Interim 18F-FDG-PET based response-adaptive dose escalation of proton therapy for head and neck cancer: a treatment planning feasibility study.

BACKGROUND: Image-driven dose escalation to tumor subvolumes has been proposed to improve treatment outcome in head and neck cancer (HNC). We used 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) acquired at baseline and into treatment (interim) to identify biologic target volumes (BTVs). We assessed the feasibility of interim dose escalation to the BTV with proton therapy by simulating the effects to organs at risk (OARs). METHODS: We used the semiautomated just-enough-interaction (JEI) method to identify BTVs in 18F-FDG-PET images from nine HNC patients. Between baseline and interim FDG-PET, patients received photon radiotherapy. BTV was identified assuming that high standardized uptake value (SUV) at interim reflected tumor radioresistance. Using Eclipse (Varian Medical Systems), we simulated a 10% (6.8 Gy(RBE1.1)) and 20% (13.6 Gy(RBE1.1)) dose escalation to the BTV with protons and compared results with proton plans without dose escalation. RESULTS: At interim 18F-FDG-PET, radiotherapy resulted in reduced SUV compared to baseline. However, spatial overlap between high-SUV regions at baseline and interim allowed for BTV identification. Proton therapy planning demonstrated that dose escalation to the BTV was feasible, and except for some 20% dose escalation plans, OAR doses did not significantly increase. CONCLUSION: Our in silico analysis demonstrated the potential for interim 18F-FDG-PET response-adaptive dose escalation to the BTV with proton therapy. This approach may give more efficient treatment to HNC with radioresistant tumor subvolumes without increasing normal tissue toxicity. Studies in larger cohorts are required to determine the full potential for interim 18F-FDG-PET-guided dose escalation of proton therapy in HNC.

Script-based automatic radiotherapy planning for cervical cancer.

BACKGROUND: This study aimed to develop fully automated script-based radiotherapy treatment plans for cervical cancer patients, and evaluate them against clinically accepted plans, as validation before clinical implementation. MATERIAL AND METHODS: In this retrospective planning study, treatment plans for 25 locally advanced cervical cancer (LACC) patients with up to three dose levels were included. Fully automated plans were created using an in-house developed Python script in RayStation, and compared to clinically accepted manually made plans. Quantitatively, relevant dose statistics were compared, and average dose volume histograms (DVHs) were analyzed. Qualitatively, a blinded plan comparison was conducted between the clinical and automatic plans. The accuracy of treatment plan delivery was verified with the Delta4 Phantom+. RESULTS: The quantitative evaluation showed that target coverage was acceptable for all the automatic and clinical plans. The automatic plans were significantly more conformal than the clinical plans; median of 1.03 vs. 1.12. Mean doses to almost all organs at risk (OARs) were reduced in the automatic plans, with a median reduction of between 0.6 Gy and 1.9 Gy. In the blinded plan comparison, the automatic plans were the preferred plans or of equal quality as the clinical plans in 99% of the cases. In addition, plan delivery was excellent, with a mean gamma passing rate of 99.8%. Complete script-based plans were generated in 30-45 min; about four to ten times faster than manually made plans. CONCLUSION: The automatic plans had acceptable target coverage, lower doses to almost all OARs, more conformal dose distributions, and were predominantly preferred by the clinicians. Based on these results, our institution has implemented the script for clinical use.

Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer.

AIM: To train and validate a comprehensive deep-learning (DL) segmentation model for loco-regional breast cancer with the aim of clinical implementation. METHODS: DL segmentation models for 7 clinical target volumes (CTVs) and 11 organs at risk (OARs) were trained on 170 left-sided breast cancer cases from two radiotherapy centres in Norway. Another 30 patient cases were used for validation, which included the evaluation of Dice similarity coefficient and Hausdorff distance, qualitative scoring according to clinical usability, and relevant dosimetric parameters. The manual inter-observer variation (IOV) was also evaluated and served as a benchmark. Delineation of the target volumes followed the ESTRO guidelines. RESULTS: Based on the geometric similarity metrics, the model performed significantly better than IOV for most structures. Qualitatively, no or only minor corrections were required for 14% and 71% of the CTVs and 72% and 26% of the OARs, respectively. Major corrections were required for 15% of the CTVs and 2% of the OARs. The most frequent corrections occurred in the cranial and caudal parts of the structures. The dose coverage, based on D98 > 95%, was fulfilled for 100% and 89% of the breast and lymph node CTVs, respectively. No differences in OAR dose parameters were considered clinically relevant. The model was implemented in a commercial treatment planning system, which generates the structures in 1.5 min. CONCLUSION: Convincing results from the validation led to the decision of clinical implementation. The clinical use will be monitored regarding applicability, standardization and efficiency.