Dataset Information

Characterization of metabolic differences between benign and malignant tumors: high-spectral-resolution diffuse optical spectroscopy.

ABSTRACT:

Purpose

To develop a near-infrared spectroscopic method to identify breast cancer biomarkers and to retrospectively determine if benign and malignant breast lesions could be distinguished by using this method.

Materials and methods

The study was HIPAA compliant and was approved by the university institutional review board. Written informed consent was obtained. By using self-referencing differential spectroscopy (SRDS) analysis, the existence of specific spectroscopic signatures of breast lesions on images acquired by using diffuse optical spectroscopy imaging in the wavelength range (650-1000 nm) was established. The SRDS method was tested in 60 subjects (mean age, 38 years; age range, 22-74 years). There were 17 patients with benign breast tumors and 22 patients with malignant breast tumors. There were 21 control subjects.

Results

Discrimination analysis helped separate malignant from benign tumors. A total of 40 lesions (22 malignant and 18 benign) were analyzed. Twenty were true-positive lesions, 17 were true-negative lesions, one was a false-positive lesion, and two were false-negative lesions (sensitivity, 91% [20 of 22]; specificity, 94% [17 of 18]; positive predictive value, 95% [20 of 21]; and negative predictive value, 89% [17 of 19]).

Conclusion

The SRDS method revealed localized tumor biomarkers specific to pathologic state.

SUBMITTER: Kukreti S

PROVIDER: S-EPMC2797652 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Publications

Characterization of metabolic differences between benign and malignant tumors: high-spectral-resolution diffuse optical spectroscopy.

Kukreti Shwayta S Cerussi Albert E AE Tanamai Wendy W Hsiang David D Tromberg Bruce J BJ Gratton Enrico E

Radiology 20100101 1

<h4>Purpose</h4>To develop a near-infrared spectroscopic method to identify breast cancer biomarkers and to retrospectively determine if benign and malignant breast lesions could be distinguished by using this method.<h4>Materials and methods</h4>The study was HIPAA compliant and was approved by the university institutional review board. Written informed consent was obtained. By using self-referencing differential spectroscopy (SRDS) analysis, the existence of specific spectroscopic signatures o ...[more]

PMID: 20032159

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Project description:Simple Summary Skin and subcutaneous tumors are among the most frequent neoplasms in dogs and cats. We studied 51 samples of canine and feline skin, lipomas, soft tissue sarcomas, and mast cell tumors using a multimodal approach based on optical coherence tomography and Raman spectroscopy. A supervised machine learning algorithm detected malignant tumors with the sensitivity and specificity of 94% and 98%, respectively. The proposed multimodal algorithm is a novel approach in veterinary oncology that can outperform the existing clinical methods such as the fine-needle aspiration method. Abstract As in humans, cancer is one of the leading causes of companion animal mortality. Up to 30% of all canine and feline neoplasms appear on the skin or directly under it. There are only a few available studies that have investigated pet tumors by biophotonics techniques. In this study, we acquired 1115 optical coherence tomography (OCT) images of canine and feline skin, lipomas, soft tissue sarcomas, and mast cell tumors ex vivo, which were subsequently used for automated machine vision analysis. The OCT images were analyzed using a scanning window with a size of 53 × 53 μm. The distributions of the standard deviation, mean, range, and coefficient of variation values were acquired for each image. These distributions were characterized by their mean, standard deviation, and median values, resulting in 12 parameters in total. Additionally, 1002 Raman spectral measurements were made on the same samples, and features were generated by integrating the intensity of the most prominent peaks. Linear discriminant analysis (LDA) was used for sample classification, and sensitivities/specificities were acquired by leave-one-out cross-validation. Three datasets were analyzed—OCT, Raman, and combined. The combined OCT and Raman data enabled the best sample differentiation with the sensitivities of 0.968, 1, and 0.939 and specificities of 0.956, 1, and 0.977 for skin, lipomas, and malignant tumors, respectively. Based on these results, we concluded that the proposed multimodal approach, combining Raman and OCT data, can accurately distinguish between malignant and benign tissues.

Project description:Purpose To investigate ultrasonography (US)-guided diffuse optical tomography to distinguish the functional differences of hemoglobin concentrations in a wide range of malignant and benign breast lesions and to improve breast cancer diagnosis in conjunction with conventional US. Materials and Methods The study protocol was approved by the institutional review boards and was HIPAA compliant. Written informed consent was obtained from all patients. Patients (288 women; mean age, 50 years; range, 17-94 years) who underwent US-guided biopsy were imaged with a handheld US and optical probe. The US-imaged lesion was used to guide reconstruction of light absorption maps at four wavelengths, and total hemoglobin (tHb), oxygenated hemoglobin (oxyHb), and deoxygenated hemoglobin (deoxyHb) were computed from the absorption maps. A threshold (80 ?mol/L) was chosen on the basis of this study population. Two radiologists retrospectively evaluated US images on the basis of the US Breast Imaging Reporting and Data System lexicon, and a lesion was considered malignant when a score of 4C or 5 was given or a lesion had tHb greater than 80 ?mol/L. A two-sample t test was used to calculate significance between groups, and Spearman ? was computed between hemoglobin parameters and tumor pathologic grades. Results Three tumors were Tis, 37 were T1, 19 were T2-T4 carcinomas, and 233 were benign lesions. The mean maximum tHb, oxyHb, and deoxyHb of Tis-T1 and T2-T4 groups were 89.3 ?mol/L ± 20.2 (standard deviation), 65.0 ?mol/L ± 20.8, and 33.5 ?mol/L ± 11.3, respectively, and 84.7 ?mol/L ± 32.8, 57.1 ?mol/L ± 19.8, and 34.7 ?mol/L ± 18.9, respectively. The corresponding values of benign lesions were 54.1 ?mol/L ± 23.5, 38.0 ?mol/L ± 17.4, and 25.2 ?mol/L ± 13.8, respectively. The mean maximum tHb, oxyHb, and deoxyHb were significantly higher in the malignant groups than the benign group (P <.001, <.001, and .041, respectively). For malignant lesions, the mean maximum tHb moderately correlated with tumor histologic grade and nuclear grade (? = 0.283 and 0.315, respectively). The mean maximum oxyHb moderately correlated with tumor nuclear grade (? = 0.267). When radiologists' US diagnosis and the tHb were used together, the sensitivity, specificity, positive predictive value, and negative predictive value were 96.6%-100%, 77.3%-83.3%, 52.7%-59.4%, and 99.0%-100%, respectively, for the combined malignant group. Conclusion The tHb and oxyHb correlate with breast cancer pathologic grade and can be used as an adjunct to US to improve sensitivity and negative predictive value in breast cancer diagnosis. (©) RSNA, 2016 Online supplemental material is available for this article.

Dataset Information

Characterization of metabolic differences between benign and malignant tumors: high-spectral-resolution diffuse optical spectroscopy.

Purpose

Materials and methods

Results

Conclusion

Publications

Characterization of metabolic differences between benign and malignant tumors: high-spectral-resolution diffuse optical spectroscopy.

Similar Datasets

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