Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–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 planning target volume (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. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. 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.

Purpose: This study aims to evaluate the treatment approaches and locoregional patterns for adenoid cystic carcinoma (ACC) in the breast, which is an uncommon malignant tumor with limited clinical data. Materials and Methods: A total of 93 patients diagnosed with primary ACC in the breast between 1992 and 2022 were collected from multi-institutions. All patients underwent surgical resection, including breast-conserving surgery (BCS) or total mastectomy (TM). Recurrence patterns and locoregional recurrence-free survival (LRFS) were assessed. Results: Seventy-five patients (80.7%) underwent BCS, and 71 of them (94.7%) received post-operative radiation therapy (PORT). Eighteen patients (19.3%) underwent TM, with five of them (27.8%) also receiving PORT. With a median follow-up of 50 months, the LRFS rate was 84.2% at 5 years. Local recurrence (LR) was observed in five patients (5.4%) and four cases (80%) of the LR occurred in the tumor bed. Three of LR (3/75, 4.0%) had a history of BCS and PORT, meanwhile, two of LR (2/18, 11.1%) had a history of mastectomy. Regional recurrence occurred in two patients (2.2%), and both cases had a history of PORT with (n=1) and without (n=1) irradiation of the regional lymph nodes. Partial breast irradiation (p=0.35), BCS (p=0.96) and PORT in BCS group (p=0.33) had no significant association with LRFS. Conclusion BCS followed by PORT was the predominant treatment approach for ACC of the breast and LR mostly occurred in the tumor bed. The findings of this study suggest that partial breast irradiation might be considered for PORT in primary breast ACC.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–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 planning target volume (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. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. 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.

Purpose: This study aims to evaluate the treatment approaches and locoregional patterns for adenoid cystic carcinoma (ACC) in the breast, which is an uncommon malignant tumor with limited clinical data. Materials and Methods: A total of 93 patients diagnosed with primary ACC in the breast between 1992 and 2022 were collected from multi-institutions. All patients underwent surgical resection, including breast-conserving surgery (BCS) or total mastectomy (TM). Recurrence patterns and locoregional recurrence-free survival (LRFS) were assessed. Results: Seventy-five patients (80.7%) underwent BCS, and 71 of them (94.7%) received post-operative radiation therapy (PORT). Eighteen patients (19.3%) underwent TM, with five of them (27.8%) also receiving PORT. With a median follow-up of 50 months, the LRFS rate was 84.2% at 5 years. Local recurrence (LR) was observed in five patients (5.4%) and four cases (80%) of the LR occurred in the tumor bed. Three of LR (3/75, 4.0%) had a history of BCS and PORT, meanwhile, two of LR (2/18, 11.1%) had a history of mastectomy. Regional recurrence occurred in two patients (2.2%), and both cases had a history of PORT with (n=1) and without (n=1) irradiation of the regional lymph nodes. Partial breast irradiation (p=0.35), BCS (p=0.96) and PORT in BCS group (p=0.33) had no significant association with LRFS. Conclusion BCS followed by PORT was the predominant treatment approach for ACC of the breast and LR mostly occurred in the tumor bed. The findings of this study suggest that partial breast irradiation might be considered for PORT in primary breast ACC.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–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 planning target volume (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. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. 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.

Purpose: This study aims to evaluate the treatment approaches and locoregional patterns for adenoid cystic carcinoma (ACC) in the breast, which is an uncommon malignant tumor with limited clinical data. Materials and Methods: A total of 93 patients diagnosed with primary ACC in the breast between 1992 and 2022 were collected from multi-institutions. All patients underwent surgical resection, including breast-conserving surgery (BCS) or total mastectomy (TM). Recurrence patterns and locoregional recurrence-free survival (LRFS) were assessed. Results: Seventy-five patients (80.7%) underwent BCS, and 71 of them (94.7%) received post-operative radiation therapy (PORT). Eighteen patients (19.3%) underwent TM, with five of them (27.8%) also receiving PORT. With a median follow-up of 50 months, the LRFS rate was 84.2% at 5 years. Local recurrence (LR) was observed in five patients (5.4%) and four cases (80%) of the LR occurred in the tumor bed. Three of LR (3/75, 4.0%) had a history of BCS and PORT, meanwhile, two of LR (2/18, 11.1%) had a history of mastectomy. Regional recurrence occurred in two patients (2.2%), and both cases had a history of PORT with (n=1) and without (n=1) irradiation of the regional lymph nodes. Partial breast irradiation (p=0.35), BCS (p=0.96) and PORT in BCS group (p=0.33) had no significant association with LRFS. Conclusion BCS followed by PORT was the predominant treatment approach for ACC of the breast and LR mostly occurred in the tumor bed. The findings of this study suggest that partial breast irradiation might be considered for PORT in primary breast ACC.

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. The time and cost of developing the prompts with LLM methods were assessed and compared with those spent on collecting information with “full manual” and “LLM-assisted manual” methods. To assess accuracy, 340 patients were randomly selected, and the extracted information by LLM method were compared with those collected by “full manual” method. Results: Data from 2,931 patients were collected. We developed 12 prompts for Extract function and 12 for Format function to extract and standardize the information. The overall accuracy was 87.7%. For lymphovascular invasion, it was 98.2%. Developing and processing the prompts took 3.5 hours and 15 minutes, respectively. Utilizing the ChatGPT application programming interface cost US $65.8 and when factoring in the estimated wage, the total cost was US $95.4. In an estimated comparison, “LLM-assisted manual” and “LLM” methods were time- and cost-efficient compared to the “full manual” method. Conclusion Developing and facilitating prompts for LLM to derive clinical factors was efficient to extract crucial information from huge medical records. This study demonstrated the potential of the application of natural language processing using LLM model in breast cancer patients. Prompts from the current study can be re-used for other research to collect clinical information.

Purpose: The value of the genomic profiling by targeted gene-sequencing on radiation therapy response prediction was evaluated through integrated analysis including clinical information. Radiation response prediction model was constructed based on the analyzed findings. Materials and Methods: Patients who had the tumor sequenced using institutional cancer panel after informed consent and received radiotherapy for the measurable disease served as the target cohort. Patients with irradiated tumor locally controlled for more than 6 months after radiotherapy were defined as the durable local control (DLC) group, otherwise, non-durable local control (NDLC) group. Significant genomic factors and domain knowledge were used to develop the Bayesian Network model to predict radiotherapy response. Results: Altogether, 88 patients were collected for analysis. Of those, 41 (43.6%) and 47 (54.4%) patients were classified as the NDLC and DLC group, respectively. Somatic mutations of NOTCH2 and BCL were enriched in the NDLC group, whereas, mutations of CHEK2, MSH2, and NOTCH1 were more frequently found in the DLC group. Altered DNA repair pathway was associated with better local failure–free survival (hazard ratio, 0.40; 95% confidence interval, 0.19 to 0.86; p=0.014). Smoking somatic signature was found more frequently in the DLC group. Area under the receiver operating characteristic curve of the Bayesian network model predicting probability of 6-month local control was 0.83. Conclusion Durable radiation response was associated with alterations of DNA repair pathway and smoking somatic signature. Bayesian network model could provide helpful insights for high precision radiotherapy. However, these findings should be verified in prospective cohort for further individualization.

Purpose: Next-generation sequencing (NGS)-based tumor panel testing has been reimbursed by the Korean government since 2017. We evaluated the use of NGS-based tumor panel testing in real-world clinical practice, focusing on molecular profiling (MP)-guided breast cancer treatment. Methods: A total of 137 breast cancer patients underwent NGS panel testing between December 2017 and July 2020 at Seoul National University Bundang Hospital (SNUBH).Samples from patients were profiled using an in-house SNUBH pan-cancer panel. Sixty-four patients were profiled on SNUBH Pan_Cancer v1.0, targeting 89 genes, while 73 patients were profiled on SNUBH Pan_Cancer v2.0, targeting 546 genes. Results: Breast cancer subtypes included hormone receptor+/human epidermal growth factor receptor 2 (HER2)− (n = 87), triple-negative (n = 44), and HER2+ (n = 6). Most patients had locally advanced or metastatic cancers (92%). Approximately 92% (126/137) of the patients had significant genomic alterations (tiers I and II), and 62% (85/137) had targetable genomic alterations. The most common targetable genomic alterations were PIK3CA (39%) and ESR1 mutations (9%), followed by ERBB2 (7%), PTEN (7%), BRCA2 (6%), and BRCA1 mutations (4%). Of the 81 patients with locally advanced/metastatic breast cancer with targetable genomic alterations, 6 (7.4%) received MP-guided treatments, including PARP inhibitor (n = 4), ERBB2-directed therapy (n = 1), and PI3K inhibitor (n = 1). Among these 6 patients, 4 participated in clinical trials, 1 underwent treatment at their own expense, and 1 received drugs through an expanded access program. The remaining 66 patients (81%) with targetable genomic alteration did not receive MP-guided treatment due to lack of matched drugs and/or clinical trials, poor performance status, and/or financial burden. Conclusion NGS panel testing allowed MP-guided treatment in only 4.7% (6/127) of patients with advanced breast cancer in a real-world setting. The availability of matched drugs is critical for the realistic implementation of personalized treatment.

Radiation therapy for patients with pN1mi or pN1 disease breast cancer undergoing mastectomy has been debated for a long time. Even in low metastatic burden in sentinel node biopsy, occult non-sentinel axillary nodal involvement can exist. Radiotherapy can sterilize axillary metastatic burden and seems to contribute a very low local recurrence rate in mastectomy patients with minimally involved lymph nodes. However, it should be considered that systemic therapy is evolving and the local recurrence difference between radiotherapy and no radiotherapy is relatively small. Regarding postmastectomy radiotherapy in patients pN1mi or pN1 cancer, published prospective clinical trial results should be considered; however, there are no such relevant results of clinical trials yet. Consideration of postmastectomy radiation therapy in pN1mi or pN1 patients should be based on identifying the high-risk group in terms of recurrence, stage, or tumor biology. When radiotherapy is determined, radiation oncologists should attempt individualized treatment approaches, such as irradiation field, and consider specific settings, such as neoadjuvant therapy. In this review, the role of radiotherapy in mastectomy patients with minimally involved lymph nodes and the relevant considerations are discussed.