Stereotactic Ablative Radiotherapy for Gynecological Oligometastatic and Oligoprogessive Tumors.

Importance: The role of stereotactic ablative radiotherapy (SABR) for gynecologic malignant tumors has yet to be clearly defined despite recent clinical uptake. Objective: To evaluate the outcomes of SABR in patients with oligometastatic and oligoprogressive gynecologic cancers. Design, Setting, and Participants: In this retrospective pooled analysis, patients with oligometastatic and oligoprogressive gynecologic cancers receiving SABR at 5 institutions from Canada and the US were studied. Data were collected from January 2011 to December 2020, and data were analyzed from January to December 2023. Exposure: Stereotactic ablative radiotherapy. Main Outcomes and Measures: Cumulative incidence of local and distant recurrence, chemotherapy-free survival (CFS), and overall survival (OS) probabilities after SABR were calculated using Kaplan-Meier methods. Univariable and multivariable analysis was conducted using Cox regression methods. Results: A total of 215 patients with 320 lesions meeting criteria were included in the analysis; the median (range) age at primary diagnosis was 59 (23-86) years. The median (range) follow-up from SABR was 18.5 (0.1-124.5) months. The primary site included the endometrium (n = 107), ovary (n = 64), cervix (n = 30), and vulva or vagina (n = 14). Local cumulative incidence of recurrence was 13.7% (95% CI, 9.4-18.9) and 18.5% (95% CI, 13.2-24.5) at 1 and 5 years, respectively. Distant cumulative incidence of recurrence was 48.5% (95% CI, 41.4-55.1) and 73.1% (95% CI, 66.0-79.0) at 1 and 5 years, respectively. OS was 75.7% (95% CI, 69.2-81.1) and 33.1% (95% CI, 25.3-41.1) at 1 and 5 years, respectively. The median CFS was 21.7 months (95% CI, 15.4-29.9). On multivariable analysis, local recurrence was significantly associated with nodal metastasis, lesion size, biologically effective dose, treatment indication, institution, and primary disease type. Distant progression-free survival was associated with nodal targets and lesion size. OS and CFS were significantly associated with lesion size. Conclusions and Relevance: In this study, SABR appeared to have excellent local control with minimal toxic effects in this large patient group, and certain patients may achieve durable distant control and OS as well. It may be possible to delay time to chemotherapy in select patient subtypes and therefore reduce associated toxic effects. Prospective multicenter trials will be critical to establish which characteristics procure the greatest benefit from SABR use and to define the ideal time to implement SABR with other oncologic treatments.

Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.

BACKGROUND: 3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multiple applicator types. The variability and scarcity of data for any given applicator type poses challenges for deep learning. PURPOSE: The goal of this work was to compare three methods of neural network training-a single model trained on all applicator data, fine-tuning the combined model to each applicator, and individual (IDV) applicator models-to determine the optimal method for dose prediction. METHODS: Models were produced for four applicator types-tandem-and-ovoid (T&O), T&O with 1-7 needles (T&ON), tandem-and-ring (T&R) and T&R with 1-4 needles (T&RN). First, the combined model was trained on 859 treatment plans from 266 cervical cancer patients treated from 2010 onwards. The train/validation/test split was 70%/16%/14%, with approximately 49%/10%/19%/22% T&O/T&ON/T&R/T&RN in each dataset. Inputs included four channels for anatomical masks (high-risk clinical target volume [HRCTV], bladder, rectum, and sigmoid), a mask indicating dwell position locations, and applicator channels for each applicator component. Applicator channels were created by mapping the 3D dose for a single dwell position to each dwell position and summing over each applicator component with uniform dwell time weighting. A 3D Cascade U-Net, which consists of two U-Nets in sequence, and mean squared error loss function were used. The combined model was then fine-tuned to produce four applicator-specific models by freezing the first U-Net and encoding layers of the second and resuming training on applicator-specific data. Finally, four IDV models were trained using only data from each applicator type. Performance of these three model types was compared using the following metrics for the test set: mean error (ME, representing model bias) and mean absolute error (MAE) over all dose voxels and ME of clinical metrics (HRCTV D90% and D2cc of bladder, rectum, and sigmoid), averaged over all patients. A positive ME indicates the clinical dose was higher than predicted. 3D global gamma analysis with the prescription dose as reference value was performed. Dice similarity coefficients (DSC) were computed for each isodose volume. RESULTS: Fine-tuned and combined models showed better performance than IDV applicator training. Fine-tuning resulted in modest improvements in about half the metrics, compared to the combined model, while the remainder were mostly unchanged. Fine-tuned MAE = 3.98%/2.69%/5.36%/3.80% for T&O/T&R/T&ON/T&RN, and ME over all voxels = -0.08%/-0.89%/-0.59%/1.42%. ME D2cc were bladder = -0.77%/1.00%/-0.66%/-1.53%, rectum = 1.11%/-0.22%/-0.29%/-3.37%, sigmoid = -0.47%/-0.06%/-2.37%/-1.40%, and ME D90 = 2.6%/-4.4%/4.8%/0.0%. Gamma pass rates (3%/3 mm) were 86%/91%/83%/89%. Mean DSCs were 0.92%/0.92%/0.88%/0.91% for isodoses ≤ 150% of prescription. CONCLUSIONS: 3D BT dose was accurately predicted for all applicator types, as indicated by the low MAE and MEs, high gamma scores and high DSCs. Training on all treatment data overcomes challenges with data scarcity in each applicator type, resulting in superior performance than can be achieved by training on IDV applicators alone. This could presumably be explained by the fact that the larger, more diverse dataset allows the neural network to learn underlying trends and characteristics in dose that are common to all treatment applicators. Accurate, applicator-specific dose predictions could enable automated, knowledge-based planning for any cervical brachytherapy treatment.

Transferring care to enhance access to early-phase cancer clinical trials: Protocol to evaluate a novel program.

Involving diverse populations in early-phase (phase I and II) cancer clinical trials is critical to informed therapeutic development. However, given the growing costs and complexities of early-phase trials, trial activation and enrollment barriers may be greatest for these studies at healthcare facilities that provide care to the most diverse patient groups, including those in historically underserved communities (e.g., safety-net healthcare systems). To promote diverse and equitable access to early-phase cancer clinical trials, we are implementing a novel program for the transfer of care to enhance access to early-phase cancer clinical trials. We will then perform a mixed-methods study to determine perceptions and impact of the program. Specifically, we will screen, recruit, and enroll diverse patients from an urban, integrated safety-net healthcare system to open and active early-phase clinical trials being conducted in a university-based cancer center. To evaluate this novel program, we will: (1) determine program impact and efficiency; and (2) determine stakeholder experience with and perceptions of the program. To achieve these goals, we will conduct preliminary cost analyses of the program. We will also conduct surveys and interviews with patients and caregivers to elucidate program impact, challenges, and areas for improvement. We hypothesize that broadening access to early-phase cancer trials conducted at experienced centers may improve equity and diversity. In turn, such efforts may enhance the efficiency and generalizability of cancer clinical research.

Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning.

Objective.Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foundation of deep learning that can use a deep neural network to represent functions accurately and flexibly, we developed a deep-learning framework that learns the probability of plan approval for cervical cancer high-dose-rate brachytherapy (HDRBT).Approach.The system consisted of a dose prediction network (DPN) and a plan-approval probability network (PPN). DPN predicts organs at risk (OAR)D2ccand CTVD90%of the current fraction from the patient's current anatomy and prescription dose of HDRBT. PPN outputs the probability of a given plan being acceptable to the physician based on the patients anatomy and the total dose combining HDRBT and external beam radiotherapy sessions. Training of the networks was achieved by first training them separately for a good initialization, and then jointly via an adversarial process. We collected approved treatment plans of 248 treatment fractions from 63 patients. Among them, 216 plans from 54 patients were employed in a four-fold cross validation study, and the remaining 32 plans from other 9 patients were saved for independent testing.Main results.DPN predicted equivalent dose of 2 Gy for bladder, rectum, sigmoidD2ccand CTVD90%with a relative error of 11.51% ± 6.92%, 8.23% ± 5.75%, 7.12% ± 6.00%, and 10.16% ± 10.42%, respectively. In a task that differentiates clinically approved plans and disapproved plans generated by perturbing doses in ground truth approved plans by 20%, PPN achieved accuracy, sensitivity, specificity, and area under the curve 0.70, 0.74, 0.65, and 0.74.Significance.We demonstrated the feasibility of developing a novel deep-learning framework that predicts a probability of plan approval for HDRBT of cervical cancer, which is an essential component in automatic treatment planning.

The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy.

Background and Purpose: Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours. Our study investigated the optimal use of such contours in cervical online cone-beam-based ART. Materials and Methods: From 136 adaptive fractions across 21 cervical cancer patients with nodal disease, we extracted 649 clinically-delivered and AID clinical target volume (CTV) lymph node boost structures. We assessed geometric alignment between AID and clinical CTVs via dice similarity coefficient, and 95% Hausdorff distance, and geometric coverage of clinical CTVs by AID planning target volumes by false positive dice. Coverage of clinical CTVs by AID contour-based plans was evaluated using D100, D95, V100%, and V95%. Results: Between AID and clinical CTVs, the median dice similarity coefficient was 0.66 and the median 95 % Hausdorff distance was 4.0 mm. The median false positive dice of clinical CTV coverage by AID planning target volumes was 0. The median D100 was 1.00, the median D95 was 1.01, the median V100% was 1.00, and the median V95% was 1.00. Increased nodal volume, fraction number, and daily adaptation were associated with reduced clinical CTV coverage by AID-based plans. Conclusion: In one of the first reports on pelvic nodal ART, AID-based plans could adequately cover nodal targets. However, physician review is required due to performance variation. Greater attention is needed for larger, daily-adapted nodes further into treatment.

A Tale of Two Hospitals: Effect of Access to Care Through a Safety Net Hospital on Adjuvant Therapy, Imaging Compliance and 5-Year Survival Rates Compared to the University Hospital Served by the Same Breast Cancer Clinical Teams.

RATIONALE AND OBJECTIVES: To compare rates of guideline-concordant care, imaging surveillance, recurrence and survival outcomes between a safety-net (SNH) and tertiary-care University Hospital (UH) served by the same breast cancer clinical teams. MATERIALS AND METHODS: 647 women with newly diagnosed breast cancer treated in affiliated SNH and UH between 11.1.2014 and 3.31.2017 were reviewed. Patient demographics, completion of guideline-concordant adjuvant chemotherapy, radiation and hormonal therapy were recorded. Two multivariable logistic regression models were performed to investigate the effect of hospital and race on cancer stage. Kaplan-Meier log-rank and Cox-regression were used to analyze five-year recurrence-free (RFS) and overall survival (OS) between hospitals and races, (p < 0.05 significant). RESULTS: Patients in SNH were younger (mean SNH 53.2 vs UH 57.9, p < 0.001) and had higher rates of cT3/T4 disease (SNH 19% vs UH 5.5%, p < 0.001). Patients in the UH had higher rates of bilateral mastectomy (SNH 17.6% vs UH 40.1% p < 0.001) while there was no difference in the positive surgical margin rate (SNH 5.0% vs UH 7.6%, p = 0.20), completion of adjuvant radiation (SNH 96.9% vs UH 98.7%, p = 0.2) and endocrine therapy (SNH 60.8% vs UH 66.2%, p = 0.20). SNH patients were less compliant with mammography surveillance (SNH 64.1% vs UH 75.1%, p = 0.02) and adjuvant chemotherapy (SNH 79.1% vs UH 96.3%, p < 0.01). RFS was lower in the SNH (SNH 54 months vs UH 57 months, HR 1.90, 95% CI: 1.18-3.94, p = 0.01) while OS was not significantly different (SNH 90.5% vs UH 94.2%, HR 1.78, 95% CI: 0.97-3.26, p = 0.06). CONCLUSION: In patients experiencing health care disparities, having access to guideline-concordant care through SNH resulted in non-inferior OS to those in tertiary-care UH.

Definitive radiation for advanced cervix cancer is not associated with vaginal shortening-a prospective vaginal length and dose correlation.

PURPOSE: Prospectively measure change in vaginal length after definitive chemoradiation (C-EBRT) with Intracavitary Brachytherapy (ICBT) for locally advanced cervix cancer (LACC) and correlate with vaginal dose (VD). MATERIALS AND METHODS: Twenty one female patients with LACC receiving C-EBRT and ICBT underwent serial vaginal length (VL) measurements. An initial measurement was made at the time of the first ICBT procedure and subsequently at 3 month intervals up to 1 year post radiation. The vagina was contoured as a 3-dimensional structure for each brachytherapy plan. The difference in VL before and at least 6 months after the last fraction of brachytherapy was considered as an indicator of toxicity. RESULTS: The mean initial VL was 8.7 cm (6.5-12) with median value of 8.5 cm. The mean VL after 6 months was 8.6 cm (6.5-12) and VL change was not found to be statistically significant. The median values (interquartile ranges) for vaginal D0.1cc, D1cc, and D2cc were 129.2 Gy (99.6-252.2), 96.9 Gy (84.2-114.9), and 89.6 Gy (82.4-102.2), respectively. No significant correlation was found between vaginal length change and the dosimetric parameters calculated for all patients. CONCLUSION: Definitive C-EBRT and ICBT did not significantly impact VL in this prospective cohort probably related to acceptable doses per ICRU constraints. Estimate of vaginal stenosis and sexual function was not performed in this cohort which is a limitation of this study and which we hope to study prospectively going forward.

Optimizing Online Adaptation Timing in the Treatment of Locally Advanced Cervical Cancer.

PURPOSE: Online adaptive radiation therapy (ART) has emerged as a new treatment modality for cervical cancer. Daily online adapting improves target coverage and organ-at-risk (OAR) sparing compared with traditional image guided radiation therapy (IGRT); however, the required resources may not be feasible in a busy clinical setting. Less frequent adapting may still benefit cervical cancer patients due to large volume changes of the uterocervix of the treatment course. In this study, the dosimetry from different online adapt-on-demand schedules was compared. MATERIALS AND METHODS: A retrospective cohort of 10 patients with cervical cancer treated with 260 fractions of definitive daily online ART was included. Plans with different adaptation schedules were simulated with adaptations weekly, every other week, once during treatment, and no adaptations (IGRT). These plans were applied to the synthetic computed tomography (CT) images and contours generated during the patient's delivered daily adaptive workflow. The dosimetry of the weekly replan, every-other-week replan, once replan, and IGRT plans were compared using a paired t test. RESULTS: Compared with traditional IGRT plans, weekly and every-other-week ART plans had similar clinical target volume (CTV) coverage, but statistically significant improved sparing of OARs. Weekly and every-other-week ART had reduced bowel bag V40 by 1.57% and 1.41%, bladder V40 by 3.82% and 1.64%, rectum V40 by 8.49% and 7.50%, and bone marrow Dmean by 0.81% and 0.61%, respectively. Plans with a single adaptation had statistically significantly worse target coverage, and moderate improvements in OAR sparing. Of the 18-dose metrics evaluated, improvements were seen in 15 for weekly ART, 14 for every-other-week ART, and 10 for single ART plans compared with IGRT. When every-other-week ART was compared with weekly ART, both plans had similar CTV coverage and OAR sparing with only small improvements in bone marrow dosimetry with weekly ART. CONCLUSIONS: This retrospective work compares different adapt-on-demand treatment schedules using data collected from patients treated with daily online adaptive radiation therapy. Results suggest weekly or every-other-week online ART is beneficial for reduced OAR dose compared with IGRT by exploiting the gradual changes in the uterocervix target volume.

Definitive ablative stereotactic partial breast irradiation in early stage inoperable breast cancer.

PURPOSE: This case series and literature review aims to investigate the efficacy and safety of definitive ablative radiation therapy as a treatment modality for non-operable patients with early stage breast cancer. We present two cases demonstrating the potential of this approach to achieve durable responses. METHODS: We assessed the long-term response of two non-operable patients diagnosed with Stage II (cT2N0M) and Stage IA (T1bN0M0) invasive ductal carcinoma (IDC), who were deemed unfit for surgery due to significant co-morbid conditions. Definitive ablative radiation therapy was administered using stereotactic partial breast irradiation with ablative doses delivered in either a single fraction or two fractions. Serial imaging was conducted to assess treatment response and monitor adverse events. RESULTS: Both patients exhibited notable treatment responses following definitive ablative radiation therapy. The first patient, an 84-year-old woman, experienced a 69% reduction in tumor size over a follow-up period exceeding 2 years. The second patient, an 87-year-old woman, achieved complete resolution of disease on imaging, with no signs of progression even 26 month post-treatment. Both patients tolerated the treatment well, without significant treatment-related adverse events. CONCLUSIONS: Our case series suggests that definitive ablative radiation therapy may serve as a safe and effective treatment option for non-operable patients with early stage breast cancer. The observed durable treatment responses and minimal toxicity support the potential of this approach. Furthermore, a longer interval between ablative radiation therapy and surgery may enhance treatment response, potentially leading to increased complete pathologic response rates.

A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI).

Accurate and efficient delineation of the clinical target volume (CTV) is of utmost significance in post-operative breast cancer radiotherapy. However, CTV delineation is challenging as the exact extent of microscopic disease encompassed by CTV is not visualizable in radiological images and remains uncertain. We proposed to mimic physicians' contouring practice for CTV segmentation in stereotactic partial breast irradiation (S-PBI) where CTV is derived from tumor bed volume (TBV) via a margin expansion followed by correcting the extensions for anatomical barriers of tumor invasion (e.g. skin, chest wall). We proposed a deep-learning model, where CT images and the corresponding TBV masks formed a multi-channel input for a 3D U-Net based architecture. The design guided the model to encode the location-related image features and directed the network to focus on TBV to initiate CTV segmentation. Gradient weighted class activation map (Grad-CAM) visualizations of the model predictions revealed that the extension rules and geometric/anatomical boundaries were learnt during model training to assist the network to limit the expansion to a certain distance from the chest wall and the skin. We retrospectively collected 175 prone CT images from 35 post-operative breast cancer patients who received 5-fraction partial breast irradiation regimen on GammaPod. The 35 patients were randomly split into training (25), validation (5) and test (5) sets. Our model achieved mean (standard deviation) of 0.94 (±0.02), 2.46 (±0.5) mm, and 0.53 (±0.14) mm for Dice similarity coefficient, 95th percentile Hausdorff distance, and average symmetric surface distance respectively on the test set. The results are promising for improving the efficiency and accuracy of CTV delineation during on-line treatment planning procedure.

Aspirin use is associated with improvement in distant metastases outcome in patients with residual disease after neoadjuvant chemotherapy.

PURPOSE: Aspirin (ASA) use has been correlated with improved outcomes in high-risk patients at risk for distant metastases. Breast cancer (BC) patients with residual disease, particularly nodal disease (ypN +) after neoadjuvant chemotherapy (NAC), are high-risk patients portending worse outcomes. We hypothesized that ASA use can reduce distant metastases and improve outcomes in these patients. METHODS: Patients at our institutions from 2005 to 2018, with BC who did not achieve complete response (pCR) after NAC were reviewed (IRB protocol STU- 052012-019). Data, including evidence of ASA use, and clinico-pathologic parameters were analyzed. Survival outcomes were obtained (Kaplan Meier analysis) and univariate (UVA) and multivariable (MVA) Cox proportional hazards regression analyses were performed. RESULTS: 637 did not achieve pCR (ypN+ = 422). 138 were ASA users. Median follow-up for the control and ASA group were 3.8 (IQR 2.2-6.3) and 3.8 (IQR 2.5-6.4) years, respectively. Majority were stage II/III. 387 were hormone receptor positive, 191 HER2 +, and 157 triple negative. On UVA, ASA use, PR status, pathologic and clinical stage showed significance for DMFS, and disease-free survival (DFS). On MVA, ASA use associated with improved 5-year DFS (p = .01, 87.0% vs 79.6%, adjusted HR = 0.48) and improved 5-year DMFS (p = .04, 92.8% vs 89.2%, adjusted HR = 0.57). In the ypN + patients, ASA use associated with improved 5-year DMFS (p = .008, 85.7% vs 70.7%, adjusted HR = 0.43) and DFS (p = .02, 86.8% vs 74.3%, adjusted HR = 0.48). CONCLUSION: For non-responders, particularly ypN + patients, ASA use associated with improved outcome. These hypotheses-generating results suggest for development of prospective clinical trials of augmented ASA use in selected very high-risk BC patients.

Aspirin Use Is Associated With Improved Outcomes in Inflammatory Breast Cancer Patients.

PURPOSE: Inflammatory breast cancer (IBC) is the most aggressive form of breast cancer and has a high propensity for distant metastases. Our previous data suggested that aspirin (acetylsalicylic acid, ASA) use may be associated with reduced risk of distant metastases in aggressive breast cancer; however, there are no reported studies on the potential benefit of ASA use in patients with IBC. METHODS: Data from patients with non-metastatic IBC treated between 2000-2017 at two institutions, were reviewed. Overall survival (OS), disease-free survival (DFS), and distant metastasis-free survival (DMFS) were performed using Kaplan-Meier analysis. Univariate and multivariable logistic regression models were used to identify significant associated factors. RESULTS: Of 59 patients meeting the criteria for analysis and available for review, 14 ASA users were identified. ASA users demonstrated increased OS (p = 0.03) and DMFS (p = 0.02), with 5-year OS and DMFS of 92% (p = 0.01) and 85% (p = 0.01) compared to 51% and 43%, respectively, for non-ASA users. In univariate analysis, pT stage, pN stage, and ASA use were significantly correlated (p < 0.05) with OS and DFS. On multivariable analysis, ASA use (hazard ratio [HR], 0.11; 95% confidence interval [CI], 0.01-0.8) and lymph node stage (HR, 5.9; 95% CI, 1.4-25.9) remained significant for OS and DFS ASA use (HR, 0.13; 95% CI, 0.03-0.56) and lymph node stage (HR, 5.6; 95% CI, 1.9-16.4). CONCLUSION: ASA use during remission was associated with significantly improved OS and DMFS in patients with IBC. These results suggest that ASA may provide survival benefits to patients with IBC. Prospective clinical trials of ASA use in patients with high-risk IBC in remission should be considered.

Towards precision radiation oncology: endocrine therapy response as a biomarker for personalization of breast radiotherapy.

Targeted therapies, such as endocrine therapies (ET), can exert selective pressure on cancer cells and promote adaptations that confer treatment resistance. In this study, we show that ET resistance in breast cancer drives radiation resistance through reprogramming of DNA repair pathways. We also show that pharmacological bromodomain and extraterminal domain inhibition reverses pathological DNA repair reprogramming in ET-resistant breast tumors and overcomes resistance to radiation therapy.

On the feasibility of improved target coverage without compromising organs at risk using online adaptive stereotactic partial breast irradiation (A-SPBI).

PURPOSE: Describe an early-adopting institution's experience with online adaptive radiation for stereotactic partial breast irradiation. METHODS AND MATERIALS: Retrospective review of 22 women treated between May 2021 and March 2022 with adaptive stereotactic partial breast irradiation. A total of 106 of 110 fractions were evaluated for dosimetric changes in target coverage and organ-at-risk (OAR) dose. Patient set up with stereotactic wooden frame and adapted per fraction. Treatment and planning times were collected prospectively by radiation therapists. RESULTS: Scheduled PTV30 Gy was <95% in 72.1% and <90% in 38.5% of fractions, and both PTV and CTV coverage were improved significantly after adaption, and 83.7% of fractions were delivered as adapted per physician choice. There was no difference in OAR coverage. Average adaptive treatment planning took 15 min and average time-on-couch was 34.4 min. CONCLUSIONS: Adaptive stereotactic breast irradiation resulted in improved target coverage with equivalent dosing to OARs in an efficient and tolerated treatment time. Improved target coverage allowed for decreased PTV margins compared to prior trial protocols that may improve acute and late toxicities.

Spare the Bowel, Don't Spoil the Target: Optimal Margin Assessment for Online Cone Beam Adaptive Radiation Therapy (OnC-ART) of the Cervix.

PURPOSE: The standard treatment for locally advanced cervical cancer involves pelvic chemoradiation. Intensity modulated radiation therapy planning requires expansion of the cervix and uterus clinical target volume (CTV) by 1.5 to 2 cm to account for motion. With online cone beam adaptive radiation therapy (OnC-ART), interfractional movement is accounted for, which can potentially lead to smaller CTV to planned target volume (PTV) margins. In this study, we attempted to determine the optimal CTV-to-PTV margin for adequate coverage with OnC-ART and factors that can affect CTV coverage. METHODS AND MATERIALS: A retrospective cohort of 21 patients with cervical cancer treated with definitive chemoradiation was included. Nine patients treated with nonadaptive radiation had the uterocervix contoured on pretreatment cone beam computed tomography (CBCT) and end-treatment CBCTs. Anterior-posterior, lateral, and superior-inferior shifts and the average shift in all directions were calculated. A CTV-to-PTV expansion was determined and verified on a validation cohort of 12 patients treated with OnC-ART. RESULTS: The average anterior-posterior, lateral, and superior-inferior shifts with standard deviation were 0.32 ± 1.55 cm, 0.12 ± 2.31 cm, and 1.67 ± 3.41 cm, respectively. A uniform 5-mm expansion around the pretreatment CTV covered 98.85% ± 1.23% of the end-treatment CTV. This 5-mm expansion was applied to our validation cohort treated with OnC-ART, and 98.39% ± 3.0% of the end-treatment CTV was covered. Time between CBCTs >30 minutes and change in bladder volume were significantly correlated to CTV coverage. CONCLUSIONS: Based on our analysis, a CTV-to-PTV margin of 5 mm is adequate to encompass 98% of the CTV. A significantly reduced margin could potentially decrease the toxicities associated with radiation for patients with cervical cancer and lead to improved patient reported toxicity outcomes. We recommend physicians begin with a 5-mm margin and assess adequate coverage with image guidance during daily adaptation.

Change in image-guided planning strategies over time impacts oncologic and survival outcomes for intracavitary cervical cancer brachytherapy.

PURPOSE: Intracavitary cervical brachytherapy (BT) has transitioned from a two-dimensional nonvolumetric (NV) dosimetry system to three-dimensional computed tomography (CT) and/or magnetic resonance imaging (MRI)-based planning techniques. The purpose of this study is to retrospectively evaluate the relative improvements in image-guided planning strategies over time with regards to dosimetry, survival, and toxicity. METHODS AND MATERIALS: A single site retrospective review of 95 locally advanced cervical cancer patients treated with concurrent chemoradiation and high dose rate BT from 2009 to 2016 were divided into three BT planning groups: point-A based NV dosimetry using CT imaging (n = 37), CT-based volumetric dosimetry (n = 33), and MRI-based volumetric dosimetry (n = 25). Overall survival (OS), progression free survival (PFS), and pelvic control (PC) at 5 years were plotted using Kaplan-Meier curves. Univariate and multivariate (MVA) cox proportional-hazards models calculated hazard-ratios (HZ). Finally, acute and late grade 3-4 toxicities were compared between the cohorts. RESULTS: Both MRI and CT had significantly less D2cc to bowel (p < 0.001) and sigmoid (p < 0.001) compared to NV-based planning. On MVA, age (<60 vs. ≥60 years) was significant for worse 5-year OS (HZ: 2.48) and PC (HZ: 5.25). MRI, with NV as the reference, had significantly improved 5-year OS (HZ: 0.26), PFS (HZ: 0.34) and PC (HZ: 0.16). There was no significant difference in grade ≥3 toxicities between the cohorts. CONCLUSIONS: CT and MRI-based 3D planning had significantly less D2cc to bowel and sigmoid. MRI-based planning had significant improvement in 5-year OS, PFS, and LC compared to NV on MVA.

The role for vaginal cuff brachytherapy boost after external beam radiation therapy in endometrial cancer.

PURPOSE: To evaluate the role and technique of a vaginal cuff brachytherapy (VB) boost to adjuvant external beam (EB) radiation for endometrial cancer through a systematic review. METHODS AND MATERIALS: Relevant trials were identified through a systematic search of the literature. RESULTS: A total of 21 prospective and retrospective studies which had a patient cohort undergoing EB + VB was identified to evaluate for rates of vaginal and pelvic recurrences, overall survival, and toxicity. Additional database studies were utilized to demonstrate differences in local control and overall survival between EB and EB + VB. CONCLUSIONS: While there is limited prospective evidence to guide the use of a VB boost after EB, the evidence suggests that patients with a higher risk of a vaginal recurrence such as those with cervical stromal involvement in select Stage III patients may derive local control and survival benefits from a VB boost. Additional individual risk factors such as grade, histology, extent of invasion, margin status, age, and the use of lower doses of EB should be considered when deciding when to add a VB boost.

A How-To Compendium for GammaPod Treatments, Clinical Workflow, and Clinical Program at an Early Adopting Institution.

In 2019, our institution became the second in the world to go live with GammaPod (Xcision Medical Systems, LLC, Columbia, MD), a device dedicated for stereotactic radiation therapy of breast cancer, with breast immobilization, real-time imaging, and highly-conformal dosimetry. At our institution, GammaPod is used for 5-fraction adjuvant partial breast irradiation, single-fraction tumor cavity boost before whole-breast irradiation, single-fraction preoperative radiation, and (in poor surgical candidates), single-fraction definitive radiation. Here, we describe our workflow, observed procedure step times, and homegrown techniques for improved efficiency in our institutional experience of 93 patients treated between 2019 and 2021.

Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.

Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative breast target delineation is challenging as the contrast between the tumor bed volume (TBV) and normal breast tissue is relatively low in CT images. In this study, we propose to mimic the marker-guidance procedure in manual target delineation. We developed a saliency-based deep learning segmentation (SDL-Seg) algorithm for accurate TBV segmentation in post-operative breast irradiation. The SDL-Seg algorithm incorporates saliency information in the form of markers' location cues into a U-Net model. The design forces the model to encode the location-related features, which underscores regions with high saliency levels and suppresses low saliency regions. The saliency maps were generated by identifying markers on CT images. Markers' location were then converted to probability maps using a distance transformation coupled with a Gaussian filter. Subsequently, the CT images and the corresponding saliency maps formed a multi-channel input for the SDL-Seg network. Our in-house dataset was comprised of 145 prone CT images from 29 post-operative breast cancer patients, who received 5-fraction partial breast irradiation (PBI) regimen on GammaPod. The 29 patients were randomly split into training (19), validation (5) and test (5) sets. The performance of the proposed method was compared against basic U-Net. Our model achieved mean (standard deviation) of 76.4(±2.7) %, 6.76(±1.83) mm, and 1.9(±0.66) mm for Dice similarity coefficient, 95 percentile Hausdorff distance, and average symmetric surface distance respectively on the test set with computation time of below 11 seconds per one CT volume. SDL-Seg showed superior performance relative to basic U-Net for all the evaluation metrics while preserving low computation cost. The findings demonstrate that SDL-Seg is a promising approach for improving the efficiency and accuracy of the on-line treatment planning procedure of PBI, such as GammaPod based PBI.