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Edelmann M, Rieken S, Dröge LH. [A 5-year follow-up of the RAPIDO trial: Back to the future of long-course radiochemotherapy in total neoadjuvant treatment (TNT) for locally advanced rectal cancer?]. Strahlenther Onkol 2024; 200:649-651. [PMID: 38647565 DOI: 10.1007/s00066-024-02232-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2024] [Indexed: 04/25/2024]
Affiliation(s)
- Marcus Edelmann
- Department of Radiotherapy and Radiation Oncology, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Deutschland
- Göttingen Comprehensive Cancer Center (G-CCC), University Medical Center Göttingen, Von-Bar-Str. 2/4, 37075, Göttingen, Deutschland
| | - Stefan Rieken
- Department of Radiotherapy and Radiation Oncology, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Deutschland
- Göttingen Comprehensive Cancer Center (G-CCC), University Medical Center Göttingen, Von-Bar-Str. 2/4, 37075, Göttingen, Deutschland
| | - Leif Hendrik Dröge
- Department of Radiotherapy and Radiation Oncology, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Deutschland.
- Göttingen Comprehensive Cancer Center (G-CCC), University Medical Center Göttingen, Von-Bar-Str. 2/4, 37075, Göttingen, Deutschland.
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Hu M, Li X, Lin H, Lu B, Wang Q, Tong L, Li H, Che N, Hung S, Han Y, Shi K, Li C, Zhang H, Liu Z, Zhang T. Easily applicable predictive score for MPR based on parameters before neoadjuvant chemoimmunotherapy in operable NSCLC: a single-center, ambispective, observational study. Int J Surg 2024; 110:2275-2287. [PMID: 38265431 PMCID: PMC11020048 DOI: 10.1097/js9.0000000000001050] [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: 10/30/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024]
Abstract
BACKGROUND Neoadjuvant chemoimmunotherapy (NACI) is promising for resectable nonsmall cell lung cancer (NSCLC), but predictive biomarkers are still lacking. The authors aimed to develop a model based on pretreatment parameters to predict major pathological response (MPR) for such an approach. METHODS The authors enrolled operable NSCLC treated with NACI between March 2020 and May 2023 and then collected baseline clinical-pathology data and routine laboratory examinations before treatment. The efficacy and safety data of this cohort was reported and variables were screened by Logistic and Lasso regression and nomogram was developed. In addition, receiver operating characteristic curves, calibration curves, and decision curve analysis were used to assess its power. Finally, internal cross-validation and external validation was performed to assess the power of the model. RESULTS In total, 206 eligible patients were recruited in this study and 53.4% (110/206) patients achieved MPR. Using multivariate analysis, the predictive model was constructed by seven variables, prothrombin time (PT), neutrophil percentage (NEUT%), large platelet ratio (P-LCR), eosinophil percentage (EOS%), smoking, pathological type, and programmed death ligand-1 (PD-L1) expression finally. The model had good discrimination, with area under the receiver operating characteristic curve (AUC) of 0.775, 0.746, and 0.835 for all datasets, cross-validation, and external validation, respectively. The calibration curves showed good consistency, and decision curve analysis indicated its potential value in clinical practice. CONCLUSION This real world study revealed favorable efficacy in operable NSCLC treated with NACI. The proposed model based on multiple clinically accessible parameters could effectively predict MPR probability and could be a powerful tool in personalized medication.
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Affiliation(s)
| | - Xiaomi Li
- Department of Oncology, Beijing Institute of Tuberculosis and Chest Tumor, Beijing, People’s Republic of China
| | | | | | | | | | | | | | - Shaojun Hung
- Department of Thoracic Surgery, Beijing Chest Hospital, Capital Medical University
| | - Yi Han
- Department of Thoracic Surgery, Beijing Chest Hospital, Capital Medical University
| | - Kang Shi
- Department of Thoracic Surgery, Beijing Chest Hospital, Capital Medical University
| | | | | | - Zhidong Liu
- Department of Thoracic Surgery, Beijing Chest Hospital, Capital Medical University
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Zeng Z, Ma D, Zhu P, Niu K, Fu S, Di X, Zhu J, Xie P. Prognostic value of the ratio of pretreatment carcinoembryonic antigen to tumor volume in rectal cancer. J Gastrointest Oncol 2023; 14:2395-2408. [PMID: 38196531 PMCID: PMC10772672 DOI: 10.21037/jgo-23-683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/10/2023] [Indexed: 01/11/2024] Open
Abstract
Background As a commonly used biomarker in rectal cancer (RC), the prognostic value of carcinoembryonic antigen (CEA) remains underexplored. This study aims to evaluate the prognostic value of pretreatment CEA/tumor volume in RC. Methods This retrospective study included patients who underwent pretreatment magnetic resonance imaging (MRI) with histologically confirmed primary rectal adenocarcinoma from November 2012 to April 2018. Patients were divided into high-risk and low-risk groups according to the median values of CEA/Diapath (CEA to pathological diameter), CEA/DiaMRI (CEA to MRI tumor diameter), and CEA/VolMRI (CEA to MRI tumor volume). Cox regression analysis was utilized to determine the prognostic value of CEA, CEA/Diapath, CEA/DiaMRI, and CEA/VolMRI. Stepwise regression was used to establish nomograms for predicting disease-free survival (DFS) and overall survival (OS). Predictive performance was estimated by using the concordance index (C-index) and area under curve receiver operating characteristic (AUC). Results A total of 343 patients [median age 58.99 years, 206 (60.06%) males] were included. After adjusting for patient-related and tumor-related factors, CEA/VolMRI was superior to CEA, CEA/Diapath, and CEA/DiaMRI in distinguishing high-risk from low-risk patients in terms of DFS [hazard ratio (HR) =1.83; P=0.010] and OS (HR =1.67; P=0.048). Subanalysis revealed that CEA/VolMRI stratified high death risk in CEA-negative individuals (HR =2.50; P=0.038), and also stratified low recurrence risk in CEA-positive individuals (HR =2.06; P=0.024). In the subanalysis of stage II or III cases, the highest HRs and the smallest P values were observed in distinguishing high-risk from low-risk patients according to CEA/VolMRI in terms of DFS (HR =2.44; P=0.046 or HR =2.41; P=0.001) and OS (HR =1.96; P=0.130 or HR =2.22; P=0.008). The nomograms incorporating CEA/VolMRI showed good performance, with a C-index of 0.72 [95% confidence interval (CI): 0.68-0.79] for DFS and 0.73 (95% CI: 0.68-0.80) for OS. Conclusions Higher CEA/VolMRI was associated with worse DFS and OS. CEA/VolMRI was superior to CEA, CEA/Diapath, and CEA/DiaMRI in predicting DFS and OS. Pretreatment CEA/VolMRI may facilitate risk stratification and treatment decision-making.
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Affiliation(s)
- Zhiming Zeng
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Decai Ma
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Pan Zhu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kexin Niu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shuai Fu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaohui Di
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Junying Zhu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Peiyi Xie
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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