Quality assessment of Ki67 staining using cell line proliferation index and stain intensity features.

Breast cancer is the most frequent cancer among women worldwide. Ki67 can be used as an immunohistochemical pseudo marker for cell proliferation to determine how aggressive the cancer is and thereby the treatment of the patient. No standard Ki67 staining protocol exists, resulting in inter-laboratory stain variability. Therefore, it is important to determine the quality control of a staining protocol to ensure correct diagnosis and treatment of patients. Currently, quality control is performed by the organization NordiQC that use an expert panel-based qualitative assessment system. However, no objective method exists to determine the quality of a staining protocol. In this study, we propose an algorithm, to objectively assess staining quality from segmented cell nuclei structures extracted from cell lines. The cell nuclei were classified into either Ki67 positive or negative to determine the Ki67 proliferation index within the cell lines. A Ki67 stain quality model based on ordinal logistic regression was developed to determine the quality of a staining protocol from features extracted from the segmented cell nuclei in the cell lines. The algorithm was able to segment and classify Ki67 positive cell nuclei with a sensitivity and positive predictive value (PPV) of 0.90 and 0.94 and Ki67 negative cell nuclei with a sensitivity and PPV of 0.78 and 0.78. The mean difference between a manual and automatic Ki67 proliferation index was -0.003 with a standard deviation of 0.056. The ordinal logistic regression model found that the stain intensity for both the Ki67 positive and Ki67 negative cell nuclei were statistically significant as parameters determining the stain quality from the cell line cores. The framework shows great promise for using cell nuclei information from cell lines to predict the staining quality of staining protocols. © 2018 International Society for Advancement of Cytometry.

Accurate Measurement of Airway Morphology on Chest CT Images.

In recent years, the ability to accurately measuring and analyzing the morphology of small pulmonary structures on chest CT images, such as airways, is becoming of great interest in the scientific community. As an example, in COPD the smaller conducting airways are the primary site of increased resistance in COPD, while small changes in airway segments can identify early stages of bronchiectasis. To date, different methods have been proposed to measure airway wall thickness and airway lumen, but traditional algorithms are often limited due to resolution and artifacts in the CT image. In this work, we propose a Convolutional Neural Regressor (CNR) to perform cross-sectional measurements of airways, considering wall thickness and airway lumen at once. To train the networks, we developed a generative synthetic model of airways that we refined using a Simulated and Unsupervised Generative Adversarial Network (SimGAN). We evaluated the proposed method by first computing the relative error on a dataset of synthetic images refined with SimGAN, in comparison with other methods. Then, due to the high complexity to create an in-vivo ground-truth, we performed a validation on an airway phantom constructed to have airways of different sizes. Finally, we carried out an indirect validation analyzing the correlation between the percentage of the predicted forced expiratory volume in one second (FEV1%) and the value of the Pi10 parameter. As shown by the results, the proposed approach paves the way for the use of CNNs to precisely and accurately measure small lung airways with high accuracy.