Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts

Nir, Guy; Hor, Soheil; Karimi, Davood; Fazli, Ladan; Skinnider, Brian; Tavassoli, Peyman; Turbin, Dmitry; Villamil, Carlos F.; Wang, Gang; Wilson, R. Storey; Iczkowski, Kenneth A.; Lucia, M. Scott; Black, Peter C.; Abolmaesumi, Purang; Goldenberg, S. Larry; Salcudean, Septimiu E.

doi:10.1016/j.media.2018.09.005

Cited by 144 publications

(119 citation statements)

References 47 publications

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“…The classification accuracy achieved for the class low vs. high grade using the proposed method is higher than other methods described in the literature. On cancer diagnosis, when classified Malignant vs. Benign, our result is better than Nir et al (2018) and Doyle et al (2006), but not higher compared to Tabesh et al (2017), because they used different types of features that are extracted from the tissue image, namely color channel histogram, fractal dimension, fractal code, wavelet, and MAGIC. The authors of Reference [4] computed the features of epithelial nuclei objects in the tissue image, whereas, our method computed the features of all nuclei objects existing in the biopsy prostate tissue image.…”

Section: One-shot Classification Binary Classification Groupscontrasting

confidence: 53%

“…Nir et al [6] proposed some novel features based on intra-and inter-nuclei properties for classification. They trained their classifier on 333 tissue microarray (TMA) cores annotated by six pathologists for different Gleason grades and used SVM classification to achieve an accuracy of 88.5% and 73.8% for cancer detection (benign vs. malignant) and low vs. high grade (Grade 3 vs. Grade 4, 5), respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2019

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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. Appl. Sci. 2019, 9, 2969 2 of 17 practitioners, for both diagnosis and treatment. Usually, the cancer detection process in histopathology consists of categorizing stained microscopic biopsy images into malignant and benign.The Gleason grade grouping system defines Gleason scores ≤ 6 as grade 1, score 3 + 4 = 7 as grade 2, score 4 + 3 = 7 as grade 3, score 4 + 4, 3 + 5 or 5 + 3 = 8 as grade 4, and score 4 + 5, 5 + 4 or 5 + 5 = 9 or 10 as grade 5. The Gleason score is obtained by adding the primary (most common) and secondary (second most common) scores from H&E stained tissue microscopic images. This system was developed by Dr. Donald F Gleason, who was a Pathologist in Minnesota, and members of the Veterans Administration Cooperative Urological Research Group (VACURG) [3]. This system was tested on a large number of patients, including long-term follow-ups and is considered an outstanding success.In recent years, an excellent and important addition to microscopy and digital imaging has been developed for microscopes that are used to convert stained tissue slides into whole slide digital images. This allows for more efficient computer-based viewing and analysis of histopathology. Early diagnosis and treatment are required, to avoid the enlargement of cancer cells in the prostate gland and control the spreading of more aggressive tumors to other parts of the body.The digital pathology field has grown dramatically over recent years, largely due to technological advancements in image processing and machine learning algorithms, and increases in computational power. As part of this field, many methods have been proposed for automatic histopathological image analysis and classification. In this paper, color segmentation, based...

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Section: One-shot Classification Binary Classification Groupscontrasting

confidence: 53%

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2019

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“…Moreover, we observed high performance for a relatively small sample size (relative to the size of machine learning data sets). Few groups have attempted to develop deep learning algorithms for the diagnosis and/or Gleason grading of prostate cancer, almost all using prostatectomy specimens, with modest performance reported [9][10][11][12][13][14]. Two groups used tissue microarrays derived from radical prostatectomy specimens to develop patch-based deep learning algorithms for prostate cancer diagnosis and Gleason grading [11,13].…”

Section: Discussionmentioning

confidence: 99%

“…Few groups have attempted to develop deep learning algorithms for the diagnosis and/or Gleason grading of prostate cancer, almost all using prostatectomy specimens, with modest performance reported [9][10][11][12][13][14]. Two groups used tissue microarrays derived from radical prostatectomy specimens to develop patch-based deep learning algorithms for prostate cancer diagnosis and Gleason grading [11,13]. Nir et al [13] reported accuracy of 92% for classification of benign versus malignant and 78% for classification of benign versus low-grade versus high-grade (Gleason 4-5) cancer.…”

Section: Discussionmentioning

confidence: 99%

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Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study

Kott

Linsley

Amin

et al. 2021

European Urology Focus

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Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images

et al. 2019

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Importance Proper evaluation of the performance of artificial intelligence techniques in the analysis of digitized medical images is paramount for the adoption of such techniques by the medical community and regulatory agencies. Objectives To compare several cross-validation (CV) approaches to evaluate the performance of a classifier for automatic grading of prostate cancer in digitized histopathologic images and compare the performance of the classifier when trained using data from 1 expert and multiple experts. Design, Setting, and Participants This quality improvement study used tissue microarray data (333 cores) from 231 patients who underwent radical prostatectomy at the Vancouver General Hospital between June 27, 1997, and June 7, 2011. Digitized images of tissue cores were annotated by 6 pathologists for 4 classes (benign and Gleason grades 3, 4, and 5) between December 12, 2016, and October 5, 2017. Patches of 192 µm 2 were extracted from these images. There was no overlap between patches. A deep learning classifier based on convolutional neural networks was trained to predict a class label from among the 4 classes (benign and Gleason grades 3, 4, and 5) for each image patch. The classification performance was evaluated in leave-patches-out CV, leave-cores-out CV, and leave-patients-out 20-fold CV. The analysis was performed between November 15, 2018, and January 1, 2019. Main Outcomes and Measures The classifier performance was evaluated by its accuracy, sensitivity, and specificity in detection of cancer (benign vs cancer) and in low-grade vs high-grade differentiation (Gleason grade 3 vs grades 4-5). The statistical significance analysis was performed using the McNemar test. The agreement level between pathologists and the classifier was quantified using a quadratic-weighted κ statistic. Results On 333 tissue microarray cores from 231 participants with prostate cancer (mean [SD] age, 63.2 [6.3] years), 20-fold leave-patches-out CV resulted in mean (SD) accuracy of 97.8% (1.2%), sensitivity of 98.5% (1.0%), and specificity of 97.5% (1.2%) for classifying benign patches vs cancerous patches. By contrast, 20-fold leave-patients-out CV resulted in mean (SD) accuracy of 85.8% (4.3%), sensitivity of 86.3% (4.1%), and specificity of 85.5% (7.2%). Similarly, 20-fold leave-cores-out CV resulted in mean (SD) accuracy of 86.7% (3.7%), sensitivity of 87.2% (4.0%), and specificity of 87.7% (5.5%). Results of McNemar tests showed that the leave-patches-out CV accuracy, sensitivity, and specificity were significantly higher than those for both leave-patients-out CV and leave-cores-out CV. Similar results were observed for classifying low-grade cancer vs high-grade cancer. When trained on a single expert, the overall agreement in grading between pathologists and the classifier ranged from 0.38 to 0.58; when trained using the majority vote among all experts, it ...

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Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts

Cited by 144 publications

References 47 publications

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

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

Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study

Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images

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