Collection efficiencies of cylindrical and plane parallel ionization chambers: analytical and numerical results and implications for experimentally determined correction factors.

Objectives.To derive a collection efficiency formula,fGauss, for cylindrical ionization chambers in pulsed radiation beams from a volume recombination model of Boaget al(1996Phys. Med. Biol.41885-97) including free electrons. To validatefGaussand a parallel plate chamber formulafexpusing an ion transport code and calculate changes in collection efficiencies caused by electric field charge screening at 0.1-100 mGy doses-per-pulse. And to determine collection efficienciesCE∞predicted at infinite voltage in the absence of avalanche effects by fitting scaled formulae to efficiencies computed for 100-400 V chamber voltages and 10 and 100 mGy doses-per-pulse.Approach.Calculations were performed for an idealized parallel plate chamber with 2 mm electrode separationd, and for an idealized cylindrical chamber with 0.5 and 2.333 mm inner and electrode radiirinandrout.Main results.fGaussandfexppredict the same collection efficiencies for cylindrical and parallel plate chambers satisfyingd2=(rout2-rin2)ln(rout/rin)/2, an equivalence condition met by the chambers studied. Without charge screening, efficiencies computed using the code equalledfGaussandfexp. With screening, efficiencies changed by ⩽0.03%, ⩽1.1% and ⩽21.3% at 1, 10 and 100 mGy doses-per-pulse, and differed between the chambers by ⩽0.9% and ⩽19.6% at ⩽10 and 100 mGy dose-per-pulse. For fits offexpandfGauss,CE∞values were ⩽1.2% and ⩽17.6% from unity at 10 and 100 mGy per pulse respectively, closer than for other formulae tested.Significance.Allowing for screening,fGaussandfexpdescribed computed collection efficiencies to within 0.03%, 1.1% and 21.3% at doses-per-pulse ⩽1, 10 and 100 mGy. Equivalence of the two chambers broke down at 100 mGy per pulse. Departures ofCE∞values from unity suggest that collection efficiencies determined experimentally by fittingfGaussorfexpto readings made at multiple voltages will be accurate to within 1.2% and 17.6% at 10 and 100 mGy per pulse respectively.

Navigating the straits: realizing the potential of proton FLASH through physics advances and further pre-clinical characterization.

Ultra-high dose-rate 'FLASH' radiotherapy may be a pivotal step forward for cancer treatment, widening the therapeutic window between radiation tumour killing and damage to neighbouring normal tissues. The extent of normal tissue sparing reported in pre-clinical FLASH studies typically corresponds to an increase in isotoxic dose-levels of 5-20%, though gains are larger at higher doses. Conditions currently thought necessary for FLASH normal tissue sparing are a dose-rate ≥40 Gy s-1, dose-per-fraction ≥5-10 Gy and irradiation duration ≤0.2-0.5 s. Cyclotron proton accelerators are the first clinical systems to be adapted to irradiate deep-seated tumours at FLASH dose-rates, but even using these machines it is challenging to meet the FLASH conditions. In this review we describe the challenges for delivering FLASH proton beam therapy, the compromises that ensue if these challenges are not addressed, and resulting dosimetric losses. Some of these losses are on the same scale as the gains from FLASH found pre-clinically. We therefore conclude that for FLASH to succeed clinically the challenges must be systematically overcome rather than accommodated, and we survey physical and pre-clinical routes for achieving this.

ImmunoChemoradiation for Non-Small Cell Lung Cancer: A Meta-Analysis of Factors Influencing Survival Benefit in Combination Trials.

PURPOSE: Adding immune checkpoint blockade (ICB) to concurrent chemoradiotherapy (cCRT) has improved overall survival (OS) for inoperable locally advanced non-small cell lung cancer. Trials of cCRT-ICB are heterogeneous for factors such as tumor stage and histology, programmed cell death ligand-1 (PDL-1) status, and cCRT-ICB schedules. We therefore aimed to determine the ICB contribution to survival across studies and identify factors associated with survival gain. METHODS AND MATERIALS: Data were collated from cCRT-ICB clinical studies published 2018 to 2022 that treated 2196 patients with non-small cell lung cancer (99% stage 3). Associations between 2-year OS and ICB, CRT, patient and tumor factors were investigated using metaregression. A published model of survival after radiation therapy (RT) or CRT was extended to include ICB effects. The model was fitted simultaneously to the cCRT-ICB data and data previously compiled for RT/CRT treatments alone. The net ICB contribution (OS gain) and its associations with factors were described by fitted values of ICB terms added to the model. Statistical significance was determined by likelihood-ratio testing. RESULTS: The gain in 2-year OS from ICB was 9.9% overall (95% CI, 7.6%, 12.2%; P = .018). Both OS gain and 2-year OS itself rose with increasing planned ICB duration (P = .008, .002, respectively) and with tumor PDL-1 ≥ 1% (P = .034, .023). Fitted OS gains were also greater for patients with stage 3B/C disease (P = .021). OS gain was not associated with tumor histology, patient performance status, radiation therapy dose, ICB drug type (anti-PDL-1 vs anti-programmed cell death-1), or whether ICB began concurrently with or after cCRT. CONCLUSIONS: Fitted gains in 2-year OS due to ICB were higher in cohorts with greater fractions of stage 3B/C patients and patients with tumor PDL-1 ≥ 1%. OS gain was also significantly higher in a single cohort with a planned ICB duration of 2 years rather than 1, but was not associated with whether ICB treatment began during versus after CRT.

Sorting lung tumor volumes from 4D-MRI data using an automatic tumor-based signal reduces stitching artifacts.

PURPOSE: To investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D-magnetic resonance (4D-MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes. METHODS: (4D-MRI) scans were collected for 10 lung cancer patients using a 2D T2-weighted single-shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor-motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison. For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test. RESULTS: The TumorPC1 signal was most strongly correlated with superior-inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior-inferior tumor motion (p < 0.05). Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p = 0.02-0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one. CONCLUSION: Tumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy.

A Biomathematical Model of Tumor Response to Radioimmunotherapy With αPDL1 and αCTLA4.

There is evidence of synergy between radiotherapy and immunotherapy. Radiotherapy can increase liberation of tumor antigens, causing activation of antitumor T-cells. This effect can be boosted with immunotherapy. Radioimmunotherapy has potential to increase tumor control rates. Biomathematical models of response to radioimmunotherapy may help on understanding of the mechanisms affecting response, and assist clinicians on the design of optimal treatment strategies. In this work we present a biomathematical model of tumor response to radioimmunotherapy. The model uses the linear-quadratic response of tumor cells to radiation (or variation of it), and builds on previous developments to include the radiation-induced immune effect. We have focused this study on the combined effect of radiotherapy and αPDL1/ αCTLA4 therapies. The model can fit preclinical data of volume dynamics and control obtained with different dose fractionations and αPDL1/ αCTLA4. A biomathematical study of optimal combination strategies suggests that a good understanding of the involved biological delays, the biokinetics of the immunotherapy drug, and the interplay between them, may be of paramount importance to design optimal radioimmunotherapy schedules. Biomathematical models like the one we present can help to interpret experimental data on the synergy between radiotherapy and immunotherapy, and to assist in the design of more effective treatments.

Roadmap for precision preclinical x-ray radiation studies.

This Roadmap paper covers the field of precision preclinical x-ray radiation studies in animal models. It is mostly focused on models for cancer and normal tissue response to radiation, but also discusses other disease models. The recent technological evolution in imaging, irradiation, dosimetry and monitoring that have empowered these kinds of studies is discussed, and many developments in the near future are outlined. Finally, clinical translation and reverse translation are discussed.

Collection efficiencies of ionization chambers in pulsed radiation beams: an exact solution of an ion recombination model including free electron effects.

Objective.Boaget al(1996) formulated a key model of collection efficiency for ionization chambers in pulsed radiation beams, in which some free electrons form negatively charged ions with a density that initially varies exponentially across the chamber. This non-uniform density complicates ion recombination calculations, in comparison with Boag's 1950 work in which a collection efficiency formula,f, was straightforwardly obtained assuming a uniform negative ion cloud. Boaget al(1996) therefore derived collection efficiency formulaef',f″ andf'″ based on three approximate descriptions of the exponentially-varying negative ion cloud, each uniform within a region. Collection efficiencies calculated by Boaget al(1996) using these formulae differed by a maximum of 5.1% relative (at 144 mGy dose-per-pulse with 212 V applied over a 1 mm electrode separation) and all three formulae are often used together. Here an exact solution of the exponentially-varying model is obtained.Approach.The exact solution was derived from a differential equation relating the number of negative ions collected from within some distance of the anode to numbers of ions initially located within that region. Using the resulting formula,fexp, collection efficiencies were calculated for a range of ionization chamber properties and doses-per-pulse, and compared withf,f',f″ andf″' values and results from an ion transport code.Main results.fexpvalues agreed to 5 decimal places with ion transport code results. The maximum relative difference betweenfexpandf″', which was often closest tofexp, was 0.78% for the chamber properties and doses-per-pulse studied by Boaget al(1996), rising to 6.1% at 1 Gy dose-per-pulse and 2 mm electrode separation.Significance.Use offexpshould reduce ambiguities in collection efficiencies calculated using the approximate formulae, although like themfexpdoes not account for electric field distortion, which becomes substantial at doses-per-pulse ≥100 mGy.

Dose-Response Analysis Describes Particularly Rapid Repopulation of Non-Small Cell Lung Cancer during Concurrent Chemoradiotherapy.

(1) Purpose: We analysed overall survival (OS) rates following radiotherapy (RT) and chemo-RT of locally-advanced non-small cell lung cancer (LA-NSCLC) to investigate whether tumour repopulation varies with treatment-type, and to further characterise the low α/ß ratio found in a previous study. (2) Materials and methods: Our dataset comprised 2-year OS rates for 4866 NSCLC patients (90.5% stage IIIA/B) belonging to 51 cohorts treated with definitive RT, sequential chemo-RT (sCRT) or concurrent chemo-RT (cCRT) given in doses-per-fraction ≤3 Gy over 16-60 days. Progressively more detailed dose-response models were fitted, beginning with a probit model, adding chemotherapy effects and survival-limiting toxicity, and allowing tumour repopulation and α/ß to vary with treatment-type and stage. Models were fitted using the maximum-likelihood technique, then assessed via the Akaike information criterion and cross-validation. (3) Results: The most detailed model performed best, with repopulation offsetting 1.47 Gy/day (95% confidence interval, CI: 0.36, 2.57 Gy/day) for cCRT but only 0.30 Gy/day (95% CI: 0.18, 0.47 Gy/day) for RT/sCRT. The overall fitted tumour α/ß ratio was 3.0 Gy (95% CI: 1.6, 5.6 Gy). (4) Conclusion: The fitted repopulation rates indicate that cCRT schedule durations should be shortened to the minimum in which prescribed doses can be tolerated. The low α/ß ratio suggests hypofractionation should be efficacious.

Quantitative Analysis of Radiation-Associated Parenchymal Lung Change.

We present a novel classification system of the parenchymal features of radiation-induced lung damage (RILD). We developed a deep learning network to automate the delineation of five classes of parenchymal textures. We quantify the volumetric change in classes after radiotherapy in order to allow detailed, quantitative descriptions of the evolution of lung parenchyma up to 24 months after RT, and correlate these with radiotherapy dose and respiratory outcomes. Diagnostic CTs were available pre-RT, and at 3, 6, 12 and 24 months post-RT, for 46 subjects enrolled in a clinical trial of chemoradiotherapy for non-small cell lung cancer. All 230 CT scans were segmented using our network. The five parenchymal classes showed distinct temporal patterns. Moderate correlation was seen between change in tissue class volume and clinical and dosimetric parameters, e.g., the Pearson correlation coefficient was ≤0.49 between V30 and change in Class 2, and was 0.39 between change in Class 1 and decline in FVC. The effect of the local dose on tissue class revealed a strong dose-dependent relationship. Respiratory function measured by spirometry and MRC dyspnoea scores after radiotherapy correlated with the measured radiological RILD. We demonstrate the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible.