Real-time diagnosis of breast cancer during core needle biopsy

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Pages 1-36 (2nd group) has title: Raman clinical instrument manual, by Chae-Ryon Kong and Michael S. Feld; with contributions from Zoya Volynskaya and Luis Galindo. Cataloged from PDF version of thesis.Includes bibliographical references.Early detection of breast cancer is critical for improved survival. Currently, breast abnormalities are diagnosed based on a histopathological evaluation of tissue removed during core needle biopsy. Microcalcifications are used as targets to position biopsy devices, as they may indicate the presence of malignancy. Despite stereotactic guidance, needle biopsy fails to retrieve target microcalcifications in up to 15% of patients. Optical techniques may help clinicians accurately diagnose and treat patients by providing important diagnostic information in real time in a minimally invasive manner. This thesis describes the results of several studies we performed to evaluate the potential of Raman, reflectance, and intrinsic fluorescence spectroscopy to provide biochemical and morphological information for discriminating breast lesions. Each modality was evaluated individually, as well as in combination, using a technique known as multimodal spectroscopy (MMS). For the first part of this project we conducted a clinical study in which spectra were acquired from excised tissue in 99 patients and physically meaningful parameters were extracted by modeling the data. The goals of the study were as follows: 1) To prospectively validate previously developed diagnostic algorithms on the data from these patients; 2) To develop a new algorithm to evaluate additional histopathology diagnoses. Diffuse reflectance (DRS) spectra were modeled using diffusion theory and provided information about tissue absorbers and scatterers. Intrinsic fluorescence (IFS) spectra were extracted from the combined fluorescence and DRS spectra and analyzed using multivariate curve resolution. Raman spectroscopy data were fit using a linear combination of Raman active components (e.g. collagen, calcium, adipose) found in breast tissue. Prospective validation of Raman spectroscopy resulted in sensitivity and specificity and negative predictive value (NPV) of 78%, 98%, and 98%, respectively. An MMS system was developed to evaluate the benefit of combining information from all three spectroscopic modalities. We found that using new 3D Raman algorithm we could discriminate among 6 histopathology categories as compared to 4 categories previously diagnosed with Raman spectroscopy. For the second part of this project, we designed and developed a portable, miniature Raman clinical spectroscopy system to evaluate the potential of spectroscopy to guide the retrieval of microcalcifications during core needle biopsies. We focused specifically on the use of Raman spectroscopy for this application, as it is particularly sensitive to calcium-containing minerals. The system employs a side-viewing Raman probe that can be used in conjunction with commercial stereotactic needle biopsy devices. Prior to core needle excision, the Raman probe was inserted into the core needle biopsy device and spectra were acquired and analyzed in real time (<Is). The results from our work indicate that spectroscopy has the potential to accurately diagnose breast lesions and enable targeted biopsies of diseased tissue and retrieval of microcalcifications.by Zoya Volynskaya.Ph.D

Multi-modal spectroscopy of breast tissue

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaves 64-66).Breast cancer is the most common form of cancer afflicting women in the United States; one out of eight women will be diagnosed with breast cancer during her lifetime. Currently, screening is performed by a combination of annual clinical breast examinations and x-ray mammography. However, only 10 to 25 percent of suspicious lesions detected during mammography are diagnosed as malignant upon biopsy, which implies that a large number of biopsies can be avoided. Although mammography images anatomic changes, it is not sensitive to the underlying morphological and biochemical changes that distinguish benign and malignant breast lesions. Presently employed diagnostic procedures are invasive, time consuming, and expensive. Thus, there is a clinical need to develop new tools for the early diagnosis of malignancy in the breast. In recent years our laboratory has explored the use of Raman spectroscopy for diagnosing disease; one important area is the detection of breast cancer. Raman spectroscopy provides information about the morphological and biochemical make up of tissue and, with the aid of our diagnostic algorithm, has provided good results in distinguishing between malignant and benign breast lesions, with a sensitivity, specificity, and an overall accuracy of 90, 96, and 86 percent, respectively [Haka,2004].(cont.) Although these initial results are promising, we would like to improve the overall accuracy. Another promising spectroscopic technique developed in our laboratory is tri-modal spectroscopy (TMS), the combination of diffuse reflectance (DRS), intrinsic fluorescence (IFS), and light scattering spectroscopy (LSS). This technique has been successfully applied to the diagnosis of epithelial neoplastic tissue, leading to the interest in exploring its application to the diagnosis of lesions in breast tissue. Finally, the Raman and DRS/IFS modalities provide complementary information and the combination of this information into a single diagnostic algorithm may provide superior diagnostic capabilities. The central theme of this research is to investigate DRS/IFS as a useful technique for the diagnosis of breast cancer and to evaluate the effectiveness of its combination with Raman spectroscopy. Through this research, we hope to aid the medical community in early diagnosis, treatment, and prevention of breast cancer.by Zoya I. Volynskaya.S.M

A multimodal spectroscopy system for real-time disease diagnosis

The combination of reflectance, fluorescence, and Raman spectroscopy—termed multimodal spectroscopy (MMS)—provides complementary and depth-sensitive information about tissue composition. As such, MMS is a promising tool for disease diagnosis, particularly in atherosclerosis and breast cancer. We have developed an integrated MMS instrument and optical fiber spectral probe for simultaneous collection of all three modalities in a clinical setting. The MMS instrument multiplexes three excitation sources, a xenon flash lamp (370–740 nm), a nitrogen laser (337 nm), and a diode laser (830 nm), through the MMS probe to excite tissue and collect the spectra. The spectra are recorded on two spectrograph/charge-coupled device modules, one optimized for visible wavelengths (reflectance and fluorescence) and the other for the near-infrared (Raman), and processed to provide diagnostic parameters. We also describe the design and calibration of a unitary MMS optical fiber probe 2 mm in outer diameter, containing a single appropriately filtered excitation fiber and a ring of 15 collection fibers, with separate groups of appropriately filtered fibers for efficiently collecting reflectance, fluorescence, and Raman spectra from the same tissue location. A probe with this excitation/collection geometry has not been used previously to collect reflectance and fluorescence spectra, and thus physical tissue models (“phantoms”) are used to characterize the probe’s spectroscopic response. This calibration provides probe-specific modeling parameters that enable accurate extraction of spectral parameters. This clinical MMS system has been used recently to analyze artery and breast tissue in vivo and ex vivo.National Institutes of Health (U.S) ( Grant No. P41-RR-02594

Ki67 quantitative interpretation: Insights using image analysis

Background: Proliferation markers, especially Ki67, are increasingly important in diagnosis and prognosis. The best method for calculating Ki67 is still the subject of debate. Materials and Methods: We evaluated an image analysis tool for quantitative interpretation of Ki67 in neuroendocrine tumors and compared it to manual counts. We expanded a primary digital pathology platform to include the Leica Biosystems image analysis nuclear algorithm. Slides were digitized using a Leica Aperio AT2 Scanner and accessed through the Cerner CoPath LIS interfaced with Aperio eSlideManager through Aperio ImageScope. Selected regions of interest (ROIs) were manually defined and annotated to include tumor cells only; they were then analyzed with the algorithm and by four pathologists counting on printed images. After validation, the algorithm was used to examine the impact of the size and number of areas selected as ROIs. Results: The algorithm provided reproducible results that were obtained within seconds, compared to up to 55 min of manual counting that varied between users. Benefits of image analysis identified by users included accuracy, time savings, and ease of viewing. Access to the algorithm allowed rapid comparisons of Ki67 counts in ROIs that varied in numbers of cells and selection of fields, the outputs demonstrated that the results vary around defined cutoffs that provide tumor grade depending on the number of cells and ROIs counted. Conclusions: Digital image analysis provides accurate and reproducible quantitative data faster than manual counts. However, access to this tool allows multiple analyses of a single sample to use variable numbers of cells and selection of variable ROIs that can alter the result in clinically significant ways. This study highlights the potential risk of hard cutoffs of continuous variables and indicates that standardization of number of cells and number of regions selected for analysis should be incorporated into guidelines for Ki67 calculations

Noise: A dialogue between social phenomenon and fashion design

Abstract Background There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Results Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Conclusion Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered

Multimodal spectroscopy detects features of vulnerable atherosclerotic plaque

Early detection and treatment of rupture-prone vulnerable atherosclerotic plaques is critical to reducing patient mortality associated with cardiovascular disease. The combination of reflectance, fluorescence, and Raman spectroscopy—termed multimodal spectroscopy (MMS)—provides detailed biochemical information about tissue and can detect vulnerable plaque features: thin fibrous cap (TFC), necrotic core (NC), superficial foam cells (SFC), and thrombus. Ex vivo MMS spectra are collected from 12 patients that underwent carotid endarterectomy or femoral bypass surgery. Data are collected by means of a unitary MMS optical fiber probe and a portable clinical instrument. Blinded histopathological analysis is used to assess the vulnerability of each spectrally evaluated artery lesion. Modeling of the ex vivo MMS spectra produce objective parameters that correlate with the presence of vulnerable plaque features: TFC with fluorescence parameters indicative of collagen presence; NC/SFC with a combination of diffuse reflectance β-carotene/ceroid [beta-carotene / ceroid] absorption and the Raman spectral signature of lipids; and thrombus with its Raman signature. Using these parameters, suspected vulnerable plaques can be detected with a sensitivity of 96% and specificity of 72%. These encouraging results warrant the continued development of MMS as a catheter-based clinical diagnostic technique for early detection of vulnerable plaques.National Institutes of Health (U.S.) (NIH grant P41-RR-02594

Additional file 1 of Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction

Table S1. Distribution of tissue types and images used for the development of the CNN training dataset. (PDF 153 kb

Diagnosing breast cancer using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy

Using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy, we have developed an algorithm that successfully classifies normal breast tissue, fibrocystic change, fibroadenoma, and infiltrating ductal carcinoma in terms of physically meaningful parameters. We acquire 202 spectra from 104 sites in freshly excised breast biopsies from 17 patients within 30 min of surgical excision. The broadband diffuse reflectance and fluorescence spectra are collected via a portable clinical spectrometer and specially designed optical fiber probe. The diffuse reflectance spectra are fit using modified diffusion theory to extract absorption and scattering tissue parameters. Intrinsic fluorescence spectra are extracted from the combined fluorescence and diffuse reflectance spectra and analyzed using multivariate curve resolution. Spectroscopy results are compared to pathology diagnoses, and diagnostic algorithms are developed based on parameters obtained via logistic regression with cross-validation. The sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy (total efficiency) of the algorithm are 100, 96, 69, 100, and 91%, respectively. All invasive breast cancer specimens are correctly diagnosed. The combination of diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy yields promising results for discrimination of breast cancer from benign breast lesions and warrants a prospective clinical study.National Center for Research Resources (U.S.) (Grant No. P41-RR-02594)Pathology Associates of University Hospital

In vivo margin assessment during partial mastectomy breast surgery using Raman spectroscopy, Cancer Res.

Abstract We present the first demonstration of in vivo collection of Raman spectra of breast tissue. Raman spectroscopy, which analyzes molecular vibrations, is a promising new technique for the diagnosis of breast cancer. We have collected 31 Raman spectra from nine patients undergoing partial mastectomy procedures to show the feasibility of in vivo Raman spectroscopy for intraoperative margin assessment. The data was fit with an established model, resulting in spectral-based tissue characterization in only 1 second. Application of our previously developed diagnostic algorithm resulted in perfect sensitivity and specificity for distinguishing cancerous from normal and benign tissues in our small data set. Significantly, we have detected a grossly invisible cancer that, upon pathologic review, required the patient to undergo a second surgical procedure. Had Raman spectroscopy been used in a real-time fashion to guide tissue excision during the procedure, the additional reexcision surgery might have been avoided. These preliminary findings suggest that Raman spectroscopy has the potential to lessen the need for reexcision surgeries resulting from positive margins and thereby reduce the recurrence rate of breast cancer following partial mastectomy surgeries. (Cancer Res 2006; 66(6): 3317-22