scholarly journals First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 356 ◽  
Author(s):  
Gabriel García ◽  
Adrián Colomer ◽  
Valery Naranjo
Keyword(s):  
Prostate Cancer ◽  
Contextual Information ◽  
Support Vector ◽  
Learning Approach ◽  
Gleason Grade ◽  
Gleason Grading ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Grade 3 ◽  
First Time

Analysis of histopathological image supposes the most reliable procedure to identify prostate cancer. Most studies try to develop computer aid-systems to face the Gleason grading problem. On the contrary, we delve into the discrimination between healthy and cancerous tissues in its earliest stage, only focusing on the information contained in the automatically segmented gland candidates. We propose a hand-driven learning approach, in which we perform an exhaustive hand-crafted feature extraction stage combining in a novel way descriptors of morphology, texture, fractals and contextual information of the candidates under study. Then, we carry out an in-depth statistical analysis to select the most relevant features that constitute the inputs to the optimised machine-learning classifiers. Additionally, we apply for the first time on prostate segmented glands, deep-learning algorithms modifying the popular VGG19 neural network. We fine-tuned the last convolutional block of the architecture to provide the model specific knowledge about the gland images. The hand-driven learning approach, using a nonlinear Support Vector Machine, reports a slight outperforming over the rest of experiments with a final multi-class accuracy of 0.876 ± 0.026 in the discrimination between false glands (artefacts), benign glands and Gleason grade 3 glands.

scholarly journals Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

2019 ◽  
Vol 9 (15) ◽  
pp. 2969 ◽  
Author(s):  
Bhattacharjee ◽  
Park ◽  
Kim ◽  
Prakash ◽  
Madusanka ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Features ◽  
Morphological Features ◽  
Support Vector ◽  
Epithelial Tissue ◽  
Gleason Grade ◽  
Svm Classification ◽  
Gleason Grading ◽  
Grade 5 ◽  
Grade 3

An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature.

scholarly journals Interchangeability of light and virtual microscopy for histopathological evaluation of prostate cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Renata Zelic ◽  
Francesca Giunchi ◽  
Luca Lianas ◽  
Cecilia Mascia ◽  
Gianluigi Zanetti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Virtual Microscopy ◽  
Observer Agreement ◽  
Gleason Grade ◽  
Gleason Grading ◽  
Gleason Pattern ◽  
Inter Observer Variability ◽  
Histopathological Evaluation ◽  
Good Repeatability ◽  
Repeatability And Reproducibility

AbstractVirtual microscopy (VM) holds promise to reduce subjectivity as well as intra- and inter-observer variability for the histopathological evaluation of prostate cancer. We evaluated (i) the repeatability (intra-observer agreement) and reproducibility (inter-observer agreement) of the 2014 Gleason grading system and other selected features using standard light microscopy (LM) and an internally developed VM system, and (ii) the interchangeability of LM and VM. Two uro-pathologists reviewed 413 cores from 60 Swedish men diagnosed with non-metastatic prostate cancer 1998–2014. Reviewer 1 performed two reviews using both LM and VM. Reviewer 2 performed one review using both methods. The intra- and inter-observer agreement within and between LM and VM were assessed using Cohen’s kappa and Bland and Altman’s limits of agreement. We found good repeatability and reproducibility for both LM and VM, as well as interchangeability between LM and VM, for primary and secondary Gleason pattern, Gleason Grade Groups, poorly formed glands, cribriform pattern and comedonecrosis but not for the percentage of Gleason pattern 4. Our findings confirm the non-inferiority of VM compared to LM. The repeatability and reproducibility of percentage of Gleason pattern 4 was poor regardless of method used warranting further investigation and improvement before it is used in clinical practice.

scholarly journals Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-assisted Decision-support Models 

2020 ◽  
Author(s):  
Hailang Liu ◽  
Kun Tang ◽  
Ejun Peng ◽  
Liang Wang ◽  
Ding Xia ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Predictive Value ◽  
Support Vector ◽  
Gleason Grade ◽  
Good Discrimination ◽  
Grade Group ◽  
Testing Dataset

Abstract Background: This study aimed to develop a machine learning (ML)-assisted model capable of accurately predicting the probability of biopsy Gleason grade group upgrading before making treatment decisions.Methods: We retrospectively collected data from prostate cancer (PCa) patients who underwent systematic biopsy and radical prostatectomy from January 2015 to December 2019 at Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. Four ML-assisted models were developed from 16 clinical features using logistic regression (LR), logistic regression optimized by least absolute shrinkage and selection operator (Lasso) regularization (Lasso-LR), random forest (RF) and support vector machine (SVM). The area under the curve (AUC) was applied to determine the model with the highest discrimination. Calibration plots were used to investigate the extent of over- or underestimation of predicted probabilities relative to the observed probabilities in models. Results: In total, 530 PCa patients were included, with 371 patients in the training dataset and 159 patients in the testing dataset. The Lasso-LR model showed good discrimination with an AUC, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.776, 0.712, 0.679, 0.745, 0.730 and 0.695, respectively, followed by SVM (AUC 0.740, 95% confidence interval [CI]: 0.690–0.790), LR (AUC 0.725, 95% CI: 0.674–0.776) and RF (AUC 0.666, 95% CI: 0.618–0.714). Validation of the model showed that the Lasso-LR model had the best discriminative power (AUC 0.735, 95% CI: 0.656–0.813), followed by SVM (AUC 0.723, 95% CI: 0.644–0.802), LR (AUC 0.697, 95% CI: 0.615–0.778) and RF (AUC 0.607, 95% CI: 0.531–0.684) in the testing dataset. Both the Lasso-LR and SVM models were well-calibrated. Conclusion: The Lasso-LR model had good discrimination in the prediction of patients at high risk of harboring incorrect Gleason grade group assignment, and the use of this model may be greatly beneficial to urologists in treatment planning, patient selection, and the decision-making process for PCa patients.

scholarly journals Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2586
Author(s):  
Sarah M. Ayyad ◽  
Mohamed Shehata ◽  
Ahmed Shalaby ◽  
Mohamed Abou El-Ghar ◽  
Mohammed Ghazal ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
The Body ◽  
Histopathological Image ◽  
Recent Developments ◽  
Histopathological Images ◽  
Conventional Machine ◽  
Histopathology Images ◽  
High Level

Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

scholarly journals A Genomic-Clinicopathologic Nomogram for the Prediction of Lymph Node Invasion in Prostate Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zongtai Zheng ◽  
Shiyu Mao ◽  
Zhuoran Gu ◽  
Ruiliang Wang ◽  
Yadong Guo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Status ◽  
Support Vector ◽  
Good Prediction ◽  
Gleason Grade ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Svm Model

Background. Lymph node status is important for treatment decision making in prostate cancer (PCa). We aimed to develop a genomic-clinicopathologic nomogram for the prediction of lymph node invasion (LNI) in PCa. Methods. Differentially expressed genes between LNI and non-LNI PCa samples were identified in the Cancer Genome Atlas database. Univariate Cox regression analysis and minimum redundancy maximum relevance were performed for gene selection. The synthetic minority oversampling technique (SMOTE) was conducted to balance the minority group (LNI group). Machine learning models were constructed in the training set and assessed in the validation set. Univariable logistic regression and multivariable logistic regression were applied to build a nomogram. Furthermore, the RNA-sequence data from our center were used to validate the expression levels of hub genes between five matched primary PCa and the corresponding LNI samples. Results. The 37-gene-based support vector machine (SVM) model had the optimal synthesized performance in the SMOTE-balanced training (area under the curve (AUC): 0.947) and validation (AUC: 0.901) sets. Incorporating the SVM-based risk score and the Gleason grade, the genomic-clinicopathologic nomogram demonstrated good prediction and calibration both in the SMOTE-balanced training (AUC: 0.946) and validation (AUC: 0.910) sets. The dysregulated expression of hub genes between PCa and LNI samples was also validated. Conclusion. The proposed nomogram combining the 37-gene-based SVM model with the Gleason grade had the potential to preoperatively predict LNI in PCa. Some of the hub genes should be prioritized for functional studies and mechanistic analyses.

Multi-Criteria Decision-Making Techniques for Histopathological Image Classification

2019 ◽  
pp. 103-138
Author(s):  
Revathi T. ◽  
Saroja S. ◽  
Haseena S. ◽  
Blessa Binolin Pepsi M.
Keyword(s):  
Decision Making ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Multi Criteria Decision Making ◽  
K Nearest Neighbor ◽  
Analysis Process ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Histopathological Image Classification

This chapter presents an overview of methods that have been proposed for analysis of histopathological images. Diagnosing and detecting abnormalities in medical images helps the pathologist in making better decisions. Different machine learning algorithms such as k-nearest neighbor, random forest, support vector machine, ensemble learning, multilayer perceptron, and convolutional neural network are incorporated for carrying out the analysis process. Further, multi-criteria decision-making (MCDM) methods such as SAW, WPM, and TOPSIS are used to improve the efficiency of the decision-making process.

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