PubMed · 8746682
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K D Altria. 1996. Fraction collection.. https://doi.org/10.1385/0-89603-315-5%3A99
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In this paper, wavelets were employed for multi-scale image analysis to extract parameters for the description of chromatin texture in the cytological diagnosis and grading of invasive breast cancer. Their value was estimated by comparing the performance of co-occurrence, densitometric, and morphometric parameters in an automated K-nearest neighbor (Knn) classification scheme based on light microscopic images of isolated nuclei of paraffin-embedded tissue. This design allowed a multifaceted cytological retrospective study of which the practical value can be judged easily. Results show that wavelets perform excellently with classification scores comparable with densitometric and co-occurrence features. Moreover, because wavelets showed a high additive value with the other textural groups, this panel allowed a very profound description with higher recognition scores than previously reported (76% for individual nuclei, 100% for cases). Morphometric parameters performed less well and only slightly increased correct classification. The major drawback, besides image segmentation errors demanding operator supervision, emanated to be the few false-negative cases, which restrict the immediate practical use. However, an enlargement of the parameter set may avoid this misclassification, resulting in an applicable expert system of practical use.
Applications of fluorescence in situ hybridization (FISH) for translocation studies and biological dosimetry would benefit substantially from reliable and efficient automatic detection of metaphase chromosomes labeled with fluorescent dyes. We replicated and evaluated a fluorescence metaphase finder previously developed at the Medical Research Council (MRC), Human Genetics Unit (Scotland) and at Lawrence Berkeley Laboratory (LBL; California). The MRC/LBL system seemed to detect nearly all of the metaphases on the test slides, but it presented an unacceptable number of false positives (about five false positives per one true positive). Furthermore, we determined that the system actually overcalled true detections by counting certain metaphase spreads twice (duplicates). Through modifications of the MRC/LBL system, we developed the Lawrence Livermore National Laboratory (LLNL) system, which minimizes the detection of duplicates, incorporates new detection features, uses a binary decision tree (BDT) for classification, and provides functionalities to improve scanning accuracy and improve the post-detection review. To test the new system, DAPI-stained preparations of metaphase chromosomes from blood lymphocytes of four unrelated donors were placed on slides in drops ranging from 7 mm to 20 mm in diameter. Drops contained between 5 and 200 scorable metaphases each. The LLNL system achieved approximately 90% detection of non-duplicated metaphases as verified by an expert cytogeneticist, with typically less than one false positive per every one true positive detected.
Extending the method for two-point linkage analysis [Zhao et al., 1998: Am J Med Genet 77:366-383], this paper introduces a semiparametric method for multipoint linkage analysis, expected to gain efficiency by using multiple markers simultaneously. Overcoming the longstanding statistical and computational challenge to the parametric approaches (or lod score methods) for multipoint linkage analysis, this semiparametric approach, based on the estimating equation technique, yields statistically efficient and yet robust estimates and enjoys the computational efficiency in processing multiple markers from large pedigrees. Its computational burden increases linearly with the sizes of pedigrees and with the number of marker loci. To illustrate this semiparametric method, we apply it to marker data gathered for the Breast Cancer Consortium. The result supports the earlier finding of the positive linkage with BRCA1 and has also shown that the multipoint linkage analysis has an improved power. In addition, we have applied this method to analyze genome scanning data that have been used to localize genes responsible for type 1 diabetes. In support of the earlier findings, the genome scanning detects the linkage signals on chromosome 6 but does not support the earlier suggestions of two major genes in that genome segment. Through sensitivity analysis, it appears that the results are robust to misspecification of penetrance and allele frequency.