title: Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study.
abstract: Several techniques are being investigated as a complement to screening mammography, to reduce its false-positive rate, but results are still insufficient to draw conclusions. This initial study explores time domain diffuse optical imaging as an adjunct method to classify non-invasively malignant vs benign breast lesions. We estimated differences in tissue composition (oxy- and deoxyhemoglobin, lipid, water, collagen) and absorption properties between lesion and average healthy tissue in the same breast applying a perturbative approach to optical images collected at 7 red-near infrared wavelengths (635-1060 nm) from subjects bearing breast lesions. The Discrete AdaBoost procedure, a machine-learning algorithm, was then exploited to classify lesions based on optically derived information (either tissue composition or absorption) and risk factors obtained from patient's anamnesis (age, body mass index, familiarity, parity, use of oral contraceptives, and use of Tamoxifen). Collagen content, in particular, turned out to be the most important parameter for discrimination. Based on the initial results of this study the proposed method deserves further investigation.
authors.affiliation: Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.|Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
authors.forename: Paola
authors.initials: P
authors.lastname: Taroni
authors.affiliation: Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
authors.forename: Anna Maria
authors.initials: AM
authors.lastname: Paganoni
authors.affiliation: Dipartimento di Matematica, Università degli Studi, Via Saldini 50, 20133 Milano, Italy.
authors.forename: Francesca
authors.initials: F
authors.lastname: Ieva
authors.affiliation: Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.|Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
authors.forename: Antonio
authors.initials: A
authors.lastname: Pifferi
authors.affiliation: Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
authors.forename: Giovanna
authors.initials: G
authors.lastname: Quarto
authors.affiliation: European Institute of Oncology, Breast Imaging Unit, Via G. Ripamonti, 435, 20141 Milano, Italy.
authors.forename: Francesca
authors.initials: F
authors.lastname: Abbate
authors.affiliation: European Institute of Oncology, Breast Imaging Unit, Via G. Ripamonti, 435, 20141 Milano, Italy.
authors.forename: Enrico
authors.initials: E
authors.lastname: Cassano
authors.affiliation: Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.|Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
authors.forename: Rinaldo
authors.initials: R
authors.lastname: Cubeddu
mesh_terms: D019540:Area Under Curve; D001823:Body Composition; D001940:Breast / Q000000981:diagnostic imaging* / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography / Q000379:methods*; D013057:Spectrum Analysis