Search

inglese

The clinical management of metastatic melanoma has been changed by BRAF (BRAFi) and MEK inhibitors (MEKi), which represent a standard treatment for BRAF-mutant melanoma. In oligoprogressive melanoma patients with BRAF mutations, target therapy can be combined with loco-regional radiotherapy (RT). However, the association of BRAF/MEK inhibitors and RT needs to be carefully monitored for potential increased toxicity. Despite the availability of some reports regarding the tolerability of RT + target therapy, data on simultaneous RT and BRAFi/MEKi are limited and mostly focused on the BRAFi vemurafenib. Here, we report a series of metastatic melanoma patients who received fractioned RT regimens for oligoprogressive disease in combination with the BRAFi dabrafenib and the MEKi trametinib, which have continued beyond progression. None of the cases developed relevant adverse events while receiving RT or interrupted dabrafenib and trametinib administration. These cases suggest that a long period of dabrafenib/trametinib interruption during radiotherapy for oligoprogressive disease can be avoided. Prospective trials are warranted to assess the efficacy and safety of the contemporary administration of BRAF/MEK inhibitors and radiotherapy for oligoprogressive disease.

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

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.

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.

Objective: The benefit of surgery and maintenance treatment with PARP inhibitors (PARPi) has been clearly demonstrated in ovarian cancer. Also, the efficacy and safety of stereotactic body radiotherapy has been shown in patients with metastatic, persistent, and recurrent disease. The aim of this study is to evaluate the management of oligometastatic progression during PARPi maintenance treatment. Methods: This is an observational, retrospective, single-arm study conducted from June 2017 to December 2020 in patients with recurrent ovarian cancer with oligometastatic progression under PARPi maintenance treatment and receiving surgery or stereotactic body radiotherapy for such recurrence. PARPi treatment was continued until further progression of the disease. The primary objective of the study was the median prolongation of the treatment-free interval-p (without platinum) after local treatment. Results: A total of 186 patients with ovarian cancer were treated with PARPi at recurrence. Of these, 30 (16%) developed oligometastatic progression. The median age was 49.5 years (range 35-73). Olaparib, niraparib and rucaparib were administered to 33%, 60%, and 7% of patients, respectively. The median prolongation of the treatment-free interval-p of patients treated with surgery or stereotactic body radiotherapy was 6 and 10 months, respectively (p=0.53). The median treatment-free interval-p of patients treated with surgery or stereotactic body radiotherapy at the time of oligometastatic progression was 32 and 29 months, respectively (p=0.44). At the time of this publication, 50% of patients are still on treatment with PARPi following progression. Conclusions: Patients with recurrent ovarian cancer who have oligometastic progression during PARPi maintenance may continue to benefit from PARPi if combined with local treatment.

N/A

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Introduction: Pancreatic cancer (PC) is one of the most aggressive tumours, and better risk stratification among patients is required to provide tailored treatment. The meaning of radiomics and texture analysis as predictive techniques are not already systematically assessed. The aim of this study is to assess the role of radiomics in PC. Methods: A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in PC. The search strategy was ‘radiomics [All Fields] AND (“pancreas” [MeSH Terms] OR “pancreas” [All Fields] OR “pancreatic” [All Fields])’ and only original articles referred to PC in humans in the English language were considered. Results: A total of 123 studies and 183 studies were obtained using the mentioned search strategy on PubMed and Embase, respectively. After the complete selection process, a total of 56 papers were considered eligible for the analysis of the results. Radiomics methods were applied in PC for assessment technical feasibility and reproducibility aspects analysis, risk stratification, biologic or genomic status prediction and treatment response prediction. Discussion: Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction. Further and larger studies are required to confirm the role and allowed to include radiomics parameter in a comprehensive decision support system.

Aims: Between 11 to 14% of patients with locally advanced rectal cancer (LARC) have positive lateral pelvic lymph nodes (LPLN) at diagnosis, related to a worse prognosis with a 5-year survival rate between 30 to 40%. The best treatment choice for this group of patients is still a challenge. The optimal radiotherapy (RT) dose for LPLN patients has been investigated. Methods: We retrospectively collected data from LARC patients with LPLN at the primary staging MRI, treated in our center from March 2003 to December 2020. Patients underwent a neoadjuvant concomitant chemo-radiotherapy (CRT) treatment on the primary tumor (T), mesorectum, and pelvic nodes, associated with a fluoride-based chemotherapy. The total reached dose was 45 Gy at 1.8 Gy/fr on the elective sites and 55 Gy at 2.2 Gy/fr on the disease and mesorectum. Patients were divided in two groups based on whether they received a simultaneous integrated RT boost on the LPLN or not. Overall Survival (OS), Disease Free Survival (DFS), Metastasis Free Survival (MFS), and Local Control (LC) were evaluated in the whole group and then compared between the two groups. Results: A total of 176 patients were evaluated: 82 were included in the RT boost group and 94 in the non-RT boost group. The median follow-up period was 57.8 months. All the clinical endpoint (OS, DFS, MFS, LC), resulted were affected by the simultaneous integrated boost on LPLN with a survival rate of 84.7%, 79.5%, 84.1%, and 92%, respectively, in the entire population. From the comparison of the two groups, there was a statistical significance towards the RT boost group with a p < 0.006, 0.030, 0.042, 0.026, respectively. Conclusions: Concomitant radiotherapy boost on positive LPLN has shown to be beneficial on the survival outcomes (OS, DFS, MFR, and LC) in patients with LARC and LPLN. This analysis demonstrates that a higher dose of radiotherapy on positive pelvic lymph nodes led not only to a higher local control but also to a better survival ra

Background: Neoadjuvant chemoradiation therapy (nCRT) is the standard treatment modality in locally advanced rectal cancer (LARC). Since response to radiotherapy (RT) is dose dependent in rectal cancer, dose escalation may lead to higher complete response rates. The possibility to predict patients who will achieve complete response (CR) is fundamental. Recently, an early tumour regression index (ERI) was introduced to predict pathological CR (pCR) after nCRT in LARC patients. The primary endpoints will be the increase of CR rate and the evaluation of feasibility of delta radiomics-based predictive MRI guided Radiotherapy (MRgRT) model. Methods: Patients affected by LARC cT2-3, N0-2 or cT4 for anal sphincter involvement N0-2a, M0 without high risk features will be enrolled in the trial. Neoadjuvant CRT will be administered using MRgRT. The initial RT treatment will consist in delivering 55 Gy in 25 fractions on Gross Tumor Volume (GTV) plus the corresponding mesorectum and 45 Gy in 25 fractions on the drainage nodes. Chemotherapy with 5-fluoracil (5-FU) or oral capecitabine will be administered continuously. A 0.35 Tesla MRI will be acquired at simulation and every day during MRgRT. At fraction 10, ERI will be calculated: if ERI will be inferior than 13.1, the patient will continue the original treatment; if ERI will be higher than 13.1 the treatment plan will be reoptimized, intensifying the dose to the residual tumor at the 11th fraction to reach 60.1 Gy. At the end of nCRT instrumental examinations are to be performed in order to restage patients. In case of stable disease or progression, the patient will undergo surgery. In case of major or complete clinical response, conservative approaches may be chosen. Patients will be followed up to evaluate toxicity and quality of life. The number of cases to be enrolled will be 63: all the patients will be treated at Fondazione Policlinico Universitario A. Gemelli IRCCS in Rome. Discussion: This clinical tri

Aim: The first prototype of the “Multidisciplinary Tumor Board Smart Virtual Assistant” is presented, aimed to (i) Automated classification of clinical stage starting from different free-text diagnostic reports; (ii) Resolution of inconsistencies by identifying controversial cases drawing the clinician’s attention to particular cases worthy for multi-disciplinary discussion; (iii) Support environment for education and knowledge transfer to junior staff; (iv) Integrated data-driven decision making and standardized language and interpretation. Patients and Method: Data from patients affected by Locally Advanced Cervical Cancer (LACC), FIGO stage IB2-IVa, treated between 2015 and 2018 were extracted. Magnetic Resonance (MR), Gynecologic examination under general anesthesia (EAU), and Positron Emission Tomography–Computed Tomography (PET-CT) performed at the time of diagnosis were the items from the Electronic Health Records (eHRs) considered for analysis. An automated extraction of eHR that capture the patient’s data before the diagnosis and then, through Natural Language Processing (NLP), analysis and categorization of all data to transform source information into structured data has been performed. Results: In the first round, the system has been used to retrieve all the eHR for the 96 patients with LACC. The system has been able to classify all patients belonging to the training set and - through the NLP procedures - the clinical features were analyzed and classified for each patient. A second important result was the setup of a predictive model to evaluate the patient’s staging (accuracy of 94%). Lastly, we created a user-oriented operational tool targeting the MTB who are confronted with the challenge of large volumes of patients to be diagnosed in the most accurate way. Conclusion: This is the first proof of concept concerning the possibility of creating a smart virtual assistant for the MTB. A significant benefit could come from the integration of these automate

In the context of the SARS-CoV-2 pandemic, it is important to ensure the quality of cancer treatment as well as patients and health professionals' safety. Individual-based treatment options should be considered in patients with advanced epithelial skin cancer, who are typically elderly and frail. Aim of this study was to assess feasibility and safety of Contact Skin Radiation Therapy (CSRT) to treat basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) during SARS-CoV-2 pandemic. Patients with advanced and difficult-to-treat BCC or SCC were discussed at skin multidisciplinary tumor board (S-MDTB) from February the 21st to May the 4th (phase 1 Italian Pandemic) and retrospectively analyzed. Patient's triage following internal recommendations was daily performed. CSRT was delivered in 8 fractions of 5 Gy each, twice a day. Beyond the clinical outcomes, treatment success indicators, such as the completion of CSRT without SARS-CoV-2 occurrence, were identified to evaluate the feasibility of CSRT during pandemic. A post-treatment psychological assessment regarding patient's safety perception was performed. Six male patients (median age 80 years; range 62–92) with histologically confirmed BCC or SCC were treated with CSRT. Complete clinical remission was achieved in 5/6 patients (83.4%). No high-grade acute toxicities occurred during treatment. No patients or healthcare personnel developed SARS-CoV-2 infection. All the treatment success indicators were achieved. CSRT represents a safe, and feasible treatment option even during the pandemic emergency period. Hypofractionation could be an option to reduce total number of fractions and, consequently, infective risk exposition.

n/a

Purpose: Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). Materials and methods: This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC). Results: The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (p = 0.6) between yMTB and CM. Conclusion: Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.