Benefit of Radiation Boost After Whole-Breast Radiotherapy

PURPOSE: To determine whether a boost to the tumor bed after breast-conserving surgery (BCS) and radiotherapy (RT) to the whole breast affects local control and disease-free survival. METHODS AND MATERIALS: A total of 1,138 patients with pT1 to pT2 breast cancer underwent adjuvant RT at the University of Florence. We analyzed only patients with a minimum follow-up of 1 year (range, 1-20 years), with negative surgical margins. The median age of the patient population was 52.0 years (+/-7.9 years). The breast cancer relapse incidence probability was estimated by the Kaplan-Meier method, and differences between patient subgroups were compared by the log rank test. Cox regression models were used to evaluate the risk of breast cancer relapse. RESULTS: On univariate survival analysis, boost to the tumor bed reduced breast cancer recurrence (p < 0.0001). Age and tamoxifen also significantly reduced breast cancer relapse (p = 0.01 and p = 0.014, respectively). On multivariate analysis, the boost and the medium age (45-60 years) were found to be inversely related to breast cancer relapse (hazard ratio [HR], 0.27; 95% confidence interval [95% CI], 0.14-0.52, and HR 0.61; 95% CI, 0.37-0.99, respectively). The effect of the boost was more evident in younger patients (HR, 0.15 and 95% CI, 0.03-0.66 for patients <45 years of age; and HR, 0.31 and 95% CI, 0.13-0.71 for patients 45-60 years) on multivariate analyses stratified by age, although it was not a significant predictor in women older than 60 years. CONCLUSION: Our results suggest that boost to the tumor bed reduces breast cancer relapse and is more effective in younger patients

Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI

Proton therapy could benefit from non-invasively gaining tumour microstructure information, at both planning and monitoring stages. The anatomical location of brain tumours, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional non-invasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion-weighted MRI (DW-MRI), are non-specific. The aim of this study was to retrieve discriminative microstructural markers from conventional ADC for meningiomas treated with proton therapy. These markers were employed for tumour grading and tumour response assessment

Optimizing b‐values schemes for diffusion MRI of the brain with segmented Intravoxel Incoherent Motion (IVIM) model

PurposeTo define an optimal set of b-values for accurate derivation of diffusion MRI parameters in the brain with segmented Intravoxel Incoherent Motion (IVIM) model. MethodsSimulations of diffusion signals were performed to define an optimal set of b-values targeting different perfusion regimes, by relying on an optimization procedure which minimizes the total relative error on estimated IVIM parameters computed with a segmented fitting procedure. Then, the optimal b-values set was acquired in vivo on healthy subjects and skull base chordoma patients to compare the optimized protocol with a clinical one. ResultsThe total relative error on simulations decreased of about 40% when adopting the optimal set of 13 b-values (0 10 20 40 50 60 200 300 400 1200 1300 1400 1500 s/mm(2)), showing significant differences and increased precision on D and f estimates with respect to simulations with a non-optimized b-values set. Similarly, in vivo acquisitions demonstrated a dependency of IVIM parameters on the b-values array, with differences between the optimal set of b-values and a clinical non-optimized acquisition. IVIM parameters were compatible to literature values, with D (0.679/0.701 [0.022/0.008] center dot 10(-3)mm(2)/s), f (5.49/5.80 [0.70/1.14] %), and D* (8.25/7.67 [0.92/0.83] center dot 10(-3)mm(2)/s) median [interquartile range] estimates for white matter/gray matter in volunteers and D (0.709/0.715/1.06 [0.035/0.023/0.271] center dot 10(-3)mm(2)/s), f (7.08/7.84/21.54 [1.20/1.06/6.05] %), and D* (10.85/11.84/2.32 [1.38/2.32/4.94] center dot 10(-3)mm(2)/s) for white matter/gray matter/Gross Tumor Volume in patients with skull-base chordoma tumor. ConclusionsThe definition of an optimal b-values set can improve the estimation of quantitative IVIM parameters. This allows setting up an optimized approach that can be adopted for IVIM studies in the brain

Microstructural parameters from DW-MRI for tumour characterization and local recurrence prediction in particle therapy of skull-base chordoma

Background Quantitative imaging such as Diffusion-Weighted MRI (DW-MRI) can be exploited to non-invasively derive patient-specific tumor microstructure information for tumor characterization and local recurrence risk prediction in radiotherapy. Purpose To characterize tumor microstructure according to proliferative capacity and predict local recurrence through microstructural markers derived from pre-treatment conventional DW-MRI, in skull-base chordoma (SBC) patients treated with proton (PT) and carbon ion (CIRT) radiotherapy. Methods Forty-eight patients affected by SBC, who underwent conventional DW-MRI before treatment and were enrolled for CIRT (n = 25) or PT (n = 23), were retrospectively selected. Clinically verified local recurrence information (LR) and histological information (Ki-67, proliferation index) were collected. Apparent diffusion coefficient (ADC) maps were calculated from pre-treatment DW-MRI and, from these, a set of microstructural parameters (cellular radius R, volume fraction vf, diffusion D) were derived by applying a fine-tuning procedure to a framework employing Monte Carlo simulations on synthetic cell substrates. In addition, apparent cellularity (ρapp) was estimated from vf and R for an easier clinical interpretation. Histogram-based metrics (mean, median, variance, entropy) from estimated parameters were considered to investigate differences (Mann-Whitney U-test, α = 0.05) in estimated tumor microstructure in SBCs characterized by low or high cell proliferation (Ki-67). Recurrence-free survival analyses were also performed to assess the ability of the microstructural parameters to stratify patients according to the risk of local recurrence (Kaplan-Meier curves, log-rank test α = 0.05). Results Refined microstructural markers revealed optimal capabilities in discriminating patients according to cell proliferation, achieving best results with mean values (p-values were 0.0383, 0.0284, 0.0284, 0.0468, and 0.0088 for ADC, R, vf, D, and ρapp, respectively). Recurrence-free survival analyses showed significant differences between populations at high and low risk of local recurrence as stratified by entropy values of estimated microstructural parameters (p = 0.0110). Conclusion Patient-specific microstructural information was non-invasively derived providing potentially useful tools for SBC treatment personalization and optimization in particle therapy

Radiomics and Dosiomics for Predicting Local Control After Carbon-Ion Radiotherapy in Skull-Base Chordoma

none11siSkull-base chordomas (SBC) are rare tumours with unfavourable outcomes, even when undergoing advanced treatments such as carbon-ion radiotherapy (CIRT). By retrospectively analysing imaging (MRI, CT), treatment (dose maps) and clinical information available before treatment, the potential use of radiomics and dosiomics for risk modelling targeting SBC treated with CIRT was explored. Despite the small sample size, dosiomic features appear to be promising factors related to local control in SBC, with worse outcomes being associated to higher dose heterogeneity. Risk models exploiting all sources of information showed slightly inferior but good performance, suggesting that multi-parametric approaches are worth being pursued for patient risk stratification. This study is put forward as groundwork for radiomic analyses targeting SBC in CIRT.noneBuizza, Giulia; Paganelli, Chiara; D’Ippolito, Emma; Fontana, Giulia; Molinelli, Silvia; Preda, Lorenzo; Riva, Giulia; Iannalfi, Alberto; Valvo, Francesca; Orlandi, Ester; Baroni, GuidoBuizza, Giulia; Paganelli, Chiara; D’Ippolito, Emma; Fontana, Giulia; Molinelli, Silvia; Preda, Lorenzo; Riva, Giulia; Iannalfi, Alberto; Valvo, Francesca; Orlandi, Ester; Baroni, Guid

From a waiting list to a priority list: A computerized model for an easy-to-manage and automatically updated priority list in the booking of patients waiting for radiotherapy

Aims and background. Waiting time for radiotherapy is a major problem in clinical practice. We developed a model to create a priority list of patients waiting for radiotherapy according to clinical criteria, where booking of patients is not on a "firstcome, first-served" basis and where prioritization has not been left up to individual discretion. Methods. The system is based on an algorithm that assigns to each patient a personal code (priority code, PC) that can be used as a continuous variable to have a priority list. PCpatient = D0patient + PWT subgroup of treatment. Palliative treatments were categorized according to the clinical urgency. Radical treatments were stratified by primary tumors, by the setting of treatment (preoperative, curative, postoperative) and by the main prognostic factors. Each subgroup of patients has a "priority waiting time" (PWT subgroup of treatment). Calculation of the PC starts from a differentiated date according to clinical scenario [Reference date (D0)], which is taken from the clinical history of the patient. Results. Patients are differentiated according to clinical criteria and according to time elapsed from diagnosis. The priority list can be automatically updated day by day. Delays in patient referral or imaging availability are minimized. Conclusions. The model represents a tool for an objective and automatic prioritization of the patients who are waiting for radiotherapy. © II Pensiero Scientifico Editore downloaded by EXCERPTA MEDICA

Investigating DWI changes in white matter of meningioma patients treated with proton therapy

Purpose To evaluate changes in diffusion and perfusion-related properties of white matter (WM) induced by proton therapy, which is capable of a greater dose sparing to organs at risk with respect to conventional X-ray radiotherapy, and to eventually expose early manifestations of delayed neuro-toxicities. Methods Apparent diffusion coefficient (ADC) and IVIM parameters (D, D* and f) were estimated from diffusion-weighted MRI (DWI) in 46 patients affected by meningioma and treated with proton therapy. The impact on changes in diffusion and perfusion-related WM properties of dose and time, as well as the influence of demographic and pre-treatment clinical information, were investigated through linear mixed-effects models. Results Decreasing trends in ADC and D were found for WM regions hit by medium–high (30–40 Gy(RBE)) and high (&gt;40 Gy(RBE)) doses, which are compatible with diffusion restriction due to radiation-induced cellular injury. Significant influence of dose and time on median ADC changes were observed. Also, D* showed a significant dependency on dose, whereas f consistently showed no dependency on dose and time. Age, gender and surgery extent were also found to affect changes in ADC. Conclusions These results overall agree with those from studies conducted on cohorts of mixed proton and X-ray radiotherapy patients. Future work should focus on relating our findings with clinical information of co-morbidities and thus exploiting such or more advanced imaging data to build normal tissue complication probability models to better integrate clinical and dose information