Relationships between the Microbiome and Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

Purpose: This study aimed to investigate the dynamic changes in the microbiome of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (nCRT), focusing on the relationship between the microbiome and response to nCRT.

Materials And Methods: We conducted a longitudinal study involving 103 samples from 26 patients with LARC. Samples were collected from both the tumor and normal rectal tissues before and after nCRT.

Prognosis prediction and immunotherapy optimisation for cryptogenic new-onset refractory status epilepticus.

Background: Cryptogenic new-onset refractory status epilepticus (cNORSE) currently lacks comprehensive knowledge regarding its clinical dynamics, prognostic factors and treatment guidance. Here we present the longitudinal clinical profiles, predictive factors for outcomes and the optimal duration of immunotherapy in patients with cNORSE.

Methods: This retrospective secondary endpoint analysis investigated patients with cNORSE identified from a prospective autoimmune encephalitis cohort at a national referral centre in Korea.

Integrating Deep Learning-Based Dose Distribution Prediction with Bayesian Networks for Decision Support in Radiotherapy for Upper Gastrointestinal cancer.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or Magnetic Resonance (MR)-guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data.

Materials And Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process.

Evaluation of cosmetic outcomes in breast reconstruction patients undergoing radiotherapy using an anomaly generative adversarial network model.

Considering the rising prevalence of breast reconstruction followed by radiotherapy (RT), evaluating the cosmetic impact of RT is crucial. Currently, there are limited tools for objectively assessing cosmetic outcomes in patients who have undergone reconstruction. Therefore, we validated the cosmetic outcome using a previously developed anomaly Generative Adversarial Network (GAN)-based model and evaluated its utility.

The impact of the new ESTRO-ACROP target volume delineation guidelines for postmastectomy radiotherapy after implant-based breast reconstruction on breast complications.

The European Society for Radiotherapy and Oncology-Advisory Committee in Radiation Oncology Practice (ESTRO-ACROP) updated a new target volume delineation guideline for postmastectomy radiotherapy (PMRT) after implant-based reconstruction. This study aimed to evaluate the impact on breast complications with the new guideline compared to the conventional guidelines. In total, 308 patients who underwent PMRT after tissue expander or permanent implant insertion from 2016 to 2021 were included; 184 received PMRT by the new ESTRO-ACROP target delineation (ESTRO-T), and 124 by conventional target delineation (CONV-T).

Prediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model.

Background: We aimed to construct an expert knowledge-based Bayesian network (BN) model for assessing the overall disease burden (ODB) in (y)pN1 breast cancer patients and compare ODB across arms of ongoing trials.

Methods: Utilizing institutional data and expert surveys, we developed a BN model for (y)pN1 breast cancer. Expert-derived probabilities and disability weights for radiotherapy-related benefit (e.

Assessment of lymph node area coverage with total marrow irradiation and implementation of total marrow and lymphoid irradiation using automated deep learning-based segmentation.

Background: Total marrow irradiation (TMI) and total marrow and lymphoid irradiation (TMLI) have the advantages. However, delineating target lesions according to TMI and TMLI plans is labor-intensive and time-consuming. In addition, although the delineation of target lesions between TMI and TMLI differs, the clinical distinction is not clear, and the lymph node (LN) area coverage during TMI remains uncertain.

B-Cell-Mediated Immunity Predicts Survival of Patients With Estrogen Receptor-Positive Breast Cancer.

Purpose: The estrogen receptor-positive (ER+) breast cancer (BC), which constitutes the majority of BC cases, exhibits highly heterogeneous clinical behavior. To aid precision treatments, we aimed to find molecular subtypes of ER+ BC representing the tumor microenvironment and prognosis.

Methods: We analyzed RNA-seq data of 113 patients with BC and classified them according to the PAM50 intrinsic subtypes using gene expression profiles.

The Impact of PM on Radiation-induced Pneumonitis in Patients With Breast Cancer.

Background/aim: Exposure to particulate matter (PM) air pollution is known to adversely affect respiratory disease, but no study has examined its effect on radiation-induced pneumonitis (RIP) in patients with breast cancer.

Patients And Methods: We conducted a retrospective review of 2,736 patients with breast cancer who received postoperative radiation therapy (RT) between 2017 and 2020 in a single institution. The distance between the PM measurement station and our institution was only 3.

Estimating the risk and benefit of radiation therapy in (y)pN1 stage breast cancer patients: A Bayesian network model incorporating expert knowledge (KROG 22-13).

Background: We aimed to evaluate the risk and benefit of (y)pN1 breast cancer patients in a Bayesian network model.

Method: We developed a Bayesian network (BN) model comprising three parts: pretreatment, intervention, and risk/benefit. The pretreatment part consisted of clinical information from a tertiary medical center.

Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study.

Purpose: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potential IOV.

Methods And Materials: In phase 1, two breast cancer cases were randomly selected and distributed to multiple institutions for contouring six clinical target volumes (CTVs) and eight OAR. In Phase 2, auto-contour sets were generated using a previously published DL Breast segmentation model and were made available for all participants.

Association between gut microbial change and acute gastrointestinal toxicity in patients with prostate cancer receiving definitive radiation therapy.

Background: This prospective study investigated the association between gut microbial changes and acute gastrointestinal toxicities in prostate cancer patients receiving definitive radiation therapy (RT).

Methods: Seventy-nine fecal samples 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-1 m), and 3 months after completion of RT (poRT-3 m).

Dynamic Responses of Circulating T Cells After Stereotactic Body Radiation Therapy for Bone Metastasis in Patients With Breast Cancer.

Purpose: Preclinical studies have shown that radiation therapy modulates antitumor immune responses. However, circulating T-cell responses after radiation therapy in patients with cancer have been poorly characterized. This study aims to explore the changes in circulating T cells after stereotactic body radiation therapy (SBRT).

Developing prompts from large language model for extracting clinical information from pathology and ultrasound reports in breast cancer.

Purpose: We aimed to evaluate the time and cost of developing prompts using large language model (LLM), tailored to extract clinical factors in breast cancer patients and their accuracy.

Materials And Methods: We collected data from reports of surgical pathology and ultrasound from breast cancer patients who underwent radiotherapy from 2020 to 2022. We extracted the information using the Generative Pre-trained Transformer (GPT) for Sheets and Docs extension plugin and termed this the "LLM" method.

A Survey of Practice Patterns for Clinical Nodal Staging Prior to Neoadjuvant Chemotherapy in Breast Cancer.

Background: The importance of clinical staging in breast cancer has increased owing to the wide use of neoadjuvant systemic therapy (NST). This study aimed to investigate the current practice patterns regarding clinical nodal staging in breast cancer in real-world settings.

Materials And Methods: A web-based survey was administered to board-certified oncologists in Korea, including breast surgical, medical, and radiation oncologists, from January to April 2022.

Development and benchmarking of a Korean audio speech recognition model for Clinician-Patient conversations in radiation oncology clinics.

Background: The purpose of this study is to develop an audio speech recognition (ASR) deep learning model for transcribing clinician-patient conversations in radiation oncology clinics.

Methods: We finetuned the pre-trained English QuartzNet 15x5 model for the Korean language using a publicly available dataset of simulated situations between clinicians and patients. Subsequently, real conversations between a radiation oncologist and 115 patients in actual clinics were then prospectively collected, transcribed, and divided into training (30.

Effect of Lactobacillus Rhamnosus GG for Regulation of Inflammatory Response in Radiation-Induced Enteritis.

The purpose of this study was to investigate the role of Lactobacillus rhamnosus GG (LGG) probiotics in radiation enteritis using in vivo mice. A total of 40 mice were randomly assigned to four groups: control, probiotics, radiotherapy (RT), and RT + probiotics. For the group of probiotics, 0.

Evaluation of initial patient setup methods for breast cancer between surface-guided radiation therapy and laser alignment based on skin marking in the Halcyon system.

Background: This study was conducted to evaluate the efficiency and accuracy of the daily patient setup for breast cancer patients by applying surface-guided radiation therapy (SGRT) using the Halcyon system instead of conventional laser alignment based on the skin marking method.

Methods And Materials: We retrospectively investigated 228 treatment fractions using two different initial patient setup methods. The accuracy of the residual rotational error of the SGRT system was evaluated by using an in-house breast phantom.

Radiosensitivity is associated with antitumor immunity in estrogen receptor-negative breast cancer.

Purpose: This study evaluated radiosensitivity and the tumor microenvironment (TME) to identify characteristics of breast cancer patients who would benefit most from radiation therapy.

Methods: We analyzed 1903 records from the Molecular Taxonomy of Breast Cancer International Consortium cohort using the radiosensitivity index and gene expression deconvolution algorithms, CIBERSORT and xCell, that estimates the TME composition of tumor samples. In this study, patients were stratified according to TME and radiosensitivity.

Protocol for the postoperative radiotherapy in N1 breast cancer patients (PORT-N1) trial, a prospective multicenter, randomized, controlled, non-inferiority trial of patients receiving breast-conserving surgery or mastectomy.

Background: Postoperative radiotherapy (PORT) could be useful for pN1 breast cancer patients who have undergone breast-conserving surgery (BCS) or mastectomy. However, the value of regional nodal irradiation (RNI) for BCS patients, and the indications for post-mastectomy radiotherapy (PMRT) for pN1 breast cancer mastectomy patients, have recently been challenged due to the absence of relevant trials in the era of modern systemic therapy. "PORT de-escalation" should be assessed in patients with pN1 breast cancer.

Exploration of biomedical knowledge for recurrent glioblastoma using natural language processing deep learning models.

Background: Efficient exploration of knowledge for the treatment of recurrent glioblastoma (GBM) is critical for both clinicians and researchers. However, due to the large number of clinical trials and published articles, searching for this knowledge is very labor-intensive. In the current study, using natural language processing (NLP), we analyzed medical research corpora related to recurrent glioblastoma to find potential targets and treatments.