Search

Current approach to identify BRCA 1/2 carriers in the general population is ineffective as most of the carriers remain undiagnosed. Radiomics is an emerging tool for large scale quantitative analysis of features from standard diagnostic imaging and has been applied also to identify gene mutational status. The objective of this study was to evaluate the clinical and economic impact of integrating a radiogenomics model with clinical and family history data in identifying BRCA mutation carriers in the general population. This cost‐effective analysis compares three different approaches to women selection for BRCA testing: established clinical criteria/family history (model 1); established clinical criteria/family history and the currently available radiogenomic model (49% sensitivity and 87% specificity) based on ultrasound images (model 2); same approach used in model 2 but simulating an improvement of the performances of the radiogenomic model (80% sensitivity and 95% specificity) (model 3). All models were trained with literature data. Direct costs were calculated according to the rates currently used in Italy. The analysis was performed simulating different scenarios on the generation of 18‐year‐old girls in Italy (274,000 people). The main outcome was to identify the most effective model comparing the number of years of BRCA‐cancer healthy life expectancy (HLYs). An incremental cost‐effectiveness ratio (ICER) was also derived to determine the cost in order to increase BRCA carriers‐healthy life span by 1 year. Compared to model 1, model 2 increases the detection rate of BRCA carriers by 41.8%, reduces the rate of BRCA‐related cancers by 23.7%, generating over a 62‐year observation period a cost increase by 2.51 €/Year/Person. Moreover, model 3 further increases BRCA carriers detection (+ 68.3%) and decrease in BRCA‐related cancers (− 38.4%) is observed compared to model 1. Model 3 increases costs by 0.7 €/Year/Person. After one generation, the estimated ICER in the

Introduction: New digital technologies can become a tool for welcoming the patient through the artistic dimension. Cancer patients, in particular, need support that accompanies and supports them throughout their treatment. Materials and methods: The Art4ART project consist in the structural proposal to cancer patients of a web-based digital platform containing several forms of art as video-entertainments; a multimedia immersive room; an art-based welcoming of the patients with several original paintings; an environment with a peacefulness vertical garden; a reconceptualization of the chemotherapy-infusion seats. Data regarding patients’ preference and choices will be stored and analysed also using artificial intelligence (AI) algorithm to measure and predict impact indicators regarding clinical outcomes (survival and toxicity), psychological indicators. Moreover, the same digital platform will contribute to a better organization of the activities. Discussion: Through the systematic acquisition of patient preferences and through integration with other clinical parameters, it will be possible to measure the clinical, psychological, organisational, and social impact of the newly implemented Art4ART project. The use of digital technology leads us to apply the reversal of viewpoint from therapeutic acts to patient-centred care.

Personalized medicine (PM) bridges several disciplines for understanding and addressing prevalent, complex, or rare situations in human health (e.g., complex phenotyping, risk stratification, etc.); therefore, digital and technological solutions have been integrated in the field to boost innovation and new knowledge generation. The open innovation (OI) paradigm proposes a method by which to respectfully manage disruptive change in biomedical organizations, as experienced by many organizations during digital transformation and the COVID-19 pandemic. In this article, we focus on how this paradigm has catalyzed the transition from PM to personalized digital medicine in a large-volume research hospital. Methods, challenges, and results are discussed. This case study is an endeavor to confirm that OI strategies could help manage urgent needs from the healthcare environment, while achieving sustainability-oriented, accountable innovation.

Purpose: The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT). Materials and methods: We retrospectively enrolled patients with LACC diagnosis who underwent NACRT followed by radical surgery in two different institutions. Radiomics features were extracted from pre-treatment 1.5 T T2w MR images. The predictive performance of each feature was quantified in terms of Wilcoxon–Mann–Whitney test. Among the significant features, Pearson correlation coefficient (PCC) was calculated to quantify the correlation among the different predictors. A logistic regression model was calculated considering the two most significant features at the univariate analysis showing the lowest PCC value. The predictive performance of the model created was quantified out using the area under the receiver operating characteristic curve (AUC). Results: A total of 175 patients were retrospectively enrolled (142 for the training cohort and 33 for the validation one). 1896 radiomic feature were extracted, 91 of which showed significance (p < 0.05) at the univariate analysis. The radiomic model showing the highest predictive value combined the features calculated starting from the gray level co-occurrence-based features. This model achieved an AUC of 0.73 in the training set and 0.91 in the validation set. Conclusions: The proposed radiomic model showed promising performances in predicting 2-year overall survival before NACRT. Nevertheless, the observed results should be tested in larger studies with consistent external validation cohorts, to confirm their potential clinical use.

Introduction: Stereotactic ablative radiotherapy (SABR) has been shown to increase survival in oligometastatic disease, but local control of colorectal metastases remains poor. We aimed to identify potential predictive factors of SBRT response through a multicenter large retrospective database and to investigate the progression to the polymetastatic disease (PMD). Material and methods: The study involved 23 centers, and was approved by the Ethical Committee (Prot. Negrar 2019-ZT). 1033 lung metastases were reported. Clinical and biological parameters were evaluated as predictive for freedom from local progression-free survival (FLP). Secondary end-point was the time to the polymetastatic conversion (tPMC). Results: Two-year FLP was 75.4%. Two-year FLP for lesions treated with a BED < 00 Gy, 100–124 Gy, and ≥125 Gy was 76.1%, 70.6%, and 94% (p = 0.000). Two-year FLP for lesion measuring ≤10 mm, 10–20 mm, and >20 mm was 79.7%, 77.1%, and 66.6% (p = 0.027). At the multivariate analysis a BED ≥125 Gy significantly reduced the risk of local progression (HR 0.24, 95%CI 0.11–0.51; p = 0.000). Median tPMC was 26.8 months. Lesions treated with BED ≥125 Gy reported a significantly longer tPMC as compared to lower BED. The median tPMC for patients treated to 1, 2–3 or 4–5 simultaneous oligometastases was 28.5, 25.4, and 9.8 months (p = 0.035). Conclusion: The present is the largest series of lung colorectal metastases treated with SABR. The results support the use of SBRT in lung oligometastatic colorectal cancer patients as it might delay the transition to PMD or offer relatively long disease-free period in selected cases. Predictive factors were identified for treatment personalization.

Item does not contain fulltext, INTRODUCTION: Care for head and neck cancers is complex in particular for the rare ones. Knowledge is limited and histological heterogeneity adds complexity to the rarity. There is a wide consensus that to support clinical research on rare cancer, clinical registries should be developed within networks specializing in rare cancers. In the EU, a unique opportunity is provided by the European Reference Networks (ERN). The ERN EURACAN is dedicated to rare adults solid cancers, here we present the protocol of the EURACAN registry on rare head and neck cancers (ClinicalTrials.gov Identifier: NCT05483374). STUDY DESIGN: Registry-based cohort study including only people with rare head and neck cancers. OBJECTIVES: to help describe the natural history of rare head and neck cancers;to evaluate factors that influence prognosis;to assess treatment effectiveness;to measure indicators of quality of care. METHODS: Settings and participants It is an hospital based registry established in hospitals with expertise in head and neck cancers. Only adult patients with epithelial tumours of nasopharynx; nasal cavity and paranasal sinuses; salivary gland cancer in large and small salivary glands; and middle ear will be included in the registry. This registry won't select a sample of patients. Each patient in the facility who meets the above mentioned inclusion criteria will be followed prospectively and longitudinally with follow-up at cancer progression and / or cancer relapse or patient death. It is a secondary use of data which will be collected from the clinical records. The data collected for the registry will not entail further examinations or admissions to the facility and/or additional appointments to those normally provided for the patient follow-up. Variables Data will be collected on patient characteristics (eg. patient demographics, lifestyle, medical history, health status); exposure data (eg. disease, procedures, treatments of interest) and outcomes (e.g. survival, progressi

Editorial In summary, ESTRO clinical and other guidelines are soundly based, but always require validation, careful and consistent implementation, and focused education and training on their use, as well as local monitoring of that. As guidelines develop and older ones become superseded, there should be a clear mechanism to inform the community of the status of specific guidelines, especially when they have been replaced by new or updated versions. An important scientific aim for the future is to further advance guidelines and their individual statements into fully evidence-based instruments. These, in principle, could be directly linked to growing data-bases, allowing feedback mechanisms and thus continuous optimization. Another challenging research topic is the interplay of guidelines with growing opportunities and demands of personalized approaches of treatment.

Background The current management of lung cancer patients has reached a high level of complexity. Indeed, besides the traditional clinical variables (e.g., age, sex, TNM stage), new omics data have recently been introduced in clinical practice, thereby making more complex the decision-making process. With the advent of Artificial intelligence (AI) techniques, various omics datasets may be used to create more accurate predictive models paving the way for a better care in lung cancer patients. Methods The LANTERN study is a multi-center observational clinical trial involving a multidisciplinary consortium of five institutions from different European countries. The aim of this trial is to develop accurate several predictive models for lung cancer patients, through the creation of Digital Human Avatars (DHA), defined as digital representations of patients using various omics-based variables and integrating well-established clinical factors with genomic data, quantitative imaging data etc. A total of 600 lung cancer patients will be prospectively enrolled by the recruiting centers and multi-omics data will be collected. Data will then be modelled and parameterized in an experimental context of cutting-edge big data analysis. All data variables will be recorded according to a shared common ontology based on variable-specific domains in order to enhance their direct actionability. An exploratory analysis will then initiate the biomarker identification process. The second phase of the project will focus on creating multiple multivariate models trained though advanced machine learning (ML) and AI techniques for the specific areas of interest. Finally, the developed models will be validated in order to test their robustness, transferability and generalizability, leading to the development of the DHA. All the potential clinical and scientific stakeholders will be involved in the DHA development process. The main goals aim of LANTERN project are: i) To develop predictive models

This issue includes a perspective contribution by Lefresne et al. [1] on face-to-face scientific conferences versus virtual meetings and the implications for climate change. Generally, it is the policy of the Green Journal not to publish contributions with non-scientific content. In this specific case, the editors believe that the contribution by Lefresne and colleagues raises an important point that needs careful consideration and discussion in our scientific community. As scientists, we are at the forefront of research and strive to positively shape the future for humankind. Therefore, it is of key importance that the environmental impact of scientific activities is considered, and that our community develops and contributes to solutions to prevent further exacerbation of climate change with its substantial impact on health-related issues. This includes that pros and cons of face-to-face scientific meetings are carefully weighed.

Hair is the matrix of choice for investigating a subject's drug history over time, usually with specific forensic applications (license renewal, workplace drug testing, toxicological evaluation), and it is generally considered difficult to be tampered with. Nevertheless, some treatments promising to lower drug concentrations in hair are described online as how to “pass” a drug test. We selected three of these practices, claiming to be effective in decreasing drug concentrations—Treatment 1: (A) baking soda, (B) salicylic acid, (C) bleach; Treatment 2: (A) bleaching and (B) dyeing; Treatment 3: (A) white vinegar, (B) salicylic acid moisturizer, (C) liquid cleanser, and (D) dyeing. Quantitative results were compared with those of untreated hair strands, used as reference. We evaluated the efficacy of the treatment on drugs of abuse and benzodiazepines. Treatment 1 proved to be the most effective, since drug concentrations in treated hair were significantly lower than in untreated ones, although methadone and tetrahydrocannabinol (THC) seemed to be less affected than cocaine and 6-monoacetylmorphine (MAM). The mean percentage values of treatment-induced decrease were up to 90% for cocaine, 81% for benzoylecgonine, 77% for morphine, 89% for MAM, 37% for methadone, 67% for ketamine, 80% for MDMA, 76% for methamphetamine, and 60% for THC, compared with the reference samples. There was no noticeable damage or discoloration of the keratin matrix, making it difficult for the technicians to determine if there was a treatment. This could be an issue for the application of cutoffs or when low concentrations of drugs are incorporated into the keratinic matrix.

Background: The current management of lung cancer patients has reached a high level of complexity. Indeed, besides the traditional clinical variables (e.g., age, sex, TNM stage), new omics data have recently been introduced in clinical practice, thereby making more complex the decision-making process. With the advent of Artificial intelligence (AI) techniques, various omics datasets may be used to create more accurate predictive models paving the way for a better care in lung cancer patients. Methods: The LANTERN study is a multi-center observational clinical trial involving a multidisciplinary consortium of five institutions from different European countries. The aim of this trial is to develop accurate several predictive models for lung cancer patients, through the creation of Digital Human Avatars (DHA), defined as digital representations of patients using various omics-based variables and integrating well-established clinical factors with genomic data, quantitative imaging data etc. A total of 600 lung cancer patients will be prospectively enrolled by the recruiting centers and multi-omics data will be collected. Data will then be modelled and parameterized in an experimental context of cutting-edge big data analysis. All data variables will be recorded according to a shared common ontology based on variable-specific domains in order to enhance their direct actionability. An exploratory analysis will then initiate the biomarker identification process. The second phase of the project will focus on creating multiple multivariate models trained though advanced machine learning (ML) and AI techniques for the specific areas of interest. Finally, the developed models will be validated in order to test their robustness, transferability and generalizability, leading to the development of the DHA. All the potential clinical and scientific stakeholders will be involved in the DHA development process. The main goals aim of LANTERN project are: i) To develop predictive mode

inglese

BackgroundThe glioblastoma's bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification. The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigate the role of radiomic analysis in GB management, to verify if radiomic features in the tissue around the resection cavity may guide the radiation target volume delineation. Materials and methodsWe retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction. The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologists and reviewed by a neuroradiologist, both with at least 10 years of experience. The Regions of interest (ROI) considered for the analysis were: the surgical cavity +/- post-surgical residual mass (CTV_cavity); the CTV a margin of 1.5 cm added to CTV_cavity and the volume resulting from subtracting the CTV_cavity from the CTV was defined as CTV_Ring. Radiomic analysis and modeling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using features extracted from the Ring to perform a binary classification and predict the PFS at 6 months. A 3-fold cross-validation repeated five times was implemented for internal validation of the model. ResultsTwo-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F_cm_merged.contrast, F_cm_merged.info.corr.2, F_rlm_merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained (p-value of 0.49 by Hosmer and Lemeshow statistical te

Background: Type II spinal muscular atrophy (SMA) often leads to scoliosis in up to 90% of cases. While pharmacological treatments have shown improvements in motor function, their impact on scoliosis progression remains unclear. This study aims to evaluate potential differences in scoliosis progression between treated and untreated SMA II patients. Methods: Treatment effect on Cobb's angle annual changes and on reaching a 50° Cobb angle was analysed in treated and untreated type II SMA patients with a minimum 1.5-year follow-up. A sliding cut-off approach identified the optimal treatment subpopulation based on age, Cobb angle and Hammersmith Functional Motor Scale Expanded at the initial visit. Mann-Whitney U-test assessed statistical significance. Results: There were no significant differences in baseline characteristics between the untreated (n=46) and treated (n=39) populations. The mean Cobb angle variation did not significantly differ between the two groups (p=0.4). Optimal cut-off values for a better outcome were found to be having a Cobb angle <26° or an age <4.5 years. When using optimal cut-off, the treated group showed a lower mean Cobb variation compared with the untreated group (5.61 (SD 4.72) degrees/year vs 10.05 (SD 6.38) degrees/year; p=0.01). Cox-regression analysis indicated a protective treatment effect in reaching a 50° Cobb angle, significant in patients <4.5 years old (p=0.016). Conclusion: This study highlights that pharmacological treatment, if initiated early, may slow down the progression of scoliosis in type II SMA patients. Larger studies are warranted to further investigate the effectiveness of individual pharmacological treatment on scoliosis progression in this patient population.

The present white paper, referring to the 4th Assisi Think Tank Meeting on breast cancer, reviews state-of-the-art data, on-going studies and research proposals. < 70% agreement in an online questionnaire identified the following clinical challenges: 1: Nodal RT in patients who have a) 1-2 positive sentinel nodes without ALND (axillary lymph node dissection); b) cN1 disease transformed into ypN0 by primary systemic therapy and c) 1-3 positive nodes after mastectomy and ALND. 2. The optimal combination of RT and immunotherapy (IT), patient selection, IT-RT timing, and RT optimal dose, fractionation and target volume. Most experts agreed that RT- IT combination does not enhance toxicity. 3: Re-irradiation for local relapse converged on the use of partial breast irradiation after second breast conserving surgery. Hyperthermia aroused support but is not widely available. Further studies are required to finetune best practice, especially given the increasing use of re-irradiation.Copyright © 2023. Published by Elsevier B.V.

Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of “multi-omics” analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.

Clinical trials in cancer treatment are imperative in enhancing patients’ survival and quality of life outcomes. The lack of communication among professionals may produce a non-optimization of patients’ accrual in clinical trials. We developed a specific platform, called “Digital Research Assistant” (DRA), to report real-time every available clinical trial and support clinician. Healthcare professionals involved in breast cancer working group agreed nine minimal fields of interest to preliminarily classify the characteristics of patients’ records (including omic data, such as genomic mutations). A progressive web app (PWA) was developed to implement a cross-platform software that was scalable on several electronic devices to share the patients’ records and clinical trials. A specialist is able to use and populate the platform. An AI algorithm helps in the matchmaking between patient’s data and clinical trial’s inclusion criteria to personalize patient enrollment. At the same time, an easy configuration allows the application of the DRA in different oncology working groups (from breast cancer to lung cancer). The DRA might represent a valid research tool supporting clinicians and scientists, in order to optimize the enrollment of patients in clinical trials. User Experience and Technology The acceptance of participants using the DRA is topic of a future analysis.

Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroentero-logical diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in “Personalized Medicine” and “Innovative Biomedical Technologies”) could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes person-alization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.

N/A

In the original article published, the last sentence in the sixth paragraph of the introduction section is incorrect. The correct sentence is “This data, were confirmed also in an Italian phase II study, in which it has been demonstrated that gemcitabine-based chemoradiotherapy was correlated with improved overall survival, especially in pts who are clinically more fit to complete the foreseen treatment schedule (CRT) (Mattiucci et al. 2010)”. The original article has been corrected.

The aim of this analysis was to assess the efficacy of stereotactic body radiotherapy (SBRT) in terms of local control (LC) and progression-free survival (PFS) in patients with lymph node metastases (NMs) from solid tumors. A systematic literature search from the earliest date to July 25th, 2019 was performed following PRISMA guidelines. Papers reporting LC and/or PFS of NMs using SBRT (< 10 fractions) were selected. The clinical outcomes rates were pooled by means of a random or fixed-effect model. Twenty-nine studies were eligible (969 patients: 938 (LC) and 698 (PFS)). LC and PFS results were reported in 28 and 18 papers, respectively. Heterogeneity was observed in terms of patient and treatment characteristics. Pooled 2-year LC reported in 11 studies was 79.3% (95%CI, 72.8%–85.7%) with substantial heterogeneity between studies (Q2 test: p = 0.0083; I2 = 57.9%), while pooled 2-year PFS reported in 8 studies was 35.9% (95%CI, 22.1%–49.7%) with very high heterogeneity between studies (Q2 test: p < 0.0001; I2 = 86.1%). Grade ≥ 3 and Grade 5 toxicity rates were 2.0% and 0.2%, respectively. SBRT of NMs seems to be safe and effective in terms of LC. However, due to the marked heterogeneity of the included series, prospective studies are required.

Purpose: Optimal timing of surgery after neoadjuvant chemoradiotherapy (Nad-CRT) is still controversial in locally advanced rectal cancer (LARC). The primary goal of this study was to determine the best surgical interval (SI) to achieve the highest rate of pathological complete response (pCR) and secondly to evaluate the effect on survival outcomes according to the SI. Patients and methods: Patients data were extracted from the international randomized trials: Accord12/0405, EORTC22921, FFCD9203, CAO/ARO/AIO-94, CAO-ARO-AIO-04, INTERACT and TROG01.04. Inclusion criteria were: age≥ 18, cT3–T4 and cN0–2, no clinical evidence of distant metastasis at diagnosis, Nad-CRT followed by surgery. Pearson's Chi-squared test with Yates’ continuity correction for categorical variables, the Mann–Whitney test for continuous variables, Mann–Kendall test, Kaplan–Meier curves with log-rank test, univariate and multivariate logistic regression model was used for data analysis. Results: 3085 patients met the inclusion criteria. Overall, the pCR rate was 14% at a median SI of 6 weeks (range 1–31). The cumulative pCR rate increased significantly when SI lengthened, with 95% of pCR events within 10 weeks from Nad-CRT. At univariate and multivariate logistic regression analysis, lengthening of SI (p< 0.01), radiotherapy dose (p< 0.01), and the addition of oxaliplatin to Nad-CRT (p< 0.01) had a favorable impact on pCR. Furthermore, lengthening of SI was not impact on local recurrences, distance metastases, and overall survival. Conclusion: This pooled analysis suggests that the best time to achieve pCR in LARC is at 10 weeks, considering that the lengthening of SI is not detrimental concerning survival outcomes.

Purpose: Hepatocellular carcinoma (HCC) in early stages benefits from local ablative treatments such as radiofrequency ablation (RFA) or transarterial chemoembolization (TACE). In this context, radiotherapy (RT) has shown promising results but has not been thoroughly evaluated. Magnetic resonance-guided RT (MRgRT) may represent a paradigm shifting improvement in stereotactic body radiotherapy (SBRT) for liver tumors. Methods: We retrospectively evaluated HCC patients treated on a hybrid low-tesla MRgRT unit. A total biologically effective dose (BED) > 100 Gy was delivered in 5 consecutive fractions, respecting the appropriate organs-at-risk constraints. Hybrid MR scans were used for treatment planning and cine MR was used for delivery gating. Patients were followed up for toxicity and treatment–response assessment. Results: Ten patients were enrolled, with a total of 12 lesions. All the lesions were irradiated with no interruptions. Six patients had already performed previous local therapies. Median follow-up after SBRT was 6.5 months (1–25). Two cases of acute toxicity were reported (G ≤ 2 according to CTCAE v4.0). At the time of the analysis, 90% of the population presented local control. Child–Pugh before and after treatment remained unchanged in all but one patient. Conclusion: MRgRT is a feasible and safe option showing favorable toxicity profile for HCC treatment.

PURPOSE/OBJECTIVE(S): stereotactic ablative radiotherapy (SABR) has been shown to increase survival rates in oligometastatic disease (OMD), but local control of colorectal metastases still remains poor. We aimed to identify potential predictive factors of SBRT response through a multicenter large retrospective database and to investigate how lung SBRT can impact on the progression to the polymetastatic disease (PMD). MATERIALS/METHODS: the study involved 22 centers, and was approved by the Ethical Committee (Prot. Negrar 2019-ZT). 1023 lung metastases treated with SBRT in 622 patients were reported. The median BED was 105 Gy10. Lesion diameter GTV, PTV volume, dose, fractionations, and site of primary tumor were evaluated as potential predictive marker for SBRT response for the primary end-point local progression-free survival (LPFS). EGFR, KRAS, NRAS, BRAF, and MSI were also evaluated. Secondary end-point was the time to the polymetastatic conversion (ttPMC). RESULTS: the median follow-up was 26 months (range 3-117 months). The median lesion diameter was 13 mm (range 4-58 mm). The 2- and 3-year LPFS were 75.6% and 71%, respectively. At the univariate analysis, BED ≥125Gy10 was associated with improved LPFS (2-year: 94.1% versus 72.6%; P = < 0.0001), single fraction SBRT correlated with better LPFS in the overall population (2-year: 80.6% versus 73.7%; P = 0.03), but no significant difference was observed when considering the population treated with BED > 100 Gy10. Lesion diameter ≤19 mm correlated with improved LPFS (2-year 80% versus 60%; P = < 0.0001). The median ttPMC was 26 months, and the 2-year ttPMC was 54.5%. The median PFS was 11.3 months. After SABR, 36% patients had polymetastatic relapse, 39.5% patients had an oligometastatic relapse, and 24.5% patients had no further relapse. CONCLUSION: the present is the largest series of lung colorectal metastases treated with SABR. The results support the use of SBRT in lung oligometastatic colorectal canc

n/a

Purpose of Review: Recognize which are the elements that predict why a person is aging faster or slower and which intervention we can arrange to slow down the process, which permits to prevent or delay the progression of multimorbidity and disability. Recent Findings: Aging is a complex process that leads to changes in all the systems of the body and all the functions of the person; however, aging develops at different rates in different people, and chronological age is not always consistent with biological age. Summary: Gerontologists are focused not only on finding the best theory able to explain aging but also on identifying one or more markers, which are able to describe aging processes. These biomarkers are necessary to better define the aging-related pathologies, manage multimorbidity, and improve the quality of life. The aim of this paper is to review the most recent evidence on aging biomarkers and the clusters related to them for personalization of treatments.