Microscopy image analysis of p63 immunohistochemically stained laryngeal cancer lesions for predicting patient 5-year survival

Abstract

The aim of the present study was to design a microscopy image analysis (MIA) system for predicting the 5-year survival of patients with laryngeal squamous cell carcinoma, employing histopathology images of lesions, which had been immunohistochemically (IHC) stained for p63 expression. Biopsy materials from 42 patients, with verified laryngeal cancer and follow-up, were selected from the archives of the University Hospital of Patras, Greece. Twenty six patients had survived more than 5 years and 16 less than 5 years after the first diagnosis. Histopathology images were IHC stained for p63 expression. Images were first processed by a segmentation method for isolating the p63-expressed nuclei. Seventy-seven features were evaluated regarding texture, shape, and physical topology of nuclei, p63 staining, and patient-specific data. Those features, the probabilistic neural network classifier, the leave-one-out (LOO), and the bootstrap cross-validation methods, were used to design the MIA-system for assessing the 5-year survival of patients with laryngeal cancer. MIA-system accuracy was about 90 % and 85 %, employing the LOO and the Bootstrap methods, respectively. The image texture of p63-expressed nuclei appeared coarser and contained more edges in the 5-year non-survivor group. These differences were at a statistically significant level (p < 0.05). In conclusion, this study has proposed an MIA-system that may be of assistance to physicians, as a second opinion tool in assessing the 5-year survival of patients with laryngeal cancer, and it has revealed useful information regarding differences in nuclei texture between 5-year survivors and non-survivors.

Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning

“…Our proposed ensemble models were able to achieve the best performance in the binary classification on the GasHisSDB dataset. To prove the effectiveness and robustness of our proposed ensemble models and also to show that the proposed work is not sample/dataset limited, we further experimented these models on a different histopathology dataset named Histology Image Collection Library (HICL) histopathology larynx dataset [53,54]. This is a multi-class dataset which consists of Grade I, II, and III tumors and has a total of 224 images across all three classes.…”

Ανάπτυξη Νέων Μεθόδων Ανάλυσης Ψηφιακού Σήματος Και Εικόνας Και Αναγνώριση Προτύπων Σε Ιατρικές Εφαρμογές

“…Σχετική εργασία έχει υποβληθεί σε διεθνές αναγνωρισμένο επιστημονικό περιοδικό με δείκτη απήχησης [27]. Στο Σχήμα 3.4 που ακολουθεί απεικονίζεται το αποτέλεσμα του φίλτρου επί της γκρι εικόνας του καρκίνου του λάρυγγα καθώς και το log φίλτρο, στο πεδίο του χώρου και στο πεδίο των συχνοτήτων.…”