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Bai G, Wang C, Sun Y, Li J, Shi X, Zhang W, Yang Y, Yang R. Quantitative analysis of contrast-enhanced ultrasound in neoadjuvant treatment of locally advanced rectal cancer: a retrospective study. Front Oncol 2024; 13:1340060. [PMID: 38322290 PMCID: PMC10844946 DOI: 10.3389/fonc.2023.1340060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 12/26/2023] [Indexed: 02/08/2024] Open
Abstract
Purpose To explore the clinical value of contrast-enhanced ultrasound (CEUS) quantitative analysis in the evaluation and prognosis of neoadjuvant chemoradiotherapy for locally advanced rectal cancer (LARC). Methods Eighty-three consecutive patients undergoing neoadjuvant chemoradiotherapy and total mesorectal excision for LARC were retrospectively included. According to pathological results, patients were categorized into complete or incomplete response groups. Differences in ultrasonic parameters, pathological results, and clinical data between groups were evaluated. The cutoff point for a complete response as determined by quantitative analysis of CEUS was assessed using a receiver operating characteristic curve; additionally, overall survival (OS) and progression-free survival (PFS) were analyzed. Results Of the 83 patients, 12 (14.5%) achieved a complete response and 71 (85.5%) did not. There were significant between-group differences in carcinoembryonic antigen (CEA) levels, differentiation degree, proportion of tumor occupying the lumen, anterior-posterior and superior-inferior diameters of the lesion, and intensity of enhancement (P<0.05). CEUS quantitative analysis showed significant between-group differences in peak intensity (PI) and area under the curve (AUC) values (P<0.05). The OS and PFS of patients with high PI, high AUC value, and poorly differentiated cancer were significantly worse than those with low PI, low AUC values, and moderately to highly differentiated cancer (P<0.05). High CEA levels (hazard ratio: 1.02, 95% confidence interval: 1.01-1.04; P=0.002) and low differentiation (2.72, 1.12-6.62; P=0.028) were independent risk factors for PFS and OS. Conclusions CEUS can predict the response to neoadjuvant treatment in patients with LARC. CEUS quantitative analysis is helpful for clinical prognosis.
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Affiliation(s)
- Gouyang Bai
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
| | - Congying Wang
- Department of Clinical Laboratory, Tang Du Hospital, Xi’an, China
| | - Yi Sun
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
| | - Jinghua Li
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
| | - Xiangzhou Shi
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
| | - Wei Zhang
- Department of Pathology, Tang Du Hospital, Xi’an, China
| | - Yilin Yang
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
| | - Ruijing Yang
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Huang H, Han L, Guo J, Zhang Y, Lin S, Chen S, Lin X, Cheng C, Guo Z, Qiu Y. Multiphase and multiparameter MRI-based radiomics for prediction of tumor response to neoadjuvant therapy in locally advanced rectal cancer. Radiat Oncol 2023; 18:179. [PMID: 37907928 PMCID: PMC10619290 DOI: 10.1186/s13014-023-02368-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/23/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND To develop and validate radiomics models for prediction of tumor response to neoadjuvant therapy (NAT) in patients with locally advanced rectal cancer (LARC) using both pre-NAT and post-NAT multiparameter magnetic resonance imaging (mpMRI). METHODS In this multicenter study, a total of 563 patients were included from two independent centers. 453 patients from center 1 were split into training and testing cohorts, the remaining 110 from center 2 served as an external validation cohort. Pre-NAT and post-NAT mpMRI was collected for feature extraction. The radiomics models were constructed using machine learning from a training cohort. The accuracy of the models was verified in a testing cohort and an independent external validation cohort. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS The model constructed with pre-NAT mpMRI had favorable accuracy for prediction of non-response to NAT in the training cohort (AUC = 0.84), testing cohort (AUC = 0.81), and external validation cohort (AUC = 0.79). The model constructed with both pre-NAT and post-NAT mpMRI had powerful diagnostic value for pathologic complete response in the training cohort (AUC = 0.86), testing cohort (AUC = 0.87), and external validation cohort (AUC = 0.87). CONCLUSIONS Models constructed with multiphase and multiparameter MRI were able to predict tumor response to NAT with high accuracy and robustness, which may assist in individualized management of LARC.
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Affiliation(s)
- Hongyan Huang
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, People's Republic of China
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Lujun Han
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China
| | - Jianbo Guo
- Department of Radiology, Meizhou People's Hospital, No. 63 Huangtang Road, Meizhou, 514000, China
| | - Yanyu Zhang
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, People's Republic of China
| | - Shiwei Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Shengli Chen
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Xiaoshan Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Caixue Cheng
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Zheng Guo
- Department of Hematology and Oncology, International Cancer Center, Shenzhen Key Laboratory of Precision Medicine for Hematological Malignancies, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University Health Science Center, Xueyuan AVE 1098, Nanshan District, Shenzhen, 518000, Guangdong, People's Republic of China
| | - Yingwei Qiu
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, People's Republic of China.
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China.
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