Predicting lymph node metastasis in colorectal cancer: An analysis of influencing factors to develop a risk model
Postcholecystectomy bile duct injury (BDI) remains a devastating iatrogenic complication that adversely impacts the quality of life with high healthcare costs. Despite a decrease in the incidence of laparoscopic cholecystectomy-related BDI, the absolute number remains high as cholecystectomy is a co...
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Published in | World journal of gastrointestinal surgery Vol. 15; no. 10; pp. 2234 - 2246 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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United States
Baishideng Publishing Group Inc
27.10.2023
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Online Access | Get full text |
ISSN | 1948-9366 1948-9366 |
DOI | 10.4240/wjgs.v15.i10.2234 |
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Abstract | Postcholecystectomy bile duct injury (BDI) remains a devastating iatrogenic complication that adversely impacts the quality of life with high healthcare costs. Despite a decrease in the incidence of laparoscopic cholecystectomy-related BDI, the absolute number remains high as cholecystectomy is a commonly performed surgical procedure. Open Roux-en-Y hepaticojejunostomy with meticulous surgical technique remains the gold standard surgical procedure with excellent long-term results in most patients. As with many hepatobiliary disorders, a minimally invasive approach has been recently explored to minimize access-related complications and improve postoperative recovery. Since patients with gallstone disease are often admitted for a minimally invasive cholecystectomy, laparoscopic and robotic approaches for repairing postcholecystectomy biliary stricture are attractive. While recent series have shown the feasibility and safety of minimally invasive post-cholecystectomy biliary stricture management, most are retrospective analyses with small sample sizes. Also, long-term follow-up is available only in a limited number of studies. The principles and technique of minimally invasive repair resemble open repair except for the extent of adhesiolysis and the suturing technique with continuous sutures commonly used in minimally invasive approaches. The robotic approach overcomes key limitations of laparoscopic surgery and has the potential to become the preferred minimally invasive approach for the repair of postcholecystectomy biliary stricture. Despite increasing use, lack of prospective studies and selection bias with available evidence precludes definitive conclusions regarding minimally invasive surgery for managing postcholecystectomy biliary stricture. High-volume prospective studies are required to confirm the initial promising outcomes with minimally invasive surgery. |
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AbstractList | Postcholecystectomy bile duct injury (BDI) remains a devastating iatrogenic complication that adversely impacts the quality of life with high healthcare costs. Despite a decrease in the incidence of laparoscopic cholecystectomy-related BDI, the absolute number remains high as cholecystectomy is a commonly performed surgical procedure. Open Roux-en-Y hepaticojejunostomy with meticulous surgical technique remains the gold standard surgical procedure with excellent long-term results in most patients. As with many hepatobiliary disorders, a minimally invasive approach has been recently explored to minimize access-related complications and improve postoperative recovery. Since patients with gallstone disease are often admitted for a minimally invasive cholecystectomy, laparoscopic and robotic approaches for repairing postcholecystectomy biliary stricture are attractive. While recent series have shown the feasibility and safety of minimally invasive post-cholecystectomy biliary stricture management, most are retrospective analyses with small sample sizes. Also, long-term follow-up is available only in a limited number of studies. The principles and technique of minimally invasive repair resemble open repair except for the extent of adhesiolysis and the suturing technique with continuous sutures commonly used in minimally invasive approaches. The robotic approach overcomes key limitations of laparoscopic surgery and has the potential to become the preferred minimally invasive approach for the repair of postcholecystectomy biliary stricture. Despite increasing use, lack of prospective studies and selection bias with available evidence precludes definitive conclusions regarding minimally invasive surgery for managing postcholecystectomy biliary stricture. High-volume prospective studies are required to confirm the initial promising outcomes with minimally invasive surgery. Colorectal cancer (CRC) is a significant global health issue, and lymph node metastasis (LNM) is a crucial prognostic factor. Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC. However, the prediction of LNM is challenging and depends on various factors such as tumor histology, clinicopathological features, and molecular characteristics. The most reliable method to detect LNM is the histopathological examination of surgically resected specimens; however, this method is invasive, time-consuming, and subject to sampling errors and interobserver variability. To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue. This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021. A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors. The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables. The model achieved high accuracy (0.86), sensitivity (0.81), specificity (0.87), positive predictive value (0.66), negative predictive value (0.94), area under the curve for the receiver operating characteristic (0.91), and a low Brier score (0.10). The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds, indicating good calibration and clinical utility. The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC. This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables, demonstrating superior performance and clinical applicability compared to existing models. The study provides new insights into the potential of deep learning to extract valuable information from tumor histology, in turn, improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice. Colorectal cancer (CRC) is a significant global health issue, and lymph node metastasis (LNM) is a crucial prognostic factor. Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC. However, the prediction of LNM is challenging and depends on various factors such as tumor histology, clinicopathological features, and molecular characteristics. The most reliable method to detect LNM is the histopathological examination of surgically resected specimens; however, this method is invasive, time-consuming, and subject to sampling errors and interobserver variability.BACKGROUNDColorectal cancer (CRC) is a significant global health issue, and lymph node metastasis (LNM) is a crucial prognostic factor. Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC. However, the prediction of LNM is challenging and depends on various factors such as tumor histology, clinicopathological features, and molecular characteristics. The most reliable method to detect LNM is the histopathological examination of surgically resected specimens; however, this method is invasive, time-consuming, and subject to sampling errors and interobserver variability.To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue.AIMTo analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue.This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021. A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors.METHODSThis study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021. A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors.The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables. The model achieved high accuracy (0.86), sensitivity (0.81), specificity (0.87), positive predictive value (0.66), negative predictive value (0.94), area under the curve for the receiver operating characteristic (0.91), and a low Brier score (0.10). The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds, indicating good calibration and clinical utility.RESULTSThe prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables. The model achieved high accuracy (0.86), sensitivity (0.81), specificity (0.87), positive predictive value (0.66), negative predictive value (0.94), area under the curve for the receiver operating characteristic (0.91), and a low Brier score (0.10). The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds, indicating good calibration and clinical utility.The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC. This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables, demonstrating superior performance and clinical applicability compared to existing models. The study provides new insights into the potential of deep learning to extract valuable information from tumor histology, in turn, improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice.CONCLUSIONThe present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC. This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables, demonstrating superior performance and clinical applicability compared to existing models. The study provides new insights into the potential of deep learning to extract valuable information from tumor histology, in turn, improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice. |
Author | Lei, Yun-Peng Lv, Guo-Qing Song, Qing-Zhi Xie, Ji-Yan Liu, Shuang |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37969707$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_7759_cureus_61832 crossref_primary_10_1016_j_surg_2024_109030 crossref_primary_10_1007_s00261_024_04668_z crossref_primary_10_3390_ijms25020874 crossref_primary_10_1016_j_aca_2024_343360 |
Cites_doi | 10.1097/EDE.0b013e3181c30fb2 10.1007/s11605-015-3026-6 10.1186/s12885-015-1632-z 10.3322/caac.21492 10.1097/SLA.0000000000000229 10.1245/s10434-013-3426-3 10.3748/wjg.v20.i22.6786 10.1080/07357900802098165 10.1001/jama.2017.14585 10.1002/ijc.28384 10.6004/jnccn.2018.0021 10.1038/s41598-018-21758-3 10.1245/s10434-010-0985-4 10.1007/s10147-017-1101-6 10.1200/JCO.2015.65.9128 10.1053/j.gastro.2004.04.022 10.1038/s43856-023-00282-0 10.1200/JCO.2012.46.2457 10.1136/gutjnl-2015-310961 10.1016/S1470-2045(13)70016-0 10.1016/j.csbj.2014.11.005 10.1038/s41591-019-0462-y 10.1136/bmj.g7594 10.1186/1479-5876-11-58 10.1200/JCO.2009.24.0952 10.1053/j.gastro.2018.01.023 10.1016/j.jamcollsurg.2013.04.018 10.1002/cncr.24396 10.1007/BF02049154 |
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Copyright | The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. 2023 |
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Keywords | Lymph node metastasis Risk prediction model Clinicopathological factors Individualized treatment strategies Colorectal cancer Machine learning |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Corresponding author: Guo-Qing Lv, MD, MS, Attending Doctor, Department of Gastrointestinal Surgery, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Peking University Shenzhen Hospital, No. 120 Lianhua Road, Futian District, Shenzhen 518036, Guangdong Province, China. 365973269@qq.com Author contributions: Lei YP, Lv GQ proposed the concept of this study; Song QZ collected the data; Liu S and Lv GQ contributed to formal analysis; Xie JY and Lei YP conducted the survey; Song QZ and Liu S contributed to these methods; Lei YP and Song QZ guided the research; Lei YP, Lv GQ validated the results of the study; Song QZ contributed to the visualization of the study; Lei YP Song QZ and Lv GQ reviewed and edited the final manuscript. Supported by “San Ming” Project of Shenzhen, No. SZSM201612051. |
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Snippet | Postcholecystectomy bile duct injury (BDI) remains a devastating iatrogenic complication that adversely impacts the quality of life with high healthcare costs.... Colorectal cancer (CRC) is a significant global health issue, and lymph node metastasis (LNM) is a crucial prognostic factor. Accurate prediction of LNM is... |
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Title | Predicting lymph node metastasis in colorectal cancer: An analysis of influencing factors to develop a risk model |
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