Prognostic value of CEA and CA19-9 in patients with local advanced rectal cancer receiving neoadjuvant chemoradiotherapy, radical surgery and postoperative chemotherapy

Shan, Jingjing; Gu, Benxing; Shi, Liming; Wang, Xuanxuan; Ye, Wenyuan; Zhou, Wuhua; Sun, Xin

doi:10.21037/tcr-20-2269

Cited by 4 publications

(2 citation statements)

References 29 publications

(41 reference statements)

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“…CEA is a superfamily of glycoproteins present on cell membranes and has been shown to be participated in the metastasis and recurrence of colorectal cancer due to an essential role in cell recognition and adhesion [23,24]. Similar to previous studies [17], the elevated CEA level contributed to the clinical application of model 1 in our study, which might indicate that the CEA level is a necessary test for preoperative and postoperative review [25]. Based on T2WI, LNs with short-axis diameter of less than 5 mm and three malignant morphologic criteria (including spiculated border, round shape and heterogenous signal intensity) or short diameter between 5 and 9 mm and two malignant criteria or greater than 9 mm were considered as suspicious metastatic LNs [18,26,27].…”

Section: Discussionsupporting

confidence: 88%

Radiomics based on RESOLVE ADC in differentiating benign and malignant lymph nodes in rectal cancer

Wang

Dai

Bai

et al. 2022

Preprint

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Background The preoperative prediction of lymph node (LN) metastasis (LNM) in rectal cancer assumes a pivotal role in risk stratification and therapeutic decision-making. This study aimed to construct and validate a clinical-radiomics nomogram in non-invasively predicting LNM based on readout segmentation of long variable echo-trains (RESOLVE) apparent diffusion coefficient (ADC). Methods This retrospective study included preoperative images of 206 rectal cancer patients, containing 261 pathologically confirmed LNs, at our hospital between August 2018 and February 2022. There were 144 patients (55 in the LNM group and 89 in the non-LNM group) with 182 LNs (76 metastatic LNs and 106 benign LNs) in the training cohort and 62 patients (25 in the LNM group and 37 in the non-LNM group) with 79 LNs (31 metastatic LNs and 48 benign LNs) in the validation cohort. Image segmentation was performed by manually delineating the whole tumor and the maximum cross-section of each LN. The maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used to construct radiomics signature. Logistic regression modeling was employed to construct models based on clinical factors and LN morphologic criteria (model 1), tumor radiomics features combined with LN radiomics features (model 2), and model 2 combined with model 1 (model 3). Then model 3 was converted as a form of clinical-radiomics nomogram. Diagnostic performance was assessed by the area under the curve (AUC). The DeLong test was conducted to compare AUCs between models. Decision curve analysis (DCA) was performed to assess the clinical usefulness of three models. Results Both model 2 and model 3 showed higher AUCs in the training (model 2 0.930, model 3 0.948) and validation cohorts (model 2 0.864, model 3 0.887) than model 1(training 0.742; validation 0.745). Model 3 showed improved diagnostic performance over model 2 (P = 0.049) and model 1(P < 0.001) in the training cohort. The DCA indicated that model 3 was regarded as the candidate model to identify LN status. Conclusions The clinical-radiomics nomogram, incorporating RESOLVE ADC based-radiomic features from tumors and LNs with clinical factors and LN morphologic criteria was promising for predicting LNM in rectal cancer.

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Section: Discussionsupporting

confidence: 88%

Radiomics based on RESOLVE ADC in differentiating benign and malignant lymph nodes in rectal cancer

Wang

Dai

Bai

et al. 2022

Preprint

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“…Previous studies have confirmed that many clinical indicators of patients with malignant tumors can be affected by chemotherapeutic drugs ( 8 , 9 ), and these indicators can also be used to predict the efficacy of neoadjuvant chemotherapy. Carcinoembryonic antigen (CEA) is a common biological indicator for CRC patients ( 10 , 11 ), but a study showed that CEA level at baseline was not significantly related to the progression-free survival (PFS) rate of CRC patients with liver metastasis who received neoadjuvant treatment ( 12 ). Therefore, it is of great significance to find new biological indicators to predict the efficacy of neoadjuvant chemotherapy in CRC patients.…”

Section: Introductionmentioning

confidence: 99%

Predictive value of apparent diffusion coefficient for neoadjuvant chemotherapy in locally advanced colorectal cancer patients

Liu¹,

Zhu²,

Liu³

et al. 2023

J Gastrointest Oncol

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Background: The efficacy of neoadjuvant chemotherapy is closely related to the long-term prognosis of colorectal cancer (CRC) patients. Apparent diffusion coefficient (ADC) is an index in dynamic enhanced magnetic resonance imaging (MRI), reflecting the density of tumor cells. ADC has been shown to be related to the efficacy of neoadjuvant chemotherapy in other malignant tumors, but there is still a lack of relevant research in CRC patients.Methods: A total of 128 patients with CRC treated with neoadjuvant chemotherapy in The First Affiliated Hospital of Xiamen University from January 2016 to January 2017 were retrospectively collected. According to the response after neoadjuvant chemotherapy, the patients were divided into an objective response group (n=80) and a control group (n=48). The clinical characteristics and ADC levels of the two groups were compared, and the predictive value of ADC on the efficacy of neoadjuvant chemotherapy was analyzed. The patients were followed up for 5 years to observe the difference of survival rate between the two groups, and further analyzed the correlation between ADC and survival rate.Results: Compared with the control group, the tumor size in the objective response group was significantly reduced (3.32±1.60 vs. 5.07±2.19 cm, P=0.000); ADC significantly increased (1.23±0.18 vs. 0.98±0.18 ×10 −3 mm 2 /s, P=0.000); albumin significantly increased (39.32±4.14 vs. 37.46±4.18 g/L, P=0.016); the proportion of patients with poorly differentiated or undifferentiated tumor cells was significantly lower (51.25% vs. 72.92%, P=0.016); and the 5-year mortality decreased significantly (40.00% vs. 58.33%, P=0.044). ADC had the highest predictive value of objective response for locally advanced CRC patients after neoadjuvant chemotherapy, and the area under the curve (AUC) was 0.834 [95% confidence interval (CI): 0.765-0.903, P=0.000]; ADC had certain predictive value for the 5-year survival of locally advanced CRC patients, and the AUC was 0.778 (95% CI: 0.696-0.861, P=0.000). ADC >1.055×10 −3 mm 2 /s, tumor size <4.1 cm, and moderately or well differentiated tumors were favorable factors for patients with locally advanced CRC to obtain objective response after neoadjuvant chemotherapy (P<0.05).Conclusions: ADC could be used as a predictor of the efficacy of neoadjuvant chemotherapy in locally advanced CRC patients.

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Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3

Fan,

Wu,

et al. 2024

BMC Gastroenterol

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Background This study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately preoperatively differentiate between stage T2 and T3 rectal cancer. Methods We included 361 patients with pathologically confirmed stage T2 or T3 rectal cancer, divided into a training set (252 patients) and a test set (109 patients) at a 7:3 ratio. The study utilized features derived from deep transfer learning and radiomics, with Spearman rank correlation and the Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques to reduce feature redundancy. Predictive models were developed using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), selecting the best-performing model for a comprehensive predictive framework incorporating clinical data. Results After removing redundant features, 24 key features were identified. In the training set, the area under the curve (AUC)values for LR, RF, DT, and SVM were 0.867, 0.834, 0.900, and 0.944, respectively; in the test set, they were 0.847, 0.803, 0.842, and 0.910, respectively. The combined model, using SVM and clinical variables, achieved AUCs of 0.946 in the trainingset and 0.920 in the test set. Conclusion The study confirms the utility of a combined model of MRI deep transfer learning, radiomic features, and clinical factors for preoperative classification of stage T2 vs. T3 rectal cancer, offering significant technological support for precise diagnosis and potential clinical application.

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Prognostic value of CEA and CA19-9 in patients with local advanced rectal cancer receiving neoadjuvant chemoradiotherapy, radical surgery and postoperative chemotherapy

Cited by 4 publications

References 29 publications

Radiomics based on RESOLVE ADC in differentiating benign and malignant lymph nodes in rectal cancer

Radiomics based on RESOLVE ADC in differentiating benign and malignant lymph nodes in rectal cancer

Predictive value of apparent diffusion coefficient for neoadjuvant chemotherapy in locally advanced colorectal cancer patients

Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3

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