2022
DOI: 10.1200/jco.2022.40.16_suppl.e13579
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Using infrared spectroscopy to analyze breath of patients diagnosed with breast cancer.

Abstract: e13579 Background: Population-level screening programs aimed at early detection and treatment of breast cancer saves lives. Analyzing breath using infrared spectroscopy offers a highly sensitive, non-invasive, and cost-effective mechanism for identifying exhaled volatile organic chemicals, and it is hypothesized that it may identify differences in the “breathprint” of women with breast cancer relative to those without a breast cancer diagnosis. Methods: Alveolar breath samples (10 L) were collected using a Br… Show more

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Cited by 4 publications
(2 citation statements)
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“…The measured spectra were used to develop a supervised ML classification model that discriminates SARS-CoV-2 positive from negative samples. First, any missing absorption coefficients were replaced in each spectrum using linear interpolation and then rescaled using vector normalization, using a previously validated approach [13][14][15]. Next, firstorder spectral derivative sequences, each comprising of 191 values were extracted from the normalized breathprints and used as features for classification.…”
Section: Machine Learning Classification Modelmentioning
confidence: 99%
See 1 more Smart Citation
“…The measured spectra were used to develop a supervised ML classification model that discriminates SARS-CoV-2 positive from negative samples. First, any missing absorption coefficients were replaced in each spectrum using linear interpolation and then rescaled using vector normalization, using a previously validated approach [13][14][15]. Next, firstorder spectral derivative sequences, each comprising of 191 values were extracted from the normalized breathprints and used as features for classification.…”
Section: Machine Learning Classification Modelmentioning
confidence: 99%
“…A linear support vector machine (SVM) learning approach was used for classification. This uses features from a set of training samples to construct an algorithm which then act as a decision boundary for classifying future samples [13][14][15].…”
Section: Machine Learning Classification Modelmentioning
confidence: 99%