Anand, Aman

Background/Objectives: Adjuvant radiation for gynecologic malignancies often exposes organs at risk (OARs), such as the bone marrow, bowel, rectum, and bladder, to radiation, leading to toxicities that impact treatment tolerance and patient quality of life. Scanning proton beam therapy, particularly with Individual Field Simultaneous Optimization (IFSO), may offer dosimetric and biological advantages over volumetric modulated arc therapy (VMAT). This study evaluates the clinical impact of IFSO-based proton planning in post-operative gynecologic cancer patients. Materials and Methods: Fourteen patients receiving adjuvant proton therapy to 45 Gy in 25 fractions were retrospectively analyzed. Comparison VMAT plans were generated on the same datasets. Dose–volume metrics for key OARs and normal tissue complication probabilities (NTCPs) were compared using paired statistical tests. Robustness evaluations accounted for setup and range uncertainties. Results: Proton plans significantly reduced dose to bone marrow (V10Gy: 58% vs. 86%, p < 0.00001; V20Gy: 47% vs. 58%, p < 0.00001), small bowel (V20Gy: 21% vs. 56%, p < 0.00001), and femoral heads (left femoral head mean: 11Gy vs. 13Gy, p = 0.032; right femoral head mean: 11Gy vs. 13Gy, p = 0.022). NTCP modeling predicted significantly lower rates of bowel urgency (9.4% vs. 3.3%, p < 0.001) and hematologic toxicity (10.2% vs. 4.9%, p < 0.001) with proton therapy. Plans remained robust across uncertainty scenarios. Conclusions: IFSO-based scanning proton therapy provides clinically meaningful sparing of bone marrow and bowel, with the potential to reduce hematologic and gastrointestinal toxicities. These findings support its use in patients receiving adjuvant pelvic radiotherapy, particularly those undergoing extended field treatment or chemotherapy.

Objectives: Identifying patients’ advantageous radiotherapy modalities prior to CT simulation is challenging. This study aimed to develop a workflow using deep learning (DL)-predicted synthetic CT (sCT) for treatment modality comparison based solely on a diagnostic CT (dCT). Methods: A DL network, U-Net, was trained utilizing 46 thoracic cases from a public database to generate sCT images predicting planning CT (pCT) scans based on the latest dCT, and tested on 15 institutional patients. The sCT accuracy was evaluated against the corresponding pCT and a commercial algorithm deformed CT (MdCT) based on Mean Absolute Error (MAE) and Universal Quality Index (UQI). To determine advantageous treatment modality, clinical dose-volume histogram (DVH) metrics and Normal Tissue Complication Probability (NTCP) differences between proton and photon treatment plans were analyzed on the sCTs via concordance correlation coefficient (CCC). Results: The AI-generated sCTs closely resembled those of the commercial deformation algorithm in the tested cases. The differences in MAE and UQI values between the sCT-vs-pCT and MdCT-vs-pCT were 19.38 HU and 0.06, respectively. The mean absolute NTCP deviation between sCT and pCT was 1.54%, 0.21%, and 2.36% for esophagus perforation, lung pneumonitis, and heart pericarditis, respectively. The CCC between sCT and pCT was 0.90 for DVH metrics and 0.97 for NTCP, indicating moderate agreement for DVH metrics and substantial agreement. Conclusions: Radiation oncologists can potentially utilize this personalized sCT based approach as a clinical support tool to rapidly compare the treatment modality benefit during patient consultation and facilitate in-depth discussion on potential toxicities at a patient-specific level.

AbstractMonte Carlo (MC) simulation has been used to generate commissioning data for the beam modeling of treatment planning system (TPS). We have developed a method called radial projection (RP) for postprocessing of MC‐simulation‐generated data. We used the RP method to reduce the statistical uncertainty of the lateral profile of proton pencil beams with axial symmetry. The RP method takes advantage of the axial symmetry of dose distribution to use the mean value of multiple independent scores as the representative score. Using the mean as the representative value rather than any individual score results in substantial reduction in statistical uncertainty. Herein, we present the concept and step‐by‐step implementation of the RP method, as well as show the advantage of the RP method over conventional measurement methods for generating lateral profile. Lateral profiles generated by both methods were compared to demonstrate the uncertainty reduction qualitatively, and standard error comparison was performed to demonstrate the reduction quantitatively. The comparisons showed that statistical uncertainty was reduced substantially by the RP method. Using the RP method to postprocess MC data, the corresponding MC simulation time was reduced by a factor of 10 without quality reduction in the generated result from the MC data. We concluded that the RP method is an effective technique to increase MC simulation efficiency for generating lateral profiles for axially symmetric pencil beams.