Search

Your search keyword '"Francesco Catucci"' showing total 14 results
Start Over Author "Francesco Catucci"
14 results on '"Francesco Catucci"'

1. A deep learning algorithm to generate synthetic computed tomography images for brain treatments from 0.35 T magnetic resonance imaging

Author

Luca Vellini, Flaviovincenzo Quaranta, Sebastiano Menna, Elisa Pilloni, Francesco Catucci, Jacopo Lenkowicz, Claudio Votta, Michele Aquilano, Andrea D’Aviero, Martina Iezzi, Francesco Preziosi, Alessia Re, Althea Boschetti, Danila Piccari, Antonio Piras, Carmela Di Dio, Alessandro Bombini, Gian Carlo Mattiucci, and Davide Cusumano

Subjects
Artificial intelligence, Synthetic CT, MRI-only workflow, GAN, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background and Purpose: The development of Magnetic Resonance Imaging (MRI)-only Radiotherapy (RT) represents a significant advancement in the field. This study introduces a Deep Learning (DL) algorithm designed to quickly generate synthetic CT (sCT) images from low-field MR images in the brain, an area not yet explored. Methods: Fifty-six patients were divided into training (32), validation (8), and test (16) groups. A conditional Generative Adversarial Network (cGAN) was trained on pre-processed axial paired images. sCTs were validated using mean absolute error (MAE) and mean error (ME) calculated within the patient body. Intensity Modulated Radiation Therapy (IMRT) plans were optimised on simulation MRI and calculated considering sCT and original CT as electron density (ED) map. Dose distributions using sCT and CT were compared using global gamma analysis at different tolerance criteria (2 %/2mm and 3 %/3mm) and evaluating the difference in estimating different Dose Volume Histogram (DVH) parameters for target and organs at risk (OARs). Results: The network generated sCTs of each single patient in less than two minutes (mean time = 103 ± 41 s). For test patients, the MAE was 62.1 ± 17.7 HU, and the ME was −7.3 ± 13.4 HU. Dose parameters on sCTs were within 0.5 Gy of those on original CTs. Gamma passing rates 2 %/2mm, and 3 %/3mm criteria were 99.5 %±0.5 %, and 99.7 %±0.3 %, respectively. Conclusion: The proposed DL algorithm generates in less than 2 min accurate sCT images in the brain for online adaptive radiotherapy, potentially eliminating the need for CT simulation in MR-only workflows for brain treatments.

Published
2025
Full Text
View/download PDF

2. Impact of bias field correction on 0.35 T pelvic MR images: evaluation on generative adversarial network-based OARs’ auto-segmentation and visual grading assessment

Author

Marica Vagni, Huong Elena Tran, Francesco Catucci, Giuditta Chiloiro, Andrea D’Aviero, Alessia Re, Angela Romano, Luca Boldrini, Maria Kawula, Elia Lombardo, Christopher Kurz, Guillaume Landry, Claus Belka, Luca Indovina, Maria Antonietta Gambacorta, Davide Cusumano, and Lorenzo Placidi

Subjects
N4ITK algorithm, bias field artifact, visual grading assessment, generative adversarial networks, 0.35 T MRIgRT, prostate cancer, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

PurposeMagnetic resonance imaging (MRI)-guided radiotherapy enables adaptive treatment plans based on daily anatomical changes and accurate organ visualization. However, the bias field artifact can compromise image quality, affecting diagnostic accuracy and quantitative analyses. This study aims to assess the impact of bias field correction on 0.35 T pelvis MRIs by evaluating clinical anatomy visualization and generative adversarial network (GAN) auto-segmentation performance.Materials and methods3D simulation MRIs from 60 prostate cancer patients treated on MR-Linac (0.35 T) were collected and preprocessed with the N4ITK algorithm for bias field correction. A 3D GAN architecture was trained, validated, and tested on 40, 10, and 10 patients, respectively, to auto-segment the organs at risk (OARs) rectum and bladder. The GAN was trained and evaluated either with the original or the bias-corrected MRIs. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95th) were computed for the segmented volumes of each patient. The Wilcoxon signed-rank test assessed the statistical difference of the metrics within OARs, both with and without bias field correction. Five radiation oncologists blindly scored 22 randomly chosen patients in terms of overall image quality and visibility of boundaries (prostate, rectum, bladder, seminal vesicles) of the original and bias-corrected MRIs. Bennett’s S score and Fleiss’ kappa were used to assess the pairwise interrater agreement and the interrater agreement among all the observers, respectively.ResultsIn the test set, the GAN trained and evaluated on original and bias-corrected MRIs showed DSC/HD95th of 0.92/5.63 mm and 0.92/5.91 mm for the bladder and 0.84/10.61 mm and 0.83/9.71 mm for the rectum. No statistical differences in the distribution of the evaluation metrics were found neither for the bladder (DSC: p = 0.07; HD95th: p = 0.35) nor for the rectum (DSC: p = 0.32; HD95th: p = 0.63). From the clinical visual grading assessment, the bias-corrected MRI resulted mostly in either no change or an improvement of the image quality and visualization of the organs’ boundaries compared with the original MRI.ConclusionThe bias field correction did not improve the anatomy visualization from a clinical point of view and the OARs’ auto-segmentation outputs generated by the GAN.

Published
2024
Full Text
View/download PDF

3. Magnetic resonance guided SBRT reirradiation in locally recurrent prostate cancer: a multicentric retrospective analysis

Author

Luca Boldrini, Angela Romano, Giuditta Chiloiro, Stefanie Corradini, Viola De Luca, Valeria Verusio, Andrea D’Aviero, Alessandra Castelluccia, Anna Rita Alitto, Francesco Catucci, Gianmarco Grimaldi, Christian Trapp, Juliane Hörner-Rieber, Domenico Marchesano, Vincenzo Frascino, Gian Carlo Mattiucci, Vincenzo Valentini, Piercarlo Gentile, and Maria Antonietta Gambacorta

Subjects
Prostate cancer, Radiotherapy, Reirradiation, MRgRT, SBRT, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Aims Reirradiation of prostate cancer (PC) local recurrences represents an emerging challenge for current radiotherapy. In this context, stereotactic body radiation therapy (SBRT) allows the delivery of high doses, with curative intent. Magnetic Resonance guided Radiation Therapy (MRgRT) has shown promising results in terms of safety, feasibility and efficacy of delivering SBRT thanks to the enhanced soft tissue contrast and the online adaptive workflow. This multicentric retrospective analysis evaluates the feasibility and efficacy of PC reirradiation, using a 0.35 T hybrid MR delivery unit. Methods Patients affected by local recurrences of PC and treated in five institutions between 2019 and 2022 were retrospectively collected. All patients had undergone previous Radiation Therapy (RT) in definitive or adjuvant setting. Re-treatment MRgSBRT was delivered with a total dose ranging from 25 to 40 Gy in 5 fractions. Toxicity according to CTCAE v 5.0 and treatment response were assessed at the end of the treatment and at follow-up. Results Eighteen patients were included in this analysis. All patients had previously undergone external beam radiation therapy (EBRT) up to a total dose of 59.36 to 80 Gy. Median cumulative biologically effective dose (BED) of SBRT re-treatment was 213,3 Gy (103,1-560), considering an α/β of 1.5. Complete response was achieved in 4 patients (22.2%). No grade ≥ 2 acute genitourinary (GU) toxicity events were recorded, while gastrointestinal (GI) acute toxicity events occurred in 4 patients (22.2%). Conclusion The low rates of acute toxicity of this experience encourages considering MRgSBRT a feasibile therapeutic approach for the treatment of clinically relapsed PC. Accurate gating of target volumes, the online adaptive planning workflow and the high definition of MRI treatment images allow delivering high doses to the PTV while efficiently sparing organs at risk (OARs).

Published
2023
Full Text
View/download PDF

4. A Deep Learning Approach for the Fast Generation of Synthetic Computed Tomography from Low-Dose Cone Beam Computed Tomography Images on a Linear Accelerator Equipped with Artificial Intelligence

Author

Luca Vellini, Sergio Zucca, Jacopo Lenkowicz, Sebastiano Menna, Francesco Catucci, Flaviovincenzo Quaranta, Elisa Pilloni, Andrea D'Aviero, Michele Aquilano, Carmela Di Dio, Martina Iezzi, Alessia Re, Francesco Preziosi, Antonio Piras, Althea Boschetti, Danila Piccari, Gian Carlo Mattiucci, and Davide Cusumano

Subjects
artificial intelligence, synthetic CT, radiotherapy, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images from low-dose Cone Beam CT (CBCT) acquired on a modern linear accelerator integrating AI. Methods: A total of 53 patients treated in the pelvic region were enrolled and split into training (30), validation (9), and testing (14). A Generative Adversarial Network (GAN) was trained for 200 epochs. The image accuracy was evaluated by calculating the mean and mean absolute error (ME and ME) between sCT and CT. RT treatment plans were calculated on CT and sCT images, and dose accuracy was evaluated considering Dose Volume Histogram (DVH) and gamma analysis. Results: A total of 4507 images were selected for training. The MAE and ME values in the test set were 36 ± 6 HU and 7 ± 6 HU, respectively. Mean gamma passing rates for 1%/1 mm, 2%/2 mm, and 3%/3 mm tolerance criteria were respectively 93.5 ± 3.4%, 98.0 ± 1.3%, and 99.2 ± 0.7%, with no difference between curative and palliative cases. All the DVH parameters analysed were within 1 Gy of the difference between sCT and CT. Conclusion: This study demonstrated that sCT generation using the DL approach is feasible on low-dose CBCT images. The proposed approach can represent a valid tool to speed up the online adaptive procedure and remove CT simulation from the RT workflow.

Published
2024
Full Text
View/download PDF

5. Dosimetric Impact of Inter-Fraction Variability in the Treatment of Breast Cancer: Towards New Criteria to Evaluate the Appropriateness of Online Adaptive Radiotherapy

Author

Martina Iezzi, Davide Cusumano, Danila Piccari, Sebastiano Menna, Francesco Catucci, Andrea D’Aviero, Alessia Re, Carmela Di Dio, Flaviovincenzo Quaranta, Althea Boschetti, Marco Marras, Domenico Piro, Flavia Tomei, Claudio Votta, Vincenzo Valentini, and Gian Carlo Mattiucci

Subjects
AI radiotherapy, predictive modeling, CBCT radiotherapy, inter-fraction dose variation, online adaptation, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

PurposeAs a discipline in its infancy, online adaptive RT (ART) needs new ontologies and ad hoc criteria to evaluate the appropriateness of its use in clinical practice. In this experience, we propose a predictive model able to quantify the dosimetric impact due to daily inter-fraction variability in a standard RT breast treatment, to identify in advance the treatment fractions where patients might benefit from an online ART approach.MethodsThe study was focused on right breast cancer patients treated using standard adjuvant RT on an artificial intelligence (AI)-based linear accelerator. Patients were treated with daily CBCT images and without online adaptation, prescribing 40.05 Gy in 15 fractions, with four IMRT tangential beams. ESTRO guidelines were followed for the delineation on planning CT (pCT) of organs at risk and targets. For each patient, all the CBCT images were rigidly aligned to pCT: CTV and PTV were manually re-contoured and the original treatment plan was recalculated. Various radiological parameters were measured on CBCT images, to quantify inter-fraction variability present in each RT fraction after the couch shifts compensation. The variation of these parameters was correlated with the variation of V95% of PTV (ΔV95%) using the Wilcoxon Mann–Whitney test. Fractions where ΔV95% > 2% were considered as adverse events. A logistic regression model was calculated considering the most significant parameter, and its performance was quantified with a receiver operating characteristic (ROC) curve.ResultsA total of 75 fractions on 5 patients were analyzed. The body variation between daily CBCT and pCT along the beam axis with the highest MU was identified as the best predictor (p = 0.002). The predictive model showed an area under ROC curve of 0.86 (95% CI, 0.82–0.99) with a sensitivity of 85.7% and a specificity of 83.8% at the best threshold, which was equal to 3 mm.ConclusionA novel strategy to identify treatment fractions that may benefit online ART was proposed. After image alignment, the measure of body difference between daily CBCT and pCT can be considered as an indirect estimator of V95% PTV variation: a difference larger than 3 mm will result in a V95% decrease larger than 2%. A larger number of observations is needed to confirm the results of this hypothesis-generating study.

Published
2022
Full Text
View/download PDF

6. Fractal-Based Radiomic Approach to Tailor the Chemotherapy Treatment in Rectal Cancer: A Generating Hypothesis Study

Author

Carmela Di Dio, Giuditta Chiloiro, Davide Cusumano, Francesco Catucci, Luca Boldrini, Angela Romano, Elisa Meldolesi, Fabio Marazzi, Barbara Corvari, Brunella Barbaro, Riccardo Manfredi, Vincenzo Valentini, and Maria Antonietta Gambacorta

Subjects
radiomics, MRI, oxaliplatin, rectal cancer, predictive modeling, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

IntroductionThe aim of this study was to create a radiomic model able to calculate the probability of 5-year disease-free survival (5yDFS) when oxaliplatin (OXA) is or not administered in patients with locally advanced rectal cancer (LARC) and treated with neoadjuvant chemoradiotherapy (nCRT), allowing physicians to choose the best chemotherapy (CT) regimen.MethodsLARC patients with cT3–4 cN0 or cT1–4 cN1–2 were treated according to an nCRT protocol that included concomitant CT schedules with or without OXA and radiotherapy dose of 55 Gy in 25 fractions. Radiomic analysis was performed on the T2-weighted (T2-w) MR images acquired during the initial tumor staging. Statistical analysis was performed separately for the cohort of patients treated with and without OXA. The ability of every single radiomic feature in predicting 5yDFS as a univariate analysis was assessed using the Wilcoxon–Mann–Whitney (WMW) test or t-test. Two logistic models (one for each cohort) were calculated, and their performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC).ResultsA total of 176 image features belonging to four families (morphological, statistical, textural, and fractal) were calculated for each patient. At the univariate analysis, the only feature showing significance in predicting 5yDFS was the maximum fractal dimension of the subpopulation identified considering 30% and 50% as threshold levels (maxFD30–50). Once the models were developed using this feature, an AUC of 0.67 (0.57–0.77) and 0.75 (0.56–0.95) was obtained for patients treated with and without OXA, respectively. A maxFD30–50 >1.6 was correlated to a higher 5yDFS probability in patients treated with OXA.ConclusionThis study suggests that radiomic analysis of MR T2-w images can be used to define the optimal concomitant CT regimen for stage III LARC cancer patients. In particular, by providing an indication of the gross tumor volume (GTV) spatial heterogeneity at initial staging, maxFD30–50 seems to be able to predict the probability of 5yDFS. New studies including a larger cohort of patients and external validation sets are recommended to verify the results of this hypothesis-generating study.

Published
2021
Full Text
View/download PDF

7. Delta Radiomics Analysis for Local Control Prediction in Pancreatic Cancer Patients Treated Using Magnetic Resonance Guided Radiotherapy

Author

Davide Cusumano, Luca Boldrini, Poonam Yadav, Calogero Casà, Sangjune Laurence Lee, Angela Romano, Antonio Piras, Giuditta Chiloiro, Lorenzo Placidi, Francesco Catucci, Claudio Votta, Gian Carlo Mattiucci, Luca Indovina, Maria Antonietta Gambacorta, Michael Bassetti, and Vincenzo Valentini

Subjects
radiomics, pancreatic cancer, magnetic resonance, MR-guided radiotherapy, Medicine (General), R5-920
Abstract

The aim of this study is to investigate the role of Delta Radiomics analysis in the prediction of one-year local control (1yLC) in patients affected by locally advanced pancreatic cancer (LAPC) and treated using Magnetic Resonance guided Radiotherapy (MRgRT). A total of 35 patients from two institutions were enrolled: A 0.35 Tesla T2*/T1 MR image was acquired for each case during simulation and on each treatment fraction. Physical dose was converted in biologically effective dose (BED) to compensate for different radiotherapy schemes. Delta Radiomics analysis was performed considering the gross tumour volume (GTV) delineated on MR images acquired at BED of 20, 40, and 60 Gy. The performance of the delta features in predicting 1yLC was investigated in terms of Wilcoxon Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC). The most significant feature in predicting 1yLC was the variation of cluster shade calculated at BED = 40 Gy, with a p-value of 0.005 and an AUC of 0.78 (0.61–0.94). Delta Radiomics analysis on low-field MR images might play a promising role in 1yLC prediction for LAPC patients: further studies including an external validation dataset and a larger cohort of patients are recommended to confirm the validity of this preliminary experience.

Published
2021
Full Text
View/download PDF

8. Evaluation of an Early Regression Index (ERITCP) as Predictor of Pathological Complete Response in Cervical Cancer: A Pilot-Study

Author

Davide Cusumano, Francesco Catucci, Angela Romano, Luca Boldrini, Antonio Piras, Sara Broggi, Claudio Votta, Lorenzo Placidi, Matteo Nardini, Giuditta Chiloiro, Alessia Nardangeli, Viola De Luca, Bruno Fionda, Maura Campitelli, Rosa Autorino, Maria Antonietta Gambacorta, Luca Indovina, Claudio Fiorino, and Vincenzo Valentini

Subjects
MR-guided radiotherapy, predictive models, cervical cancer, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Background: Recent studies have highlighted the potentialities of a radiobiological parameter, the early regression index (ERITCP), in the treatment response prediction for rectal cancer patients treated with chemoradiotherapy followed by surgery. The aim of this study is to evaluate the performance of this parameter in predicting pathological complete response (pCR) in the context of low field MR guided radiotherapy (MRgRT) for cervical cancer (CC). Methods: A total of 16 patients affected by CC were enrolled. All patients underwent a MRgRT treatment, with prescription of 50.6 Gy in 22 fractions. A daily MR acquisition was performed at simulation and on each treatment fraction. Gross tumor volume (GTV) was delineated on the MR images acquired at the following biological effective dose (BED) levels: 14, 28, 42, 54 and 62 Gy. The ERITCP was calculated at the different BED levels and its predictive performance was quantified in terms of receiver operating characteristic (ROC) curve. Results: pCR was observed in 11/16 cases. The highest discriminative power of ERITCP was reported when a BED value of 28 Gy is reached, obtaining an area under curve (AUC) of 0.84. Conclusion: This study confirmed ERITCP as a promising response biomarker also for CC, although further studies with larger cohort of patients are recommended.

Published
2020
Full Text
View/download PDF

9. Evaluation of early regression index as response predictor in cervical cancer: A retrospective study on T2 and DWI MR images

Author

Davide Cusumano, Luca Russo, Benedetta Gui, Rosa Autorino, Luca Boldrini, Luca D'Erme, Salvatore Persiani, Francesco Catucci, Sara Broggi, Giulia Panza, Alessia Nardangeli, Maura Campitelli, Gabriella Ferrandina, Gabriella Macchia, Claudio Fiorino, Vincenzo Valentini, Giovanni Scambia, Riccardo Manfredi, and Maria Antonietta Gambacorta

Subjects
Radiomics, Rectal Neoplasms, Uterine Cervical Neoplasms, Chemoradiotherapy, Hematology, Cervical Cancer, Magnetic Resonance Imaging, Neoadjuvant Therapy, Diffusion Magnetic Resonance Imaging, Treatment Outcome, Oncology, Predictive model, Humans, Image-based biomarker, Female, Radiology, Nuclear Medicine and imaging, Retrospective Studies, Settore MED/36 - DIAGNOSTICA PER IMMAGINI E RADIOTERAPIA
Abstract

Early Regression Index (ERIA total of 88 patients affected by LACC (FIGO IB2-IVA) and treated with CRT were enrolled. An MRI protocol consisting in two acquisitions (T2-w and DWI) in two times (before treatment and at mid-therapy) was applied. Gross Tumor Volume (GTV) was delineated and ERIERIThis study identified ERI

Published
2022
Full Text
View/download PDF

10. A deep learning approach to generate synthetic CT in low field MR-guided radiotherapy for lung cases

Author

Jacopo Lenkowicz, Claudio Votta, Matteo Nardini, Flaviovincenzo Quaranta, Francesco Catucci, Luca Boldrini, Marica Vagni, Sebastiano Menna, Lorenzo Placidi, Angela Romano, Giuditta Chiloiro, Maria Antonietta Gambacorta, Gian Carlo Mattiucci, Luca Indovina, Vincenzo Valentini, and Davide Cusumano

Subjects
Radiotherapy Planning, Computer-Assisted, Radiotherapy Dosage, Hematology, Thorax, Magnetic Resonance Imaging, MR-guided Radiotherapy, Deep Learning, Oncology, Artificial Intelligence, Humans, Radiology, Nuclear Medicine and imaging, Tomography, X-Ray Computed, MR-only Radiotherapy, Lung, Synthetic CT, Settore MED/36 - DIAGNOSTICA PER IMMAGINI E RADIOTERAPIA, Radiotherapy, Image-Guided
Abstract

This study aims to apply a conditional Generative Adversarial Network (cGAN) to generate synthetic Computed Tomography (sCT) from 0.35 Tesla Magnetic Resonance (MR) images of the thorax.Sixty patients treated for lung lesions were enrolled and divided into training (32), validation (8), internal (10,TNo significant difference was observed between the test sets for image and dose accuracy parameters. Considering the whole test cohort, a MAE of 54.9 ± 10.5 HU and a ME of 4.4 ± 7.4 HU was obtained. Mean gamma passing rates for 2%/2mm, and 3%/3mm tolerance criteria were 95.5 ± 5.9% and 98.2 ± 4.1% for pure sCT, 96.1 ± 5.1% and 98.5 ± 3.9% for hybrid sCT: the difference between the two approaches was significant (p = 0.01). As regards DVH analysis, differences in target parameters estimation were found to be within 5% using hybrid approach and 20% using pure sCT.The DL algorithm here presented can generate sCT images in the thorax with good image and dose accuracy, especially when the hybrid approach is used. The algorithm does not suffer from inter-scanner variability, making feasible the implementation of MR-only workflows for palliative treatments.

Published
2022

11. Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center

Author

Andrea D’Aviero, Alessia Re, Francesco Catucci, Danila Piccari, Claudio Votta, Domenico Piro, Antonio Piras, Carmela Di Dio, Martina Iezzi, Francesco Preziosi, Sebastiano Menna, Flaviovincenzo Quaranta, Althea Boschetti, Marco Marras, Francesco Miccichè, Roberto Gallus, Luca Indovina, Francesco Bussu, Vincenzo Valentini, Davide Cusumano, and Gian Carlo Mattiucci

Subjects
Deep Learning, Head and Neck Neoplasms, Radiotherapy Planning, Computer-Assisted, Health, Toxicology and Mutagenesis, Image Processing, Computer-Assisted, Radiation Oncology, Public Health, Environmental and Occupational Health, Humans, head and neck, radiotherapy artificial intelligence, deep-learning, auto-contouring, Software
Abstract

Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-generated auto-contours (AC) with manual contours (MC) was performed by three expert radiation oncologists from a single center. Methods: Planning computed tomography (CT) scans of patients undergoing RT treatments for H&N cancers were considered. CT scans were processed by Limbus Contour auto-segmentation software, a commercial deep-learning auto-segmentation based software to generate AC. H&N protocol was used to perform AC, with the structure set consisting of bilateral brachial plexus, brain, brainstem, bilateral cochlea, pharyngeal constrictors, eye globes, bilateral lens, mandible, optic chiasm, bilateral optic nerves, oral cavity, bilateral parotids, spinal cord, bilateral submandibular glands, lips and thyroid. Manual revision of OARs was performed according to international consensus guidelines. The AC and MC were compared using the Dice similarity coefficient (DSC) and 95% Hausdorff distance transform (DT). Results: A total of 274 contours obtained by processing CT scans were included in the analysis. The highest values of DSC were obtained for the brain (DSC 1.00), left and right eye globes and the mandible (DSC 0.98). The structures with greater MC editing were optic chiasm, optic nerves and cochleae. Conclusions: In this preliminary analysis, deep-learning auto-segmentation seems to provide acceptable H&N OAR delineations. For less accurate organs, AC could be considered a starting point for review and manual adjustment. Our results suggest that AC could become a useful time-saving tool to optimize workload and resources in RT departments.

Published
2022
Full Text
View/download PDF

12. Disease-specific and general health-related quality of life in newly diagnosed prostate cancer patients: The Pros-IT CNR study

Author

Angelo, Porreca, Marianna, Noale, Walter, Artibani, Pier Francesco Bassi, Filippo, Bertoni, Sergio, Bracarda, Giario Natale Conti, Renzo, Corvò, Mauro, Gacci, Pierpaolo, Graziotti, Magrini, STEFANO MARIA, Vincenzo, Mirone, Rodolfo, Montironi, Giovanni, Muto, Stefano, Pecoraro, Umberto, Ricardi, Elvio, Russi, Andrea, Tubaro, Vittorina, Zagonel, Gaetano, Crepaldi, Stefania, Maggi, Pierfrancesco, Bassi, Stefano Maria Magrini, Anna Rita Alitto, Enrica, Ambrosi, Alessandro, Antonelli, Cynthia, Aristei, Michele, Barbieri, Franco, Bardari, Lilia, Bardoscia, Salvina, Barra, Sara, Bartoncini, Umberto, Basso, Carlotta, Becherini, Rita, Bellavita, Franco, Bergamaschi, Stefania, Berlingheri, Berruti, Alfredo, Marco, Borghesi, Roberto, Bortolus, Valentina, Borzillo, Davide, Bosetti, Giuseppe, Bove, Pierluigi, Bove, Maurizio, Brausi, Alessio, Bruni, Giorgio, Bruno, Eugenio, Brunocilla, Alberto, Buffoli, BUGLIONE DI MONALE E BASTIA, Michela, Consuelo, Buttigliero, Giovanni, Cacciamani, Michela, Caldiroli, Giuseppe, Cardo, Giorgio, Carmignani, Giuseppe, Carrieri, Emanuele, Castelli, Elisabetta, Castrezzati, Gianpiero, Catalano, Susanna, Cattarino, Francesco, Catucci, Francolini Dario Cavallini, Ofelia, Ceccarini, Antonio, Celia, Francesco, Chiancone, Tommaso, Chini, Claudia, Cianci, Antonio, Cisternino, Devis, Collura, Franco, Corbella, Matteo, Corinti, Paolo, Corsi, Fiorenza, Cortese, Luigi, Corti, Cosimo de Nunzio, Olga, Cristiano, Rolando, M D'Angelillo, Luigi Da Pozzo, Daniele, D'Agostino, Carolina, D'Elia, Matteo, Dandrea, Michele De Angelis, Paolo De Angelis, Ottavio De Cobelli, Bernardino De Concilio, Antonello De Lisa, Stefano De Luca, Agostina De Stefani, Chiara Lucrezia Deantoni, Esposti Claudio Degli, Anna, Destito, Beatrice, Detti, Nadia Di Muzio, Andrea Di Stasio, Calogero Di Stefano, Danilo Di Trapani, Giuseppe, Difino, Sara, Falivene, Giuseppe, Farullo, Paolo, Fedelini, Ilaria, Ferrari, Francesco, Ferrau, Matteo, Ferro, Andrei, Fodor, Francesco, Fontanta, Francesco, Francesca, Giulio, Francolini, Paolo, Frata, Giovanni, Frezza, Pietro, Gabriele, Maria, Galeandro, Elisabetta, Garibaldi, Pietro Giovanni Gennari, Alessandro, Gentilucci, Alessandro, Giacobbe, Laura, Giussani, Giuseppe, Giusti, Paolo, Gontero, Alessia, Guarneri, Cesare, Guida, Alberto, Gurioli, Dorijan, Huqi, Ciro, Imbimbo, Gianluca, Ingrosso, Cinzia, Iotti, Corrado, Italia, Pierdaniele La Mattina, Enza, Lamanna, Luciana, Lastrucci, Grazia, Lazzari, Fabiola, Liberale, Giovanni, Liguori, Roberto, Lisi, Frank, Lohr, Riccardo, Lombardo, Jon A, J Lovisolo, Giuseppe Mario Ludovico, Nicola, Macchione, Francesca, Maggio, Michele, Malizia, Gianluca, Manasse, Giovanni, Mandoliti, Giovanna, Mantini, Luigi, Marafioti, Luisa, Marciello, Alberto Mario Marconi, Antonietta, Martilotta, Salvino, Marzano, Stefano, Masciullo, Gloria, Maso, Adele, Massenzo, Ercole, Mazzeo, Luigi, Mearini, Serena, Medoro, Rosa, Molè, Giorgio, Monesi, Emanuele, Montanari, Franco, Montefiore, Giampaolo, Montesi, Giuseppe, Morgia, Gregorio, Moro, Giorgio, Muscas, Daniela, Musio, Paolo, Muto, Giovanni, Muzzonigro, Giorgio, Napodano, Carlo Luigi Augusto Negro, Mattia, Nidini, Maria, Ntreta, Marco, Orsatti, Carmela, Palazzolo, Isabella, Palumbo, Alessandro, Parisi, Paolo, Parma, Nicola, Pavan, Martina, Pericolini, Francesco, Pinto, Antonio, Pistone, Valerio, Pizzuti, Angelo, Platania, Caterina, Polli, Giorgio, Pomara, Elisabetta, Ponti, Antonio Benito Porcaro, Francesco, Porpiglia, Dario, Pugliese, Armin, Pycha, Giuseppe, Raguso, Andrea, Rampini, Donato Franco Randone, Valentina, Roboldi, Marco, Roscigno, Maria Paola Ruggieri, Giuseppe, Ruoppo, Roberto, Sanseverino, Anna, Santacaterina, Michele, Santarsieri, Riccardo, Santoni, Sarah, Scagliarini, Giorgio Vittorio Scagliotti, Mauro, Scanzi, Marcello, Scarcia, Riccardo, Schiavina, Alessandro, Sciarra, Carmine, Sciorio, Tindaro, Scolaro, Salvatore, Scuzzarella, Oscar, Selvaggio, Armando, Serao, Sergio, Serni, Marco Andrea Signor, Mauro, Silvani, Giovanni, Silvano, Franco, Silvestris, Simeone, Claudio, Valeria, Simone, Girolamo, Spagnoletti, Matteo Giulio Spinelli, Luigi, Squillace, Vincenzo, Tombolini, Mariastella, Toninelli, Triggiani, Luca, Alberto, Trinchieri, Luca Eolo Trodella, Lucio, Trodella, Carlo, Trombetta, Lidia, Tronnolone, Marcello, Tucci, Daniele, Urzì, Riccardo, Valdagni, Maurizio, Valeriani, Maurizio, Vanoli, Elisabetta, Vitali, Alessandro, Volpe, Stefano, Zaramella, Guglielmo, Zeccolini, Giampaolo, Zini, Porreca, Angelo, Noale, Marianna, Artibani, Walter, Bassi, Pier Francesco, Bertoni, Filippo, Bracarda, Sergio, Conti, Giario Natale, Corvò, Renzo, Gacci, Mauro, Graziotti, Pierpaolo, Magrini, Stefano Maria, Mirone, Vincenzo, Montironi, Rodolfo, Muto, Giovanni, Pecoraro, Stefano, Ricardi, Umberto, Russi, Elvio, Tubaro, Andrea, Zagonel, Vittorina, Crepaldi, Gaetano, Maggi, Stefania, Gaetano, Crepaldi, Umberto, Basso, Luigi, Corti, D'Agostino, Daniele, Matteo, Dandrea, Davide, Bosetti, Gianpiero, Catalano, Ottavio, De Cobelli, Lucrezia, Deantoni Chiara, Nadia, Di Muzio, Ferro, Matteo, Andrei, Fodor, Pierdaniele, La Mattina, Emanuele, Montanari, Barbieri, Michele, Valentina, Borzillo, Chiancone, Francesco, Sara, Falivene, Paolo, Fedelini, Imbimbo, Ciro, Paolo, Muto, Sarah, Scagliarini, Giovanni, Muzzonigro, Enrica, Ambrosi, Alessandro, Antonelli, Lilia, Bardoscia, Stefania, Berlingheri, Alfredo, Berruti, Alberto, Buffoli, Michela, Buglione, Mauro, Scanzi, Elisabetta, Castrezzati, Paolo, Frata, Giulio, Francolini, Beatrice, Detti, Tommaso, Chini, Carlotta, Becherini, Olga, Cristiano, Cesare, Guida, Sara, Bartoncini, Consuelo, Buttigliero, Emanuele, Castelli, Devis, Collura, Stefano, De Luca, Pietro, Gabriele, Elisabetta, Garibaldi, Alessandro, Giacobbe, Paolo, Gontero, Alessia, Guarneri, Alberto, Gurioli, Francesco, Porpiglia, Franco, Randone Donato, Vittorio, Scagliotti Giorgio, Cynthia, Aristei, Rita, Bellavita, Isabella, Palumbo, Franco, Bardari, Augusto, Negro Carlo Luigi, Franco, Bergamaschi, Maria, Galeandro, Cinzia, Iotti, Giuseppe, Raguso, Paola, Ruggieri Maria, Giuseppe, Ruoppo, Marco, Borghesi, Eugenio, Brunocilla, Claudio, Degli Esposti, Giovanni, Frezza, Michele, Malizia, Maria, Ntreta, Alessandro, Parisi, Riccardo, Schiavina, Roberto, Bortolu, Giuseppe, Bove, Antonio, Cisternino, Carrieri, Giuseppe, Giuseppe, Difino, Oscar, Selvaggio, Maurizio, Brausi, Alessio, Bruni, Frank, Lohr, Ercole, Mazzeo, Enza, Lamanna, Calogero, Di Stefano, Giorgio, Bruno, Michela, Caldiroli, Ilaria, Ferrari, Laura, Giussani, Lovisolo Jon, A. J., Mario, Marconi Alberto, Giuseppe, Cardo, Mario, Ludovico Giuseppe, Marcello, Scarcia, Giorgio, Carmignani, Salvina, Barra, Dario, Cavallini Francolini, Franco, Corbella, Ofelia, Ceccarini, Luigi, Da Pozzo, Agostina, De Stefani, Corrado, Italia, Stefano, Masciullo, Valentina, Roboldi, Marco, Roscigno, Antonio, Celia, Bernardino, De Concilio, Claudia, Cianci, Francesco, Francesca, Giorgio, Pomara, Michele, Santarsieri, Fiorenza, Cortese, Andrea, Di Stasio, Franco, Montefiore, Armando, Serao, D'Elia, Carolina, Armin, Pycha, Dorijan, Huqi, Paolo, De Angeli, Nicola, Macchione, Francesco, Fontanta, Giorgio, Monesi, Antonello, De Lisa, Giuseppe, Giusti, Giorgio, Musca, Anna, Destito, Rosa, Molè, Danilo, Di Trapani, Francesco, Ferrau, Carmela, Palazzolo, Angelo, Platania, Anna, Santacaterina, Grazia, Lazzari, Fabiola, Liberale, Gregorio, Moro, Giovanni, Liguori, Nicola, Pavan, Francesca, Maggio, Marco, Orsatti, Giovanni, Mandoliti, Giampaolo, Montesi, Luigi, Marafioti, Antonietta, Martilotta, Adele, Massenzo, Luisa, Marciello, Salvino, Marzano, Caterina, Polli, Gloria, Maso, Serena, Medoro, Giuseppe, Morgia, Napodano, Giorgio, Pistone, Antonio, Roberto, Sanseverino, Mattia, Nidini, Paolo, Parma, Valerio, Pizzuti, Sciorio, Carmine, Scuzzarella, Salvatore, Tindaro, Scolaro, Porreca A, Noale M, Artibani W, Bassi PF, Bertoni F, Bracarda S, Conti GN, Corvò R, Gacci M, Graziotti P, Magrini SM, Mirone V, Montironi R, Muto G, Pecoraro S, Ricardi U, Russi E, Tubaro A, Zagonel V, Crepaldi G, Maggi S, Crepaldi G, Maggi S, Noale M, Porreca A, Artibani W, Bassi P, Bracarda S, Conti GN, Corvò R, Graziotti P, Russi E, Mirone V, Montironi R, Bertoni F, Gacci M, Magrini SM, Muto G, Pecoraro S, Ricardi U, Tubaro A, Zagonel V, Alitto AR, Ambrosi E, Antonelli A, Aristei C, Barbieri M, Bardari F, Bardoscia L, Barra S, Bartoncini S, Basso U, Becherini C, Bellavita R, Bergamaschi F, Berlingheri S, Berruti A, Borghesi M, Bortolus R, Borzillo V, Bosetti D, Bove G, Bove P, Brausi M, Bruni A, Bruno G, Brunocilla E, Buffoli A, Buglione M, Buttigliero C, Cacciamani G, Caldiroli M, Cardo G, Carmignani G, Carrieri G, Castelli E, Castrezzati E, Catalano G, Cattarino S, Catucci F, Cavallini FD, Ceccarini O, Celia A, Chiancone F, Chini T, Cianci C, Cisternino A, Collura D, Corbella F, Corinti M, Corsi P, Cortese F, Corti L, de Nunzio C, Cristiano O, D'Angelillo RM, Da Pozzo L, D'agostino D, D'Elia C, Dandrea M, De Angelis M, De Angelis P, De Cobelli O, De Concilio B, De Lisa A, De Luca S, De Stefani A, Deantoni CL, Degli EC, Destito A, Detti B, Di Muzio N, Di Stasio A, Di Stefano C, Di Trapani D, Difino G, Falivene S, Farullo G, Fedelini P, Ferrari I, Ferrau F, Ferro M, Fodor A, Fontanta F, Francesca F, Francolini G, Frata P, Frezza G, Gabriele P, Galeandro M, Garibaldi E, Gennari PG, Gentilucci A, Giacobbe A, Giussani L, Giusti G, Gontero P, Guarneri A, Guida C, Gurioli A, Huqi D, Imbimbo C, Ingrosso G, Iotti C, Italia C, La Mattina P, Lamanna E, Lastrucci L, Lazzari G, Liberale F, Liguori G, Lisi R, Lohr F, Lombardo R, Lovisolo JAJ, Ludovico GM, Macchione N, Maggio F, Malizia M, Manasse G, Mandoliti G, Mantini G, Marafioti L, Marciello L, Marconi AM, Martilotta A, Marzano S, Masciullo S, Maso G, Massenzo A, Mazzeo E, Mearini L, Medoro S, Molè R, Monesi G, Montanari E, Montefiore F, Montesi G, Morgia G, Moro G, Muscas G, Musio D, Muto P, Muzzonigro G, Napodano G, Negro CLA, Nidini M, Ntreta M, Orsatti M, Palazzolo C, Palumbo I, Parisi A, Parma P, Pavan N, Pericolini M, Pinto F, Pistone A, Pizzuti V, Platania A, Polli C, Pomara G, Ponti E, Porcaro AB, Porpiglia F, Pugliese D, Pycha A, Raguso G, Rampini A, Randone DF, Roboldi V, Roscigno M, Ruggieri MP, Ruoppo G, Sanseverino R, Santacaterina A, Santarsieri M, Santoni R, Scagliarini S, Scagliotti GV, Scanzi M, Scarcia M, Schiavina R, Sciarra A, Sciorio C, Scolaro T, Scuzzarella S, Selvaggio O, Serao A, Serni S, Signor MA, Silvani M, Silvano G, Silvestris F, Simeone C, Simone V, Spagnoletti G, Spinelli MG, Squillace L, Tombolini V, Toninelli M, Triggiani L, Trinchieri A, Trodella LE, Trodella L, Trombetta C, Tronnolone L, Tucci M, Urzì D, Valdagni R, Valeriani M, Vanoli M, Vitali E, Volpe A, Zaramella S, Zeccolini G, Zini G, Porreca, A., Noale, M., Artibani, W., Bassi, P. F., Bertoni, F., Bracarda, S., Conti, G. N., Corvo, R., Gacci, M., Graziotti, P., Magrini, S. M., Mirone, V., Montironi, R., Muto, G., Pecoraro, S., Ricardi, U., Russi, E., Tubaro, A., Zagonel, V., Crepaldi, G., Maggi, S., Alitto, A. R., Ambrosi, E., Antonelli, A., Aristei, C., Barbieri, M., Bardari, F., Bardoscia, L., Barra, S., Bartoncini, S., Basso, U., Becherini, C., Bellavita, R., Bergamaschi, F., Berlingheri, S., Berruti, A., Borghesi, M., Bortolus, R., Borzillo, V., Bosetti, D., Bove, G., Bove, P., Maurizio, B., Alessio, B., Giorgio, B., Eugenio, B., Alberto, B., Michela, B., Consuelo, B., Giovanni, C., Michela, C., Giuseppe, C., Giorgio, C., Emanuele, C., Elisabetta, C., Gianpiero, C., Susanna, C., Catucci, F., Dario, C. F., Ofelia, C., Antonio, C., Francesco, C., Tommaso, C., Claudia, C., Devis, C., Franco, C., Matteo, C., Paolo, C., Fiorenza, C., Luigi, C., Cosimo, N., Cristiano, O., D'Angelillo, R. M., Da Pozzo, L., D'Agostino, D., D'Elia, C., Dandrea, M., De Angelis, M., De Angelis, P., De Cobelli, O., De Concilio, B., De Lisa, A., De Luca, S., De Stefani, A., Deantoni, C. L., Degli, E. C., Destito, A., Detti, B., Di Muzio, N., Di Stasio, A., Di Stefano, C., Di Trapani, D., Difino, G., Falivene, S., Farullo, G., Fedelini, P., Ferrari, I., Ferrau, F., Ferro, M., Fodor, A., Fontanta, F., Francesca, F., Francolini, G., Frata, P., Frezza, G., Gabriele, P., Galeandro, M., Garibaldi, E., Gennari, P. G., Gentilucci, A., Giacobbe, A., Giussani, L., Giusti, G., Gontero, P., Guarneri, A., Guida, C., Gurioli, A., Huqi, D., Imbimbo, C., Ingrosso, G., Iotti, C., Italia, C., La Mattina, P., Lamanna, E., Lastrucci, L., Lazzari, G., Liberale, F., Liguori, G., Lisi, R., Lohr, F., Lombardo, R., Lovisolo, J. A. J., Ludovico, G. M., Macchione, N., Maggio, F., Malizia, M., Manasse, G., Mandoliti, G., Mantini, G., Marafioti, L., Marciello, L., Marconi, A. M., Martilotta, A., Marzano, S., Masciullo, S., Maso, G., Massenzo, A., Mazzeo, E., Mearini, L., Medoro, S., Mole, R., Monesi, G., Montanari, E., Montefiore, F., Montesi, G., Morgia, G., Moro, G., Muscas, G., Musio, D., Muto, P., Muzzonigro, G., Napodano, G., Negro, C. L. A., Nidini, M., Ntreta, M., Orsatti, M., Palazzolo, C., Palumbo, I., Parisi, A., Parma, P., Pavan, N., Pericolini, M., Pinto, F., Pistone, A., Pizzuti, V., Platania, A., Polli, C., Pomara, G., Ponti, E., Porcaro, A. B., Porpiglia, F., Pugliese, D., Pycha, A., Raguso, G., Rampini, A., Randone, D. F., Roboldi, V., Roscigno, M., Ruggieri, M. P., Ruoppo, G., Sanseverino, R., Santacaterina, A., Santarsieri, M., Santoni, R., Scagliarini, S., Scagliotti, G. V., Scanzi, M., Scarcia, M., Schiavina, R., Sciarra, A., Sciorio, C., Scolaro, T., Scuzzarella, S., Selvaggio, O., Serao, A., Serni, S., Signor, M. A., Silvani, M., Silvano, G., Silvestris, F., Simeone, C., Simone, V., Spagnoletti, G., Spinelli, M. G., Squillace, L., Tombolini, V., Toninelli, M., Triggiani, L., Trinchieri, A., Trodella, L. E., Trodella, L., Trombetta, C., Tronnolone, L., Tucci, M., Urzi, D., Valdagni, R., Valeriani, M., Vanoli, M., Vitali, E., Volpe, A., Zaramella, S., Zeccolini, G., Zini, G., Porreca, A, Noale, M, Artibani, W, Bassi, P, Bertoni, F, Bracarda, S, Conti, G, Corvò, R, Gacci, M, Graziotti, P, Magrini, S, Mirone, V, Montironi, R, Muto, G, Pecoraro, S, Ricardi, U, Russi, E, Tubaro, A, Zagonel, V, Crepaldi, G, Maggi, S, and Da Pozzo, L

Subjects
Male, 030232 urology & nephrology, Severity of Illness Index, Prostate cancer, 0302 clinical medicine, Quality of life, Activities of Daily Living, Diagnosis, Medicine, Age Factor, Prospective Studies, Prospective cohort study, Settore MED/36 - DIAGNOSTICA PER IMMAGINI E RADIOTERAPIA, Age Factors, Pros-IT CNR study, General Medicine, Health Survey, Middle Aged, Neoadjuvant Therapy, 3. Good health, Italy, 030220 oncology & carcinogenesis, lcsh:R858-859.7, Regression Analysis, Public Health, Human, Diagnosi, prostate cancer, survival, quality of life, medicine.medical_specialty, lcsh:Computer applications to medicine. Medical informatics, Regression Analysi, 03 medical and health sciences, Percutaneous Coronary Intervention, Internal medicine, Severity of illness, Humans, Aged, Cancer staging, business.industry, Research, Environmental and Occupational Health, Public Health, Environmental and Occupational Health, Prostatic Neoplasms, Cancer, medicine.disease, Health Surveys, Comorbidity, Quality of Life, Prospective Studie, Settore MED/24, Prostatic Neoplasm, business, Sexual function
Abstract

Background The National Research Council (CNR) prostate cancer monitoring project in Italy (Pros-IT CNR) is an observational, prospective, ongoing, multicentre study aiming to monitor a sample of Italian males diagnosed as new cases of prostate cancer. The present study aims to present data on the quality of life at time prostate cancer is diagnosed. Methods One thousand seven hundred five patients were enrolled. Quality of life is evaluated at the time cancer was diagnosed and at subsequent assessments via the Italian version of the University of California Los Angeles-Prostate Cancer Index (UCLA-PCI) and the Short Form Health Survey (SF-12). Results At diagnosis, lower scores on the physical component of the SF-12 were associated to older ages, obesity and the presence of 3+ moderate/severe comorbidities. Lower scores on the mental component were associated to younger ages, the presence of 3+ moderate/severe comorbidities and a T-score higher than one. Urinary and bowel functions according to UCLA-PCI were generally good. Almost 5% of the sample reported using at least one safety pad daily to control urinary loss; less than 3% reported moderate/severe problems attributable to bowel functions, and sexual function was a moderate/severe problem for 26.7%. Diabetes, 3+ moderate/severe comorbidities, T2 or T3-T4 categories and a Gleason score of eight or more were significantly associated with lower sexual function scores at diagnosis. Conclusions Data collected by the Pros-IT CNR study have clarified the baseline status of newly diagnosed prostate cancer patients. A comprehensive assessment of quality of life will allow to objectively evaluate outcomes of different profile of care.

Published
2018

13. Solid phase microextraction coupled to gas chromatography- mass spectrometry for the determination of the adsorption coefficients of triazines in soil

Author

Francesco Catucci, Francesco Palmisano, and Carlo G. Zambonin

Subjects
chemistry.chemical_classification, Chromatography, Analytical chemistry, Solid-phase microextraction, Mass spectrometry, Biochemistry, Analytical Chemistry, Adsorption, chemistry, Electrochemistry, Environmental Chemistry, Organic matter, Sample preparation, Freundlich equation, Gas chromatography, Gas chromatography–mass spectrometry, Spectroscopy
Abstract

Solid phase microextraction coupled to gas chromatography-mass spectrometry (SPME-GC-MS) was used to determine the adsorption coefficients (Koc) for six target triazines in soil and sediment samples with different organic matter contents and textures. SPME-GC-MS was used in conjunction with a conventional batch equilibrium method. The recoveries of the selected triazines from soils and sediments having organic carbon contents in the range 0.2–2.4% were satisfactory. Drawbacks of conventional analytical techniques are avoided; owing to the selectivity of SPME-GC-MS, no sample pre-treatment or solvent enrichment steps were required so that Koc values could be easily determined. Moreover, the sensitivity of the technique makes it possible to work at low triazine concentrations (50–500 ng g–1) where the Freundlich adsorption isotherm can be considered linear.

Published
1998
Full Text
View/download PDF

14. [Air quality assessment in an urban-industrial area: the Taranto case study]

Author

Giuseppe, Viviano, Giovanni, Ziemacki, Gaetano, Settimo, Giorgio, Cattani, Maria, Spartera, Francesco, Catucci, and Giovanni, Carbotti

Subjects
Italy, Air Pollution, Metals, Heavy, Urban Health, Industry, Benzopyrenes
Abstract

The chemical-physical characterization of suspended particulate matter and particulate deposition, heavy metals and benzo[a]pyrene, found in the town of Taranto and the nearby industrial area (steelworks, cement production, refinery).Atmospheric deposition Bulks, air of High Volume (HV) samplers equipped with PM10 probe and total suspended particulate matter samplers were used.4 sites of monitoring in different urban areas: two of them close to the cokery, one near the school, and the last one in the town of Statte (Taranto).Determination of suspended and deposition particulate matter concentration, further determination of heavy metal concentration in the suspended and deposition particulate matter, and determination of BaP concentration.Data evidenced that in the site of Tamburi Cemetery (near the cokery) suspended particulate matter PM10 concentrations vary with the different seasons (warm period: 67 microg/m3, cold period: 39 microg/m3). Lead concentrations are lower than the national limit (0,5 microg/m3); for those heavy metals not yet regulated by law, values are comparable with other industrial areas. Deposition particulate matter concentrations found nearby the steelworks (warm period: 699 mg/m2d, cold period 575 mg/m2d), vary in each different monitoring site. In the town of Statte (Taranto) the concentrations found are: 236 mg/m2d in the warm period, and 154 mg/m2d in the cold period. BaP concentrations for PM10 found in the area of Tamburi near the cookery are the following: 1,9 ng/m3 in the warm period, and 2,6 ng/m3 in the cold period.A very compromised situation is found in the area under study as regard air quality, especially the particulate matter in its different fractions and its content of organic and inorganic pollutants.

Published
2006

Catalog

Books, media, physical & digital resources
See catalog results