Clinical evaluation of deep learning-based clinical target volume three-channel auto-segmentation algorithm for adaptive radiotherapy in cervical cancer.

OBJECTIVES: Accurate contouring of the clinical target volume (CTV) is a key element of radiotherapy in cervical cancer. We validated a novel deep learning (DL)-based auto-segmentation algorithm for CTVs in cervical cancer called the three-channel adaptive auto-segmentation network (TCAS). METHODS: A total of 107 cases were collected and contoured by senior radiation oncologists (ROs). Each case consisted of the following: (1) contrast-enhanced CT scan for positioning, (2) the related CTV, (3) multiple plain CT scans during treatment and (4) the related CTV. After registration between (1) and (3) for the same patient, the aligned image and CTV were generated. Method 1 is rigid registration, method 2 is deformable registration, and the aligned CTV is seen as the result. Method 3 is rigid registration and TCAS, method 4 is deformable registration and TCAS, and the result is generated by a DL-based method. RESULTS: From the 107 cases, 15 pairs were selected as the test set. The dice similarity coefficient (DSC) of method 1 was 0.8155 ± 0.0368; the DSC of method 2 was 0.8277 ± 0.0315; the DSCs of method 3 and 4 were 0.8914 ± 0.0294 and 0.8921 ± 0.0231, respectively. The mean surface distance and Hausdorff distance of methods 3 and 4 were markedly better than those of method 1 and 2. CONCLUSIONS: The TCAS achieved comparable accuracy to the manual delineation performed by senior ROs and was significantly better than direct registration.

Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer.

OBJECTIVES: Because radiotherapy is indispensible for treating cervical cancer, it is critical to accurately and efficiently delineate the radiation targets. We evaluated a deep learning (DL)-based auto-segmentation algorithm for automatic contouring of clinical target volumes (CTVs) in cervical cancers. METHODS: Computed tomography (CT) datasets from 535 cervical cancers treated with definitive or postoperative radiotherapy were collected. A DL tool based on VB-Net was developed to delineate CTVs of the pelvic lymph drainage area (dCTV1) and parametrial area (dCTV2) in the definitive radiotherapy group. The training/validation/test number is 157/20/23. CTV of the pelvic lymph drainage area (pCTV1) was delineated in the postoperative radiotherapy group. The training/validation/test number is 272/30/33. Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) were used to evaluate the contouring accuracy. Contouring times were recorded for efficiency comparison. RESULTS: The mean DSC, MSD, and HD values for our DL-based tool were 0.88/1.32 mm/21.60 mm for dCTV1, 0.70/2.42 mm/22.44 mm for dCTV2, and 0.86/1.15 mm/20.78 mm for pCTV1. Only minor modifications were needed for 63.5% of auto-segmentations to meet the clinical requirements. The contouring accuracy of the DL-based tool was comparable to that of senior radiation oncologists and was superior to that of junior/intermediate radiation oncologists. Additionally, DL assistance improved the performance of junior radiation oncologists for dCTV2 and pCTV1 contouring (mean DSC increases: 0.20 for dCTV2, 0.03 for pCTV1; mean contouring time decrease: 9.8 min for dCTV2, 28.9 min for pCTV1). CONCLUSIONS: DL-based auto-segmentation improves CTV contouring accuracy, reduces contouring time, and improves clinical efficiency for treating cervical cancer.

Microbiome profiling and Co-metabolism pathway analysis in cervical cancer patients with acute radiation enteritis.

Background: Intestinal bacteria significantly contribute to the metabolism of intestinal epithelial tissues. As the occurrence and development of radiation enteritis (RE) depend on the "co-metabolism" microenvironment formed by the host and intestinal microbiota, which involves complex influencing factors and strong correlations, ordinary techniques struggle to fully explain the underlying mechanisms. However, given that it is based on systems biology, metabolomics analysis is well-suited to address these issues. This study aimed to analyze the metabolomic changes in urine, serum, and fecal samples during volumetric modulated arc therapy (VMAT) for cervical cancer and screen for characteristic metabolites of severe acute radiation enteritis (SARE) and RE. Methods: We enrolled 50 patients who received radiotherapy for cervical cancer. Urine, serum, and fecal samples of patients were collected at one day before radiotherapy and the second week, fourth week, and sixth week after the start of radiotherapy. Control group samples were collected during the baseline period. Differential metabolites were identified by metabolomics analysis; co-metabolic pathways were clarified. We used the mini-SOM library for incorporating characteristic metabolites, and established metabolite classification models for predicting SARE and RE. Results: Urine and serum sample data showed remarkable clustering effect; metabolomics data of the fecal supernatant were evidently disturbed. Patient sample analyses during VMAT revealed the following. Urine samples: Downregulation of the pyrimidine and riboflavin metabolism pathways as well as initial upregulation followed by downregulation of arginine and proline metabolism pathways and the arginine biosynthesis pathway. Fecal samples: Upregulation of linoleic acid and phenylalanine metabolic pathways and initial downregulation followed by upregulation of arachidonic acid (AA) metabolic pathways. Serum samples: Initial upregulation followed by downregulation of the arginine biosynthesis pathway and downregulation of glutathione, AA, and arginine and proline metabolic pathways. Conclusion: Patients with cervical cancer exhibited characteristic metabolic pathways and characteristic metabolites predicting RE and SARE were screened out. An effective RE mini-SOM classification model was successfully established.

Analysis of nutritional risk, skeletal muscle depletion, and lipid metabolism phenotype in acute radiation enteritis.

BACKGROUND: Radiation enteritis, which often occurs during radiation-induced acute intestinal symptoms (RIAIS), is the most common and important complication during radiotherapy for cervical cancer. RIAIS caused by abdominal and pelvic radiotherapy will affect nutrient intake, digestion, absorption, and metabolism, leading to malnutrition or poorer nutritional status. In patients with malignant tumors, malnutrition can adversely affect the curative effect and response of radiotherapy by reducing radiosensitivity, affecting the precision of radiotherapy placement and increasing the incidence of radiotherapy-related adverse reactions. AIM: To analyze nutritional risk, skeletal muscle depletion, and lipid metabolism phenotype in acute radiation enteritis. METHODS: Fifty patients with cervical cancer received external beam radiotherapy, and 15 patients received brachytherapy after external beam radiotherapy. Body weight, body composition parameters, nutritional risk screening (NRS) 2002 score, and blood biochemical indices of patients with cervical cancer during periradiation were tested by a one-way repeated measures analysis of variance. Metabolomics analysis was used to identify characteristic lipid metabolism pathways. Clinical factors that affect linoleic acid changes were screened using the generalized evaluation equation. RESULTS: Among the 50 patients, 37 had RIAIS, including 34 patients with grade 1-2 RIAIS and 3 patients with grade 3 RIAIS. The NRS 2002 score of patients who underwent cervical cancer radiotherapy continued to increase during the periradiation period, and 42 patients who underwent cancer radiotherapy had nutritional deficits (NRS 2002 score ≥ 3 points) at the end of radiotherapy. Correlation analyses revealed that body weight and body mass index changes were closely associated with body fat content (R2 = 0.64/0.51). The results of the univariate analysis showed that radiotherapy time, percentage reduction of serum albumin, and percentage reduction of serum prealbumin were the key factors affecting skeletal muscle exhaustion (P < 0.05). Metabolomic analysis of fecal supernatants of cervical cancer patients during the periradiation period revealed the involvement of linoleic acid, cholic acid, arachidonic acid, and N-acetyl-L-benzene alanine in the metabolic pathway of linoleic acid. CONCLUSION: Cervical cancer radiotherapy patients faced nutritional risks, decreased serum albumin synthesis, and increased risk of skeletal muscle exhaustion. Linoleic acid was a biomarker of high nutritional risk.

Establishment of a prediction model for severe acute radiation enteritis associated with cervical cancer radiotherapy.

BACKGROUND: Cervical cancer is one of the most common gynecological malignant tumors. Radiation enteritis (RE) leads to radiotherapy intolerance or termination of radiotherapy, which negatively impacts the therapeutic effect and seriously affects the quality of life of patients. If the incidence of RE in patients can be predicted in advance, and targeted clinical preventive treatment can be carried out, the side effects of radiotherapy in cervical cancer patients can be significantly reduced. Furthermore, accurate prediction of RE is essential for the selection of individualized radiation dose and the optimization of the radiotherapy plan. AIM: To analyze the relationships between severe acute RE (SARE) of cervical cancer radiotherapy and clinical factors and dose-volume parameters retrospectively. METHODS: We included 50 cervical cancer patients who received volumetric modulated arc therapy (VMAT) from September 2017 to June 2018 in the Department of Radiotherapy at The First Affiliated Hospital Soochow University. Clinical and dose-volume histogram factors of patients were collected. Logistic regression analysis was used to evaluate the predictive value of each factor for SARE. A nomogram to predict SARE was developed (SARE scoring system ≥ 3 points) based on the multiple regression coefficients; validity was verified by an internal verification method. RESULTS: Gastrointestinal and hematological toxicity of cervical cancer VMAT gradually increased with radiotherapy and reached the peak at the end of radiotherapy. The main adverse reactions were diarrhea, abdominal pain, colitis, anal swelling, and blood in the stool. There was no significant difference in the incidence of gastrointestinal toxicity between the radical and postoperative adjuvant radiotherapy groups (P > 0.05). There were significant differences in the small intestine V20, V30, V40, and rectal V40 between adjuvant radiotherapy and radical radiotherapy after surgery (P < 0.05). Univariate and multivariate analyses revealed anal bulge rating (OR: 14.779, 95%CI: 1.281-170.547, P = 0.031) and disease activity index (DAI) score (OR: 53.928, 95%CI: 3.822-760.948, P = 0.003) as independent predictors of SARE. CONCLUSION: Anal bulge rating (> 0.500 grade) and DAI score (> 2.165 points) can predict SARE. The nomogram shows potential value in clinical practice.