Diffuse optical tomography (DOT) is a noninvasive, nonionizing imaging modality that uses near-infrared light to visualize optically relevant chromophores. A recently developed dynamic DOT imaging system enables the study of hemodynamic effects in the breast during a breath-hold. Dynamic DOT imaging was performed in a total of 21 subjects (age 54±10  years) including 3 healthy subjects and 18 subjects with benign (n=8) and malignant (n=14) masses. Three-dimensional time-series images of the percentage change in oxygenated and deoxygenated hemoglobin concentrations ([HbO2] and [Hb]) from baseline are obtained over the course of a breath-hold. At a time point of 15 s following the end of the breath-hold, [Hb] in healthy breasts has returned to near-baseline values (1.6%±0.5%), while tumor-bearing breasts have increased levels of [Hb] (6.8%±3.6%, p<0.01). Further, healthy subjects have a higher correlation between the breasts over the course of the breath-hold as compared with the subjects with breast cancer (healthy: 0.96±0.02; benign: 0.89±0.02; malignant: 0.78±0.23, p<0.05). Therefore this study shows that dynamic features extracted from DOT measurements can differentiate healthy and diseased breast tissues. These features provide a physiologic method for identifying breast cancer without the need for ionizing radiation.

No abstract

Colorectal cancer (CRC) is the 3rd most common and the 2nd most deadly type of cancer worldwide. Understanding the biochemical and microstructural aspects of carcinogenesis is a critical step towards developing new technologies for accurate CRC detection. To date, optical detection through analyzing tissue chromophore concentrations and scattering parameters has been mostly limited to chromophores in the visible region and analytical light diffusion models. In this study, tissue parameters were extracted by fitting diffuse reflectance spectra (DRS) within the range 350–1900 nm based on reflectance values from a look-up table built using Monte Carlo simulations of light propagation in tissues. This analysis was combined with machine learning models to estimate parameter thresholds leading to best differentiation between mucosa and tumor tissues based on almost 3000 DRS recorded from fresh ex vivo tissue samples from 47 subjects. DRS spectra were measured with a probe for superficial tissue and another for slightly deeper tissue layers. By using the classification and regression tree algorithm, the most important parameters for CRC detection were the total lipid content (f lipid), the reduced scattering amplitude (α′), and the Mie scattering power (b Mie). Successful classification with an area under the receiver operating characteristic curve higher than 90% was achieved. To the best of our knowledge, this is the first study to evaluate the potential tissue biomolecule concentrations and scattering properties in superficial and deeper tissue layers for CRC detection in the luminal wall. This may have important clinical applications for the rapid diagnosis of colorectal neoplasia.

Purpose To identify dynamic optical imaging features that associate with the degree of pathologic response in patients with breast cancer during neoadjuvant chemotherapy (NAC). Materials and Methods Of 40 patients with breast cancer who participated in a longitudinal study between June 2011 and March 2016, 34 completed the study. There were 13 patients who obtained a pathologic complete response (pCR) and 21 patients who did not obtain a pCR. Imaging data from six subjects were excluded from the study because either the patients dropped out of the study before it was finished or there was an instrumentation malfunction. Two weeks into the treatment regimen, three-dimensional images of both breasts during a breath hold were acquired by using dynamic diffuse optical tomography. Features from the breath-hold traces were used to distinguish between response groups. Receiver operating characteristic (ROC) curves and sensitivity analysis were used to determine the degree of association with 5-month treatment outcome. Results An ROC curve analysis showed that this method could identify patients with a pCR with a positive predictive value of 70.6% (12 of 17), a negative predictive value of 94.1% (16 of 17), a sensitivity of 92.3% (12 of 13), a specificity of 76.2% (16 of 21), and an area under the ROC curve of 0.85. Conclusion Several dynamic optical imaging features obtained within 2 weeks of NAC initiation were identified that showed statistically significant differences between patients with pCR and patients without pCR as determined 5 months after treatment initiation. If confirmed in a larger cohort prospective study, these dynamic imaging features may be used to predict treatment outcome as early as 2 weeks after treatment initiation. RSNA, 2018 Online supplemental material is available for this article.

No abstract