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Zehra T, Moeen S, Shams M, Raza M, Khurshid A, Jafri A, Abdul-Ghafar J. Model for detecting metastatic deposits in lymph nodes of colorectal carcinoma on digital/ non-WSI images. Diagn Pathol 2024; 19:125. [PMID: 39285483 PMCID: PMC11404020 DOI: 10.1186/s13000-024-01547-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Accepted: 09/03/2024] [Indexed: 09/22/2024] Open
Abstract
INTRODUCTION Colorectal cancer (CRC) constitutes around 10% of global cancer diagnoses and death due to cancer. Treatment involves the surgical resection of the tumor and regional lymph nodes. Assessment of multiple lymph node demands meticulous examination by skilled pathologists, which can be arduous, prompting consideration for an artificial intelligence (AI)-supported workflow due to the growing number of slides to be examined, demanding heightened precision and the global shortage of pathologists. METHOD This was a retrospective cross-sectional study including digital images of glass slides containing sections of positive and negative lymph nodes obtained from radical resection of primary CRC. Lymph nodes from 165 previously diagnosed cases were selected from Agha Khan University Hospital, from Jan 2021 to Jan 2022. The images were prepared at 10X and uploaded into an open source software, Q path and deep learning model Ensemble was applied for the identification of tumor deposits in lymph node. RESULTS Out of the 87 positive lymph nodes detected by AI, 73(84%) were true positive and 14(16%) were false positive. The total number of negative lymph nodes detected by AI was 78. Out of these, 69(88.5%) were true negative and 9 (11.5%) were false negative. The sensitivity was 89% and specificity 83.1%. The odds ratio was 40 with a confidence interval of 16.26-98.3. P-value was < 0.05 (< 0.0001). CONCLUSION Though it was a small study but its results were really appreciating and we encourage more such studies with big sample data in future.
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Affiliation(s)
- Talat Zehra
- Jinnah Sindh Medical University, Karachi, Pakistan
| | | | - Mahin Shams
- United Medical and Dental College, Karachi, Pakistan
| | | | - Amna Khurshid
- Histopathology Department, Liaquat National Hospital, Karachi, Pakistan
| | - Asad Jafri
- Histopathology Department, Liaquat National Hospital, Karachi, Pakistan
| | - Jamshid Abdul-Ghafar
- Department of Pathology and Clinical Laboratory, French Medical Institute for Mothers and Children (FMIC), Kabul, Afghanistan.
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Tanaka R, Tsuboshita Y, Okodo M, Settsu R, Hashimoto K, Tachibana K, Tanabe K, Kishimoto K, Fujiwara M, Shibahara J. Artificial Intelligence Recognition Model Using Liquid-Based Cytology Images to Discriminate Malignancy and Histological Types of Non-Small-Cell Lung Cancer. Pathobiology 2024:1-11. [PMID: 39197433 DOI: 10.1159/000541148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 08/24/2024] [Indexed: 09/01/2024] Open
Abstract
INTRODUCTION Artificial intelligence image recognition has applications in clinical practice. The purpose of this study was to develop an automated image classification model for lung cancer cytology using a deep learning convolutional neural network (DCNN). METHODS Liquid-based cytology samples from 8 normal parenchymal (N), 22 adenocarcinoma (ADC), and 15 squamous cell carcinoma (SQCC) surgical specimens were prepared, and 45 Papanicolaou-stained slides were scanned using whole-slide imaging. The final dataset of 9,141 patches consisted of 2,737 N, 4,756 ADC, and 1,648 SQCC samples. Densenet-121 was used as the DCNN to classify N versus malignant (ADC+SQCC) and ADC versus SQCC images. AdamW optimizer and 5-fold cross-validation were used in the training. RESULTS For malignancy prediction, the sensitivity, specificity, and accuracy were 0.97, 0.85, and 0.94, respectively, in the patch-level classification, and 0.92, 0.88, and 0.91, respectively, in the case-level classification. For SQCC prediction, the sensitivity, specificity, and accuracy were 0.86, 0.91, and 0.90, respectively, in the patch-level classification and 0.73, 0.82, and 0.78, respectively, in the case-level classification. CONCLUSION The DCNN model performed excellently in predicting malignancy and histological types of lung cancer. This model may be useful for predicting cytopathological diagnosis in clinical situations by reinforcing training.
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Affiliation(s)
- Ryota Tanaka
- Department of Thoracic and Thyroid Surgery, Kyorin University, Tokyo, Japan
| | - Yukihiro Tsuboshita
- Center for Data Science Education and Research, Kyorin University, Tokyo, Japan
| | - Mitsuaki Okodo
- Department of Medical Technology, Faculty of Health Sciences, Kyorin University, Tokyo, Japan
| | - Rei Settsu
- Department of Medical Technology, Faculty of Health Sciences, Kyorin University, Tokyo, Japan
| | - Kohei Hashimoto
- Department of Thoracic and Thyroid Surgery, Kyorin University, Tokyo, Japan
| | - Keisei Tachibana
- Department of Thoracic and Thyroid Surgery, Kyorin University, Tokyo, Japan
| | - Kazumasa Tanabe
- Department of Pathology, Kyorin University School of Medicine, Tokyo, Japan
| | - Koji Kishimoto
- Department of Pathology, Kyorin University School of Medicine, Tokyo, Japan
| | - Masachika Fujiwara
- Department of Pathology, Kyorin University School of Medicine, Tokyo, Japan
| | - Junji Shibahara
- Department of Pathology, Kyorin University School of Medicine, Tokyo, Japan
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Lee Y, Alam MR, Park H, Yim K, Seo KJ, Hwang G, Kim D, Chung Y, Gong G, Cho NH, Yoo CW, Chong Y, Choi HJ. Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology. Thyroid 2024; 34:723-734. [PMID: 38874262 DOI: 10.1089/thy.2023.0384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Background: Artificial intelligence (AI) is increasingly being applied in pathology and cytology, showing promising results. We collected a large dataset of whole slide images (WSIs) of thyroid fine-needle aspiration cytology (FNA), incorporating z-stacking, from institutions across the nation to develop an AI model. Methods: We conducted a multicenter retrospective diagnostic accuracy study using thyroid FNA dataset from the Open AI Dataset Project that consists of digitalized images samples collected from 3 university hospitals and 215 Korean institutions through extensive quality check during the case selection, scanning, labeling, and reviewing process. Multiple z-layer images were captured using three different scanners and image patches were extracted from WSIs and resized after focus fusion and color normalization. We pretested six AI models, determining Inception ResNet v2 as the best model using a subset of dataset, and subsequently tested the final model with total datasets. Additionally, we compared the performance of AI and cytopathologists using randomly selected 1031 image patches and reevaluated the cytopathologists' performance after reference to AI results. Results: A total of 10,332 image patches from 306 thyroid FNAs, comprising 78 malignant (papillary thyroid carcinoma) and 228 benign from 86 institutions were used for the AI training. Inception ResNet v2 achieved highest accuracy of 99.7%, 97.7%, and 94.9% for training, validation, and test dataset, respectively (sensitivity 99.9%, 99.6%, and 100% and specificity 99.6%, 96.4%, and 90.4% for training, validation, and test dataset, respectively). In the comparison between AI and human, AI model showed higher accuracy and specificity than the average expert cytopathologists beyond the two-standard deviation (accuracy 99.71% [95% confidence interval (CI), 99.38-100.00%] vs. 88.91% [95% CI, 86.99-90.83%], sensitivity 99.81% [95% CI, 99.54-100.00%] vs. 87.26% [95% CI, 85.22-89.30%], and specificity 99.61% [95% CI, 99.23-99.99%] vs. 90.58% [95% CI, 88.80-92.36%]). Moreover, after referring to the AI results, the performance of all the experts (accuracy 96%, 95%, and 96%, respectively) and the diagnostic agreement (from 0.64 to 0.84) increased. Conclusions: These results suggest that the application of AI technology to thyroid FNA cytology may improve the diagnostic accuracy as well as intra- and inter-observer variability among pathologists. Further confirmatory research is needed.
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Affiliation(s)
- Yujin Lee
- Department of Hospital Pathology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Mohammad Rizwan Alam
- Department of Hospital Pathology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Hongsik Park
- Department of Hospital Pathology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Kwangil Yim
- Department of Hospital Pathology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Kyung Jin Seo
- Department of Hospital Pathology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | | | | | | | - Gyungyub Gong
- Department of Pathology, Asan Medical Center, Seoul, Korea
| | - Nam Hoon Cho
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Chong Woo Yoo
- Department of Pathology, National Cancer Center, Ilsan, Republic of Korea
| | - Yosep Chong
- Department of Hospital Pathology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Hyun Joo Choi
- Department of Hospital Pathology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
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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] [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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Kazemi A, Rasouli-Saravani A, Gharib M, Albuquerque T, Eslami S, Schüffler PJ. A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes. Comput Biol Med 2024; 173:108306. [PMID: 38554659 DOI: 10.1016/j.compbiomed.2024.108306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 03/04/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024]
Abstract
The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect. Incorporating machine learning (ML) based applications in clinical routine may promote diagnosis reliability. Recently, ML has shown potential for making progress in routine clinical procedures. We aim to systematically review the TILs analysis based on ML in CRC histological images. Deep learning (DL) and non-DL techniques can aid pathologists in identifying TILs, and automated TILs are associated with patient outcomes. However, a large multi-institutional CRC dataset with a diverse and multi-ethnic population is necessary to generalize ML methods.
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Affiliation(s)
- Azar Kazemi
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Institute of General and Surgical Pathology, Technical University of Munich, Munich, Germany.
| | - Ashkan Rasouli-Saravani
- Student Research Committee, Department of Immunology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Masoumeh Gharib
- Department of Pathology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| | | | - Saeid Eslami
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Pharmaceutical Sciences Research Center, Institute of Pharmaceutical Technology, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical Informatics, University of Amsterdam, Amsterdam, the Netherlands.
| | - Peter J Schüffler
- Institute of General and Surgical Pathology, Technical University of Munich, Munich, Germany; TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; Munich Center for Machine Learning, Munich, Germany; Munich Data Science Institute, Munich, Germany.
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Kim HK, Han E, Lee J, Yim K, Abdul-Ghafar J, Seo KJ, Seo JW, Gong G, Cho NH, Kim M, Yoo CW, Chong Y. Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid. Cancers (Basel) 2024; 16:1064. [PMID: 38473421 DOI: 10.3390/cancers16051064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/17/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
Ascites cytology is a cost-effective test for metastatic colorectal cancer (CRC) in the abdominal cavity. However, metastatic carcinoma of the peritoneum is difficult to diagnose based on biopsy findings, and ascitic aspiration cytology has a low sensitivity and specificity and a high inter-observer variability. The aim of the present study was to apply artificial intelligence (AI) to classify benign and malignant cells in ascites cytology patch images of metastatic CRC using a deep convolutional neural network. Datasets were collected from The OPEN AI Dataset Project, a nationwide cytology dataset for AI research. The numbers of patch images used for training, validation, and testing were 56,560, 7068, and 6534, respectively. We evaluated 1041 patch images of benign and metastatic CRC in the ascitic fluid to compare the performance of pathologists and an AI algorithm, and to examine whether the diagnostic accuracy of pathologists improved with the assistance of AI. This AI method showed an accuracy, a sensitivity, and a specificity of 93.74%, 87.76%, and 99.75%, respectively, for the differential diagnosis of malignant and benign ascites. The diagnostic accuracy and sensitivity of the pathologist with the assistance of the proposed AI method increased from 86.8% to 90.5% and from 73.3% to 79.3%, respectively. The proposed deep learning method may assist pathologists with different levels of experience in diagnosing metastatic CRC cells of ascites.
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Affiliation(s)
- Hyung Kyung Kim
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea
- Department of Pathology, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Eunkyung Han
- Department of Pathology, Soonchunyang University Hospital Bucheon, Bucheon 14584, Republic of Korea
| | - Jeonghyo Lee
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea
| | - Kwangil Yim
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Jamshid Abdul-Ghafar
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Kyung Jin Seo
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Jang Won Seo
- AI Team, MTS Company Inc., Seoul 06178, Republic of Korea
| | - Gyungyub Gong
- Department of Pathology, Asan Medical Center, Seoul 05505, Republic of Korea
| | - Nam Hoon Cho
- Department of Pathology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Milim Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Chong Woo Yoo
- Department of Pathology, National Cancer Center, Goyang 10408, Republic of Korea
| | - Yosep Chong
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
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Neto PC, Montezuma D, Oliveira SP, Oliveira D, Fraga J, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Reinhard S, Zlobec I, Pinto IM, Cardoso JS. An interpretable machine learning system for colorectal cancer diagnosis from pathology slides. NPJ Precis Oncol 2024; 8:56. [PMID: 38443695 PMCID: PMC10914836 DOI: 10.1038/s41698-024-00539-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 02/08/2024] [Indexed: 03/07/2024] Open
Abstract
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.
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Affiliation(s)
- Pedro C Neto
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
| | - Diana Montezuma
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal.
- Cancer Biology and Epigenetics Group, Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center (Porto.CCC), R. Dr. António Bernardino de Almeida 865, Porto, 4200-072, Porto, Portugal.
- Doctoral Programme in Medical Sciences, School of Medicine and Biomedical Sciences - University of Porto (ICBAS-UP), R. Jorge de Viterbo Ferreira 228, Porto, 4050-313, Porto, Portugal.
| | - Sara P Oliveira
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal.
| | - Domingos Oliveira
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - João Fraga
- Department of Pathology, IPO-Porto, R. Dr. António Bernardino de Almeida 865, Porto, 4200-072, Porto, Portugal
| | - Ana Monteiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - João Monteiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Liliana Ribeiro
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Sofia Gonçalves
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Stefan Reinhard
- Institute of Pathology, University of Bern, Uni Bern, Murtenstrasse 31, Bern, 3008, Bern, Switzerland
| | - Inti Zlobec
- Institute of Pathology, University of Bern, Uni Bern, Murtenstrasse 31, Bern, 3008, Bern, Switzerland
| | - Isabel M Pinto
- IMP Diagnostics, Praça do Bom Sucesso, 61, sala 808, Porto, 4150-146, Porto, Portugal
| | - Jaime S Cardoso
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
- Faculty of Engineering, University of Porto (FEUP), R. Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
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Grignaffini F, Barbuto F, Troiano M, Piazzo L, Simeoni P, Mangini F, De Stefanis C, Onetti Muda A, Frezza F, Alisi A. The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review. Diagnostics (Basel) 2024; 14:388. [PMID: 38396427 PMCID: PMC10887838 DOI: 10.3390/diagnostics14040388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.
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Affiliation(s)
- Flavia Grignaffini
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Francesco Barbuto
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Maurizio Troiano
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
| | - Lorenzo Piazzo
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Patrizio Simeoni
- National Transport Authority (NTA), D02 WT20 Dublin, Ireland;
- Faculty of Lifelong Learning, South East Technological University (SETU), R93 V960 Carlow, Ireland
| | - Fabio Mangini
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Cristiano De Stefanis
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
| | | | - Fabrizio Frezza
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Anna Alisi
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
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Lee TH, Yoon K, Lee S, Choi WR, Kim KG. Comparative Sensing and Judgment Control System for Temperature Maintenance for Optimal Treatment in Hyperthermic Intraperitoneal Chemotherapy Surgery. SENSORS (BASEL, SWITZERLAND) 2024; 24:596. [PMID: 38257692 PMCID: PMC10821041 DOI: 10.3390/s24020596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/05/2024] [Accepted: 01/11/2024] [Indexed: 01/24/2024]
Abstract
For tumors wherein cancer cells remain in the tissue after colorectal cancer surgery, a hyperthermic anticancer agent is injected into the abdominal cavity to necrotize the remaining cancer cells with heat using a hyperthermic intraperitoneal chemotherapy system. However, during circulation, the processing temperature is out of range and the processing result is deteriorated. This paper proposes a look-up table (LUT) module design method that can stably maintain the processing temperature range during circulation via feedback. If the temperature decreases or increases, the LUT transmits a command signal to the heat exchanger to reduce or increase heat input, thereby maintaining the treatment temperature range. The command signal for increasing and decreasing heat input is Tp and Ta, respectively. The command signal for the treatment temperature range is Ts. If drug temperatures below 41 and above 43 °C are input to the LUT, it sends a Tp or Ta signal to the heat exchanger to increase or decrease the input heat, respectively. If the drug's temperature is 41-43 °C, the LUT generates a Ts signal and proceeds with the treatment. The proposed system can automatically control drug temperature using temperature feedback to ensure rapid, accurate, and safe treatment.
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Affiliation(s)
- Tae-Hyeon Lee
- Department of Electronic Engineering, Gyeonggi University of Science and Technology, 269 Gyeonggigwagi–dearo, Gyeonggi–do, Siheung City 15073, Republic of Korea;
| | - Kicheol Yoon
- Medical Devices R&D Center, Gachon University Gil Medical Center, 21, 774 beon–gil, Namdong–daero Namdong–gu, Incheon 21565, Republic of Korea; (K.Y.); (S.L.); (W.R.C.)
- Department of Premedicine Course, College of Medicine, Gachon University, 38–13, 3 Dokjom–ro, Namdong–gu, Incheon 21565, Republic of Korea
| | - Sangyun Lee
- Medical Devices R&D Center, Gachon University Gil Medical Center, 21, 774 beon–gil, Namdong–daero Namdong–gu, Incheon 21565, Republic of Korea; (K.Y.); (S.L.); (W.R.C.)
- Department of Health and Safety Convergence Sciences & Health and Environmental Convergence Sciences, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
- Department of Biomedical Engineering, Gachon University, 38–13, 3 Dokjom–ro, Namdong–gu, Incheon 21565, Republic of Korea
| | - Woong Rak Choi
- Medical Devices R&D Center, Gachon University Gil Medical Center, 21, 774 beon–gil, Namdong–daero Namdong–gu, Incheon 21565, Republic of Korea; (K.Y.); (S.L.); (W.R.C.)
- Department of Biomedical Engineering, Gachon University, 38–13, 3 Dokjom–ro, Namdong–gu, Incheon 21565, Republic of Korea
- School of Electrical Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Kwang Gi Kim
- Medical Devices R&D Center, Gachon University Gil Medical Center, 21, 774 beon–gil, Namdong–daero Namdong–gu, Incheon 21565, Republic of Korea; (K.Y.); (S.L.); (W.R.C.)
- Department of Biomedical Engineering, Gachon University, 38–13, 3 Dokjom–ro, Namdong–gu, Incheon 21565, Republic of Korea
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, 38–13, 3 Dokjom–ro, Namdong–gu, Incheon 21565, Republic of Korea
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10
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Ramzan M, Raza M, Sharif MI, Azam F, Kim J, Kadry S. Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization. PLoS One 2023; 18:e0292601. [PMID: 37831692 PMCID: PMC10575542 DOI: 10.1371/journal.pone.0292601] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset.
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Affiliation(s)
- Muhammad Ramzan
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Mudassar Raza
- Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
| | - Muhammad Irfan Sharif
- Department of Information Sciences, University of Education Lahore, Jauharabad Campus, Jauharabad, Pakistan
| | - Faisal Azam
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Jungeun Kim
- Department of Software and CMPSI, Kongju National University, Cheonan, Korea
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
- MEU Research Unit, Middle East University, Amman, Jordan
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11
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Beniwal SS, Lamo P, Kaushik A, Lorenzo-Villegas DL, Liu Y, MohanaSundaram A. Current Status and Emerging Trends in Colorectal Cancer Screening and Diagnostics. BIOSENSORS 2023; 13:926. [PMID: 37887119 PMCID: PMC10605407 DOI: 10.3390/bios13100926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/27/2023] [Accepted: 10/07/2023] [Indexed: 10/28/2023]
Abstract
Colorectal cancer (CRC) is a prevalent and potentially fatal disease categorized based on its high incidences and mortality rates, which raised the need for effective diagnostic strategies for the early detection and management of CRC. While there are several conventional cancer diagnostics available, they have certain limitations that hinder their effectiveness. Significant research efforts are currently being dedicated to elucidating novel methodologies that aim at comprehending the intricate molecular mechanism that underlies CRC. Recently, microfluidic diagnostics have emerged as a pivotal solution, offering non-invasive approaches to real-time monitoring of disease progression and treatment response. Microfluidic devices enable the integration of multiple sample preparation steps into a single platform, which speeds up processing and improves sensitivity. Such advancements in diagnostic technologies hold immense promise for revolutionizing the field of CRC diagnosis and enabling efficient detection and monitoring strategies. This article elucidates several of the latest developments in microfluidic technology for CRC diagnostics. In addition to the advancements in microfluidic technology for CRC diagnostics, the integration of artificial intelligence (AI) holds great promise for further enhancing diagnostic capabilities. Advancements in microfluidic systems and AI-driven approaches can revolutionize colorectal cancer diagnostics, offering accurate, efficient, and personalized strategies to improve patient outcomes and transform cancer management.
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Affiliation(s)
| | - Paula Lamo
- Escuela Superior de Ingeniería y Tecnología, Universidad Internacional de La Rioja, 26006 Logroño, Spain
| | - Ajeet Kaushik
- NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL 33805, USA
| | | | - Yuguang Liu
- Departments of Physiology and Biomedical Engineering, Immunology and Surgery, Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
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12
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Keshtkar K, Reza Safarpour A, Heshmat R, Sotoudehmanesh R, Keshtkar A. A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer. THE TURKISH JOURNAL OF GASTROENTEROLOGY : THE OFFICIAL JOURNAL OF TURKISH SOCIETY OF GASTROENTEROLOGY 2023; 34:985-997. [PMID: 37681266 PMCID: PMC10645297 DOI: 10.5152/tjg.2023.22491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 03/22/2023] [Indexed: 09/09/2023]
Abstract
Convolutional neural networks are a class of deep neural networks used for different clinical purposes, including improving the detection rate of colorectal lesions. This systematic review and meta-analysis aimed to assess the performance of convolutional neural network-based models in the detection or classification of colorectal polyps and colorectal cancer. A systematic search was performed in MEDLINE, SCOPUS, Web of Science, and other related databases. The performance measures of the convolutional neural network models in the detection of colorectal polyps and colorectal cancer were calculated in the 2 scenarios of the best and worst accuracy. Stata and R software were used for conducting the meta-analysis. From 3368 searched records, 24 primary studies were included. The sensitivity and specificity of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged from 84.7% to 91.6% and from 86.0% to 93.8%, respectively. These values in predicting colorectal cancer varied between 93.2% and 94.1% and between 94.6% and 97.7%. The positive and negative likelihood ratios varied between 6.2 and 14.5 and 0.09 and 0.17 in these scenarios, respectively, in predicting colorectal polyps, and 17.1-41.2 and 0.07-0.06 in predicting colorectal polyps. The diagnostic odds ratio and accuracy measures of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged between 36% and 162% and between 80.5% and 88.6%, respectively. These values in predicting colorectal cancer in the worst and the best scenarios varied between 239.63% and 677.47% and between 88.2% and 96.4%. The area under the receiver operating characteristic varied between 0.92 and 0.97 in the worst and the best scenarios in colorectal polyps, respectively, and between 0.98 and 0.99 in colorectal polyps prediction. Convolutional neural network-based models showed an acceptable accuracy in detecting colorectal polyps and colorectal cancer.
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Affiliation(s)
- Kamyab Keshtkar
- University of Tehran School of Electrical and Computer Engineering, Tehran, Iran
| | - Ali Reza Safarpour
- Gastroenterohepatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ramin Heshmat
- Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Rasoul Sotoudehmanesh
- Department of Gastroenterology, Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Keshtkar
- Department of Health Sciences Education Development, Tehran University of Medical Sciences School of Public Health, Tehran, Iran
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13
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Jing Y, Li C, Du T, Jiang T, Sun H, Yang J, Shi L, Gao M, Grzegorzek M, Li X. A comprehensive survey of intestine histopathological image analysis using machine vision approaches. Comput Biol Med 2023; 165:107388. [PMID: 37696178 DOI: 10.1016/j.compbiomed.2023.107388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/06/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023]
Abstract
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.
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Affiliation(s)
- Yujie Jing
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
| | - Tianming Du
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Tao Jiang
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China; International Joint Institute of Robotics and Intelligent Systems, Chengdu University of Information Technology, Chengdu, China
| | - Hongzan Sun
- Shengjing Hospital of China Medical University, Shenyang, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Liyu Shi
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Minghe Gao
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany; Department of Knowledge Engineering, University of Economics in Katowice, Katowice, Poland
| | - Xiaoyan Li
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China.
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Shao Z, Dai L, Jonnagaddala J, Chen Y, Wang Y, Fang Z, Zhang Y. Generalizability of Self-Supervised Training Models for Digital Pathology: A Multicountry Comparison in Colorectal Cancer. JCO Clin Cancer Inform 2023; 7:e2200178. [PMID: 37703507 DOI: 10.1200/cci.22.00178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 06/15/2023] [Accepted: 06/27/2023] [Indexed: 09/15/2023] Open
Abstract
PURPOSE In this multicountry study, we aim to explore the effectiveness of self-supervised learning (SSL) in colorectal cancer (CRC)-related predictive tasks using large amount of unlabeled digital pathology imaging data. METHODS We adopted SimSiam to conduct self-supervised pretraining on two large whole-slide image CRC data sets from the United States and Australia. The SSL pretrained encoder is then used in several predictive tasks, including supervised predictive tasks (tissue classification, microsatellite instability v microsatellite stability classification), and weakly supervised predictive tasks (polyp type classification and adenoma grading, and 5-year survival prediction). Performance on the tasks was compared between models using SSL pretraining and those using ImageNet pretraining, and performance for one-country pretraining was compared with two-country pretraining. RESULTS We demonstrate that SSL pretraining outperforms ImageNet pretraining in predictive tasks, that is, SSL pretraining outperforms the ImageNet pretraining by 3.01% of F 1 score on average over supervised predictive tasks and 1.53% of AUC on average over weakly supervised predictive tasks. Furthermore, two-country SSL pretraining has shown more stable performance than single-country pretraining, that is, two-country pretraining outperforms at least one of the single-country pretrainings by 1.93% of F 1 on average over supervised predictive tasks and 1.36% of AUC on average over weakly-supervised predictive tasks. CONCLUSION We find that using unlabeled image data for SSL pretraining in CRC related tasks is more effective than using ImageNet pretraining. Furthermore, SSL pretraining using data from multiple countries achieve more stable performance and better generalization than single-country pretraining.
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Affiliation(s)
- Zhuchen Shao
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Liuxi Dai
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
| | | | - Yang Chen
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Yifeng Wang
- School of Science, Harbin Institute of Technology, Shenzhen, China
| | - Zijie Fang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Yongbing Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
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15
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Davri A, Birbas E, Kanavos T, Ntritsos G, Giannakeas N, Tzallas AT, Batistatou A. Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review. Cancers (Basel) 2023; 15:3981. [PMID: 37568797 PMCID: PMC10417369 DOI: 10.3390/cancers15153981] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists' routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist's routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.
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Affiliation(s)
- Athena Davri
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
| | - Effrosyni Birbas
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Theofilos Kanavos
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Georgios Ntritsos
- Department of Hygiene and Epidemiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece;
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Alexandros T. Tzallas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Anna Batistatou
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
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16
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Zhou J, Foroughi Pour A, Deirawan H, Daaboul F, Aung TN, Beydoun R, Ahmed FS, Chuang JH. Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality. EBioMedicine 2023; 94:104726. [PMID: 37499603 PMCID: PMC10388166 DOI: 10.1016/j.ebiom.2023.104726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 06/19/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient stratification in the clinic remains a challenging task. Here we assess how image, clinical, and genomic features can be combined to predict risk. METHODS We developed and evaluated integrative deep learning models combining formalin-fixed, paraffin-embedded (FFPE) whole slide images (WSIs), clinical variables, and mutation signatures to stratify colon adenocarcinoma (COAD) patients based on their risk of mortality. Our models were trained using a dataset of 108 patients from The Cancer Genome Atlas (TCGA), and were externally validated on newly generated dataset from Wayne State University (WSU) of 123 COAD patients and rectal adenocarcinoma (READ) patients in TCGA (N = 52). FINDINGS We first observe that deep learning models trained on FFPE WSIs of TCGA-COAD separate high-risk (OS < 3 years, N = 38) and low-risk (OS > 5 years, N = 25) patients (AUC = 0.81 ± 0.08, 5 year survival p < 0.0001, 5 year relative risk = 1.83 ± 0.04) though such models are less effective at predicting overall survival (OS) for moderate-risk (3 years < OS < 5 years, N = 45) patients (5 year survival p-value = 0.5, 5 year relative risk = 1.05 ± 0.09). We find that our integrative models combining WSIs, clinical variables, and mutation signatures can improve patient stratification for moderate-risk patients (5 year survival p < 0.0001, 5 year relative risk = 1.87 ± 0.07). Our integrative model combining image and clinical variables is also effective on an independent pathology dataset (WSU-COAD, N = 123) generated by our team (5 year survival p < 0.0001, 5 year relative risk = 1.52 ± 0.08), and the TCGA-READ data (5 year survival p < 0.0001, 5 year relative risk = 1.18 ± 0.17). Our multicenter integrative image and clinical model trained on combined TCGA-COAD and WSU-COAD is effective in predicting risk on TCGA-READ (5 year survival p < 0.0001, 5 year relative risk = 1.82 ± 0.13) data. Pathologist review of image-based heatmaps suggests that nuclear size pleomorphism, intense cellularity, and abnormal structures are associated with high-risk, while low-risk regions have more regular and small cells. Quantitative analysis shows high cellularity, high ratios of tumor cells, large tumor nuclei, and low immune infiltration are indicators of high-risk tiles. INTERPRETATION The improved stratification of colorectal cancer patients from our computational methods can be beneficial for treatment plans and enrollment of patients in clinical trials. FUNDING This study was supported by the National Cancer Institutes (Grant No. R01CA230031 and P30CA034196). The funders had no roles in study design, data collection and analysis or preparation of the manuscript.
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Affiliation(s)
- Jie Zhou
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Department of Genetics and Genome Sciences, UCONN Health, Farmington, CT, USA
| | | | - Hany Deirawan
- Department of Pathology, Wayne State University, Detroit, MI, USA; Department of Dermatology, Wayne State University, Detroit, MI, USA
| | - Fayez Daaboul
- Department of Pathology, Wayne State University, Detroit, MI, USA
| | - Thazin Nwe Aung
- Department of Pathology, Yale University, New Haven, CT, USA
| | - Rafic Beydoun
- Department of Pathology, Wayne State University, Detroit, MI, USA
| | | | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Department of Genetics and Genome Sciences, UCONN Health, Farmington, CT, USA.
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Yoon K, Lee S, Lee TH, Kim KG. The Design of an Automatic Temperature Compensation System through Smart Heat Comparison/Judgment and Control for Stable Thermal Treatment in Hyperthermic Intraperitoneal Chemotherapy (HIPEC) Surgery. SENSORS (BASEL, SWITZERLAND) 2023; 23:6722. [PMID: 37571507 PMCID: PMC10422214 DOI: 10.3390/s23156722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/06/2023] [Accepted: 07/18/2023] [Indexed: 08/13/2023]
Abstract
After surgery for ovarian cancer or colorectal cancer, residual tumors are left around. A practical way to treat residual tumors is to destroy them with heat by injecting high-temperature drugs into the abdominal cavity. The injected medicinal substances are induced to flow out of the abdominal cavity; then, the spilled drug flows back into the abdominal cavity through feedback. During this process, the heat starts to decrease; thus, the treatment performance reduces. To overcome this problem, this study compares and assesses the temperature needed to maintain the heat for treatment and transmits a command signal to the heat exchanger through a look-up table (LUT). When the temperature decreases during the circulation of medications leaking out of the abdominal cavity, the LUT transmits a control signal (Tp) to the heat exchanger, which increases or vice versa. However, if the temperature (To) is within the treatment range, the LUT sends a Ts signal to the heat exchanger. This principle generates a pulse signal for the temperature difference (Tdif) in TC by comparing and determining the temperature (To) of the substance flowing out of the abdominal cavity with the reference temperature (Tref) through the temperature comparator (TC). At this time, if the signal is 41 °C or less, the LUT generates (heats) a Tp signal so that the temperature of the heat exchanger can be maintained in the range of 41 °C to 43 °C. If the Tdif is 44 °C or higher, the LUT generates (cools) the Ta signal and maintains the temperature of the heat exchanger at 41-43 °C. If the Tdif is maintained at 41-43 °C, the LUT generates a Tx signal to stop the system performance. At this time, the TC operation performance and Tdif generation process for comparing and determining the signal of To and Tref for drugs leaking out of the abdominal cavity is very important. It was observed that the faster the response signal, the lower the comparison and judgment error was; therefore, the response signal was confirmed to be 0.209 μs. The proposed method can guarantee rapid/accurate/safe treatment and automatically induce temperature adjustment; thus, it could be applied to the field of surgery.
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Affiliation(s)
- Kicheol Yoon
- Medical Devices R&D Center, Gachon University Gil Medical Center, 21, 774 Beon-gil, Namdong-daero, Namdong-gu, Incheon 21565, Republic of Korea; (K.Y.); (S.L.)
- Department of Biomedical Engineering, College of Medicine, Gachon University, 38–13, 3 Beon-gil, Dokjom-ro 3, Namdong-gu, Incheon 21565, Republic of Korea
| | - Sangyun Lee
- Medical Devices R&D Center, Gachon University Gil Medical Center, 21, 774 Beon-gil, Namdong-daero, Namdong-gu, Incheon 21565, Republic of Korea; (K.Y.); (S.L.)
- Department of Biomedical Engineering, College of Medicine, Gachon University, 38–13, 3 Beon-gil, Dokjom-ro 3, Namdong-gu, Incheon 21565, Republic of Korea
| | - Tae-Hyeon Lee
- Department of Electronic Engineering, Gyeonggi University of Science and Technology, Gyeonggigwagi-dearo 269, Siheung City 15073, Gyeonggi-do, Republic of Korea;
| | - Kwang Gi Kim
- Medical Devices R&D Center, Gachon University Gil Medical Center, 21, 774 Beon-gil, Namdong-daero, Namdong-gu, Incheon 21565, Republic of Korea; (K.Y.); (S.L.)
- Department of Biomedical Engineering, College of Medicine, Gachon University, 38–13, 3 Beon-gil, Dokjom-ro 3, Namdong-gu, Incheon 21565, Republic of Korea
- Department of Biomedical Engineering, College of Health Science, Gachon University, 191 Hambak-moero, Yeonsu-gu, Incheon 21936, Republic of Korea
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, 38–13, 3 Beon-gil, Dokjom-ro, Namdong-gu, Incheon 21565, Republic of Korea
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18
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Reyes-Aldasoro CC. Modelling the Tumour Microenvironment, but What Exactly Do We Mean by "Model"? Cancers (Basel) 2023; 15:3796. [PMID: 37568612 PMCID: PMC10416922 DOI: 10.3390/cancers15153796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/19/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.
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Alam MR, Seo KJ, Abdul-Ghafar J, Yim K, Lee SH, Jang HJ, Jung CK, Chong Y. Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers. Brief Bioinform 2023; 24:bbad151. [PMID: 37114657 DOI: 10.1093/bib/bbad151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/24/2023] [Accepted: 03/24/2023] [Indexed: 04/29/2023] Open
Abstract
PURPOSE Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review. METHODS A literature search using the MEDLINE, Embase and Cochrane databases was conducted in August 2021. The articles were shortlisted by titles and abstracts. After a full-text review, publication trends, study characteristic analysis and comparison of performance metrics were performed. RESULTS Twenty-four studies were found mostly from developed countries, and their number is increasing. The major targets were gastrointestinal, genitourinary, gynecological, lung and head and neck cancers. Most studies used the Cancer Genome Atlas, with a few using an in-house dataset. The area under the curve of some of the cancer driver gene mutations in particular organs was satisfactory, such as 0.92 of BRAF in thyroid cancers and 0.79 of EGFR in lung cancers, whereas the average of all gene mutations was 0.64, which is still suboptimal. CONCLUSION AI has the potential to predict gene mutations on histologic images with appropriate caution. Further validation with larger datasets is still required before AI models can be used in clinical practice to predict gene mutations.
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Affiliation(s)
- Mohammad Rizwan Alam
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Kyung Jin Seo
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Jamshid Abdul-Ghafar
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Kwangil Yim
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Sung Hak Lee
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Hyun-Jong Jang
- Catholic Big Data Integration Center, Department of Physiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Chan Kwon Jung
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Yosep Chong
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Mansur A, Saleem Z, Elhakim T, Daye D. Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions. Front Oncol 2023; 13:1065402. [PMID: 36761957 PMCID: PMC9905815 DOI: 10.3389/fonc.2023.1065402] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 01/09/2023] [Indexed: 01/26/2023] Open
Abstract
Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.
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Affiliation(s)
- Arian Mansur
- Harvard Medical School, Boston, MA, United States
| | | | - Tarig Elhakim
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
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21
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Mei WJ, Mi M, Qian J, Xiao N, Yuan Y, Ding PR. Clinicopathological characteristics of high microsatellite instability/mismatch repair-deficient colorectal cancer: A narrative review. Front Immunol 2022; 13:1019582. [PMID: 36618386 PMCID: PMC9822542 DOI: 10.3389/fimmu.2022.1019582] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022] Open
Abstract
Colorectal cancers (CRCs) with high microsatellite instability (MSI-H) and deficient mismatch repair (dMMR) show molecular and clinicopathological characteristics that differ from those of proficient mismatch repair/microsatellite stable CRCs. Despite the importance of MSI-H/dMMR status in clinical decision making, the testing rates for MSI and MMR in clinical practice remain low, even in high-risk populations. Additionally, the real-world prevalence of MSI-H/dMMR CRC may be lower than that reported in the literature. Insufficient MSI and MMR testing fails to identify patients with MSI-H/dMMR CRC, who could benefit from immunotherapy. In this article, we describe the current knowledge of the clinicopathological features, molecular landscape, and radiomic characteristics of MSI-H/dMMR CRCs. A better understanding of the importance of MMR/MSI status in the clinical characteristics and prognosis of CRC may help increase the rates of MMR/MSI testing and guide the development of more effective therapies based on the unique features of these tumors.
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Affiliation(s)
- Wei-Jian Mei
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - Mi Mi
- Department of Medical Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Qian
- Global Medical Affairs, MSD China, Shanghai, China
| | - Nan Xiao
- Global Medical Affairs, MSD China, Shanghai, China
| | - Ying Yuan
- Department of Medical Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for CANCER, Hangzhou, China
- Cancer Center of Zhejiang University, Hangzhou, China
| | - Pei-Rong Ding
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, China
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22
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Nakanishi R, Morooka K, Omori K, Toyota S, Tanaka Y, Hasuda H, Koga N, Nonaka K, Hu Q, Nakaji Y, Nakanoko T, Ando K, Ota M, Kimura Y, Oki E, Oda Y, Yoshizumi T. Artificial Intelligence-Based Prediction of Recurrence after Curative Resection for Colorectal Cancer from Digital Pathological Images. Ann Surg Oncol 2022; 30:3506-3514. [PMID: 36512260 DOI: 10.1245/s10434-022-12926-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 10/19/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND To develop an artificial intelligence-based model to predict recurrence after curative resection for stage I-III colorectal cancer from digitized pathological slides. PATIENTS AND METHODS In this retrospective study, 471 consecutive patients who underwent curative resection for stage I-III colorectal cancer at our institution from 2004 to 2015 were enrolled, and 512 randomly selected tiles from digitally scanned images of hematoxylin and eosin-stained tumor tissue sections were used to train a convolutional neural network. Five-fold cross-validation was used to validate the model. The association between recurrence and the model's output scores were analyzed in the test cohorts. RESULTS The area under the receiver operating characteristic curve of the cross-validation was 0.7245 [95% confidence interval (CI) 0.6707-0.7783; P < 0.0001]. The score successfully classified patients into those with better and worse recurrence free survival (P < 0.0001). Multivariate analysis revealed that a high score was significantly associated with worse recurrence free survival [odds ratio (OR) 1.857; 95% CI 1.248-2.805; P = 0.0021], which was independent from other predictive factors: male sex (P = 0.0238), rectal cancer (P = 0.0396), preoperative abnormal carcinoembryonic antigen (CEA) level (P = 0.0216), pathological T3/T4 stage (P = 0.0162), and pathological positive lymph node metastasis (P < 0.0001). CONCLUSIONS The artificial intelligence-based prediction model discriminated patients with a high risk of recurrence. This approach could help decision-makers consider the benefits of adjuvant chemotherapy.
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Thakur N, Alam MR, Abdul-Ghafar J, Chong Y. Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review. Cancers (Basel) 2022; 14:cancers14143529. [PMID: 35884593 PMCID: PMC9316753 DOI: 10.3390/cancers14143529] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 11/27/2022] Open
Abstract
Simple Summary Artificial intelligence (AI) has attracted significant interest in the healthcare sector due to its promising results. Cytological examination is a critical step in the initial diagnosis of cancer. Here, we conducted a systematic review with quantitative analysis to understand the current status of AI applications in non-gynecological (non-GYN) cancer cytology. In our analysis, we found that most of the studies focused on classification and segmentation tasks. Overall, AI showed promising results for non-GYN cancer cytopathology analysis. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation across all studies. Abstract State-of-the-art artificial intelligence (AI) has recently gained considerable interest in the healthcare sector and has provided solutions to problems through automated diagnosis. Cytological examination is a crucial step in the initial diagnosis of cancer, although it shows limited diagnostic efficacy. Recently, AI applications in the processing of cytopathological images have shown promising results despite the elementary level of the technology. Here, we performed a systematic review with a quantitative analysis of recent AI applications in non-gynecological (non-GYN) cancer cytology to understand the current technical status. We searched the major online databases, including MEDLINE, Cochrane Library, and EMBASE, for relevant English articles published from January 2010 to January 2021. The searched query terms were: “artificial intelligence”, “image processing”, “deep learning”, “cytopathology”, and “fine-needle aspiration cytology.” Out of 17,000 studies, only 26 studies (26 models) were included in the full-text review, whereas 13 studies were included for quantitative analysis. There were eight classes of AI models treated of according to target organs: thyroid (n = 11, 39%), urinary bladder (n = 6, 21%), lung (n = 4, 14%), breast (n = 2, 7%), pleural effusion (n = 2, 7%), ovary (n = 1, 4%), pancreas (n = 1, 4%), and prostate (n = 1, 4). Most of the studies focused on classification and segmentation tasks. Although most of the studies showed impressive results, the sizes of the training and validation datasets were limited. Overall, AI is also promising for non-GYN cancer cytopathology analysis, such as pathology or gynecological cytology. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation found across all studies. Future studies with larger datasets with high-quality annotations and external validation are required.
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Jiang W, Mei WJ, Xu SY, Ling YH, Li WR, Kuang JB, Li HS, Hui H, Li JB, Cai MY, Pan ZZ, Zhang HZ, Li L, Ding PR. Clinical actionability of triaging DNA mismatch repair deficient colorectal cancer from biopsy samples using deep learning. EBioMedicine 2022; 81:104120. [PMID: 35753152 PMCID: PMC9240789 DOI: 10.1016/j.ebiom.2022.104120] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 11/19/2022] Open
Abstract
Background We aimed to develop a deep learning (DL) model to predict DNA mismatch repair (MMR) status in colorectal cancers (CRC) based on hematoxylin and eosin-stained whole-slide images (WSIs) and assess its clinical applicability. Methods The DL model was developed and validated through three-fold cross validation using 441 WSIs from the Cancer Genome Atlas (TCGA) and externally validated using 78 WSIs from the Pathology AI Platform (PAIP), and 355 WSIs from surgical specimens and 341 WSIs from biopsy specimens of the Sun Yet-sun University Cancer Center (SYSUCC). Domain adaption and multiple instance learning (MIL) techniques were adopted for model development. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUROC). A dual-threshold strategy was also built from the surgical cohorts and validated in the biopsy cohort. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, and the percentage of patients avoiding IHC testing were evaluated. Findings The MIL model achieved an AUROC of 0·8888±0·0357 in the TCGA-validation cohort, 0·8806±0·0232 in the PAIP cohort, 0·8457±0·0233 in the SYSUCC-surgical cohort, and 0·7679±0·0342 in the SYSUCC-biopsy cohort. A dual-threshold triage strategy was used to rule-in and rule-out dMMR patients with remaining uncertain patients recommended for further IHC testing, which kept sensitivity higher than 90% and specificity higher than 95% on deficient MMR patient triage from both the surgical and biopsy specimens, result in more than half of patients avoiding IHC based MMR testing. Interpretation A DL-based method that could directly predict CRC MMR status from WSIs was successfully developed, and a dual-threshold triage strategy was established to minimize the number of patients for further IHC testing. Funding The study was funded by the National Natural Science Foundation of China (82073159, 81871971 and 81700576), the Natural Science Foundation of Guangdong Province (No. 2021A1515011792 and No.2022A1515012403) and Medical Scientific Research Foundation of Guangdong Province of China (No. A2020392).
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Affiliation(s)
- Wu Jiang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China; Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Wei-Jian Mei
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China; Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Shuo-Yu Xu
- Bio-totem Pte Ltd, Foshan, PR China; Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, PR China
| | - Yi-Hong Ling
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Wei-Rong Li
- Department of General Surgery, Guangzhou First People's Hospital, Guangzhou, PR China
| | | | | | - Hui Hui
- Bio-totem Pte Ltd, Foshan, PR China
| | - Ji-Bin Li
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China; Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Mu-Yan Cai
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Zhi-Zhong Pan
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China; Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Hui-Zhong Zhang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China; Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, PR China.
| | - Li Li
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China; Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, PR China.
| | - Pei-Rong Ding
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China; Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, Guangzhou, PR China.
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Neto PC, Oliveira SP, Montezuma D, Fraga J, Monteiro A, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images. Cancers (Basel) 2022; 14:cancers14102489. [PMID: 35626093 PMCID: PMC9139905 DOI: 10.3390/cancers14102489] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Nowadays, colorectal cancer is the third most incident cancer worldwide and, although it can be detected by imaging techniques, diagnosis is always based on biopsy samples. This assessment includes neoplasia grading, a subjective yet important task for pathologists. With the growing availability of digital slides, the development of robust and high-performance computer vision algorithms can help to tackle such a task. In this work, we propose an approach to automatically detect and grade lesions in colorectal biopsies with high sensitivity. The presented model attempts to support slide decision reasoning in terms of the spatial distribution of lesions, focusing the pathologist’s attention on key areas. Thus, it can be integrated into clinical practice as a second opinion or as a flag for details that may have been missed at first glance. Abstract Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.
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Affiliation(s)
- Pedro C. Neto
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
- Correspondence:
| | - Sara P. Oliveira
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
| | - Diana Montezuma
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
- School of Medicine and Biomedical Sciences, University of Porto (ICBAS), 4050-313 Porto, Portugal
- Cancer Biology and Epigenetics Group, IPO-Porto, 4200-072 Porto, Portugal
| | - João Fraga
- Department of Pathology, IPO-Porto, 4200-072 Porto, Portugal;
| | - Ana Monteiro
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Liliana Ribeiro
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Sofia Gonçalves
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Isabel M. Pinto
- IMP Diagnostics, 4150-146 Porto, Portugal; (D.M.); (A.M.); (L.R.); (S.G.); (I.M.P.)
| | - Jaime S. Cardoso
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; (S.P.O.); (J.S.C.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
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Hong J, Li Q, Wang X, Li J, Ding W, Hu H, He L. Development and validation of apoptosis-related signature and molecular subtype to improve prognosis prediction in osteosarcoma patients. J Clin Lab Anal 2022; 36:e24501. [PMID: 35576501 PMCID: PMC9280000 DOI: 10.1002/jcla.24501] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Previous evidence has shown that apoptosis performs integral functions in the tumorigenesis and development of various tumors. Therefore, this study aimed to establish a molecular subtype and prognostic signature based on apoptosis-related genes (ARGs) to understand the molecular mechanisms and predict prognosis in patients with osteosarcoma. METHODS The GEO and TARGET databases were utilized to obtain the expression levels of ARGs and clinical information of osteosarcoma patients. Consensus clustering analysis was used to explore the different molecular subtypes based on ARGs. GO, KEGG, GSEA, ESTIMATE, and ssGSEA analyses were performed to examine the differences in biological functions and immune characteristics between the distinct molecular subtypes. Then, we constructed an ARG signature by LASSO analysis. The prognostic significance of the ARG signature in osteosarcoma was determined by Kaplan-Meier plotter, Cox regression, and nomogram analyses. RESULTS Two apoptosis-related subtypes were identified. Cluster 1 had a better prognosis, higher immunogenicity, and immune cell infiltration, as well as a better response to immunotherapy than Cluster 2. We discovered that patients in the high-risk cohort had a lower survival rate than those in the low-risk cohort according to the ARG signature. Furthermore, Cox regression analysis confirmed that a high risk score independently acted as an unfavorable prognostic marker. Additionally, the nomogram combining risk scores with clinical characteristics can improve prediction efficiency. CONCLUSION We demonstrated that patients suffering from osteosarcoma may be classified into two apoptosis-related subtypes. Moreover, we developed an ARG prognostic signature to predict the prognosis status of osteosarcoma patients.
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Affiliation(s)
- Jinjiong Hong
- Department of Hand Surgery, Department of Plastic Reconstructive Surgery, Ningbo No. 6 Hospital, Ningbo, China
| | - Qun Li
- Department of Otorhinolaryngology Head and Neck Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo, China
| | - Xiaofeng Wang
- Department of Hand Surgery, Department of Plastic Reconstructive Surgery, Ningbo No. 6 Hospital, Ningbo, China
| | - Jie Li
- Department of Orthopedics, Ningbo Medical Center Lihuili Hospital, Ningbo, China
| | - Wenquan Ding
- Department of Hand Surgery, Department of Plastic Reconstructive Surgery, Ningbo No. 6 Hospital, Ningbo, China
| | - Haoliang Hu
- Department of Hand Surgery, Department of Plastic Reconstructive Surgery, Ningbo No. 6 Hospital, Ningbo, China
| | - Lingfeng He
- Department of Hand Surgery, Department of Plastic Reconstructive Surgery, Ningbo No. 6 Hospital, Ningbo, China
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Ailia MJ, Thakur N, Abdul-Ghafar J, Jung CK, Yim K, Chong Y. Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape. Cancers (Basel) 2022; 14:cancers14102400. [PMID: 35626006 PMCID: PMC9139645 DOI: 10.3390/cancers14102400] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/04/2022] [Accepted: 05/10/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary The combination of digital pathology (DP) with artificial intelligence (AI) offers faster, more accurate, and more comprehensive diagnoses, resulting in more precise individualized treatment. As this technology is constantly evolving, it is critical to understand the current state of AI applications in DP. Thus, it is necessary to analyze AI patent applications, assignees, and leaders in the field. In this study, five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, were searched using key phrases, such as DP, AI, machine learning, and deep learning, and 523 patents were shortlisted based on the inclusion criteria. Our data demonstrated that the key areas of the patents were whole-slide imaging, segmentation, classification, and detection. In the past five years, an increasing trend in patent filing has been observed, mainly in a few prominent countries, with a focus on the digitization of pathological images and AI technologies that support the critical role of pathologists. Abstract The integration of digital pathology (DP) with artificial intelligence (AI) enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. As technology is advancing rapidly, it is critical to understand the current state of AI applications in DP. Therefore, a patent analysis of AI in DP is required to assess the application and publication trends, major assignees, and leaders in the field. We searched five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, from 1974 to 2021, using keywords such as DP, AI, machine learning, and deep learning. We discovered 6284 patents, 523 of which were used for trend analyses on time series, international distribution, top assignees; word cloud analysis; and subject category analyses. Patent filing and publication have increased exponentially over the past five years. The United States has published the most patents, followed by China and South Korea (248, 117, and 48, respectively). The top assignees were Paige.AI, Inc. (New York City, NY, USA) and Siemens, Inc. (Munich, Germany) The primary areas were whole-slide imaging, segmentation, classification, and detection. Based on these findings, we expect a surge in DP and AI patent applications focusing on the digitalization of pathological images and AI technologies that support the vital role of pathologists.
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Affiliation(s)
| | | | | | | | | | - Yosep Chong
- Correspondence: ; Tel.: +82-2-2258-1620; Fax: +82-2-783-6648
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Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12040837. [PMID: 35453885 PMCID: PMC9028395 DOI: 10.3390/diagnostics12040837] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 02/04/2023] Open
Abstract
Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.
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Qiu H, Ding S, Liu J, Wang L, Wang X. Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer. Curr Oncol 2022; 29:1773-1795. [PMID: 35323346 PMCID: PMC8947571 DOI: 10.3390/curroncol29030146] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/28/2022] [Accepted: 03/03/2022] [Indexed: 12/29/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.
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Affiliation(s)
- Hang Qiu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- Correspondence: (H.Q.); (X.W.)
| | - Shuhan Ding
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA;
| | - Jianbo Liu
- West China School of Medicine, Sichuan University, Chengdu 610041, China;
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Liya Wang
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Xiaodong Wang
- West China School of Medicine, Sichuan University, Chengdu 610041, China;
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Correspondence: (H.Q.); (X.W.)
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30
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Su A, Lee H, Tan X, Suarez CJ, Andor N, Nguyen Q, Ji HP. A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images. NPJ Precis Oncol 2022; 6:14. [PMID: 35236916 PMCID: PMC8891271 DOI: 10.1038/s41698-022-00252-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 12/16/2021] [Indexed: 12/19/2022] Open
Abstract
Deep-learning classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches so far depends on prior pathological annotations and large training datasets. The manual annotation is low-resolution, time-consuming, highly variable and subject to observer variance. To address this issue, we developed a method, H&E Molecular neural network (HEMnet). HEMnet utilizes immunohistochemistry as an initial molecular label for cancer cells on a H&E image and trains a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, HEMnet successfully generated and labeled 21,939 tumor and 8782 normal tiles from ten whole-slide images for model training. After building the model, HEMnet accurately identified colorectal cancer regions, which achieved 0.84 and 0.73 of ROC AUC values compared to p53 staining and pathological annotations, respectively. Our validation study using histopathology images from TCGA samples accurately estimated tumor purity, which showed a significant correlation (regression coefficient of 0.8) with the estimation based on genomic sequencing data. Thus, HEMnet contributes to addressing two main challenges in cancer deep-learning analysis, namely the need to have a large number of images for training and the dependence on manual labeling by a pathologist. HEMnet also predicts cancer cells at a much higher resolution compared to manual histopathologic evaluation. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at: https://github.com/BiomedicalMachineLearning/HEMnet.
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Affiliation(s)
- Andrew Su
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - HoJoon Lee
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Xiao Tan
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Carlos J Suarez
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Noemi Andor
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA.
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31
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Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2:141-156. [DOI: 10.35712/aig.v2.i6.141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/19/2021] [Accepted: 12/27/2021] [Indexed: 02/06/2023] Open
Abstract
The integration of artificial intelligence (AI) has shown promising benefits in many fields of diagnostic histopathology, including for gastrointestinal cancers (GCs), such as tumor identification, classification, and prognosis prediction. In parallel, recent evidence suggests that AI may help reduce the workload in gastrointestinal pathology by automatically detecting tumor tissues and evaluating prognostic parameters. In addition, AI seems to be an attractive tool for biomarker/genetic alteration prediction in GC, as it can contain a massive amount of information from visual data that is complex and partially understandable by pathologists. From this point of view, it is suggested that advances in AI could lead to revolutionary changes in many fields of pathology. Unfortunately, these findings do not exclude the possibility that there are still many hurdles to overcome before AI applications can be safely and effectively applied in actual pathology practice. These include a broad spectrum of challenges from needs identification to cost-effectiveness. Therefore, unlike other disciplines of medicine, no histopathology-based AI application, including in GC, has ever been approved either by a regulatory authority or approved for public reimbursement. The purpose of this review is to present data related to the applications of AI in pathology practice in GC and present the challenges that need to be overcome for their implementation.
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Affiliation(s)
- Anil Alpsoy
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Aysen Yavuz
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Gulsum Ozlem Elpek
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
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32
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Pettit RW, Fullem R, Cheng C, Amos CI. Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerg Top Life Sci 2021; 5:ETLS20210246. [PMID: 34927670 PMCID: PMC8786279 DOI: 10.1042/etls20210246] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022]
Abstract
AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.
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Affiliation(s)
- Rowland W. Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
| | - Robert Fullem
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, U.S.A
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, U.S.A
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Großerueschkamp F, Jütte H, Gerwert K, Tannapfel A. Advances in Digital Pathology: From Artificial Intelligence to Label-Free Imaging. Visc Med 2021; 37:482-490. [PMID: 35087898 DOI: 10.1159/000518494] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 07/14/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Digital pathology, in its primary meaning, describes the utilization of computer screens to view scanned histology slides. Digitized tissue sections can be easily shared for a second opinion. In addition, it allows tissue image analysis using specialized software to identify and measure events previously observed by a human observer. These tissue-based readouts were highly reproducible and precise. Digital pathology has developed over the years through new technologies. Currently, the most discussed development is the application of artificial intelligence to automatically analyze tissue images. However, even new label-free imaging technologies are being developed to allow imaging of tissues by means of their molecular composition. SUMMARY This review provides a summary of the current state-of-the-art and future digital pathologies. Developments in the last few years have been presented and discussed. In particular, the review provides an outlook on interesting new technologies (e.g., infrared imaging), which would allow for deeper understanding and analysis of tissue thin sections beyond conventional histopathology. KEY MESSAGES In digital pathology, mathematical methods are used to analyze images and draw conclusions about diseases and their progression. New innovative methods and techniques (e.g., label-free infrared imaging) will bring significant changes in the field in the coming years.
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Affiliation(s)
- Frederik Großerueschkamp
- Center for Protein Diagnostics (PRODI), Biospectroscopy, Ruhr University Bochum, Bochum, Germany.,Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, Bochum, Germany
| | - Hendrik Jütte
- Center for Protein Diagnostics (PRODI), Biospectroscopy, Ruhr University Bochum, Bochum, Germany.,Institute of Pathology, Ruhr University Bochum, Bochum, Germany
| | - Klaus Gerwert
- Center for Protein Diagnostics (PRODI), Biospectroscopy, Ruhr University Bochum, Bochum, Germany.,Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, Bochum, Germany
| | - Andrea Tannapfel
- Center for Protein Diagnostics (PRODI), Biospectroscopy, Ruhr University Bochum, Bochum, Germany.,Institute of Pathology, Ruhr University Bochum, Bochum, Germany
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34
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Kim H, Yoon H, Thakur N, Hwang G, Lee EJ, Kim C, Chong Y. Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain. Sci Rep 2021; 11:22520. [PMID: 34795365 PMCID: PMC8602325 DOI: 10.1038/s41598-021-01905-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 10/28/2021] [Indexed: 02/06/2023] Open
Abstract
Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis.
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Affiliation(s)
- Hyeongsub Kim
- Departments of Electrical Engineering, Creative IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Medical Device Innovation Center, and Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, 37674, South Korea.,Deepnoid Inc., Seoul, 08376, South Korea
| | | | - Nishant Thakur
- Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Uijeongbu St. Mary's Hospital, Seoul, South Korea
| | - Gyoyeon Hwang
- Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Yeouido St. Mary's Hospital, Seoul, South Korea
| | - Eun Jung Lee
- Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Yeouido St. Mary's Hospital, Seoul, South Korea.,Department of Pathology, Shinwon Medical Foundation, Gwangmyeong-si, Gyeonggi-do, South Korea
| | - Chulhong Kim
- Departments of Electrical Engineering, Creative IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Medical Device Innovation Center, and Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, 37674, South Korea.
| | - Yosep Chong
- Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Uijeongbu St. Mary's Hospital, Seoul, South Korea.
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Li LS, Guo XY, Sun K. Recent advances in blood-based and artificial intelligence-enhanced approaches for gastrointestinal cancer diagnosis. World J Gastroenterol 2021; 27:5666-5681. [PMID: 34629793 PMCID: PMC8473600 DOI: 10.3748/wjg.v27.i34.5666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/14/2021] [Accepted: 08/03/2021] [Indexed: 02/06/2023] Open
Abstract
Gastrointestinal (GI) cancers are among the most common cancer types and leading causes of cancer-related deaths worldwide. There is a tremendous clinical need for effective early diagnosis for better healthcare of GI cancer patients. In this article, we provide a short overview of the recent advances in GI cancer diagnosis. In the first part, we discuss the applications of blood-based biomarkers, such as plasma circulating cell-free DNA, circulating tumor cells, extracellular vesicles, and circulating cell-free RNA, for cancer liquid biopsies. In the second part, we review the current trends of artificial intelligence (AI) for pathology image and tissue biopsy analysis for GI cancer, as well as deep learning-based approaches for purity assessment of tissue biopsies. We further provide our opinions on the future directions in blood-based and AI-enhanced approaches for GI cancer diagnosis, and we think that these fields will have more intensive integrations with clinical needs in the near future.
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Affiliation(s)
- Li-Shi Li
- School of Chemical Biology and Biotechnology, Shenzhen Graduate School, Peking University, Shenzhen 518055, Guangdong Province, China
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, Guangdong Province, China
| | - Xiang-Yu Guo
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, Guangdong Province, China
| | - Kun Sun
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, Guangdong Province, China
- BGI-Shenzhen, Shenzhen 518083, Guangdong Province, China
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36
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Yang H, Hu B. Early gastrointestinal cancer: The application of artificial intelligence. Artif Intell Gastrointest Endosc 2021; 2:185-197. [DOI: 10.37126/aige.v2.i4.185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 06/25/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Early gastrointestinal (GI) cancer has been the core of clinical endoscopic work. Its early detection and treatment are tightly associated with patients’ prognoses. As a novel technology, artificial intelligence has been improved and applied in the field of endoscopy. Studies on detection, diagnosis, risk, and prognosis evaluation of diseases in the GI tract have been in development, including precancerous lesions, adenoma, early GI cancers, and advanced GI cancers. In this review, research on esophagus, stomach, and colon was concluded, and associated with the process from precancerous lesions to early GI cancer, such as from Barrett’s esophagus to early esophageal cancer, from dysplasia to early gastric cancer, and from adenoma to early colonic cancer. A status quo of research on early GI cancers and artificial intelligence was provided.
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Affiliation(s)
- Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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37
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Topalovic N, Mazic S, Nesic D, Vukovic O, Cumic J, Laketic D, Stasevic Karlicic I, Pantic I. Association between Chromatin Structural Organization of Peripheral Blood Neutrophils and Self-Perceived Mental Stress: Gray-Level Co-occurrence Matrix Analysis. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2021; 27:1-7. [PMID: 34334154 DOI: 10.1017/s143192762101240x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Methods based on the evaluation of textural patterns in microscopy, such as the “gray-level co-occurrence matrix” (GLCM) analysis are modern and innovative computer and mathematical algorithms that can be used to quantify subtle structural changes in cells and their organelles. Potential application of GLCM method in the fields of psychophysiology and psychiatry to this date has not been systematically investigated. The main objective of our study was to test the existence and strength of the association between chromatin structural organization of peripheral blood neutrophils and levels of self-perceived mental stress. The research was done on a sample of 100 healthy student athletes, and the Depression, Anxiety, and Stress Scales (DASS-21) were used for the estimation of psychological distress. Chromatin textural homogeneity and uniformity were negatively correlated (p < 0.01) with mental distress and had relatively good discriminatory power in differentiating participants with normal and elevated stress levels. As an addition, we propose the creation of a machine learning model based on binomial logistic regression that uses these and other GLCM features to predict stress elevation. To the best of our knowledge, these results are one of the first to establish the link between neutrophil chromatin structural organization quantified by the GLCM method and indicators of normal psychological functioning.
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Affiliation(s)
- Nikola Topalovic
- University of Belgrade, Faculty of Medicine, Institute of Medical Physiology, Visegradska 26/II, RS-11129, Belgrade, Serbia
| | - Sanja Mazic
- University of Belgrade, Faculty of Medicine, Institute of Medical Physiology, Visegradska 26/II, RS-11129, Belgrade, Serbia
| | - Dejan Nesic
- University of Belgrade, Faculty of Medicine, Institute of Medical Physiology, Visegradska 26/II, RS-11129, Belgrade, Serbia
| | - Olivera Vukovic
- University of Belgrade, Faculty of Medicine, Institute of Mental Health, Palmoticeva 37, RS-11000, Belgrade, Serbia
| | - Jelena Cumic
- University of Belgrade, Faculty of Medicine, University Clinical Centre of Serbia, Dr. Koste Todorovica 8, RS-11129, Belgrade, Serbia
| | - Darko Laketic
- University of Belgrade, Faculty of Medicine, Institute of Anatomy, Dr Subotica 4/2, RS-11129, Belgrade, Serbia
| | | | - Igor Pantic
- University of Belgrade, Faculty of Medicine, Institute of Medical Physiology, Visegradska 26/II, RS-11129, Belgrade, Serbia
- University of Haifa, 199 Abba Hushi Blvd. Mount Carmel, HaifaIL-3498838, Israel
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38
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Oliveira SP, Neto PC, Fraga J, Montezuma D, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance. Sci Rep 2021; 11:14358. [PMID: 34257363 PMCID: PMC8277780 DOI: 10.1038/s41598-021-93746-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/28/2021] [Indexed: 02/07/2023] Open
Abstract
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.
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Affiliation(s)
- Sara P Oliveira
- INESCTEC, 4200-465, Porto, Portugal.
- Faculty of Engineering (FEUP), University of Porto, 4200-465, Porto, Portugal.
| | - Pedro C Neto
- INESCTEC, 4200-465, Porto, Portugal
- Faculty of Engineering (FEUP), University of Porto, 4200-465, Porto, Portugal
| | - João Fraga
- IMP Diagnostics, 4150-146, Porto, Portugal
| | - Diana Montezuma
- IMP Diagnostics, 4150-146, Porto, Portugal
- ICBAS, University of Porto, 4050-313, Porto , Portugal
- Cancer Biology and Epigenetics Group, IPO-Porto, 4200-072, Porto, Portugal
| | | | | | | | | | | | - Jaime S Cardoso
- INESCTEC, 4200-465, Porto, Portugal
- Faculty of Engineering (FEUP), University of Porto, 4200-465, Porto, Portugal
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Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol 2021; 27:2818-2833. [PMID: 34135556 PMCID: PMC8173389 DOI: 10.3748/wjg.v27.i21.2818] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/16/2021] [Accepted: 04/28/2021] [Indexed: 02/06/2023] Open
Abstract
Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless of these promising advances, unlike the areas of radiology or cardiology imaging, no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The challenges have been identified at different stages of the development process, such as needs identification, data curation, model development, validation, regulation, modification of daily workflow, and cost-effectiveness balance. The aim of this review is to present challenges in the process of AI development, validation, and regulation that should be overcome for its implementation in real-life GI pathology practice.
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Affiliation(s)
- Hiroshi Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan
| | - Tomoharu Kiyuna
- Digital Healthcare Business Development Office, NEC Corporation, Tokyo 108-8556, Japan
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40
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Kobayashi S, Saltz JH, Yang VW. State of machine and deep learning in histopathological applications in digestive diseases. World J Gastroenterol 2021; 27:2545-2575. [PMID: 34092975 PMCID: PMC8160628 DOI: 10.3748/wjg.v27.i20.2545] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/27/2021] [Accepted: 04/29/2021] [Indexed: 02/06/2023] Open
Abstract
Machine learning (ML)- and deep learning (DL)-based imaging modalities have exhibited the capacity to handle extremely high dimensional data for a number of computer vision tasks. While these approaches have been applied to numerous data types, this capacity can be especially leveraged by application on histopathological images, which capture cellular and structural features with their high-resolution, microscopic perspectives. Already, these methodologies have demonstrated promising performance in a variety of applications like disease classification, cancer grading, structure and cellular localizations, and prognostic predictions. A wide range of pathologies requiring histopathological evaluation exist in gastroenterology and hepatology, indicating these as disciplines highly targetable for integration of these technologies. Gastroenterologists have also already been primed to consider the impact of these algorithms, as development of real-time endoscopic video analysis software has been an active and popular field of research. This heightened clinical awareness will likely be important for future integration of these methods and to drive interdisciplinary collaborations on emerging studies. To provide an overview on the application of these methodologies for gastrointestinal and hepatological histopathological slides, this review will discuss general ML and DL concepts, introduce recent and emerging literature using these methods, and cover challenges moving forward to further advance the field.
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Affiliation(s)
- Soma Kobayashi
- Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, United States
| | - Joel H Saltz
- Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, United States
| | - Vincent W Yang
- Department of Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, United States
- Department of Physiology and Biophysics, Renaissance School of Medicine, Stony Brook University, Stony Brook , NY 11794, United States
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41
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Alloro R, Sinagra E. Artificial intelligence and colorectal cancer: How far can you go? Artif Intell Cancer 2021; 2:7-11. [DOI: 10.35713/aic.v2.i2.7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/01/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence is an emerging technology whose application is rapidly increasing in several medical fields. The numerous applications of artificial intelligence in gastroenterology have shown promising results, especially in the setting of gastrointestinal oncology. Therefore, we would like to highlight and summarize the research progress and clinical application value of artificial intelligence in the diagnosis, treatment, and prognosis of colorectal cancer to provide evidence for its use as a promising diagnostic and therapeutic tool in this setting.
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Affiliation(s)
- Rita Alloro
- Department of Surgical, Oncological and Oral Sciences (Di.Chir.On.S.), Unit of General and Oncological Surgery, Paolo Giaccone University Hospital, University of Palermo, Palermo 90127, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto G. Giglio, Palermo 90015, Italy
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Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. JOURNAL OF BIG DATA 2021; 8:53. [PMID: 33816053 PMCID: PMC8010506 DOI: 10.1186/s40537-021-00444-8] [Citation(s) in RCA: 775] [Impact Index Per Article: 258.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 03/22/2021] [Indexed: 05/04/2023]
Abstract
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.
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Affiliation(s)
- Laith Alzubaidi
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000 Australia
- AlNidhal Campus, University of Information Technology & Communications, Baghdad, 10001 Iraq
| | - Jinglan Zhang
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000 Australia
| | - Amjad J. Humaidi
- Control and Systems Engineering Department, University of Technology, Baghdad, 10001 Iraq
| | - Ayad Al-Dujaili
- Electrical Engineering Technical College, Middle Technical University, Baghdad, 10001 Iraq
| | - Ye Duan
- Faculty of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211 USA
| | - Omran Al-Shamma
- AlNidhal Campus, University of Information Technology & Communications, Baghdad, 10001 Iraq
| | - J. Santamaría
- Department of Computer Science, University of Jaén, 23071 Jaén, Spain
| | - Mohammed A. Fadhel
- College of Computer Science and Information Technology, University of Sumer, Thi Qar, 64005 Iraq
| | - Muthana Al-Amidie
- Faculty of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211 USA
| | - Laith Farhan
- School of Engineering, Manchester Metropolitan University, Manchester, M1 5GD UK
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Chong Y, Thakur N, Lee JY, Hwang G, Choi M, Kim Y, Yu H, Cho MY. Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation. Diagn Pathol 2021; 16:19. [PMID: 33706755 PMCID: PMC7953791 DOI: 10.1186/s13000-021-01081-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 03/01/2021] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions. METHODS We developed ImmunoGenius, a machine-learning-based expert system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on Bayesian theorem, the most probable diagnoses can be drawn by calculating the probabilities of the IHC results in each disease. We prepared IHC profile data of 584 antibodies in 2009 neoplasms based on the relevant textbooks. We developed the reactive native mobile application for iOS and Android platform that can provide 10 most possible differential diagnoses based on the IHC input. RESULTS We trained the software using 562 real case data, validated it with 382 case data, tested it with 164 case data and compared the precision hit rate. Precision hit rate was 78.5, 78.0 and 89.0% in training, validation and test dataset respectively. Which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases. CONCLUSION The results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.
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Affiliation(s)
- Yosep Chong
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, 271, Cheonbo-ro, Uijeongbu, 11765, Gyeonggi-do, Republic of Korea. .,Postech-Catholic Biomedical Engineering institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
| | - Nishant Thakur
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, 271, Cheonbo-ro, Uijeongbu, 11765, Gyeonggi-do, Republic of Korea
| | - Ji Young Lee
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, 271, Cheonbo-ro, Uijeongbu, 11765, Gyeonggi-do, Republic of Korea
| | - Gyoyeon Hwang
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, 271, Cheonbo-ro, Uijeongbu, 11765, Gyeonggi-do, Republic of Korea
| | | | - Yejin Kim
- Department of Creative Information Technology, POSTECH, Pohang, Republic of Korea.,University of Texas Health Science Center, Houston, TX, USA
| | - Hwanjo Yu
- Computer Science and Engineering, POSTECH, Pohang, Republic of Korea
| | - Mee Yon Cho
- Department of Pathology, Yonsei University, Wonju College of Medicine, Wonju, Republic of Korea
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Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6683931. [PMID: 33542924 PMCID: PMC7843175 DOI: 10.1155/2021/6683931] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 12/22/2020] [Accepted: 01/06/2021] [Indexed: 02/08/2023]
Abstract
Colorectal imaging improves on diagnosis of colorectal diseases by providing colorectal images. Manual diagnosis of colorectal disease is labor-intensive and time-consuming. In this paper, we present a method for automatic colorectal disease classification and segmentation. Because of label unbalanced and difficult colorectal data, the classification based on self-paced transfer VGG network (STVGG) is proposed. ImageNet pretraining network parameters are transferred to VGG network with training colorectal data to acquire good initial network performance. And self-paced learning is used to optimize the network so that the classification performance of label unbalanced and difficult samples is improved. In order to assist the colonoscopist to accurately determine whether the polyp needs surgical resection, feature of trained STVGG model is shared to Unet segmentation network as the encoder part and to avoid repeat learning of polyp segmentation model. The experimental results on 3061 colorectal images illustrated that the proposed method obtained higher classification accuracy (96%) and segmentation performance compared with a few other methods. The polyp can be segmented accurately from around tissues by the proposed method. The segmentation results underpin the potential of deep learning methods for assisting colonoscopist in identifying polyps and enabling timely resection of these polyps at an early stage.
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Colon Cancer Biomarkers: Implications for Personalized Medicine. J Pers Med 2020; 10:jpm10040167. [PMID: 33066312 PMCID: PMC7711712 DOI: 10.3390/jpm10040167] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 10/08/2020] [Indexed: 12/14/2022] Open
Abstract
The heterogeneity of colon cancers and their reactions presents both a challenge and promise for personalized medicine. The challenge is to develop effective biologically personalized therapeutics guided by predictive and prognostic biomarkers. Presently, there are several classes of candidate biomarkers, including genomic probes, inhibitory RNAs, assays for immunity dysfunction and, not to be forgotten, specific histopathologic and histochemical features. To develop effective therapeutics, candidate biomarkers must be qualified and validated in comparable independent cohorts, no small undertaking. This process and subsequent deployment in clinical practice involves not only the strong association of the biomarker with the treatment but also careful attention to the prosaic aspects of representative tumor site selection, obtaining a fully adequate sample which is preserved and prepared to optimize high quality analysis. In the future, the clinical utility of biomarker analytical results will benefit from associated clinical and basic science data with the assistance of artificial intelligence techniques. By application of an individualized, selected suite of biomarkers, comprehensively interpreted, individualized, more effective and less toxic therapy for colon cancer will be enabled, thereby fulfilling the promise of personalized medicine.
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