Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. Data Acquisition
- Grade 3: Gleason score 4 + 3 = 7. Distinctly infiltrative margin.
- Grade 4: Gleason score 4 + 4 = 8. Irregular masses of neoplastic glands. Cancer cells have lost their ability to form glands.
- Grade 5: Gleason score 4 + 5, 5 + 4, or 5 + 5 = 9 or 10. Only occasional gland formation. Sheets of cancer cells throughout the tissue.
3.2. Research Pipeline
3.2.1. Image Preprocessing
3.2.2. Nuclear Segmentation of Cancer Cells
3.2.3. Cluster Analysis
- Create an adjacent grid matrix using the input image.
- Calculate the total grid numbers in the rows and columns.
- Generate a graph from an adjacent matrix, which must contain the minimum and maximum weights of all vertices.
- Create an MST-set to track all vertices.
- Find a minimum weight for all vertices in the input graph.
- Assign that weight to the first vertex.
- As the MST-set does not include all vertices:
- Select a vertex u not present in the MST-set that has the minimum weight;
- Add u to the MST-set;
- Update the minimum weights of all vertices adjacent to u by iterating through all adjacent vertices. For every adjacent vertex v, if the weight of edge u-v is less than the previous key value of v, update that minimum weight;
- Iterate step 7 until the MST is complete.
3.2.4. Feature Extraction and Selection
3.2.5. AI Classification
4. Experimental Results and Discussion
- The size of the image datasets was too small to perform cluster analysis and apply deep learning-based algorithms, such as graph convolution neural network (GCNN) and LSTM network, and the study could be improved by increasing the data samples.
- Cell nuclei segmentation using traditional-based algorithms is a major issue, but we can improve this problem gradually by performing cell-level analysis applying different state-of-the-art methods.
- We know that unsupervised classification is very important in the real-world environment, the classifiers used in our study performed well but did not achieve astounding results compared to supervised classification. Therefore, we can improve this problem by analyzing the feature dissimilarities between the PCa grades.
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Author | Techniques | Classification Types | Description and Performance |
---|---|---|---|
Uthappa et al., 2019 [13] | CNN-based texture analysis | Multiclass (grade 2, 3, 4, and 5) | Developed a hybrid unified deep learning network to grade the PCa and achieved an accuracy of 98.0% |
Khouzani et al., 2003 [14] | Handcrafted-based texture analysis | Multiclass (grade 2, 3, 4, and 5) | Calculated energy and entropy features of multiwavelet coefficients of the image and used ML classifier to classify each image to the appropriate grade. They achieved an accuracy of 97.0% |
Kwak et al., 2017 [15] | CNN-based texture and nuclear architectural analysis | Binary class (benign and cancer) | The author presented a CNN approach to identify PCa. In addition, they extracted handcrafted nuclear architecture features and performed ML classification. The performance of their CNNs (0.95 AUC) was significantly better than that of other ML algorithms |
Linkon et al., 2021 [16] | Different techniques related to PCa detection and histopathology image analysis have been discussed | N/A | The author discussed recent advances in CAD systems using DL for automatic detection and recognition. In addition, they discussed the current state and existing techniques as well as unique insights in PCa detection and described research findings, current limitations, and future scope for research |
Wang et al., 2020 [17] | Morphological, texture, and contrastive predictive coding feature analysis | Binary class (score 3 + 3 and 3 + 4) | The author proposed a weakly supervised approach for grade classification in tissue micro-arrays using graph CNN. An accuracy of 88.6% and an AUC of 0.96 were achieved using their proposed model |
Bhattacharjee et al., 2019 [18] | Morphological analysis | Binary class (benign vs. malignant, grade 3 vs. grade 4, 5, and grade 4 vs. grade 5) Multiclass (benign, grade 3, grade 4, and grade 5) | The author used histopathology images to perform morphological analysis of cell nucleus and lumen and carried out multiclass and binary classification. The best accuracy of 92.5% was achieved for binary classification (grade 4 vs. grade 5 using support vector machine classifier |
Bhattacharjee et al., 2020 [19] | Handcrafted and non-handcrafted feature analysis using AI techniques | Binary class (benign vs. malignant) | The author introduced two lightweight CNN models for histopathology image classification and performed a comparative analysis with other state-of-the-art models. An accuracy of 94.0% was achieved using the proposed DL model |
Nir et al., 2018 [20] | Glandular-, nuclear-, and image-based feature analysis | Binary class (benign vs. all grades) and (grade 3 vs. grade 4, 5) | Proposed some novel features based on intra- and inter-nuclei properties for classification using ML and DL algorithms and achieved the best accuracy of 91.6% for benign vs. all grades using linear discriminant analysis |
Ali et al., 2013 [21] | Morphological and architectural feature analysis from cell cluster graph | Binary class (no recurrence vs. recurrence) | The author defined cells clusters as a node and constructed a novel graph called Cell Cluster Graph (CCG). In addition, they extracted global and local features from the CCG that best capture the morphology of the tumor. A randomized three-fold cross-validation was applied via support vector machine classifier and achieved an accuracy of 83.1% |
Kim et al., 2021 [22] | Texture analysis using DL and ML techniques | Binary class (benign vs. malignant) and (low- vs. high-grade) | The author used DL (long short-term memory network) and ML (logistic regression, bagging tree, boosting tree, and support vector machine) techniques to classify dual-channel tissue features extracted from hematoxylin and eosin tissue images |
Features | FS | IG | ANOVA | RFE | PI | Boruta | Votes | Select/Reject | |
---|---|---|---|---|---|---|---|---|---|
total intra-cluster total MST distance | True | True | True | True | True | True | True | 7 | Select |
total intra-cluster nucleus to nucleus maximum distance | True | True | True | True | True | True | True | 7 | Select |
inter-cluster centroid to centroid total distance | True | False | True | True | True | True | True | 6 | Select |
inter-cluster total MST distance | True | True | True | True | True | False | True | 6 | Select |
number of clusters | True | True | True | True | True | False | True | 6 | Select |
total intra-cluster maximum MST distance | True | True | True | True | True | False | True | 6 | Select |
average intra-cluster nucleus to nucleus minimum distance | False | True | True | True | True | False | True | 5 | Select |
average intra-cluster nucleus to nucleus maximum distance | False | True | True | True | True | False | True | 5 | Select |
average intra-cluster maximum MST distance | False | True | True | True | True | False | True | 5 | Select |
average cluster area | True | True | False | False | True | True | True | 5 | Select |
total intra-cluster nucleus to nucleus total distance | True | False | False | True | True | True | True | 5 | Select |
total intra-cluster minimum MST distance | True | True | True | True | False | False | True | 5 | Select |
total intra-cluster nucleus to nucleus minimum distance | True | True | True | True | False | False | True | 5 | Select |
inter-cluster maximum MST distance | True | True | False | False | True | False | True | 4 | Select |
average intra-cluster total MST distance | False | True | True | False | True | False | True | 4 | Select |
average intra-cluster minimum MST distance | False | True | True | True | False | False | True | 4 | Select |
total cluster area | True | False | False | False | False | True | True | 3 | Reject |
inter-cluster average MST distance | False | False | True | True | False | False | True | 3 | Reject |
average intra-cluster nucleus to nucleus average distance | False | False | True | True | False | False | True | 3 | Reject |
inter-cluster centroid to centroid average distance | False | True | False | False | True | False | False | 2 | Reject |
minimum area of cluster | True | False | False | False | True | False | False | 2 | Reject |
average intra-cluster nucleus to nucleus total distance | True | False | False | False | False | False | True | 2 | Reject |
inter-cluster centroid to centroid minimum distance | False | False | False | False | False | False | True | 1 | Reject |
inter-cluster centroid to centroid maximum distance | False | False | False | False | False | False | True | 1 | Reject |
maximum area of cluster | True | False | False | False | False | False | False | 1 | Reject |
inter-cluster minimum MST distance | False | False | False | False | False | False | True | 1 | Reject |
(A) Supervised Ensemble Classification—Modern AI Techniques | ||||
---|---|---|---|---|
Multiclass Classification (Grade 3 vs. Grade 4 vs. Grade 5) | ||||
Test Split | Accuracy | Precision | Recall | F1-Score |
Split 1 | 97.2% | 97.3% | 97.3% | 97.3% |
Split 2 | 91.7% | 92.0% | 91.7% | 91.7% |
Split 3 | 97.2% | 97.3% | 97.3% | 97.3% |
Split 4 | 94.4% | 94.7% | 94.7% | 94.7% |
Split 5 | 91.7% | 91.7% | 91.7% | 91.7% |
Average Split | 94.4% | 94.7% | 94.3% | 94.7% |
Binary Classification (Grade 3 vs. Grade 5) | ||||
Test Split | Accuracy | Precision | Recall | F1-Score |
Split 1 | 91.7% | 91.6 | 0.916 | 0.916 |
Split 2 | 100% | 100% | 100% | 100% |
Split 3 | 95.8% | 96.2% | 95.8% | 95.9% |
Split 4 | 95.8% | 96.2% | 95.8% | 95.9% |
Split 5 | 91.7% | 92.8% | 91.6% | 92.2% |
Average Split | 95.0% | 95.0% | 95.0% | 95.0% |
(B) K-Medoids Unsupervised Classification—Traditional AI Technique | ||||
Multiclass Classification (Grade 3 vs. Grade 4 vs. Grade 5) | ||||
Data Split | Accuracy | Precision | Recall | F1-Score |
Split 1 | 86.1% | 87.0% | 86.0% | 86.3% |
Split 2 | 92.3% | 92.7% | 92.0% | 92.3% |
Split 3 | 86.7% | 88.3% | 86.7% | 87.0% |
Split 4 | 88.3% | 88.3% | 88.3% | 88.0% |
Split 5 | 91.6% | 91.7% | 91.7% | 91.7% |
Average Split | 88.5% | 89.7% | 88.3% | 88.7% |
Binary Classification (Grade 3 vs. Grade 5) | ||||
Data Split | Accuracy | Precision | Recall | F1-Score |
Split 1 | 81.7% | 82.0% | 81.5% | 81.5% |
Split 2 | 96.7% | 96.5% | 96.5% | 97.0% |
Split 3 | 89.2% | 89.5% | 89.0% | 89.0% |
Split 4 | 86.7% | 87.5% | 86.5% | 86.5% |
Split 5 | 93.3% | 93.5% | 93.5% | 93.5% |
Average Split | 88.3% | 88.5% | 88.5% | 88.5% |
Authors | Methods | Classification Type | Performance | |
---|---|---|---|---|
Uthappa et al., 2019 [13] | Hybrid DL | Multiclass (grade 2, 3, 4, and 5) | 98.0% (Accuracy) | |
Khouzani et al., 2003 [14] | ML | Multiclass (grade 2, 3, 4, and 5) | 97.0% (Accuracy) | |
Kwak et al., 2017 [15] | CNN | Binary (benign and cancer) | 0.95 (AUC) | |
Wang et al., 2020 [16] | Graph CNN | Binary (score 3 + 3 and 3 + 4) | 88.6% (Accuracy) | |
Bhattacharjee et al., 2019 [18] | ML | Binary | benign vs. malignant | 88.7% (Accuracy) |
grade 3 vs. grade 4, 5 | 85.0% (Accuracy) | |||
grade 4 vs. grade 5 | 92.5% (Accuracy) | |||
Bhattacharjee et al., 2020 [19] | DL | Binary (benign vs. malignant) | 94.0% (Accuracy) | |
Nir et al., 2018 [20] | ML | Binary | benign vs. all grades | 88.5% (Accuracy) |
grade 3 vs. grade 4, 5 | 73.8% (Accuracy) | |||
Ali et al., 2013 [21] | ML | Binary (no recurrence vs. recurrence) | 83.1% (Accuracy) | |
Kim et al., 2021 [22] | DL | Binary | benign vs. malignant | 98.6% (Accuracy) |
low- vs. high-grade | 93.6% (Accuracy) | |||
Proposed | ML | Binary (Split 2) | grade 3 vs. grade 5 | 100% (Accuracy) |
Multiclass (Split 1) | grade 3 vs. grade 4 vs. grade 5 | 97.2% (Accuracy) | ||
K-Medoids Clustering | Binary (Split 2) | grade 3 vs. grade 5 | 96.7% (Accuracy) | |
Multiclass (Split 2) | grade 3 vs. grade 4 vs. grade 5 | 92.3% (Accuracy) |
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Bhattacharjee, S.; Ikromjanov, K.; Carole, K.S.; Madusanka, N.; Cho, N.-H.; Hwang, Y.-B.; Sumon, R.I.; Kim, H.-C.; Choi, H.-K. Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques. Diagnostics 2022, 12, 15. https://doi.org/10.3390/diagnostics12010015
Bhattacharjee S, Ikromjanov K, Carole KS, Madusanka N, Cho N-H, Hwang Y-B, Sumon RI, Kim H-C, Choi H-K. Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques. Diagnostics. 2022; 12(1):15. https://doi.org/10.3390/diagnostics12010015
Chicago/Turabian StyleBhattacharjee, Subrata, Kobiljon Ikromjanov, Kouayep Sonia Carole, Nuwan Madusanka, Nam-Hoon Cho, Yeong-Byn Hwang, Rashadul Islam Sumon, Hee-Cheol Kim, and Heung-Kook Choi. 2022. "Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques" Diagnostics 12, no. 1: 15. https://doi.org/10.3390/diagnostics12010015
APA StyleBhattacharjee, S., Ikromjanov, K., Carole, K. S., Madusanka, N., Cho, N. -H., Hwang, Y. -B., Sumon, R. I., Kim, H. -C., & Choi, H. -K. (2022). Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques. Diagnostics, 12(1), 15. https://doi.org/10.3390/diagnostics12010015