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Liu J, Jiang P, Zhang Z, Yang H, Zhou Y, Li P, Zeng Q, Zhang X, Sun Y. Survival analysis in rectal cancer patients after lateral lymph node dissection: Exploring the necessity of nCRT for suspected lateral lymph node metastasis. Curr Probl Surg 2024; 61:101525. [PMID: 39098341 DOI: 10.1016/j.cpsurg.2024.101525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 08/06/2024]
Affiliation(s)
- Jiafei Liu
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, People's Republic of China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, People's Republic of China; Tianjin Institute of Coloproctology, Tianjin, People's Republic of China
| | - Peishi Jiang
- Nankai University, Tianjin, People's Republic of China
| | - Zhichun Zhang
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, People's Republic of China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, People's Republic of China; Tianjin Institute of Coloproctology, Tianjin, People's Republic of China; Nankai University, Tianjin, People's Republic of China
| | - Hongjie Yang
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, People's Republic of China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, People's Republic of China; Tianjin Institute of Coloproctology, Tianjin, People's Republic of China; Nankai University, Tianjin, People's Republic of China
| | - Yuanda Zhou
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, People's Republic of China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, People's Republic of China; Tianjin Institute of Coloproctology, Tianjin, People's Republic of China
| | - Peng Li
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, People's Republic of China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, People's Republic of China; Tianjin Institute of Coloproctology, Tianjin, People's Republic of China
| | - Qingsheng Zeng
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, People's Republic of China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, People's Republic of China; Tianjin Institute of Coloproctology, Tianjin, People's Republic of China
| | - Xipeng Zhang
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, People's Republic of China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, People's Republic of China; Tianjin Institute of Coloproctology, Tianjin, People's Republic of China; Nankai University, Tianjin, People's Republic of China
| | - Yi Sun
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, People's Republic of China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, People's Republic of China; Tianjin Institute of Coloproctology, Tianjin, People's Republic of China; Nankai University, Tianjin, People's Republic of China.
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Sun Y, Lu Z, Yang H, Jiang P, Zhang Z, Liu J, Zhou Y, Li P, Zeng Q, Long Y, Li L, Du B, Zhang X. Prediction of lateral lymph node metastasis in rectal cancer patients based on MRI using clinical, deep transfer learning, radiomic, and fusion models. Front Oncol 2024; 14:1433190. [PMID: 39099685 PMCID: PMC11294238 DOI: 10.3389/fonc.2024.1433190] [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: 05/15/2024] [Accepted: 07/02/2024] [Indexed: 08/06/2024] Open
Abstract
Introduction Lateral lymph node (LLN) metastasis in rectal cancer significantly affects patient treatment and prognosis. This study aimed to comprehensively compare the performance of various predictive models in predicting LLN metastasis. Methods In this retrospective study, data from 152 rectal cancer patients who underwent lateral lymph node (LLN) dissection were collected. The cohort was divided into a training set (n=86) from Tianjin Union Medical Center (TUMC), and two testing cohorts: testing cohort (TUMC) (n=37) and testing cohort from Gansu Provincial Hospital (GSPH) (n=29). A clinical model was established using clinical data; deep transfer learning models and radiomics models were developed using MRI images of the primary tumor (PT) and largest short-axis LLN (LLLN), visible LLN (VLLN) areas, along with a fusion model that integrates features from both deep transfer learning and radiomics. The diagnostic value of these models for LLN metastasis was analyzed based on postoperative LLN pathology. Results Models based on LLLN image information generally outperformed those based on PT image information. Rradiomics models based on LLLN demonstrated improved robustness on external testing cohorts compared to those based on VLLN. Specifically, the radiomics model based on LLLN imaging achieved an AUC of 0.741 in the testing cohort (TUMC) and 0.713 in the testing cohort (GSPH) with the extra trees algorithm. Conclusion Data from LLLN is a more reliable basis for predicting LLN metastasis in rectal cancer patients with suspicious LLN metastasis than data from PT. Among models performing adequately on the internal test set, all showed declines on the external test set, with LLLN_Rad_Models being less affected by scanning parameters and data sources.
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Affiliation(s)
- Yi Sun
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Zhongxiang Lu
- The First Clinical College of Medicine, Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, China
| | - Hongjie Yang
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | | | - Zhichun Zhang
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Jiafei Liu
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Yuanda Zhou
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Peng Li
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Qingsheng Zeng
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Yu Long
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
| | - Laiyuan Li
- Gansu Provincial Hospital, Gansu Clinical Medical Research Center for Anorectal Diseases, Lanzhou, Gansu, China
| | - Binbin Du
- Gansu Provincial Hospital, Gansu Clinical Medical Research Center for Anorectal Diseases, Lanzhou, Gansu, China
| | - Xipeng Zhang
- Nankai University, Tianjin, China
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China
- Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China
- Tianjin Institute of Coloproctology, Tianjin, China
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Abbaspour E, Karimzadhagh S, Monsef A, Joukar F, Mansour-Ghanaei F, Hassanipour S. Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis. Int J Surg 2024; 110:3795-3813. [PMID: 38935817 PMCID: PMC11175807 DOI: 10.1097/js9.0000000000001239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/19/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data. METHODS Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models' diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted. RESULTS Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022-2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78-0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001). CONCLUSION Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.
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Affiliation(s)
- Elahe Abbaspour
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Sahand Karimzadhagh
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Abbas Monsef
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Farahnaz Joukar
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Fariborz Mansour-Ghanaei
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Soheil Hassanipour
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
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Granata V, Fusco R, Brunese MC, Ferrara G, Tatangelo F, Ottaiano A, Avallone A, Miele V, Normanno N, Izzo F, Petrillo A. Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment. Diagnostics (Basel) 2024; 14:152. [PMID: 38248029 PMCID: PMC10814152 DOI: 10.3390/diagnostics14020152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
PURPOSE We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. METHODS Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. RESULTS The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%. CONCLUSIONS Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli”, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy;
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy;
| | - Gerardo Ferrara
- Division of Pathology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli”, 80131 Naples, Italy; (G.F.); (F.T.)
| | - Fabiana Tatangelo
- Division of Pathology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli”, 80131 Naples, Italy; (G.F.); (F.T.)
| | - Alessandro Ottaiano
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131 Naples, Italy; (A.O.); (A.A.)
| | - Antonio Avallone
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131 Naples, Italy; (A.O.); (A.A.)
| | - Vittorio Miele
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Nicola Normanno
- Department of Radiology, University of Florence—Azienda Ospedaliero—Universitaria Careggi, 50134 Florence, Italy;
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli”, 80131 Naples, Italy;
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