Shape analysis of microcalcifications using Radon transform

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

PURPOSE

The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process.

METHODS

Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies.

RESULTS

Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials.

CONCLUSIONS

The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.

Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques

This chapter introduces a new method for knowledge extraction from databases for the purpose of finding a discriminative set of features that is also a robust set for within-class classification. Our method is generic and we introduce it here in the field of breast cancer diagnosis from digital mammography data. The mathematical formalism is based on a generalization of the k-Feature Set problem called (α, β)-k-Feature Set problem, introduced by Cotta and Moscato (J Comput Syst Sci 67(4):686-690, 2003). This method proceeds in two steps: first, an optimal (α, β)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (α, β)-k-feature set in two phases; first a series of extremely powerful reduction techniques, which do not lose the optimal solution, are employed; and second, a metaheuristic search to identify the remaining features to be considered or disregarded. Two algorithms were tested with a public domain digital mammography dataset composed of 71 malignant and 75 benign cases. Based on the results provided by the algorithms, we obtain classification rules that employ only a subset of these features.

High-resolution 3D micro-CT imaging of breast microcalcifications: a preliminary analysis

BACKGROUND

Detection of microcalcifications on mammograms indicates the presence of breast lesion, and the shapes of the microcalcifications as seen by conventional mammography correlates with the probability of malignancy. This preliminary study evaluated the 3D shape of breast microcalcifications using micro-computed tomography (micro-CT) and compared the findings with those obtained using anatomopathological analysis.

METHODS

The study analyzed breast biopsy samples from 11 women with findings of suspicious microcalcifications on routine mammograms. The samples were imaged using a micro-CT (SkyScan 1076) at a resolution of 35 μm. Images were reconstructed using filtered back-projection and analyzed in 3D using surface rendering. The samples were subsequently analyzed by the pathology service. Reconstructed 3D images were compared with the corresponding histological slices.

RESULTS

Anatomopathological analysis showed that 5 of 11 patients had ductal breast carcinoma in situ. One patient was diagnosed with invasive ductal carcinoma.Individual object analysis was performed on 597 microcalcifications. Malignant microcalcifications tended to be thinner and to have a smaller volume and surface area, while their surface area-to-volume ratio was greater than that of benign microcalcifications. The structure model index values were the same for malignant and benign microcalcifications.

CONCLUSIONS

This is the first study to use micro-CT for quantitative 3D analysis of microcalcifications. This high-resolution imaging technique will be valuable for gaining a greater understanding of the morphologic characteristics of malignant and benign microcalcifications. The presence of many small microcalcifications can be an indication of malignancy. For the larger microcalcifications, 3D parameters confirmed the more irregular shape of malignant microcalcifications.

Hybrid cosine and Radon transform-based processing for digital mammogram feature extraction and classification with SVM

A new methodology to automatically extract features from mammograms and classify them is presented. It relies on a hybrid processing system that sequentially uses the discrete cosine transform (DCT) to obtain the high frequency component of the mammogram and then applies the Radon transform to the obtained DCT image in order to extract its directional features. The features are subsequently fed to a support vector machine for classification. The approach was tested on a database of one hundred images and shows improved classification accuracy in comparison to using the discrete cosine transform or the Radon transform alone, as done in others works.

Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer

PURPOSE

The authors propose an image-retrieval based approach for case-adaptive classifier design in computer-aided diagnosis (CADx). The conventional approach in CADx is to first train a pattern-classifier based on a set of existing training samples and then apply this classifier to subsequent new cases. The purpose of this work is to improve the classification accuracy of a CADx classifier by making use of a set of known cases retrieved from a reference library that are similar to the case under consideration.

METHODS

In the proposed approach, the authors will first apply image-retrieval to obtain a set of lesion images from a library of known cases that have similar image features to a case being diagnosed (i.e., query). These retrieved cases are then used to optimize a pattern-classifier toward boosting its classification accuracy on the query case. The basic idea is to put more emphasis on those cases that are similar to the query. The proposed approach is demonstrated first using a linear classifier and then extended to a nonlinear classifier induced by kernel principal component analysis.

RESULTS

The proposed retrieval-driven approach was tested on a library of mammogram images from 1006 cases (646 benign and 360 malignant) obtained from multiple institutions and was demonstrated to yield significant improvement in classification performance. Measured by the area under the receiver operating characteristic curve (AUC), the case-adaptive approach could boost the classification performance of a linear classifier from AUC = 0.7415 to AUC = 0.7807; similar improvement was also obtained for a nonlinear classifier, with AUC boosted from 0.7527 to 0.7838.

CONCLUSIONS

Use of additional cases from a reference library that have similar image features can improve the classification accuracy of a CADx classifier on a query case. It can even outperform retraining the classifier with all the cases from the entire reference library. This implies that cases with similar image features are more relevant in defining the local decision boundary of the CADx classifier around the query.