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Song X, Li J, Zhu J, Kong YF, Zhou YH, Wang ZK, Zhang J. Predictors of early colorectal cancer metastasis to lymph nodes: providing rationale for therapy decisions. Front Oncol 2024; 14:1371599. [PMID: 39035744 PMCID: PMC11257837 DOI: 10.3389/fonc.2024.1371599] [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: 01/17/2024] [Accepted: 06/24/2024] [Indexed: 07/23/2024] Open
Abstract
With the improvement of national health awareness and the popularization of a series of screening methods, the number of patients with early colorectal cancer is gradually increasing, and accurate prediction of lymph node metastasis of T1 colorectal cancer is the key to determining the optimal therapeutic solutions. Whether patients with T1 colorectal cancer undergoing endoscopic resection require additional surgery and regional lymph node dissection is inconclusive in current guidelines. However, we can be sure that in early colorectal cancer without lymph node metastasis, endoscopic resection alone does not affect the prognosis, and it greatly improves the quality of life and reduces the incidence of surgical complications while preserving organ integrity. Therefore, it is vital to discriminate patients without lymph node metastasis in T1 colorectal cancer, and this requires accurate predictors. This paper briefly explains the significance and shortcomings of traditional pathological factors, then extends and states the new pathological factors, clinical test factors, molecular biomarkers, and the risk assessment models of lymph node metastasis based on artificial intelligence.
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Affiliation(s)
| | | | | | | | | | | | - Jin Zhang
- Department of General Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China
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2
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Borowczyk M, Kaczmarek-Ryś M, Hryhorowicz S, Sypniewski M, Filipowicz D, Dobosz P, Oszywa M, Ruchała M, Ziemnicka K. Germline polymorphisms of the NOD2 pathway may predict the effectiveness of radioiodine in differentiated thyroid cancer treatment. J Endocrinol Invest 2024:10.1007/s40618-024-02389-0. [PMID: 38755492 DOI: 10.1007/s40618-024-02389-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 05/02/2024] [Indexed: 05/18/2024]
Abstract
PURPOSE Differentiated thyroid cancer (DTC) presents a complex clinical challenge, especially in patients with distant metastases and resistance to standard treatments. This study aimed to investigate the influence of specific genes and their germline single nucleotide polymorphisms (SNPs) linked to both inflammatory processes and other neoplasms on the clinical and pathological characteristics of DTC, particularly their potential impact on radioiodine (RAI) treatment efficacy. METHODS This retrospective analysis involved a cohort of 646 patients diagnosed with DTC after thyroidectomy. Study covering 1998-2014, updated in 2023, included 567 women and 79 men (median age: 49; range: 7-83). SNP selection targeted functional significance, while mutational status was assessed by pyrosequencing for comprehensive characterization. Patient genetic profiles were assessed for associations with disease characteristics, RAI response, and cancer pathology. RESULTS Significant correlations emerged between certain SNPs and DTC features. Notably, the NOD2 c.802 T > C variant (rs2066842) was identified as a marker distinguishing between papillary thyroid cancer (PTC) and follicular thyroid cancer (FTC). Moreover, the c.802 T allele was associated with an enhanced response to RAI treatment, indicating a more substantial decrease in posttreatment stimulated thyroglobulin (sTg) concentrations. The NFKB1A allele c.126A (rs696) exhibited connections with lower FTC stages and a reduced probability of multifocality. CONCLUSION This study explored the molecular mechanisms of particular SNPs, highlighting the role of NOD2 in innate immunity and the stress response, and its potential impact on RAI efficacy. This research underscores the clinical promise of SNP analysis and contributes to personalized treatment strategies for DTC, emphasizing the relevance of genetic factors in cancer progression and treatment outcomes.
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Affiliation(s)
- M Borowczyk
- Department of Endocrinology, Metabolism and Internal Medicine, Poznan University of Medical Sciences, 49 Przybyszewskiego Street, 60-355, Poznan, Poland.
| | - M Kaczmarek-Ryś
- Institute of Human Genetics, Polish Academy of Sciences, Poznan, Poland
| | - S Hryhorowicz
- Institute of Human Genetics, Polish Academy of Sciences, Poznan, Poland
| | - M Sypniewski
- University Cancer Diagnostic Center, Poznan University of Medical Sciences, Poznan, Poland
| | - D Filipowicz
- Department of Endocrinology, Metabolism and Internal Medicine, Poznan University of Medical Sciences, 49 Przybyszewskiego Street, 60-355, Poznan, Poland
| | - P Dobosz
- University Cancer Diagnostic Center, Poznan University of Medical Sciences, Poznan, Poland
- Institute of Genetics and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - M Oszywa
- Department of Endocrinology, Metabolism and Internal Medicine, Poznan University of Medical Sciences, 49 Przybyszewskiego Street, 60-355, Poznan, Poland
| | - M Ruchała
- Department of Endocrinology, Metabolism and Internal Medicine, Poznan University of Medical Sciences, 49 Przybyszewskiego Street, 60-355, Poznan, Poland
| | - K Ziemnicka
- Department of Endocrinology, Metabolism and Internal Medicine, Poznan University of Medical Sciences, 49 Przybyszewskiego Street, 60-355, Poznan, Poland
- University Cancer Diagnostic Center, Poznan University of Medical Sciences, Poznan, Poland
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Yu L, Huang Z, Xiao Z, Tang X, Zeng Z, Tang X, Ouyang W. Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review). Oncol Rep 2024; 51:60. [PMID: 38456540 PMCID: PMC10940877 DOI: 10.3892/or.2024.8719] [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: 10/08/2023] [Accepted: 01/22/2024] [Indexed: 03/09/2024] Open
Abstract
Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.
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Affiliation(s)
- Liqing Yu
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Zhenjun Huang
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
| | - Ziqi Xiao
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Xiaofu Tang
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Ziqiang Zeng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
- School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Xiaoli Tang
- School of Basic Medicine, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Wenhao Ouyang
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
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Wang H, Huo R, He K, Cheng L, Zhang S, Yu M, Zhao W, Li H, Xue J. Perineural invasion in colorectal cancer: mechanisms of action and clinical relevance. Cell Oncol (Dordr) 2024; 47:1-17. [PMID: 37610689 PMCID: PMC10899381 DOI: 10.1007/s13402-023-00857-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND In recent years, the significance of the nervous system in the tumor microenvironment has gained increasing attention. The bidirectional communication between nerves and cancer cells plays a critical role in tumor initiation and progression. Perineural invasion (PNI) occurs when tumor cells invade the nerve sheath and/or encircle more than 33% of the nerve circumference. PNI is a common feature in various malignancies and is associated with tumor invasion, metastasis, cancer-related pain, and unfavorable clinical outcomes. The colon and rectum are highly innervated organs, and accumulating studies support PNI as a histopathologic feature of colorectal cancer (CRC). Therefore, it is essential to investigate the role of nerves in CRC and comprehend the mechanisms of PNI to impede tumor progression and improve patient survival. CONCLUSION This review elucidates the clinical significance of PNI, summarizes the underlying cellular and molecular mechanisms, introduces various experimental models suitable for studying PNI, and discusses the therapeutic potential of targeting this phenomenon. By delving into the intricate interactions between nerves and tumor cells, we hope this review can provide valuable insights for the future development of CRC treatments.
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Affiliation(s)
- Hao Wang
- Department of Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, P.R. China
| | - Ruixue Huo
- Department of Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, P.R. China
| | - Kexin He
- Department of Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, P.R. China
| | - Li Cheng
- Department of Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, P.R. China
| | - Shan Zhang
- State Key Laboratory of Oncogenes and Related Genes, Ren Ji Hospital, School of Medicine, Shanghai Cancer Institute, Shanghai Jiao Tong University, Shanghai, 200240, P.R. China
| | - Minhao Yu
- Department of Gastrointestinal Surgery, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200217, P.R. China
| | - Wei Zhao
- Department of Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, P.R. China.
| | - Hui Li
- State Key Laboratory of Oncogenes and Related Genes, Ren Ji Hospital, School of Medicine, Shanghai Cancer Institute, Shanghai Jiao Tong University, Shanghai, 200240, P.R. China.
| | - Junli Xue
- Department of Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, P.R. China.
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Dang H, Verhoeven DA, Boonstra JJ, van Leerdam ME. Management after non-curative endoscopic resection of T1 rectal cancer. Best Pract Res Clin Gastroenterol 2024; 68:101895. [PMID: 38522888 DOI: 10.1016/j.bpg.2024.101895] [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] [Received: 09/30/2023] [Revised: 02/03/2024] [Accepted: 02/15/2024] [Indexed: 03/26/2024]
Abstract
Since the introduction of population-based screening, increasing numbers of T1 rectal cancers are detected and removed by local endoscopic resection. Patients can be cured with endoscopic resection alone, but there is a possibility of residual tumor cells remaining after the initial resection. These can be located intraluminally at the resection site or extraluminally in the form of (lymph node) metastases. To decrease the risk of residual cells progressing towards more advanced disease, additional treatment is usually needed. However, with the currently available risk stratification models, it remains challenging to determine who should and should not be further treated after non-curative endoscopic resection. In this review, the different management strategies for patients with non-curatively treated T1 rectal cancers are discussed, along with the available evidence for each strategy and relevant considerations for clinical decision making. Furthermore, we provide practical guidance on the management and surveillance following non-curative endoscopic resection of T1 rectal cancer.
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Affiliation(s)
- Hao Dang
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Daan A Verhoeven
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Jurjen J Boonstra
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Monique E van Leerdam
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands
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Ose I, Levic K, Thygesen LC, Bulut O, Bisgaard T, Gögenur I, Kuhlmann TP. Prediction of disease recurrence or residual disease after primary endoscopic resection of pT1 colorectal cancer-results from a large nationwide Danish study. Int J Colorectal Dis 2023; 38:274. [PMID: 38036699 PMCID: PMC10689518 DOI: 10.1007/s00384-023-04570-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/23/2023] [Indexed: 12/02/2023]
Abstract
PURPOSE Risk assessment of disease recurrence in pT1 colorectal cancer is crucial in order to select the appropriate treatment strategy. The study aimed to develop a prediction model, based on histopathological data, for the probability of disease recurrence and residual disease in patients with pT1 colorectal cancer. METHODS The model dataset consisted of 558 patients with pT1 CRC who had undergone endoscopic resection only (n = 339) or endoscopic resection followed by subsequent bowel resection (n = 219). Tissue blocks and slides were retrieved from Pathology Departments from all regions in Denmark. All original slides were evaluated by one experienced gastrointestinal pathologist (TPK). New sections were cut and stained for haematoxylin and eosin (HE) and immunohistochemical markers. Missing values were multiple imputed. A logistic regression model with backward elimination was used to construct the prediction model. RESULTS The final prediction model for disease recurrence demonstrated good performance with AUC of 0.75 [95% CI 0.72-0.78], HL chi-squared test of 0.59 and scaled Brier score of 10%. The final prediction model for residual disease demonstrated medium performance with an AUC of 0.68 [0.63-0.72]. CONCLUSION We developed a prediction model for the probability of disease recurrence in pT1 CRC with good performance and calibration based on histopathological data. Together with lymphatic and venous invasion, an involved resection margin (0 mm) as opposed to a margin of ≤ 1 mm was an independent risk factor for both disease recurrence and residual disease.
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Affiliation(s)
- Ilze Ose
- Center for Surgical Science, Department of Surgery, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark.
| | - Katarina Levic
- Department of Surgical Gastroenterology, Copenhagen University Hospital, Hvidovre, Denmark
| | - Lau Caspar Thygesen
- National Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | - Orhan Bulut
- Department of Surgical Gastroenterology, Copenhagen University Hospital, Hvidovre, Denmark
| | - Thue Bisgaard
- Department of Surgical Gastroenterology, Holbæk Hospital, Holbæk, Denmark
| | - Ismail Gögenur
- Center for Surgical Science, Department of Surgery, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark
| | - Tine Plato Kuhlmann
- Department of Pathology, University Hospital of Copenhagen, Herlev Hospital, Herlev, Denmark
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Xu W, Mesa-Eguiagaray I, Kirkpatrick T, Devlin J, Brogan S, Turner P, Macdonald C, Thornton M, Zhang X, He Y, Li X, Timofeeva M, Farrington S, Din F, Dunlop M, Theodoratou E. Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms. J Pers Med 2023; 13:1065. [PMID: 37511678 PMCID: PMC10381199 DOI: 10.3390/jpm13071065] [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: 05/19/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models' calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models' prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.
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Affiliation(s)
- Wei Xu
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Ines Mesa-Eguiagaray
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Theresa Kirkpatrick
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Jennifer Devlin
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Stephanie Brogan
- Clinical Research Team, Oncology Department, Forth Valley Royal Hospital, Stirling Road, Larbert FK5 4WR, UK
| | - Patricia Turner
- Clinical Research Team, Oncology Department, Forth Valley Royal Hospital, Stirling Road, Larbert FK5 4WR, UK
| | - Chloe Macdonald
- University Hospital Wishaw & University Hospital Monklands, NHS Lanarkshire, Airdrie ML6 0JS, UK
| | | | - Xiaomeng Zhang
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Yazhou He
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Xue Li
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Maria Timofeeva
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Danish Institute for Advanced Study, Research Unit of Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, 5230 Odense M, Denmark
| | - Susan Farrington
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Farhat Din
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Malcolm Dunlop
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
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Wan L, Hu J, Chen S, Zhao R, Peng W, Liu Y, Hu S, Zou S, Wang S, Zhao X, Zhang H. Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning. Eur Radiol 2023; 33:3638-3646. [PMID: 36905470 DOI: 10.1007/s00330-023-09450-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/01/2022] [Accepted: 02/03/2023] [Indexed: 03/12/2023]
Abstract
OBJECTIVES This study aimed to investigate whether a deep learning (DL) model based on preoperative MR images of primary tumors can predict lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. METHODS In this retrospective study, patients with stage T1-2 rectal cancer who underwent preoperative MRI between October 2013 and March 2021 were included and assigned to the training, validation, and test sets. Four two-dimensional and three-dimensional (3D) residual networks (ResNet18, ResNet50, ResNet101, and ResNet152) were trained and tested on T2-weighted images to identify patients with LNM. Three radiologists independently assessed LN status on MRI, and diagnostic outcomes were compared with the DL model. Predictive performance was assessed with AUC and compared using the Delong method. RESULTS In total, 611 patients were evaluated (444 training, 81 validation, and 86 test). The AUCs of the eight DL models ranged from 0.80 (95% confidence interval [CI]: 0.75, 0.85) to 0.89 (95% CI: 0.85, 0.92) in the training set and from 0.77 (95% CI: 0.62, 0.92) to 0.89 (95% CI: 0.76, 1.00) in the validation set. The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set, with an AUC of 0.79 (95% CI: 0.70, 0.89) that was significantly greater than that of the pooled readers (AUC, 0.54 [95% CI: 0.48, 0.60]; p < 0.001). CONCLUSION The DL model based on preoperative MR images of primary tumors outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer. KEY POINTS • Deep learning (DL) models with different network frameworks showed different diagnostic performance for predicting lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. • The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set. • The DL model based on preoperative MR images outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.
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Affiliation(s)
- Lijuan Wan
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Jiesi Hu
- Department of Pharmaceutical Diagnosis, GE Healthcare, Life Sciences, #1 Tongji South Road, Beijing, 100176, China
- Harbin Institute of Technology, 518000, Shenzhen, China
| | - Shuang Chen
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Rui Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Wenjing Peng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yuan Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shangying Hu
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shuangmei Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Sicong Wang
- Department of Pharmaceutical Diagnosis, GE Healthcare, Life Sciences, #1 Tongji South Road, Beijing, 100176, China
| | - Xinming Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Hongmei Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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Ma L, Lian S, Liu H, Meng T, Zeng W, Zhong R, Zhong L, Xie C. Diagnostic performance of synthetic magnetic resonance imaging in the prognostic evaluation of rectal cancer. Quant Imaging Med Surg 2022; 12:3580-3591. [PMID: 35782274 PMCID: PMC9246756 DOI: 10.21037/qims-22-24] [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: 01/10/2022] [Accepted: 03/22/2022] [Indexed: 11/06/2022]
Abstract
Background Numerous factors are related to the prognosis of rectal cancer, including T stage, N stage, metastasis, extramural venous invasion (EMVI), circumferential resection margin (CRM), and tumor differentiation. However, it is still a challenge to precisely evaluate them before therapy; therefore, we investigate whether synthetic magnetic resonance imaging and apparent diffusion coefficient (ADC) values could help predict the prognostic factors of rectal cancer. Methods Eighty-seven patients (55 men and 32 women; mean age, 59±11 years) with pathologically confirmed rectal cancer were enrolled. Preoperative quantitative metrics, including T1, T2, proton density (PD), and ADC values, were measured with diffusion-weighted imaging (DWI) acquired by a single-shot echo-planar sequence and synthetic magnetic resonance imaging acquired by a multi-dynamic multi-echo sequence at 3.0 T, in patients with rectal cancer by two radiologists. We evaluated the diagnostic performance of synthetic magnetic resonance imaging using the independent sample t-test or Mann-Whitney U test and receiver operating characteristic (ROC) curve and multivariate logistic regression analyses and compared the area under the ROC curve of quantitative values using the DeLong test. Results The T2 and PD values showed a significant reduction among patients with poor differentiation and lymph node metastasis in rectal cancer. The area under the ROC curve values of T2 and PD values for predicting magnetic resonance imaging N stage and differentiation were 0.734, 0.682, and 0.673, 0.686, respectively. Moreover, combining T2 and PD values for magnetic resonance imaging N stage slightly improved the area under the ROC curve value of 0.774 (95% CI, 0.673-0.876). In the present study, the ADC and T1 values were not significant in the differentiation or clinical stage of rectal cancer (RC). Conclusions Quantitative T2 and PD values obtained by synthetic magnetic resonance imaging might be used for evaluating prognostic factors of rectal cancer noninvasively. Furthermore, combining T2 and PD values further improved the diagnostic performance of magnetic resonance imaging N staging in rectal cancer. The ADC and T1 values were not significant in the differentiation or clinical stage of RC.
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Affiliation(s)
- Lidi Ma
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Shanshan Lian
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Huimin Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Tiebao Meng
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Weilong Zeng
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Rui Zhong
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Linchang Zhong
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Chuanmiao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
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Liu Z, Huang C, Tian H, Liu Y, Huang Y, Zhu Z. Establishment of a Dynamic Nomogram for Predicting the Risk of Lymph Node Metastasis in T1 Stage Colorectal Cancer. Front Surg 2022; 9:845666. [PMID: 35388361 PMCID: PMC8977409 DOI: 10.3389/fsurg.2022.845666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 02/16/2022] [Indexed: 12/03/2022] Open
Abstract
Background Accurate prediction of the risk of lymph node metastasis in patients with stage T1 colorectal cancer is crucial for the formulation of treatment plans for additional surgery and lymph node dissection after endoscopic resection. The purpose of this study was to establish a predictive model for evaluating the risk of LNM in patients with stage T1 colorectal cancer. Methods The clinicopathological and imaging data of 179 patients with T1 stage colorectal cancer who underwent radical resection of colorectal cancer were collected. LASSO regression and a random forest algorithm were used to screen the important risk factors for LNM, and a multivariate logistic regression equation and dynamic nomogram were constructed. The C index, Calibration curve, and area under the ROC curve were used to evaluate the discriminant and prediction ability of the nomogram. The net reclassification index (NRI), comprehensive discriminant improvement index (IDI), and clinical decision curve (DCA) were compared with traditional ESMO criteria to evaluate the accuracy, net benefit, and clinical practicability of the model. Results The probability of lymph node metastasis in patients with T1 colorectal cancer was 11.17% (20/179). Multivariate analysis showed that the independent risk factors for LNM in T1 colorectal cancer were submucosal invasion depth, histological grade, CEA, lymphovascular invasion, and imaging results. The dynamic nomogram model constructed with independent risk factors has good discrimination and prediction capabilities. The C index was 0.914, the corrected C index was 0.890, the area under the ROC curve was 0.914, and the accuracy, sensitivity, and specificity were 93.3, 80.0, and 91.8%, respectively. The NRI, IDI, and DCA show that this model is superior to the ESMO standard. Conclusion This study establishes a dynamic nomogram that can effectively predict the risk of lymph node metastasis in patients with stage T1 colorectal cancer, which will provide certain help for the formulation of subsequent treatment plans for patients with stage T1 CRC after endoscopic resection.
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Zhang L, Yang L, Jiang S, Yu M. Nerve Dependence in Colorectal Cancer. Front Cell Dev Biol 2022; 10:766653. [PMID: 35223829 PMCID: PMC8866866 DOI: 10.3389/fcell.2022.766653] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 01/13/2022] [Indexed: 12/28/2022] Open
Abstract
Cancerous invasion of nerves has been reported in a list of malignant tumors as a high-risk pathological feature and marker of poor disease outcome especially in neurotrophic cancers (such as in pancreas and prostate), indicating that although once neglected, nerves could have played a pivotal role in tumorigenesis and cancer progression. In colorectal cancer, perineural invasion, a specific form of tumor-nerve interaction referring to the identification of tumor cells in proximity to the nerve, has been recognized as a strong and independent prognosis predictor; denervation of autonomic nerves and enteric nerves have shown that the existence of these nerves in the gut are accompanied by promoted cancer proliferation, further supporting that nerve is a potential accomplice to shield and nurture tumor cells. However, the precise role of nerve in CRC and the pattern of interaction between CRC cells and nerve has not been unveiled yet. Here we aim to review some basic knowledge of the importance of nerves in CRC and attempt to depict a mechanistic view of tumor-nerve interaction during CRC development.
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Affiliation(s)
- Lincheng Zhang
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ludi Yang
- Department of Ophthalmology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuheng Jiang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Minhao Yu, ; Shuheng Jiang,
| | - Minhao Yu
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Minhao Yu, ; Shuheng Jiang,
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12
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Zhang X, Duan R, Wang Y, Liu X, Zhang W, Zhu X, Chen Z, Shen W, He Y, Wang HQ, Huang M, Wang C, Zhang Z, Zhao X, Qiu L, Luo J, Sheng X, Guo W. FOLFIRI (folinic acid, fluorouracil, and irinotecan) increases not efficacy but toxicity compared with single-agent irinotecan as a second-line treatment in metastatic colorectal cancer patients: a randomized clinical trial. Ther Adv Med Oncol 2022; 14:17588359211068737. [PMID: 35069808 PMCID: PMC8771434 DOI: 10.1177/17588359211068737] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 12/02/2021] [Indexed: 12/15/2022] Open
Abstract
Background: FOLFIRI [irinotecan, folinic acid (CF), and fluorouracil] is considered a standard second-line chemotherapy regimen for patients with metastatic colorectal cancer (mCRC) who failed first-line XELOX/FOLFOX regimens. However, it remains unknown whether fluorouracil is still necessary in this case. This trial was designed to test the superiority of FOLFIRI over single-agent irinotecan as a second-line treatment for patients with mCRC. Methods: This randomized clinical trial was conducted in five hospitals in China. From 4 November 2016 to 17 January 2020, patients aged 18 years or older with histologically confirmed unresectable mCRC and who had failed first-line XELOX/FOLFOX regimens were screened and enrolled. Patients were randomized to receive either FOLFIRI or irinotecan. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS), objective response rate (ORR), and toxicity. Data were analyzed on an intention-to-treat basis. Results: A total of 172 patients with mCRC were randomly treated with FOLFIRI (n = 88) or irinotecan (n = 84). The median PFS was 104 and 112 days (3.5 and 3.7 months) in the FOLFIRI and irinotecan groups, respectively [hazard ratio (HR) = 1.084, 95% confidence interval (CI) = 0.7911–1.485; p = 0.6094], and there was also no significant difference in OS and ORR between the two groups. The incidence of the following adverse events (AEs) was significantly higher in the FOLFIRI group than in the irinotecan group: any grade AEs including leucopenia (73.9% versus 55.4%), neutropenia (72.7% versus 56.6%), thrombocytopenia (31.8% versus 18.1%), jaundice (18.2% versus 7.2%), mucositis (40.9% versus 14.5%), vomiting (37.5% versus 21.7%), and fever (19.3% versus 7.2%) and grade 3–4 neutropenia (47.7% versus 21.7%). Conclusion: This is the first head-to-head trial showing that single-agent irinotecan yielded PFS, OS, and ORR similar to FOLFIRI, with a more favorable toxicity profile; therefore, it might be a more favorable standard chemotherapy regimen for mCRC patients who failed first-line XELOX/FOLFOX regimens. Trial registration: This study is registered with ClinicalTrials.gov, number NCT02935764, registered 17 October 2016, https://clinicaltrials.gov/ct2/show/NCT02935764.
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Affiliation(s)
- Xiaowei Zhang
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ran Duan
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yusheng Wang
- Shanxi Tumor Hospital, Taiyuan, China
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xin Liu
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wen Zhang
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaodong Zhu
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhiyu Chen
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Shen
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yifu He
- Department of Medical Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
| | - Hong Qiang Wang
- Department of Oncology, Zhejiang Province Zhoushan Hospital, Zhoushan, China
| | - Mingzhu Huang
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chenchen Wang
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhe Zhang
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaoying Zhao
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lixin Qiu
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jianfeng Luo
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Xuedan Sheng
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weijian Guo
- Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
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Zhai LL, Zhu AK, Tang ZG. Noninvasive Identification of Lymph Node Metastasis in T1 Colorectal Cancer by Transcriptomic Panel Based on Liquid Biopsy Assay: Additional Analytical Strategies Are Needed. Gastroenterology 2022; 162:347-348. [PMID: 34224742 DOI: 10.1053/j.gastro.2021.06.075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 06/30/2021] [Indexed: 12/02/2022]
Affiliation(s)
- Lu-Lu Zhai
- Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - A-Kao Zhu
- Department of General Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhi-Gang Tang
- Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan, China
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14
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Wada Y, Zhu Z, Goel A. Reply. Gastroenterology 2022; 162:348-349. [PMID: 34529991 DOI: 10.1053/j.gastro.2021.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 09/05/2021] [Accepted: 09/07/2021] [Indexed: 12/02/2022]
Affiliation(s)
- Yuma Wada
- Department of Surgery, Tokushima University, Tokushima, Japan; Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope, Biomedical Research Center, Monrovia, California
| | - Zhongxu Zhu
- Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope, Biomedical Research Center, Monrovia, California; Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
| | - Ajay Goel
- Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope, Biomedical Research Center, Monrovia, California; City of Hope Comprehensive Cancer Center, Duarte, California
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15
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Luo Z, Fu Z, Li T, Zhang Y, Zhang J, Yang Y, Yang Z, Li Q, Qiu Z, Huang C. Development and Validation of the Individualized Prognostic Nomograms in Patients With Right- and Left-Sided Colon Cancer. Front Oncol 2021; 11:709835. [PMID: 34790565 PMCID: PMC8591050 DOI: 10.3389/fonc.2021.709835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/24/2021] [Indexed: 12/23/2022] Open
Abstract
Background The overall survival (OS) of patients diagnosed with colon cancer (CC) varied greatly, so did the patients with the same tumor stage. We aimed to design a nomogram that is capable of predicting OS in resected left-sided colon cancers (LSCC) and right-sided colon cancers (RSCC), and thus to stratify patients into different risk groups, respectively. Methods Records from a retrospective cohort of 577 patients with complete information were used to construct the nomogram. Univariate and multivariate analyses screened risk factors associated with overall survival. The performance of the nomogram was evaluated with concordance index (c-index), calibration plots, and decision curve analyses for discrimination, accuracy, calibration ability, and clinical net benefits, respectively, which was further compared with the American Joint Committee on Cancer (AJCC) 8th tumor-node-metastasis (TNM) classification. Risk stratification based on nomogram scores was performed with recursive partitioning analysis. Results The LSCC nomogram incorporated carbohydrate antigen 12-5 (CA12-5), age and log odds of positive lymph nodes (LODDS), and RSCC nomogram enrolled tumor stroma percentage (TSP), age and LODDS. Compared with the TNM classification, the LSCC and RSCC nomograms both had a statistically higher C-index (0.837, 95% CI: 0.827-0.846 and 0.780, 95% CI 0.773-0.787, respectively) and more clinical net benefits, respectively. Calibration plots revealed no deviations from reference lines. All results were reproducible in the validation cohort. Conclusions An original predictive nomogram was constructed and validated for OS in patients with CC after surgery, which had facilitated physicians to appraise the individual survival of postoperative patients accurately and to identify high-risk patients who were in need of more aggressive treatment and follow-up strategies.
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Affiliation(s)
- Zai Luo
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhongmao Fu
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Tengfei Li
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yuan Zhang
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jianming Zhang
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yan Yang
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhengfeng Yang
- Institute of Translational Medicine, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qi Li
- Department of Medical Oncology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhengjun Qiu
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Chen Huang
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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16
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Kiehl L, Kuntz S, Höhn J, Jutzi T, Krieghoff-Henning E, Kather JN, Holland-Letz T, Kopp-Schneider A, Chang-Claude J, Brobeil A, von Kalle C, Fröhling S, Alwers E, Brenner H, Hoffmeister M, Brinker TJ. Deep learning can predict lymph node status directly from histology in colorectal cancer. Eur J Cancer 2021; 157:464-473. [PMID: 34649117 DOI: 10.1016/j.ejca.2021.08.039] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/23/2021] [Accepted: 08/30/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC). OBJECTIVES The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM). METHODS Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set. RESULTS On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage. CONCLUSION Deep learning-based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.
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Affiliation(s)
- Lennard Kiehl
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sara Kuntz
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Julia Höhn
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tanja Jutzi
- 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
| | - Jakob N Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Tim Holland-Letz
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany; Tissue Bank of the National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Christof von Kalle
- Berlin Institute of Health (BIH) and Charité University Medicine, Berlin, Germany
| | - Stefan Fröhling
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Elizabeth Alwers
- Division of Clinical Epidemiology and Aging Research, 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; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
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17
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Yan S, Ding H, Zhao X, Wang J, Deng W. Development and validation of a nomogram for further decision of radical surgery in pT1 colorectal cancer after local resection. Int J Colorectal Dis 2021; 36:1499-1506. [PMID: 33864506 DOI: 10.1007/s00384-021-03928-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/07/2021] [Indexed: 02/04/2023]
Abstract
PURPOSE The aim of this study was to develop and validate a nomogram to assist physicians making further decisions on the requirement of a radical surgery for T1 colorectal cancer (CRC) after local excision through preoperative prediction of lymph node metastasis (LNM). METHODS A total of 141 T1 CRC patients were enrolled from January 2013 to August 2020. The independent predictive parameters were determined in multivariate analyses. The nomogram was constructed based on predictors of LNM and its performance was evaluated with respect to its calibration, discrimination, and decision curve analysis. Internal validation by bootstrapping was performed to verify the applicability of the nomogram. RESULTS cN in CT/MRI (N+), histologic type (poorly differentiated, mucinous adenocarcinoma, and signet-ring cell carcinoma), tumor budding (G3), and lymphovascular invasion were identified in the multivariable analysis (p<0.05). The developed nomogram incorporated these four predictors and it yielded good discrimination and calibration, with an area under the curve (AUC) of 0.89 (95% confidence interval [CI]: 0.80-0.97). However, the Japanese guideline yielded an AUC of 0.75 (95% CI: 0.63-0.87). A decision curve analysis showed that the predictive scoring system had a high clinical application value, and the nomogram conferred a greater benefit than the Japanese guideline did (range of threshold within 10%-80%). CONCLUSIONS This study proposed a novel predictive model to assist physicians in making treatment decisions regarding additional surgery after local excision.
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Affiliation(s)
- Shu Yan
- Department of Gastrointestinal Surgery, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, People's Republic of China.,Beijing Key Laboratory of Cancer Invasion and Metastasis Research & National Clinical Research Center for Digestive Diseases, Beijing, People's Republic of China
| | - Haiyang Ding
- Graduate School of Capital Medical University, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, People's Republic of China
| | - Xiaomu Zhao
- Department of Gastrointestinal Surgery, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, People's Republic of China.,Beijing Key Laboratory of Cancer Invasion and Metastasis Research & National Clinical Research Center for Digestive Diseases, Beijing, People's Republic of China
| | - Jin Wang
- Department of Gastrointestinal Surgery, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, People's Republic of China. .,Beijing Key Laboratory of Cancer Invasion and Metastasis Research & National Clinical Research Center for Digestive Diseases, Beijing, People's Republic of China.
| | - Wei Deng
- Department of Gastrointestinal Surgery, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, People's Republic of China. .,Beijing Key Laboratory of Cancer Invasion and Metastasis Research & National Clinical Research Center for Digestive Diseases, Beijing, People's Republic of China.
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Gong Z, Zhang X, Geng Q, Li W, Huang M, Chen Z, Sheng X, Zhang W, Guo W. AKP and GGT level can provide an early prediction of first-line treatment efficacy in colorectal cancer patients with hepatic metastases. Biomark Med 2021; 15:697-713. [PMID: 34169734 DOI: 10.2217/bmm-2020-0667] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 03/18/2021] [Indexed: 02/08/2023] Open
Abstract
Aim: It is important to early evaluate or predict the efficacy to avoid ineffective treatment for most colorectal cancer (CRC) patients with liver metastases. Patients & methods: The medical records of 440 patients with histologically confirmed primary CRC admitted to the Fudan University Shanghai Cancer Center were reviewed. Results: High baseline serum alkaline phosphatase (AKP) and γ-glutamyl transferase (GGT) is associated with worse overall survival. In patients with a high serum AKP and GGT a decreased percentage had high objective response rate and better progression-free survival. Conclusion: Measuring the changes of serum AKP or GGT in CRC patients with hepatic metastases before and after the first cycle of treatment is a convenient, fast and economical way to early predict antitumor treatment efficacy.
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Affiliation(s)
- Zhe Gong
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, PR China
| | - Xiaowei Zhang
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, PR China
| | - Qirong Geng
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, PR China
| | - Wenhua Li
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, PR China
| | - Mingzhu Huang
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, PR China
| | - Zhiyu Chen
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, PR China
| | - Xuedan Sheng
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, PR China
| | - Wen Zhang
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, PR China
| | - Weijian Guo
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, PR China
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Steffen P, Li J, Chandra J, Ahadi MS, Gill AJ, Engel AF, Molloy MP. Molecular Features of Lymph Node Metastasis in T1/2 Colorectal Cancer from Formalin-Fixed Paraffin-Embedded Archival Specimens. J Proteome Res 2021; 20:1304-1312. [PMID: 33427478 DOI: 10.1021/acs.jproteome.0c00693] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Histological risk factors for lymph node metastasis (LNM) in early-stage colorectal cancers (CRC) have been described, although the predictive utility of these factors varies. Improved LNM risk assessment based on findings in endoscopic colon and rectal excisions is necessary for optimal surgical management of CRC patients with pathologic T1- /T2-staged invasive depth (i.e., tumor not invading beyond the muscularis propria layer); as the current system is overly conservative, and results in many unnecessary radical surgeries. To identify molecular features in early CRC with elevated LNM potential, we carried out proteomic and gene expression profiling to compare T1 lymph node (LN) negative with T1/2 LN positive CRC tumors from formalin-fixed paraffin-embedded (FFPE) specimens. Using a data-independent acquisition mass spectrometry workflow, we detected over 7400 proteins and quantified over 4400 in all 21 specimens. Proteins from tumors with LN metastasis were enriched with effectors of epithelial-mesenchymal transition (EMT) and gene expression profiling confirmed activation of key transcription factors, SNAI1 and ZEB1, as well as a reduction in E-cadherin expression. Toward an implementation pathway, we investigated immunohistochemistry assays targeting four EMT-related proteins. While MS could reliably discern twofold protein abundance changes, we found the semiquantitative nature of IHC scoring limited confirmation of this degree of protein expression difference. This study demonstrated that EMT effectors are associated with locoregional metastasis in T1/T2 CRC and could be used to augment metastatic risk assessment, although further developments are required to enable routine implementation.
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Affiliation(s)
- Pascal Steffen
- Bowel Cancer and Biomarker Laboratory, Northern Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia
| | - Jun Li
- Bowel Cancer and Biomarker Laboratory, Northern Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia
| | - Jason Chandra
- Bowel Cancer and Biomarker Laboratory, Northern Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia
| | - Mahsa S Ahadi
- NSW Health Pathology, Department of Anatomical Pathology, Royal North Shore Hospital, St. Leonards, New South Wales 2065, Australia.,Sydney Medical School, University of Sydney, Sydney, New South Wales 2006, Australia.,Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, New South Wales 2065, Australia
| | - Anthony J Gill
- NSW Health Pathology, Department of Anatomical Pathology, Royal North Shore Hospital, St. Leonards, New South Wales 2065, Australia.,Sydney Medical School, University of Sydney, Sydney, New South Wales 2006, Australia.,Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, New South Wales 2065, Australia
| | - Alexander F Engel
- Bowel Cancer and Biomarker Laboratory, Northern Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia.,Sydney Medical School, University of Sydney, Sydney, New South Wales 2006, Australia.,Department of Colorectal Surgery, Royal North Shore Hospital, St Leonards, New South Wales 2065, Australia
| | - Mark P Molloy
- Bowel Cancer and Biomarker Laboratory, Northern Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia.,Sydney Medical School, University of Sydney, Sydney, New South Wales 2006, Australia
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