Lorenzo Vinante

To compare dosimetric outcomes between Free Breath (FB) and Deep Inspiration Breath Hold (DIBH) across different radiotherapy modalities, establish patient selection criteria for DIBH, and optimizing the setup margin (SM) in left breast cancer treatment. 26 patients with left breast cancer were studied at CRO, Aviano in Italy. FB and DIBH simulations were done using CT with a real-time position management system. 3DCRT and IMRT plans were prepared for both simulations of each patient. The setup margin was measured by Van Herk’s formula and compared with residual uncertainties. The dose coverage of PTV and spare OARs were better with DIBH. The distance of more than 1.6 cm between (Left Anterior Descending artery) LAD and PTV was no significantly different for FB and DIBH. The setup margin by Van Herk’s formula was calculated as 0.9 cm for DIBH_IMRT. The average duration of DIBH per respiration was 19 ± 4 s. So, holding one breath at least 19 s would be the criteria for choosing a patient to apply DIBH. DIBH enhances PTV dose coverage and OAR sparing in both 3DCRT and IMRT. When the distance between the LAD and PTV exceeds 1.6 cm, the application of DIBH depends on the availability of a LINAC with RPM and the patient’s breathholding ability.

Introduction: Randomised evidence supports the use of partial breast irradiation (PBI) with targeted intraoperative radiotherapy (TARGIT-IORT) for early stage breast cancer, but prospective data from real-world adoption of this technique is also important. The aim of this study was to determine if the outcome reported in TARGIT-A trial could be replicated in large cohort of early stage breast cancer treated with TARGIT-IORT.Methods: This prospective observational study analysed all patients treated with TARGIT-IORT between 2004 and 2021 in a single national cancer institute. TARGIT-IORT during lumpectomy was performed according to the risk-adapted TARGIT-A protocol using the Intrabeam^® device. We analysed the completeness of follow up, 5-year in-breast-tumour-recurrence (IBTR), long term local recurrence free survival, distant disease-free survival, overall survival and breast-cancer-related survival, using the Kaplan-Meier method. A covariate analysis was performed to investigate risk factors for IBTR. We also analysed high grade toxicity events.Results: The study included 814 patients and the a median follow up was 72 months. The majority of patients (60.3%) received TARGIT-IORT as PBI modality (“exclusive IORT” group); 39.7% received additional EBRT. There was no significant difference between the 5 years IBTR for the whole study population and the “exclusive IORT” cohort (1.6% (95%CI=1.1-2.1%) and 2.5% (95%CI=1.7%-3.3%) respectively). 5 years overall survival and tumour related survival were >95%. In 21% of patients with recurrence, breast was preserved. Radiotherapy toxicity (CTCAE Grade>2) was very rare (0.9%).Conclusions: This large single institute study found that breast cancer control and survival outcomes with TARGIT-IORT were consistent with TARGIT-A trial results. This “real world” experience confirmed that the randomised evidence showing the value of TARGIT-IORT as partial breast irradiation modality that can be replicated in routine clinical practice.

Cellular and humoral immunity are both required for SARS-CoV-2 infection recovery and vaccine efficacy. The factors affecting mRNA vaccination-induced immune responses, in healthy and fragile subjects, are still under investigation. Thus, we monitored the vaccine-induced cellular and humoral immunity in healthy subjects and cancer patients after vaccination to define whether a different antibody titer reflected similar rates of cellular immune responses and if cancer has an impact on vaccination efficacy. We found that higher titers of antibodies were associated with a higher probability of positive cellular immunity and that this greater immune response was correlated with an increased number of vaccination side effects. Moreover, active T-cell immunity after vaccination was associated with reduced antibody decay. The vaccine-induced cellular immunity appeared more likely in healthy subjects rather than in cancer patients. Lastly, after boosting, we observed a cellular immune conversion in 20% of subjects, and a strong correlation between pre- and post-boosting IFN-γ levels, while antibody levels did not display a similar association. Finally, our data suggested that integrating humoral and cellular immune responses could allow the identification of SARS-CoV-2 vaccine responders and that T-cell responses seem more stable over time compared to antibodies, especially in cancer patients.

Increasing evidence pinpoints metronomic chemotherapy, a frequent and low dose drug administration with no prolonged drug-free intervals, as a potential tool to fight certain types of cancers. The primary identified targets of metronomic chemotherapy were the tumor endothelial cells involved in angiogenesis. After this, metronomic chemotherapy has been shown to efficiently target the heterogeneous population of tumor cells and, more importantly, elicit the innate and adaptive immune system reverting the “cold” to ”hot” tumor immunologic phenotype. Although metronomic chemotherapy is primarily used in the context of a palliative setting, with the development of new immunotherapeutic drugs, a synergistic therapeutic role of the combined metronomic chemotherapy and immune checkpoint inhibitors has emerged at both the preclinical and clinical levels. However, some aspects, such as the dose and the most effective scheduling, still remain unknown and need further investigation. Here, we summarize what is currently known of the underlying anti-tumor effects of the metronomic chemotherapy, the importance of the optimal therapeutic dose and time-exposure, and the potential therapeutic effect of the combined administration of metronomic chemotherapy with checkpoint inhibitors in preclinical and clinical settings.

Purpose: to predict eligibility for deep inspiration breath-hold (DIBH) radiotherapy (RT) treatment of patients with left breast cancer from analysis of respiratory signal, using Deep Bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks. Methods: The respiratory traces from 36 patients who underwent DIBH RT were collected. The patients’ RT treatment plans were generated for both DIBH and free-breathing (FB) modalities. The patients were divided into two classes (patient eligible or not), based on the decrease of maximum dose to the left anterior descending (LAD) artery achieved with DIBH, compared to that achieved with FB and ΔD_L. Patients with ΔD_L > median value of ΔD_L within the patient cohort were assumed to be those selected for DIBH. A BLSTM-RNN was trained for classification of patients eligible for DIBH by analysis of their respiratory signals, as acquired during acquisition of the pre-treatment computed tomography (CT), for selecting the window for DIBH. The dataset was split into training (60%) and test groups (40%), and the hyper-parameters, including the number of hidden layers, the optimizer, the learning rate, and the number of epochs, were selected for optimising model performance. The BLSTM included 2 layers of 100 neural units, each followed by a dropout layer with 20% dropout, and was trained in 35 epochs using the Adam optimizer, with an initial learning rate of 0.0003. Results: The system achieved accuracy, specificity, and sensitivity of, F1 score and area under the receiving operating characteristic curve (AUC) of 71.4%, 66.7%, 80.1%, 72.4%, and 69.4% in the test dataset, respectively. Conclusions: The proposed BLSTM-RNN classified patients in the test set eligible for DIBH with good accuracy. These results look promising for building an accurate and robust decision system to provide automated assistance to the radiotherapy team in assigning patients to DIBH.

Importance Conventional adjuvant radiotherapy for breast cancer given daily for several weeks is onerous and expensive. Some patients may be obliged to choose a mastectomy instead, and some may forgo radiotherapy altogether. We proposed a clinical trial to test whether radiotherapy could be safely limited to the tumor bed. Objective To determine whether delayed second-procedure targeted intraoperative radiotherapy (TARGIT-IORT) is noninferior to whole-breast external beam radiotherapy (EBRT) in terms of local control. Design, Setting, and Participants In this prospective, randomized (1:1 ratio) noninferiority trial, 1153 patients aged 45 years or older with invasive ductal breast carcinoma smaller than 3.5 cm treated with breast conservation were enrolled from 28 centers in 9 countries. Data were locked in on July 3, 2019. Interventions The TARGIT-A trial was started in March 2000; patients were randomized after needle biopsy to receive TARGIT-IORT immediately after lumpectomy under the same anesthetic vs EBRT and results have been shown to be noninferior. A parallel study, described in this article, was initiated in 2004; patients who had their cancer excised were randomly allocated using separate randomization tables to receive EBRT or delayed TARGIT-IORT given as a second procedure by reopening the lumpectomy wound. Main Outcomes and Measures A noninferiority margin for local recurrence rate of 2.5% at 5 years, and long-term survival outcomes. Results Overall, 581 women (mean [SD] age, 63 [7] years) were randomized to delayed TARGIT-IORT and 572 patients (mean [SD] age, 63 [8] years) were randomized to EBRT. Sixty patients (5%) had tumors larger than 2 cm, or had positive nodes and only 32 (2.7%) were younger than 50 years. Delayed TARGIT-IORT was not noninferior to EBRT. The local recurrence rates at 5-year complete follow-up were: delayed TARGIT-IORT vs EBRT (23/581 [3.96%] vs 6/572 [1.05%], respectively; difference, 2.91%; upper 90% CI, 4.4%). With long-term follow-up (median [IQR], 9.0 [7.5-10.5] years), there was no statistically significant difference in local recurrence-free survival (HR, 0.75; 95% CI, 0.57-1.003;P = .052), mastectomy-free survival (HR, 0.88; 95% CI, 0.65-1.18;P = .38), distant disease-free survival (HR, 1.00; 95% CI, 0.72-1.39;P = .98), or overall survival (HR, 0.96; 95% CI, 0.68-1.35;P = .80). Conclusions and Relevance These long-term data show that despite an increase in the number of local recurrences with delayed TARGIT-IORT, there was no statistically significant decrease in mastectomy-free survival, distant disease-free survival, or overall survival. Trial Registration ISRCTN34086741, ClinicalTrials.gov Identifier:NCT00983684

Purpose: to predict the occurrence of late subcutaneous radiation induced fibrosis (RIF) after partial breast irradiation (PBI) for breast carcinoma by using machine learning (ML) models and radiomic features from 3D Biologically Effective Dose (3D-BED) and Relative Electron Density (3D-RED). Methods: 165 patients underwent external PBI following a hypo-fractionation protocol consisting of 40 Gy/10 fractions, 35 Gy/7 fractions, and 28 Gy/4 fractions, for 73, 60, and 32 patients, respectively. Physicians evaluated toxicity at regular intervals by the Common Terminology Adverse Events (CTAE) version 4.0. RIF was assessed every 3 months after the completion of radiation course and scored prospectively. RIF was experienced by 41 (24.8%) patients after average 5 years of follow up. The Hounsfield Units (HU) of the CT-images were converted into relative electron density (3D-RED) and Dose maps into Biologically Effective Dose (3D-BED), respectively. Shape, first-order and textural features of 3D-RED and 3D-BED were calculated in the planning target volume (PTV) and breast. Clinical and demographic variables were also considered (954 features in total). Imbalance of the dataset was addressed by data augmentation using ADASYN technique. A subset of non-redundant features that best predict the data was identified by sequential feature selection. Support Vector Machines (SVM), ensemble machine learning (EML) using various aggregation algorithms and Naive Bayes (NB) classifiers were trained on patient dataset to predict RIF occurrence. Models were assessed using sensitivity and specificity of the ML classifiers and the area under the receiver operator characteristic curve (AUC) of the score functions in repeated 5-fold cross validation on the augmented dataset. Results: The SVM model with seven features was preferred for RIF prediction and scored sensitivity 0.83 (95% CI 0.80–0.86), specificity 0.75 (95% CI 0.71–0.77) and AUC of the score function 0.86 (0.85–0.88) on cross-validation. The selected features included cluster shade and Run Length Non-uniformity of breast 3D-BED, kurtosis and cluster shade from PTV 3D-RED, and 10th percentile of PTV 3D-BED. Conclusion: Textures extracted from 3D-BED and 3D-RED in the breast and PTV can predict late RIF and may help better select patient candidates to exclusive PBI.