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Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers?
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Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers? Neal Shahidi MD FRCPC , Michael J. Bourke MBBS FRACP PII: DOI: Reference:
S1096-2883(19)30078-6 https://doi.org/10.1016/j.tgie.2019.150639 YTGIE 150639
To appear in:
Techniques in Gastrointestinal Endoscopy
Received date: Accepted date:
25 July 2019 6 August 2019
Please cite this article as: Neal Shahidi MD FRCPC , Michael J. Bourke MBBS FRACP , Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers?, Techniques in Gastrointestinal Endoscopy (2019), doi: https://doi.org/10.1016/j.tgie.2019.150639
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers?
Neal Shahidi MD FRCPC1-3, Michael J. Bourke MBBS FRACP1,2 1. Westmead Hospital, Department of Gastroenterology and Hepatology, Sydney, New South Wales, Australia 2. University of Sydney, Westmead Clinical School, Sydney, New South Wales, Australia 3. University of British Columbia, Department of Medicine, Vancouver, British Columbia, Canada
Corresponding Author Michael J. Bourke MBBS FRACP Clinical Professor of Medicine Westmead Hospital, Department of Gastroenterology and Hepatology University of Sydney, Westmead Clinical School Suite 106a 151-155 Hawkesbury Road Sydney, New South Wales, Australia 2145 Email: [email protected] Fax: +61 2 9845 5637
Abstract Endoscopic tissue resection is a rapidly evolving field. En bloc resection techniques, specifically endoscopic submucosal dissection (ESD), allow for organsparing curative endoscopic resection for early gastrointestinal cancers. However, using current techniques to quantify depth of invasion, it remains difficult for endoscopists to reliably select optimal ESD candidates. In this review, we highlight that artificial intelligence platforms can now quantify the depth of invasion of esophageal, gastric and colorectal neoplasia. While real-time performance evaluation is needed, this represents a significant advancement in endoscopic tissue resection and carries the potential to provide real-time guidance for selecting the appropriate tissue resection technique.
Highlights
En bloc endoscopic resection techniques have the ability to cure early gastrointestinal cancers of the esophagus, stomach and colorectum
Optical evaluation is the primary method for identifying endoscopically curable gastrointestinal cancers, with modest performance characteristics
Computer-aided diagnosis platforms can quantify lesion depth of invasion and therefore have the potential to improve both patient outcomes and resource utilization by optimizing tissue resection technique selection
Key Words: Adenoma, Endoscopy, Colonoscopy, Polyp
Abbreviations AC; Adenocarcinoma AI; Artificial intelligence CADe; Computer-aided detection CADx; Computer-aided diagnosis EGC; Early gastric cancer EMR; Endoscopic mucosal resection ESGE; European Society of Gastrointestinal Endoscopy ESD; Endoscopic submucosal dissection IPCL; Intra-papillary capillary loops JGES; Japanese Gastroenterological Endoscopy Society JES; Japan Esophageal Society M; Mucosa NBI; Narrow-band imaging NPV; Negative predictive value PPV; Positive predictive value SM; Submucosa SCC; Squamous cell cancer
Introduction With the integration of deep learning methodology, artificial intelligence (AI) has rapidly proliferated throughout medicine. This is readily apparent in endoscopy, with computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems for gastrointestinal lesions (1-3). Computer-aided diagnosis has the potential to differentiate lesion histopathology, but also to quantify the depth of invasion of early gastrointestinal cancers of the esophagus, stomach, and colorectum (4-9). Early gastrointestinal cancers, whether confined to the mucosa or superficially invading into the submucosa carry a low risk of lymph node positivity. In the absence of other high-risk features (poor differentiation; lymphovascular invasion; tumor budding), R0 resection is considered curative (10-12). Given the morbidity and mortality of gastrointestinal surgery (13-15), most notably esophagectomy, gastrectomy and distal colorectal surgery, organ-sparing curative endoscopic resection is a ground-breaking advancement in the field of endoscopic tissue resection. Allowing for this advancement is the ability to perform safe, effective and sizeindependent en bloc removal of gastrointestinal lesions by endoscopic submucosal dissection (ESD) (16, 17). Utilizing an electrosurgical knife within the fluid-expanded submucosal plane, ESD is a meticulous cap-assisted tissue resection technique allowing for precise control over the deep and lateral margins; thereby empowering the endoscopist to perform radical excision without surgery. However, it is technically demanding, and is associated with a heightened risk of adverse events including
bleeding and perforation (18-20). Therefore, it is predominantly advocated for the removal of early gastrointestinal cancers as benign lesions, especially in the colorectum (21-25), are effectively and efficiently removed by endoscopic mucosal resection (EMR). To delineate a selective resection algorithm, incorporating EMR, ESD, and surgery, the endoscopist has to differentiate between benign lesions, early cancers amenable to curative endoscopic resection and deep cancers which should be referred directly to surgery. Biopsy, although commonly performed, is of limited clinical benefit. It is prone to false negativity due to sampling error and may complicate endoscopic resection by precipitating fibrosis (26, 27). Moreover, radiographic and endosonographic evaluations have limited ability to reliably differentiate clinically relevant depths of invasion and are prone to both under-staging and over-staging (28-31). Given the above limitations, real-time optical evaluation is used as the primary modality for delineating endoscopically curable disease. Using high-definition endoscopes with advanced imaging techniques (optical magnification, chromoendoscopy, virtual chromoendoscopy), pit pattern and vascular pattern changes consistent with invasive disease can be identified. However, optical evaluation is operator dependent and has modest performance characteristics in quantifying depth of invasion, even amongst expert endoscopists (32-35). Moreover, magnifying endoscopy and other advanced imaging techniques (confocal laser endomicroscopy, endocytoscopy) are not readily available worldwide. This has forced endoscopists to stratify the risk of invasive disease by lesion location, size, morphology and topography in an attempt to diagnose invisible or “covert” cancer (36).
Accurate computer-aided depth of invasion evaluation would be a transformative paradigm shift. By providing real-time guidance on selecting the appropriate resection technique, it carries the potential to define selective resection algorithms throughout the gastrointestinal tract. Therefore, we sought out to appraise the literature in this space.
Esophagus Histological Criteria for Curative Endoscopic Resection For squamous neoplasia, the frequency of lymph node positivity for M1 (intraepithelial) or M2 (lamina propria) disease is negligible. It increases to 8-18%, 11-53% and 30-54% for M3 (muscularis mucosae), SM1 (submucosa ≤ 200 μm) and ≥ SM2 (submucosa > 200 μm) disease, respectively (37-39). It is imperative to recognize that this data is commonly not stratified by the absence of high-risk features. In their absence, the frequency of lymph node positivity for M3-SM1 disease approximates the risk of M1-M2 disease (38). Concordantly, the European Society of Gastrointestinal Endoscopy (ESGE) recommends M1-M2 and M3-SM1 squamous neoplasia, without other high-risk features, as absolute and relative indications for curative endoscopic resection, respectively (11). Concerning Barrett’s neoplasia, in the absence of high-risk features, disease limited to the mucosa carries a limited risk of lymph node positivity (40, 41). This appears extendable to lesions confined to SM1 (submucosa ≤ 500 μm) (42), based on long-term follow-up data after endoscopic resection. Therefore, the ESGE considers mucosal disease and disease limited to SM1, without other high-risk features, as absolute and relative indications for curative endoscopic resection, respectively (11).
Depth of Invasion Platforms Three AI platforms (4-6) have automated depth of invasion analysis for squamous cell cancer (SCC) and adenocarcinoma (AC) of the esophagus. Utilizing a deep convolutional neural network, Horie and colleagues (4) evaluated the ability to differentiate early (T1) vs. advanced (T2-T4) esophageal SCC and AC (Figure 1). High-definition non-magnified white-light and narrow-banding imaging (NBI) images were used to create the training (397 SSC, 32 AC: 8428 images) and validation (41 SSC, 8 AC, 50 normal: 1118 images) datasets. Median lesion size in the validation dataset was 20mm (range 5-70mm). The diagnostic accuracy for differentiating early vs. advanced esophageal cancer was 98%. Diagnostic accuracy varied between SCC (99%) and AC (90%). Involving contributors from the study above, Nakagawa and colleagues (5), evaluated the performance of a deep convolutional neural network to differentiate depth of invasion amongst superficial squamous neoplasia. The platform was trained and validated using non-magnified and magnified white-light, NBI and iodinechromoendoscopy images (Training dataset: 804 SCC, 8660 non-magnified images, 5679 magnified images; Validation dataset: 155 SCC, 405 non-magnified images, 509 magnified images). The median lesion size in the validation dataset was 18mm (495mm). When differentiating M-SM1 disease vs. SM2-3 disease, the AI platform’s accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 91%, 90%, 96%, 99% and 64%, respectively. In comparison, the
accuracy, sensitivity, specificity, PPV and NPV of 16 board-certified experienced endoscopists were 90%, 90%, 88%, 98% and 66%, respectively. When differentiating mucosal (M) disease vs. submucosal (SM) disease, accuracy, sensitivity, specificity, PPV and NPV were 90%, 89%, 93%, 98% and 65%, respectively. Zhao et al. (6), evaluated the ability to automate the classification of intrapapillary capillary loops (IPCLs) in squamous neoplasia. Emanating from the subepithelium, IPCLs are capillary loops which are perpendicularly aligned from smooth branching vessels (35). Progressive variation in IPCL caliber and configuration correlates with depth of invasion and has been classified by the Japan Esophageal Society (JES)(43): type A (normal epithelium, inflammation, low grade intra-epithelial neoplasia), type B1 (high grade intra-epithelial neoplasia/M1-2 disease), type B2 (M3SM1 disease) and type B3 (≥ SM2 disease). High-definition magnified NBI images (207 type A, 970 type B1, 206 type B2) were evaluated. Utilizing a deep convolutional neural network, 3-fold cross validation was performed whereby 3 image groups were created, and 3 AI models were trained independently (training with 2 image groups, validation with remaining image group). Due to limited numbers of IPCL type B3, this grouping was excluded from analysis. Mean sensitivity, specificity, and accuracy were 87%, 84% and 89%, respectively. Automated performance was most similar to senior endoscopists (> 15 years experience: sensitivity 91%, specificity 94%, accuracy 92%), in comparison to mid-level endoscopists (10-15 years experience: sensitivity 79%, specificity 71%, accuracy 82%) or junior endoscopists (5-10 years experience: sensitivity 68%, specificity 76%, accuracy 73%). Automated IPCL classification accuracy was significantly higher than mid-level endoscopists and junior endoscopists (p < 0.001).
Stomach Histological Criteria for Curative Endoscopic Resection Histologic criteria for curative endoscopic resection of early gastric cancer (EGC) is stratified by lesion size, presence of ulceration, depth of invasion and lesion differentiation. As defined by the Japanese Gastroenterological Endoscopy Society (JGES), absolute criteria are well-differentiated, < 2 cm intramucosal (T1a) EGC without ulceration or lymphovascular invasion (10). The risk of lymph node positivity is effectively zero (44). Under expanded criteria: 1) intramucosal EGC, well-differentiated, ulcer negative, > 2 cm; 2) intramucosal EGC, well-differentiated, ulcer positive, < 3 cm; 3) intramucosal EGC, poorly-differentiated, ulcer negative, < 2 cm and 4) SM1 (≤ 500 μm) EGC, well-differentiated, ulcer negative, < 3 cm are considered curative. This is due to the low risk of lymph node positivity, as supported by a large series of 5265 EGCs which underwent gastrectomy (44).
Depth of Invasion Platforms Two AI platforms (7, 8) have automated depth of invasion assessment in gastric cancer. Kubota et al. (7), evaluated the ability to automate T-staging for 344 gastric cancers (mean size 40 mm; range 10 mm-240 mm). 902 pre-operative endoscopic images were evaluated (T1 448, T2 106, T3 149, T4 199). A multi-layer neural network was trained and validated using a 10-fold cross validation method. Overall, depth of
invasion accuracy was 65%. Stratified by T-stage, the diagnostic accuracies were 77%, 49%, 51% and 55% for T1, T2, T3 and T4, respectively. When evaluating T1a vs. T1b EGC accuracy was 69% and 64%, respectively. The PPVs were 80%, 42%, 51%, 56%, 69% and 68% for T1, T2, T3, T4, T1a and T1b, respectively. Zhu et al. (8) evaluated the ability to differentiate intramucosal-SM1 (≤ 500 μm) vs. ≥ SM2 (> 500 μm) using a deep convolutional neural network. Training (790 lesions) and validation (203 lesions) datasets using white-light images were used. For the training dataset, data augmentation was performed by rotating and flipping images, leading to an 8-fold increase in dataset size (6320 images). Automated sensitivity, specificity, accuracy, PPV and NPV were 77%, 96%, 89%, 90% and 89%, respectively. In comparison to 17 endoscopists with varying experience, sensitivity, specificity, accuracy, PPV and NPV were 72%, 88%, 63%, 56% and 91%. Significant differences in accuracy and specificity, favoring CADx, were identified.
Colorectum Histological Criteria for Curative Endoscopic Resection Within the colorectum, cancer is defined by invasion into the submucosa. This is because the mucosa does not have lymphatic drainage and therefore does not carry the potential for lymphatic or metastatic spread (45). The overall risk of lymph node positivity is 13% for T1 colorectal cancer. However, it decreases to 1.9% in welldifferentiated lesions, confined to SM1 (≤ 1000 μm), without lymphovascular invasion or tumor budding (46). Both the JGES (12) and the ESGE (11) utilize the above criteria for curative endoscopic resection.
Depth of Invasion Platform Takeda et al. (9), evaluated the ability of a machine learning platform to differentiate adenomatous lesions from invasive cancer (Figure 2). Endocytoscopy images were used from 375 lesions (mean size +/- standard deviation: adenoma 11 mm +/- 10 mm; invasive cancer: 31 mm +/- 14 mm). Endocytoscopy, performed after application of crystal violet and methylene blue dye, allows for x380 magnification and enables in vivo evaluation of both nuclei and gland lumens. A total of 5843 endocytoscopy images were evaluated, with 200 of them used for validation. Sensitivity, specificity, accuracy, PPV and NPV were 89%, 99%, 94%, 98% and 90%, respectively. High-confidence (≥ 90% probability of being correct) was achieved in 72%, and under these conditions, sensitivity, specificity, accuracy, PPV and NPV were 98%, 100%, 99%, 100% and 99%, respectively.
Limitations of Current Platforms Artificial intelligence depth of invasion platforms represent a critical advancement in the fields of optical evaluation and endoscopic tissue resection, with impressive performance outcomes. However, they do have limitations; as can be expected in this new and rapidly evolving space. Not all platforms differentiated clinically relevant depths of invasion. For squamous neoplasia and EGC, ESD is the recommended method for endoscopic resection. Therefore, delineating intramucosal-SM1 versus ≥ SM2 should be the primary
outcome. For Barrett’s neoplasia, it is important to differentiate between intramucosal verus SM1 and SM1 versus ≥ SM2. Endoscopic mucosal resection, is an established modality for the long-term treatment of Barrett’s neoplasia limited to the mucosal layer (47, 48). Concordantly, the ESGE recommends EMR as the preferred modality with ESD being considered for lesions > 15mm, bulky lesions, non-lifting lesions or lesions with a suspicion of superficial submucosal invasion. Within the colorectum, EMR is an effective, efficient, safe and durable method for managing colorectal lesions (21-25). Economic analyses show that a selective resection algorithm utilizing both EMR and ESD is the most cost-effective strategy (49). Therefore, delineating between benign lesions, superficial invasive cancer and deep invasive cancer would allow endoscopists to correctly select between EMR, ESD or direct referral to surgery, respectively. All platforms were trained and validated on retrospectively collected datasets, introducing the potential for selection bias. Moreover, all studies evaluated static images with the majority excluding poor quality images. Only Takeda and colleagues (9) purposefully included unclear images. This impairs the ability of these platforms to be representative of real-world performance and their ability to reflect future reproducibility in clinical practice. Future platforms or future refinement of the above platforms should utilize either prospectively collected videos or real-time evaluation. A central advantage of AI is the ability to eliminate operator-dependence increasing its universality. By utilizing technology which invariably requires skill to capture the relevant image, such as magnifying endoscopy and endocytoscopy, directly negates this advantage. Moreover, these technologies are not widely available and impair the applicability of these platforms.
Future Directions Mori and colleagues (50) have outlined an AI-specific pathway for the assimilation of CADe and CADx in endoscopy. This includes: 1) product development and feasibility studies; 2) clinical trials; 3) obtaining regulatory approval; and 4) establishing insurance reimbursement. Recently, Vinsard and colleagues (51) outlined quality assurance guidelines for future endoscopy-related AI research. Recommended features of future AI platforms include: 1) appropriate algorithm selection; 2) real-time performance; 3) appropriate output format; and 4) smooth incorporation with existing endoscopy technology. Combining these recommendations will ensure high-quality AI platforms are produced and are more readily incorporated into clinical practice. For depth of invasion solutions, it is critical that two audiences are targeted: 1) the general endoscopist and 2) the tissue resection specialist. For the former, AI strategies need to reliably identify which lesions are amenable to endoscopic resection vs. those that should be referred directly to surgery. For the endoscopic resection specialist, facilitating the selection of the appropriate resection technique carries the potential to optimize both patient outcomes and resource utilization. In conclusion, endoscopic tissue resection is a rapidly evolving field. Advances in optical evaluation and endoscopic resection techniques are allowing endoscopists to reliably identify and subsequently remove early gastrointestinal cancers effectively, efficiently and safely; thus, permitting organ-sparing curative endoscopic resection. Artificial intelligence carries the potential to revolutionize optical evaluation, and surpass
endoscopist’s optical limitations. It is therefore imperative that all endoscopists, even those that do not perform advanced tissue resection, be aware of the full array of therapeutic options for superficial gastrointestinal lesions. Moreover, all endoscopists should be aware of current optical evaluation classification systems as AI will likely emerge as a decision support tool, and the endoscopist will remain the ultimate decision maker.
Acknowledgements Neal Shahidi is supported by the University of British Columbia Clinician Investigator Program.
Conflicts of Interest None
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Figure Legends
Figure 1: Computer-aided diagnosis of esophageal cancer. (Reprinted from Horie Y et al (4): Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc 89:25-32, 2019. Copyright © 2019 with permission from Elsevier Inc.)
Figure 2: Computer-aided diagnosis of colorectal neoplasia (Reprinted from Takeda K et al (9): Accuracy of diagnosing invasive colorectal cancer using computeraided endocytoscopy. Endoscopy 49:798-802, 2017. Copyright © 2017 with permission from Thieme Medical Publishers Inc.)
Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers? Neal Shahidi MD FRCPC , Michael J. Bourke MBBS FRACP PII: DOI: Reference:
S1096-2883(19)30078-6 https://doi.org/10.1016/j.tgie.2019.150639 YTGIE 150639
To appear in:
Techniques in Gastrointestinal Endoscopy
Received date: Accepted date:
25 July 2019 6 August 2019
Please cite this article as: Neal Shahidi MD FRCPC , Michael J. Bourke MBBS FRACP , Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers?, Techniques in Gastrointestinal Endoscopy (2019), doi: https://doi.org/10.1016/j.tgie.2019.150639
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers?
Neal Shahidi MD FRCPC1-3, Michael J. Bourke MBBS FRACP1,2 1. Westmead Hospital, Department of Gastroenterology and Hepatology, Sydney, New South Wales, Australia 2. University of Sydney, Westmead Clinical School, Sydney, New South Wales, Australia 3. University of British Columbia, Department of Medicine, Vancouver, British Columbia, Canada
Corresponding Author Michael J. Bourke MBBS FRACP Clinical Professor of Medicine Westmead Hospital, Department of Gastroenterology and Hepatology University of Sydney, Westmead Clinical School Suite 106a 151-155 Hawkesbury Road Sydney, New South Wales, Australia 2145 Email: [email protected] Fax: +61 2 9845 5637
Abstract Endoscopic tissue resection is a rapidly evolving field. En bloc resection techniques, specifically endoscopic submucosal dissection (ESD), allow for organsparing curative endoscopic resection for early gastrointestinal cancers. However, using current techniques to quantify depth of invasion, it remains difficult for endoscopists to reliably select optimal ESD candidates. In this review, we highlight that artificial intelligence platforms can now quantify the depth of invasion of esophageal, gastric and colorectal neoplasia. While real-time performance evaluation is needed, this represents a significant advancement in endoscopic tissue resection and carries the potential to provide real-time guidance for selecting the appropriate tissue resection technique.
Highlights
En bloc endoscopic resection techniques have the ability to cure early gastrointestinal cancers of the esophagus, stomach and colorectum
Optical evaluation is the primary method for identifying endoscopically curable gastrointestinal cancers, with modest performance characteristics
Computer-aided diagnosis platforms can quantify lesion depth of invasion and therefore have the potential to improve both patient outcomes and resource utilization by optimizing tissue resection technique selection
Key Words: Adenoma, Endoscopy, Colonoscopy, Polyp
Abbreviations AC; Adenocarcinoma AI; Artificial intelligence CADe; Computer-aided detection CADx; Computer-aided diagnosis EGC; Early gastric cancer EMR; Endoscopic mucosal resection ESGE; European Society of Gastrointestinal Endoscopy ESD; Endoscopic submucosal dissection IPCL; Intra-papillary capillary loops JGES; Japanese Gastroenterological Endoscopy Society JES; Japan Esophageal Society M; Mucosa NBI; Narrow-band imaging NPV; Negative predictive value PPV; Positive predictive value SM; Submucosa SCC; Squamous cell cancer
Introduction With the integration of deep learning methodology, artificial intelligence (AI) has rapidly proliferated throughout medicine. This is readily apparent in endoscopy, with computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems for gastrointestinal lesions (1-3). Computer-aided diagnosis has the potential to differentiate lesion histopathology, but also to quantify the depth of invasion of early gastrointestinal cancers of the esophagus, stomach, and colorectum (4-9). Early gastrointestinal cancers, whether confined to the mucosa or superficially invading into the submucosa carry a low risk of lymph node positivity. In the absence of other high-risk features (poor differentiation; lymphovascular invasion; tumor budding), R0 resection is considered curative (10-12). Given the morbidity and mortality of gastrointestinal surgery (13-15), most notably esophagectomy, gastrectomy and distal colorectal surgery, organ-sparing curative endoscopic resection is a ground-breaking advancement in the field of endoscopic tissue resection. Allowing for this advancement is the ability to perform safe, effective and sizeindependent en bloc removal of gastrointestinal lesions by endoscopic submucosal dissection (ESD) (16, 17). Utilizing an electrosurgical knife within the fluid-expanded submucosal plane, ESD is a meticulous cap-assisted tissue resection technique allowing for precise control over the deep and lateral margins; thereby empowering the endoscopist to perform radical excision without surgery. However, it is technically demanding, and is associated with a heightened risk of adverse events including
bleeding and perforation (18-20). Therefore, it is predominantly advocated for the removal of early gastrointestinal cancers as benign lesions, especially in the colorectum (21-25), are effectively and efficiently removed by endoscopic mucosal resection (EMR). To delineate a selective resection algorithm, incorporating EMR, ESD, and surgery, the endoscopist has to differentiate between benign lesions, early cancers amenable to curative endoscopic resection and deep cancers which should be referred directly to surgery. Biopsy, although commonly performed, is of limited clinical benefit. It is prone to false negativity due to sampling error and may complicate endoscopic resection by precipitating fibrosis (26, 27). Moreover, radiographic and endosonographic evaluations have limited ability to reliably differentiate clinically relevant depths of invasion and are prone to both under-staging and over-staging (28-31). Given the above limitations, real-time optical evaluation is used as the primary modality for delineating endoscopically curable disease. Using high-definition endoscopes with advanced imaging techniques (optical magnification, chromoendoscopy, virtual chromoendoscopy), pit pattern and vascular pattern changes consistent with invasive disease can be identified. However, optical evaluation is operator dependent and has modest performance characteristics in quantifying depth of invasion, even amongst expert endoscopists (32-35). Moreover, magnifying endoscopy and other advanced imaging techniques (confocal laser endomicroscopy, endocytoscopy) are not readily available worldwide. This has forced endoscopists to stratify the risk of invasive disease by lesion location, size, morphology and topography in an attempt to diagnose invisible or “covert” cancer (36).
Accurate computer-aided depth of invasion evaluation would be a transformative paradigm shift. By providing real-time guidance on selecting the appropriate resection technique, it carries the potential to define selective resection algorithms throughout the gastrointestinal tract. Therefore, we sought out to appraise the literature in this space.
Esophagus Histological Criteria for Curative Endoscopic Resection For squamous neoplasia, the frequency of lymph node positivity for M1 (intraepithelial) or M2 (lamina propria) disease is negligible. It increases to 8-18%, 11-53% and 30-54% for M3 (muscularis mucosae), SM1 (submucosa ≤ 200 μm) and ≥ SM2 (submucosa > 200 μm) disease, respectively (37-39). It is imperative to recognize that this data is commonly not stratified by the absence of high-risk features. In their absence, the frequency of lymph node positivity for M3-SM1 disease approximates the risk of M1-M2 disease (38). Concordantly, the European Society of Gastrointestinal Endoscopy (ESGE) recommends M1-M2 and M3-SM1 squamous neoplasia, without other high-risk features, as absolute and relative indications for curative endoscopic resection, respectively (11). Concerning Barrett’s neoplasia, in the absence of high-risk features, disease limited to the mucosa carries a limited risk of lymph node positivity (40, 41). This appears extendable to lesions confined to SM1 (submucosa ≤ 500 μm) (42), based on long-term follow-up data after endoscopic resection. Therefore, the ESGE considers mucosal disease and disease limited to SM1, without other high-risk features, as absolute and relative indications for curative endoscopic resection, respectively (11).
Depth of Invasion Platforms Three AI platforms (4-6) have automated depth of invasion analysis for squamous cell cancer (SCC) and adenocarcinoma (AC) of the esophagus. Utilizing a deep convolutional neural network, Horie and colleagues (4) evaluated the ability to differentiate early (T1) vs. advanced (T2-T4) esophageal SCC and AC (Figure 1). High-definition non-magnified white-light and narrow-banding imaging (NBI) images were used to create the training (397 SSC, 32 AC: 8428 images) and validation (41 SSC, 8 AC, 50 normal: 1118 images) datasets. Median lesion size in the validation dataset was 20mm (range 5-70mm). The diagnostic accuracy for differentiating early vs. advanced esophageal cancer was 98%. Diagnostic accuracy varied between SCC (99%) and AC (90%). Involving contributors from the study above, Nakagawa and colleagues (5), evaluated the performance of a deep convolutional neural network to differentiate depth of invasion amongst superficial squamous neoplasia. The platform was trained and validated using non-magnified and magnified white-light, NBI and iodinechromoendoscopy images (Training dataset: 804 SCC, 8660 non-magnified images, 5679 magnified images; Validation dataset: 155 SCC, 405 non-magnified images, 509 magnified images). The median lesion size in the validation dataset was 18mm (495mm). When differentiating M-SM1 disease vs. SM2-3 disease, the AI platform’s accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 91%, 90%, 96%, 99% and 64%, respectively. In comparison, the
accuracy, sensitivity, specificity, PPV and NPV of 16 board-certified experienced endoscopists were 90%, 90%, 88%, 98% and 66%, respectively. When differentiating mucosal (M) disease vs. submucosal (SM) disease, accuracy, sensitivity, specificity, PPV and NPV were 90%, 89%, 93%, 98% and 65%, respectively. Zhao et al. (6), evaluated the ability to automate the classification of intrapapillary capillary loops (IPCLs) in squamous neoplasia. Emanating from the subepithelium, IPCLs are capillary loops which are perpendicularly aligned from smooth branching vessels (35). Progressive variation in IPCL caliber and configuration correlates with depth of invasion and has been classified by the Japan Esophageal Society (JES)(43): type A (normal epithelium, inflammation, low grade intra-epithelial neoplasia), type B1 (high grade intra-epithelial neoplasia/M1-2 disease), type B2 (M3SM1 disease) and type B3 (≥ SM2 disease). High-definition magnified NBI images (207 type A, 970 type B1, 206 type B2) were evaluated. Utilizing a deep convolutional neural network, 3-fold cross validation was performed whereby 3 image groups were created, and 3 AI models were trained independently (training with 2 image groups, validation with remaining image group). Due to limited numbers of IPCL type B3, this grouping was excluded from analysis. Mean sensitivity, specificity, and accuracy were 87%, 84% and 89%, respectively. Automated performance was most similar to senior endoscopists (> 15 years experience: sensitivity 91%, specificity 94%, accuracy 92%), in comparison to mid-level endoscopists (10-15 years experience: sensitivity 79%, specificity 71%, accuracy 82%) or junior endoscopists (5-10 years experience: sensitivity 68%, specificity 76%, accuracy 73%). Automated IPCL classification accuracy was significantly higher than mid-level endoscopists and junior endoscopists (p < 0.001).
Stomach Histological Criteria for Curative Endoscopic Resection Histologic criteria for curative endoscopic resection of early gastric cancer (EGC) is stratified by lesion size, presence of ulceration, depth of invasion and lesion differentiation. As defined by the Japanese Gastroenterological Endoscopy Society (JGES), absolute criteria are well-differentiated, < 2 cm intramucosal (T1a) EGC without ulceration or lymphovascular invasion (10). The risk of lymph node positivity is effectively zero (44). Under expanded criteria: 1) intramucosal EGC, well-differentiated, ulcer negative, > 2 cm; 2) intramucosal EGC, well-differentiated, ulcer positive, < 3 cm; 3) intramucosal EGC, poorly-differentiated, ulcer negative, < 2 cm and 4) SM1 (≤ 500 μm) EGC, well-differentiated, ulcer negative, < 3 cm are considered curative. This is due to the low risk of lymph node positivity, as supported by a large series of 5265 EGCs which underwent gastrectomy (44).
Depth of Invasion Platforms Two AI platforms (7, 8) have automated depth of invasion assessment in gastric cancer. Kubota et al. (7), evaluated the ability to automate T-staging for 344 gastric cancers (mean size 40 mm; range 10 mm-240 mm). 902 pre-operative endoscopic images were evaluated (T1 448, T2 106, T3 149, T4 199). A multi-layer neural network was trained and validated using a 10-fold cross validation method. Overall, depth of
invasion accuracy was 65%. Stratified by T-stage, the diagnostic accuracies were 77%, 49%, 51% and 55% for T1, T2, T3 and T4, respectively. When evaluating T1a vs. T1b EGC accuracy was 69% and 64%, respectively. The PPVs were 80%, 42%, 51%, 56%, 69% and 68% for T1, T2, T3, T4, T1a and T1b, respectively. Zhu et al. (8) evaluated the ability to differentiate intramucosal-SM1 (≤ 500 μm) vs. ≥ SM2 (> 500 μm) using a deep convolutional neural network. Training (790 lesions) and validation (203 lesions) datasets using white-light images were used. For the training dataset, data augmentation was performed by rotating and flipping images, leading to an 8-fold increase in dataset size (6320 images). Automated sensitivity, specificity, accuracy, PPV and NPV were 77%, 96%, 89%, 90% and 89%, respectively. In comparison to 17 endoscopists with varying experience, sensitivity, specificity, accuracy, PPV and NPV were 72%, 88%, 63%, 56% and 91%. Significant differences in accuracy and specificity, favoring CADx, were identified.
Colorectum Histological Criteria for Curative Endoscopic Resection Within the colorectum, cancer is defined by invasion into the submucosa. This is because the mucosa does not have lymphatic drainage and therefore does not carry the potential for lymphatic or metastatic spread (45). The overall risk of lymph node positivity is 13% for T1 colorectal cancer. However, it decreases to 1.9% in welldifferentiated lesions, confined to SM1 (≤ 1000 μm), without lymphovascular invasion or tumor budding (46). Both the JGES (12) and the ESGE (11) utilize the above criteria for curative endoscopic resection.
Depth of Invasion Platform Takeda et al. (9), evaluated the ability of a machine learning platform to differentiate adenomatous lesions from invasive cancer (Figure 2). Endocytoscopy images were used from 375 lesions (mean size +/- standard deviation: adenoma 11 mm +/- 10 mm; invasive cancer: 31 mm +/- 14 mm). Endocytoscopy, performed after application of crystal violet and methylene blue dye, allows for x380 magnification and enables in vivo evaluation of both nuclei and gland lumens. A total of 5843 endocytoscopy images were evaluated, with 200 of them used for validation. Sensitivity, specificity, accuracy, PPV and NPV were 89%, 99%, 94%, 98% and 90%, respectively. High-confidence (≥ 90% probability of being correct) was achieved in 72%, and under these conditions, sensitivity, specificity, accuracy, PPV and NPV were 98%, 100%, 99%, 100% and 99%, respectively.
Limitations of Current Platforms Artificial intelligence depth of invasion platforms represent a critical advancement in the fields of optical evaluation and endoscopic tissue resection, with impressive performance outcomes. However, they do have limitations; as can be expected in this new and rapidly evolving space. Not all platforms differentiated clinically relevant depths of invasion. For squamous neoplasia and EGC, ESD is the recommended method for endoscopic resection. Therefore, delineating intramucosal-SM1 versus ≥ SM2 should be the primary
outcome. For Barrett’s neoplasia, it is important to differentiate between intramucosal verus SM1 and SM1 versus ≥ SM2. Endoscopic mucosal resection, is an established modality for the long-term treatment of Barrett’s neoplasia limited to the mucosal layer (47, 48). Concordantly, the ESGE recommends EMR as the preferred modality with ESD being considered for lesions > 15mm, bulky lesions, non-lifting lesions or lesions with a suspicion of superficial submucosal invasion. Within the colorectum, EMR is an effective, efficient, safe and durable method for managing colorectal lesions (21-25). Economic analyses show that a selective resection algorithm utilizing both EMR and ESD is the most cost-effective strategy (49). Therefore, delineating between benign lesions, superficial invasive cancer and deep invasive cancer would allow endoscopists to correctly select between EMR, ESD or direct referral to surgery, respectively. All platforms were trained and validated on retrospectively collected datasets, introducing the potential for selection bias. Moreover, all studies evaluated static images with the majority excluding poor quality images. Only Takeda and colleagues (9) purposefully included unclear images. This impairs the ability of these platforms to be representative of real-world performance and their ability to reflect future reproducibility in clinical practice. Future platforms or future refinement of the above platforms should utilize either prospectively collected videos or real-time evaluation. A central advantage of AI is the ability to eliminate operator-dependence increasing its universality. By utilizing technology which invariably requires skill to capture the relevant image, such as magnifying endoscopy and endocytoscopy, directly negates this advantage. Moreover, these technologies are not widely available and impair the applicability of these platforms.
Future Directions Mori and colleagues (50) have outlined an AI-specific pathway for the assimilation of CADe and CADx in endoscopy. This includes: 1) product development and feasibility studies; 2) clinical trials; 3) obtaining regulatory approval; and 4) establishing insurance reimbursement. Recently, Vinsard and colleagues (51) outlined quality assurance guidelines for future endoscopy-related AI research. Recommended features of future AI platforms include: 1) appropriate algorithm selection; 2) real-time performance; 3) appropriate output format; and 4) smooth incorporation with existing endoscopy technology. Combining these recommendations will ensure high-quality AI platforms are produced and are more readily incorporated into clinical practice. For depth of invasion solutions, it is critical that two audiences are targeted: 1) the general endoscopist and 2) the tissue resection specialist. For the former, AI strategies need to reliably identify which lesions are amenable to endoscopic resection vs. those that should be referred directly to surgery. For the endoscopic resection specialist, facilitating the selection of the appropriate resection technique carries the potential to optimize both patient outcomes and resource utilization. In conclusion, endoscopic tissue resection is a rapidly evolving field. Advances in optical evaluation and endoscopic resection techniques are allowing endoscopists to reliably identify and subsequently remove early gastrointestinal cancers effectively, efficiently and safely; thus, permitting organ-sparing curative endoscopic resection. Artificial intelligence carries the potential to revolutionize optical evaluation, and surpass
endoscopist’s optical limitations. It is therefore imperative that all endoscopists, even those that do not perform advanced tissue resection, be aware of the full array of therapeutic options for superficial gastrointestinal lesions. Moreover, all endoscopists should be aware of current optical evaluation classification systems as AI will likely emerge as a decision support tool, and the endoscopist will remain the ultimate decision maker.
Acknowledgements Neal Shahidi is supported by the University of British Columbia Clinician Investigator Program.
Conflicts of Interest None
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Figure Legends
Figure 1: Computer-aided diagnosis of esophageal cancer. (Reprinted from Horie Y et al (4): Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc 89:25-32, 2019. Copyright © 2019 with permission from Elsevier Inc.)
Figure 2: Computer-aided diagnosis of colorectal neoplasia (Reprinted from Takeda K et al (9): Accuracy of diagnosing invasive colorectal cancer using computeraided endocytoscopy. Endoscopy 49:798-802, 2017. Copyright © 2017 with permission from Thieme Medical Publishers Inc.)