The recovery of intestinal barrier function and changes in oral microbiota after radiation therapy injury

Introduction

Colorectal cancer (CRC) is the third most common malignant tumor, and neoadjuvant chemo-radiotherapy is usually recommended for advanced stage colorectal cancer. Radiotherapy can cause damage to intestinal mucosal barrier, which may be related to perioperative complications. Intestinal microbiota is one of the constituents of the intestinal mucosal biological barrier, and literature reports that patients with CRC have changes in corresponding oral microbiota. This study aims to analyze the levels of immunoglobulin SIgA, inflammatory factors, lymphocyte subsets quantity, and proportion in surgical specimens of intestinal mucosa at different time intervals after radiotherapy, in order to seek investigation for the optimal surgical time after radiotherapy and to provide evidence for finding probiotics or immunomodulators through high-throughput sequencing of bacterial 16s rRNA in patients' saliva microbiota. Ultimately, this may provide new ideas for reducing perioperative complications caused by radiotherapy-induced intestinal damage.

Methods

We selected intestinal mucosal tissue and saliva samples from over 40 patients in our center who did not undergo radiotherapy and underwent surgery at different time intervals after radiotherapy. Detection of SIgA was performed using ELISA assay. Western Blotting was used to detect IL-1β, IL-6, and IL-17 in the intestinal mucosal tissue. Flow cytometry was used to detect CD4 and CD8. And the microbial community changes in saliva samples were detected through 16s rRNA sequencing.

Results

After radiotherapy, changes in SIgA, various cytokines, CD4CD8 lymphocyte subsets, and oral microbiota in the intestinal mucosal tissue of rectal cancer patients may occur. Over time, this change may gradually recover.

Discussion

In colorectal cancer, oncological aspects often receive more attention, while studies focusing on the intestinal mucosal barrier are less common. This study aims to understand the repair mechanisms of the intestinal mucosal barrier and reduce complications arising from radiotherapy-induced damage. The relationship between oral microbiota and systemic diseases has gained interest in recent years. However, the literature on the oral microbiota after radiotherapy for rectal cancer remains scarce. This study addresses this gap by analysing changes in the salivary microbiota of rectal cancer patients before and after radiotherapy, shedding light on microbiota changes. It aims to lay the groundwork for identifying suitable probiotics or immunomodulators to alleviate perioperative complications and improve the prognosis of CRC.

An atlas-guided automatic planning approach for rectal cancer intensity-modulated radiotherapy

We try to develop an atlas-guided automatic planning (AGAP) approach and evaluate its feasibility and performance in rectal cancer intensity-modulated radiotherapy. The developed AGAP approach consisted of four independent modules: patient atlas, similar patient retrieval, beam morphing (BM), and plan fine-tuning (PFT) modules. The atlas was setup using anatomy and plan data from Pinnacle auto-planning (P-auto) plans. Given a new patient, the retrieval function searched the top similar patient by a generic Fourier descriptor algorithm and retrieved its plan information. The BM function generated an initial plan for the new patient by morphing the beam aperture from the top similar patient plan. The beam aperture and calculated dose of the initial plan were used to guide the new plan optimization in the PFT function. The AGAP approach was tested on 96 patients by the leave-one-out validation and plan quality was compared with the P-auto plans. The AGAP and P-auto plans had no statistical difference for target coverage and dose homogeneity in terms ofV_100%(p = 0.76) and homogeneity index (p = 0.073), respectively. The CI index showed they had a statistically significant difference. But the ΔCI was both 0.02 compared to the perfect CI index of 1. The AGAP approach reduced the bladder mean dose by 152.1 cGy (p < 0.05) andV₅₀by 0.9% (p < 0.05), and slightly increased the left and right femoral head mean dose by 70.1 cGy (p < 0.05) and 69.7 cGy (p < 0.05), respectively. This work developed an efficient and automatic approach that could fully automate the IMRT planning process in rectal cancer radiotherapy. It reduced the plan quality dependence on the planner experience and maintained the comparable plan quality with P-auto plans.

A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer

PURPOSE

Convolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning (prone or supine) for radiotherapy. In this study, we investigated the effect of position orientation on segmentation using CNN.

METHODS

Data of 100 patients (50 in supine and 50 in prone) with rectal cancer were collected for this study. We designed three sets of experiments for comparison: (a) segmentation using the model trained with data from the same orientation; (b) segmentation using the model trained with data from the opposite orientation; (c) segmentation using the model trained with data from both orientations. We performed fivefold cross-validation. The performance was evaluated on segmentation of the clinical target volume (CTV), bladder, and femurs with Dice similarity coefficient (DSC) and Hausdorff distance (HD).

RESULTS

Compared with models trained on cases positioned in the same orientation, the models trained with cases positioned in the opposite orientation performed significantly worse (P < 0.05) on CTV and bladder segmentation, but had comparable accuracy for femurs (P > 0.05). The average DSC values were 0.74 vs 0.84, 0.85 vs 0.88, and 0.91 vs 0.91 for CTV, bladder, and femurs, respectively. The corresponding HD values (mm) were 16.6 vs 14.6, 8.4 vs 8.1, and 6.3 vs 6.3, respectively. The models trained with data from both orientations have comparable accuracy (P > 0.05), with average DSC of 0.84, 0.88, and 0.91 and HD of 14.4, 8.1, and 6.3, respectively.

CONCLUSIONS

Orientation affects the accuracy for CTV and bladder, but has negligible effect on the femurs. The model trained from data combining both orientations performs as well as a model trained with data from the same orientation for all the organs. These observations can offer guidance on the choice of training data for accurate segmentation.