Biomedical subjects

Andrea Sboner

Publications and source records attributed to Andrea Sboner.

5 recordsLinked to original sources

PubMed2006

TMABoost: an integrated system for comprehensive management of tissue microarray data.

In the last decade, high-throughput technologies such as DNA and tissue microarrays (TMAs) have become a means of large-scale investigation of gene expression, providing a plethora of new biomedical data in a relatively short time. Data collection and organization are critical aspects in this process to ensure the quality and reliability of future data interpretation. In this work, we propose a comprehensive approach to handle TMA data with the aim of supporting and promoting biomarker development. We describe a web-based system for the complete management of tissue microarray data in the field of pathology. The system has been in use since June, 2003. Our approach includes automatic localization and identification of tissue microarray samples, and quantitative image analysis that allows high-throughput screening of TMAs by ensuring nonsubjective measures and novel prognosis associations. In this paper, we present the architecture and the components of this system.

Algorithms↗

PubMed2005

An automated procedure to properly handle digital images in large scale tissue microarray experiments.

Tissue Microarray (TMA) methodology has been recently developed to enable "genome-scale" molecular pathology studies. To enable high-throughput screening of TMAs automation is mandatory, both to speed up the process and to improve data quality. In particular, in acquiring digital images of single tissues (core sections) a crucial step is the correct recognition of each tissue position in the array. In fact, further reliable data analysis is based on the exact assignment of each tissue to the corresponding tumor. As most of the times tissue alignment in the microarray grid is far from being perfect, simple strategies to perform proper acquisition do not fit well. The present paper describes a new solution to automatically perform grid location assignment. We developed an ad hoc image processing procedure and a robust algorithm for object recognition. Algorithm accuracy tests and assessment of working constraints are discussed. Our approach speeds up TMA data collection and enables large scale investigation.

Algorithms↗

PubMed2005

Modeling clinical judgment and implicit guideline compliance in the diagnosis of melanomas using machine learning.

We explore several machine learning techniques to model clinical decision making of 6 dermatologists in the clinical task of melanoma diagnosis of 177 pigmented skin lesions (76 malignant, 101 benign). In particular we apply Support Vector Machine (SVM) classifiers to model clinician judgments, Markov Blanket and SVM feature selection to eliminate clinical features that are effectively ignored by the dermatologists, and a novel explanation technique whereby regression tree induction is run on the reduced SVM model's output to explain the physicians' implicit patterns of decision making. Our main findings include: (a) clinician judgments can be accurately predicted, (b) subtle decision making rules are revealed enabling the explanation of differences of opinion among physicians, and (c) physician judgment is non-compliant with the diagnostic guidelines that physicians self-report as guiding their decision making.

Artificial Intelligence↗

PubMed2004

Clinical validation of an automated system for supporting the early diagnosis of melanoma.

BACKGROUND: Early diagnosis and surgical excision is the most effective treatment of melanoma. Well-trained dermatologists reach a high level of diagnostic accuracy with good sensitivity and specificity. Their performances increase using some technical aids as digital epiluminescence microscopy. Several studies describe the development of computerized systems whose aim is supporting dermatologists in the early diagnosis of melanoma. In many cases, the performances of those systems were comparable to those of dermatologists. However, this cannot tell us whether a system is able to support dermatologists. Actually, the computerized system might correctly recognize the same lesions that the dermatologist does, without providing them any useful advice and therefore being useless in recognizing early malignant lesions. PURPOSE: We present a novel approach to enhance dermatologists' performances in the diagnosis of early melanoma. We provide results of our evaluation of a computerized system combined with dermatologists. METHODS: A Multiple-Classifier system was developed on a set of 152 cases and combined to a group of eight dermatologists to support them by improving their sensitivity. RESULTS: The eight dermatologists have average sensitivity and specificity values of 0.83 and 0.66, respectively. The Multiple-Classifier system performs as well as the eight dermatologists (sensitivity range: 0.75-0.86; specificity range: 0.64-0.89). The combination with the dermatologists shows an average improvement of 11% (P=0.022) of dermatologists' sensitivity. CONCLUSION: Our results suggest that an automated system can be effective in supporting dermatologists because it recognizes different malignant melanomas with respect to the dermatologists.

Dermatology↗

PubMed2003

A multiple classifier system for early melanoma diagnosis.

Melanoma is the most dangerous skin cancer and early diagnosis is the key factor in its successful treatment. Well-trained dermatologists reach a diagnosis via visual inspection, and reach sensitivity and specificity levels of about 80%. Several computerised diagnostic systems were reported in the literature using different classification algorithms. In this paper, we will illustrate a novel approach by which a suitable combination of different classifiers is used in order to improve the diagnostic performances of single classifiers. We used three different kinds of classifiers, namely linear discriminant analysis (LDA), k-nearest neighbour (k-NN) and a decision tree, the inputs of which are 38 geometric and colorimetric features automatically extracted from digital images of skin lesions. Multiple classifiers were generated by combining the diagnostic outputs of single classifiers with appropriate voting schemata. This approach was evaluated on a set of 152 digital skin images. We compared the performances of multiple classifiers (2- and 3-classifier groups) between them and with respect to single ones (1-classifier group). We further compared the classifiers' performances with those of eight dermatologists. Classifiers' performances were measured in terms of distance from the ideal classifier. Compared with 1- and 2-classifier groups, performances of 3-classifier systems were significantly higher (P<0.0005 and P<0.001, respectively). No statistically significant differences were found between the 1- and 2-classifier groups (P=0.352). While the dermatologists group showed a level of performances significantly higher than the 1-classifier systems (P<0.020), no differences were found between the multiple classifier groups and the dermatologists groups, indicating comparable performances. This work suggests that a suitable combination of different kinds of classifiers can improve the performances of an automatic diagnostic system.

Algorithms↗