Int J Comput Assist Radiol Surg - Computer-aided diagnostics in digital pathology: automated evaluation of early-phase pancreatic cancer in mice.

Tópicos

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Resumo

RPOSE: Digital pathology diagnostics are often based on subjective qualitative measures. A murine model of early-phase pancreatic ductal adenocarcinoma provides a controlled environment with a priori knowledge of the genetic mutation and stage of the disease. Use of this model enables the application of supervised learning methods to digital pathology. A computerized diagnostics system for histological detection of pancreatic adenocarcinoma was developed and tested. METHODS : Pathological H&E-stained specimens with early pancreatic lesions were identified and evaluated with a system that models cancer detection using a top-down object learning paradigm, mimicking the way a pathologist learns. First, the dominant primitives were identified and segmented in the images, i.e., the ducts, nuclei and tumor stroma. A boost-based machine learning technique was used for duct segmentation, classification and outlier pruning. Second, a set of morphological features traditionally used for cancer diagnosis which provides quantitative image features was employed to quantify subtle findings such as duct deformation and nuclei malformations. Finally, a visually interpretable predictive model was trained to distinguish between normal tissue and premalignant cancer lesions, given ground truth samples. RESULTS : A predictive success rate of 92% was achieved using tenfold cross-validation and 93% on an independent test set. Comparison was made with state-of-the-art classification algorithms that are not interpretable as visible features yielded the contribution of individual primitive features to the prediction outcome.CONCLUSIONS: Quantitative image analysis and classification were successful in preclinical histology diagnosis for early-stage pancreatic adenocarcinoma. The usage of annotated contours coupled with interpretable supervised learning methods and outlier pruning can be adapted to other medical imaging tasks. The usage of interpretable supervised learning techniques may improve the success of CAD in histopathological diagnosis.

Resumo Limpo

rpose digit patholog diagnost often base subject qualit measur murin model earlyphas pancreat ductal adenocarcinoma provid control environ priori knowledg genet mutat stage diseas use model enabl applic supervis learn method digit patholog computer diagnost system histolog detect pancreat adenocarcinoma develop test method patholog hestain specimen earli pancreat lesion identifi evalu system model cancer detect use topdown object learn paradigm mimick way pathologist learn first domin primit identifi segment imag ie duct nuclei tumor stroma boostbas machin learn techniqu use duct segment classif outlier prune second set morpholog featur tradit use cancer diagnosi provid quantit imag featur employ quantifi subtl find duct deform nuclei malform final visual interpret predict model train distinguish normal tissu premalign cancer lesion given ground truth sampl result predict success rate achiev use tenfold crossvalid independ test set comparison made stateoftheart classif algorithm interpret visibl featur yield contribut individu primit featur predict outcomeconclus quantit imag analysi classif success preclin histolog diagnosi earlystag pancreat adenocarcinoma usag annot contour coupl interpret supervis learn method outlier prune can adapt medic imag task usag interpret supervis learn techniqu may improv success cad histopatholog diagnosi

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