The Role of Local Prostate and Metastasis-Directed Radiotherapy in the Treatment of Oligometastatic Prostate Cancer.

Background/Objectives: Oligometastatic prostate cancer (OMPC) represents an early stage of metastatic disease characterized by a limited number of lesions. Recent advancements in imaging and treatment have revived interest in personalized therapies, including metastasis-directed radiotherapy (OMDRT) and primary prostate radiotherapy (PPR). This study evaluates the impact of OMDRT timing and the role of PPR on survival outcomes in OMPC patients; Methods: In this retrospective cohort study, 82 patients with OMPC who underwent OMDRT between 2010 and 2019 were analyzed. Patients were classified based on OMDRT timing (early vs. late) and disease type (synchronous vs. metachronous). Progression-free survival (PFS) and overall survival (OS) were the primary endpoints, assessed via Kaplan-Meier analysis and Cox proportional hazards models; Results: Among the patients, 36 (43.9%) had synchronous and 46 (56.1%) had metachronous OMD. With a median follow-up of 32 months, the 5-year PFS and OS rates were 77.5% and 88.5%, respectively. Early OMDRT significantly improved PFS (HR 0.461, 95% CI: 0.257-0.826, p = 0.009) and OS (HR 0.219, 95% CI: 0.080-0.603, p = 0.003). Subgroup analysis showed the most favorable outcomes for synchronous OMD patients receiving early OMDRT, with a median PFS of 22.2 months and a 5-year survival rate of 42.1%. The treatment of the primary prostate provided a survival benefit in the OS of synchronous OMD patients (5-year 83.1% vs. 50%, p = 0.025), and there was a further improvement in OS after PPR (5-year 87.7% vs. 50%, p = 0.015). Conclusions: Early OMDRT significantly enhances survival outcomes in OMPC, in both synchronous and metachronous cases. The integration of PPR can further improve results, emphasizing the importance of early intervention and personalized treatment strategies. To more definitively clarify our findings across various clinical situations, further studies with larger cohorts or prospective designs are necessary.

Personalized Deep Learning Model for Clinical Target Volume on Daily Cone Beam Computed Tomography in Breast Cancer Patients.

Purpose: Herein, we developed a deep learning algorithm to improve the segmentation of the clinical target volume (CTV) on daily cone beam computed tomography (CBCT) scans in breast cancer radiation therapy. By leveraging the Intentional Deep Overfit Learning (IDOL) framework, we aimed to enhance personalized image-guided radiation therapy based on patient-specific learning. Methods and Materials: We used 240 CBCT scans from 100 breast cancer patients and employed a 2-stage training approach. The first stage involved training a novel general deep learning model (Swin UNETR, UNET, and SegResNET) on 90 patients. The second stage used intentional overfitting on the remaining 10 patients for patient-specific CBCT outputs. Quantitative evaluation was conducted using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), mean surface distance (MSD), and independent samples t test with expert contours on CBCT scans from the first to 15th fractions. Results: IDOL integration significantly improved CTV segmentation, particularly with the Swin UNETR model (P values < .05). Using patient-specific data, IDOL enhanced the DSC, HD, and MSD metrics. The average DSC for the 15th fraction improved from 0.9611 to 0.9819, the average HD decreased from 4.0118 mm to 1.3935 mm, and the average MSD decreased from 0.8723 to 0.4603. Incorporating CBCT scans from the initial treatments and first to third fractions further improved results, with an average DSC of 0.9850, an average HD of 1.2707 mm, and an average MSD of 0.4076 for the 15th fraction, closely aligning with physician-drawn contours. Conclusion: Compared with a general model, our patient-specific deep learning-based training algorithm significantly improved CTV segmentation accuracy of CBCT scans in patients with breast cancer. This approach, coupled with continuous deep learning training using daily CBCT scans, demonstrated enhanced CTV delineation accuracy and efficiency. Future studies should explore the adaptability of the IDOL framework to diverse deep learning models, data sets, and cancer sites.

Comparative analysis of molecular and histological glioblastomas: insights into prognostic variance.

PURPOSE: Whether molecular glioblastomas (GBMs) identify with a similar dismal prognosis as a "classical" histological GBM is controversial. This study aimed to compare the clinical, molecular, imaging, surgical factors, and prognosis between molecular GBMs and histological GBMs. METHODS: Retrospective chart and imaging review was performed in 983 IDH-wildtype GBM patients (52 molecular GBMs and 931 histological GBMs) from a single institution between 2005 and 2023. Propensity score-matched analysis was additionally performed to adjust for differences in baseline variables between molecular GBMs and histological GBMs. RESULTS: Molecular GBM patients were substantially younger (58.1 vs. 62.4, P = 0.014) with higher rate of TERTp mutation (84.6% vs. 50.3%, P < 0.001) compared with histological GBM patients. Imaging showed higher incidence of gliomatosis cerebri pattern (32.7% vs. 9.2%, P < 0.001) in molecular GBM compared with histological GBM, which resulted in lesser extent of resection (P < 0.001) in these patients. The survival was significantly better in molecular GBM compared to histological GBM (median OS 30.2 vs. 18.4 months, P = 0.001). The superior outcome was confirmed in propensity score analyses by matching histological GBM to molecular GBM (P < 0.001). CONCLUSION: There are distinct clinical, molecular, and imaging differences between molecular GBMs and histological GBMs. Our results suggest that molecular GBMs have a more favorable prognosis than histological GBMs.

Integrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning.

Purpose: We aimed to develop a machine learning-based prediction model for severe radiation pneumonitis (RP) by integrating relevant clinicopathological and genetic factors, considering the associations of clinical, dosimetric parameters, and single nucleotide polymorphisms (SNPs) of genes in the TGF-ß1 pathway with RP. Methods: We prospectively enrolled 59 primary lung cancer patients undergoing radiotherapy and analyzed pretreatment blood samples, clinicopathological/dosimetric variables, and 11 functional SNPs in TGFß pathway genes. Using the Synthetic Minority Over-sampling Technique (SMOTE) and nested cross-validation, we developed a machine learning-based prediction model for severe RP (grade ≥ 2). Feature selection was conducted using four methods (filtered-based, wrapper-based, embedded, and logistic regression), and performance was evaluated using three machine learning models. Results: Severe RP occurred in 20.3 % of patients with a median follow-up of 39.7 months. In our final model, age (>66 years), smoking history, PTV volume (>300 cc), and AG/GG genotype in BMP2 rs1979855 were identified as the most significant predictors. Additionally, incorporating genomic variables for prediction alongside clinicopathological variables significantly improved the AUC compared to using clinicopathological variables alone (0.822 vs. 0.741, p = 0.029). The same feature set was selected using both the wrapper-based method and logistic model, demonstrating the best performance across all machine learning models (AUC: XGBoost 0.815, RF 0.805, SVM 0.712, respectively). Conclusion: We successfully developed a machine learning-based prediction model for RP, demonstrating age, smoking history, PTV volume, and BMP2 rs1979855 genotype as significant predictors. Notably, incorporating SNP data significantly enhanced predictive performance compared to clinicopathological factors alone.

Revisiting gliomatosis cerebri in adult-type diffuse gliomas: a comprehensive imaging, genomic and clinical analysis.

Although gliomatosis cerebri (GC) has been removed as an independent tumor type from the WHO classification, its extensive infiltrative pattern may harbor a unique biological behavior. However, the clinical implication of GC in the context of the 2021 WHO classification is yet to be unveiled. This study investigated the incidence, clinicopathologic and imaging correlations, and prognostic implications of GC in adult-type diffuse glioma patients. Retrospective chart and imaging review of 1,211 adult-type diffuse glioma patients from a single institution between 2005 and 2021 was performed. Among 1,211 adult-type diffuse glioma patients, there were 99 (8.2%) patients with GC. The proportion of molecular types significantly differed between patients with and without GC (P = 0.017); IDH-wildtype glioblastoma was more common (77.8% vs. 66.5%), while IDH-mutant astrocytoma (16.2% vs. 16.9%) and oligodendroglioma (6.1% vs. 16.5%) were less common in patients with GC than in those without GC. The presence of contrast enhancement, necrosis, cystic change, hemorrhage, and GC type 2 were independent risk factors for predicting IDH mutation status in GC patients. GC remained as an independent prognostic factor (HR = 1.25, P = 0.031) in IDH-wildtype glioblastoma patients on multivariable analysis, along with clinical, molecular, and surgical factors. Overall, our data suggests that although no longer included as a distinct pathological entity in the WHO classification, recognition of GC may be crucial considering its clinical significance. There is a relatively high incidence of GC in adult-type diffuse gliomas, with different proportion according to molecular types between patients with and without GC. Imaging may preoperatively predict the molecular type in GC patients and may assist clinical decision-making. The prognostic role of GC promotes its recognition in clinical settings.

Identification of prognostic imaging biomarkers in H3 K27-altered diffuse midline gliomas in adults: impact of tumor oxygenation imaging biomarkers on survival.

PURPOSE: To investigate prognostic markers for H3 K27-altered diffuse midline gliomas (DMGs) in adults with clinical, qualitative and quantitative imaging phenotypes, including tumor oxygenation characteristics. METHODS: Retrospective chart and imaging reviews were conducted on 32 adults with H3 K27-altered DMGs between 2017 and 2023. Clinical and qualitative imaging characteristics were analyzed. Quantitative imaging assessment was performed from the tumor mask via automatic segmentation to calculate normalized cerebral blood volume (nCBV), capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), relative cerebral metabolic rate of oxygen (rCMRO2), and mean ADC values. Leptomeningeal metastases (LM) was diagnosed with imaging. Cox analyses were conducted to determine predictors of overall survival (OS) in entire patients and a subgroup of patients with contrast-enhancing (CE) tumor. RESULTS: The median patient age was 40.5 years (range 19.9-75.7), with an OS of 30.3 months (interquartile range 11.3-32.3). In entire patients, the presence of LM was the only independent predictor of OS (hazard ratio [HR] = 6.01, P = 0.009). In the subgroup of 23 (71.9%) patients with CE tumors, rCMRO2 of CE tumor (HR = 1.08, P = 0.019) and the presence of LM (HR = 5.92, P = 0.043) were independent predictors of OS. CONCLUSION: The presence of LM was independently associated with poor prognosis in adult patients with H3 K27-altered DMG. In patients with CE tumors, higher rCMRO2 of CE tumor, which may reflect higher metabolic activity in the tumor oxygenation microenvironment, may be a useful imaging biomarker to predict poor prognosis.

Revisiting oligodendroglioma grading in the 2021 WHO classification: calcification and larger contrast-enhancing tumor volume may predict higher oligodendroglioma grade.

PURPOSE: To investigate whether qualitative and quantitative imaging phenotypes can predict the grade of oligodendroglioma. METHODS: Retrospective chart and imaging reviews were conducted on 180 adults with oligodendroglioma (IDH-mutant and 1p/19q codeleted) between 2005 and 2021. Qualitative imaging characteristics including tumor location, calcification, gliomatosis cerebri, cystic change, necrosis, and infiltrative pattern were analyzed. Quantitative imaging assessment was performed from the tumor mask via automatic segmentation to calculate total, contrast-enhancing (CE), non-enhancing (NE), and necrotic tumor volumes. Logistic analyses were conducted to determine predictors of oligodendroglioma grade. RESULTS: This study included 180 patients (84 [46.7%] with grade 2 and 96 [53.3%] with grade 3 oligodendrogliomas), with a median age of 42 years (range 23-76 years), comprising 91 females and 89 males. On univariable analysis, calcification (odds ratio [OR] = 6.00, P < 0.001), necrosis (OR = 21.84, P = 0.003), presence of CE tumor (OR = 7.86, P < 0.001), larger total (OR = 1.01, P < 0.001), larger CE (OR = 2.22, P = 0.010), and larger NE (OR = 1.01, P < 0.001) tumor volumes were predictors of grade 3 oligodendroglioma. On multivariable analysis, calcification (OR = 3.79, P < 0.001) and larger CE tumor volume (OR = 2.70, P = 0.043) remained as independent predictors of grade 3 oligodendroglioma. The multivariable model exhibited an AUC, accuracy, sensitivity, specificity of 0.78 (95% confidence interval 0.72-0.84), 72.8%, 79.2%, 69.1%, respectively. CONCLUSION: Presence of calcification and larger CE tumor volume may serve as useful imaging biomarkers for prediction of oligodendroglioma grade. CLINICAL RELEVANCE STATEMENT: Assessment of intratumoral calcification and CE tumor volume may facilitate accurate preoperative estimation of oligodendroglioma grade. Presence of intratumoral calcification and larger contrast-enhancing tumor volume were the significant predictors of higher grade oligodendroglioma based on the 2021 WHO classification.

Dose-toxicity surface histogram-based prediction of radiation dermatitis severity and shape.

Objective.This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade.Approach.Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose-toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity.Main results.The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients.Significance. The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.

Revisiting prognostic factors of gliomatosis cerebri in adult-type diffuse gliomas.

PURPOSE: There is lack of comprehensive analysis evaluating the impact of clinical, molecular, imaging, and surgical data on survival of patients with gliomatosis cerebri (GC). This study aimed to investigate prognostic factors of GC in adult-type diffuse glioma patients. METHODS: Retrospective chart and imaging review was performed in 99 GC patients from adult-type diffuse glioma (among 1,211 patients; 6 oligodendroglioma, 16 IDH-mutant astrocytoma, and 77 IDH-wildtype glioblastoma) from a single institution between 2005 and 2021. Predictors of overall survival (OS) of entire patients and IDH-wildtype glioblastoma patients were determined. RESULTS: The median OS was 16.7 months (95% confidence interval [CI] 14.2-22.2) in entire patients and 14.3 months (95% CI 12.2-61.9) in IDH-wildtype glioblastoma patients. In entire patients, KPS (hazard ratio [HR] = 0.98, P = 0.004), no 1p/19q codeletion (HR = 10.75, P = 0.019), MGMTp methylation (HR = 0.54, P = 0.028), and hemorrhage (HR = 3.45, P = 0.001) were independent prognostic factors on multivariable analysis. In IDH-wildtype glioblastoma patients, KPS (HR = 2.24, P = 0.075) was the only independent prognostic factor on multivariable analysis. In subgroup of IDH-wildtype glioblastoma with CE tumors, total resection of CE tumor did not remain as a significant prognostic factor (HR = 1.13, P = 0.685). CONCLUSIONS: The prognosis of GC patients is determined by its underlying molecular type and patient performance status. Compared with diffuse glioma without GC, aggressive surgery of CE tumor in GC patients does not improve survival.

Efficacy of stereotactic ablative radiotherapy in patients with oligometastatic hepatocellular carcinoma: A phase II study.

BACKGROUND & AIMS: Stereotactic ablative radiotherapy (SABR) can extend survival and offers the potential for cure in some patients with oligometastatic disease (OMD). However, limited evidence exists regarding its use in oligometastatic hepatocellular carcinoma (HCC). We aimed to prospectively investigate the efficacy and safety of SABR in patients with oligometastatic HCC. METHODS: We enrolled patients with controlled primary HCC and one to five metastatic lesions amenable to SABR. The primary endpoint was treatment efficacy defined as overall survival (OS) and progression-free survival (PFS). The secondary endpoints included time to local progression, objective response rate, disease control rate, toxicities, and quality of life (QOL), assessed using the EORTC QLQ-C30 before, and 0, 1, and 3 months after SABR. RESULTS: Overall, 40 consecutive patients received SABR on 62 lesions between 2021 and 2022. The most common locations for OMD were the lungs (48.4%), lymph nodes (22.6%), and bone (17.7%). After a median follow-up of 15.5 months, the 2-year OS was 80%. Median PFS was 5.3 months, with 1- and 2-year PFS rates of 21.2% and 0%, respectively. A shorter time to OMD from the controlled primary independently correlated with PFS (p = 0.039, hazard ratio 2.127) alongside age, Child-Pugh class, and alpha-fetoprotein (p = 0.002, 0.004, 0.019, respectively). The 2-year time to local progression, objective response rate, and disease control rate were 91.1%, 75.8%, and 98.4%, respectively. Overall, 10% of patients experienced acute toxicity, and 7.5% experienced late toxicity, with no grade 3+ toxicity. All QOL scores remained stable, whereas the patients without systemic treatments had improved insomnia and social functioning scores. CONCLUSIONS: SABR is an effective and feasible option for oligometastatic HCC that leads to excellent local tumor control and improves survival without adversely affecting QOL. IMPACT AND IMPLICATIONS: Stereotactic ablative radiotherapy (SABR) is a non-invasive treatment approach capable of efficiently ablating the target lesion; however, neither the oligometastatic disease concept nor the potential benefits of SABR have been well-defined in hepatocellular carcinoma (HCC). According to this study, SABR is an effective and safe treatment option for oligometastatic HCC, yielding excellent local tumor control and survival improvement without worsening patients' quality of life, regardless of tumor sites. We also demonstrated that patients with a later presentation of OMD from the controlled primary and lower alpha-fetoprotein levels achieved better survival outcomes. This is the first prospective study of SABR in oligometastatic HCC, providing insights for the development of novel strategies to improve oncologic outcomes. CLINICAL TRIAL NUMBER: NCT05173610.

Experience of Implementing Deep Learning-Based Automatic Contouring in Breast Radiation Therapy Planning: Insights From Over 2000 Cases.

PURPOSE: This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments. METHODS AND MATERIALS: The auto-contouring system was implemented in 2019. We evaluated data from 2428 patients who underwent adjuvant breast radiation therapy before and after the system's introduction. We collected the treatment's finalized contours, which were reviewed and revised by a multidisciplinary team. After implementation, the treatment contours underwent a finalization process that involved manual review and adjustment of the initial auto-contours. For the preimplementation group (n = 369), auto-contours were generated retrospectively. We compared the auto-contours and final contours using the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95). RESULTS: We analyzed 22,215 structures from final and corresponding auto-contours. The final contours were generally larger, encompassing more slices in the superior or inferior directions. Among organs at risk (OAR), the heart, esophagus, spinal cord, and contralateral breast demonstrated significantly increased DSC and decreased HD95 postimplementation (all P < .05), except for the lungs, which presented inaccurate segmentation. Among target volumes, CTVn_L2, L3, L4, and the internal mammary node showed increased DSC and decreased HD95 postimplementation (all P < .05), although the increase was less pronounced than the OAR outcomes. The analysis also covered factors contributing to significant differences, pattern identification, and outlier detection. CONCLUSIONS: In our study, the adoption of an auto-contouring system was associated with an increased reliance on automated settings, underscoring its utility and the potential risk of automation bias. Given these findings, we underscore the importance of considering the integration of stringent risk assessments and quality management strategies as a precautionary measure for the optimal use of such systems.

Normal tissue complication probability models of hypothyroidism after radiotherapy for breast cancer.

Purpose: We aimed to develop Lyman-Kutcher-Burman (LKB) and multivariable normal tissue complication probability (NTCP) models to predict the risk of radiation-induced hypothyroidism (RIHT) in breast cancer patients. Materials and methods: A total of 1,063 breast cancer patients who underwent whole breast irradiation between 2009 and 2016 were analyzed. Individual dose-volume histograms were used to generate LKB and multivariable logistic regression models. LKB model was fit using the thyroid radiation dose-volume parameters. A multivariable model was constructed to identify potential dosimetric and clinical parameters associated with RIHT. Internal validation was conducted using bootstrapping techniques, and model performance was evaluated using the area under the curve (AUC) and Hosmer-Lemeshow (HL) goodness-of-fit test. Results: RIHT developed in 4 % of patients with a median follow-up of 77.7 months. LKB and multivariable NTCP models exhibited significant agreement between the predicted and observed results (HL P values > 0.05). The multivariable NTCP model outperformed the LKB model in predicting RIHT (AUC 0.62 vs. 0.54). In the multivariable model, systemic therapy, age, and percentage of thyroid volume receiving ≥ 10 Gy (V10) were significant prognostic factors for RIHT. The cumulative incidence of RIHT was significantly higher in patients who exceeded the cut-off values for all three risk predictors (systemic therapy, age ≥ 40 years, and thyroid V10 ≥ 26 %, P < 0.005). Conclusions: Systemic therapy, age, and V10 of the thyroid were identified as strong risk factors for the development of RIHT. Our NTCP models provide valuable insights to clinicians for predicting and preventing hypothyroidism by identifying high-risk patients.

Clinical, qualitative imaging biomarkers, and tumor oxygenation imaging biomarkers for differentiation of midline-located IDH wild-type glioblastomas and H3 K27-altered diffuse midline gliomas in adults.

PURPOSE: To compare the clinical, qualitative and quantitative imaging phenotypes, including tumor oxygenation characteristics of midline-located IDH-wildtype glioblastomas (GBMs) and H3 K27-altered diffuse midline gliomas (DMGs) in adults. METHODS: Preoperative MRI data of 55 adult patients with midline-located IDH-wildtype GBM or H3 K27-altered DMG (32 IDH-wildtype GBM and 23 H3 K27-altered DMG patients) were included. Qualitative imaging assessment was performed. Quantitative imaging assessment including the tumor volume, normalized cerebral blood volume, capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), relative cerebral metabolic rate of oxygen values, and mean ADC value were performed from the tumor mask via automatic segmentation. Univariable and multivariable logistic analyses were performed. RESULTS: On multivariable analysis, age (odds ratio [OR] = 0.92, P = 0.015), thalamus or medulla location (OR = 10.48, P = 0.013), presence of necrosis (OR = 0.15, P = 0.038), and OEF (OR = 0.01, P = 0.042) were independent predictors to differentiate H3 K27-altered DMG from midline-located IDH-wildtype GBM. The area under the curve, accuracy, sensitivity, and specificity of the multivariable model were 0.88 (95 % confidence interval: 0.77-0.95), 81.8 %, 82.6 %, and 81.3 %, respectively. CONCLUSIONS: Along with younger age, tumor location, less frequent necrosis, and lower OEF may be useful imaging biomarkers to differentiate H3 K27-altered DMG from midline-located IDH-wildtype GBM. Tumor oxygenation imaging biomarkers may reflect the less hypoxic nature of H3 K27-altered DMG than IDH-wildtype GBM and may contribute to differentiation.

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. The difference in IOV of submitted contours in phases 1 and 2 was investigated quantitatively using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). The qualitative analysis involved using contour heat maps to visualize the extent and location of these variations and the required modification. RESULTS: Over 800 pairwise comparisons were analysed for each structure in each case. Quantitative phase 2 metrics showed significant improvement in the mean DSC (from 0.69 to 0.77) and HD (from 34.9 to 17.9 mm). Quantitative analysis showed increased interobserver agreement in phase 2, specifically for CTV structures (5-19 %), leading to fewer manual adjustments. Underlying IOV differences causes were reported using a questionnaire and hierarchical clustering analysis based on the volume of CTVs. CONCLUSION: DL-based auto-contours improved the contour agreement for OARs and CTVs significantly, both qualitatively and quantitatively, suggesting its potential role in minimizing radiation therapy protocol deviation.

Large institutional experience of early outcomes and dosimetric findings with postoperative stereotactic partial breast irradiation in breast cancer.

PURPOSE: To analyze the dosimetric and toxicity outcomes of patients treated with postoperative stereotactic partial breast irradiation (S-PBI). METHODS: We identified 799 women who underwent S-PBI at our institution between January 2016 and December 2022. The most commonly used dose-fraction and technique were 30 Gy in 5 fractions (91.7 %) and a robotic stereotactic radiation system with real-time tracking (83.7 %). The primary endpoints were dosimetric parameters and radiation-related toxicities. For comparison, a control group undergoing ultra-hypofractionated whole breast irradiation (UF-WBI, n = 468) at the same institution was selected. RESULTS: A total of 815 breasts from 799 patients, with a median planning target volume (PTV) volume of 89.6 cm3, were treated with S-PBI. Treatment plans showed that the mean and maximum doses received by the PTV were 96.2 % and 104.8 % of the prescription dose, respectively. The volume of the ipsilateral breast that received 50 % of the prescription dose was 32.3 ± 8.9 %. The mean doses for the ipsilateral lung and heart were 2.5 ± 0.9 Gy and 0.65 ± 0.39 Gy, respectively. Acute toxicity occurred in 175 patients (21.5 %), predominantly of grade 1. Overall rate of late toxicity was 4 % with a median follow-up of 31.6 months. Compared to the UF-WBI group, the S-PBI group had comparably low acute toxicity (21.5 % vs. 25.2 %, p = 0.12) but significantly lower dosimetric parameters for all organs-at-risks (all p < 0.05). CONCLUSION: In this large cohort, S-PBI demonstrated favorable dosimetric and toxicity profiles. Considering the reduced radiation exposure to surrounding tissues, external beam PBI with advanced techniques should at least be considered over traditional WBI-based approaches for PBI candidates.

Effects of Saffron Extract (Affron®) with 100 mg/kg and 200 mg/kg on Hypothalamic-Pituitary-Adrenal Axis and Stress Resilience in Chronic Mild Stress-Induced Depression in Wistar Rats.

Stress-related symptoms are a global concern, impacting millions of individuals, yet effective and safe treatments remain scarce. Although multiple studies have highlighted the stress- alleviating properties of saffron extract, the underlying mechanisms remain unclear. This study employs the unpredictable chronic mild stress (CMS) animal model to investigate the impact of a standardized saffron extract, Affron® (AFN), on hypothalamic-pituitary-adrenal (HPA) axis regulation and neuroplasticity in Wistar rats following repeated oral administration. The research evaluates AFN's effects on various stress-related parameters, including hypothalamic gene expression, stress hormone levels, and the sucrose preference test. In animals subjected to continuous unpredictable CMS, repetitive administration of AFN at doses of 100 mg/kg and 200 mg/kg effectively normalized HPA axis dysregulation and enhanced neuroplasticity. Increased concentrations of AFN demonstrated greater efficacy. Following AFN oral administration, adrenocorticotropic and corticosterone hormone levels exhibited significant or nearly significant reductions in comparison to subjects exposed to stress only. These changes align with the alleviation of stress and the normalization of the HPA axis. These findings elucidate AFN's role in stress mitigation, affirm its health benefits, validate its potential as a treatment for stress-related symptoms, confirm its physiological effectiveness, and emphasize its therapeutic promise.

Active involvement of patients, radiation oncologists, and surgeons in a multidisciplinary team approach: Guiding local therapy in recurrent, metastatic rectal cancer.

BACKGROUND: Despite the extensive implementation of an organized multidisciplinary team (MDT) approach in cancer treatment, there is little evidence regarding the optimal format of MDT. We aimed to investigate the impact of patient participation in MDT care on the actual application rate of metastasis-directed local therapy. METHODS: We identified all 1211 patients with locally advanced rectal cancer treated with neoadjuvant radiochemotherapy at a single institution from 2006 to 2018. Practice patterns, tumor burden and OMD state were analyzed in recurrent, metastatic cases. RESULTS: With a median follow-up of 60.7 months, 281 patients developed metastases, and 96 (34.2%), 92 (32.7%), and 93 (33.1%) patients had 1, 2-5, and >5 lesions, respectively. In our study, 27.1% were managed in the MDT clinic that mandated the participation of at least four to five board-certified multidisciplinary experts and patients in decision-making processes, while the rest were managed through diverse MDT approaches such as conferences, tumor board meetings, and discussions conducted via phone calls or email. Management in MDT clinic was significantly associated with more use of radiotherapy (p = 0.003) and more sessions of local therapy (p < 0.001). At the time of MDT clinic, the number of lesions was 1, 2-5, and >5 in 9 (13.6%), 35 (53.1%), and 19 (28.8%) patients, respectively. The most common states were repeat OMD (28.8%) and de novo OMD (27.3%), followed by oligoprogression (15%) and induced OMD (10.6%). CONCLUSION: Our findings suggest that active involvement of patients and radiation oncologists, and surgeons in MDT care has boosted the probability of using local therapies for various types of OMD throughout the course of the disease.

MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma.

OBJECTIVES: Sinonasal squamous cell carcinoma (SCC) follows a poor prognosis with high tendency for local recurrence. We aimed to evaluate whether MRI radiomics can predict early local failure in sinonasal SCC. METHODS: Sixty-eight consecutive patients with node-negative sinonasal SCC (January 2005-December 2020) were enrolled, allocated to the training (n = 47) and test sets (n = 21). Early local failure, which occurred within 12 months of completion of initial treatment, was the primary endpoint. For clinical features (age, location, treatment modality, and clinical T stage), binary logistic regression analysis was performed. For 186 extracted radiomic features, different feature selections and classifiers were combined to create two prediction models: (1) a pure radiomics model; and (2) a combined model with clinical features and radiomics. The areas under the receiver operating characteristic curves (AUCs) were calculated and compared using DeLong's method. RESULTS: Early local failure occurred in 38.3% (18/47) and 23.8% (5/21) in the training and test sets, respectively. We identified several radiomic features which were strongly associated with early local failure. In the test set, both the best-performing radiomics model and the combined model (clinical + radiomic features) yielded higher AUCs compared to the clinical model (AUC, 0.838 vs. 0.438, p = 0.020; 0.850 vs. 0.438, p = 0.016, respectively). The performances of the best-performing radiomics model and the combined model did not differ significantly (AUC, 0.838 vs. 0.850, p = 0.904). CONCLUSION: MRI radiomics integrated with a machine learning classifier may predict early local failure in patients with sinonasal SCC. CLINICAL RELEVANCE STATEMENT: MRI radiomics intergrated with machine learning classifiers may predict early local failure in sinonasal squamous cell carcinomas more accurately than the clinical model. KEY POINTS: • A subset of radiomic features which showed significant association with early local failure in patients with sinonasal squamous cell carcinomas was identified. • MRI radiomics integrated with machine learning classifiers can predict early local failure with high accuracy, which was validated in the test set (area under the curve = 0.838). • The combined clinical and radiomics model yielded superior performance for early local failure prediction compared to that of the radiomics (area under the curve 0.850 vs. 0.838 in the test set), without a statistically significant difference.

Selective Inhibition of PI3K Isoforms in Brain Tumors Suppresses Tumor Growth by Increasing Radiosensitivity.

PURPOSE: Glioblastoma (GBM) is a malignant brain tumor with poor prognosis. Radioresistance is a major challenge in the treatment of brain tumors. The development of several types of tumors, including GBM, involves the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway. Upon activation, this pathway induces radioresistance. In this study, we investigated whether additional use of selective inhibitors of PI3K isoforms would enhance radiosensitivity in GBM. MATERIALS AND METHODS: We evaluated whether radiation combined with PI3K isoform selective inhibitors can suppress radioresistance in GBM. Glioma 261 expressing luciferase (GL261-luc) and LN229 were used to confirm the effect of combination of radiation and PI3K isoform inhibitors in vitro. Cell viability was confirmed by clonogenic assay, and inhibition of PI3K/AKT signaling activation was observed by Western blot. To confirm radiosensitivity, the expression of phospho-γ-H2AX was observed by immunofluorescence. In addition, to identify the effect of a combination of radiation and PI3K-α isoform inhibitor in vivo, an intracranial mouse model was established by implanting GL261-luc. Tumor growth was observed by IVIS imaging, and survival was analyzed using Kaplan-Meier survival curves. RESULTS: Suppression of the PI3K/AKT signaling pathway increased radiosensitivity, and PI3K-α inhibition had similar effects on PI3K-pan inhibition in vitro. The combination of radiotherapy and PI3K-α isoform inhibitor suppressed tumor growth and extended survival in vivo. CONCLUSION: This study verified that PI3K-α isoform inhibition improves radiosensitivity, resulting in tumor growth suppression and extended survival in GBM mice.