Search Results (52)

Background: Online adaptive radiotherapy (oART) can change the dose distribution briefly to match the shape of the targets and organs at risk. Cone-beam computed tomography (CBCT)-guided oART has been introduced in clinical practice and is expected to improve clinical outcomes. This technology allows treatment as an extension of conventional CT-based image-guided radiotherapy technology and has the advantage of relatively short treatment times. However, the clinical and dosimetric benefits of oART have not yet been adequately reported. Methods: We systematically reviewed previous studies evaluating CBCT-guided oART in patients with malignancies. Case reports, conference abstracts, letters, editorials, and reviews were excluded. PubMed was searched from January 2019 to 22 November 2025, supplemented by hand searching. Study design, cancer site, workflow, treatment time, dosimetric outcomes, feasibility, and clinical outcomes were extracted. Owing to clinical and methodological heterogeneity, findings were synthesized narratively. Results: A total of 127 studies were included. Across most studies, adapted plans improved target coverage and reduced or maintained doses to organs at risk compared with scheduled plans. This review provides an overview of the workflows and latest developments in CBCT-guided oART. Furthermore, the clinical and dosimetric benefits at each site, including the chest, abdomen, pelvis, head, and neck, are shown. Conclusions: The development of oART is expected to lead to the introduction of personalized medicine and improve the clinical outcomes of patients with various cancers. Further research and clinical trials are warranted to establish CBCT-guided oART. This review received no external funding and was registered in PROSPERO (CRD420251237632). Full article

Background/Objectives: Adaptive radiotherapy (ART) is essential yet expensive in prostate cancer treatment. This study aims to preliminarily validate the feasibility of an integrated AI workflow for CBCT-guided adaptive radiotherapy in prostate cancer, and to provide a technical foundation for subsequent clinical translation. Methods: Planning CT and CBCT images from 120 patients were used for training and validation, while 21 patients were reserved for testing. A CycleGAN-ResNet generated synthetic CT (sCT) from CBCT, and an nnU-Net model performed auto-segmentation on the sCT. Image quality and segmentation accuracy were quantitatively assessed. The original plan was recalculated on sCT to evaluate actual dose delivery; if clinical constraints were unmet, adaptive re-optimization was performed, and plans were compared. Results: The total time per fraction in this study was approximately 19 ± 6 min, falling within the reported feasibility range for online ART. sCT image quality was significantly improved, making them suitable for subsequent auto-segmentation and treatment planning. The auto-segmentation technique substantially enhanced contouring efficiency, with the automatically generated contours requiring only minor modifications to meet clinical standards. In dosimetric analysis, the adaptive plans provided superior target coverage, CI, and HI. Compared with the actual dose delivered by the original plan, the adaptive plans yielded lower bladder V40 and lower rectal mean dose/V30/V40/V50. Conclusions: This study preliminarily validated the feasibility of an integrated AI workflow that concatenates CycleGAN-based sCT generation, nnU-Net-based auto-segmentation, and sCT-based adaptive plan re-optimization. Compared with conventional segmented studies, this integrated exploration facilitates a more comprehensive assessment of the potential value of AI technologies in CBCT-guided prostate cancer ART, offering a preliminary solution for promoting a cost-effective adaptive radiotherapy approach. Full article

Background/Objectives: Moderately hypofractionated radiotherapy (MHRT) may shorten treatment duration for cervical cancer but raises concerns regarding toxicity due to substantial interfractional pelvic organ motion. This prospective study evaluated the feasibility, workflow efficiency, dosimetric benefits, and acute toxicities of daily online adaptive radiotherapy (oART)-guided MHRT. Methods: Thirty patients with FIGO 2018 stage IB1–IIB or IIIC1 cervical squamous cell carcinoma receiving definitive chemoradiotherapy were prospectively included between September 2023 and April 2024 (NCT05994300). All patients underwent daily oART, receiving 43.35 Gy in 17 fractions to the pelvic target volume, with a simultaneous integrated boost to 54.40 Gy in 17 fractions for involved lymph nodes. The adaptive workflow consisted of iterative cone-beam computed tomography acquisition, artificial intelligence-assisted contouring, physician review, plan adaptation, and treatment verification. Workflow efficiency, plan selection, target coverage, organ-at-risk (OAR) sparing, treatment completion, early tumor response and acute toxicity were prospectively assessed. Results: A total of 510 adaptive fractions were delivered. The first-attempt adaptation success rate was 99.0%, and adapted plans were selected in 99.4% of fractions. The mean adaptive workflow and total treatment times were 17.3 and 23.3 min per fraction, respectively. Compared with scheduled plans, adapted plans significantly improved target coverage, with V_100% increasing by 7.26% for the planning target volume of the uterus and 8.79% for planning target volume of the cervix (both p < 0.001), while significantly reducing doses to the bladder, rectum, small bowel, bone marrow, and femoral heads. All patients achieved complete clinical response at 3 months. Acute toxicity was generally manageable; Grade ≥ 3 gastrointestinal, genitourinary, and hematologic toxicities occurred in 10%, 0%, and 40% of patients, respectively, including one Grade 4 neutropenia event, and no treatment interruptions. Conclusions: Daily oART-guided MHRT was feasible and efficient, providing improved target coverage and reduced OAR doses compared with scheduled plans. Acute toxicity was acceptable. These findings provide early prospective evidence supporting the feasibility of this treatment strategy and warrant further validation in larger prospective studies with longer follow-up. Full article

Adaptive radiotherapy (ART) transforms radiotherapy from a static treatment plan into a monitored, feedback-driven process that can respond to anatomic and biologic change. This critical narrative review synthesizes foundational physics literature, consensus reports, prospective studies, randomized trials, implementation reports, and ART-specific artificial-intelligence validation studies, with emphasis on image guidance, evidence quality, and clinical actionability. Further the review separates image guidance, reduced margins, gating, tracking, plan-of-the-day selection, offline replanning, and online reoptimization because these related interventions have different evidence bases. ART is most compelling when anatomic change is frequent, dosimetrically important, measurable, and not adequately managed by population margins or conventional image guidance. Quantitative evidence is strongest for dosimetric recovery, organ-at-risk sparing, workflow feasibility, and selected toxicity reduction; evidence for survival improvement remains limited and heterogeneous. Offline ART has mature roles for progressive or systematic change, whereas online MR-guided and cone-beam CT-guided workflows address daily anatomy through same-session contour review, plan selection or reoptimization, and rapid quality assurance. Artificial intelligence can accelerate segmentation, synthetic CT generation, planning, and quality assurance, but clinically meaningful validation requires geometric, dosimetric, time, failure-rate, bias, and post-update monitoring. ART should be deployed through explicit adaptation triggers, validated dose-accumulation methods, independent quality management, and prospective endpoints connecting adaptive decisions to toxicity, tumor control, workflow efficiency, cost, access, and equity. Full article

Accurate positioning remains a central challenge in head and neck radiotherapy because highly conformal dose distributions are delivered near multiple critical organs, and clinically relevant discrepancies may not be resolved by rigid image-guided correction alone. This narrative review synthesizes evidence on positioning management in head and neck radiotherapy, with particular emphasis on the transition from couch correction to action-oriented clinical decision-making. Peer-reviewed studies, guidelines, and key reviews published between January 2005 and April 2026 were identified using PubMed/MEDLINE and reference screening. Positioning uncertainty is often region-specific and dynamic, reflecting lower-neck and shoulder mismatch, mandibular variation, mask-fit deterioration, functional motion, and progressive anatomical changes during treatment. Thermoplastic immobilization, customized headrests, bite blocks, cone-beam computed tomography, six-degree-of-freedom correction, and adaptive workflows have improved reproducibility and verification; however, these approaches do not eliminate mismatches caused by non-rigid posture, immobilization failure, or evolving anatomy. In such situations, the key clinical task is to distinguish rigid setup deviations suitable for online correction from discrepancies requiring repositioning, re-immobilization, repeat simulation, or adaptive replanning. Emerging technologies, including artificial intelligence, dose-of-the-day estimation, corrected CBCT, synthetic CT, rapid replanning, and workflow-integrated decision support, may help close the gap between mismatch detection and intervention selection. Positioning management should therefore evolve from a correction-centered model to an action-oriented framework that links observed geometric or anatomical change to the most appropriate clinical response. Full article

MR-guided adaptive radiotherapy (ART) enables daily plan optimization for prostate cancer but is resource-intensive. This study evaluated dosimetric and clinical outcomes following transition from radiation oncologist (RO)-led to radiation therapist (RTT)-led MR-guided ART. All prostate cancer patients treated with MR-guided ART on a 1.5T MR-linac were retrospectively reviewed. Consecutive RO-led (September 2019–November 2021) and RTT-led (April 2022–October 2023) cohorts were compared, excluding the actual transition period. Toxicities (CTCAE v5.0), dose–volume metrics from daily adapted plans, target volume variation, and biochemical recurrence-free survival (BRFS) were analyzed. A total of 166 patients were included (78 RO-led, 88 RTT-led; median follow-up 40 and 35 months). Dosimetric differences between the cohorts were statistically small (<1%). Rates of G2+ GI adverse events were similar across all timepoints. An increase in on-treatment GU events was observed in the RTT-led cohort (G2+ 27% vs. 9%, G3 incidence n = 2 vs. n = 0), likely reflecting higher baseline urinary dysfunction; no post-treatment differences persisted. Early biochemical outcomes were comparable, with 36-month BRFS of 93.5% (RO-led) and 95.0% (RTT-led). RTT-led MR-guided ART achieved comparable dosimetric quality and early biochemical outcomes to RO-led workflows with adverse advents that resolved in the long term. With structured training and a mature practice setting, RTT-led ART represents a scalable model to support future adaptive radiotherapy practice. Full article

Background/Objectives: Online adaptive radiotherapy (oART) generates plans at each fraction by exploiting AI-assisted optimization engines without explicit user control over modulation. This process challenges quality assurance since measurement-based Patient Specific Quality Assurance (PSQA) cannot be performed daily. This study aimed: (i) to characterize plan complexity in IOE-generated plans for prostate cancer using a reproducible set of PCMs, including the decomposition of inter-patient and intra-patient variability sources; (ii) to evaluate the association between PCMs and delivery accuracy within a cohort-informed SPC framework validated through leave-one-patient-out cross-validation; (iii) to investigate whether inter-fraction anatomical variations explain the observed plan complexity patterns, or whether complexity is predominantly an intrinsic signature of the AI-assisted optimizer. Methods: Twenty-one prostate cancer patients treated on a CBCT-based oART platform were retrospectively analyzed across three anatomical targets: prostatic bed (PrB), prostate (Pr), and prostate with seminal vesicles (PrSV). Six PCMs, namely MU/cGy, Modulation Complexity Score (MCS), Aperture Area Variability (AAV), Leaf Sequence Variability (LSV), Average Leaf Gap (ALG) and Plan Irregularity, were extracted. Additionally, five anatomical metrics (AMs) were computed from daily contours. Linear mixed-effects models (LMEMs) compared reference/online plans, decomposed variance via intraclass correlation coefficients (ICCs), and assessed PCM–gamma passing rate (GPR) associations. Leave-one-patient-out cross-validation (LOPO-CV) evaluated SPC threshold stability. The relationships between PCMs and AMs were investigated using LMEMs. Results: The AI-assisted optimization engine generated plans characterized by elevated monitor unit demand (average MU/cGy ≥ 6.8 ± 0.9) and narrow MLC apertures (ALG ≤ 17.7 mm ± 1.9 mm). No complexity differences emerged between offline and online-adapted plans, nor between anatomical targets. All PCMs showed significant associations with global GPR (p ≤ 0.027), though marginal R² remained low (≤0.122). Notably, GPR dispersion increased systematically at higher complexity values, indicating that highly modulated plans exhibit reduced delivery predictability. LOPO-CV demonstrated stable tolerance/action limits. Anatomical variations explained less than 35% of the total variance in PCMs. Conclusions: Plan complexity in oART reflects the optimization paradigm and patient-specific anatomy rather than daily adaptation. PCMs can serve as surveillance indicators flagging high-risk fractions to support SPC-based monitoring. Full article

Background/Objectives: Patients with early-stage non-small cell lung cancer (NSCLC) or limited lung metastases and compromised lung function, such as those with interstitial lung disease (ILD) or chronic obstructive pulmonary disease (COPD), or other factors rendering them high-risk for surgery or medically inoperable, face increased risks of treatment-related toxicity from stereotactic ablative radiation therapy (SABR). This study evaluated a novel treatment approach to mitigate these risks. Methods: We investigated Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR), delivered as pulsed radiation every three weeks, in patients with <5 cm lung tumors and ILD, COPD, or prior therapy. Treatment occurred between 2022 and 2024. Online adaptive radiotherapy (o-ART) was employed in 20 patients (80%) to modify treatment plans when anatomical changes warranted replanning. Primary outcomes included volumetric tumor response, changes in dose to organs at risk (OARs) and acute events, while secondary outcomes included local and tumor control, and overall survival. Results: Twenty-three patients received PULSAR treatment at doses between 40 Gy and 60 Gy in 5 fractions and one patient received 54 Gy in 3 fractions, with a median follow-up time of 16.2 months. Approximately half of treated patients demonstrated volumetric tumor response, with median residual volume of 70% (range 36–100%) at maximal response. Among the 20 patients (80%) who underwent online adaptive replanning, significant reductions in OAR dosimetry were observed for all organs assessed including the Dmax for heart (p = 0.0053), bronchus (p = 0.0003), esophagus (p = 0.0005), spinal cord (p = 0.025), and the lung V20 Gy and V12.5 Gy (p < 0.0001). Treatment-related toxicity included two grade 1–2 adverse events and six grade 3 events consisting of pneumonitis, dyspnea or lung infection, with no grade 4 or 5 events. Median progression-free survival was 21.1 months, with 1-year overall survival of 74% and 1-year local control of 100%. Conclusions: PULSAR shows promise as a feasible treatment option for high-risk patients with NSCLC or lung metastases, demonstrating no grade 5 events and complete tumor control. Additional research is needed to fully evaluate the safety profile of PULSAR in the high-risk subgroups and whether PULSAR’s treatment intervals and adaptive planning advantages lead to improved long-term outcomes compared to conventional, uninterrupted SABR regimens. Full article

Online adaptive radiotherapy (ART) is rapidly transforming clinical radiation oncology by enabling adaptation of treatment plans based on patient-specific anatomical and biological changes. However, most medical physics training programs lack structured education in ART. To address this critical gap, the Medical Physics Adaptive Radiotherapy (MPART) Fellowship was established at our center to train post-residency or practicing physicists in advanced adaptive technologies and workflows. The MPART Fellowship is a two-year program that provides immersive, platform-specific training in CBCT-guided (Varian Ethos), MR-guided (Elekta Unity), and PET-guided (RefleXion X1) radiotherapy. Fellows undergo modular clinical rotations, hands-on training, and dedicated research projects. The curriculum incorporates competencies in imaging, contouring, online planning, quality assurance, and team-based decision-making. Evaluation is based on the Accreditation Council for Graduate Medical Education competency domains and includes milestone tracking, mentor reviews, and structured presentations. The fellowship attracted applicants from both domestic and international institutions, reflecting strong demand for formal ART training. Out of 22 applications, two fellows have been successfully recruited into the program since 2024. Fellows actively participate in all phases of adaptive workflows and are expected to function at near-attending levels by the second year of their training. Each fellow also leads at least one translational or operational research project aimed at improving ART delivery. Fellows contribute to clinical coverage and lead developmental projects, resulting in presentations and publications at the national and international levels. The MPART Fellowship addresses a vital educational need by equipping medical physicists with the advanced competencies necessary for implementing and leading ART. This program offers a replicable framework for other institutions seeking to advance precision radiation therapy through structured post-residency training in adaptive radiotherapy. As this fellowship program is still in its early phase of establishment, the primary goal of this paper is to introduce the structure, framework, and implementation model of the program. Comprehensive outcome analyses—such as quantitative assessments, fellow feedback, and longitudinal competency evaluations—will be incorporated in future work as additional cohorts complete training. Full article

With the rapid development of artificial intelligence (AI), various deep learning (DL) methods have been introduced into radiation oncology. Among them, the generation of synthetic Computed Tomography (sCT) images has attracted increasing attention, as it supports different clinical scenarios, from image-guided adaptive radiotherapy (IGART) to the simulation-free workflow. This review provides a comprehensive overview of recent studies on DL-based sCT synthesis in radiotherapy from multiple imaging modalities, including Cone-Beam CT (CBCT), Magnetic Resonance Imaging (MRI), and diagnostic CT, and discusses their clinical applications in CBCT-based online adaptive radiotherapy, MRI-guided radiotherapy, and simulation-free workflows. We also examine the architectures of representative DL models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) and summarize emerging training strategies. Finally, we discuss current challenges of clinical translation of DL algorithms into clinical practice and suggest potential directions for future research. Overall, this paper highlights the potential of AI-driven sCT generation to advance treatment planning by reducing imaging burden, improving dose accuracy, and accelerating workflow efficiency, thus ultimately improving the treatment outcome of patient care. Full article

Online adaptive radiotherapy mitigates errors in absorbed dose delivery due to daily anatomical changes during hypofractionated breast treatment, particularly when comprehensive nodal therapy includes the internal mammary chain. To illustrate this, we present a case of a 65-year-old woman with left-sided luminal B invasive carcinoma, who underwent segmentectomy and level 1–2 dissection. Pathology revealed an 18 × 15 × 13 mm primary tumor with lymphovascular invasion, two of eleven axillary nodes positive, and intramammary metastasis, staged pT1cN1a. She received adjuvant docetaxel–cyclophosphamide followed by letrozole. Hypofractionated radiotherapy (40 Gy in 15 fractions) was administered in an inspiration breath-hold setting using a CBCT-guided online-adaptive platform. Adaptive planning improved V95% coverage over the planned treatment for all targets: on average, whole breast coverage increased from 88.4% to 96.3%, supraclavicular from 93.0% to 97.1%, axilla from 90.6% to 96.7%, and internal mammary from 91.8% to 95.9%. Organ-at-risk metrics remained within limits: the mean heart dose increased slightly (from an average of 0.12 Gy in scheduled to 0.15 Gy in adaptive plans). At the same time, the LAD D0.03 cm³ decreased, and the heart V4 Gy fell modestly (from 13.3% in the scheduled plan to 8.2% in the adaptive plan), reflecting low-dose redistribution without exceeding constraints. Lung and thyroid mean doses remained comparable. The patient tolerated treatment well, with no acute toxicity or local recurrence. This case highlights the importance of daily adaptation for complex left-sided radiation treatment involving internal mammary nodes, demonstrating target recovery without exceeding absorbed dose constraints and supporting future studies on control, toxicity, and quality of life. Full article

Background and Objectives: Ultra-hypofractionated radiotherapy (uhRT) is increasingly used for low- and intermediate-risk localized prostate cancer, necessitating exceptional precision compared to conventional fractionation. CBCT-based online-adaptive uhRT may help mitigate pelvic organ motion but has not yet been established in clinical routine. We report initial clinical experiences focusing on the feasibility and technical aspects of treatment delivery. Materials and Methods: Seven patients (35 fractions) with low- or intermediate-risk prostate cancer were treated with online-adaptive uhRT on the Varian Ethos^® system within routine clinical care. The target included the prostate and proximal seminal vesicles (CTV1, 5 × 7.25 Gy), with an integrated boost to the prostate (CTV2, 5 × 8.00 Gy). For each fraction, dose–volume histogram (DVH) parameters for targets and organs at risk (OARs) were recorded retrospectively for both scheduled and adaptive plans, along with the plan selection decision. Plan quality was evaluated per clinical DVH constraints and target coverage. The treatment time was recorded. Results: Online-adaptive uhRT was successfully delivered every day in 5 patients and on alternate days in 2 patients. Mean treatment time was 30:17 (±05:49 SD) minutes per fraction. The median recorded change in target and OAR volumes was <10%. Adaptive plans resulted in a statistically significantly improved target coverage for CTV1 (V100%, p = 0.01), PTV1 (D98%, p < 0.001), PTV2 boost (D98%, p < 0.001) in Wilcoxon signed-rank tests. OAR dose reduction was limited, with a small improvement in bladder V40Gy (p = 0.02). Adaptive plans were applied in 32/35 fractions (91.4%). To encompass intra-fractional motion in 95% of fractions, positional adjustments up to 0.77 cm (longitudinal), 0.37 cm (lateral), and 0.59 cm (sagittal) were required. Conclusions: Online-adaptive uhRT appears feasible, leading to optimized target volume coverage. Considerable treatment times must be taken into account. A second CBCT is recommended to compensate for intra-fractional motion. Further research regarding patient-related endpoints and cost-effectiveness is highly needed. Full article

Background/Objectives: In Spain, pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related death, with only 20% of patients eligible for surgery at diagnosis. For the remaining majority, prognosis is poor and effective non-surgical strategies are needed. Stereotactic MR-guided adaptive radiotherapy (SMART) may facilitate the delivery of ablative doses of radiation safely with low toxicity. This study reports the first national experience in Spain with SMART for patients with borderline resectable (BRPC) or locally advanced pancreatic cancer and evaluates its feasibility, safety, and early clinical outcomes. Methods: A prospective observational study was conducted including 28 patients with histologically confirmed BRPC or LAPC treated between August 2023 and December 2024. All patients received induction chemotherapy—mainly FOLFIRINOX (57.1%)—followed by SMART delivered in five fractions (40–50 Gy) using a 0.35T MR-guided linear accelerator. Daily online adaptive recontouring and replanning were performed for all 140 treatment fractions. Toxicities were assessed using CTCAE v5.0, and survival outcomes were estimated using Kaplan–Meier analysis. Results: The median patient age was 67 years, and 71.4% of tumors were located in the pancreatic head. At a median follow-up of 7.4 months after SMART (12.25 months from diagnosis), 6-month local progression-free survival (LPFS) was 89.3% from the start of SMART and 82.1% from diagnosis. Distant progression-free survival (DPFS) at 6 and 12 months was 92.9% and 68.2%, respectively. Median progression-free survival (PFS) was 11.5 months, and the median treatment-free interval was 5.7 months. Median overall survival (OS) was not reached; 6- and 12-month OS rates were 89.3% and 74.1%, respectively. Treatment-related toxicity was limited to grade 2 abdominal pain in 14.3% of patients, with no grade ≥3 adverse events attributed to SMART. Conclusions: SMART is a feasible and safe treatment modality for BRPC and LAPC in real-world clinical practice. These encouraging early outcomes support further clinical investigation and broader implementation. Full article

Purpose or Objective: To evaluate the feasibility and clinical utility of integrating sequential PSMA-PET imaging into an offline–online adaptive workflow for response-based dominant intraprostatic lesion (DIL)-boosting high-risk prostate cancer treated with stereotactic ablative radiotherapy (SABR). Materials and Methods: As part of a prospective trial, patients were treated on MR- or CBCT-guided adaptive radiotherapy (ART) systems with prostate/pelvic node 5-fraction SABR (36.25 Gy/25 Gy) with DIL boost (50 Gy). Whereas traditional DIL boost volumes delineate full pre-therapy imaging-defined disease (GTVinitial), this study serially refined DIL boost volumes based on treatment response defined by PSMA-PET scans after neoadjuvant androgen deprivation therapy (nADT, GTVmb1) and fraction 3 SABR (GTVmb2). DIL delineation employed PET-PSMA fusion to CT/MR simulation and was guided by a rule-based %SUVmax threshold approach. Comparisons of GTV volumes and OAR dosimetry were performed between plans using GTVinitial versus GTVmb1/GTVmb2 for DIL boost, for each of the initial cohorts of five patients from the initially treated cohorts. Results: Five patients treated on MR-Linac (n = 3) or CBCT-based ART (n = 2) were analyzed. Three patients exhibited complete imaging response after nADT, omitting GTVmb boosts. Offline GTVmb refinements based on PSMA-PET were seamlessly integrated into ART workflows without introducing additional treatment time. DIL GTV volumes significantly decreased (p = 0.03) from an initial mean of 11.4 cc (GTVinitial) to 4.1 cc (GTVmb1) and 3.0 cc (GTVmb2). Dosimetric analysis showed meaningful reductions in OAR doses: rectal wall D0.035 cc decreased by up to 12 Gy, while bladder wall D0.035 cc and V18.3 Gy reduced from 52.3 Gy and 52.3 cc (Plan_initial) to 42.9 Gy and 24.9 cc (Plan_mb2), respectively. Urethra doses remained stable, with minor reductions. Sigmoid and femoral head doses remained within acceptable limits. Online adaptation efficiently addressed daily anatomical variations, enabling simulation-free plan re-optimization. Conclusion: PSMA-PET-guided adaptive microboosting for HRPCa SABR is feasible and effective. Standard MR-Linac and CBCT systems offer practical alternatives to BgRT platforms, enabling biology-driven dose personalization and potentially reducing toxicity. Full article

Background/Objectives: This study aims to present a structured clinical workflow for offline adaptive Biology-guided Radiotherapy (BgRT) using the RefleXion X1 PET-linac system, addressing challenges introduced by inter-treatment anatomical and biological changes. Methods: We propose a decision tree offline adaptation framework based on real-time assessments of Activity Concentration (AC), Normalized Target Signal (NTS), and bounded dose-volume histogram (bDVH%) metrics. Three offline strategies were developed: (1) preemptive adaptation for minor changes, (2) partial re-simulation for moderate changes, and (3) full re-simulation for major anatomical or metabolic alterations. Two clinical cases demonstrating strategies 1 and 2 are presented. Results: The preemptive adaptation strategy was applied in a case with early tumor shrinkage, maintaining delivery parameters within acceptable limits while updating contours and dose distribution. In the partial re-Simulation case, significant changes in PET signal necessitated a same-day PET functional modeling session and plan re-optimization, effectively restoring safe deliverability. Both cases showed reduced target volumes and improved OAR sparing without additional patient visits or tracer injections. Conclusions: Offline adaptive workflows for BgRT provide practical solutions to address inter-fractional changes in tumor structure and function. These strategies can help maintain the safety and accuracy of BgRT delivery and support clinical adoption of PET-guided radiotherapy, paving the way for future online adaptive capabilities. Full article