Association of T Stage and Serum CEA Levels in Determining Survival of Rectal Cancer

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

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.

Machine learning for predicting liver and/or lung metastasis in colorectal cancer: A retrospective study based on the SEER database

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.

Navigating through novelties concerning mCRC treatment-the role of immunotherapy, chemotherapy, and targeted therapy in mCRC

Over the course of nearly six decades since the inception of initial trials involving 5-FU in the treatment of mCRC (metastatic colorectal cancer), our progressive comprehension of the pathophysiology, genetics, and surgical techniques related to mCRC has paved the way for the introduction of novel therapeutic modalities. These advancements not only have augmented the overall survival but have also positively impacted the quality of life (QoL) for affected individuals. Despite the remarkable progress made in the last two decades in the development of chemotherapy, immunotherapy, and target therapies, mCRC remains an incurable disease, with a 5-year survival rate of 14%. In this comprehensive review, our primary goal is to present an overview of mCRC treatment methods following the latest guidelines provided by the National Comprehensive Cancer Network (NCCN), the American Society of Clinical Oncology (ASCO), and the American Society of Colon and Rectal Surgeons (ASCRS). Emphasis has been placed on outlining treatment approaches encompassing chemotherapy, immunotherapy, targeted therapy, and surgery's role in managing mCRC. Furthermore, our review delves into prospective avenues for developing new therapies, offering a glimpse into the future of alternative pathways that hold potential for advancing the field.

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study

BACKGROUND

To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer.

METHODS

Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms.

RESULTS

Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians.

CONCLUSION

The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

Application of machine learning techniques in real-world research to predict the risk of liver metastasis in rectal cancer

Background

The liver is the most common site of distant metastasis in rectal cancer, and liver metastasis dramatically affects the treatment strategy of patients. This study aimed to develop and validate a clinical prediction model based on machine learning algorithms to predict the risk of liver metastasis in patients with rectal cancer.

Methods

We integrated two rectal cancer cohorts from Surveillance, Epidemiology, and End Results (SEER) and Chinese multicenter hospitals from 2010-2017. We also built and validated liver metastasis prediction models for rectal cancer using six machine learning algorithms, including random forest (RF), light gradient boosting (LGBM), extreme gradient boosting (XGB), multilayer perceptron (MLP), logistic regression (LR), and K-nearest neighbor (KNN). The models were evaluated by combining several metrics, such as the area under the curve (AUC), accuracy score, sensitivity, specificity and F1 score. Finally, we created a network calculator using the best model.

Results

The study cohort consisted of 19,958 patients from the SEER database and 924 patients from two hospitals in China. The AUC values of the six prediction models ranged from 0.70 to 0.95. The XGB model showed the best predictive power, with the following metrics assessed in the internal test set: AUC (0.918), accuracy (0.884), sensitivity (0.721), and specificity (0.787). The XGB model was assessed in the outer test set with the following metrics: AUC (0.926), accuracy (0.919), sensitivity (0.740), and specificity (0.765). The XGB algorithm also shows a good fit on the calibration decision curves for both the internal test set and the external validation set. Finally, we constructed an online web calculator using the XGB model to help generalize the model and to assist physicians in their decision-making better.

Conclusion

We successfully developed an XGB-based machine learning model to predict liver metastasis from rectal cancer, which was also validated with a real-world dataset. Finally, we developed a web-based predictor to guide clinical diagnosis and treatment strategies better.

Increased Carcinoembryonic Antigen (CEA) Level Is Highly Associated with Low Skeletal Muscle Mass in Asymptomatic Adults: A Population-Based Study

We investigated the relationship between high carcinoembryonic antigen (CEA) levels and low skeletal muscle mass (LMM) in asymptomatic adults in a population-based study. A total of 202,602 adults (mean age 41.7 years) without malignancy, stroke, cardiovascular disease, or chronic lung/liver disease were included. A high CEA level was defined as ≥5 ng/mL. Skeletal muscle mass index (SMI) was calculated based on appendicular muscle mass (kg)/height (m)2. Participants were classified into three groups based on SMI: “normal”, “mild LMM”, and “severe LMM.” The prevalence of elevated CEA levels was the highest in subjects with severe LMM (4.2%), followed by those with mild LMM (1.6%) and normal muscle mass (1.1%) (p for trend < 0.001). In multivariate logistic regression analysis, high CEA was independently associated with having mild LMM (adjusted odds ratio, 1.139 [95% confidence interval, 1.092−1.188]) and severe LMM (2.611 [2.055−3.319]) compared to normal muscle mass. Furthermore, the association between high CEA and severe LMM was stronger in women than that in men (women, 5.373 [2.705−10.669]; men, 2.273 [1.762−2.933]). Elevated CEA levels were significantly associated with a higher prevalence of LMM. Therefore, increased CEA could be used as a biomarker for detecting LMM in adults without cancer.

Quantitative T2*-Weighted Imaging and Reduced Field-of-View Diffusion-Weighted Imaging of Rectal Cancer: Correlation of R2* and Apparent Diffusion Coefficient With Histopathological Prognostic Factors

Purpose

To assess T2*-weighted imaging (T2*WI) and reduced field-of-view diffusion-weighted Imaging (rDWI) derived parameters and their relationships with histopathological factors in patients with rectal cancer.

Methods

Fifty-four patients with pathologically-proven rectal cancer underwent preoperative T2*-weighted imaging and rDWI in this retrospective study. R2* values from T2*-weighted imaging and apparent diffusion coefficient (ADC) values from rDWI were compared in terms of different histopathological prognostic factors using student’s t-test or Mann-Whitney U-test. The correlations of R2* and ADC with prognostic factors were assessed by Spearman correlation analysis. The diagnostic performances of R2* and ADC were analyzed by receiver operating characteristic curves (ROC) separately and jointly.

Results

Significant positive correlation was found between R2* values and T stage, lymph node involvement, histological grades, CEA level, the presence of EMVI and tumor deposit (r = 0.374 ~ 0.673, p = 0.000–0.006), with the exception of CA19-9 level, CRM status and tumor involvement in the circumference lumen (TIL). Meanwhile, ADC values negatively correlated with almost all the prognostic factors (r = −0.588 to −0.299, p = 0.000–0.030), except CA19-9 level. The AUC range was 0.724–0.907 for R2* and 0.674–0.887 for ADC in discrimination of different prognostic factors. While showing the highest AUC of 0.913 (0.803–1.000) in differentiation of T stage, combination of R2* and ADC with reference to different prognostic factors did not significantly improve the diagnostic performance in comparison with individual R2*/ADC parameter.

Conclusions

R2* and ADC were associated with important histopathological prognostic factors of rectal cancer. R2* might act as additional quantitative imaging marker for tumor characterization of rectal cancer.

Prognostic Impact of Pretreatment Elevated and Normalized Carcinoembryonic Antigen Levels After Neoadjuvant Chemoradiotherapy in Resected Locally Advanced Rectal Cancer Patients

Purpose

The prognostic significance of pretreatment elevated and normalized CEA after neoadjuvant chemoradiotherapy (nCRT) was evaluated.

Materials and Methods

The characteristics of 951 locally advanced rectal cancer patients with nCRT were retrieved and were analyzed retrospectively. Pretreatment CEA levels were defined as CEA evaluated one week prior to the nCRT. CEA after nCRT was deemed as CEA measured one week before surgery. The normal CEA levels were set at <5 ng/mL. The normal CEA group was defined as patients with normal pretreatment CEA levels. The normalized CEA group was defined as patients with elevated pretreatment CEA levels and normal CEA levels after nCRT. The elevated CEA group was defined as patients with elevated pretreatment CEA levels and elevated CEA levels after nCRT.

Results

Compared with the elevated CEA group, the normalized CEA group was associated with better overall survival (OS) (HR: 0.625, 95%CI: 0.416-0.938, P=0.022). There was no difference between the normalized CEA group and the normal CEA group (HR: 1.143, 95%CI: 0.84-1.557, P=0.395).

Conclusion

In conclusion, the study indicated that OS of the normalized CEA group and the normal CEA group was better than the elevated CEA group.