1
|
Xiaojian Y, Zhanbo Q, Jian C, Zefeng W, Jian L, Jin L, Yuefen P, Shuwen H. Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace. J Cancer Res Clin Oncol 2024; 150:467. [PMID: 39422817 PMCID: PMC11489169 DOI: 10.1007/s00432-024-05992-z] [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: 04/06/2024] [Accepted: 10/09/2024] [Indexed: 10/19/2024]
Abstract
BACKGROUND The advancements in artificial intelligence (AI) technology for image recognition were propelling molecular pathology research into a new era. OBJECTIVE To summarize the hot spots and research trends in the field of molecular pathology image recognition. METHODS Relevant articles from January 1st, 2010, to August 25th, 2023, were retrieved from the Web of Science Core Collection. Subsequently, CiteSpace was employed for bibliometric and visual analysis, generating diverse network diagrams illustrating keywords, highly cited references, hot topics, and research trends. RESULTS A total of 110 relevant articles were extracted from a pool of 10,205 articles. The overall publication count exhibited a rising trend each year. The leading contributors in terms of institutions, countries, and authors were Maastricht University (11 articles), the United States (38 articles), and Kather Jacob Nicholas (9 articles), respectively. Half of the top ten research institutions, based on publication volume, were affiliated with Germany. The most frequently cited article was authored by Nicolas Coudray et al. accumulating 703 citations. The keyword "Deep learning" had the highest frequency in 2019. Notably, the highlighted keywords from 2022 to 2023 included "microsatellite instability", and there were 21 articles focusing on utilizing algorithms to recognize microsatellite instability (MSI) in colorectal cancer (CRC) pathological images. CONCLUSION The use of DL is expected to provide a new strategy to effectively solve the current problem of time-consuming and expensive molecular pathology detection. Therefore, further research is needed to address issues, such as data quality and standardization, model interpretability, and resource and infrastructure requirements.
Collapse
Affiliation(s)
- Yu Xiaojian
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, China
- Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China
| | - Qu Zhanbo
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, China
- Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China
| | - Chu Jian
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, China
- Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China
| | - Wang Zefeng
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, China
- Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China
| | - Liu Jian
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, China
- Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China
| | - Liu Jin
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, China
- Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China
| | - Pan Yuefen
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China.
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, China.
- Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China.
| | - Han Shuwen
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558, Sanhuan North Road, Wuxing District, Huzhou, 313000, Zhejiang Province, China.
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, China.
- Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China.
- ASIR(Institute - Association of intelligent systems and robotics), Rueil-Malmaison, France.
| |
Collapse
|
2
|
Faa G, Coghe F, Pretta A, Castagnola M, Van Eyken P, Saba L, Scartozzi M, Fraschini M. Artificial Intelligence Models for the Detection of Microsatellite Instability from Whole-Slide Imaging of Colorectal Cancer. Diagnostics (Basel) 2024; 14:1605. [PMID: 39125481 PMCID: PMC11311951 DOI: 10.3390/diagnostics14151605] [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: 07/08/2024] [Revised: 07/19/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
With the advent of whole-slide imaging (WSI), a technology that can digitally scan whole slides in high resolution, pathology is undergoing a digital revolution. Detecting microsatellite instability (MSI) in colorectal cancer is crucial for proper treatment, as it identifies patients responsible for immunotherapy. Even though universal testing for MSI is recommended, particularly in patients affected by colorectal cancer (CRC), many patients remain untested, and they reside mainly in low-income countries. A critical need exists for accessible, low-cost tools to perform MSI pre-screening. Here, the potential predictive role of the most relevant artificial intelligence-driven models in predicting microsatellite instability directly from histology alone is discussed, focusing on CRC. The role of deep learning (DL) models in identifying the MSI status is here analyzed in the most relevant studies reporting the development of algorithms trained to this end. The most important performance and the most relevant deficiencies are discussed for every AI method. The models proposed for algorithm sharing among multiple research and clinical centers, including federal learning (FL) and swarm learning (SL), are reported. According to all the studies reported here, AI models are valuable tools for predicting MSI status on WSI alone in CRC. The use of digitized H&E-stained sections and a trained algorithm allow the extraction of relevant molecular information, such as MSI status, in a short time and at a low cost. The possible advantages related to introducing DL methods in routine surgical pathology are underlined here, and the acceleration of the digital transformation of pathology departments and services is recommended.
Collapse
Affiliation(s)
- Gavino Faa
- Dipartimento di Scienze Mediche e Sanità Pubblica, University of Cagliari, 09123 Cagliari, Italy;
| | - Ferdinando Coghe
- UOC Laboratorio Analisi, AOU of Cagliari, 09123 Cagliari, Italy;
| | - Andrea Pretta
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (A.P.); (M.S.)
| | - Massimo Castagnola
- Laboratorio di Proteomica, Centro Europeo di Ricerca sul Cervello, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy;
| | - Peter Van Eyken
- Division of Pathology, Genk Regional Hospital, 3600 Genk, Belgium;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, University of Cagliari, 40138 Cagliari, Italy;
| | - Mario Scartozzi
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (A.P.); (M.S.)
| | - Matteo Fraschini
- Dipartimento di Ingegneria Elettrica ed Elettronica, University of Cagliari, 09123 Cagliari, Italy
| |
Collapse
|
3
|
Xu Z, Li W, Dong X, Chen Y, Zhang D, Wang J, Zhou L, He G. Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence. Clin Chim Acta 2024; 559:119686. [PMID: 38663471 DOI: 10.1016/j.cca.2024.119686] [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: 11/27/2023] [Revised: 04/22/2024] [Accepted: 04/22/2024] [Indexed: 05/03/2024]
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients.
Collapse
Affiliation(s)
- Zishan Xu
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Wei Li
- School of Forensic Medicine, Xinxiang Medical University, Xinxiang 453000, China
| | - Xiangyang Dong
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Yingying Chen
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang 453000, China
| | - Dan Zhang
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Jingnan Wang
- Xinxiang Medical University SanQuan Medical College, Xinxiang 453003, China
| | - Lin Zhou
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Guoyang He
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China.
| |
Collapse
|
4
|
Guitton T, Allaume P, Rabilloud N, Rioux-Leclercq N, Henno S, Turlin B, Galibert-Anne MD, Lièvre A, Lespagnol A, Pécot T, Kammerer-Jacquet SF. Artificial Intelligence in Predicting Microsatellite Instability and KRAS, BRAF Mutations from Whole-Slide Images in Colorectal Cancer: A Systematic Review. Diagnostics (Basel) 2023; 14:99. [PMID: 38201408 PMCID: PMC10795725 DOI: 10.3390/diagnostics14010099] [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: 11/22/2023] [Revised: 12/22/2023] [Accepted: 12/24/2023] [Indexed: 01/12/2024] Open
Abstract
Mismatch repair deficiency (d-MMR)/microsatellite instability (MSI), KRAS, and BRAF mutational status are crucial for treating advanced colorectal cancer patients. Traditional methods like immunohistochemistry or polymerase chain reaction (PCR) can be challenged by artificial intelligence (AI) based on whole slide images (WSI) to predict tumor status. In this systematic review, we evaluated the role of AI in predicting MSI status, KRAS, and BRAF mutations in colorectal cancer. Studies published in PubMed up to June 2023 were included (n = 17), and we reported the risk of bias and the performance for each study. Some studies were impacted by the reduced number of slides included in the data set and the lack of external validation cohorts. Deep learning models for the d-MMR/MSI status showed a good performance in training cohorts (mean AUC = 0.89, [0.74-0.97]) but slightly less than expected in the validation cohort when available (mean AUC = 0.82, [0.63-0.98]). Contrary to the MSI status, the prediction of KRAS and BRAF mutations was less explored with a less robust methodology. The performance was lower, with a maximum of 0.77 in the training cohort, 0.58 in the validation cohort for KRAS, and 0.82 AUC in the training cohort for BRAF.
Collapse
Affiliation(s)
- Theo Guitton
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Pierre Allaume
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Noémie Rabilloud
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, CEDEX 09, 35033 Rennes, France
| | - Nathalie Rioux-Leclercq
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Sébastien Henno
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Bruno Turlin
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
| | - Marie-Dominique Galibert-Anne
- Department of Molecular Genetics and Medical Genomics CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (M.-D.G.-A.); (A.L.)
| | - Astrid Lièvre
- Department of Gastro-Entrology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France;
| | - Alexandra Lespagnol
- Department of Molecular Genetics and Medical Genomics CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (M.-D.G.-A.); (A.L.)
| | - Thierry Pécot
- Facility for Artificial Intelligence and Image Analysis (FAIIA), Biosit UAR 3480 CNRS-US18 INSERM, Rennes University, 2 Avenue du Professeur Léon Bernard, 35042 Rennes, France
| | - Solène-Florence Kammerer-Jacquet
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 Rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France; (P.A.); (N.R.-L.); (S.-F.K.-J.)
- Impact TEAM, Laboratoire Traitement du Signal et de l’Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, CEDEX 09, 35033 Rennes, France
| |
Collapse
|
5
|
Saillard C, Dubois R, Tchita O, Loiseau N, Garcia T, Adriansen A, Carpentier S, Reyre J, Enea D, von Loga K, Kamoun A, Rossat S, Wiscart C, Sefta M, Auffret M, Guillou L, Fouillet A, Kather JN, Svrcek M. Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides. Nat Commun 2023; 14:6695. [PMID: 37932267 PMCID: PMC10628260 DOI: 10.1038/s41467-023-42453-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023] Open
Abstract
Mismatch Repair Deficiency (dMMR)/Microsatellite Instability (MSI) is a key biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is now recommended, but contributes to increased workload for pathologists and delayed therapeutic decisions. Deep learning has the potential to ease dMMR/MSI testing and accelerate oncologist decision making in clinical practice, yet no comprehensive validation of a clinically approved tool has been conducted. We developed MSIntuit, a clinically approved artificial intelligence (AI) based pre-screening tool for MSI detection from haematoxylin-eosin (H&E) stained slides. After training on samples from The Cancer Genome Atlas (TCGA), a blind validation is performed on an independent dataset of 600 consecutive CRC patients. Inter-scanner reliability is studied by digitising each slide using two different scanners. MSIntuit yields a sensitivity of 0.96-0.98, a specificity of 0.47-0.46, and an excellent inter-scanner agreement (Cohen's κ: 0.82). By reaching high sensitivity comparable to gold standard methods while ruling out almost half of the non-MSI population, we show that MSIntuit can effectively serve as a pre-screening tool to alleviate MSI testing burden in clinical practice.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | - Diana Enea
- Department of Pathology, Saint-Antoine Hospital - Sorbonne Université, AP-HP, Paris, France
| | | | | | | | | | | | | | | | | | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
| | - Magali Svrcek
- Department of Pathology, Saint-Antoine Hospital - Sorbonne Université, AP-HP, Paris, France
| |
Collapse
|
6
|
Yu J, Ding PR, Jiang W. Screening and Management of Lynch Syndrome: The Chinese Experience. Clin Colon Rectal Surg 2023; 36:369-377. [PMID: 37795465 PMCID: PMC10547539 DOI: 10.1055/s-0043-1767706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Lynch syndrome (LS), caused by germline mutations in the mismatch repair genes, is the most common hereditary colorectal cancer. While LS is also associated with various cancers, early detection of the proband is meaningful for tumor prevention, treatment, and familial management. It has been a dramatic shift on the screening approaches for LS. As the rapid development of the molecular biological methods, a comprehensive understanding of the LS screening strategies will help to improve the clinical care for this systematic disease. The current screening strategies have been well validated but mainly by evidence derived from western population, lacking consideration of the ethnic heterogeneity, which hampers the universality and clinical application in China. Hence, this review will focus on the Chinese experience in LS screening, aiming to help better understand the ethnic diversity and further optimize the screening strategies.
Collapse
Affiliation(s)
- Jiehai Yu
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou Guangdong, P. R. 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, Guangzhou Guangdong, P. R. China
| | - Wu Jiang
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou Guangdong, P. R. China
| |
Collapse
|
7
|
Wu Y, Li Y, Xiong X, Liu X, Lin B, Xu B. Recent advances of pathomics in colorectal cancer diagnosis and prognosis. Front Oncol 2023; 13:1094869. [PMID: 37538112 PMCID: PMC10396402 DOI: 10.3389/fonc.2023.1094869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 06/13/2023] [Indexed: 08/05/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most common malignancies, with the third highest incidence and the second highest mortality in the world. To improve the therapeutic outcome, the risk stratification and prognosis predictions would help guide clinical treatment decisions. Achieving these goals have been facilitated by the fast development of artificial intelligence (AI) -based algorithms using radiological and pathological data, in combination with genomic information. Among them, features extracted from pathological images, termed pathomics, are able to reflect sub-visual characteristics linking to better stratification and prediction of therapeutic responses. In this paper, we review recent advances in pathological image-based algorithms in CRC, focusing on diagnosis of benign and malignant lesions, micro-satellite instability, as well as prediction of neoadjuvant chemoradiotherapy and the prognosis of CRC patients.
Collapse
Affiliation(s)
- Yihan Wu
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Yi Li
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Xiaomin Xiong
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Xiaohua Liu
- Bioengineering College, Chongqing University, Chongqing, China
| | - Bo Lin
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Bo Xu
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| |
Collapse
|
8
|
Cao W, Hu H, Guo J, Qin Q, Lian Y, Li J, Wu Q, Chen J, Wang X, Deng Y. CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study. J Transl Med 2023; 21:214. [PMID: 36949511 PMCID: PMC10035255 DOI: 10.1186/s12967-023-04023-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 02/27/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Stratification of DNA mismatch repair (MMR) status in patients with colorectal cancer (CRC) enables individual clinical treatment decision making. The present study aimed to develop and validate a deep learning (DL) model based on the pre-treatment CT images for predicting MMR status in CRC. METHODS 1812 eligible participants (training cohort: n = 1124; internal validation cohort: n = 482; external validation cohort: n = 206) with CRC were enrolled from two institutions. All pretherapeutic CT images from three dimensions were trained by the ResNet101, then integrated by Gaussian process regression (GPR) to develop a full-automatic DL model for MMR status prediction. The predictive performance of the DL model was evaluated using the area under the receiver operating characteristic curve (AUC) and then tested in the internal and external validation cohorts. Additionally, the participants from institution 1 were sub-grouped by various clinical factors for subgroup analysis, then the predictive performance of the DL model for identifying MMR status between participants in different groups were compared. RESULTS The full-automatic DL model was established in the training cohort to stratify the MMR status, which presented promising discriminative ability with the AUCs of 0.986 (95% CI 0.971-1.000) in the internal validation cohort and 0.915 (95% CI 0.870-0.960) in the external validation cohort. In addition, the subgroup analysis based on the thickness of CT images, clinical T and N stages, gender, the longest diameter, and the location of tumors revealed that the DL model showed similar satisfying prediction performance. CONCLUSIONS The DL model may potentially serve as a noninvasive tool to facilitate the pre-treatment individualized prediction of MMR status in patients with CRC, which could promote the personalized clinical-making decision.
Collapse
Affiliation(s)
- Wuteng Cao
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
| | - Huabin Hu
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
- Department of Medical Oncology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
| | - Jirui Guo
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
- Department of Colorectal Surgery, Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
| | - Qiyuan Qin
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
- Department of Colorectal Surgery, Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
| | - Yanbang Lian
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Jiao Li
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
| | - Qianyu Wu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
| | - Junhong Chen
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Xinhua Wang
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
| | - Yanhong Deng
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China.
- Department of Medical Oncology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China.
| |
Collapse
|
9
|
Jiang Z, Yan L, Deng S, Gu J, Qin L, Mao F, Xue Y, Cai W, Nie X, Liu H, Shang F, Tao K, Wang J, Wu K, Cao Y, Cai K. Development and Interpretation of a Clinicopathological-Based Model for the Identification of Microsatellite Instability in Colorectal Cancer. DISEASE MARKERS 2023; 2023:5178750. [PMID: 36860582 PMCID: PMC9969972 DOI: 10.1155/2023/5178750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/05/2023] [Accepted: 01/28/2023] [Indexed: 02/20/2023]
Abstract
Chemotherapy is not recommended for patients with deficient mismatch repair (dMMR) in colorectal cancer (CRC); therefore, assessing the status of MMR is crucial for the selection of subsequent treatment. This study is aimed at building predictive models to accurately and rapidly identify dMMR. A retrospective analysis was performed at Wuhan Union Hospital between May 2017 and December 2019 based on the clinicopathological data of patients with CRC. The variables were subjected to collinearity, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) feature screening analyses. Four sets of machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and RF) and a conventional logistic regression (LR) model were built for model training and testing. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the developed models. In total, 2279 patients were included in the study and were randomly divided into either the training or test group. Twelve clinicopathological features were incorporated into the development of the predictive models. The area under curve (AUC) values of the five predictive models were 0.8055 for XGBoost, 0.8174 for SVM, 0.7424 for NB, 8584 for RF, and 0.7835 for LR (Delong test, P value < 0.05). The results showed that the RF model exhibited the best recognition ability and outperformed the conventional LR method in identifying dMMR and proficient MMR (pMMR). Our predictive models based on routine clinicopathological data can significantly improve the diagnostic performance of dMMR and pMMR. The four machine learning models outperformed the conventional LR model.
Collapse
Affiliation(s)
- Zhenxing Jiang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Lizhao Yan
- Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Shenghe Deng
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Junnan Gu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Le Qin
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
- Department of General Surgery, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, Xinjiang 832008, China
| | - Fuwei Mao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Yifan Xue
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Wentai Cai
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Xiu Nie
- Department of Pathology, Union Hospital, Tongji Medical, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Hongli Liu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Fumei Shang
- Department of Medical Oncology, Nanyang Central Hospital, Nanyang, Henan, China
| | - Kaixiong Tao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Jiliang Wang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Ke Wu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Yinghao Cao
- Department of Digestive Surgical Oncology, Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Kailin Cai
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|