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Sun Z, Xia F, Lv W, Li J, Zou Y, Wu J. Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer. Am J Surg 2024; 232:59-67. [PMID: 38272767 DOI: 10.1016/j.amjsurg.2024.01.002] [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: 10/17/2023] [Revised: 12/17/2023] [Accepted: 01/03/2024] [Indexed: 01/27/2024]
Abstract
AIM Preoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer. MATERIALS AND METHODS A total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n = 159) and a validation set (n = 40). The total incidence of TDs was 37.2 % (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI + ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores. RESULTS The Rad-score (T2WI + ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI + ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 % in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 %, 92.0 %, 83.3 %, and 82.1 %, respectively. CONCLUSION The radiomics nomogram based on Rad-score (T2WI + ADC) and clinical factors provides a promising and effective method for the preoperative prediction of TDs in patients with rectal cancer.
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Affiliation(s)
- Zhen Sun
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Feng Xia
- Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, 430030, China
| | - Jin Li
- Computer Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - You Zou
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jianhong Wu
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
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Guo Z, Zhang Z, Liu L, Zhao Y, Liu Z, Zhang C, Qi H, Feng J, Yang C, Tai W, Banchini F, Inchingolo R. Machine learning for predicting liver and/or lung metastasis in colorectal cancer: A retrospective study based on the SEER database. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108362. [PMID: 38704899 DOI: 10.1016/j.ejso.2024.108362] [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: 12/03/2023] [Revised: 04/11/2024] [Accepted: 04/20/2024] [Indexed: 05/07/2024]
Abstract
OBJECTIVE This study aims to establish a machine learning (ML) model for predicting the risk of liver and/or lung metastasis in colorectal cancer (CRC). METHODS Using the National Institutes of Health (NIH)'s Surveillance, Epidemiology, and End Results (SEER) database, a total of 51265 patients with pathological diagnosis of colorectal cancer from 2010 to 2015 were extracted for model development. On this basis, We have established 7 machine learning algorithm models. Evaluate the model based on accuracy, and AUC of receiver operating characteristics (ROC) and explain the relationship between clinical pathological features and target variables based on the best model. We validated the model among 196 colorectal cancer patients in Beijing Electric Power Hospital of Capital Medical University of China to evaluate its performance and universality. Finally, we have developed a network-based calculator using the best model to predict the risk of liver and/or lung metastasis in colorectal cancer patients. RESULTS 51265 patients were enrolled in the study, of which 7864 (15.3 %) had distant liver and/or lung metastasis. RF had the best predictive ability, In the internal test set, with an accuracy of 0.895, AUC of 0.956, and AUPR of 0.896. In addition, the RF model was evaluated in the external validation set with an accuracy of 0.913, AUC of 0.912, and AUPR of 0.611. CONCLUSION In this study, we constructed an RF algorithm mode to predict the risk of colorectal liver and/or lung metastasis, to assist doctors in making clinical decisions.
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Affiliation(s)
- Zhentian Guo
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Zongming Zhang
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China.
| | - Limin Liu
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Yue Zhao
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Zhuo Liu
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Chong Zhang
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Hui Qi
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Jinqiu Feng
- Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China; Department of Immunology, Peking University School of Basic Medical Sciences, Peking University, Beijing, 100191, China
| | - Chunmin Yang
- Department of Gastroenterology, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China
| | - Weiping Tai
- Department of Gastroenterology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Filippo Banchini
- General Surgery Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy
| | - Riccardo Inchingolo
- Interventional Radiology Unit, "F. Miulli" Regional General Hospital, Acquaviva delle Fonti, 70021, Italy
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Lieto E, Auricchio A, Ronchi A, Del Sorbo G, Panarese I, Ferraraccio F, De Vita F, Galizia G, Cardella F. Presence of tumor deposits is a strong indicator of poor outcome in patients with stage III colorectal cancers undergoing radical surgery. J Gastrointest Surg 2024; 28:47-56. [PMID: 38353074 DOI: 10.1016/j.gassur.2023.11.003] [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: 09/08/2023] [Revised: 10/29/2023] [Accepted: 11/04/2023] [Indexed: 02/16/2024]
Abstract
BACKGROUND Tumor deposits (TDs) are emerging as an adverse prognostic factor in colorectal cancers (CRCs). However, TDs are somewhat neglected in the current staging system. It has been proposed either to add the TD count to the number of metastatic lymph nodes or to consider TDs as distant metastases; however, the scientific basis for these proposals seems questionable. This study aimed to investigate a new staging system. METHODS A total of 243 consecutive patients with stage III CRC who were undergoing curative resection and adjuvant chemotherapy were included. Each substage of stage III TNM was split according to the absence or presence of TDs. Receiver operating characteristic (ROC) curves and bootstrap methods were used to compare the current vs the new competing staging system in terms of oncologic outcome prediction. RESULTS A high rate of TDs was recorded (124 cases [51%]). TDs were correlated with other adverse prognostic indicators, particularly vascular and perineural invasions, and showed a negative correlation with the number of removed lymph nodes, suggesting a possible multimodal origin. In addition, TDs were confirmed to have a negative impact on oncologic outcome, regardless of their counts. Compared with the current staging system, the new classification displayed higher values at survival ROC analysis, a significantly better stratification of patients, and effective identification of patients at high risk of recurrence. CONCLUSIONS TDs negatively affect the prognosis in CRCs. A revision of the staging system could be useful to optimize treatments. The proposed new classification is easy to implement and more accurate than the current one. This study was registered online on the ClinicalTrials.gov website under the following identifier: NCT05923450.
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Affiliation(s)
- Eva Lieto
- Division of Gastrointestinal Surgical Oncology, Department of Translational Medical Sciences, School of Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Annamaria Auricchio
- Division of Gastrointestinal Surgical Oncology, Department of Translational Medical Sciences, School of Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Andrea Ronchi
- Division of Pathology, Department of Mental and Physical Health and Rehabilitation Medicine, School of Medicine, University of Campania "Luigi Vanvitelli," Naples, Italy
| | - Giovanni Del Sorbo
- Division of Gastrointestinal Surgical Oncology, Department of Translational Medical Sciences, School of Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Iacopo Panarese
- Division of Pathology, Department of Mental and Physical Health and Rehabilitation Medicine, School of Medicine, University of Campania "Luigi Vanvitelli," Naples, Italy
| | - Francesca Ferraraccio
- Division of Pathology, Department of Mental and Physical Health and Rehabilitation Medicine, School of Medicine, University of Campania "Luigi Vanvitelli," Naples, Italy
| | - Ferdinando De Vita
- Division of Medical Oncology, Department of Precision Medicine, School of Medicine, University of Campania "Luigi Vanvitelli," Naples, Italy
| | - Gennaro Galizia
- Division of Gastrointestinal Surgical Oncology, Department of Translational Medical Sciences, School of Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy.
| | - Francesca Cardella
- Division of Gastrointestinal Surgical Oncology, Department of Translational Medical Sciences, School of Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
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Zheng HD, Hu YH, Ye K, Xu JH. Development and validation of a nomogram for preoperative prediction of tumor deposits in colorectal cancer. World J Gastroenterol 2023; 29:5483-5493. [PMID: 37900997 PMCID: PMC10600810 DOI: 10.3748/wjg.v29.i39.5483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/21/2023] [Accepted: 09/28/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Based on the clinical data of colorectal cancer (CRC) patients who underwent surgery at our institution, a model for predicting the formation of tumor deposits (TDs) in this patient population was established. AIM To establish an effective model for predicting TD formation, thus enabling clinicians to identify CRC patients at high risk for TDs and implement personalized treatment strategies. METHODS CRC patients (n = 645) who met the inclusion criteria were randomly divided into training (n = 452) and validation (n = 193) cohorts using a 7:3 ratio in this retrospective analysis. Least absolute shrinkage and selection operator regression was employed to screen potential risk factors, and multivariable logistic regression analysis was used to identify independent risk factors. Subsequently, a predictive model for TD formation in CRC patients was constructed based on the independent risk factors. The discrimination ability of the model, its consistency with actual results, and its clinical applicability were evaluated using receiver-operating characteristic curves, area under the curve (AUC), calibration curves, and decision curve analysis (DCA). RESULTS Thirty-four (7.5%) patients with TDs were identified in the training cohort based on postoperative pathological specimens. Multivariate logistic regression analysis identified female sex, preoperative intestinal obstruction, left-sided CRC, and lymph node metastasis as independent risk factors for TD formation. The AUCs of the nomogram models constructed using these variables were 0.839 and 0.853 in the training and validation cohorts, respectively. The calibration curve demonstrated good consistency, and the training cohort DCA yielded a threshold probability of 7%-78%. CONCLUSION This study developed and validated a nomogram with good predictive performance for identifying TDs in CRC patients. Our predictive model can assist surgeons in making optimal treatment decisions.
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Affiliation(s)
- Hui-Da Zheng
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, Fujian Province, China
| | - Yun-Huang Hu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, Fujian Province, China
| | - Kai Ye
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, Fujian Province, China
| | - Jian-Hua Xu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, Fujian Province, China
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Jin Y, Wang Y, Zhu Y, Li W, Tang F, Liu S, Song B. A nomogram for preoperative differentiation of tumor deposits from lymph node metastasis in rectal cancer: A retrospective study. Medicine (Baltimore) 2023; 102:e34865. [PMID: 37832071 PMCID: PMC10578668 DOI: 10.1097/md.0000000000034865] [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: 05/13/2023] [Accepted: 07/31/2023] [Indexed: 10/15/2023] Open
Abstract
The objective is to develop and validate a combined model for noninvasive preoperative differentiating tumor deposits (TDs) from lymph node metastasis (LNM) in patients with rectal cancer (RC). A total of 204 patients were enrolled and randomly divided into 2 sets (training and validation set) at a ratio of 8:2. Radiomics features of tumor and peritumor fat were extracted by using Pyradiomics software from the axial T2-weighted imaging of MRI. Rad-score based on extracted Radiomics features were calculated by combination of feature selection and the machine learning method. Factors (Rad-score, laboratory test factor, clinical factor, traditional characters of tumor on MRI) with statistical significance were integrated to build a combined model. The combined model was visualized by a nomogram, and its distinguish ability, diagnostic accuracy, and clinical utility were evaluated by the receiver operating characteristic curve (ROC) analysis, calibration curve, and clinical decision curve, respectively. Carbohydrate antigen (CA) 19-9, MRI reported node stage (MRI-N stage), tumor volume (cm3), and Rad-score were all included in the combined model (odds ratio = 3.881 for Rad-score, 2.859 for CA19-9, 0.411 for MRI-N stage, and 1.055 for tumor volume). The distinguish ability of the combined model in the training and validation cohorts was area under the summary receiver operating characteristic curve (AUC) = 0.863, 95% confidence interval (CI): 0.8-0.911 and 0.815, 95% CI: 0.663-0.919, respectively. And the combined model outperformed the clinical model in both training and validation cohorts (AUC = 0.863 vs 0.749, 0.815 vs 0.627, P = .0022, .0302), outperformed the Rad-score model only in training cohorts (AUC = 0.863 vs 0.819, P = .0283). The combined model had highest net benefit and showed good diagnostic accuracy. The combined model incorporating Rad-score and clinical factors could provide a preoperative differentiation of TD from LNM and guide clinicians in making individualized treatment strategy for patients with RC.
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Affiliation(s)
- Yumei Jin
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
- Department of Radiology, Sichuan University, West China Hospital, Sichuan, China
- Department of Radiology, Sanya People’s Hospital, Sanya, Hainan, China
| | - Yewu Wang
- Department of Joint and Sports Medicine, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Yonghua Zhu
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Wenzhi Li
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Fengqiong Tang
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Shengmei Liu
- Department of Radiology, Sichuan University, West China Hospital, Sichuan, China
| | - Bin Song
- Department of Radiology, Sichuan University, West China Hospital, Sichuan, China
- Department of Radiology, Sanya People’s Hospital, Sanya, Hainan, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Sichuan University, West China Hospital, Sichuan, China
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Li M, Xu G, Chen Q, Xue T, Peng H, Wang Y, Shi H, Duan S, Feng F. Computed Tomography-based Radiomics Nomogram for the Preoperative Prediction of Tumor Deposits and Clinical Outcomes in Colon Cancer: a Multicenter Study. Acad Radiol 2023; 30:1572-1583. [PMID: 36566155 DOI: 10.1016/j.acra.2022.11.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/16/2022] [Accepted: 11/07/2022] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a computed tomography (CT)-based radiomics nomogram for the preoperative prediction of tumor deposits (TDs) and clinical outcomes in patients with colon cancer. MATERIALS AND METHODS This retrospective study included 383 consecutive patients with colon cancer from two centers. Radiomics features were extracted from portal venous phase CT images. Least absolute shrinkage and selection operator regression was applied for feature selection and radiomics signature construction. The multivariate logistic regression model was used to establish a radiomics nomogram. The performance of the nomogram was assessed by using receiver operating characteristic curves, calibration curves and decision curve analysis. Kaplan‒Meier survival analysis was used to assess the difference of the overall survival (OS) in the TDs-positive and TDs-negative groups. RESULTS The radiomics signature was composed of 11 TDs status related features. The AUCs of the radiomics model in the training cohort, internal validation and external validation cohorts were 0.82, 0.78 and 0.78, respectively. The radiomics nomogram that incorporated the radiomics signature and clinical independent predictors (CT-N, CEA and CA199) showed good calibration and discrimination with AUCs of 0.88, 0.80 and 0.81 in the training cohort, internal validation and external validation cohorts, respectively. The radiomics nomogram-predicted high-risk groups had a worse OS than the low-risk groups (p < 0.001). The radiomics nomogram-predicted TDs was an independent preoperative predictor of OS. CONCLUSION The radiomics nomogram based on CT radiomics features and clinical independent predictors could effectively predict the preoperative TDs status and OS of colon cancer. IMPORTANT FINDINGS CT-based radiomics nomogram may be applied in the individual preoperative prediction of TDs status in colon cancer. Additionally, there was a significant difference in OS between the high-risk and low-risk groups defined by the radiomics nomogram, in which patients with high-risk TDs had a significantly worse OS, compared with those with low-risk TDs.
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Affiliation(s)
- Manman Li
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Guodong Xu
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu, PR China
| | - Qiaoling Chen
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Ting Xue
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Hui Peng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Yuwei Wang
- Department of Record room, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China
| | - Hui Shi
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361.
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Peparini N. Oncological outcome after lateral pelvic lymphadenectomy for low rectal carcinoma: not only an N-status matter. ANZ J Surg 2023; 93:54-58. [PMID: 36190012 DOI: 10.1111/ans.18067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/06/2022] [Accepted: 09/11/2022] [Indexed: 11/27/2022]
Abstract
Tumour deposits (TDs), novel pathological entities, should be considered when estimating the regional and systemic spread of rectal carcinoma and formulating treatment strategies. In fact, TDs may have more severe prognostic impact than lymph node positivity or the lymph node ratio. The assessment of the presence of TDs can be performed only through accurate postoperative pathological examination; however, the detection of TDs is not part of any of the procedures currently used to assess preoperative or intraoperative staging. This review aims to analyse and discuss the impact of TDs on the oncological outcome of patients who undergo surgery for advanced low rectal carcinoma. No prospective study has evaluated the impact of lateral pelvic TDs on oncological outcomes following total mesorectal excision with lateral pelvic lymphadenectomy. Although adequate total mesorectal excision allows for the excision of intramesorectal TDs, lateral pelvic lymph node dissection cannot guarantee the removal of lateral pelvic TDs; moreover, it remains to be determined whether surgical excision of lateral pelvic TDs can impact long-term outcomes. However, the identification of lateral pelvic TDs strengthens the 'staging effect' and limits the 'therapeutic effect' of lateral pelvic lymphadenectomy, supporting the rationale for the use of neoadjuvant chemoradiotherapy for rectal cancer. When evaluating the oncological outcomes after total mesorectal excision with lateral pelvic lymphadenectomy, the impact of lateral pelvic TDs should be considered.
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Affiliation(s)
- Nadia Peparini
- Distretto 3, Azienda Sanitaria Locale Roma 6, Ciampino (Rome), Italy
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Distinguishing mesorectal tumor deposits from metastatic lymph nodes by using diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging in rectal cancer. Eur Radiol 2022; 33:4127-4137. [PMID: 36520180 DOI: 10.1007/s00330-022-09328-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 11/18/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVES This study aimed to identify whether apparent diffusion coefficient (ADC) values and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters are helpful in distinguishing mesorectal tumor deposits (TD) from metastatic lymph nodes (MLN) in rectal cancer (RC). METHODS Thirty patients (59 lesions, including 30 TD and 29 MLN) with RC who underwent pretreatment-MRI between February 2016 and August 2018 were enrolled. The morphological features, ADC values, and semi-quantitative parameters of DCE-MRI, including relative enhancement (RE), maximum enhancement (ME), maximum relative enhancement (MRE), time to peak (TTP), wash-in rates (WIR), wash-out rates (WOR), brevity of enhancement (BRE), and area under the curve (AUC) were measured on lesions (TD or MLN) and RC. The parameters were compared between TD and MLN, tumor with and without TD group by using Fisher's exact test, independent-samples t-test, and Mann-Whitney U test. The ratio (lesion-to-tumor) of the parameters was compared between TD and MLN. Receiver operating characteristic curve analysis and binary logistic regression analysis were used to assess the diagnostic ability of single and combined metrics for distinguishing TD from MLN. RESULTS The morphological features, including size, shape, and border, were significantly different between TD and MLN. TD exhibited significantly lower RE, MRE, RE-ratio, MRE-ratio, ADCmin-ratio, and ADCmean-ratio than MLN. RE-ratio showed the highest AUC (0.749) and accuracy (77.97%) among single parameters. The combination of DCE-MRI and DWI parameters together showed higher diagnostic efficiency (AUC = 0.825). CONCLUSIONS Morphological features, ADC values, and DCE-MRI parameters can preoperatively help distinguish TD from MLN in RC. KEY POINTS • DWI and DCE-MRI can facilitate early detection and distinguishing mesorectal TD (tumor deposits) from MLN (metastatic lymph nodes) in rectal cancer preoperatively. • TD has some specific morphological features, including relatively larger size, lower short- to long-axis ratio, irregular shape, and ill-defined border on T2-weighted MR images in rectal cancer. • The combination of ADC values and semi-quantitative parameters of DCE-MRI (RE, MRE) can help to improve the diagnostic efficiency of TD in rectal cancer.
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Wang X, Cheng W, Dou X, Tan F, Yan S, Zhou Z, Li Y, Xu B, Liu C, Ge H, Tian M, Liu F, Li L, Zhang S, Li Q, Pei H, Pei Q. The new 'coN' staging system combining lymph node metastasis and tumour deposit provides a more accurate prognosis for TNM stage III colon cancer. Cancer Med 2022; 12:2538-2550. [PMID: 35912894 PMCID: PMC9939212 DOI: 10.1002/cam4.5099] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 07/14/2022] [Accepted: 07/19/2022] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE Despite controversy over its origin and definition, the significance of tumour deposit (TD) has been underestimated in the tumour node metastasis (TNM) staging system for colon cancer, especially in stage III patients. We aimed to further confirm the prognostic value of TD in stage III colon cancer and to establish a more accurate 'coN' staging system combining TD and lymph node metastasis (LNM). METHODS Information on stage III colon cancer patients with a definite TD status was retrospectively collected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017. The effect of TD on prognosis was estimated using Cox regression analysis. Maximally selected rank statistics were used to select the optimal cut-off value of TD counts. The predictive power of conventional N staging and the new coN staging was evaluated and compared by Akaike's information criterion (AIC), Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curves. Clinicopathological data of stage III colon cancer patients in the Xiangya database from 2014 to 2018 were collected to validate the coN staging system. RESULTS A total of 39,185 patients with stage III colon cancer were included in our study: 38,446 in the SEER cohort and 739 in the Xiangya cohort. The incidence of TD in stage III colon cancer was approximately 30% (26% in SEER and 30% in the Xiangya database). TD was significantly associated with poorer overall survival (OS) (HR = 1.37, 95% CI 1.31-1.44, p < 0.001 in SEER). The optimal cut-off value of TD counts was 4, and the patients were classified into the TD0 (count = 0), TD1 (count = 1-3) and TD2 (count ≥ 4) groups accordingly. The estimated 5-year OS was significantly different among the three groups (69.4%, 95% CI 68.8%-70.0% in TD0; 60.5%, 95% CI 58.9%-62.2% in TD1 and 42.6%, 95% CI 39.2%-46.4% in TD2, respectively, p < 0.001). The coN system integrating LNM and TD was established, and patients with stage III colon cancer were reclassified into five subgroups (coN1a, coN1b, coN2a, coN2b and coN2c). Compared with conventional N staging, the coN staging Cox model had a smaller AIC (197097.581 vs. 197358.006) and a larger C-index (0.611 vs. 0.601). The AUCs of coN staging at 3, 5 and 7 years were also greater than those of conventional N staging (0.6305, 0.6326, 0.6314 vs. 0.6186, 0.6197, 0.6160). Concomitant with the SEER cohort results, the coN staging Cox model of the Xiangya cohort also had a smaller AIC (2883.856 vs. 2906.741) and a larger C-index (0.669 vs. 0.633). Greater AUCs at 3, 5 and 7 years for coN staging were also observed in the Xiangya cohort (0.6983, 0.6774, 0.6502 vs. 0.6512, 0.6368, 0.6199). CONCLUSIONS Not only the presence but also the number of TDs is associated with poor prognosis in stage III colon cancer. A combined N staging system integrating LNM and TD provides more accurate prognostic prediction than the latest AJCC N staging in stage III colon cancer.
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Affiliation(s)
- Xitao Wang
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China,Key Laboratory of Molecular Radiation Oncology Hunan ProvinceChangshaPeople's Republic of China,National Clinical Research Center for Geriatric Disorders (Xiangya Hospital)Central South UniversityChangshaPeople's Republic of China
| | - Wei Cheng
- Department of Hepatobiliary Surgery, Hunan Provincial People's HospitalThe First Affiliated Hospital of Hunan Normal UniversityChangshaPeople's Republic of China
| | - Xiaolin Dou
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Fengbo Tan
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Shipeng Yan
- Department of Cancer Prevention and Control, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaPeople's Republic of China
| | - Zhongyi Zhou
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Yuqiang Li
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Biaoxiang Xu
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Chongshun Liu
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Heming Ge
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Mengxiang Tian
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Fangchun Liu
- Department of Gastroenterology, The Third Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Liling Li
- Department of Pathology, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Sai Zhang
- Institute of Medical Sciences, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Qingling Li
- Department of Pathology, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Haiping Pei
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China
| | - Qian Pei
- Department of General Surgery, Xiangya HospitalCentral South UniversityChangshaPeople's Republic of China,Key Laboratory of Molecular Radiation Oncology Hunan ProvinceChangshaPeople's Republic of China,National Clinical Research Center for Geriatric Disorders (Xiangya Hospital)Central South UniversityChangshaPeople's Republic of China
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