title: Automated melanoma detection with a novel multispectral imaging system: results of a prospective study.
abstract: The aim of this research was to evaluate the performance of a new spectroscopic system in the diagnosis of melanoma. This study involves a consecutive series of 1278 patients with 1391 cutaneous pigmented lesions including 184 melanomas. In an attempt to approach the 'real world' of lesion population, a further set of 1022 not excised clinically reassuring lesions was also considered for analysis. Each lesion was imaged in vivo by a multispectral imaging system. The system operates at wavelengths between 483 and 950 nm by acquiring 15 images at equally spaced wavelength intervals. From the images, different lesion descriptors were extracted related to the colour distribution and morphology of the lesions. Data reduction techniques were applied before setting up a neural network classifier designed to perform automated diagnosis. The data set was randomly divided into three sets: train (696 lesions, including 90 melanomas) and verify (348 lesions, including 53 melanomas) for the instruction of a proper neural network, and an independent test set (347 lesions, including 41 melanomas). The neural network was able to discriminate between melanomas and non-melanoma lesions with a sensitivity of 80.4% and a specificity of 75.6% in the 1391 histologized cases data set. No major variations were found in classification scores when train, verify and test subsets were separately evaluated. Following receiver operating characteristic (ROC) analysis, the resulting area under the curve was 0.85. No significant differences were found among areas under train, verify and test set curves, supporting the good network ability to generalize for new cases. In addition, specificity and area under ROC curve increased up to 90% and 0.90, respectively, when the additional set of 1022 lesions without histology was added to the test set. Our data show that performance of an automated system is greatly population dependent, suggesting caution in the comparison with results reported in the literature. In our opinion, scientific reports should provide, at least, the median values of thickness and dimension of melanomas, as well as the number of small (6 mm) melanomas.
authors.affiliation: Department of Medical Physics, Istituto Nazionale per lo Studio e la Cura dei Tumori, Milan, Italy. stefano.tomatis@istitutotumori.mi.it
authors.forename: Stefano
authors.initials: S
authors.lastname: Tomatis
authors.forename: Mauro
authors.initials: M
authors.lastname: Carrara
authors.forename: Aldo
authors.initials: A
authors.lastname: Bono
authors.forename: Cesare
authors.initials: C
authors.lastname: Bartoli
authors.forename: Manuela
authors.initials: M
authors.lastname: Lualdi
authors.forename: Gabrina
authors.initials: G
authors.lastname: Tragni
authors.forename: Ambrogio
authors.initials: A
authors.lastname: Colombo
authors.forename: Renato
authors.initials: R
authors.lastname: Marchesini
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D002648:Child; D002675:Child, Preschool; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D006801:Humans; D008297:Male; D008545:Melanoma / Q000145:classification / Q000175:diagnosis*; D008875:Middle Aged; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011446:Prospective Studies; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000145:classification / Q000175:diagnosis*; D013057:Spectrum Analysis / Q000379:methods*