Potential skin morbidity reduction with intensity-modulated proton therapy for breast cancer with nodal involvement.

Background: Different modern radiation therapy treatment solutions for breast cancer (BC) and regional nodal irradiation (RNI) have been proposed. In this study, we evaluate the potential reduction in radiation-induced skin morbidity obtained by intensity modulated proton therapy (IMPT) compared with intensity modulated photon therapy (IMXT) for left-side BC and RNI. Material and Methods: Using CT scans from 10 left-side BC patients, treatment plans were generated using IMXT and IMPT techniques. A dose of 50 Gy (or Gy [RBE] for IMPT) was prescribed to the target volume (involved breast, the internal mammary, supraclavicular, and infraclavicular nodes). Two single filed optimization IMPT (IMPT1 and IMPT2) plans were calculated without and with skin optimization. For each technique, skin dose-metrics were extracted and normal tissue complication probability (NTCP) models from the literature were employed to estimate the risk of radiation-induced skin morbidity. NTCPs for relevant organs-at-risk (OARs) were also considered for reference. The non-parametric Anova (Friedman matched-pairs signed-rank test) was used for comparative analyses. Results: IMPT improved target coverage and dose homogeneity even if the skin was included into optimization strategy (HIIMPT2 = 0.11 vs. HIIMXT = 0.22 and CIIMPT2 = 0.96 vs. CIIMXT = 0.82, p < .05). A significant relative skin risk reduction (RR = NTCPIMPT/NTCPIMXT) was obtained with IMPT2 including the skin in the optimization with a RR reduction ranging from 0.3 to 0.9 depending on the analyzed skin toxicity endpoint/model. Both IMPT plans attained significant OARs dose sparing compared with IMXT. As expected, the heart and lung doses were significantly reduced using IMPT. Accordingly, IMPT always provided lower NTCP values. Conclusions: IMPT guarantees optimal target coverage, OARs sparing, and simultaneously minimizes the risk of skin morbidity. The applied model-based approach supports the potential clinical relevance of IMPT for left-side BC and RNI and might be relevant for the setup of cost-effectiveness evaluation strategies based on NTCP predictions, as well as for establishing patient selection criteria.

Model-based approach for quantitative estimates of skin, heart, and lung toxicity risk for left-side photon and proton irradiation after breast-conserving surgery.

BACKGROUND: Proton beam therapy represents a promising modality for left-side breast cancer (BC) treatment, but concerns have been raised about skin toxicity and poor cosmesis. The aim of this study is to apply skin normal tissue complication probability (NTCP) model for intensity modulated proton therapy (IMPT) optimization in left-side BC. MATERIAL AND METHODS: Ten left-side BC patients undergoing photon irradiation after breast-conserving surgery were randomly selected from our clinical database. Intensity modulated photon (IMRT) and IMPT plans were calculated with iso-tumor-coverage criteria and according to RTOG 1005 guidelines. Proton plans were computed with and without skin optimization. Published NTCP models were employed to estimate the risk of different toxicity endpoints for skin, lung, heart and its substructures. RESULTS: Acute skin NTCP evaluation suggests a lower toxicity level with IMPT compared to IMRT when the skin is included in proton optimization strategy (0.1% versus 1.7%, p < 0.001). Dosimetric results show that, with the same level of tumor coverage, IMPT attains significant heart and lung dose sparing compared with IMRT. By NTCP model-based analysis, an overall reduction in the cardiopulmonary toxicity risk prediction can be observed for all IMPT compared to IMRT plans: the relative risk reduction from protons varies between 0.1 and 0.7 depending on the considered toxicity endpoint. CONCLUSIONS: Our analysis suggests that IMPT might be safely applied without increasing the risk of severe acute radiation induced skin toxicity. The quantitative risk estimates also support the potential clinical benefits of IMPT for left-side BC irradiation due to lower risk of cardiac and pulmonary morbidity. The applied approach might be relevant on the long term for the setup of cost-effectiveness evaluation strategies based on NTCP predictions.

Dose-surface analysis for prediction of severe acute radio-induced skin toxicity in breast cancer patients.

BACKGROUND: Severe acute radiation-induced skin toxicity (RIST) after breast irradiation is a side effect impacting the quality of life in breast cancer (BC) patients. The aim of the present study was to develop normal tissue complication probability (NTCP) models of severe acute RIST in BC patients. PATIENTS AND METHODS: We evaluated 140 consecutive BC patients undergoing conventional three-dimensional conformal radiotherapy (3D-CRT) after breast conserving surgery in a prospective study assessing acute RIST. The acute RIST was classified according to the RTOG scoring system. Dose-surface histograms (DSHs) of the body structure in the breast region were extracted as representative of skin irradiation. Patient, disease, and treatment-related characteristics were analyzed along with DSHs. NTCP modeling by Lyman-Kutcher-Burman (LKB) and by multivariate logistic regression using bootstrap resampling techniques was performed. Models were evaluated by Spearman's Rs coefficient and ROC area. RESULTS: By the end of radiotherapy, 139 (99%) patients developed any degree of acute RIST. G3 RIST was found in 11 of 140 (8%) patients. Mild-moderate (G1-G2) RIST was still present at 40 days after treatment in six (4%) patients. Using DSHs for LKB modeling of acute RIST severity (RTOG G3 vs. G0-2), parameter estimates were TD50=39 Gy, n=0.38 and m=0.14 [Rs = 0.25, area under the curve (AUC) = 0.77, p = 0.003]. On multivariate analysis, the most predictive model of acute RIST severity was a two-variable model including the skin receiving ≥30 Gy (S30) and psoriasis [Rs = 0.32, AUC = 0.84, p < 0.001]. CONCLUSIONS: Using body DSH as representative of skin dose, the LKB n parameter was consistent with a surface effect for the skin. A good prediction performance was obtained using a data-driven multivariate model including S30 and a pre-existing skin disease (psoriasis) as a clinical factor.

Predicting radiation-induced valvular heart damage.

PURPOSE: To develop a predictive multivariate normal tissue complication probability (NTCP) model for radiation-induced heart valvular damage (RVD). The influence of combined heart-lung irradiation on RVD development was included. MATERIAL AND METHODS: Multivariate logistic regression modeling with the least absolute shrinkage and selection operator (LASSO) was used to build an NTCP model to predict RVD based on a cohort of 90 Hodgkin lymphoma patients treated with sequential chemo-radiation therapy. In addition to heart irradiation factors, clinical variables, along with left and right lung dose-volume histogram statistics, were included in the analysis. To avoid overfitting, 10-fold cross-validation (CV) was used for LASSO logistic regression modeling, with 50 reshuffled cycles. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and Spearman's correlation coefficient (Rs). RESULTS: At a median follow-up time of 55 months (range 12-92 months) after the end of radiation treatment, 27 of 90 patients (30%) manifested at least one kind of RVD (mild or moderate), with a higher incidence of left-sided valve defects (64%). Fourteen prognostic factors were frequently selected (more than 100/500 model fits) by LASSO, which included mainly heart and left lung dosimetric variables along with their volume variables. The averaged cross-validated performance was AUC-CV = 0.685 and Rs = 0.293. The overall performance of a final NTCP model for RVD obtained applying LASSO logistic regression to the full dataset was satisfactory (AUC = 0.84, Rs = 0.55, p < 0.001). CONCLUSION: LASSO proved to be an improved and flexible modeling method for variable selection. Applying LASSO, we showed, for the first time, the importance of jointly considering left lung irradiation and left lung volume size in the prediction of subclinical radiation-related heart disease resulting in RVD.

Ionizing radiation detectors based on Ge-doped optical fibers inserted in resonant cavities.

The measurement of ionizing radiation (IR) is a crucial issue in different areas of interest, from environmental safety and industrial monitoring to aerospace and medicine. Optical fiber sensors have recently proven good candidates as radiation dosimeters. Here we investigate the effect of IR on germanosilicate optical fibers. A piece of Ge-doped fiber enclosed between two fiber Bragg gratings (FBGs) is irradiated with gamma radiation generated by a 6 MV medical linear accelerator. With respect to other FBG-based IR dosimeters, here the sensor is only the bare fiber without any special internal structure. A near infrared laser is frequency locked to the cavity modes for high resolution measurement of radiation induced effects on the fiber optical parameters. In particular, we observe a variation of the fiber thermo-optic response with the radiation dose delivered, as expected from the interaction with Ge defect centers, and demonstrate a detection limit of 360 mGy. This method can have an impact in those contexts where low radiation doses have to be measured both in small volumes or over large areas, such as radiation therapy and radiation protection, while bare optical fibers are cheap and disposable.

A prognostic model comprising pT stage, N status, and the chemokine receptors CXCR4 and CXCR7 powerfully predicts outcome in neoadjuvant resistant rectal cancer patients.

Despite the optimization of the local treatment of advanced rectal cancer (LARC), combination of preoperative chemoradiotherapy (CRT) and surgery, approximately one third of patients will develop distant metastases. Since the chemokine receptor CXCR4 has been implicated in metastasis development and prognosis in colorectal cancer, the role of the entire axis CXCR4-CXCL12-CXCR7 was evaluated to identify high relapse risk rectal cancer patients. Tumor specimens of 68 LARC patients undergoing surgery after neoadjuvant-CRT were evaluated for CXCR4, CXCR7, and CXCL12 expression through immunohistochemistry. Multivariable prognostic model was developed using classical prognostic factors along with chemokine receptor expression profiles. High CXCR4 correlated with a shorter relapse-free survival (RFS) (p = 0.0006) and cancer specific survival (CSS) (p = 0.0004). Concomitant high CXCR4-negative/low CXCR7 or high CXCR4-negative/low CXCL12 significantly impaired RFS (p = 0.0003 and p = 0.0043) and CSS (p = 0.0485 and p = 0.0026). High CXCR4/N+ identified the worst prognostic category for RFS (p < 0.0001) and CSS (p = 0.0003). The optimal multivariable predictive model for RFS was a five-variable model consisting of gender, pT stage, N status, CXCR4, and CXCR7 (AUC = 0.92, 95% CI = 0.77-0.98). The model is informative and supportive for adjuvant treatment and identifies CXCR4 as a new therapeutic target in rectal cancer.

Pulmonary damage in Hodgkin's lymphoma patients treated with sequential chemo-radiotherapy: Predictors of radiation-induced lung injury.

BACKGROUND: Our aim was to define predictors of late radiation-induced lung injury (RILI) in Hodgkin's lymphoma (HL) survivors treated with bleomycin-containing chemotherapy and radiotherapy. MATERIAL AND METHODS: Eighty consecutive patients treated with chemotherapy and subsequent supradiaphragmatic radiation therapy for HL were retrospectively reviewed for symptoms and/or radiological signs of RILI. Median patient age was 26 years (range 14-55). Left, right, and total lung dosimetric parameters along with clinical, disease, and treatment-related characteristics were analyzed. Multivariate logistic regression analyses were performed. A receiver operator characteristic (ROC) curve analysis was performed to find possible cutoff values dividing patients into high- and low-risk groups. RESULTS: Seven of 80 (9%) patients had lung disease at baseline. Four of 80 (5%) had toxicity after chemotherapy and before the beginning of radiotherapy. These patients were excluded from further evaluation. At a median time of 10 months (range 9-18), 9/69 patients (13%) developed lung radiological changes on computed tomography (CT) after treatment. Four of nine patients were diagnosed RTOG grade ≥ 2. On multivariate analyses, left-lung V30 (p = 0.004, OR = 1.108 95% CI 1.033-1.189) and total-lung V30 (p = 0.009, OR = 1.146 95% CI 1.035-1.270) resulted to be predictors of lung CT changes with a cutoff value of 16% and 15%, respectively. When only symptomatic RILI was considered a left-lung V30 cutoff value of 32% was estimated. CONCLUSION: Bleomycin and RT may cause lung injury in a small, but significant fraction of HL patients. Left-lung V30 predicts the risk of developing asymptomatic or symptomatic RILI after sequential chemo-radiotherapy.

Modeling frameworks for radiation induced lymphopenia: A critical review.

Radiation-induced lymphopenia (RIL) is a frequent, and often considered unavoidable, side effect of radiation therapy (RT), whether or not chemotherapy is included. However, in the last few years several studies have demonstrated the detrimental effect of RIL on therapeutic outcomes, with conflicting findings concerning possible inferior patient survival. In addition, since immunotherapeutic treatment has become an integral part of cancer therapy, preserving the immune system is recognized as crucial. Given this background, various research groups have reported on different frameworks for modelling RIL, frequently based on different definitions of RIL itself, and discordant results have been reported. Our aim is to critically review the current literature on RIL modelling and summarize the different approaches recently proposed to improve the prediction of RIL after RT and aimed at immunity-sparing RT. A detailed description of these approaches will be outlined and illustrated through their applications as found in the literature from the last five years. Such a critical analysis represents the necessary starting step to develop an effective strategy that ultimately could harmonize the diverse modelling methods.

Incidence of alopecia in brain tumour patients treated with pencil scanning proton therapy and validation of existing NTCP models.

BACKGROUND AND PURPOSE: Radiation-induced alopecia (RIA) is one of the most frequent and upsetting cosmetic side effects after radiotherapy (RT) for brain cancer. We report the incidence of RIA in a cohort of brain tumours patients treated with Proton Therapy (PT) and externally validate published NTCP models of grade 2 (G2) RIA for their implementation in clinical practice. METHODS: Data for patients treated for brain tumours with scanning beam PT between 2018 and 2022 were extracted. Acute, late and permanent RIA events were evaluated according to CTCAE 5.0. Lyman-Kutcher-Burman (LKB) and multivariable logistic regression (MLR) published models were computed from the relative dose-surface histogram of the scalp. External validity of models was assessed in terms of discrimination and calibration. RESULTS: In the 264 patients analysed, rates of any grade acute (≤90 days after PT completion), late (>90 days) and permanent RIA (persisting for> 12 months) were 61.8 %, 24.7 % and 14.4 %, respectively. In our independent cohort, LKB- and MLR-NTCP showed a good discrimination for G2 RIA (0.71≤ROC-AUC≤0.83) while model calibration was unsatisfactory possibly due to a different outcome evaluation between training and validation cohorts, as well as differences in clinical and treatment related variables between the two groups. CONCLUSIONS: Despite the reasonable sensitivity and specificity of the NTCP models for RIA in the validation cohort, our study emphasizes the significance of differences between the cohorts utilized for model development and validation. Specifically, variations in the reporting of clinical outcomes inevitably jeopardize the validation of NTCP models. A standardize and objective RIA scoring system is essential.

Neoadjuvant Versus Adjuvant Systemic Therapy in Breast Cancer: A Matched Case-control Study.

BACKGROUND/AIM: Neoadjuvant systemic therapy (NAT) in breast cancer can make tumors resectable or reduce the extent of surgery needed for locally advanced cancers. It can also better prevent distant relapse and possibly modulate drug therapy by adjusting adjuvant therapy (AD) based on the response to NAT, either by escalating or de-escalating the treatment. However, clear evidence of improved outcomes is currently missing. Here, we report on breast cancer patients treated with NAT at our institution. PATIENTS AND METHODS: One hundred twenty-seven patients treated at our Radiation Oncology department between 2004 and 2021 were retrospectively analyzed. All patients had localized or locally advanced breast cancer, were treated with NAT, and received postoperative radiotherapy. The outcomes considered were overall survival (OS), loco-regional recurrence-free survival (LRRFS), and distant metastases-free survival (DMFS). A matched patient population treated with AD during the same period and at the same center was used for comparison. RESULTS: The 5-year predicted OS was 87% in the NAT group and 81.5% in the AD group (p-value=0.179), while LRRFS was 93.2% in the NAT group and 100% in the AD group (p=0.005). The 5-year predicted DMFS was 84.6% in the NAT group and 82.1% in AD patients (p=0.367). In the NAT group, the only prognostic factor significantly related to improved outcomes was the pathological node response, with an OS of 95.6% in patients without residual node disease compared to 75.1% in patients with evidence of residual node disease. CONCLUSION: Our study, despite the limitations of a small number of patients and its retrospective nature, confirms the data of previous larger studies. In terms of DMFS and OS, NAT is at least as effective as AD. NAT represents a great opportunity for personalized modulation of treatment in node-positive breast cancer patients.

Technical note: MAMBA-Multi-pAradigM voxel-Based Analysis: A computational cookbot.

BACKGROUND: Voxel-Based (VB) analysis embraces a multifaceted ensemble of sophisticated techniques, lying at the boundary between image processing and statistical modeling, that allow for a frequentist inference of pathophysiological properties anchored to an anatomical reference. VB methods has been widely adopted in neuroimaging studies and, more recently, they are gaining momentum in radiation oncology research. However, the price for the power of VB analysis is the complexity of the underlying mathematics and algorithms. PURPOSE: In this paper, we present the Multi-pAradigM voxel-Based Analysis (MAMBA) toolbox, which is intended for a flexible application of VB analysis in a wide variety of scenarios in medical imaging and radiation oncology. METHODS: The MAMBA toolbox is implemented in Matlab. It provides open-source functions to compute VB statistical models of the input data, according to a great variety of regression schemes, and to derive VB maps of the observed significance level, performing a non-parametric permutation inference. The toolbox allows for including VB and global outcomes, as well as an arbitrary amount of VB and global Explanatory Variables (EVs). In addition, the Matlab Parallel Computing Toolbox is exploited to take advantage of the perfect parallelizability of most workloads. RESULTS: The use of MAMBA was demonstrated by means of several realistic examples on a synthetic dataset mimicking a radiation oncology scenario. CONCLUSION: MAMBA is an open-source toolbox, freely available for academic and non-commercial purposes. It is designed to make state-of-the-art VB analysis accessible to research scientists without the programming resources needed to build from scratch their own software solutions. At the same time, the source code is handed out for more experienced users to complement their own tools, also customizing user-defined models. MAMBA guarantees high generality and flexibility in the design of the statistical models, significantly expanding on the features of available free tools for VB analysis. The presented toolbox aims at increasing the reach of VB studies as well as the sharing of research results.

Stereotactic Radiation Therapy for Brain Metastases: Factors Affecting Outcomes and Radiation Necrosis.

Stereotactic radiation therapy (SRT) is a proven effective treatment for brain metastases (BM); however, symptomatic radiation necrosis (RN) is a late effect that may impact on patient's quality of life. The aim of our study was to retrospectively evaluate survival outcomes and characterize the occurrence of RN in a cohort of BM patients treated with ablative SRT at Federico II University Hospital. Clinical and dosimetric factors of 87 patients bearing a total of 220 BMs treated with SRT from 2016 to 2022 were analyzed. Among them, 46 patients with 127 BMs having clinical and MRI follow-up (FUP) ≥ 6 months were selected for RN evaluation. Dosimetric parameters of the uninvolved brain (brain without GTV) were extracted. The crude local control was 91% with neither clinical factors nor prescription dose correlating with local failure (LF). At a median FUP of 9 (1-68) months, the estimated median overall survival (OS), progression-free survival (PFS), and brain progression-free survival (bPFS) were 16, 6, and 9 months, respectively. The estimated OS rates at 1 and 3 years were 59.8% and 18.3%, respectively; bPFS at 1 and 3 years was 29.9% and 13.5%, respectively; PFS at 1 and 3 years was 15.7% and 0%, respectively; and local failure-free survival (LFFS) at 1 and 3 years was 87.2% and 83.8%, respectively. Extracranial disease status was an independent factor related to OS. Fourteen (30%) patients manifested RN. At multivariate analysis, adenocarcinoma histology, left location, and absence of chemotherapy were confirmed as independent risk factors for any-grade RN. Nine (20%) patients developed symptomatic (G2) RN, which improved or stabilized after 1-16 months of steroid therapy. With prompt recognition and, when necessary, medical therapy, RN radiological and clinical amelioration can be obtained.

Voxel-based analysis: Roadmap for clinical translation.

Voxel-based analysis (VBA) allows the full, 3-dimensional, dose distribution to be considered in radiotherapy outcome analysis. This provides new insights into anatomical variability of pathophysiology and radiosensitivity by removing the need for a priori definition of organs assumed to drive the dose response associated with patient outcomes. This approach may offer powerful biological insights demonstrating the heterogeneity of the radiobiology across tissues and potential associations of the radiotherapy dose with further factors. As this methodological approach becomes established, consideration needs to be given to translating VBA results to clinical implementation for patient benefit. Here, we present a comprehensive roadmap for VBA clinical translation. Technical validation needs to demonstrate robustness to methodology, where clinical validation must show generalisability to external datasets and link to a plausible pathophysiological hypothesis. Finally, clinical utility requires demonstration of potential benefit for patients in order for successful translation to be feasible. For each step on the roadmap, key considerations are discussed and recommendations provided for best practice.

Radiation Therapy in Thoracic Tumors: Recent Trends and Current Issues.

Radiation therapy (RT) plays a fundamental role in the multidisciplinary treatment and management of thoracic cancers, and in particular, RT is the most used non-surgical treatment modality for lung cancer, which in turn is the most common type of thoracic malignancy [...].

Radiation-Induced Esophagitis in Non-Small-Cell Lung Cancer Patients: Voxel-Based Analysis and NTCP Modeling.

The aim of our study is to characterize the risk of radiation-induced esophagitis (RE) in a cohort of Non-Small-Cell Lung Cancer (NSCLC) patients treated with concurrent chemotherapy and photon/proton therapy. For each patient, the RE was graded according to the CTCAE v.3. The esophageal dose-volume histograms (DVHs) were extracted. Voxel-based analyses (VBAs) were performed to assess the spatial patterns of the dose differences between patients with and without RE of grade ≥ 2. Two hierarchical NTCP models were developed by multivariable stepwise logistic regression based on non-dosimetric factors and on the DVH metrics for the whole esophagus and its anatomical subsites identified by the VBA. In the 173 analyzed patients, 76 (44%) developed RE of grade ≥ 2 at a median follow-up time of 31 days. The VBA identified regions of significant association between dose and RE in a region encompassing the thoracic esophagus. We developed two NTCP models, including the RT modality and a dosimetric factor: V55Gy for the model related to the whole esophagus, and the mean dose for the model designed on the thoracic esophagus. The cross-validated performance showed good predictions for both models (ROC-AUC of 0.70 and 0.73, respectively). The only slight improvement provided by the analysis of the thoracic esophageal subsites might be due to the relevant sparing of cervical and lower thoracic esophagus in the analyzed cohort. Further studies on larger cohorts and a more heterogeneous set of dose distributions are needed to validate these preliminary findings and shed further light on the spatial patterns of RE development.

On the interplay between dosiomics and genomics in radiation-induced lymphopenia of lung cancer patients.

PURPOSE: To investigate the interplay between spatial dose patterns and single nucleotide polymorphisms in the development of radiation-induced lymphopenia (RIL) in 186 non-small-cell lung cancer (NSCLC) patients undergoing chemo-radiotherapy (RT). METHODS: This study included NSCLC patients enrolled in a randomized trial of protons vs. photons with available absolute lymphocyte counts at baseline and during RT and XRCC1-rs25487 genotyping data. After masking the GTV, planning CT scans and dose maps were spatially normalized to a common anatomical reference. A Voxel-Based Analysis (VBA) was performed to assess voxel-wise relationships of dosiomic and genomic explanatory variables with RIL. The underlying generalized linear model was designed to include both the explanatory variables (3D dose distributions and the XRCC1-rs25487 genotypes) and possible nuisance variables significantly correlated with RIL. The maps of model coefficients as well as their significance maps were generated. RESULTS: Measures for RIL definition during RT were characterized, including kinetic parameters for lymphocyte loss. The VBA generated three-dimensional maps of correlation between RIL and dose in lymphoid organs as well as organs with abundant blood pools. The identified voxel-wise relationships account for XRCC1-rs25487 polymorphism and demonstrate the variant AA genotype being detrimental to lymphocyte depletion (p = 0.03). CONCLUSION: The performed analyses blindly highlighted relevant anatomical regions that contributed most to lymphocyte depletion during RT and the interplay of the variant XRCC1-rs25487 AA genotype with the dose delivered to the primary lymphoid organs. These findings may help to guide the development of dosimetric RIL mitigation strategies for the application of effective individualized RT.

Local tumor control and treatment related toxicity after plaque brachytherapy for uveal melanoma: A systematic review and a data pooled analysis.

Uveal melanoma (UM) represents the most common primary intraocular tumor, and nowadays eye plaque brachytherapy (EPB) is the most frequently used visual acuity preservation treatment option for small to medium sized UMs. The excellent local tumor control (LTC) rate achieved by EPB may be associated with severe complications and adverse events. Several dosimetric and clinical risk factors for the development of EPB-related ocular morbidity can be identified. However, morbidity predictive models specifically developed for EPB are still scarce. PRISMA methodology was used for the present systematic review of articles indexed in PubMed in the last sixteen years on EPB treatment of UM which aims at determining the major factors affecting local tumor control and ocular morbidities. To our knowledge, for the first time in EPB field, local tumor control probability (TCP) and normal tissue complication probability (NTCP) modelling on pooled clinical outcomes were performed. The analyzed literature (103 studies including 21,263 UM patients) pointed out that Ru-106 EPB provided high local control outcomes while minimizing radiation induced complications. The use of treatment planning systems (TPS) was the most influencing factor for EPB outcomes such as metastasis occurrence, enucleation, and disease specific survival, irrespective of radioactive implant type. TCP and NTCP parameters were successfully extracted for 5-year LTC, cataract and optic neuropathy. In future studies, more consistent recordings of ocular morbidities along with accurate estimation of doses through routine use of TPS are needed to expand and improve the robustness of toxicity risk prediction in EPB.

Potential benefits of using radioactive ion beams for range margin reduction in carbon ion therapy.

Sharp dose gradients and high biological effectiveness make ions such as 12C an ideal tool to treat deep-seated tumors, however, at the same time, sensitive to errors in the range prediction. Tumor safety margins mitigate these uncertainties, but during the irradiation they lead to unavoidable damage to the surrounding healthy tissue. To fully exploit the Bragg peak benefits, a large effort is put into establishing precise range verification methods. Despite positron emission tomography being widely in use for this purpose in 12C therapy, the low count rates, biological washout, and broad activity distribution still limit its precision. Instead, radioactive beams used directly for treatment would yield an improved signal and a closer match with the dose fall-off, potentially enabling precise in vivo beam range monitoring. We have performed a treatment planning study to estimate the possible impact of the reduced range uncertainties, enabled by radioactive 11C ions treatments, on sparing critical organs in tumor proximity. Compared to 12C treatments, (i) annihilation maps for 11C ions can reflect sub- millimeter shifts in dose distributions in the patient, (ii) outcomes of treatment planning with 11C significantly improve and (iii) less severe toxicities for serial and parallel critical organs can be expected.

RESUMEN: A flexible class of multi-parameter qMRI protocols.

PURPOSE: To introduce a class of fast 3D quantitative MRI (qMRI) schemes (RESUMEN, for N=1,…,4) that allow for a thorough characterization of microstructural properties of brain tissues. METHODS: An arbitrary multi-echo GRE acquisition optimized for quantitative susceptibility mapping (QSM) is complemented with an appropriate low flip-angle GRE sequence drawn from four possible choices. The acquired signals are processed to analytically derive the longitudinal relaxation (R1) and free induction decay (R2∗) rates, as well as the proton density (PD) and QSM. A comprehensive modeling of the excitation and B1- profiles and of the RF-spoiling is included in the acquisition and processing pipeline. RESULTS: The RESUMEN maps appear homogeneous throughout the field-of-view and exhibit comparable values and high SNR across the considered range of N values. CONCLUSIONS: The introduced schemes represent a class of robust and flexible strategies to derive a thorough and fast qMRI study, suitable for a whole-brain acquisition with isotropic voxel resolution of 700 µm in less than 15 min.

Radiation-Induced Dyspnea in Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy.

In this study, we investigated the prognostic factors for radiation-induced dyspnea after hypo-fractionated radiation therapy (RT) in 106 patients treated with Stereotactic Body RT for Non-Small-Cell Lung Cancer (NSCLC). The median prescription dose was 50 Gy (range: 40-54 Gy), delivered in a median of four fractions (range: 3-12). Dyspnea within six months after SBRT was scored according to CTCAE v.4.0. Biologically Effective Dose (α/ß = 3 Gy) volume histograms for lungs and heart were extracted. Dosimetric parameters along with patient-specific and treatment-related factors were analyzed, multivariable logistic regression method with Leave-One-Out (LOO) internal validation applied. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot parameters. Fifty-seven patients (53.8%) out of 106 developed dyspnea of any grade after SBRT (25/57 grade ≥ 2 cases). A three-variable predictive model including patient comorbidity (COPD), heart volume and the relative lungs volume receiving more than 15 Gy was selected. The model displays an encouraging performance given by a training ROC-AUC = 0.71 [95%CI 0.61-0.80] and a LOO-ROC-AUC = 0.64 [95%CI 0.53-0.74]. Further modeling efforts are needed for dyspnea prediction in hypo-fractionated treatments in order to identify patients at high risk for developing lung toxicity more accurately.