Abstract
.Significance: Current imaging paradigms for differential diagnosis of suspicious breast lesions suffer from high false positive rates that force patients to undergo unnecessary biopsies. Diffuse optical spectroscopic imaging (DOSI) noninvasively probes functional hemodynamic and compositional parameters in deep tissue and has been shown to be sensitive to contrast between normal and malignant tissues.Aim: DOSI methods are under investigation as an adjunct to mammography and ultrasound that could reduce false positive rates and unnecessary biopsies, particularly in radiographically dense breasts.Methods: We performed a retrospective analysis of 212 subjects with suspicious breast lesions who underwent DOSI imaging. Physiological tissue parameters were -score normalized to the patient’s contralateral breast tissue and input to univariate logistic regression models to discriminate between malignant tumors and the surrounding normal tissue. The models were then used to differentiate malignant lesions from benign lesions.Results: Models incorporating several individual hemodynamic parameters were able to accurately distinguish malignant tumors from both the surrounding background tissue and benign lesions with area under the curve (AUC) . -score normalization improved the discriminatory ability and calibration of these predictive models relative to unnormalized or ratio-normalized data.Conclusions: Findings from a large subject population study show how DOSI data normalization that accounts for normal tissue heterogeneity and quantitative statistical regression approaches can be combined to improve the ability of DOSI to diagnose malignant lesions. This improved diagnostic accuracy, combined with the modality’s inherent logistical advantages of portability, low cost, and nonionizing radiation, could position DOSI as an effective adjunct modality that could be used to reduce the number of unnecessary invasive biopsies.
Highlights
In standard clinical practice, breast lesions are imaged using x-ray mammography and ultrasound;[1] this requires the differentiation of suspicious lesions from surrounding healthy tissue
Each model can be evaluated by two performance metrics: (1) ability to distinguish between malignant tumors and healthy tissue, which is what the model was trained to do, and (2) ability to distinguish malignant tumors from benign lesions
The z-score normalized HHb model had an area under the curve (AUC) 1⁄4 0.90 [95% Confidence Interval (CI): 0.85 to 0.95] for malignant versus normal tissue, and an AUC 1⁄4 0.85 for malignant versus benign lesions (Fig. 3)
Summary
Breast lesions are imaged using x-ray mammography and ultrasound;[1] this requires the differentiation of suspicious lesions from surrounding healthy tissue. X-ray mammography has very high sensitivity to breast tumors, it has relatively low specificity,[2] which produces a high false positive rate, i.e., ∼10% across all ages,[3] with higher rates in younger patients.[4] ultrasound imaging is susceptible to this high false positive rate,[5] which prompts more than 500,000 unnecessary negative biopsies per year[5,6] and can lead to excessive cost[3,7] and stress for patients.[8] women with radiographically dense breasts, who may be at increased risk for breast cancer,[9,10] are more difficult to image with mammography, leading to even higher false positive rates.[11] Functional information can help distinguish benign lesions from the more metabolically active malignant tumors. Other imaging modalities such as positron emission tomography (PET) or magnetic resonance imaging (MRI) can augment the diagnostic ability of mammography, albeit with logistical constraints such as ionizing radiation, cost, and low throughput
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