Background: In pencil beam scanning (PBS) proton therapy, plans are delivered as proton spot maps (PSMs). Although deep learning can rapidly predict 3D dose, direct conversion of dose into deliverable spot patterns remains limited. Purpose: We developed GenSpot, a two stage framework that infers deliverable PSMs from CT and dose, and evaluated it in prostate SBRT by comparing Monte Carlo (MC) doses from GenSpot and clinical PSMs. Methods: GenSpot uses a physics informed projected proton spot map (PrPSM) representation, projecting spots through CT with water equivalent thickness and PDD information to align spots with the CT/dose grid while preserving linearity with spot weights. The dataset included 1,036 fields from 259 prostate SBRT plans, split 80%/10%/10% for training, validation, and testing. A 3D SwinUNETR predicted PrPSMs from CT and dose. Field specific PSMs were reconstructed using column wise nonnegative Lasso regression with precomputed PDD curves. GenSpot and clinical MC doses were compared using MAE, 3D gamma analysis, and composite plan DVH metrics. Results: On the test set, SwinUNETR achieved PrPSM MAE of 0.06 +/- 0.02 with high similarity to clinical PrPSMs. GenSpot MC doses showed low MAE of 0.07 +/- 0.03 Gy in the nonzero dose region and gamma passing rates of 0.90 at the field level and 0.97 at plan level. Composite DVH differences were within 1 Gy for targets and organs at risk, though the CTV showed a modest high dose increase. Spot complexity was similar to clinical plans, with slightly more spots. Prediction and reconstruction averaged 0.02 s and 2.1 s/field. Conclusions: GenSpot generated machine deliverable PSMs from CT and dose whose MC doses closely matched clinical PSM doses in a single institution prostate SBRT cohort. This physics informed dose to spots framework may support automated PBS planning and adaptive replanning, pending broader validation.

Background: To our knowledge, no tools have been installed in clinic for delivered and planned dose differences evaluation. This difference could be large for head and neck patients who suffer the most anatomy changes compared with other treatment sites due to long treatment courses and difficulty in eating. At the same time, variable RBE dose is an increasing concern for proton therapy. The constant RBE 1.1 is widely applied in clinics, however, the real RBE is larger than 1.1 especially at the end of beam range. How they are different and what the influence on plan evaluation are an interesting topic to investigate.

Since the publication of our review article Hyperspectral imaging solutions for brain tissue metabolic and hemodynamic monitoring: past, current and future developments in 2018, the technological and applicational landscape of the use of hyperspectral imaging (HSI) in brain sciences has evolved and transformed significantly. The number of studies and works where HSI has been deployed in its many forms to map and monitor the haemodynamic and metabolic states of cerebral tissues have grown exponentially, to such a point where an update on the cur-rent state of the art is timely, and we believe would be desirable for both long-term experts in the field, as well as for any new researcher approaching it for the first time. In this commentary, we provide a renewed perspective on the newest and latest developments in brain haemodynamic and metabolic monitoring with HSI over the past eight years. Our hope is that even greater breakthroughs and broader, more numerous novel applications will come forward in the future for the technology, that may benefit from this new overview, as they did from the original one.

The variation of the oxygen enhancement ratio (OER) across linear energy transfer (LET) currently lacks a comprehensive mechanistic interpretation and a mechanistic model. Our earlier research revealed a significant correlation between the distribution of double-strand breaks (DSBs) within 3D genome and radiation-induced cell death, which offers valuable insights into the oxygen effect. We propose a model where the reaction of oxygen is represented as the probability of inducing DNA strand breaks. Then it is integrated into a track-structure Monte Carlo simulation to investigate the impact of oxygen on the distribution of DSBs within 3D genome. Using the parameters from our previous study, we calculate the OER values related to cell survival. Results show that the incidence ratios of clustered DSBs within a single topologically associating domain (TAD) (case 2) and within frequently interacting TADs (case 3) under aerobic and hypoxic conditions align with the trend in the OER of cell survival across LET. Our OER curves exhibit good correspondence with experimental data. This study provides a potentially mechanistic explanation for changes in OER across LET. High-LET irradiation leads to dense ionization events, resulting in an overabundance of lesions that readily induce case 2 and case 3, which have substantially higher probabilities of cell killing than other damage patterns. This may contribute to the main mechanism governing the variation of OER for high LET. Our study further underscores the importance of the DSB distribution within 3D genome in the context of radiation-induced cell death.