Local Control and Toxicity of Adaptive Radiotherapy Using Weekly CT Imaging: Results from the LARTIA Trial in Stage III NSCLC

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Chemo-radiotherapy in non-small cell lung cancer: the role of gemcitabine.

Gemcitabine (2'-2'-difluorodeoxycytidine) is a well-known cytotoxic drug and a potent radio-enhancer. We herein report the in vitro evidence of its activity, and the clinical experiences when this drug is administered concurrently with radiation. The phase I-II trials are analyzed, focusing on the recent ability to deliver irradiation with low incidence of side effects

Radiation-induced pneumonitis in the era of the COVID-19 pandemic: artificial intelligence for differential diagnosis

: (1) Aim: To test the performance of a deep learning algorithm in discriminating radiation therapy-related pneumonitis (RP) from COVID-19 pneumonia. (2) Methods: In this retrospective study, we enrolled three groups of subjects: pneumonia-free (control group), COVID-19 pneumonia and RP patients. CT images were analyzed by mean of an artificial intelligence (AI) algorithm based on a novel deep convolutional neural network structure. The cut-off value of risk probability of COVID-19 was 30%; values higher than 30% were classified as COVID-19 High Risk, and values below 30% as COVID-19 Low Risk. The statistical analysis included the Mann-Whitney U test (significance threshold at p < 0.05) and receiver operating characteristic (ROC) curve, with fitting performed using the maximum likelihood fit of a binormal model. (3) Results: Most patients presenting RP (66.7%) were classified by the algorithm as COVID-19 Low Risk. The algorithm showed high sensitivity but low specificity in the detection of RP against COVID-19 pneumonia (sensitivity = 97.0%, specificity = 2%, area under the curve (AUC = 0.72). The specificity increased when an estimated COVID-19 risk probability cut-off of 30% was applied (sensitivity 76%, specificity 63%, AUC = 0.84). (4) Conclusions: The deep learning algorithm was able to discriminate RP from COVID-19 pneumonia, classifying most RP cases as COVID-19 Low Risk

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

In reply to Fiorino et al.: The central role of the radiation oncologist in the multidisciplinary & multiprofessional model of modern radiation therapy

12nononenoneAlongi, Filippo; Arcangeli, Stefano; Cuccia, Francesco; Maria D'Angelillo, Rolando; Gisella Di Muzio, Nadia; Riccardo Filippi, Andrea; Alicja Jereczek-Fossa, Barbara; Livi, Lorenzo; Pergolizzi, Stefano; Scorsetti, Marta; Corvò, Renzo; Maria Magrini, StefanoAlongi, Filippo; Arcangeli, Stefano; Cuccia, Francesco; Maria D'Angelillo, Rolando; Gisella Di Muzio, Nadia; Riccardo Filippi, Andrea; Alicja Jereczek-Fossa, Barbara; Livi, Lorenzo; Pergolizzi, Stefano; Scorsetti, Marta; Corvò, Renzo; Maria Magrini, Stefan

A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients.

The primary goal of precision medicine is to minimize side effects and optimize efficacy of treatments. Recent advances in medical imaging technology allow the use of more advanced image analysis methods beyond simple measurements of tumor size or radiotracer uptake metrics. The extraction of quantitative features from medical images to characterize tumor pathology or heterogeneity is an interesting process to investigate, in order to provide information that may be useful to guide the therapies and predict survival. This paper discusses the rationale supporting the concept of radiomics and the feasibility of its application to Non-Small Cell Lung Cancer in the field of radiation oncology research. We studied 91 stage III patients treated with concurrent chemoradiation and adaptive approach in case of tumor reduction during treatment. We considered 12 statistics features and 230 textural features extracted from the CT images. In our study, we used an ensemble learning method to classify patients' data into either the adaptive or non-adaptive group during chemoradiation on the basis of the starting CT simulation. Our data supports the hypothesis that a specific signature can be identified (AUC 0.82). In our experience, a radiomic signature mixing semantic and image-based features has shown promising results for personalized adaptive radiotherapy in non-small cell lung cancer

Treatment of muscle-invasive bladder cancer in patients without comorbidities and fit for surgery: Trimodality therapy vs radical cystectomy. Development of GRADE (Grades of Recommendation, Assessment, Development and Evaluation) recommendation by the Italian Association of Radiotherapy and Clinical Oncology (AIRO)

Aim: To compare trimodality therapy (TMT) versus radical cystectomy (RC) and develop GRADE (Grades of Recommendation, Assessment, Development and Evaluation) Recommendation by the Italian Association of Radiotherapy and Clinical Oncology (AIRO) for treatment of muscle-invasive bladder cancer (MIBC). Material and methods: Prospective and retrospective studies comparing TMT and RC for MIBC patients were included. Qualitative and quantitative evaluation of evidence was made. Results: Ten studies were included in the analysis. Pooled analysis showed salvage cystectomy and pathological complete response rates after TMT of 12 % and 72-77.5 %, respectively. Pooled rates of G3-G4 GU toxicity and serious toxicity rate were 18 vs 3% and 45 vs 29 % for patients undergoing TMT vs RC, respectively. The panel assessed a substantial equivalence in terms of OS and CSS at 5 years between TMT and RC. Conclusions: TMT could be suggested as an alternative treatment to RC in non-metastatic MIBC patients, deemed fit for surgery