Whole Regional Lymph Node Area Delineation with Deep Learning Model for Total Marrow and Lymphoid Irradiation

PURPOSE/OBJECTIVE(S)

Total body irradiation (TBI) has been performed for conditioning before hematopoietic stem cell transplantation. However, TBI can be related to diverse adverse events including radiation pneumonitis and cataract. Efforts to reduce these events include the total marrow irradiation (TMI) and total marrow and lymphoid irradiation (TMLI). Compared to TMI, TMLI requires more target delineations with lymph nodes which can be labor-intensive and time-consuming. However, with the TMI plans, the coverage to lymph node might be lower than TMLI and its clinical significance is unknown. In the current study, we aimed to develop a deep learning model for automatic delineation of whole regional lymph nodes area and assess the dose coverage of lymph nodes with TMI plans.

MATERIALS/METHODS

Whole regional lymph nodes (cervical, axillary, mediastinal, para-aortic, common iliac, external iliac, internal iliac, obturator, presacral, inguinal lymph nodes) were manually contoured by 3 radiation oncologists in 26 patients having whole body computed tomography (CT) images. Twenty patients were designated as the training/validation set and 6 patients as the testing set, and model was developed using the 'nnUNET' framework. The trained model was evaluated with dice coefficient score (DCS), precision, and recall. In addition, dose coverage of the automatically or manually delineated lymph nodes in TMI plans was calculated.

RESULTS

The mean value of DCS, precision, and recall of the trained model was 0.76, 0.81, and 0.74, respectively. Dose parameters for manually delineated lymph nodes in previously treated TMI plans showed the mean value of V100% (the percentage of volume receiving 100% of the prescribed dose), V95%, and V90% to be 46.50%, 62.12%, and 73.68%, respectively. The highest V90% was observed in presacral (93.61%), axillary (90.40%), obturator (88.78%), and internal iliac lymph nodes (84.67%). In contrast, the lowest V90% was identified in inguinal (47.95%), cervical (61.69%), and para-aortic (65.75%) and external iliac lymph nodes (68.97%). For automatically delineated lymph nodes, the mean value of V100%, V95%, and V90% of TMI plan was 38.35%, 55.06%, and 67.84%, respectively. The difference with dose coverage of lymph node between delineated manually and automatically was not statistically significant.

CONCLUSION

Automatic delineation of lymph node using deep learning showed the potential to reduce the labor-intensive process of TMLI. When treated with TMI, the coverage of inguinal, cervical, para-aortic and external iliac lymph nodes was lower than expected.

Association between Gut Microbial Change and Acute Gastrointestinal Toxicity in Patients with Prostate Cancer Receiving Definitive Radiation Therapy: A Prospective Pilot Analysis

PURPOSE/OBJECTIVE(S)

The gut microbiome is an emerging biomarker that is known to have a pivotal role in the development of diverse human diseases. This prospective cohort study aimed to investigate the association between gut microbial changes and acute gastrointestinal (GI) toxicities in prostate cancer patients receiving definitive radiation therapy (RT).

MATERIALS/METHODS

Seventy-nine fecal samples from 16 prostate cancer patients were analyzed. Stool samples were collected at the following timepoints: pre-RT (prRT), 2 weeks after the start of RT (RT-2w), 5 weeks after the start of RT (RT-5w), 1 month after completion of RT (poRT-1m), and 3 months after completion of RT (poRT-3m). Total RT doses were 69.6‒74.4 Gy at 2.4 Gy per fraction in the high-dose area and 45‒50.4 Gy at 1.8 Gy per fraction in the low-dose area. Alpha- and beta-diversity were estimated. We computed the microbial community polarization index (MCPI) as an indicator of RT-induced dysbiosis. A linear mixed effect model was adopted to evaluate time effects after RT. Metabolic pathway abundances were inferred using bioinformatics tools.

RESULTS

Seven patients experienced ≥ grade 1 acute GI toxicities. Patients experiencing toxicity had lower alpha diversity, especially at RT-2w (P = 0.037) and RT-5w (P = 0.003), with the microbiota enriched in Fusobacteria, Fusobacterium, and Bacteroides fragilis. Patients receiving a large RT field had a trend of lower alpha diversity, particularly at poRT-1m (P = 0.027), with the microbiota enriched in Propionibacteriaceae, Cutibacterium, and Prevotella stercorea. Compared with the MCPI at prRT, the MCPI observed at poRT-1m in patients experiencing toxicities was significantly elevated (P = 0.007). In terms of predicted metabolic pathways, we found linearly decreasing pathways, including carbon fixation pathways in prokaryotes (P = 0.035) and the bacterial secretion system (P = 0.005), in patients who experienced toxicities. Regarding the RT field, no linear trend of functional pathways was found across timepoints.

CONCLUSION

We showed RT-induced dysbiosis in the gut microbiome among patients with prostate cancer who experienced toxicities or received a large RT field. Reduced diversity and elevated RT-related MCPI could be helpful for developing individualized RT approaches. Longitudinal analysis revealed dynamic changes in several microbes and metabolic pathways, which should be validated in a whole metagenome sequencing study.

Relationships between Microbiome and Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer

PURPOSE/OBJECTIVE(S)

Gut microbiome is known to be involved in antitumor immunotherapy and chemotherapy responses; however, few research has focused on the role of gut microbiome in the setting of concurrent chemoradiotherapy (CCRT). In this study, we investigated the tumor microbiome dynamics in patients undergoing neoadjuvant CCRT for locally advanced rectal cancer and sought to determine whether the diversity and composition of microbiome affect treatment response.

MATERIALS/METHODS

A total of 103 samples from 26 patients with locally advanced rectal cancer were collected and 16S ribosomal RNA amplicon sequencing was performed. All patients underwent neoadjuvant CCRT followed by surgical resection between 2008 and 2016. Samples were obtained from both tumor and normal rectal tissue at pre- and post-CCRT. According to the American Joint Committee on Cancer tumor regression grading (TRG) system, patients were divided into responders (TRG 0, 1) and non-responders (TRG 2, 3). We performed diversity, taxonomy, and network analyses to compare responders and non-responders. Then, we established the Bayesian network model to predict treatment response in patients with rectal cancer.

RESULTS

Overall, we detected 1260 microbial genera from 287 families, 132 orders, 56 classes, and 32 phyla in the bacteria kingdom. Between tumor and normal rectal tissues, there was no difference in microbial diversity and composition. On the other hand, there was a significant decrease in diversity and compositional alterations when comparing pre- and post-CCRT samples (all p<0.001). Ten patients (38.5%) were classified as responders and 16 patients (61.5%) were classified as non-responders. In both groups, CCRT significantly reduced microbial diversity and altered their composition, but it was more pronounced in non-responders. In taxonomic analysis of pre-CCRT samples, butyrate-producing bacteria were differentially enriched in responders. Meanwhile, in post-CCRT samples, opportunistic pathogen were overrepresented in non-responders. The network analysis revealed that butyrate-producing bacteria had strong interactions in responders, whereas opportunistic pathogen demonstrated strong interactions in non-responders (Pearson's coefficient>0.5). Finally, five microbes were selected as the optimal set for the response prediction model, which yielded an area under the curve value of 82.33%.

CONCLUSION

CCRT significantly changed the diversity and composition of microbiome, especially in non-responders. Several microbes might be related with treatment response. These findings highlight the potential of microbiome to play an important role as a biomarker in patients with rectal cancer. (NCT02533271).

Impact of the New ESTRO-ACROP Target Volume Delineation Guideline on Breast-Related Complications after Implant-Based Reconstruction and Postmastectomy Radiotherapy

PURPOSE/OBJECTIVE(S)

The European Society for Radiotherapy and Oncology Advisory Committee in Radiation Oncology Practice (ESTRO-ACROP) recently updated a new target volume delineation guideline for postmastectomy radiotherapy (PMRT) after implant-based reconstruction. This study aimed whether this change has impact on breast-related complications.

MATERIALS/METHODS

We retrospectively reviewed patients who underwent PMRT after mastectomy with tissue expander or permanent implant insertion from 2016 to 2021. In total, 412 patients were included; 277 received RT by the new ESTRO-ACROP target delineation (ESTRO-T), and 135 received RT by conventional target delineation (CONV-T). The primary endpoint was comparison between the target groups of major breast-related complication, including infection, capsular contracture, deformity and necrosis requiring re-operation or re-hospitalization during follow-up after RT or delayed implant replacement. Complications were evaluated according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0., and capsular contracture was graded by the Baker Classification.

RESULTS

The median follow-up was 29.5 months (range, 0.3-76.8). The 1-, 2-, and 3-year incidence rates of major breast-related complication were 5.7%, 10.0%, and 11.6% in the ESTRO-T group, and 8.2%, 13.8%, and 14.7% in the CONV-T groups; it did not show a difference between the groups (P = 0.55). In multivariate analyses, target delineation is not significantly associated with the major complications (hazard ratio [HR] = 0.93; P = 0.83, Table 1). There was no significant difference between the ESTRO-T and CONV-T groups in the incidence of any breast-related complications (3-year cumulative incidence, 37.3% vs. 29.4%, respectively; P = 0.28). Symptomatic RT-induced pneumonitis rates were 2.7% in the ESTRO-T group (7 patients) and 2.2% in the CONV-T group (3 patients). Only one local recurrence event occurred in the ESTRO-T group, which was within the ESTRO-target volume.

CONCLUSION

Target volume delineation according to the new ESTRO-ACROP guideline did not reduce the risk of major or any breast-related complications. As the dosimetric benefits of heart and lung have been reported, further analyses with long-term follow-up are necessary to evaluate whether it could be connected to better clinical outcomes.

Pattern and Complication of Reconstructed Breast Cancer Patients Who Received Postmastectomy Radiotherapy in the National Health Insurance Service Cohort

PURPOSE/OBJECTIVE(S)

This study aimed to analyze the nationwide pattern of reconstruction after mastectomy in patients with breast cancer who received postmastectomy radiotherapy (PMRT) and to compare complications according to fractionation and reconstruction procedures.

MATERIALS/METHODS

By using claim data from the National Health Insurance Service (NHIS) database, we analyzed breast cancer patients who received PMRT and underwent reconstruction between 2015 and 2020. We defined the grade ≥ 3 complications as the primary endpoint which involved hospital admission to the plastic surgery department. The complication was identified by using the procedure code for debridement and the International Classification of Diseases 10th codes for wound infections, dehiscence, necrosis, and mechanical complication of breast prosthesis and implant. The propensity score matching method was adopted to constitute the matched cohort between the hypofractionated fractionation (HF) and the conventional fractionation (CF), adjusted for age, diabetes, hypertension, obesity, smoking history, PMRT technique, use of bolus, year of PMRT delivery, and reconstruction method. Logistic regression was performed to evaluate the association between complication and variables.

RESULTS

Altogether 4,553 patients were analyzed: 1,395 (30.6%) in the HF group and 3,158 (69.4.%) in the CF group. The use of HF has steadily increased from 20.1% in 2015 to 42.2% in 2020. Immediate implant reconstruction (36.8%) method was the most frequently used, followed by immediate autologous (33.3%) and two-stage implant reconstruction methods (19.6%). In the matched cohort (N = 2,052), the major complication rate was not significantly different between the HF group and the CF group (5.9% [60/1,026] vs. 5.4% [55/1,026], P = 0.568) with the median follow-up of 30.9 months (range, 6.0-82.1). Surgical debridement was performed in 3.3% [34/1026] of the HF group and 3.5% [36/1026] of the CF group (P = 0.808). HF was not associated with major complications (odds ratio (OR) 1.09, 95% CI 0.75-1.59, P = 0.128).

CONCLUSION

In a nationwide insurance cohort, the complication rate was not significantly different between the HF group and the CF group. Our data suggest HF for reconstructed breasts is comparable to CF. However, consultation for fractionation regimen for reconstructed breast cancer patients may be still required at time of consideration of PMRT.

Preparation of 153Sm-chitosan complex for radiation synovectomy

A samarium 153-chitosan complex was prepared by simply mixing acidic solutions of chitosan and (153)SmCl(3). When a solution of this complex was injected into the knee joints of rabbits, minimal extra-articular leakage was observed. This can be attributed to the rapid change in the pH of the complex solution from acidic to neutral, resulting in the formation of gel followed by the subsequent retention in the administered site. Thus, the complex solution represents a promising candidate for radiation synovectomy.

Synthesis of 4-hydroxy-1-thiocoumarin derivatives-1: an efficient synthesis of thioflocoumafen

An efficient procedure for the preparation of 4-hydroxy-3-{1,2,3,4-tetra-hydro-3-[4-(4-triflu-oromethylbenzyl oxy)phenyl]-1-naphthyl}thiocoumarin (thioflocoumafen, 1a and 1b) is described. The key step in the synthesis involves the condensation reaction of 3-(4-methoxyphenyl)-1-tetralol (2) with 4-hydroxy-1-thiocoumarin (3).