Purpose: To demonstrate the feasibility of improving prostate cancer patient outcomes with PBS proton LET_d optimization. Methods: SFO, IPT-SIB, and LET-optimized plans were created for 12 patients, and generalized-tissue and disease-specific LET-dependent RBE models were applied. The mean LET_d in several structures was determined and used to calculate mean RBEs. LET_d- and dose–volume histograms (LVHs/DVHs) are shown. TODRs were defined based on clinical dose goals and compared between plans. The impact of robust perturbations on LET_d, TODRs, and DVH spread was evaluated. Results: LET_d optimization achieved statistically significant increased target volume LET_d of ~4 keV/µm compared to SFO and IPT-SIB LET_d of ~2 keV/µm while mitigating OAR LET_d increases. A disease-specific RBE model predicted target volume RBEs > 1.5 for LET-optimized plans, up to 18% higher than for SFO plans. LET-optimized target LVHs/DVHs showed a large increase not present in OARs. All RBE models showed a statistically significant increase in TODRs from SFO to IPT-SIB to LET-optimized plans. RBE = 1.1 does not accurately represent TODRs when using LET_d optimization. Robust evaluations demonstrated a trade-off between increased mean target LET_d and decreased DVH spread. Conclusion: The demonstration of improved TODRs provided via LET_d optimization shows potential for improved patient outcomes.

This study reports the potential operational improvement provided by reduced energy layer switching time and total beam delivery time of a 3-room IBA ProteusPLUS system. Eight treatment fields, including various disease sites and a reference cube, were analyzed for energy layer switching time and total delivery time before and after an energy layer switching time upgrade. The upgraded beam delivery time estimated the total treatment capacity for a 12-h treatment day using a 3-room system with a single common cyclotron. The potential increase in revenue was estimated using Medicare reimbursement rates for proton therapy intermediate. The average layer switching time was reduced from 6.33 to 1.86 s, reducing the average field delivery time from 240 to 113 s. Overall, the average energy layer switching time reduction was 70.6%, and the average total treatment field delivery time was reduced by 52.6%. Head and neck cancer fields experienced the greatest treatment time reduction of 59.5%, reducing from 236 to 95.5 s. The treatment field delivery time reduction allowed an increase from 60 to 127 patients per day, an increase of 112%. Reductions in energy layer switching time significantly decreased treatment field delivery time. Reduced treatment field delivery time may provide clinical benefits to patients through improved patient alignment and comfort. Increased treatment capacity could also improve patient access to proton therapy for disease sites that may experience clinical benefits, such as head and neck cancer.

Proton beam therapy for head and neck cancers traditionally employs a fixed relative biological effectiveness (RBE) of 1.1, which may underestimate actual biological effects in critical structures. This study evaluates how Linear Energy Transfer (LET) optimization could potentially prevent radiation-induced brachial plexopathy (RIBP). (1) Case presentation: A 65-year-old male with stage IVA p16-positive oropharyngeal squamous cell carcinoma received pencil-beam-scanning intensity-modulated proton therapy with concurrent cisplatin. Due to a right level 4 neck node, the high-risk target volume overlapped with the brachial plexus, resulting in a D0.1cc of 70.3 Gy (RBE = 1.1). Four years post-treatment, the patient developed progressive right upper extremity paresthesia, weakness, and dysesthesia. Electromyography revealed myokymia consistent with brachial plexopathy, while MRI showed hyperintensity of the right brachial plexus corresponding to the radiation field. Conservative treatment with pentoxifylline, gabapentin, and physical therapy improved his symptoms. (2) Methods: The original treatment plan was retrospectively analyzed using Monte Carlo dose algorithms and LET-dependent RBE models from McMahon and McNamara. An LET-optimized plan was created to limit LET_d to 2.0 keV/µm in the brachial plexus. (3) Results: The relative biological equivalent (RBE) dose to 0.1cc of the brachial plexus was 77.8 Gy (CGE RBE), exceeding tolerance. The LET-optimized plan reduced the brachial plexus D0.1cc to 59.4 Gy (RBE = 1.1) and 63.2 Gy (CGE RBE), an 18.8% decrease, while maintaining target coverage. LET_d, within the brachial plexus enhancement, decreased from 5.3 to 2.6 keV/μm. (4) Conclusion: This case highlights the potential clinical importance of LET optimization in proton therapy planning, particularly when organs-at-risk overlap with target volumes. By reducing LET_d from 5.3 to 2.6 keV/μm and biological equivalent dose by 18.8%, LET optimization could potentially prevent late toxicities, like RIBP, while maintaining target coverage.

Securing insurance authorization for proton therapy remains a challenge for many centers. When health insurance or employer-sponsored health plans deny coverage, Independent Review Organizations (IROs) can review proton therapy cases. However, despite providing an independent review pathway, IROs are often underutilized in securing approvals for care following a denial. We analyzed trends in IRO approvals, strategies, and legal procedures using publicly available data from California (CA), Washington (WA), and New York (NY). The aggregate analysis of the 3 states revealed an IRO average approval rate for proton therapy of 42.1%, with varying trends across states. All 3 states showed increases in IRO approval rates over time, averaging annual increases of +5.0%, +2.3%, and +7.2% for CA, WA, and NY, respectively. Sarcoma showed the highest IRO approval rate at 84.6%, followed by GYN cancers at 55.6% and breast cancer at 51.4%. CNS tumors and lymphomas had moderate approval rates at 44.7% and 40.0% respectively. Head and neck cancers had a 33.3% approval rate, while thoracic malignancies were at 36.8%. The lowest IRO approval rate was seen in prostate cancer at 16.5%. Qualitative analysis revealed that referencing guidelines, discussing published studies, citing trial inclusion, and submitting personalized letters were associated with higher IRO approval rates. IRO reviews provide a more objective remedy for patients denied care through internal appeals, particularly for plans with historically unfavorable proton policies. Our study demonstrates that IRO appeals provide a valuable pathway to proton therapy access with higher overturn rates improving significantly in recent years. Nearly half of initially denied patients eventually received approval through this process. Proton centers should strategically utilize IRO reviews to increase patient access and improve approval chances.

Purpose: This study introduces the prostate cancer linear energy transfer sensitivity index (PCLSI) as a novel method to predict relative biological effectiveness (RBE) in prostate cancer using linear energy transfer (LET) in proton therapy based on screening for DNA repair mutations. Materials and Methods: Five prostate cancer cell lines with DNA repair mutations known to cause sensitivity to LET and DNA repair inhibitors were examined using published data. Relative Du145 LET sensitivity data were leveraged to deduce the LET equivalent of olaparib doses. The PCLSI model was built using three of the prostate cancer cell lines (LNCaP, 22Rv1 and Du145) with DNA mutation frequency from patient cohorts. The PCLSI model was compared against two established RBE models, McNamara and McMahon, for LET-optimized prostate cancer treatment plans. Results: The PCLSI model relies on the presence of eight DNA repair mutations: AR, ATM, BRCA1, BRCA2, CDH1, ETV1, PTEN and TP53, which are most likely to predict increased LET sensitivity and RBE in proton therapy. In the LET-optimized plan, the PCLSI model indicates that prostate cancer cells with these DNA repair mutations are more sensitive to increased LET than the McNamara and McMahon RBE models, with expected RBE increases ranging from 11%–33% at 2keV/µm. Conclusions: The PCLSI model predicts increasing RBE as a function of LET in the presence of certain genetic mutations. The integration of LET-optimized proton therapy and genetic mutation profiling could be a significant step toward the use of individualized medicine to improve outcomes using RBE escalation without the potential toxicity of physical dose escalation.