Prognostic significance of carcinoembryonic antigen combined with carbohydrate antigen 19-9 following neoadjuvant chemoradiotherapy in patients with locally advanced 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.

Prognostic Significance of Preoperative and Postoperative Evaluation of Combined Tumor Markers for Patients With Colon Cancer

BACKGROUND

The combined value of the tumor markers carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) in patients with colon cancer (CC) is unclear. This study aimed to investigate the role of composite tumor markers in the prognosis of CC.

METHODS

Patients who underwent curative resection of colon adenocarcinoma were enrolled. The tumor marker status before and after the operation was used to divide the patients into groups according to the number of tumor markers with abnormal expression, and recurrence-free survival (RFS) and overall survival (OS) of different groups were compared. The impact of changes in composite tumor markers in the perioperative period on outcomes was further explored.

RESULTS

Ultimately, 531 patients were enrolled in the study. As the number of preoperative and postoperative elevated tumor markers increased, both RFS and OS rates became lower (both P <0.05). Further analysis revealed that the number of elevated tumor markers after resection can significantly affect the outcomes (both P <0.05). In patients with abnormal preoperative tumor markers, normalization of markers after surgery was a protective factor for prognosis (both P <0.05), and patients with postoperative elevated levels of both tumor markers had a 5.5-fold and 6-fold increase in the risk of recurrence and death. In addition, patients with elevated markers after surgery had a high risk of recurrence within 5 years after colectomy.

CONCLUSIONS

Postoperative tumor markers had a better ability to differentiate postoperative outcomes in patients with CC than preoperative tumor markers. Patients whose tumor markers normalized after surgery had a better prognosis.

Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

OBJECTIVES

To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model.

MATERIALS AND METHODS

This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors' region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models' overall performance.

RESULTS

The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481-0.868), CEUS 0.821 (95%CI: 0.660-0.983), Combined_1 0.829 (95%CI: 0.673-0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509-0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P < 0.01). Both univariate and multivariate logistic regression analyses showed that age (P < 0.01) and clinical stage (P < 0.01) could be an independent predictor of efficacy after nCRT in patients with LARC.

CONCLUSION

The ultrasomics model had better diagnostic performance to predict efficacy to nCRT in patients with LARC than the Clinical model.

Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning

OBJECTIVE

In this study, we aimed to investigate the predictive efficacy of magnetic resonance imaging (MRI) radiomics features at different time points of neoadjuvant therapy for rectal cancer in patients with pathological complete response (pCR). Furthermore, we aimed to develop and validate a radiomics space-time model (RSTM) using machine learning for artificial intelligence interventions in predicting pCR in patients.

METHODS

Clinical and imaging data of 83 rectal cancer patients were retrospectively analyzed, and the patients were classified as pCR and non-pCR patients according to their postoperative pathological results. All patients received one MRI examination before and after neoadjuvant therapy to extract radiomics features, including pre-treatment, post-treatment, and delta features. Delta features were defined by the ratio of the difference between the pre- and the post-treatment features to the pre-treatment feature. After feature dimensionality reduction based on the above three feature types, the RSTM was constructed using machine learning methods, and its performance was evaluated using the area under the curve (AUC).

RESULTS

The AUC values of the individual basic models constructed by pre-treatment, post-treatment, and delta features were 0.771, 0.681, and 0.871, respectively. Their sensitivity values were 0.727, 0.864, and 0.909, respectively, and their specificity values were 0.803, 0.492, and 0.656, respectively. The AUC, sensitivity, and specificity values of the combined basic model constructed by combining pre-treatment, post-treatment, and delta features were 0.901, 0.909, and 0.803, respectively. The AUC, sensitivity, and specificity values of the RSTM constructed using the K-Nearest Neighbor (KNN) classifier on the basis of the combined basic model were 0.944, 0.871, and 0.983, respectively. The Delong test showed that the performance of RSTM was significantly different from that of pre-treatment, post-treatment, and delta models (P < 0.05) but not significantly different from the combined basic model of the three (P > 0.05).

CONCLUSIONS

The RSTM constructed using the KNN classifier based on the combined features of before and after neoadjuvant therapy and delta features had the best predictive efficacy for pCR of neoadjuvant therapy. It may emerge as a new clinical tool to assist with individualized management of rectal cancer patients.

Efficacy and safety of different radiotherapy doses in neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: A retrospective study

Background

This study aims to compare the efficacy and safety of neoadjuvant chemoradiotherapy (nCRT) with different radiotherapy doses (45Gy and 50.4Gy) in patients with locally advanced rectal cancer (LARC).

Methods

Herein, 120 patients with LARC were retrospectively enrolled between January 2016 and June 2021. All patients underwent two courses of induction chemotherapy (XELOX), chemoradiotherapy, and total mesorectum excision (TME). A total of 72 patients received a radiotherapy dose of 50.4 Gy, while 48 patients received a dose of 45 Gy. Surgery was then performed within 5-12 weeks following nCRT.

Results

There was no statistically significant difference between the baseline characteristics of the two groups. The rate of good pathological response in the 50.4Gy group was 59.72% (43/72), while in the 45Gy group achieved 64.58% (31/48) (P>0.05). The disease control rate (DCR) in the 50.4Gy group was 88.89% (64/72), compared to 89.58% (43/48) in the 45Gy group (P>0.05). The incidence of adverse reactions for radioactive proctitis, myelosuppression, and intestinal obstruction or perforation differed significantly between the two groups (P<0.05). The anal retention rate in the 50.4Gy group was significantly higher in contrast to the 45Gy group (P<0.05).

Conclusions

Patients receiving a radiotherapy dose of 50.4Gy have a better anal retention rate but also a higher incidence of adverse events such as radioactive proctitis, myelosuppression, and intestinal obstruction or perforation, and a comparable prognosis to patients treated with a radiotherapy dose of 45Gy.

Predicting neoadjuvant chemoradiotherapy response with functional imaging and liquid biomarkers in locally advanced rectal cancer

INTRODUCTION

Non-invasive predictive quantitative biomarkers are required to guide treatment individualization in patients with locally advanced rectal cancer (LARC) in order to maximise therapeutic outcomes and minimise treatment toxicity. Magnetic resonance imaging (MRI), positron emission tomography (PET) and blood biomarkers have the potential to predict chemoradiotherapy (CRT) response in LARC.

AREAS COVERED

This review examines the value of functional imaging (MRI and PET) and liquid biomarkers (circulating tumor cells (CTCs) and circulating tumor nucleic acid (ctNA)) in the prediction of CRT response in LARC. Selected imaging and liquid biomarker studies are presented and the current status of the most promising imaging (apparent diffusion co-efficient (ADC), K^trans, SUV_max, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) and liquid biomarkers (circulating tumor cells (CTCs), circulating tumor nucleic acid (ctNA)) is discussed. The potential applications of imaging and liquid biomarkers for treatment stratification and a pathway to clinical translation are presented.

EXPERT OPINION

Functional imaging and liquid biomarkers provide novel ways of predicting CRT response. The clinical and technical validation of the most promising imaging and liquid biopsy biomarkers in multi-centre studies with harmonised acquisition techniques is required. This will enable clinical trials to investigate treatment escalation or de-escalation pathways in rectal cancer.

The number of cycles of adjuvant chemotherapy in stage III and high-risk stage II rectal cancer: a nomogram and recursive partitioning analysis

Objective

The prognostic role of the number of cycles of adjuvant chemotherapy (ACT) after total mesorectal excision in stage III and high-risk stage II rectal cancer is unknown. As a result of this, our study was designed to assess the effect of the number of cycles of ACT on the prediction of cancer-specific survival.

Methods

Four hundred patients that were diagnosed as stage III and high-risk stage II rectal cancer from January 2012 to January 2018 and who had received total mesorectal excision were enrolled in this study. A nomogram incorporating the number of cycles of ACT was also developed in this study. For internal validation, the bootstrap method was used and the consistency index was used to evaluate the accuracy of the model. The patients were stratified into risk groups according to their tumor characteristics by recursive partitioning analysis.

Results

We found that the risk of death was decreased by 26% (HR = 0.74, 95% CI: 0.61–0.89, P = 0.0016) with each increasing ACT cycle. The N stage, positive lymph node ratio (PLNR), carcinoembryonic antigen, neutrophil-to-lymphocyte ratio, and the number of cycles of ACT were chosen and entered into the nomogram model. Recursive partitioning analysis-based risk stratification revealed a significant difference in the prognosis in rectal cancer patients with high-risk, intermediate-risk, and low-risk (3-year cancer-specific survival: 0.246 vs. 0.795 vs. 0.968, P < 0.0001). Seven or more cycles of ACT yielded better survival in patients with PLNR ≥ 0.28 but not in patients with PLNR < 0.28.

Conclusion

In conclusion, the nomogram prognosis model based on the number of cycles of ACT predicted individual prognosis in rectal cancer patients who had undergone total mesorectal excision. These findings further showed that in patients with PLNR ≥ 0.28, no fewer than 7 cycles of ACT are needed to significantly reduce the patient’s risk of death.

C-Reactive Protein as Predictive Biomarker for Response to Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer: A Retrospective Study

Simple Summary

Most patients with locally advanced rectal cancer present resistance or a moderate response to neoadjuvant chemoradiotherapy (nCRT), which is considered the standard of care. To select patients who could benefit from nCRT, while avoiding unnecessary treatment-induced toxicity and surgery-associated morbidity, it is urgent to find biomarkers of response to chemoradiotherapy. Therefore, the aim of our retrospective study was to assess the potential of classical blood analytes collected before chemoradiotherapy as biomarkers of response to treatment and prognostics in locally advanced rectal cancer. Our results identified C-reactive protein ≤3.5 as a strong independent predictor of response to treatment and an independent predictor of disease-free survival (DFS) and overall survival (OS). Additionally, platelets were found to be independent predictors of DFS and OS and hemoglobin of DFS. These data might contribute to the personalization of rectal cancer treatment by guiding clinicians in decision-making regarding the best treatment strategy for each patient.

Abstract

The standard of care for the treatment of locally advanced rectal cancer is neoadjuvant chemoradiotherapy (nCRT) followed by surgery, but complete response rates are reduced. To find predictive biomarkers of response to therapy, we conducted a retrospective study evaluating blood biomarkers before nCRT. Hemoglobin (Hg), C-reactive protein (CRP), platelets, carcinoembryonic antigen, carbohydrate antigen 19.9 levels, and neutrophil/lymphocyte ratio were obtained from 171 rectal cancer patients before nCRT. Patients were classified as responders (Ryan 0–1; ycT0N0), 59.6% (n = 102), or nonresponders (Ryan 2–3), 40.3% (n = 69), in accordance with the Ryan classification. A logistic regression using prognostic pretreatment factors identified CRP ≤ 3.5 (OR = 0.05; 95%CI: 0.01–0.21) as a strong independent predictor of response to treatment. Multivariate analysis showed that CRP was an independent predictor of disease-free survival (DFS) (HR = 5.48; 95%CI: 1.54–19.48) and overall survival (HR = 6.10; 95%CI 1.27–29.33) in patients treated with nCRT. Platelets were an independent predictor of DFS (HR = 3.068; 95%CI: 1.29–7.30) and OS (HR= 4.65; 95%CI: 1.66–13.05) and Hg was revealed to be an independent predictor of DFS (HR = 0.37; 95%CI: 0.15–0.90) in rectal cancer patients treated with nCRT. The lower expression of CRP is independently associated with an improved response to nCRT, DFS, and OS.

Prognostic value of FDG PET-CT in suspected recurrence of colorectal carcinoma: survival outcomes of a 10-year follow-up : FDG PET in recurrent colorectal CA

OBJECTIVE

We aimed to evaluate the predictive value of FDG PET-CT scan and CEA measurements in recurrent colorectal cancer (CRC) patients.

METHODS

The records of 211 CRC patients who had FDG PET-CT scans between April 2009 and June 2011 due to suspicion of recurrence were extracted from the data of our previous report of 235 patients after 24 patients were excluded from the study due to lack of follow-up data or death unrelated to CRC. FDG PET-CT findings, simultaneous CEA levels, and survival data were evaluated retrospectively to determine the prognostic factors that affected the overall survival (OS) of the patients.

RESULTS

The mean age of 211 patients was 60.2 ± 12.8 years. The median follow-up time was 39 months (CI 95%: 4-123 months). The CRC-related death rate was 71.6% and the median OS time was measured 39 months (CI 95%: 27-50 months) for 211 patients. The median OS time for the patients with positive findings for recurrence in PET scans was 28 months (CI 95%: 22-33 months) which was significantly shorter (p < 0.001) than that of PET-negative patients (median OS was not reached; mean OS: 105 months; CI 95%: 95-116 months). CEA positivity also had a significant negative effect on survival (p < 0.001). Median OS times in patients with elevated and normal levels of CEA were 24 months (CI 95%: 17-30 months) and 85 months (CI 95%: 62-107 months), respectively. When the effect of CEA positivity was evaluated in patients with negative PET scans for recurrence, no statistically significant difference was determined (p = 0.209), but PET positivity had a significant negative effect on OS in patients with normal levels of CEA (p < 0.001). On the other hand, PET negativity had a significant positive effect on OS in patients with elevated CEA levels (p = 0.002). The extend of recurrent disease had also a significant effect on OS. The patients with distant metastasis had less favorable OS than those patients with only local recurrence (p < 0.001). The presence of liver metastasis also diminished the OS, but this effect was not statistically significant (p = 0.177).

CONCLUSION

FDG PET-CT scan which is a reliable imaging method to detect recurrence in CRC patients, regardless of CEA levels, can also provide valuable prognostic information, even superior to that of CEA measurement.