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McGenity C, Clarke EL, Jennings C, Matthews G, Cartlidge C, Freduah-Agyemang H, Stocken DD, Treanor D. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med 2024; 7:114. [PMID: 38704465 PMCID: PMC11069583 DOI: 10.1038/s41746-024-01106-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 04/12/2024] [Indexed: 05/06/2024] Open
Abstract
Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.
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Affiliation(s)
- Clare McGenity
- University of Leeds, Leeds, UK.
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - Emily L Clarke
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Charlotte Jennings
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | | | | | | | - Darren Treanor
- University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Department of Clinical Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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2
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Iwashita T, Uemura S, Ryuichi T, Senju A, Iwata S, Ohashi Y, Shimizu M. Advances and efficacy in specimen handling for endoscopic ultrasound-guided fine needle aspiration and biopsy: A comprehensive review. DEN Open 2024; 4:e350. [PMID: 38495467 PMCID: PMC10941515 DOI: 10.1002/deo2.350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 02/19/2024] [Accepted: 02/23/2024] [Indexed: 03/19/2024]
Abstract
Endoscopic ultrasound-guided fine needle aspiration and biopsy have significantly evolved since they offer a minimally invasive approach for obtaining pathological specimens from lesions adjacent to or within the intestine. This paper reviews advancements in endoscopic ultrasound-guided fine needle aspiration and biopsy techniques and devices, emphasizing the importance of handling specimens for diagnostic accuracy. Innovations of fine needle biopsy needles with features like side holes and Franseen shapes have enhanced histological sampling capabilities. Techniques for specimen handling, including rapid on-site evaluation and macroscopic on-site evaluation, play pivotal roles in assessing sample adequacy, thereby influencing diagnostic outcomes. The utility of artificial intelligence in augmenting rapid on-site evaluation and macroscopic on-site evaluation, although still in experimental stages, presents a promising avenue for improving procedural efficiency and diagnostic precision. The choice of specimen handling technique is dependent on various factors including endoscopist preference, procedure objectives, and available resources, underscoring the need for a comprehensive understanding of each method's characteristics to optimize diagnostic efficacy and procedural safety.
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Affiliation(s)
- Takuji Iwashita
- First Department of Internal MedicineGifu University HospitalGifuJapan
| | - Shinya Uemura
- First Department of Internal MedicineGifu University HospitalGifuJapan
| | - Tezuka Ryuichi
- First Department of Internal MedicineGifu University HospitalGifuJapan
| | - Akihiko Senju
- First Department of Internal MedicineGifu University HospitalGifuJapan
| | - Shota Iwata
- First Department of Internal MedicineGifu University HospitalGifuJapan
| | - Yosuke Ohashi
- First Department of Internal MedicineGifu University HospitalGifuJapan
| | - Masahito Shimizu
- First Department of Internal MedicineGifu University HospitalGifuJapan
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3
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Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Fukui T, Urata M, Yamamoto Y. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN Open 2024; 4:e267. [PMID: 37397344 PMCID: PMC10312781 DOI: 10.1002/deo2.267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/18/2023] [Indexed: 07/04/2023]
Abstract
Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high-quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.
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Affiliation(s)
| | - Kazuo Hara
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nobumasa Mizuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Shin Haba
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nozomi Okuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Toshitaka Fukui
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Minako Urata
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
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Irmakci I, Nateghi R, Zhou R, Vescovo M, Saft M, Ross AE, Yang XJ, Cooper LAD, Goldstein JA. Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology. Mod Pathol 2024; 37:100422. [PMID: 38185250 PMCID: PMC10960671 DOI: 10.1016/j.modpat.2024.100422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 11/13/2023] [Accepted: 12/15/2023] [Indexed: 01/09/2024]
Abstract
Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, such as floaters, represent unexpected tissue. Although human pathologists are extensively trained to consider and detect tissue contaminants, we examined their impact on ML models. We trained 4 whole-slide models. Three operate in placenta for the following functions: (1) detection of decidual arteriopathy, (2) estimation of gestational age, and (3) classification of macroscopic placental lesions. We also developed a model to detect prostate cancer in needle biopsies. We designed experiments wherein patches of contaminant tissue are randomly sampled from known slides and digitally added to patient slides and measured model performance. We measured the proportion of attention given to contaminants and examined the impact of contaminants in the t-distributed stochastic neighbor embedding feature space. Every model showed performance degradation in response to one or more tissue contaminants. Decidual arteriopathy detection--balanced accuracy decreased from 0.74 to 0.69 ± 0.01 with addition of 1 patch of prostate tissue for every 100 patches of placenta (1% contaminant). Bladder, added at 10% contaminant, raised the mean absolute error in estimating gestational age from 1.626 weeks to 2.371 ± 0.003 weeks. Blood, incorporated into placental sections, induced false-negative diagnoses of intervillous thrombi. Addition of bladder to prostate cancer needle biopsies induced false positives, a selection of high-attention patches, representing 0.033 mm2, and resulted in a 97% false-positive rate when added to needle biopsies. Contaminant patches received attention at or above the rate of the average patch of patient tissue. Tissue contaminants induce errors in modern ML models. The high level of attention given to contaminants indicates a failure to encode biological phenomena. Practitioners should move to quantify and ameliorate this problem.
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Affiliation(s)
- Ismail Irmakci
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Ramin Nateghi
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Rujoi Zhou
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Mariavittoria Vescovo
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Madeline Saft
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Ashley E Ross
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Ximing J Yang
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Lee A D Cooper
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
| | - Jeffery A Goldstein
- Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois.
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Yamaguchi R, Morikawa H, Akatsuka J, Numata Y, Noguchi A, Kokumai T, Ishida M, Mizuma M, Nakagawa K, Unno M, Miyake A, Tamiya G, Yamamoto Y, Furukawa T. Machine Learning of Histopathological Images Predicts Recurrences of Resected Pancreatic Ductal Adenocarcinoma With Adjuvant Treatment. Pancreas 2024; 53:e199-e204. [PMID: 38127849 DOI: 10.1097/mpa.0000000000002289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
OBJECTIVES Pancreatic ductal adenocarcinoma is an intractable disease with frequent recurrence after resection and adjuvant therapy. The present study aimed to clarify whether artificial intelligence-assisted analysis of histopathological images can predict recurrence in patients with pancreatic ductal adenocarcinoma who underwent resection and adjuvant chemotherapy with tegafur/5-chloro-2,4-dihydroxypyridine/potassium oxonate. MATERIALS AND METHODS Eighty-nine patients were enrolled in the study. Machine-learning algorithms were applied to 10-billion-scale pixel data of whole-slide histopathological images to generate key features using multiple deep autoencoders. Areas under the curve were calculated from receiver operating characteristic curves using a support vector machine with key features alone and by combining with clinical data (age and carbohydrate antigen 19-9 and carcinoembryonic antigen levels) for predicting recurrence. Supervised learning with pathological annotations was conducted to determine the significant features for predicting recurrence. RESULTS Areas under the curves obtained were 0.73 (95% confidence interval, 0.59-0.87) by the histopathological data analysis and 0.84 (95% confidence interval, 0.73-0.94) by the combinatorial analysis of histopathological data and clinical data. Supervised learning model demonstrated that poor tumor differentiation was significantly associated with recurrence. CONCLUSIONS Results indicate that machine learning with the integration of artificial intelligence-driven evaluation of histopathological images and conventional clinical data provides relevant prognostic information for patients with pancreatic ductal adenocarcinoma.
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Affiliation(s)
- Ruri Yamaguchi
- From the Department of Investigative Pathology, Tohoku University Graduate School of Medicine, Sendai
| | - Hiromu Morikawa
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo
| | - Jun Akatsuka
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo
| | - Yasushi Numata
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo
| | - Aya Noguchi
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Takashi Kokumai
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Masaharu Ishida
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Masamichi Mizuma
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Kei Nakagawa
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Michiaki Unno
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Akimitsu Miyake
- Department of AI and Innovative Medicine, Tohoku University Graduate School of Medicine, Sendai
| | | | | | - Toru Furukawa
- From the Department of Investigative Pathology, Tohoku University Graduate School of Medicine, Sendai
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Rawlani P, Ghosh NK, Kumar A. Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis. Artif Intell Gastroenterol 2023; 4:48-63. [DOI: 10.35712/aig.v4.i3.48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/11/2023] [Accepted: 10/08/2023] [Indexed: 12/07/2023] Open
Abstract
Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.
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Affiliation(s)
- Palash Rawlani
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Nalini Kanta Ghosh
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
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Dhali A, Kipkorir V, Srichawla BS, Kumar H, Rathna RB, Ongidi I, Chaudhry T, Morara G, Nurani K, Cheruto D, Biswas J, Chieng LR, Dhali GK. Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space-occupying lesion: a systematic review and meta-analysis. Int J Surg 2023; 109:4298-4308. [PMID: 37800594 PMCID: PMC10720860 DOI: 10.1097/js9.0000000000000717] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/21/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce interobserver variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities. METHODS Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized. RESULTS A total of 21 studies were included in the review with four studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4-96.8%) was found using the random-effects model on four studies that showed significant heterogeneity ( P <0.05) in the Cochrane's Q test. Further, a pooled sensitivity of 93.9% (CI 92.4-95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane's Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane's Q test and determined as 93.1% (CI 90.7-95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3-95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4-96.8%). CONCLUSION AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.
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Affiliation(s)
- Arkadeep Dhali
- NIHR Academic Clinical Fellow in Gastroenterology, University of Sheffield; Internal Medicine Trainee, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Vincent Kipkorir
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | | | | | | | - Ibsen Ongidi
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Talha Chaudhry
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Gisore Morara
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Khulud Nurani
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Doreen Cheruto
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | | | - Leonard R. Chieng
- NIHR Academic Clinical Fellow in Gastroenterology, University of Sheffield; Internal Medicine Trainee, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Gopal Krishna Dhali
- School of Digestive and Liver Diseases, Institute of Postgraduate Medical Education and Research, Kolkata, India
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Ishikawa T, Yamao K, Mizutani Y, Iida T, Kawashima H. Cutting edge of endoscopic ultrasound-guided fine-needle aspiration for solid pancreatic lesions. J Med Ultrason (2001) 2023:10.1007/s10396-023-01375-y. [PMID: 37914883 DOI: 10.1007/s10396-023-01375-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 08/31/2023] [Indexed: 11/03/2023]
Abstract
This article provides an extensive review of the advancements and future perspectives related to endoscopic ultrasound-guided tissue acquisition (EUS-TA) for the diagnosis of solid pancreatic lesions (SPLs). EUS-TA, including fine-needle aspiration (EUS-FNA) and fine-needle biopsy (EUS-FNB), has revolutionized the collection of specimens from intra-abdominal organs, including the pancreas. Improvements in the design of needles, collection methods, and specimen processing techniques have improved the diagnostic performance. This review highlights the latest findings regarding needle evolution, actuation number, sampling methods, specimen evaluation techniques, application of artificial intelligence (AI) for diagnostic purposes, and use of comprehensive genomic profiling (CGP). It acknowledges the rising use of Franseen and fork-tip needles for EUS-FNB and emphasizes that the optimal number of actuations requires further study. Methods such as the door-knocking and fanning techniques have shown promise for increasing diagnostic performance. Macroscopic on-site evaluation (MOSE) is presented as a practical rapid specimen evaluation method, and the integration of AI is identified as a potentially impactful development. The study also underscores the importance of optimal sampling for CGP, which can enhance the precision of cancer treatment. Ongoing research and technological innovations will further improve the accuracy and efficacy of EUS-TA.
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Affiliation(s)
- Takuya Ishikawa
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan.
| | - Kentaro Yamao
- Department of Endoscopy, Nagoya University Hospital, Nagoya, Japan
| | - Yasuyuki Mizutani
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Tadashi Iida
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Hiroki Kawashima
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
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Abu-Khudir R, Hafsa N, Badr BE. Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning. Diagnostics (Basel) 2023; 13:3091. [PMID: 37835833 PMCID: PMC10572229 DOI: 10.3390/diagnostics13193091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/08/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.
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Affiliation(s)
- Rasha Abu-Khudir
- Chemistry Department, College of Science, King Faisal University, P.O. Box 380, Hofuf 31982, Al-Ahsa, Saudi Arabia
- Chemistry Department, Biochemistry Branch, Faculty of Science, Tanta University, Tanta 31527, Egypt
| | - Noor Hafsa
- Computer Science Department, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Hofuf 31982, Al-Ahsa, Saudi Arabia;
| | - Badr E. Badr
- Egyptian Ministry of Labor, Training and Research Department, Tanta 31512, Egypt;
- Botany Department, Microbiology Unit, Faculty of Science, Tanta University, Tanta 31527, Egypt
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10
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Qin X, Ran T, Chen Y, Zhang Y, Wang D, Zhou C, Zou D. Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges. Diagnostics (Basel) 2023; 13:3054. [PMID: 37835797 PMCID: PMC10572518 DOI: 10.3390/diagnostics13193054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/06/2023] [Accepted: 09/06/2023] [Indexed: 10/15/2023] Open
Abstract
Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.
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Affiliation(s)
| | | | | | | | | | - Chunhua Zhou
- Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (X.Q.); (T.R.); (Y.C.); (Y.Z.); (D.W.)
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (X.Q.); (T.R.); (Y.C.); (Y.Z.); (D.W.)
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11
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Qin X, Zhang M, Zhou C, Ran T, Pan Y, Deng Y, Xie X, Zhang Y, Gong T, Zhang B, Zhang L, Wang Y, Li Q, Wang D, Gao L, Zou D. A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma. Cancer Med 2023; 12:17005-17017. [PMID: 37455599 PMCID: PMC10501295 DOI: 10.1002/cam4.6335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 06/12/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND AND AIMS Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens. METHODS HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model. RESULTS A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei. CONCLUSIONS An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.
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Affiliation(s)
- Xianzheng Qin
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Minmin Zhang
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Chunhua Zhou
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Taojing Ran
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yundi Pan
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yingjiao Deng
- Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina
| | - Xingran Xie
- Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina
| | - Yao Zhang
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Tingting Gong
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Benyan Zhang
- Department of PathologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Ling Zhang
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yan Wang
- Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina
| | - Dong Wang
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Lili Gao
- Department of PathologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Duowu Zou
- Department of GastroenterologyRuijin Hospital, School of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
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12
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Yamada R, Tsuboi J, Murashima Y, Tanaka T, Nose K, Nakagawa H. Advances in the Early Diagnosis of Pancreatic Ductal Adenocarcinoma and Premalignant Pancreatic Lesions. Biomedicines 2023; 11:1687. [PMID: 37371782 DOI: 10.3390/biomedicines11061687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/23/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Pancreatic cancer is one of the most lethal human malignancies, in part because it is often diagnosed at late stages when surgery and systemic therapies are either unfeasible or ineffective. Therefore, diagnosing pancreatic cancer in earlier stages is important for effective treatment. However, because the signs and symptoms may be nonspecific and not apparent until the disease is at a late stage, the timely diagnoses of pancreatic cancer can be difficult to achieve. Recent studies have shown that selective screening and increased usage of biomarkers could improve the early diagnosis of pancreatic cancer. In this review, we discuss recent advancements in the early detection of pancreatic ductal carcinoma and precancerous lesions. These include innovations in imaging modalities, the diagnostic utility of various biomarkers, biopsy techniques, and population-based surveillance approaches. Additionally, we discuss how machine learning methods are being applied to develop integrated methods of identifying individuals at high risk of developing pancreatic disease. In the future, the overall survival of pancreatic cancer patients could be improved by the development and adoption of these new methods and techniques.
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Affiliation(s)
- Reiko Yamada
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Junya Tsuboi
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Yumi Murashima
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Takamitsu Tanaka
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Kenji Nose
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
| | - Hayato Nakagawa
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan
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13
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Sugiyama T, Tajiri T, Kurata M, Hiraiwa S, Fujita H, Machida T, Ito H, Muraki T, Yoshii H, Izumi H, Suzuki T, Mukai M, Nakamura N. Sensitivity of endoscopic ultrasound-guided fine-needle aspiration cytology and biopsy for a diagnosis of pancreatic ductal adenocarcinoma: A comparative analysis. Pathol Int 2023. [PMID: 37154509 DOI: 10.1111/pin.13328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/16/2023] [Indexed: 05/10/2023]
Abstract
The utility of endoscopic ultrasound fine-needle aspiration cytology (EUS-FNAC) or endoscopic ultrasound fine-needle aspiration biopsy (EUS-FNAB) for diagnosis of small and large pancreatic ductal adenocarcinomas (PDACs) remains in question. We addressed this by analyzing 97 definitively diagnosed cases of PDAC, for which both EUS-FNAC and EUS-FNAB had been performed. We subclassified the 97 solid masses into small (n = 35) or large (n = 62) according to the maximum tumor diameter (<24 mm or ≥24 mm) and compared the diagnostic sensitivity (truly positive rate) of EUS-FNAC and of EUS-FNAB for small and large masses. Diagnostic sensitivity of EUS-FNAC did not differ between large and small masses (79.0% vs. 60.0%; p = 0.0763). However, the diagnostic sensitivity of EUS-FNAB was significantly higher for large masses (85.5% vs. 62.9%; p = 0.0213). Accurate EUS-FNAC-based diagnosis appeared to depend on the degree of cytological atypia of cancer cells, which was not associated with quantity of cancer cells. The accuracy of EUS-FNAB-based diagnosis appeared to depend on cancer cell viability in large masses and cancer volume in small masses. Based on the advantages or disadvantages in each modality, both modalities play an important role in the qualitative diagnosis of PDAC as a complementary procedure.
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Affiliation(s)
- Tomoko Sugiyama
- Department of Diagnostic Pathology, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Takuma Tajiri
- Department of Diagnostic Pathology, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Makiko Kurata
- Department of Diagnostic Pathology, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Shinichiro Hiraiwa
- Department of Diagnostic Pathology, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Hirotaka Fujita
- Department of Laboratory Medicine, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Tomohisa Machida
- Department of Laboratory Medicine, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Hiroyuki Ito
- Department of Gastroenterology, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Takashi Muraki
- Department of Gastroenterology, Kita-Alps Medical Center Azumino Hospital, Nagano, Japan
| | - Hisamichi Yoshii
- Department of Surgery, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Hideki Izumi
- Department of Surgery, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Takayoshi Suzuki
- Department of Gastroenterology, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Masaya Mukai
- Department of Surgery, Tokai University Hachioji Hospital, Tokyo, Japan
| | - Naoya Nakamura
- Department of Pathology, Tokai University School of Medicine, Isehara, Japan
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14
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Irmakci I, Nateghi R, Zhou R, Ross AE, Yang XJ, Cooper LAD, Goldstein JA. Tissue contamination challenges the credibility of machine learning models in real world digital pathology. medRxiv 2023:2023.04.28.23289287. [PMID: 37205404 PMCID: PMC10187357 DOI: 10.1101/2023.04.28.23289287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, such as floaters, represent unexpected tissue. While human pathologists are extensively trained to consider and detect tissue contaminants, we examined their impact on ML models. We trained 4 whole slide models. Three operate in placenta for 1) detection of decidual arteriopathy (DA), 2) estimation of gestational age (GA), and 3) classification of macroscopic placental lesions. We also developed a model to detect prostate cancer in needle biopsies. We designed experiments wherein patches of contaminant tissue are randomly sampled from known slides and digitally added to patient slides and measured model performance. We measured the proportion of attention given to contaminants and examined the impact of contaminants in T-distributed Stochastic Neighbor Embedding (tSNE) feature space. Every model showed performance degradation in response to one or more tissue contaminants. DA detection balanced accuracy decreased from 0.74 to 0.69 +/- 0.01 with addition of 1 patch of prostate tissue for every 100 patches of placenta (1% contaminant). Bladder, added at 10% contaminant raised the mean absolute error in estimating gestation age from 1.626 weeks to 2.371 +/ 0.003 weeks. Blood, incorporated into placental sections, induced false negative diagnoses of intervillous thrombi. Addition of bladder to prostate cancer needle biopsies induced false positives, a selection of high-attention patches, representing 0.033mm2, resulted in a 97% false positive rate when added to needle biopsies. Contaminant patches received attention at or above the rate of the average patch of patient tissue. Tissue contaminants induce errors in modern ML models. The high level of attention given to contaminants indicates a failure to encode biological phenomena. Practitioners should move to quantify and ameliorate this problem.
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Affiliation(s)
| | | | | | | | | | | | - Jeffery A. Goldstein
- To whom correspondence should be addressed: Olson 2-455, 710 N. Fairbanks Ave, Chicago IL, 60611,
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15
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Faur AC, Lazar DC, Ghenciu LA. Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis. World J Gastroenterol 2023; 29:1811-1823. [PMID: 37032728 PMCID: PMC10080704 DOI: 10.3748/wjg.v29.i12.1811] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/23/2022] [Accepted: 03/16/2023] [Indexed: 03/28/2023] Open
Abstract
Pancreatic cancer (PC) has a low incidence rate but a high mortality, with patients often in the advanced stage of the disease at the time of the first diagnosis. If detected, early neoplastic lesions are ideal for surgery, offering the best prognosis. Preneoplastic lesions of the pancreas include pancreatic intraepithelial neoplasia and mucinous cystic neoplasms, with intraductal papillary mucinous neoplasms being the most commonly diagnosed. Our study focused on predicting PC by identifying early signs using noninvasive techniques and artificial intelligence (AI). A systematic English literature search was conducted on the PubMed electronic database and other sources. We obtained a total of 97 studies on the subject of pancreatic neoplasms. The final number of articles included in our study was 44, 34 of which focused on the use of AI algorithms in the early diagnosis and prediction of pancreatic lesions. AI algorithms can facilitate diagnosis by analyzing massive amounts of data in a short period of time. Correlations can be made through AI algorithms by expanding image and electronic medical records databases, which can later be used as part of a screening program for the general population. AI-based screening models should involve a combination of biomarkers and medical and imaging data from different sources. This requires large numbers of resources, collaboration between medical practitioners, and investment in medical infrastructures.
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Affiliation(s)
- Alexandra Corina Faur
- Department of Anatomy and Embriology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Timișoara 300041, Timiș, Romania
| | - Daniela Cornelia Lazar
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, University of Medicine and Pharmacy “Victor Babes” Timișoara, Timișoara 300041, Timiș, Romania
| | - Laura Andreea Ghenciu
- Department III, Discipline of Pathophysiology, “Victor Babeș” University of Medicine and Pharmacy, Timișoara 300041, Timiș, Romania
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16
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Abstract
Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.
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Affiliation(s)
- Siddhi Ramesh
- Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA
| | - James M Dolezal
- Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA
| | - Alexander T Pearson
- Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; University of Chicago Comprehensive Cancer Center, Chicago, IL, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA.
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17
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Ohshima H, Mishima K. Oral biosciences: The annual review 2022. J Oral Biosci 2023; 65:1-12. [PMID: 36740188 DOI: 10.1016/j.job.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND The Journal of Oral Biosciences is devoted to advancing and disseminating fundamental knowledge concerning every aspect of oral biosciences. HIGHLIGHT This review features review articles in the fields of "Bone Cell Biology," "Tooth Development & Regeneration," "Tooth Bleaching," "Adipokines," "Milk Thistle," "Epithelial-Mesenchymal Transition," "Periodontitis," "Diagnosis," "Salivary Glands," "Tooth Root," "Exosome," "New Perspectives of Tooth Identification," "Dental Pulp," and "Saliva" in addition to the review articles by the winner of the "Lion Dental Research Award" ("Plastic changes in nociceptive pathways contributing to persistent orofacial pain") presented by the Japanese Association for Oral Biology. CONCLUSION The review articles in the Journal of Oral Biosciences have inspired its readers to broaden their knowledge about various aspects of oral biosciences. The current editorial review introduces these exciting review articles.
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Affiliation(s)
- Hayato Ohshima
- Division of Anatomy and Cell Biology of the Hard Tissue, Department of Tissue Regeneration and Reconstruction, Niigata University Graduate School of Medical and Dental Sciences, 2-5274 Gakkocho-dori, Chuo-ku, Niigata 951-8514, Japan.
| | - Kenji Mishima
- Division of Pathology, Department of Oral Diagnostic Sciences, Showa University School of Dentistry, 1-5-8, Hatanodai, Shinagawa-ku, Tokyo 142-8555, Japan
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18
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Tsuneki M, Abe M, Ichihara S, Kanavati F. Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer. BMC Cancer 2023; 23:11. [PMID: 36600203 DOI: 10.1186/s12885-022-10488-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 12/26/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Prostate cancer is often a slowly progressive indolent disease. Unnecessary treatments from overdiagnosis are a significant concern, particularly low-grade disease. Active surveillance has being considered as a risk management strategy to avoid potential side effects by unnecessary radical treatment. In 2016, American Society of Clinical Oncology (ASCO) endorsed the Cancer Care Ontario (CCO) Clinical Practice Guideline on active surveillance for the management of localized prostate cancer. METHODS Based on this guideline, we developed a deep learning model to classify prostate adenocarcinoma into indolent (applicable for active surveillance) and aggressive (necessary for definitive therapy) on core needle biopsy whole slide images (WSIs). In this study, we trained deep learning models using a combination of transfer, weakly supervised, and fully supervised learning approaches using a dataset of core needle biopsy WSIs (n=1300). In addition, we performed an inter-rater reliability evaluation on the WSI classification. RESULTS We evaluated the models on a test set (n=645), achieving ROC-AUCs of 0.846 for indolent and 0.980 for aggressive. The inter-rater reliability evaluation showed s-scores in the range of 0.10 to 0.95, with the lowest being on the WSIs with both indolent and aggressive classification by the model, and the highest on benign WSIs. CONCLUSION The results demonstrate the promising potential of deployment in a practical prostate adenocarcinoma histopathological diagnostic workflow system.
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Affiliation(s)
- Masayuki Tsuneki
- Medmain Research, Medmain Inc., 2-4-5-104, Akasaka, Chuo-ku, Fukuoka, 810-0042, Japan.
| | - Makoto Abe
- Department of Pathology, Tochigi Cancer Center, 4-9-13 Yohnan, Utsunomiya, 320-0834, Japan
| | - Shin Ichihara
- Department of Surgical Pathology, Sapporo Kosei General Hospital, 8-5 Kita-3-jo Higashi, Chuo-ku, Sapporo, 060-0033, Japan
| | - Fahdi Kanavati
- Medmain Research, Medmain Inc., 2-4-5-104, Akasaka, Chuo-ku, Fukuoka, 810-0042, Japan
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19
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Tsuneki M, Kanavati F. Weakly Supervised Learning for Poorly Differentiated Adenocarcinoma Classification in GastricEndoscopic Submucosal Dissection Whole Slide Images. Technol Cancer Res Treat 2022; 21:15330338221142674. [PMID: 36476107 PMCID: PMC9742706 DOI: 10.1177/15330338221142674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Objective: Endoscopic submucosal dissection (ESD) is the preferred technique for treating early gastric cancers including poorly differentiated adenocarcinoma without ulcerative findings. The histopathological classification of poorly differentiated adenocarcinoma including signet ring cell carcinoma is of pivotal importance for determining further optimum cancer treatment(s) and clinical outcomes. Because conventional diagnosis by pathologists using microscopes is time-consuming and limited in terms of human resources, it is very important to develop computer-aided techniques that can rapidly and accurately inspect large number of histopathological specimen whole-slide images (WSIs). Computational pathology applications which can assist pathologists in detecting and classifying gastric poorly differentiated adenocarcinoma from ESD WSIs would be of great benefit for routine histopathological diagnostic workflow. Methods: In this study, we trained the deep learning model to classify poorly differentiated adenocarcinoma in ESD WSIs by transfer and weakly supervised learning approaches. Results: We evaluated the model on ESD, endoscopic biopsy, and surgical specimen WSI test sets, achieving and ROC-AUC up to 0.975 in gastric ESD test sets for poorly differentiated adenocarcinoma. Conclusion: The deep learning model developed in this study demonstrates the high promising potential of deployment in a routine practical gastric ESD histopathological diagnostic workflow as a computer-aided diagnosis system.
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Affiliation(s)
- Masayuki Tsuneki
- Medmain Research, Medmain Inc., Fukuoka, Japan,Masayuki Tsuneki, Medmain Research, Medmain Inc., Fukuoka, 810-0042, Japan.
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Mohamad Sehmi MN, Ahmad Fauzi MF, Wan Ahmad WSHM, Wan Ling Chan E. Pancreatic cancer grading in pathological images using deep learning convolutional neural networks. F1000Res 2022; 10:1057. [PMID: 37767358 PMCID: PMC10521057 DOI: 10.12688/f1000research.73161.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/08/2022] [Indexed: 09/29/2023] Open
Abstract
Background: Pancreatic cancer is one of the deadliest forms of cancer. The cancer grades define how aggressively the cancer will spread and give indication for doctors to make proper prognosis and treatment. The current method of pancreatic cancer grading, by means of manual examination of the cancerous tissue following a biopsy, is time consuming and often results in misdiagnosis and thus incorrect treatment. This paper presents an automated grading system for pancreatic cancer from pathology images developed by comparing deep learning models on two different pathological stains. Methods: A transfer-learning technique was adopted by testing the method on 14 different ImageNet pre-trained models. The models were fine-tuned to be trained with our dataset. Results: From the experiment, DenseNet models appeared to be the best at classifying the validation set with up to 95.61% accuracy in grading pancreatic cancer despite the small sample set. Conclusions: To the best of our knowledge, this is the first work in grading pancreatic cancer based on pathology images. Previous works have either focused only on detection (benign or malignant), or on radiology images (computerized tomography [CT], magnetic resonance imaging [MRI] etc.). The proposed system can be very useful to pathologists in facilitating an automated or semi-automated cancer grading system, which can address the problems found in manual grading.
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Tian G, Xu D, He Y, Chai W, Deng Z, Cheng C, Jin X, Wei G, Zhao Q, Jiang T. Deep learning for real-time auxiliary diagnosis of pancreatic cancer in endoscopic ultrasonography. Front Oncol 2022; 12:973652. [PMID: 36276094 PMCID: PMC9586286 DOI: 10.3389/fonc.2022.973652] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
In recent year, many deep learning have been playing an important role in the detection of cancers. This study aimed to real-timely differentiate a pancreatic cancer (PC) or a non-pancreatic cancer (NPC) lesion via endoscopic ultrasonography (EUS) image. A total of 1213 EUS images from 157 patients (99 male, 58 female) with pancreatic disease were used for training, validation and test groups. Before model training, regions of interest (ROIs) were manually drawn to mark the PC and NPC lesions using Labelimage software. Yolov5m was used as the algorithm model to automatically distinguish the presence of pancreatic lesion. After training the model based on EUS images using YOLOv5, the parameters achieved convergence within 300 rounds (GIoU Loss: 0.01532, Objectness Loss: 0.01247, precision: 0.713 and recall: 0.825). For the validation group, the mAP0.5 was 0.831, and mAP@.5:.95 was 0.512. In addition, the receiver operating characteristic (ROC) curve analysis showed this model seemed to have a trend of more AUC of 0.85 (0.665 to 0.956) than the area under the curve (AUC) of 0.838 (0.65 to 0.949) generated by physicians using EUS detection without puncture, although pairwise comparison of ROC curves showed that the AUC between the two groups was not significant (z= 0.15, p = 0.8804). This study suggested that the YOLOv5m would generate attractive results and allow for the real-time decision support for distinction of a PC or a NPC lesion.
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Affiliation(s)
- Guo Tian
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, China
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Danxia Xu
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, China
| | - Yinghua He
- Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, Hangzhou, China
| | - Weilu Chai
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, China
| | - Zhuang Deng
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Cheng
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xinyan Jin
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Guyue Wei
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiyu Zhao
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, China
| | - Tianan Jiang
- Department of Ultrasound Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, China
- Zhejiang University Cancer Center, Hangzhou, China
- *Correspondence: Tianan Jiang,
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22
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Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic cancer. Am J Cancer Res 2022; 12:6931-6954. [PMID: 36276650 PMCID: PMC9576619 DOI: 10.7150/thno.77949] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.
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Affiliation(s)
- Bowen Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Haoran Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Shuting Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Dingyue Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Qingya Shi
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jianzhou Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Junchao Guo
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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Naito Y, Notohara K, Omori Y, Aishima S, Itoi T, Ohike N, Okabe Y, Kojima M, Tajiri T, Tanaka M, Tsuneki M, Nakagohri T, Norose T, Hirabayashi K, Fukumura Y, Mitsuhashi T, Yamaguchi H, Fukushima N, Furukawa T. Diagnostic Categories and Key Features for Pathological Diagnosis of Endoscopic Ultrasound-Guided Fine Needle Aspiration Biopsy Samples of Pancreatic Lesions: A Consensus Study. Pancreas 2022; 51:1105-1111. [PMID: 37078931 PMCID: PMC10144294 DOI: 10.1097/mpa.0000000000002179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 12/29/2022] [Indexed: 04/21/2023]
Abstract
OBJECTIVES This study aimed to establish a reliable and reproducible categorized diagnostic classification system with identification of key features to achieve accurate pathological diagnosis of endoscopic ultrasound-guided fine needle aspiration biopsy (EUS-FNAB) samples of pancreatic lesions. METHODS Twelve pathologists examined virtual whole-slide images of EUS-FNAB samples obtained from 80 patients according to proposed diagnostic categories and key features for diagnosis. Fleiss κ was used to assess the concordance. RESULTS A hierarchical diagnostic system consisting of the following 6 diagnostic categories was proposed: inadequate, nonneoplasm, indeterminate, ductal carcinoma, nonductal neoplasm, and unclassified neoplasm. Adopting these categories, the average κ value of participants was 0.677 (substantial agreement). Among these categories, ductal carcinoma and nonductal neoplasm showed high κ values of 0.866 and 0.837, respectively, which indicated the almost perfect agreement. Key features identified for diagnosing ductal carcinoma were necrosis in low-power appearance; structural atypia/abnormalities recognized by irregular glandular contours, including cribriform and nonuniform shapes; cellular atypia, including enlarged nuclei, irregular nuclear contours, and foamy gland changes; and haphazard glandular arrangement and stromal desmoplasia. CONCLUSIONS The proposed hierarchical diagnostic classification system was proved to be useful for achieving reliable and reproducible diagnosis of EUS-FNAB specimens of pancreatic lesions based on evaluated histological features.
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Affiliation(s)
- Yoshiki Naito
- From the Department of Clinical Laboratory Medicine, Kurume University Hospital, Fukuoka
| | - Kenji Notohara
- Department of Anatomic Pathology, Kurashiki Central Hospital, Okayama
| | - Yuko Omori
- Department of Investigative Pathology, Tohoku University Graduate School of Medicine, Sendai
| | - Shinichi Aishima
- Department of Pathology and Microbiology, Faculty of Medicine, Saga University, Saga
| | - Takao Itoi
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo
| | - Nobuyuki Ohike
- Department of Pathology, St Marianna University School of Medicine, Kawasaki
| | - Yoshinobu Okabe
- Division of Gastroenterology, Department of Medicine, Kurume University School of Medicine, Fukuoka
| | - Motohiro Kojima
- Division of Pathology, Research Center for Innovative Oncology, National Cancer Center, Chiba
| | - Takuma Tajiri
- Department of Diagnostic Pathology, Tokai University Hachioji Hospital
| | - Mariko Tanaka
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo
| | | | - Toshio Nakagohri
- Department of Surgery, Tokai University School of Medicine, Kanagawa
| | - Tomoko Norose
- Department of Pathology, St Marianna University School of Medicine, Kawasaki
| | - Kenichi Hirabayashi
- Department of Diagnostic Pathology, Faculty of Medicine, University of Toyama, Toyama
| | - Yuki Fukumura
- Department of Human Pathology, School of Medicine, Juntendo University, Tokyo
| | - Tomoko Mitsuhashi
- Department of Surgical Pathology, Hokkaido University Hospital, Hokkaido
| | | | | | - Toru Furukawa
- Department of Investigative Pathology, Tohoku University Graduate School of Medicine, Sendai
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Spadaccini M, Koleth G, Emmanuel J, Khalaf K, Facciorusso A, Grizzi F, Hassan C, Colombo M, Mangiavillano B, Fugazza A, Anderloni A, Carrara S, Repici A. Enhanced endoscopic ultrasound imaging for pancreatic lesions: The road to artificial intelligence. World J Gastroenterol 2022; 28:3814-3824. [PMID: 36157539 PMCID: PMC9367228 DOI: 10.3748/wjg.v28.i29.3814] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 06/07/2022] [Accepted: 07/06/2022] [Indexed: 02/06/2023] Open
Abstract
Early detection of pancreatic cancer has long eluded clinicians because of its insidious nature and onset. Often metastatic or locally invasive when symptomatic, most patients are deemed inoperable. In those who are symptomatic, multi-modal imaging modalities evaluate and confirm pancreatic ductal adenocarcinoma. In asymptomatic patients, detected pancreatic lesions can be either solid or cystic. The clinical implications of identifying small asymptomatic solid pancreatic lesions (SPLs) of < 2 cm are tantamount to a better outcome. The accurate detection of SPLs undoubtedly promotes higher life expectancy when resected early, driving the development of existing imaging tools while promoting more comprehensive screening programs. An imaging tool that has matured in its reiterations and received many image-enhancing adjuncts is endoscopic ultrasound (EUS). It carries significant importance when risk stratifying cystic lesions and has substantial diagnostic value when combined with fine needle aspiration/biopsy (FNA/FNB). Adjuncts to EUS imaging include contrast-enhanced harmonic EUS and EUS-elastography, both having improved the specificity of FNA and FNB. This review intends to compile all existing enhancement modalities and explore ongoing research around the most promising of all adjuncts in the field of EUS imaging, artificial intelligence.
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Affiliation(s)
- Marco Spadaccini
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Glenn Koleth
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - James Emmanuel
- Department of Gastroenterology and Hepatology, Queen Elizabeth, Kota Kinabalu 88200, Sabah, Malaysia
| | - Kareem Khalaf
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Antonio Facciorusso
- Section of Gastroenterology, Department of Medical Sciences, University of Foggia, Foggia 71122, Italy
| | - Fabio Grizzi
- Department of Immunology and Inflammation, Humanitas Clinical and Research Hospital, Rozzano 20089, Italy
| | - Cesare Hassan
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Matteo Colombo
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Benedetto Mangiavillano
- Digestive Endoscopy Unit, Division of Gasteroenterology, Humanitas Mater Domini, Castellanza 21053, Italy
| | - Alessandro Fugazza
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Andrea Anderloni
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Silvia Carrara
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Alessandro Repici
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
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Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers (Basel) 2022; 14:cancers14143498. [PMID: 35884559 PMCID: PMC9316850 DOI: 10.3390/cancers14143498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/07/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed. Abstract Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.
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Affiliation(s)
- Megan Schuurmans
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Natália Alves
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Pierpaolo Vendittelli
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - John Hermans
- Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands;
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Yamada R, Nakane K, Kadoya N, Matsuda C, Imai H, Tsuboi J, Hamada Y, Tanaka K, Tawara I, Nakagawa H. Development of “Mathematical Technology for Cytopathology,” an Image Analysis Algorithm for Pancreatic Cancer. Diagnostics (Basel) 2022; 12:diagnostics12051149. [PMID: 35626304 PMCID: PMC9139930 DOI: 10.3390/diagnostics12051149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/01/2022] [Accepted: 05/03/2022] [Indexed: 12/24/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related death worldwide. The accuracy of a PDAC diagnosis based on endoscopic ultrasonography-guided fine-needle aspiration cytology can be strengthened by performing a rapid on-site evaluation (ROSE). However, ROSE can only be performed in a limited number of facilities, due to a relative lack of available resources or cytologists with sufficient training. Therefore, we developed the Mathematical Technology for Cytopathology (MTC) algorithm, which does not require teaching data or large-scale computing. We applied the MTC algorithm to support the cytological diagnosis of pancreatic cancer tissues, by converting medical images into structured data, which rendered them suitable for artificial intelligence (AI) analysis. Using this approach, we successfully clarified ambiguous cell boundaries by solving a reaction–diffusion system and quantitating the cell nucleus status. A diffusion coefficient (D) of 150 showed the highest accuracy (i.e., 74%), based on a univariate analysis. A multivariate analysis was performed using 120 combinations of evaluation indices, and the highest accuracies for each D value studied (50, 100, and 150) were all ≥70%. Thus, our findings indicate that MTC can help distinguish between adenocarcinoma and benign pancreatic tissues, and imply its potential for facilitating rapid progress in clinical diagnostic applications.
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Affiliation(s)
- Reiko Yamada
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan; (Y.H.); (H.N.)
- Correspondence: ; Tel.: +81-59-232-1111
| | - Kazuaki Nakane
- Department of Molecular Pathology, Osaka University, Osaka 565-0871, Japan;
| | - Noriyuki Kadoya
- Department of Radiation Oncology, School of Medicine, Tohoku University, Sendai 980-8577, Japan;
| | - Chise Matsuda
- Department of Pathology, Mie University Hospital, Tsu 514-8507, Japan; (C.M.); (H.I.)
| | - Hiroshi Imai
- Department of Pathology, Mie University Hospital, Tsu 514-8507, Japan; (C.M.); (H.I.)
| | - Junya Tsuboi
- Department of Endoscopic Medicine, Mie University Hospital, Tsu 514-8507, Japan; (J.T.); (K.T.)
| | - Yasuhiko Hamada
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan; (Y.H.); (H.N.)
| | - Kyosuke Tanaka
- Department of Endoscopic Medicine, Mie University Hospital, Tsu 514-8507, Japan; (J.T.); (K.T.)
| | - Isao Tawara
- Department of Hematology and Oncology, School of Medicine, Mie University, Tsu 514-8507, Japan;
| | - Hayato Nakagawa
- Department of Gastroenterology and Hepatology, School of Medicine, Mie University, Tsu 514-8507, Japan; (Y.H.); (H.N.)
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Tsuneki M, Abe M, Kanavati F. A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning. Diagnostics (Basel) 2022; 12:768. [PMID: 35328321 DOI: 10.3390/diagnostics12030768] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/08/2022] [Accepted: 03/18/2022] [Indexed: 02/04/2023] Open
Abstract
The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual system and limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately screen large numbers of histopathological prostate needle biopsy specimens. Computational pathology applications that can assist pathologists in detecting and classifying prostate adenocarcinoma from whole-slide images (WSIs) would be of great benefit for routine pathological practice. In this paper, we trained deep learning models capable of classifying needle biopsy WSIs into adenocarcinoma and benign (non-neoplastic) lesions. We evaluated the models on needle biopsy, transurethral resection of the prostate (TUR-P), and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.978 in needle biopsy test sets and up to 0.9873 in TCGA test sets for adenocarcinoma.
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Abstract
BACKGROUND Deep learning is a state-of-the-art technology that has rapidly become the method of choice for medical image analysis. Its fast and robust object detection, segmentation, tracking, and classification of pathophysiological anatomical structures can support medical practitioners during routine clinical workflow. Thus, deep learning-based applications for diseases diagnosis will empower physicians and allow fast decision-making in clinical practice. HIGHLIGHT Deep learning can be more robust with various features for differentiating classes, provided the training set is large and diverse for analysis. However, sufficient medical images for training sets are not always available from medical institutions, which is one of the major limitations of deep learning in medical image analysis. This review article presents some solutions for this issue and discusses efforts needed to develop robust deep learning-based computer-aided diagnosis applications for better clinical workflow in endoscopy, radiology, pathology, and dentistry. CONCLUSION The introduction of deep learning-based applications will enhance the traditional role of medical practitioners in ensuring accurate diagnoses and treatment in terms of precision, reproducibility, and scalability.
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Affiliation(s)
- Masayuki Tsuneki
- Medmain Research, Medmain Inc., Fukuoka, Japan; Division of Anatomy and Cell Biology of the Hard Tissue, Department of Tissue Regeneration and Reconstruction, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
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Prabhu S, Prasad K, Robels-Kelly A, Lu X. AI-based carcinoma detection and classification using histopathological images: A systematic review. Comput Biol Med 2022; 142:105209. [DOI: 10.1016/j.compbiomed.2022.105209] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/01/2022] [Accepted: 01/01/2022] [Indexed: 02/07/2023]
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Ishikawa T, Hayakawa M, Suzuki H, Ohno E, Mizutani Y, Iida T, Fujishiro M, Kawashima H, Hotta K. Development of a Novel Evaluation Method for Endoscopic Ultrasound-Guided Fine-Needle Biopsy in Pancreatic Diseases Using Artificial Intelligence. Diagnostics (Basel) 2022; 12:diagnostics12020434. [PMID: 35204524 PMCID: PMC8871496 DOI: 10.3390/diagnostics12020434] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 01/28/2022] [Accepted: 02/07/2022] [Indexed: 12/12/2022] Open
Abstract
We aimed to develop a new artificial intelligence (AI)-based method for evaluating endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) specimens in pancreatic diseases using deep learning and contrastive learning. We analysed a total of 173 specimens from 96 patients who underwent EUS-FNB with a 22 G Franseen needle for pancreatic diseases. In the initial study, the deep learning method based on stereomicroscopic images of 98 EUS-FNB specimens from 63 patients showed an accuracy of 71.8% for predicting the histological diagnosis, which was lower than that of macroscopic on-site evaluation (MOSE) performed by EUS experts (81.6%). Then, we used image analysis software to mark the core tissues in the photomicrographs of EUS-FNB specimens after haematoxylin and eosin staining and verified whether the diagnostic performance could be improved by applying contrastive learning for the features of the stereomicroscopic images and stained images. The sensitivity, specificity, and accuracy of MOSE were 88.97%, 53.5%, and 83.24%, respectively, while those of the AI-based diagnostic method using contrastive learning were 90.34%, 53.5%, and 84.39%, respectively. The AI-based evaluation method using contrastive learning was comparable to MOSE performed by EUS experts and can be a novel objective evaluation method for EUS-FNB.
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Affiliation(s)
- Takuya Ishikawa
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 4668550, Japan; (H.S.); (E.O.); (Y.M.); (T.I.)
- Correspondence: ; Tel./Fax: +81-(52)-744-2602
| | - Masato Hayakawa
- Department of Electrical and Electronic Engineering, Faculty of Science and Technology, Meijo University, Nagoya 4688502, Japan; (M.H.); (K.H.)
| | - Hirotaka Suzuki
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 4668550, Japan; (H.S.); (E.O.); (Y.M.); (T.I.)
- Department of Gastroenterology, Toyohashi Municipal Hospital, Toyohashi 4418570, Japan
| | - Eizaburo Ohno
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 4668550, Japan; (H.S.); (E.O.); (Y.M.); (T.I.)
| | - Yasuyuki Mizutani
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 4668550, Japan; (H.S.); (E.O.); (Y.M.); (T.I.)
| | - Tadashi Iida
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya 4668550, Japan; (H.S.); (E.O.); (Y.M.); (T.I.)
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan;
| | - Hiroki Kawashima
- Department of Endoscopy, Nagoya University Hospital, Nagoya 4668550, Japan;
| | - Kazuhiro Hotta
- Department of Electrical and Electronic Engineering, Faculty of Science and Technology, Meijo University, Nagoya 4688502, Japan; (M.H.); (K.H.)
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Kanavati F, Tsuneki M. Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning. Cancers (Basel) 2021; 13:cancers13215368. [PMID: 34771530 PMCID: PMC8582388 DOI: 10.3390/cancers13215368] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/22/2021] [Accepted: 10/23/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary In this study, we have trained deep learning models using transfer learning and weakly-supervised learning for the classification of breast invasive ductal carcinoma (IDC) in whole slide images (WSIs). We evaluated the models on four test sets: one biopsy (n = 522) and three surgical (n = 1129) achieving AUCs in the range 0.95 to 0.99. We have also compared the trained models to existing pre-trained models on different organs for adenocarcinoma classification and they have achieved lower AUC performances in the range 0.66 to 0.89 despite adenocarcinoma exhibiting some structural similarity to IDC. Therefore, performing fine-tuning on the breast IDC training set was beneficial for improving performance. The results demonstrate the potential use of such models to aid pathologists in clinical practice. Abstract Invasive ductal carcinoma (IDC) is the most common form of breast cancer. For the non-operative diagnosis of breast carcinoma, core needle biopsy has been widely used in recent years for the evaluation of histopathological features, as it can provide a definitive diagnosis between IDC and benign lesion (e.g., fibroadenoma), and it is cost effective. Due to its widespread use, it could potentially benefit from the use of AI-based tools to aid pathologists in their pathological diagnosis workflows. In this paper, we trained invasive ductal carcinoma (IDC) whole slide image (WSI) classification models using transfer learning and weakly-supervised learning. We evaluated the models on a core needle biopsy (n = 522) test set as well as three surgical test sets (n = 1129) obtaining ROC AUCs in the range of 0.95–0.98. The promising results demonstrate the potential of applying such models as diagnostic aid tools for pathologists in clinical practice.
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Mohamad Sehmi MN, Ahmad Fauzi MF, Wan Ahmad WSHM, Wan Ling Chan E. Pancreatic cancer grading in pathological images using deep learning convolutional neural networks. F1000Res 2021. [DOI: 10.12688/f1000research.73161.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: Pancreatic cancer is one of the deadliest forms of cancer. The cancer grades define how aggressively the cancer will spread and give indication for doctors to make proper prognosis and treatment. The current method of pancreatic cancer grading, by means of manual examination of the cancerous tissue following a biopsy, is time consuming and often results in misdiagnosis and thus incorrect treatment. This paper presents an automated grading system for pancreatic cancer from pathology images developed by comparing deep learning models on two different pathological stains. Methods: A transfer-learning technique was adopted by testing the method on 14 different ImageNet pre-trained models. The models were fine-tuned to be trained with our dataset. Results: From the experiment, DenseNet models appeared to be the best at classifying the validation set with up to 95.61% accuracy in grading pancreatic cancer despite the small sample set. Conclusions: To the best of our knowledge, this is the first work in grading pancreatic cancer based on pathology images. Previous works have either focused only on detection (benign or malignant), or on radiology images (computerized tomography [CT], magnetic resonance imaging [MRI] etc.). The proposed system can be very useful to pathologists in facilitating an automated or semi-automated cancer grading system, which can address the problems found in manual grading.
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