Development and comparative assessment of Raman spectroscopic classification algorithms for lesion discrimination in stereotactic breast biopsies with microcalcifications

Dingari, Narahara Chari; Barman, Ishan; Saha, Anushree; McGee, Sasha; Galindo, Luis H.; Liu, Wendy; Plecha, Donna; Klein, Nina; Dasari, Ramachandra R.; Fitzmaurice, Maryann

doi:10.1002/jbio.201200098

Cited by 34 publications

(27 citation statements)

References 37 publications

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“…Here, we attribute the inaccuracies in classification (i.e., the ten false negatives from two cancerous sites) to spectroscopy-histopathology registration errors [29,30]. In other words, the spectroscopic measurements were potentially performed on a grossly normal site of the biopsied tissue whereas the histopathological results were obtained for a marginally different position on the tissue where cancerous cells could be observed.…”

Section: Resultsmentioning

confidence: 99%

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

et al. 2012

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In recent years, Raman spectroscopy has shown substantive promise in diagnosing bladder cancer, especially due to its exquisite molecular specificity. The ability to reduce false detection rates in comparison to existing diagnostic tools such as photodynamic diagnosis makes Raman spectroscopy particularly attractive as a complementary diagnostic tool for real-time guidance of transurethral resection of bladder tumor (TURBT). Nevertheless, the state-ofthe-art high-volume Raman spectroscopic probes have not reached the expected levels of specificity thereby impeding their clinical translation. To address this issue, we propose the use of a confocal Raman probe for bladder cancer diagnosis that can boost the specificity of the diagnostic algorithm based on its suppression of the out-of-focus nonanalyte-specific signals emanating from the neighboring normal tissue. In this article, we engineer and apply such a probe, having depth of field of approximately 280 μm, for Raman spectral acquisition from ex vivo normal and cancerous TURBT samples. Using this clinical dataset, a diagnostic algorithm based on principal component analysis and logistic regression is developed. We demonstrate that this approach results in comparable sensitivity but significantly higher specificity in relation to high-volume Raman spectral data. The application of only two principal components is sufficient for the discrimination of the samples underlining the robustness of the algorithm. Further, no discordance between replicate spectra is observed emphasizing the reproducible nature of the current diagnostic assessment. The high levels of sensitivity and specificity achieved in this proof-of-concept study opens substantive avenues for application of a confocal Raman probe during endoscopic procedures related to diagnosis and treatment of bladder cancer.

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Section: Resultsmentioning

confidence: 99%

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

et al. 2012

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“…(18) with an SVM algorithm developed for diagnosis of lesions irrespective of microcalcification status described in Dingari et al . (29). In the first step of this two-step naïve algorithm, LR was performed to first discriminate the entire dataset into three classes: normal breast tissue, lesions without microcalcifications and lesions with microcalcifications.…”

Section: Methodsmentioning

confidence: 99%

“…One possible approach is to combine the previous algorithm for microcalcification detection (18) with a new algorithm for diagnosis of breast lesions (irrespective of microcalcification status) (29), thereby constructing a sequential two-step algorithm to identify the breast lesion(s) associated with the microcalcifications. However, this is a laborious and unwieldy process (that also does not provide the required level of accuracy as detailed below), which can be simplified by devising a single algorithm to simultaneously detect microcalcifications and diagnose the associated breast lesion(s).…”

Section: Introductionmentioning

confidence: 99%

Application of Raman Spectroscopy to Identify Microcalcifications and Underlying Breast Lesions at Stereotactic Core Needle Biopsy

et al. 2013

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Microcalcifications are a feature of diagnostic significance on a mammogram and a target for stereotactic breast needle biopsy. Here, we report development of a Raman spectroscopy technique to simultaneously identify microcalcification status and diagnose the underlying breast lesion, in real-time, during stereotactic core needle biopsy procedures. Raman spectra were obtained ex vivo from 146 tissue sites from fresh stereotactic breast needle biopsy tissue cores from 33 patients, including 50 normal tissue sites, 77 lesions with microcalcifications, and 19 lesions without microcalcifications, using a compact clinical system. The Raman spectra were modeled based on the breast tissue components and a support vector machine framework was used to develop a single-step diagnostic algorithm to distinguish normal tissue, fibrocystic change (FCC), fibroadenoma (FA) and breast cancer, in the absence and presence of microcalcifications. This algorithm was subjected to leave-one-site-out cross-validation, yielding a positive predictive value, negative predictive value, sensitivity and specificity of 100%, 95.6%, 62.5% and 100% for diagnosis of breast cancer (with or without microcalcifications) and an overall accuracy of 82.2% for classification into specific categories of normal tissue, FCC, FA or breast cancer (with and without microcalcifications). Notably, the majority of breast cancers diagnosed are ductal carcinoma in situ (DCIS), the most common lesion associated with microcalcifications, which could not be diagnosed using previous Raman algorithm(s). Our study demonstrates the potential of Raman spectroscopy to concomitantly detect microcalcifications and diagnose associated lesions, including DCIS, and thus provide real-time feedback to radiologists during such biopsy procedures, reducing non-diagnostic and false negative biopsies.

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“…So, it is interesting to observe what happens to performance metrics like "sensitivity" and "specificity" as this threshold is varied from '0' to '1'. The ROC curves capture this behavior by plotting "sensitivity" against "FPR (1-specificity, which indicates false alarms)" for the different possible probability thresholds of a classification model [9,38,68,95]. It exhibits the tradeoff between "sensitivity" and "specificity" i.e.…”

Section: Receiver Operating Characteristics (Roc) Curvesmentioning

confidence: 99%

Review of multidimensional data processing approaches for Raman and infrared spectroscopy

et al. 2015

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Raman and Infrared (IR) spectroscopies provide information about the structure, functional groups and environment of the molecules in the sample. In combination with a microscope, these techniques can also be used to study molecular distributions in heterogeneous samples. Over the past few decades Raman and IR microspectroscopy based techniques have been extensively used to understand fundamental biology and responses of living systems under diverse physiological and pathological conditions. The spectra from biological systems are complex and diverse, owing to their heterogeneous nature consisting of bio-molecules such as proteins, lipids, nucleic acids, carbohydrates etc. Sometimes minor differences may contain critical information. Therefore, interpretation of the results obtained from Raman and IR spectroscopy is difficult and to overcome these intricacies and for deeper insight we need to employ various data mining methods. These methods must be suitable for handling large multidimensional data sets and for exploring the complete spectral information simultaneously. The effective implementation of these multivariate data analysis methods requires the pretreatment of data. The preprocessing of raw data helps in the elimination of noise (unwanted signals) and the enhancement of discriminating features. This review provides an outline of the state-of-the-art data processing tools for multivariate analysis and the various preprocessing methods that are widely used in Raman and IR spectroscopy including imaging for better qualitative and quantitative analysis of biological samples.

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Development and comparative assessment of Raman spectroscopic classification algorithms for lesion discrimination in stereotactic breast biopsies with microcalcifications

Cited by 34 publications

References 37 publications

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

Application of Raman Spectroscopy to Identify Microcalcifications and Underlying Breast Lesions at Stereotactic Core Needle Biopsy

Review of multidimensional data processing approaches for Raman and infrared spectroscopy

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