Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Search Strategy
2.2. Study Selection
2.3. Inclusion and Exclusion Criteria
2.4. Data Extraction
3. Results
3.1. Qualitative Synthesis of the Included Studies
3.2. Study Characteristics
3.3. Outcome Measures
3.4. Risk of Bias Assessment and Applicability Concern
3.5. Assessment of the Strength of Evidence
4. Discussion
4.1. Effectiveness of AI in the Diagnosis of Oral Cancer
4.2. Effectiveness of AI in Differentiating Normal from Malignant Regions
4.3. Effectiveness of AI in Early Diagnosis of OC
4.4. Effectiveness of AI in Predicting Survival of OC Patients
4.5. Effectiveness of AI in the Grading of OC
5. Future Perspectives and Limitations
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Research Question | What is the Performance of the Artificial Intelligence Models That Have Been Widely Used in Oral Cancer Detection, Diagnosis, Classification, and Prediction Using Histopathological Images? |
---|---|
Population | Patients who underwent investigations for oral cancer (histological images). |
Intervention | AI-based models designed for oral cancer diagnosis, classification, and prediction of prognosis. |
Comparison | Expert opinions and reference standards. |
Outcome | Measurable or predictive outcomes, such as accuracy, sensitivity, specificity, precision, recall, receiver operating characteristic curve (ROC), area under the curve (AUC), statistical significance, F1 scores, positive predictive value (PPV), negative predictive value (NPV), discrete wavelet transform (DWT), local binary pattern (LBP), fuzzy color histogram (FCH), gray level co-occurrence matrix (GLCM), mean intersection-over-union (mIOU), Dice coefficient, and Jaccard index. |
Sl No | Authors | Year of Publication | Study Design | Algorithm Architecture | Objective of the Study | No. of Images/Photographs for Testing | Study Factor | Modality | Comparison if any | Evaluation Accuracy/Average Accuracy/Statistical Significance | Results (+) Effective, (−) Non-Effective (N) Neutral | Outcomes | Authors’ Suggestions/Conclusions |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Das DK et al. [22] | 2015 | Observational study | CNN | To develop a computer-assisted quantitative microscopic methodology for automated identification of keratinization and keratin pearl areas from in situ oral histological images | 10 OSCC patients’ oral histological slides | Diagnosis of OSCC | Histopathological images | Manual experts | 95.08% segmentation accuracy | (+) Effective | This provides a computer-aided diagnostic framework for microscopic image-based OSCC diagnosis, which can assist clinicians/pathologists for rapid evaluation. | The proposed methodology would be able to provide more robust performance when the sample size is large and can also be recommended as one of the tele-pathology applications. |
2 | Hameed KA et al. [23] | 2016 | Observational study | CNN | To develop automatic IHC scoring of p53-immunostained tissue images of oral cancer | 400 sub-regions of tissue images | Diagnosis of OSCC | Histopathological images | State-of-the-art methods, including intensity and texture features (manual) | Classification accuracy of 96.09% achieved by the proposed method for LDA classifiers | (+) Effective | The automatic scoring methods presented have high potential in IHC image analysis. | It helps the pathologist during the diagnostic and prognostic evaluation of oral cancer. |
3 | Deif MA et al. [24] | 2022 | Observational study | CNN | To diagnose OSCC using deep neural networks | Histopathological images of 230 individuals | Diagnosis of OSCC | Histopathological images | VGG16, AlexNet, ResNet50, and Inception V3 | Accuracy of 96.3% was obtained when using Inception V3 with BPSO. | (+) Effective | Best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. | This approach significantly contributes to improve the diagnostic efficiency of OCSCC patients while reducing diagnostic costs. |
4 | Yang SY et al. [25] | 2022 | Observational study | CNN | To develop a custom-made deep learning model to assist pathologists in detecting OSCC from histopathology images | 2025 images | Diagnosis of OSCC | Histopathological images | 3 junior pathologists, 3 senior pathologists, and 1 chief pathologist | Sensitivity of 0.98, specificity of 0.92, positive predictive value of 0.924, negative predictive value of 0.978, and F1 score of 0.951 | (+) Effective | The results demonstrated that the automated deep learning method could evaluate OSCC approximately 249 times faster than a junior pathologist. | These findings indicate that deep learning can improve the accuracy and speed of OSCC diagnosis from histopathology images. |
5 | Das DK et al. [26] | 2018 | Observational study | CNN | To identify clinically relevant regions from oral tissue histological images for OSCC diagnosis | 42 tissue slides | Computer-aided diagnosis and screening of oral cancers | Histopathological images | Gabor filter-trained random forest | Epithelial layer segmentation: 98.42% segmentation accuracy, 97.76% sensitivity, 90.63% Jaccard index, and 95.03% Dice coefficient. Keratin pearls: 98.05% segmentation accuracy, 71.87% Jaccard index, and 75.19% Dice coefficient. | (+) Effective | Proposed approach is good enough to extract epithelial, subepithelial, and keratin regions from oral histological images. | Segmentation of epithelial and subepithelial layers and detection of keratin pearls can be utilized for oral precancerous screening and OSCC grading, respectively. |
6 | Das DK et al. [27] | 2019 | Observational study | CNN | To develop a two-stage computational pipeline for automatic detection of nucleus and its segmentation from oral histology images | 42 tissue slides | Computer-aided diagnosis of OSCC | Histopathological images | Chan–Vese model | 94.22% Dice coefficient, 89.38% Jaccard index, 88.87% recall, and 82.03% precision | (+) Effective | The proposed segmentation methodology performed well, with 94.22% Dice coefficient, 89.38% Jaccard index, 97.56% precision, and 91.58% recall. | This is the first attempt on oral tissue histology image computation for joint nucleus detection and segmentation to diagnose OSCC. |
7 | Yoshizawa K et al. [28] | 2022 | Observational study | CNN | To determine the mode of invasion based on digital images of the invasive front of an OSCC. | 101 digitized photographic images | Diagnosis of OSCC | Histopathological images | Yamamoto–Kohama grades (1, 2, 3, 4C, 4D) determined by a human oral and maxillofacial surgeon | F-measure value of 87% | (+) Effective | These results suggest that the output of the classifier was very similar to the judgments of the clinician. | This system may be valuable for diagnostic support to provide an accurate determination of the mode of invasion. |
8 | Rahman TY et al. [29] | 2019 | Observational study | CNN | To develop a CAD system for OSCC classification using textural features on real histopathologic images | 134 images with normal tissue and 135 images with malignant tissue | Differentiating normal and malignant | Histopathological images | Linear support vector machine (SVM) | 100% accuracy AUC = 0.92 | (+) Effective | The linear support vector machine classifier provided 100% accuracy for the automated diagnosis of oral cancer. | It can be used to assist clinicians in the rapid evaluation and differentiation of tumorous lesions and normal tissue. |
9 | Martino F et al. [30] | 2020 | Observational study | SSNs | To compare four different deep learning-based architectures for oral cancer segmentation | 188 images | Differentiating normal and malignant areas | Histopathological images | SegNet, U-Net, U-Net with VGG16 encoder, and U-Net with ResNet50 encoder | mIOU SegNet = 0.54 U-Net = 0.57 U-Net with VGG16 encoder = 0.62 U-Net with ResNet50 encoder = 0.63 | (+) Effective | The deeper network, U-Net modified with ResNet50 as the encoder, performed better than the original U-Net (having a more shallow encoder). | This will help those who work in generalist diagnostic centres, not specialized in the diagnosis of an infrequent but extremely lethal disease. |
10 | Das N et al. [31] | 2020 | Observational study | CNN | To classify OSCC into its four classes as per Broder’s system of histological grading | 156 slide images | Differentiation of malignant lesions in biopsy images | Histopathological images | Alexnet, Resnet-50, VGG 16, and VGG 19 | Accuracy of 97.5%. | (+) Effective | Highest classification accuracy of 92.15% was achieved with the Resnet-50 model. The proposed CNN model outperformed the transfer learning approaches, displaying an accuracy of 97.5%. | It can be concluded that the proposed CNN-based multi-class grading method of OSCC could be used for the diagnosis of patients with OSCC. |
11 | Fraz MM et al. [32] | 2020 | Observational study | CNN | To propose a deep network for simultaneous segmentation of microvessels and nerves in routinely used H&E-stained histology images | 7780 images | Differentiating normal from malignant areas | Histopathological images | FCN-8, U-Net, Segnet, and DeepLabv3 | Accuracies of 96.3% and 97.05% for nerves and blood vessels | (+) Effective | The proposed network outperformed the current deep neural networks used for semantic segmentation. | The proposed network also provides robust segmentation performance when applied to the full digital whole slide image. |
12 | Rahman TY et al. [33] | 2021 | Observational study | CNN | To propose an automated efficient computer-aided system to distinguish normal from malignant OSCC categories | 42 slides | Classification of cell nuclei into normal and malignant categories | Histopathological images | Decision tree classifier, SVM, and logistic regression | 99.4% accuracy using decision tree classifier, 100% accuracy using both SVM and logistic regression, and 100% accuracy using SVM | (+) Effective | The in-depth analysis showed SVM and linear discriminant classifiers provided the best results for texture and color features, respectively. | This system is fast, cost-effective, and accurate. Hence, physicians can use it in their daily clinical screening as an assistant diagnostic tool. |
13 | Amin I et al. [34] | 2021 | Observational study | CNN | To propose an automated classification of cancerous oral histo pathological images | 290 normal and 934 cancerous oral histo pathological images | Differentiating normal and malignant areas | Histopathological images | VGG16, InceptionV3, and Resnet50 | 96.66% accuracy, 95.16% precision, 98.33% recall, and 95.00% specificity; concatenated model AUC = 0.997 | (+) Effective | The concatenated model yielded the best results and outperformed the individual models. | These results demonstrate that the concatenated model can effectively replace the use of a single DL architecture. |
14 | Panigrahi S et al. [35] | 2022 | Observational study | CNN | To propose three ResNet architectures for the multistage classification of OSCC into benign and malignant | 400 image patches | Differentiating normal from malignant | Histopathological images | ResNet-based model | 97.59% accuracy | (+) Effective | The Optimal ResNet model (ResNet13-A) was chosen as the best model, which is an automated computer-aided method to obtain high-performance results with less computational complexity and small datasets. | The proposed ResNet model is an efficient model for detecting multistage oral cancer, and it can be utilized as a diagnostic tool to help physicians in daily clinical screening. |
15 | Panigrahi S et al. [36] | 2022 | Observational study | Capsule network | To classify oral cancer using a deep learning technique known as capsule network to discriminate between cancerous and non-cancerous images | 82 malignant and 68 benign images | Differentiating normal and malignant areas | Histopathological images | Regular CNN model | 97.78% sensitivity, 96.92% specificity, and 97.35% accuracy | (+) Effective | Capsule networks have better capabilities in capturing the pose information and spatial relationship and can better discriminate between cancerous and non-cancerous images compared to the CNN model. | The proposed system can be extended to classify the different stages of oral cancer in the future. |
16 | Fati SM et al. [37] | 2022 | Observational study | CNN ANN | To achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features | 5192 images | Early diagnosis of OSCC | Histopathological images | CNN models (AlexNet and ResNet-18) and SVM algorithm ANN models (ResNet-18, DWT, LBP, FCH, and GLCM) | ResNet-18, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.3%, specificity of 99.42%, sensitivity of 99.26%, precision of 99.71%, and AUC of 99.39% | (+) Effective | The ANN algorithm based on hybrid features yielded promising results in histological image diagnostics for early diagnosis of OSCC. | This study highlights the tremendous potential of artificial intelligence techniques to diagnose OSCC and increase cure rates among patients. |
17 | Lu C et al. [38] | 2017 | Observational study | CNN | To construct an oral cavity histomorphometric-based image classifier for risk stratification of OSCC patients | Slides from 115 patients | To risk stratify patients for disease-specific survival | Histopathological image | Standard clinical and pathologic parameters | ROC = 0.72, hazard ratio = 11.02 | (+) Effective | Patients with positive results were 11 times more likely to develop disease recurrence and die from it. | Quantitative histomorphometric features of local nuclear architecture derived from digitized H&E slides of OSCCs are independently predictive of patient survival |
18 | Shaban M et al. [39] | 2019 | Observational study | CNN | To obtain an automated TIL abundance score and explore its prognostic significance for disease-free survival (DFS) of OSCC patients | Slides from 70 patients | Prognostic significance for disease-free survival of OSCC patients | Histopathological images | Manual TIL score | High accuracy of 96.31% | (+) Effective | The automated TILAb score had a significantly higher prognostic value than the manual TIL score (p = 0.0024). | The TILAb score can be used as an independent prognostic parameter in OSCC patients. |
19 | Anuradha K et al. [40] | 2017 | Observational study | CNN | To histologically grade oral tumors using fuzzy cognitive map (FCM) | Histopathological images from 123 cases | Computer-aided grading of oral tumors | Histopathological images | Active Hebbian learning (AHL) | Accuracy of 90.58% for oral tumors of low grade and 89.47% of high grade | (+) Effective | The proposed method used an FCM grading model to categorize tumor cases into low grade and high grade. In addition, to improve the values, an active Hebbian learning algorithm was used. | Features can be extracted using feature extraction methods and can be given as input to the FCM. |
Outcome | Inconsistency | Indirectness | Imprecision | Risk of Bias | Publication Bias | Strength of Evidence |
---|---|---|---|---|---|---|
Application of AI in the diagnosis of OSCC [22,23,24,25,26,27,28] | Not Present | Not Present | Not Present | Present | Not Present | ⨁⨁⨁◯ |
Application of AI in differentiating between normal and malignant conditions [29,30,31,32,33,34,35,36] | Not Present | Not Present | Not Present | Not Present | Not Present | ⨁⨁⨁⨁ |
Application of AI in the early diagnosis of OSCC [37] | Not Present | Not Present | Not Present | Not Present | Not Present | ⨁⨁⨁⨁ |
Application of AI in predicting survival of OSCC patients [38,39] | Not Present | Not Present | Not Present | Present | Not Present | ⨁⨁⨁◯ |
Application of AI in severity grading of OSCC [40] | Not Present | Not Present | Not Present | Not Present | Not Present | ⨁⨁⨁⨁ |
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Khanagar, S.B.; Alkadi, L.; Alghilan, M.A.; Kalagi, S.; Awawdeh, M.; Bijai, L.K.; Vishwanathaiah, S.; Aldhebaib, A.; Singh, O.G. Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review. Biomedicines 2023, 11, 1612. https://doi.org/10.3390/biomedicines11061612
Khanagar SB, Alkadi L, Alghilan MA, Kalagi S, Awawdeh M, Bijai LK, Vishwanathaiah S, Aldhebaib A, Singh OG. Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review. Biomedicines. 2023; 11(6):1612. https://doi.org/10.3390/biomedicines11061612
Chicago/Turabian StyleKhanagar, Sanjeev B., Lubna Alkadi, Maryam A. Alghilan, Sara Kalagi, Mohammed Awawdeh, Lalitytha Kumar Bijai, Satish Vishwanathaiah, Ali Aldhebaib, and Oinam Gokulchandra Singh. 2023. "Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review" Biomedicines 11, no. 6: 1612. https://doi.org/10.3390/biomedicines11061612