Multi-Stage Classification-Based Deep Learning for Gleason System Grading Using Histopathological Images.

In this work, we introduced an automated diagnostic system for Gleason system grading and grade groups (GG) classification using whole slide images (WSIs) of digitized prostate biopsy specimens (PBSs). Our system first classifies the Gleason pattern (GP) from PBSs and then identifies the Gleason score (GS) and GG. We developed a comprehensive DL-based approach to develop a grading pipeline system for the digitized PBSs and consider GP as a classification problem (not segmentation) compared to current research studies (deals with as a segmentation problem). A multilevel binary classification was implemented to enhance the segmentation accuracy for GP. Also, we created three levels of analysis (pyramidal levels) to extract different types of features. Each level has four shallow binary CNN to classify five GP labels. A majority fusion is applied for each pixel that has a total of 39 labeled images to create the final output for GP. The proposed framework is trained, validated, and tested on 3080 WSIs of PBS. The overall diagnostic accuracy for each CNN is evaluated using several metrics: precision (PR), recall (RE), and accuracy, which are documented by the confusion matrices.The results proved our system's potential for classifying all five GP and, thus, GG. The overall accuracy for the GG is evaluated using two metrics, PR and RE. The grade GG results are between 50% to 92% for RE and 50% to 92% for PR. Also, a comparison between our CNN architecture and the standard CNN (ResNet50) highlights our system's advantage. Finally, our deep-learning system achieved an agreement with the consensus grade groups.

A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens.

Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs' edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 (CNNS), 150 × 150 (CNNM), and 200 × 200 (CNNL), pixels, and 75% overlap. Those three sizes of patches represent the three pyramidal levels. This pyramidal technique allows us to extract rich information, such as that the larger patches give more global information, while the small patches provide local details. After that, the patch-wise technique assigns each overlapped patch a label as GP categories (1 to 5). Then, the majority voting is the core approach for getting the pixel-wise classification that is used to get a single label for each overlapped pixel. The results after applying those techniques are three images of the same size as the original, and each pixel has a single label. We utilized the majority voting technique again on those three images to obtain only one. The proposed framework is trained, validated, and tested on 608 whole slide images (WSIs) of the digitized PBSs. The overall diagnostic accuracy is evaluated using several metrics: precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged. The (CNNL) has the best accuracy results for patch classification among the three CNNs, and its classification accuracy is 0.76. The macro-averaged and weighted-average metrics are found to be around 0.70-0.77. For GG, our CAD results are about 80% for precision, and between 60% to 80% for recall and F1-score, respectively. Also, it is around 94% for accuracy and NPV. To highlight our CAD systems' results, we used the standard ResNet50 and VGG-16 to compare our CNN's patch-wise classification results. As well, we compared the GG's results with that of the previous work.

Impact of 18F-2-fluoro-2-deoxy-D-glucose PET/computerized tomography on the initial staging and changing the management intent in head and neck squamous cell carcinoma.

OBJECTIVE: To assess the role of 18F-FDG PET/CT in initial staging of head and neck squamous cell carcinoma and its impact on changing the management compared to other conventional methods. PATIENTS AND METHODS: A prospective study of 31 patients (21 male and 10 female), mean age 49.3 ± 12.1 years with histologically confirmed squamous cell carcinoma of the head and neck (nasopharynx was the commonest cancer in 15 patients (48.4%), poorly differentiated grade represented 64.5% of all tumors. Initial staging was achieved according to routine physical examination, endoscopy, CT, U/S, MRI. RESULTS: The overall change in TNM staging by 18F-FDG PET/CT in relation to conventional methods was encountered in 15/31 patients (48.4%). PET/CT changed; T staging in three patients (9.6%), upstaging in two patients and downstaging in one patient. PET/CT upstaged all 13/31 patients in N staging (41.9%). 18F-FDG PET/CT changed; M staging in 3/31 (9.6%) patients, upstaging in two and downstaging in one patient. PET/CT results caused radiotherapy modification in 21/31 patients (67.7%). PET/CT detected intra-parotid nodule in four patients, so additional radiation was added to the parotid in the treatment field. Retropharyngeal nodes were detected by PET/CT in three patients that were missed by conventional imaging. 18F-FDG PET/CT detected two patients of thyroid papillary carcinoma and one case of sigmoid neoplasm confirmed by histopathology. CONCLUSION: 18F-FDG-PET/CT is considered a valuable diagnostic test in head and neck squamous cell carcinoma at initial assessment which would change staging and radiotherapy planning and hence proper management.