A nationwide study of breast reconstruction after mastectomy in patients with breast cancer receiving postmastectomy radiotherapy: comparison of complications according to radiotherapy fractionation and reconstruction procedures.

BACKGROUND: We examined the patterns of breast reconstruction postmastectomy in breast cancer patients undergoing postmastectomy radiotherapy (PMRT) and compared complications based on radiotherapy fractionation and reconstruction procedures. METHODS: Using National Health Insurance Service (NHIS) data (2015-2020), we analysed 4669 breast cancer patients with PMRT and reconstruction. Using propensity matching, cohorts for hypofractionated fractionation (HF) and conventional fractionation (CF) were created, adjusting for relevant factors and identifying grade ≥3 complications. RESULT: Of 4,669 patients, 30.6% underwent HF and 69.4% CF. The use of HF has increased from 19.4% in 2015 to 41.0% in 2020. Immediate autologous (32.9%) and delayed two-stage implant reconstruction (33.9%) were common. Complication rates for immediate (N = 1286) and delayed two-stage (N = 784) reconstruction were similar between HF and CF groups (5.1% vs. 5.4%, P = 0.803, and 10.5% vs. 10.7%, P = 0.856, respectively) with median follow-ups of 2.5 and 2.6 years. HF showed no increased risk of complications across reconstruction methods. CONCLUSION: A nationwide cohort study revealed no significant difference in complication rates between the HF and CF groups, indicating HF for reconstructed breasts is comparable to CF. However, consultation regarding the fractionation for reconstructed breast cancer patients may still be necessary.

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. The main outcomes included longitudinal functional scales, seizure frequency and the number of antiseizure medications. Measures encompassed NORSE-related clinical parameters such as the duration of unconsciousness, immunotherapy profiles, cytokine/chemokine analysis, and serial MRI scans. RESULTS: A total of 74 patients with cNORSE were finally analysed (mean age: 38.0±18.2; 36 (48.6%) male). All patients received first-line immunotherapy, and 91.9% (68/74) received second-line immunotherapy. A total of 83.8% (62/74) regained consciousness within a median duration of 30 days (14-56), and 50% (31/62) achieved good outcome (mRS ≤2) at 2 years. Poor 1-year outcomes (mRS ≥3) were predicted by the presence of mesial temporal lobe (mTL) and extra-mTL lesions at 3-month MRI, and prolonged unconsciousness (≥60 days). Those with mTL atrophy exhibited a higher seizure burden post-NORSE. The optimal duration of immunotherapy appeared to be between 18 weeks and 1-year post-NORSE onset. CONCLUSIONS: This study elucidates longitudinal clinical dynamics, functional outcomes, prognostic factors and immunotherapy response in patients with cNORSE. These findings might contribute to a more standardised understanding and clinical decision-making for cNORSE.

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. The survey included 19 general questions and 4 case-based questions. RESULTS: In total, 122 oncologists (45 radiation, 44 surgical, and 33 medical oncologists) completed the survey. Among them, 108 (88%) responded that clinical staging before NST was primarily performed by breast surgeons. All the respondents referred to imaging studies during nodal staging. Overall, 64 (52.5%) responders determined the stage strictly based on the radiology reports, whereas 58 (47.5%) made their own decision while noting radiology reports. Of those who made their own decisions, 88% referred to the number or size of the suspicious node. Of the 75 respondents involved in prescribing regimens for neoadjuvant chemotherapy, 58 (77.3%) responded that the reimbursement regulations in the selection of NST regimens affected nodal staging in clinical practice. In the case-based questions, high variability was observed among the clinicians in the same cases. CONCLUSIONS: Diverse assessments by specialists owing to the lack of a clear, harmonized staging system for the clinical nodal staging of breast cancer can lead to diverse practice patterns. Thus, practical, harmonized, and objective methods for clinical nodal staging and for the outcomes of post-NST response are warranted for appropriate treatment decisions and accurate outcome evaluation.

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. We performed integrative analyses of clinical and immuno-genomic data to characterize molecular features associated with radiosensitivity. RESULTS: Radiosensitivity was significantly associated with activation of antitumor immunity. In contrast, radioresistance was associated with a reactive stromal microenvironment. The immuno-genomic analysis revealed that estrogen receptor (ER) pathway activity was correlated with suppression of antitumor immunity. In ER-negative disease, the best prognosis was shown in the immune-high and radiosensitive group patients, and the lowest was in the immune-low and radioresistant group patients. In ER-positive disease, immune signature and radiosensitivity had no prognostic significance. CONCLUSION: Taken together, these results suggest that tumor radiosensitivity is associated with activation of antitumor immunity and a better prognosis, particularly in patients with ER-negative breast cancer.

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. METHODS: The PORT-N1 trial is a multicenter, randomized, phase 3 clinical trial for patients with pN1 breast cancer that compares the outcomes of control [whole-breast irradiation (WBI) and RNI/PMRT] and experimental (WBI alone/no PMRT) groups. PORT-N1 aims to demonstrate non-inferiority of the experimental group by comparing 7-year disease-free survival rates with the control group. Female breast cancer patients with pT1-3 N1 status after BCS or mastectomy are eligible. Participants will be randomly assigned to the two groups in a 1:1 ratio. Randomization will be stratified by surgery type (BCS vs. mastectomy) and histologic subtype (triple-negative vs. non-triple-negative). In patients who receive mastectomy, dissection of ≥5 nodes is required when there is one positive node, and axillary lymph node dissection when there are two or three positive nodes. Patients receiving neoadjuvant chemotherapy are not eligible. RNI includes a "high-tangent" or wider irradiation field. This study will aim to recruit 1106 patients. DISCUSSION: The PORT-N1 trial aims to verify that PORT de-escalation after BCS or mastectomy is safe for pN1 breast cancer patients in terms of oncologic outcomes and capable of reducing toxicity rates. This trial will provide information crucial for designing PORT de-escalation strategies for patients with pN1 breast cancer. TRIAL REGISTRATION: This trial was registered at ClinicalTrials.gov (NCT05440149) on June 30, 2022.

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. METHODS: We fine-tuned the 'SAPBERT', which was pretrained on biomedical ontologies, to perform question/answering (QA) and name entity recognition (NER) tasks for medical corpora. The model was fine-tuned with the SQUAD2 dataset and multiple NER datasets designed for QA task and NER task, respectively. Corpora were collected by searching the terms "recurrent glioblastoma" and "drug target", published from 2000 to 2020 in the Web of science (N = 288 articles). Also, clinical trial corpora were collected from 'clinicaltrial.gov' using the searching term of 'recurrent glioblastoma" (N = 587 studies). RESULTS: For the QA task, the model showed an F1 score of 0.79. For the NER task, the model showed F1 scores of 0.90 and 0.76 for drug and gene name recognition, respectively. When asked what the molecular targets were promising for recurrent glioblastoma, the model answered that RTK inhibitors or LPA-1 antagonists were promising. From collected clinical trials, the model summarized them in the order of bevacizumab, temozolomide, lomustine, and nivolumab. Based on published articles, the model found the many drug-gene pairs with the NER task, and we presented them with a circus plot and related summarization ( https://github.com/bigwiz83/NLP_rGBM ). CONCLUSION: Using NLP deep learning models, we could explore potential targets and treatments based on medical research and clinical trial corpora. The knowledge found by the models may be used for treating recurrent glioblastoma.

Evaluation of the dosimetric and radiobiological parameters in four radiotherapy regimens for synchronous bilateral breast cancer.

This study is to investigate the optimal treatment option for synchronous bilateral breast cancer (SBBC) by comparing dosimetric and radiobiological parameters of intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) plans using single and dual isocenters. Twenty patients with SBBC without lymph node involvement were selected retrospectively. Four treatment plans were generated for each patient using the Eclipse treatment planning system (Varian Medical System, Palo Alto, CA, USA) following two delivery techniques with two isocenter conditions-IMRT using a single isocenter (IMRT_Iso1), VMAT using a single isocenter (VMAT_Iso1), IMRT using dual isocenters (IMRT_Iso2), and VMAT using dual isocenters (VMAT_Iso2). A dose of 42.56 Gy in 16 fractions was prescribed for the planning target volume (PTV). All plans were calculated using the Acuros XB algorithm and a photon optimizer for a 6-MV beam of a Vital Beam linear accelerator. PTV-related dosimetric parameters were analyzed. Further, the homogeneity index, conformity index, and conformation number were computed to evaluate plan quality. Dosimetric parameters were also measured for the organs at risk (OARs). In addition, the equivalent uniform dose corresponding to an equivalent dose related to a reference of 2 Gy per fraction, the tumor control probability, and the normal tissue complication probability were calculated based on the dose-volume histogram to investigate the radiobiological impact on PTV and OARs. IMRT_Iso1 exhibited similar target coverage and a certain degree of dosimetric improvement in OAR sparing compared to the other techniques. It also exhibited some radiobiological improvement, albeit insignificant. Although IMRT_Iso1 significantly increased monitor unit compared to VMAT_Iso1, which is the best option in terms of delivery efficiency, there was only a 22% increase in delivery time. Therefore, in conclusion, IMRT_Iso1, the complete treatment of which can be completed using a single setup, is the most effective method for treating SBBC.

Role of Adjuvant Chemoradiotherapy for Duodenal Cancer: An Updated Analysis of Long-Term Follow-Up from Single Institution.

BACKGROUND: There are only limited data on the failure patterns after surgical resection for duodenal cancer, and the role of adjuvant chemoradiotherapy (CRT) also remains controversial. In this study, the treatment outcomes of surgery alone were compared to those of surgery plus adjuvant CRT for duodenal cancer. METHODS: Between January 1991 and February 2013, a total of 47 patients with duodenal cancer had pancreaticoduodenectomy, and their age ranged from 31 to 80 (median 62). Twenty-five patients (53%) underwent surgery alone, while 22 (47%) underwent surgery plus adjuvant CRT. Postoperative radiotherapy with concomitant 5-fluorouracil was given to tumor bed and regional lymph nodes up to 40-55.4 Gy. Median duration of follow-up was 31 months (range 6-286) for all patients and 90 months (range 14-286) for survivors. RESULTS: CRT (+) group included more patients with advanced nodal stage and overall stage group (p = 0.003 and 0.002, respectively). The 5-year overall survival rates were not different between CRT (-) and CRT (+) groups (50.1 vs. 46.7%, p = 0.794). CRT (+) group achieved a superior 5-year loco-regional relapse-free survival rate compared with CRT (-) group, but the difference did not reach a statistical significance (80.1 vs. 68.4%, p = 0.267). On multivariate analysis, however, the addition of CRT was the only favorable prognosticator predicting loco-regional relapse-free survival (p = 0.046). Two patients experienced grade 3 neutropenia during CRT. CONCLUSIONS: Adjuvant CRT after pancreaticoduodenectomy was correlated with an improved loco-regional control in duodenal cancer. Considering the high loco-regional recurrence in surgery alone group, CRT may be considered as adjuvant treatment.

Analysis of survival outcomes based on molecular subtypes in breast cancer brain metastases: A single institutional cohort.

PURPOSE: To evaluate the survival outcomes based on molecular subtypes of breast cancer in patients with brain metastasis. MATERIALS AND METHODS: We retrospectively reviewed 106 breast cancer patients treated for brain metastases, from January 2005 to May 2016. Patients were divided into four groups based on the tumor molecular subtype: luminal A (Estrogen Receptor [ER]/Progesterone Receptor [PR] positive, human epithelial growth factor receptor-2 [HER2] negative), luminal B (ER/PR positive, HER2 Positive), HER2 (HER2 positive and ER/PR negative), and Triple negative (TNBC). RESULTS: The median follow-up time for surviving patients was 22 months (range: 11.2-51.1 months). The median survival of all patients was 14 months, with a 1-year overall survival (OS) rate of 57.5% and a 2-year OS rate of 32.1%. Thirty patients (28.3%) had a solitary brain metastasis while 62 (58.5%) patients had multiple metastases. A significant difference was observed in the survival rates of the two groups. Based on the Karnofsky performance score, the performance status of the patients at the time of brain metastasis was also found to affect survival. Patients with different molecular subtypes had different survival rates; the luminal A group showed the highest median survival (luminal A: 23.1, luminal B: 15.0, HER2: 12.5 and TNBC: 6.4 months, respectively), which was statistically significant. CONCLUSION: In breast cancer patients with brain metastasis, survival rates were different based on the molecular subtype of the tumor, despite various local and systemic treatments. Appropriate and tailored treatment approaches should, therefore, be considered for the different molecular subtypes.

Surgery vs. radiotherapy in patients with uveal melanoma : Analysis of the SEER database using propensity score matching and weighting.

BACKGROUND: The treatment modalities for uveal melanoma (UM) include surgery and radiotherapy (RT). The utilization of RT as a strategy for organ preservation has been increasing, but the survival difference between the two aforementioned treatment modalities has not been reported. METHODS: An observational and cohort study was performed using a propensity score with an already existing public database. Patients diagnosed with UM within the period from 2004-2013 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. One-to-one matching and inverse probability of treatment weighting (IPTW) using the propensity score were used to estimate and compare survival rates. RESULTS: Overall, 3291 patients were treated: 2503 received RT only (RT group) and 788 received surgical resection only (surgery group). The RT group had an improved crude 5­year overall survival (OS) rate compared with the surgery group (76% vs. 60%, P < 0.001), and an improved 5­year melanoma-specific survival (MSS) rate (89% vs. 73%, P < 0.001). Compared to the surgery group, the RT group was associated with improved OS (hazard ratio [HR] 0.52, 95% confidence interval [CI] 0.38-0.73, P < 0.001) and MSS (HR 0.48, 95% CI 0.35-0.65, P < 0.001) in the matched cohort. The survival benefit of the RT group maintained after adjustment with IPTW, both in OS and MSS. CONCLUSIONS: To our knowledge, the present study was the first to demonstrate the survival difference between the two treatment modalities for UM using both the propensity score matching and weighting methods with the SEER database. The current study suggests that RT may provide a survival advantage over surgery in the treatment of UM.

Clinical outcomes of stereotactic ablative radiotherapy in patients with pulmonary metastasis.

BACKGROUNDS: In addition to its curative use for early stage lung cancer, stereotactic ablative radiotherapy is also indicated for pulmonary metastatic disease. Aims of this study were to retrospectively analyze treatment outcomes and to find prognostic factors for survivals. METHODS: Treatment outcomes and toxicities of 85 cases of SABR in 72 patients were retrospectively reviewed from September 2012 to April 2015. Prognostic factors were analyzed using Cox proportional hazards regression. RESULTS: The local failure-free survival rate at 2 years was 98%. Of the case, 1-year and 2-year progression-free survival rates were 62% and 48%, and overall survival rates were 90% and 72%, respectively. Multivariate analyses demonstrated that controlled primary cancer (P = 0.01), absence of extra-pulmonary metastatic disease (P < 0.01) and disease-free interval longer than 1 year (P < 0.01) favorably affected progression-free survival. Furthermore, the absence of extra-pulmonary metastatic disease (P < 0.01) increased overall survival as well. Grade 1 or 2 radiation pneumonitis was found in 37 cases, and Grade 1 chest wall pain was found in 1 case. CONCLUSIONS: Stereotactic ablative radiotherapy demonstrated good local control with tolerable adverse effects for pulmonary metastasis. The presence or absence of extra-pulmonary metastasis was found to be prognostic factor of mortality after stereotactic ablative radiotherapy treatment.

The Impact of PM2.5 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 3.43 km. PM data, including PM2.5 and PM10, were retrieved from the open dataset in the official government database. RESULTS: Overall incidence rate of RIP was 1.74%. After adjusting for age, RT technique, regional irradiation, fractionation and boost, the average value of PM2.5 was significantly associated with a higher risk of RIP (p=0.047) when patients received ≥20 fractions of RT. Specifically, PM2.5 ≥35 (µg/m3) showed a significantly higher risk of RIP (p=0.019) in patients with ≥20 fractions of RT. CONCLUSION: This is the first study to reveal the association between PM2.5 and RIP in patients with breast cancer who received 20 fractions or more of postoperative RT. We demonstrated that high PM2.5 levels around the RT institution were associated with RIP, suggesting that reducing PM air pollution may be a modifiable risk factor.

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. Between January 2016 and December 2020, we collected computed tomography (CT) images from 82 breast cancer patients who underwent immediate reconstruction surgery followed by radiotherapy. Among these patients, 38 received immediate implant insertion, while 44 underwent autologous breast reconstruction. Anomaly scores (AS) were estimated using an anomaly GAN model at pre-RT, 1st follow-up, 1-year (Post-1Y) and 2-year (Post-2Y) after RT. Subsequently, the scores were analyzed in a time-series manner, considering reconstruction types (implant versus autologous), RT techniques, and the incidence of major complications. The median age of the patients was 46 years (range 29-62). The AS between Post-1Y and Post-2Y demonstrated a positive relationship (coefficient 0.515, P < 0.001). The AS was significantly associated with objective cosmetic indices, namely Breast Contour Difference (P = 0.009) and Breast Area Difference (P = 0.004), at both Post-1Y and Post-2Y. Subgroup analysis stratified by type of breast reconstruction revealed significantly higher AS values in patients who underwent prosthetic implant insertion compared to those with autologous reconstruction at all follow-up time points (1st follow-up, P = 0.001; Post-1Y, P < 0.001; and Post-2Y, P < 0.001). A threshold AS of ≥ 1.9 was associated with a 10% predicted risk of developing major complications. The feasibility of an AS generated by a GAN model for predicting both cosmetic outcomes and the likelihood of complications following RT has been successfully validated. Further investigation involving a larger patient cohort is warranted.

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. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the PTV and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. Conclusion: We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

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. The intervention part regarded the field of radiotherapy. The risk/benefit component encompasses radiotherapy (RT)-related side effects and effectiveness, including factors such as recurrence, cardiac toxicity, lymphedema, and radiation pneumonitis. These factors were evaluated in terms of disability weights and probabilities from a nationwide expert survey. The overall disease burden (ODB) was calculated as the sum of the probability multiplied by the disability weight. A higher value of ODB indicates a greater disease burden for the patient. RESULTS: Among the 58 participants, a BN model utilizing discretization and clustering techniques revealed five distinct clusters. Overall, factors associated with breast reconstruction and RT exhibited high discrepancies (24-34 %), while RT-related side effects demonstrated low discrepancies (3-11 %) among the experts. When incorporating recurrence and RT-related side effects, the mean ODB of (y)pN1 patients was 0.258 (range, 0.244-0.337), with a higher tendency observed in triple-negative breast cancer (TNBC) or mastectomy cases. The ODB for TNBC patients undergoing mastectomy without postmastectomy radiotherapy was 0.327, whereas for non-TNBC patients undergoing breast conserving surgery with RT, the disease burden was 0.251. There was an increasing trend in ODB as the field of RT increased. CONCLUSION: We developed a Bayesian network model based on an expert survey, which helps to understand treatment patterns and enables precise estimations of RT-related risk and benefit in (y)pN1 patients.

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. Among them, we further focused on 44 patients with luminal-type (ER+) BC for subclassification. The Cancer Genome Atlas (TCGA) data of patients with BC were used as a validation data set to verify the new classification. We estimated the immune cell composition using CIBERSORT and further analyzed its association with clinical or molecular parameters. RESULTS: Principal component analysis clearly divided the patients into two subgroups separately from the luminal A and B classification. The top differentially expressed genes between the subgroups were distinctly characterized by immunoglobulin and B-cell-related genes. We could also cluster a separate cohort of patients with luminal-type BC from TCGA into two subgroups on the basis of the expression of a B-cell-specific gene set, and patients who were predicted to have high B-cell immune activity had better prognoses than other patients. CONCLUSION: Our transcriptomic approach emphasize a molecular phenotype of B-cell immunity in ER+ BC that may help to predict disease prognosis. Although further researches are required, B-cell immunity for patients with ER+ BC may be helpful for identifying patients who are good responders to chemotherapy or immunotherapy.

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.g., 7-year disease-free survival [DFS]) and toxicities were integrated into the model. ODB was defined as the sum of disability weights multiplied by probabilities. In silico predictions were conducted for Alliance A011202, PORT-N1, RAPCHEM, and RT-CHARM trials, comparing ODB, 7-year DFS, and side effects. RESULTS: In the Alliance A011202 trial, 7-year DFS was 80.1% in both arms. Axillary lymph node dissection led to higher clinical lymphedema and ODB compared to sentinel lymph node biopsy with full regional nodal irradiation (RNI). In the PORT-N1 trial, the control arm (whole-breast irradiation [WBI] with RNI or post-mastectomy radiotherapy [PMRT]) had an ODB of 0.254, while the experimental arm (WBI alone or no PMRT) had an ODB of 0.255. In the RAPCHEM trial, the radiotherapy field did not impact the 7-year DFS in ypN1 patients. However, there was a mild ODB increase with a larger irradiation field. In the RT-CHARM trial, we identified factors associated with the major complication rate, which ranged from 18.3% to 22.1%. CONCLUSIONS: The expert knowledge-based BN model predicted ongoing trial outcomes, validating reported results and assumptions. In addition, the model demonstrated the ODB in different arms, with an emphasis on quality of life.

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). METHODS AND MATERIALS: Peripheral blood samples of 30 patients with breast cancer who underwent SBRT for bone metastasis were analyzed using multicolor flow cytometry. Phenotypes of PD-1+ CD8+ T cells and regulatory T (TREG) cells were examined. Additionally, plasma protein levels were analyzed using a bead-based immunoassay. RESULTS: Circulating PD-1+ CD8+ T cells, which are enriched for tumor-specific clonotypes, were activated at 1 week after SBRT. However, circulating TREG cells were also activated after SBRT; this pattern was also evident among effector Foxp3hiCD45RA- TREG cells. We observed no difference in T-cell responses according to the fraction size and number. Notably, activation of TREG cells was more prominent in patients who experienced greater activation of PD-1+ CD8+ T cells. Plasma level changes in TGF-ß1, soluble CTLA-4, and soluble 4-1BB at 1 week after SBRT were associated with PD-1+ CD8+ T-cell responses. Activation of TREG cells at 1 week after SBRT was associated with worse progression-free survival. Clinical factors including molecular subtype were not associated with the T-cell responses. CONCLUSIONS: SBRT induced activation of both potentially tumor-specific CD8+ T cells and TREG cells, which were tightly associated with each other. These results may support the use of TREG cell-modulating strategies with SBRT to improve the antitumor immune response.

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, 0.2 mL of solution that contained 1.0 × 108 colony-forming units (CFU) of LGG was used and orally administered daily until sacrifice. For RT, a single dose of 14 Gy was administered using a 6 mega-voltage photon beam to the abdominopelvic area. Mice were sacrifice at day 4 (S1) and day 7 (S2) after RT. Their jejunum, colon, and stool were collected. A multiplex cytokine assay and 16 s ribosomal RNA amplicon sequencing were then performed. Regarding cytokine concentrations in tissues, pro-inflammatory cytokines, such as tumor necrosis factor-α, interleukin-6 and monocyte chemotactic protein-1, showed significantly decreased protein levels in colon tissues of the RT + probiotics group than in the RT alone group (all p < 0.05). As for comparing microbial abundance through alpha-diversity and beta-diversity, no significant differences were observed between the RT + probiotics and RT alone groups, except for an increase in alpha-diversity in the stool of the RT + probiotics group. Upon analysis of differential microbes based on treatment, the dominance of anti-inflammatory-related microbes, such as Porphyromonadaceae, Bacteroides acidifaciens, and Ruminococcus, was observed in the jejunum, colon, and stool of the RT + probiotics group. With regard to predicted metabolic pathway abundances, the pathways associated with anti-inflammatory processes, such as biosynthesis of pyrimidine nucleotides, peptidoglycans, tryptophan, adenosylcobalamin, and propionate, were differentially identified in the RT + probiotics group compared to the RT alone group. Protective effects of probiotics on radiation enteritis were potentially derived from dominant anti-inflammation-related microbes and metabolites.

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. Accordingly, this study calculates the LN area coverage according to the TMI plan. Further, a deep learning-based model for delineating LN areas is trained and evaluated. METHODS: Whole-body regional LN areas were manually contoured in patients treated according to a TMI plan. The dose coverage of the delineated LN areas in the TMI plan was estimated. To train the deep learning model for automatic segmentation, additional whole-body computed tomography data were obtained from other patients. The patients and data were divided into training/validation and test groups and models were developed using the "nnU-NET" framework. The trained models were evaluated using Dice similarity coefficient (DSC), precision, recall, and Hausdorff distance 95 (HD95). The time required to contour and trim predicted results manually using the deep learning model was measured and compared. RESULTS: The dose coverage for LN areas by TMI plan had V100% (the percentage of volume receiving 100% of the prescribed dose), V95%, and V90% median values of 46.0%, 62.1%, and 73.5%, respectively. The lowest V100% values were identified in the inguinal (14.7%), external iliac (21.8%), and para-aortic (42.8%) LNs. The median values of DSC, precision, recall, and HD95 of the trained model were 0.79, 0.83, 0.76, and 2.63, respectively. The time for manual contouring and simply modified predicted contouring were statistically significantly different. CONCLUSIONS: The dose coverage in the inguinal, external iliac, and para-aortic LN areas was suboptimal when treatment is administered according to the TMI plan. This research demonstrates that the automatic delineation of LN areas using deep learning can facilitate the implementation of TMLI.