; Zhou, Jiao-Qun; Chen, Qiang; Deng, Yongchuan

doi:10.1097/md.0000000000026531

Cited by 34 publications

(18 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, MRI still has some limitations and still needs to be combined with other diagnostic measures for joint diagnosis. For example, if there is only a small amount and subtle degree of calcification appears in the tumor tissue in the early stage, the diagnostic sensitivity of MRI is not high, and it still needs to be combined with X-ray for diagnosis [ 19 , 20 ]. Moreover, the thoracic tumor tissue is complex, and when benign and malignant tumors exist at the same time, the overlapping effects will interfere with MRI diagnosis, and other three-dimensional effects are also needed to cooperate with multiangle and multilevel collection of impact information [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

Study on the Effect of MRI in the Diagnosis of Benign and Malignant Thoracic Tumors

Sui²,

Chi³

et al. 2021

Disease Markers

View full text Add to dashboard Cite

In order to investigate the effectiveness and accuracy of magnetic resonance imaging (MRI) in the diagnosis of benign and malignant thoracic tumors, the research retrospectively selected 80 patients with thoracic tumors admitted from May 2019 to May 2020 as the study subject and all patients were underwent MRI detection. Using pathological diagnostic results as the gold standard, the research analyzed the detection of benign and malignant thoracic tumors by MRI, as well as the diagnostic sensitivity and specificity. After pathological diagnosis, there were 35 malignant tumors and 45 benign tumors. 41 cases of malignant tumors and 39 cases of benign tumors were diagnosed by MRI, with a diagnostic sensitivity of 80.00%, a diagnostic specificity of 71.11%, and a diagnostic compliance rate of 75.00%. In the MRI diagnosis of tumors in different parts of the chest, the diagnostic sensitivity for lung tumors, mediastinal tumors, chest wall tumors, and esophageal tumors was 83.33%, 71.43%, 83.33%, 75.00%, and 87.50%, respectively, and the specificity was 66.67%, 77.78%, 57.14%, 50.00%, and 91.67% according to and breast tumors, respectively. And the accuracy was 73.33%, 75.00%, 69.23, 62.50%, and 90.00%, respectively, with the highest diagnostic sensitivity, specificity, and accuracy for breast tumors. MRI has a good effect on the diagnosis of benign and malignant thoracic tumors and has a high diagnostic value, which is helpful to identify the location, nature, source, and lesion scope of the tumor. It is safe and worthy of promotion.

show abstract

Section: Discussionmentioning

confidence: 99%

Study on the Effect of MRI in the Diagnosis of Benign and Malignant Thoracic Tumors

Sui²,

Chi³

et al. 2021

Disease Markers

View full text Add to dashboard Cite

show abstract

“…Medical imaging, which is superior in measuring tumor size and volume, might be used to obtain anatomic information accurately and non-invasively ( 6 – 10 ). Among the imaging methods, magnetic resonance imaging (MRI) is a better diagnostic technique with the highest resolution and quantitative information for preoperative prediction and prognosis evaluation ( 11 – 14 ). However, it takes considerable time and a great deal of expertise to process images by trained radiologists.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging

et al. 2022

View full text Add to dashboard Cite

PurposeIn clinical work, accurately measuring the volume and the size of breast cancer is significant to develop a treatment plan. However, it is time-consuming, and inter- and intra-observer variations among radiologists exist. The purpose of this study was to assess the performance of a Res-UNet convolutional neural network based on automatic segmentation for size and volumetric measurement of mass enhancement breast cancer on magnetic resonance imaging (MRI).Materials and methodsA total of 1,000 female breast cancer patients who underwent preoperative 1.5-T dynamic contrast-enhanced MRI prior to treatment were selected from January 2015 to October 2021 and randomly divided into a training cohort (n = 800) and a testing cohort (n = 200). Compared with the masks named ground truth delineated manually by radiologists, the model performance on segmentation was evaluated with dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). The performance of tumor (T) stage classification was evaluated with accuracy, sensitivity, and specificity.ResultsIn the test cohort, the DSC of automatic segmentation reached 0.89. Excellent concordance (ICC > 0.95) of the maximal and minimal diameter and good concordance (ICC > 0.80) of volumetric measurement were shown between the model and the radiologists. The trained model took approximately 10–15 s to provide automatic segmentation and classified the T stage with an overall accuracy of 0.93, sensitivity of 0.94, 0.94, and 0.75, and specificity of 0.95, 0.92, and 0.99, respectively, in T1, T2, and T3.ConclusionsOur model demonstrated good performance and reliability for automatic segmentation for size and volumetric measurement of breast cancer, which can be time-saving and effective in clinical decision-making.

show abstract

“…Detection of breast cancer by mammography has an overall sensitivity of 75 % to 85 % but when the mammary tissue is dense the sensitivity decreases sharply [10].…”

Section: Resultsmentioning

confidence: 99%

Comparison of BIRADS lexicon to breast biopsy findings in low resource countries

Prabhala

Srirambhatla

Pasula

2022

SR:MS

View full text Add to dashboard Cite

Breast cancer is the most common malignancy in women worldwide and early detection is of utmost importance. In developed countries, mandatory mammographic screening programs help in early detection, whereas, in developing countries cancer is often detected at an advanced stage. The BIRADS guidelines permit a standard approach and follow up for breast lesions. Many newer imaging modalities are being available for better diagnosis. Breast lesions have a varied spectrum and the gold standard for diagnosis of breast cancer is based on histopathological examination of tissue. At times, even on trucut biopsy, it is difficult to categorize the lesion as the tissue studied is limited and some evolving lesions may have overlapping features. As there are limitations to both radiologic and pathologic approaches, the general and accepted way is to combine both modalities to arrive at a diagnosis. The aim: The aim of the study was to find out how well the BIRADS radiological findings correlate with histopathological findings on breast biopsies. Materials and methods: A MEDLINE search for articles published in English language, with key words as breast biopsy histopathology and BIRADS was done for the years between 1985 and 2021. In addition, other cross-referenced articles were also searched for relevant data. Results: There is good correlation between BIRADS category 1, 2 and 5 with the findings on core needle biopsy in breast lumps i.e., good correlation is seen at the end of spectrum of breast lesions in totally benign and unequivocally malignant lesions. But this correlation is lacking in the middle of the spectrum i.e., in borderline/intermediate category of BIRADS. Conclusion: The non-suspicious (BIRADS 1/ 2) and highly suspicious (BIRADS 4C/5) compare very well with the histopathologic findings. It is the grey zone i.e., BIRADS 3/4A which has a wide and variable predictive value for breast cancer when compared with histopathology and imaging study alone is insufficient and mandates histopathology in all such cases

show abstract

Comparison of the sensitivity of mammography, ultrasound, magnetic resonance imaging and combinations of these imaging modalities for the detection of small (≤2 cm) breast cancer

Cited by 34 publications

References 28 publications

Study on the Effect of MRI in the Diagnosis of Benign and Malignant Thoracic Tumors

Study on the Effect of MRI in the Diagnosis of Benign and Malignant Thoracic Tumors

Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging

Comparison of BIRADS lexicon to breast biopsy findings in low resource countries

Contact Info

Product

Resources

About