Tumor stage in patients operated for rectal cancer: a comparison of the pre-operative MR and the resection specimen, with specific attention to the effect of neo-adjuvant radiotherapy

Impacts of neoadjuvant therapy on the number of dissected lymph nodes in esophagogastric junction cancer patients

BACKGROUND

Neoadjuvant therapy favors the prognosis of various cancers, including esophagogastric junction cancer (EGC). However, the impacts of neoadjuvant therapy on the number of dissected lymph nodes (LNs) have not yet been evaluated in EGC.

METHODS

We selected EGC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2006-2017). The optimal number of resected LNs was determined using X-tile software. Overall survival (OS) curves were plotted with the Kaplan-Meier method. Prognostic factors were evaluated using univariate and multivariate COX regression analyses.

RESULTS

Neoadjuvant radiotherapy significantly decreased the mean number of LN examination compared to the mean number of patients without neoadjuvant therapy (12.2 vs. 17.5, P = 0.003). The mean LN number of patients with neoadjuvant chemoradiotherapy was 16.3, which was also statistically lower than 17.5 (P = 0.001). In contrast, neoadjuvant chemotherapy caused a significant increase in the number of dissected LNs (21.0, P < 0.001). For patients with neoadjuvant chemotherapy, the optimal cutoff value was 19. Patients with > 19 LNs had a better prognosis than those with 1-19 LNs (P < 0.05). For patients with neoadjuvant chemoradiotherapy, the optimal cutoff value was 9. Patients with  > 9 LNs had a better prognosis than those with 1-9 LNs (P < 0.05).

CONCLUSIONS

Neoadjuvant radiotherapy and chemoradiotherapy decreased the number of dissected LNs, while neoadjuvant chemotherapy increased it in EGC patients. Hence, at least 10 LNs should be dissected for neoadjuvant chemoradiotherapy and 20 for neoadjuvant chemotherapy, which could be applied in clinical practice.

How We Treat Localized Rectal Cancer-An Institutional Paradigm for Total Neoadjuvant Therapy

Total neoadjuvant therapy (TNT)-the neoadjuvant employment of radiotherapy (RT) or chemoradiation (CRT) as well as chemotherapy (CHT) before surgery-may lead to increased pathological complete response (pCR) rates as well as a reduction in the risk of distant metastases in locally advanced rectal cancer. Furthermore, increased response rates may allow organ-sparing strategies in a growing number of patients with low rectal cancer and upfront immunotherapy has shown very promising early results in patients with microsatellite instability (MSI)-high/mismatch-repair-deficient (dMMR) tumors. Despite the lack of a generally accepted treatment standard, we strongly believe that existing data is sufficient to adopt the concept of TNT and immunotherapy in clinical practice. The treatment algorithm presented in the following is based on our interpretation of the current data and should serve as a practical guide for treating physicians-without any claim to general validity.

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

BACKGROUND

The purpose of this study was to investigate and validate multiparametric magnetic resonance imaging (MRI)-based machine learning classifiers for early identification of poor responders after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).

METHODS

Patients with LARC who underwent nCRT were included in this retrospective study (207 patients). After preprocessing of multiparametric MRI, radiomics features were extracted and four feature selection methods were used to select robust features. The selected features were used to build five machine learning classifiers, and 20 (four feature selection methods × five machine learning classifiers) predictive models for the screening of poor responders were constructed. The predictive models were evaluated according to the area under the curve (AUC), F1 score, accuracy, sensitivity, and specificity.

RESULTS

Eighty percent of all predictive models constructed achieved an AUC of more than 0.70. A predictive model using a support vector machine classifier with the minimum redundancy maximum relevance (mRMR) selection method followed by the least absolute shrinkage and selection operator (LASSO) selection method showed superior prediction performance, with an AUC of 0.923, an F1 score of 88.14%, and accuracy of 91.03%. The predictive performance of the constructed models was not improved by ComBat compensation.

CONCLUSIONS

In rectal cancer patients who underwent neoadjuvant chemoradiotherapy, machine learning classifiers with radiomics features extracted from multiparametric MRI were able to accurately discriminate poor responders from good responders. The techniques should provide additional information to guide patient-tailored treatment.

Survival benefit of statins in older patients with rectal cancer: A Swedish population-based cohort study

OBJECTIVES

Increasing evidence suggests that statins may have antitumor effects but their role in rectal cancer appears inconclusive. The aim of this study was to investigate whether statins may have an impact on survival of older and younger patients with rectal cancer.

MATERIALS AND METHODS

This study included 238 patients ≥70 years and 227 patients <70 years old, from the Southeast Health Care Region of Sweden, who were diagnosed with rectal adenocarcinoma between 2004 and 2013.

RESULTS

In the older group (n = 238), statin use at the time of diagnosis was related to better cancer-specific survival (CSS) and overall survival (OS), compared to non-use (CSS: Hazard Ratio (HR), 0.37; 95% CI, 0.19-0.72; P = .003; OS: HR, 0.62; 95% CI, 0.39-0.96; P = .032). In the older group with stages I-III disease (n = 199), statin use was associated with better disease-free survival (DFS) compared to non use (HR, 0.18; 95% CI, 0.06-0.59; P = .005). The improvement of CSS, OS and DFS remained significant after adjusting for potential confounders. In the older group with stage III disease, statin users had better CSS and DFS compared to non-users (log rank P = .043; log-rank P = .028, respectively). In the older group with short course radiotherapy, statin use was related to better CSS (log-rank P = .032). No such association was present in the younger group.

CONCLUSION

Statin use was related to improved survival in older patients with rectal cancer. This observation is important given the low cost and safety of statins as a drug.