ArticleRadiology2016
Assessment of Functional Differences in Malignant and Benign Breast Lesions and Improvement of Diagnostic Accuracy by Using US-guided Diffuse Optical Tomography in Conjunction with Conventional US.
Article in Radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06468566 (Identification of Benign and Malignant Breast Nodules Using Opalogram Ultrasonography), which is not on this map. Cited by 49 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Identification of Benign and Malignant Breast Nodules Using Opalogram Ultrasonography: A Multicenter Study
Who cites it
49 citing papers in PubMed, 67 citations in OpenAlex.
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- Ultrasound-guided diffuse optical tomography: an adjunct to ultrasound that can reduce unnecessary breast biopsies.Breast cancer research : BCR · 2025Article
- Multimodal PA/US imaging and radiomics for the prediction of HER2-zero, -low, and -positive breast cancers: A novel approach for targeted therapy selection.Photoacoustics · 2025Article
- Deep Learning Enables Fast and Accurate Quantification of MRI-Guided Near-Infrared Spectral Tomography for Breast Cancer Diagnosis.IEEE transactions on medical imaging · 2025Article
- High-speed photoacoustic and ultrasonic computed tomography of the breast tumor for early diagnosis with enhanced accuracy.Science advances · 2025Article
- Deep learning and radiomics integration of photoacoustic/ultrasound imaging for non-invasive prediction of luminal and non-luminal breast cancer subtypes.Breast cancer research : BCR · 2025Article
- B1 corrected T1 mapping in the differentiation and prediction of breast cancer.Scientific reports · 2025Article
- Prognosticating axillary lymph node metastasis in breast cancer through integrated photoacoustic imaging, ultrasound, and clinical parameters.Breast cancer research : BCR · 2025Article
- Model-Based Convolution Neural Network for 3D Near-Infrared Spectral Tomography.IEEE transactions on medical imaging · 2025Article
- Innovative Discrete Multi-Wavelength Near-Infrared Spectroscopic (DMW-NIRS) Imaging for Rapid Breast Lesion Differentiation: Feasibility Study.Diagnostics (Basel, Switzerland) · 2025Article
- Abnormal Breast Temperature and Cortical Enlargement of the Axillary Lymph Nodes Through the Thermography.Clinical case reports · 2024Article
- Detection of breast cancer using machine learning on time-series diffuse optical transillumination data.Journal of biomedical optics · 2024Article
- Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model.Journal of biomedical optics · 2024Article
- Optimizing breast cancer diagnosis with photoacoustic imaging: An analysis of intratumoral and peritumoral radiomics.Photoacoustics · 2024Article
- Semiconducting polymer dots for multifunctional integrated nanomedicine carriers.Materials today. Bio · 2024Review
- Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment.Journal of biophotonics · 2024Article
- Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.Biomedical optics express · 2023Article
- Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.Journal of biomedical optics · 2023Article
- FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.IEEE transactions on medical imaging · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Purpose To investigate ultrasonography (US)-guided diffuse optical tomography to distinguish the functional differences of hemoglobin concentrations in a wide range of malignant and benign breast lesions and to improve breast cancer diagnosis in conjunction with conventional US. Materials and Methods The study protocol was approved by the institutional review boards and was HIPAA compliant. Written informed consent was obtained from all patients. Patients (288 women; mean age, 50 years; range, 17-94 years) who underwent US-guided biopsy were imaged with a handheld US and optical probe. The US-imaged lesion was used to guide reconstruction of light absorption maps at four wavelengths, and total hemoglobin (tHb), oxygenated hemoglobin (oxyHb), and deoxygenated hemoglobin (deoxyHb) were computed from the absorption maps. A threshold (80 μmol/L) was chosen on the basis of this study population. Two radiologists retrospectively evaluated US images on the basis of the US Breast Imaging Reporting and Data System lexicon, and a lesion was considered malignant when a score of 4C or 5 was given or a lesion had tHb greater than 80 μmol/L. A two-sample t test was used to calculate significance between groups, and Spearman ρ was computed between hemoglobin parameters and tumor pathologic grades. Results Three tumors were Tis, 37 were T1, 19 were T2-T4 carcinomas, and 233 were benign lesions. The mean maximum tHb, oxyHb, and deoxyHb of Tis-T1 and T2-T4 groups were 89.3 μmol/L ± 20.2 (standard deviation), 65.0 μmol/L ± 20.8, and 33.5 μmol/L ± 11.3, respectively, and 84.7 μmol/L ± 32.8, 57.1 μmol/L ± 19.8, and 34.7 μmol/L ± 18.9, respectively. The corresponding values of benign lesions were 54.1 μmol/L ± 23.5, 38.0 μmol/L ± 17.4, and 25.2 μmol/L ± 13.8, respectively. The mean maximum tHb, oxyHb, and deoxyHb were significantly higher in the malignant groups than the benign group (P <.001, <.001, and .041, respectively). For malignant lesions, the mean maximum tHb moderately correlated with tumor histologic grade and nuclear grade (ρ = 0.283 and 0.315, respectively). The mean maximum oxyHb moderately correlated with tumor nuclear grade (ρ = 0.267). When radiologists' US diagnosis and the tHb were used together, the sensitivity, specificity, positive predictive value, and negative predictive value were 96.6%-100%, 77.3%-83.3%, 52.7%-59.4%, and 99.0%-100%, respectively, for the combined malignant group. Conclusion The tHb and oxyHb correlate with breast cancer pathologic grade and can be used as an adjunct to US to improve sensitivity and negative predictive value in breast cancer diagnosis. (©) RSNA, 2016 Online supplemental material is available for this article.
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