|
1
|
Elomaa H, Tarkiainen V, Äijälä VK, Sirniö P, Ahtiainen M, Sirkiä O, Karjalainen H, Kastinen M, Tapiainen VV, Rintala J, Meriläinen S, Saarnio J, Rautio T, Tuomisto A, Helminen O, Wirta EV, Seppälä TT, Böhm J, Mäkinen MJ, Mecklin JP, Väyrynen JP. Associations of mucinous differentiation and mucin expression with immune cell infiltration and prognosis in colorectal adenocarcinoma. Br J Cancer 2025; 132:660-669. [PMID: 39966658 PMCID: PMC11961615 DOI: 10.1038/s41416-025-02960-3] [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: 08/04/2024] [Revised: 01/21/2025] [Accepted: 02/10/2025] [Indexed: 02/20/2025] Open
Abstract
BACKGROUND The production of extracellular mucus and expression of mucins are commonly aberrant in colorectal cancer, yet their roles in tumour progression remain unclear. METHODS To investigate the potential influence of mucus on immune response and prognosis, we analysed mucinous differentiation (non-mucinous, 0%; mucinous component, 1-50%; mucinous, >50%) and its associations with immune cell densities (determined with three multiplex immunohistochemistry assays or conventional immunohistochemistry) and survival in 1049 colorectal cancer patients and a validation cohort of 771 patients. We also assessed expression patterns of transmembrane (MUC1, MUC4) and secreted (MUC2, MUC5AC and MUC6) mucins using immunohistochemistry. RESULTS Mucinous differentiation was associated with higher densities of CD14+HLADR- immature monocytic cells and M2-like macrophages in mismatch repair (MMR) proficient tumours, and lower T-cell densities in MMR-deficient tumours. Mucinous differentiation was not associated with cancer-specific survival in multivariable Cox regression models. Higher cytoplasmic MUC1 expression independently predicted worse cancer-specific survival (multivariable HR for high vs. negative to low expression, 2.14; 95% CI: 1.26-3.64). It was also associated with increased myeloid cell infiltration in MMR-proficient tumours. CONCLUSIONS Although mucinous differentiation did not independently predict survival, extracellular mucus and MUC1 expression could promote tumour progression through immunosuppression.
Collapse
Affiliation(s)
- Hanna Elomaa
- Department of Education and Research, Hospital Nova of Central Finland, Well Being Services County of Central Finland, Jyväskylä, Finland
| | - Vilma Tarkiainen
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
| | - Ville K Äijälä
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Päivi Sirniö
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Maarit Ahtiainen
- Central Finland Biobank, Hospital Nova of Central Finland, Well Being Services County of Central Finland, Jyväskylä, Finland
| | - Onni Sirkiä
- Department of Pathology, Hospital Nova of Central Finland, Well Being Services County of Central Finland, Jyväskylä, Finland
- Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland
| | - Henna Karjalainen
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Meeri Kastinen
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Vilja V Tapiainen
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Jukka Rintala
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Sanna Meriläinen
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Juha Saarnio
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Tero Rautio
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Anne Tuomisto
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Olli Helminen
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Erkki-Ville Wirta
- Department of Gastroenterology and Alimentary Tract Surgery, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere University Hospital, Tampere, Finland
| | - Toni T Seppälä
- Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere University Hospital, Tampere, Finland
- Department of Gastrointestinal Surgery, Helsinki University Hospital, Helsinki, Finland
- Applied Tumor Genomics Research Program, University of Helsinki, Helsinki, Finland
| | - Jan Böhm
- Department of Pathology, Hospital Nova of Central Finland, Well Being Services County of Central Finland, Jyväskylä, Finland
| | - Markus J Mäkinen
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland
| | - Jukka-Pekka Mecklin
- Department of Education and Research, Hospital Nova of Central Finland, Well Being Services County of Central Finland, Jyväskylä, Finland
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Juha P Väyrynen
- Translational Medicine Research Unit, University of Oulu, Medical Research Center Oulu, and Oulu University Hospital, Oulu, Finland.
| |
Collapse
|
|
2
|
Wang T, Shin SJ, Wang M, Xu Q, Jiang G, Cong F, Kang J, Xu H. Multi-Task Adaptive Resolution Network for Lymph Node Metastasis Diagnosis From Whole Slide Images of Colorectal Cancer. IEEE J Biomed Health Inform 2025; 29:420-432. [PMID: 39446536 DOI: 10.1109/jbhi.2024.3485703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2024]
Abstract
Automated detection of lymph node metastasis (LNM) holds great potential to alleviate the workload of doctors and reduce misinterpretations. Despite the practical successes achieved, effectively addressing the highly complex and heterogeneous tumor microenvironment remains an open and challenging problem, especially when tumor subtypes intermingle and are difficult to delineate. In this paper, we propose a multi-task adaptive resolution network, named MAR-Net, for LNM detection and subtyping in complex mixed-type cancers. Specifically, we construct a resolution-aware module to mine heterogeneous diagnostic information, which exploits the multi-scale pyramid information and adaptively combines multi-resolution structured features for comprehensive representation. Additionally, we adopt a multi-task learning approach that simultaneously addresses LNM detection and subtyping, reducing model instability during optimization and improving performance across both tasks. More importantly, to rectify the potential misclassification of tumor subtypes, we elaborately design a hierarchical subtying refinement (HSR) algorithm that leverages a generic segmentation model informed by pathologists' prior knowledge. Evaluations have been conducted on three private and one public cancer datasets (554 WSIs, 4.8 million patches). Our experimental results demonstrate that the proposed method consistently achieves superior performance compared to the state-of-the-art methods, achieving 0.5% to 3.2% higher AUC in LNM detection and 3.8% to 4.4% higher AUC in LNM subtyping.
Collapse
|
|
3
|
Yang J, Huang J, Han D, Ma X. Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review. Clin Med Insights Oncol 2024; 18:11795549231220320. [PMID: 38187459 PMCID: PMC10771756 DOI: 10.1177/11795549231220320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 11/26/2023] [Indexed: 01/09/2024] Open
Abstract
Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly revealed molecular mechanisms diversify therapeutic approaches for colorectal cancer. However, the selection of personalized treatment among multiple treatment options has become another challenge in the era of precision medicine. Artificial intelligence has recently been increasingly investigated in the treatment of colorectal cancer. This narrative review mainly discusses the applications of artificial intelligence in the treatment of colorectal cancer patients. A comprehensive literature search was conducted in MEDLINE, EMBASE, and Web of Science to identify relevant papers, resulting in 49 articles being included. The results showed that, based on different categories of data, artificial intelligence can predict treatment outcomes and essential guidance information of traditional and novel therapies, thus enabling individualized treatment strategy selection for colorectal cancer patients. Some frequently implemented machine learning algorithms and deep learning frameworks have also been employed for long-term prognosis prediction in patients with colorectal cancer. Overall, artificial intelligence shows encouraging results in treatment strategy selection and prognosis evaluation for colorectal cancer patients.
Collapse
Affiliation(s)
- Jiaqing Yang
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Huang
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China
| | - Deqian Han
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| |
Collapse
|
|
4
|
Höhn J, Krieghoff-Henning E, Wies C, Kiehl L, Hetz MJ, Bucher TC, Jonnagaddala J, Zatloukal K, Müller H, Plass M, Jungwirth E, Gaiser T, Steeg M, Holland-Letz T, Brenner H, Hoffmeister M, Brinker TJ. Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning. NPJ Precis Oncol 2023; 7:98. [PMID: 37752266 PMCID: PMC10522577 DOI: 10.1038/s41698-023-00451-3] [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: 04/11/2023] [Accepted: 09/06/2023] [Indexed: 09/28/2023] Open
Abstract
Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction. Therefore, we established survival curve-based CRC survival predictors and benchmarked them against standard binary survival predictors, comparing their performance extensively on the clinical high and low risk subsets of one internal and three external cohorts. Survival curve-based risk prediction achieved a very similar risk stratification to binary risk prediction for this task. Exchanging other components of the pipeline, namely input tissue and feature extractor, had largely identical effects on model performance independently of the type of risk prediction. An ensemble of all survival curve-based models exhibited a more robust performance, as did a similar ensemble based on binary risk prediction. Patients could be further stratified within clinical risk groups. However, performance still varied across cohorts, indicating limited generalization of all investigated image analysis pipelines, whereas models using clinical data performed robustly on all cohorts.
Collapse
Affiliation(s)
- Julia Höhn
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eva Krieghoff-Henning
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christoph Wies
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty, University Heidelberg, Heidelberg, Germany
| | - Lennard Kiehl
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin J Hetz
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tabea-Clara Bucher
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jitendra Jonnagaddala
- School of Population Health, Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, Australia
| | - Kurt Zatloukal
- Diagnostic and Research Center for Molecular BioMedicine, Diagnostic & Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Heimo Müller
- Diagnostic and Research Center for Molecular BioMedicine, Diagnostic & Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Markus Plass
- Diagnostic and Research Center for Molecular BioMedicine, Diagnostic & Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Emilian Jungwirth
- Diagnostic and Research Center for Molecular BioMedicine, Diagnostic & Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Timo Gaiser
- Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany
- Institute of Applied Pathology, Speyer, Germany
| | - Matthias Steeg
- Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany
| | - Tim Holland-Letz
- Department of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| |
Collapse
|
|
5
|
Liu Y, Wang Y, Yao S, Liang C, Li Q, Liu Z, Zhu Y, Cui Y, Zhao K. Development and validation of a scoring system incorporating tumor growth pattern and perineural invasion for risk stratification in colorectal cancer. J Investig Med 2023; 71:674-685. [PMID: 37073507 DOI: 10.1177/10815589231167359] [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] [Indexed: 04/20/2023]
Abstract
Tumor growth pattern (TGP) and perineural invasion (PNI) at the invasive margin have been recognized as indicators of tumor invasiveness and prognostic events in colorectal cancer (CRC). This study aims to develop a scoring system incorporating TGP and PNI, and further investigate its prognostic significance for CRC risk stratification. A scoring system, termed tumor-invasion score, was established by summing TGP and PNI scores. The discovery cohort (N = 444) and the validation cohort (N = 339) were used to explore the prognostic significance of the tumor-invasion score. The endpoints of the event were disease-free survival (DFS) and overall survival (OS) which were analyzed by the Cox proportional hazard model. In the discovery cohort, Cox regression analysis showed that DFS and OS were inferior for score 4 group compared with score 1 group (DFS, hazard ratio (HR) 4.44, 95% confidence interval (CI) 2.49-7.92, p < 0.001; OS, 4.41, 2.37-8.19,p < 0.001). The validation cohort showed similar results (DFS, 4.73, 2.39-9.37, p < 0.001; OS, 5.52, 2.55-12.0, p < 0.001). The model combining tumor-invasion score and clinicopathologic information showed good discrimination performance than single predictors. TGP and PNI were associated with tumor invasiveness and survival in CRC. The tumor-invasion score generated by TGP and PNI scores served as an independent prognostic parameter of DFS and OS for CRC patients.
Collapse
Affiliation(s)
- Yulin Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Yiting Wang
- Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Qian Li
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Yaxi Zhu
- Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yanfen Cui
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Ke Zhao
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| |
Collapse
|
|
6
|
Lee M. Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis. Bioengineering (Basel) 2023; 10:897. [PMID: 37627783 PMCID: PMC10451210 DOI: 10.3390/bioengineering10080897] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/21/2023] [Accepted: 07/27/2023] [Indexed: 08/26/2023] Open
Abstract
This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review's findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.
Collapse
Affiliation(s)
- Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| |
Collapse
|
|
7
|
Yavuz A, Alpsoy A, Gedik EO, Celik MY, Bassorgun CI, Unal B, Elpek GO. Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology. Artif Intell Gastroenterol 2022; 3:142-162. [DOI: 10.35712/aig.v3.i5.142] [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: 10/16/2022] [Revised: 11/25/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022] Open
Abstract
Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.
Collapse
Affiliation(s)
- Aysen Yavuz
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Anil Alpsoy
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Elif Ocak Gedik
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | | | | | - Betul Unal
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Gulsum Ozlem Elpek
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| |
Collapse
|
|
8
|
Ozemir IA, Aydemir MA, Gapbarov A, Ekinci O, Alimoglu O. The Effect of the Mucinous Component Presence on the Clinical Outcomes of Colorectal Cancer. GALICIAN MEDICAL JOURNAL 2022. [DOI: 10.21802/gmj.2022.4.6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Background. The effect of colorectal cancer (CRC) histological subtypes on the prognosis is still a controversial issue. We aimed to compare clinical findings, histopathologic data, and survival outcomes in CRC patients with classical and mucinous subtypes.
Methods. Patients who were operated on for CRC between 2010 and 2017 were included in the study. Patients were classified into two groups according to the presence of a mucinous component: mucinous adenocarcinoma (MAC) - mucinous component > 50% and classical adenocarcinoma (CAC). Clinical and histopathologic findings, recurrence, metastasis, and survival rates were compared.
Results. Data of the 484 CRC patients were documented. Sixty-nine patients (14.3%) were in the MAC group and 415 (85.7%) patients were in the CAC group. The mean age of patients with MAC and CAC was 63.4 ± 13.5 and 68.5 ± 12.7 years, respectively (p = 0.002). Proximal colon localization was found in 30 (43.5%) MAC patients and 123 (29.6%) CAC patients (p = 0.029). The number of patients with metastatic lymph nodes was higher in the MAC group (58% vs. 41.2%, p = 0.03). Nevertheless, there was no significant difference between the CAC and MAC groups in terms of disease-free survival (63.1% vs. 69.6%, p = 0.37) and disease-related mortality (23.6% vs. 23.2%, p = 0.94) over the follow-up period. Multivariate analysis showed that the presence of perineural invasion, patient’s age, and disease stage were associated with mortality in CRC patients.
Conclusions. MACs occurred at a younger age than CACs and were more likely localized in the proximal colon as compared to CACs. Despite increased lymph node metastasis in MAC patients, no statistical significance was detected in overall survival or disease-free survival. Multivariate analysis revealed that age, perineural invasion, and disease stage were relevant to mortality in CRC patients.
Collapse
|