Ideally, neoadjuvant chemotherapy (NAC) assessment should predict pathologic complete response (pCR), a surrogate clinical endpoint for 5-year survival, as early as possible during typical 3- to 6-month breast cancer treatments. We introduce and demonstrate an approach for predicting pCR within 10 days of initiating NAC. The method uses a bedside diffuse optical spectroscopic imaging (DOSI) technology and logistic regression modeling. Tumor and normal tissue physiological properties were measured longitudinally throughout the course of NAC in 33 patients enrolled in the American College of Radiology Imaging Network multicenter breast cancer DOSI trial (ACRIN-6691). An image analysis scheme, employing z-score normalization to healthy tissue, produced models with robust predictions. Notably, logistic regression based on z-score normalization using only tissue oxygen saturation (StO2) measured within 10 days of the initial therapy dose was found to be a significant predictor of pCR (AUC = 0.92; 95% CI: 0.82 to 1). This observation suggests that patients who show rapid convergence of tumor tissue StO2 to surrounding tissue StO2 are more likely to achieve pCR. This early predictor of pCR occurs prior to reductions in tumor size and could enable dynamic feedback for optimization of chemotherapy strategies in breast cancer.
Tissue water content and molecular microenvironment can provide important intrinsic contrast for cancer imaging. In this work, we examine the relationship between water optical spectroscopic features related to binding state and magnetic resonance imaging (MRI)-measured water diffusion dynamics. Broadband diffuse optical spectroscopic imaging (DOSI) and MR images were obtained from eight patients with locally-advanced infiltrating ductal carcinomas (tumor size = 5.5±3.2 cm). A DOSI-derived bound water index (BWI) was compared to the apparent diffusion coefficient (ADC) of diffusion weighted (DW) MRI. BWI and ADC were positively correlated (R = 0.90, p-value = 0.003) and BWI and ADC both decreased as the bulk water content increased (R = −0.81 and −0.89, respectively). BWI correlated inversely with tumor size (R = −0.85, p-value = 0.008). Our results suggest underlying sensitivity differences between BWI and ADC to water in different tissue compartments (e.g., extracellular vs cellular). These data highlight the potential complementary role of DOSI and DW-MRI in providing detailed information on the molecular disposition of water in breast tumors. Because DOSI is a portable technology that can be used at the bedside, BWI may provide a low-cost measure of tissue water properties related to breast cancer biology.
Differences in tissue water state have been measured in normal and malignant breast tissues. Broadband
Diffuse Optical Spectroscopy (DOS) has been used to acquire 650-1000 nm absorption spectra of normal
and tumor breast tissues from 7 patients <i>in vivo</i>. The absolute values of spectral differences between
normalized tissue water spectra and pure water spectra were combined and divided by the number of points
in the sum to form the bound water index (BWI). In all subjects, the average BWIs of line scan points were
significantly lower in tumor tissues (1.62 ± 0.27 x10<sup>-3</sup>) than normal tissues (3.06 ±0.51 x10<sup>-3</sup>, Wilcoxon
Ranked Sum Test z= 0.003 and power=0.98). These results imply that the water in tumors behaves more
like free water than the water in normal tissue.
Independent component analysis (ICA) method was applied as a processing step for Raman spectra. 136 Raman spectra were acquired from urine samples from 18 subjects. Each spectrum was acquired from different sample. 785nm, 100mW (at sample) laser with 2048 element linear silicon TE cooled CCD were used. In order to separate information of glucose, creatinine, urea nitrogen, uric acid and invaluable information from the urine spectrum, ICA by Maximum Likelihood (ML) fast fixed-point estimation algorithm was applied. By looking for maximum likelihood, independent information could be separated from the urine spectra. Among separated information, high frequency noise which could be generated by ambient noise and low frequency noise which contain information of baseline shift were observed. Additionally, peak information of each component was observed. The processing time was very short because fast fixed point algorithm was added to ML estimation method. Before applying ICA, all spectra were mean centered in order to enhance the peak information. In addition, all spectra were pre-processed to have unit variance in order to shorten calculation time. This first study about applying ICA suggested that this algorithm can be used as a pattern recognition algorithm to extract information from Raman spectra. Additionally, because ICA can provide information with statistical independency sufficiently, further studies about ICA which can substitute PCA will be performed.
As a part of non-invasive and unaware measurement of physiological signal in the house of live-alone person, Raman spectroscopy was applied for urine component analysis in the toilet set. 785nm, 250-300mW output solid state diode laser and 2048 element linear silicon TE cooled CCD array were incorporated for this system. Several tests were performed for setting up Raman spectroscopy in non-constrained situation: toilet set in the house. The effect of dark current, integration time, warming up time of laser, property of probe and interference of water in the toilet were tested and controlled for appropriate measurement in this environment. The spectra were obtained immediately when the subject uses the toilet set, and they can be transmitted to the server though Bluetooth. Those spectra were pre-processed for removing or correcting the effect of undesired light scattering, sample path-length difference and baseline-effect. The preprocessed data were enhanced for more exact result of multivariate analysis. The training data was prepared for predicting unknown component and its concentration by using multivariate methods. Several kinds of multivariate methods: PCA, PCR, PLS were performed to validate what is the fittest method in this environment. Through quantitative and qualitative analysis of Raman spectroscopy’s spectra obtained in the house's toilet set, we could know the component and its concentration of urine which can be index of disease.