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Ultrasound-guided diffuse optical tomography (US-DOT) can potentially detect breast carcinomas by measuring total tumour haemoglobin concentrations (TTHC). The purpose of this study was to evaluate whether vascular haemoglobin concentrations (VHC) affect the ability of US-DOT to distinguish breast carcinomas from benign. In 85 women (97 palpable lesions) referred for core breast biopsy, we measured VHC with a complete blood count and calculated TTHCs for each lesion with US-DOT. Anaemia was defined as a VHC less than 120.0 g/L. Mean TTHCs were significantly higher in malignant lesions (n = 53) than in benign lesions (n = 44), regardless of whether the lesions were from women with anaemia (TTHC, 248.5 vs. 123.3 μmol/L; P = 0.001) or from those without (TTHC, 229.7 vs. 173.9 μmol/L; P = 0.016). A cut-off TTHC of 155.1 μmol/L provided 81.3 % sensitivity, 81.8 % specificity and 81.5 % accuracy for detecting malignant tumours in women with anaemia and 78.4 % sensitivity, 54.5 % specificity and 67.1 % accuracy for women without. There was no significant difference in sensitivity (P = 0.813), specificity (P = 0.108) and accuracy (P = 0.162) between the anaemic group and the non-anaemic group. Vascular haemoglobin concentrations did not affect the ability of US-DOT to differentiate breast carcinomas from benign lesions. • US-DOT can differentiate benign from malignant breast lesions by measuring TTHC. • No difference in TTHC between the anaemia and non-anaemia group. • Vascular haemoglobin concentrations do not affect the diagnostic ability of US-DOT.

Background The apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-MRI). The combination of ADC and other pictorial characteristics has improved lesion type identification accuracy. The objective of this study was to reassess the findings on an independent patient group by changing the magnetic field from 1.5-Tesla to 3.0-Tesla. Methods This retrospective study consisted of a training group of 234 female patients, including 85 benign and 149 malignant lesions, imaged using 1.5-Tesla MRI, and a test group of 95 female patients, including 19 benign and 85 malignant lesions, imaged using 3.0-Tesla MRI. The lesion of interest was segmented from the raw image and four sets of measurements describing the morphology, kinetics, DW-MRI, and texture of the pictorial properties of each lesion were obtained. Each lesion was characterized by 28 features in total. Three classical machine-learning algorithms were used to build prediction models on the training group, which evaluated the prognostic performance of the multi-sided features in three scenarios. To reduce information redundancy, five highly diagnostic factors were selected to obtain a compact yet informative characterization of the lesion status. Results Three classification models were built on the training of 1.5-Tesla patients and were tested on the independent 3.0-Tesla test group. The following results were found. i) Characterization of breast masses in a multi-sided way dramatically increased prediction performance. The usage of all features gave a higher performance in both sensitivity and specificity than any individual feature groups or their combinations. ii) ADC was a highly effective factor in improving the sensitivity in discriminating malignant from benign masses. iii) Five features, namely ADC, Sum Average, Entropy, Elongation, and Sum Variance, were selected to achieve the highest performance in diagnosis of the 3.0-Tesla patient group. Conclusions The combination of ADC and other multi-sided characteristics can increase the capability of discriminating malignant and benign breast lesions, even under different imaging protocols. The selected compact feature subsets achieved a high diagnostic performance and thus are promising in clinical applications for discriminating lesion type and for personalized treatment planning.