Search

Your search keyword '"Cremonesi, Marta"' showing total 473 results
Start Over Author "Cremonesi, Marta"
473 results on '"Cremonesi, Marta"'

1. The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study

Author

Tagliabue, Marta, Ruju, Francesca, Mossinelli, Chiara, Gaeta, Aurora, Raimondi, Sara, Volpe, Stefania, Zaffaroni, Mattia, Isaksson, Lars Johannes, Garibaldi, Cristina, Cremonesi, Marta, Rapino, Anna, Chiocca, Susanna, Pietrobon, Giacomo, Alterio, Daniela, Trisolini, Giuseppe, Morbini, Patrizia, Rampinelli, Vittorio, Grammatica, Alberto, Petralia, Giuseppe, Jereczek-Fossa, Barbara Alicja, Preda, Lorenzo, Ravanelli, Marco, Maroldi, Roberto, Piazza, Cesare, Benazzo, Marco, and Ansarin, Mohssen

Published
2024
Full Text
View/download PDF

3. Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models

Author

Marvaso, Giulia, Isaksson, Lars Johannes, Zaffaroni, Mattia, Vincini, Maria Giulia, Summers, Paul Eugene, Pepa, Matteo, Corrao, Giulia, Mazzola, Giovanni Carlo, Rotondi, Marco, Mastroleo, Federico, Raimondi, Sara, Alessi, Sarah, Pricolo, Paola, Luzzago, Stefano, Mistretta, Francesco Alessandro, Ferro, Matteo, Cattani, Federica, Ceci, Francesco, Musi, Gennaro, De Cobelli, Ottavio, Cremonesi, Marta, Gandini, Sara, La Torre, Davide, Orecchia, Roberto, Petralia, Giuseppe, and Jereczek-Fossa, Barbara Alicja

Published
2024
Full Text
View/download PDF

4. Application of the nnU-Net for automatic segmentation of lung lesion on CT images, and implication on radiomic models

Author

Ferrante, Matteo, Rinaldi, Lisa, Botta, Francesca, Hu, Xiaobin, Dolp, Andreas, Minotti, Marta, De Piano, Francesca, Funicelli, Gianluigi, Volpe, Stefania, Bellerba, Federica, De Marco, Paolo, Raimondi, Sara, Rizzo, Stefania, Shi, Kuangyu, Cremonesi, Marta, Jereczek-Fossa, Barbara A., Spaggiari, Lorenzo, De Marinis, Filippo, Orecchia, Roberto, and Origgi, Daniela

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Lesion segmentation is a crucial step of the radiomic workflow. Manual segmentation requires long execution time and is prone to variability, impairing the realisation of radiomic studies and their robustness. In this study, a deep-learning automatic segmentation method was applied on computed tomography images of non-small-cell lung cancer patients. The use of manual vs automatic segmentation in the performance of survival radiomic models was assessed, as well. METHODS A total of 899 NSCLC patients were included (2 proprietary: A and B, 1 public datasets: C). Automatic segmentation of lung lesions was performed by training a previously developed architecture, the nnU-Net, including 2D, 3D and cascade approaches. The quality of automatic segmentation was evaluated with DICE coefficient, considering manual contours as reference. The impact of automatic segmentation on the performance of a radiomic model for patient survival was explored by extracting radiomic hand-crafted and deep-learning features from manual and automatic contours of dataset A, and feeding different machine learning algorithms to classify survival above/below median. Models' accuracies were assessed and compared. RESULTS The best agreement between automatic and manual contours with DICE=0.78 +(0.12) was achieved by averaging predictions from 2D and 3D models, and applying a post-processing technique to extract the maximum connected component. No statistical differences were observed in the performances of survival models when using manual or automatic contours, hand-crafted, or deep features. The best classifier showed an accuracy between 0.65 and 0.78. CONCLUSION The promising role of nnU-Net for automatic segmentation of lung lesions was confirmed, dramatically reducing the time-consuming physicians' workload without impairing the accuracy of survival predictive models based on radiomics.

Published
2022

5. Results from an EANM survey on time estimates and personnel responsible for main tasks in molecular radiotherapy dosimetry

Author

Gabiña, Pablo Mínguez, Gleisner, Katarina Sjögreen, Cremonesi, Marta, Stokke, Caroline, Flux, Glenn, Cicone, Francesco, Konijnenberg, Mark, Aldridge, Matt, Sandstrom, Mattias, Chiesa, Carlo, Paphiti, Maria, Hippeläinen, Eero, Uribe, Carlos, Solny, Pavel, Gnesin, Silvano, Bernhardt, Peter, Chouin, Nicolas, Costa, Pedro Fragoso, Glatting, Gerhard, Verburg, Frederik, and Gear, Jonathan

Published
2023
Full Text
View/download PDF

6. Multi-omics integrative modelling for stereotactic body radiotherapy in early-stage non-small cell lung cancer: clinical trial protocol of the MONDRIAN study

Author

Volpe, Stefania, Zaffaroni, Mattia, Piperno, Gaia, Vincini, Maria Giulia, Zerella, Maria Alessia, Mastroleo, Federico, Cattani, Federica, Fodor, Cristiana Iuliana, Bellerba, Federica, Bonaldi, Tiziana, Bonizzi, Giuseppina, Ceci, Francesco, Cremonesi, Marta, Fusco, Nicola, Gandini, Sara, Garibaldi, Cristina, Torre, Davide La, Noberini, Roberta, Petralia, Giuseppe, Spaggiari, Lorenzo, Venetis, Konstantinos, Orecchia, Roberto, Casiraghi, Monica, and Jereczek-Fossa, Barbara Alicja

Published
2023
Full Text
View/download PDF

7. Comparison of automated segmentation techniques for magnetic resonance images of the prostate

Author

Isaksson, Lars Johannes, Pepa, Matteo, Summers, Paul, Zaffaroni, Mattia, Vincini, Maria Giulia, Corrao, Giulia, Mazzola, Giovanni Carlo, Rotondi, Marco, Lo Presti, Giuliana, Raimondi, Sara, Gandini, Sara, Volpe, Stefania, Haron, Zaharudin, Alessi, Sarah, Pricolo, Paola, Mistretta, Francesco Alessandro, Luzzago, Stefano, Cattani, Federica, Musi, Gennaro, Cobelli, Ottavio De, Cremonesi, Marta, Orecchia, Roberto, Marvaso, Giulia, Petralia, Giuseppe, and Jereczek-Fossa, Barbara Alicja

Published
2023
Full Text
View/download PDF

12. A multicenter study on radiomic features from T$_2$-weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics

Author

Bianchini, Linda, Santinha, Joao, Loução, Nuno, Figueiredo, Mario, Botta, Francesca, Origgi, Daniela, Cremonesi, Marta, Cassano, Enrico, Papanikolaou, Nikolaos, and Lascialfari, Alessandro

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

In this study we investigated the repeatability and reproducibility of radiomic features extracted from MRI images and provide a workflow to identify robust features. 2D and 3D T$_2$-weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of the radiomic features were assessed respectively by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC), considering repeated acquisitions with or without phantom repositioning, and with different scanner/acquisition type, and acquisition parameters. The features showing ICC/CCC > 0.9 were selected, and their dependence on shape information (Spearman's $\rho$> 0.8) was analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities. From 944 2D features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. Much lower range (11.2% to 85.4%) was obtained after phantom repositioning. 3D extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. 82.4% to 94.9% of features showed excellent agreement when extracted from images acquired with TEs 5 ms apart (values decreased when increasing TE intervals) and 90.7% of the features exhibited excellent reproducibility for changes in TR. 2.0% of non-shape features were identified as providing only shape information. This study demonstrates that radiomic features are affected by specific MRI protocols. The use of our radiomic pelvic phantom allowed to identify unreliable features for radiomic analysis on T$_2$-weighted images. This paper proposes a general workflow to identify repeatable, reproducible, and informative radiomic features, fundamental to ensure robustness of clinical studies., Comment: 32 pages, 8 figures (7 + 1 supplemental), 8 tables (5 + 3 supplemental); Submitted to Magnetic Resonance in Medicine

Published
2020

14. Radiobiology and Radiation Dosimetry in Nuclear Medicine

Author

Salvatori, Massimo, Cremonesi, Marta, Indovina, Luca, Chianelli, Marco, Pacilio, Massimiliano, Danieli, Rachele, Chiesa, Carlo, Zanzonico, Pat, Volterrani, Duccio, editor, Erba, Paola A., editor, Strauss, H. William, editor, Mariani, Giuliano, editor, and Larson, Steven M., editor

Published
2022
Full Text
View/download PDF

15. High-performance prediction models for prostate cancer radiomics

Author

Isaksson, Lars Johannes, Repetto, Marco, Summers, Paul Eugene, Pepa, Matteo, Zaffaroni, Mattia, Vincini, Maria Giulia, Corrao, Giulia, Mazzola, Giovanni Carlo, Rotondi, Marco, Bellerba, Federica, Raimondi, Sara, Haron, Zaharudin, Alessi, Sarah, Pricolo, Paula, Mistretta, Francesco Alessandro, Luzzago, Stefano, Cattani, Federica, Musi, Gennaro, De Cobelli, Ottavio, Cremonesi, Marta, Orecchia, Roberto, La Torre, Davide, Marvaso, Giulia, Petralia, Giuseppe, and Jereczek-Fossa, Barbara Alicja

Published
2023
Full Text
View/download PDF

19. Quality assurance for automatically generated contours with additional deep learning

Author

Isaksson, Lars Johannes, Summers, Paul, Bhalerao, Abhir, Gandini, Sara, Raimondi, Sara, Pepa, Matteo, Zaffaroni, Mattia, Corrao, Giulia, Mazzola, Giovanni Carlo, Rotondi, Marco, Lo Presti, Giuliana, Haron, Zaharudin, Alessi, Sara, Pricolo, Paola, Mistretta, Francesco Alessandro, Luzzago, Stefano, Cattani, Federica, Musi, Gennaro, De Cobelli, Ottavio, Cremonesi, Marta, Orecchia, Roberto, Marvaso, Giulia, Petralia, Giuseppe, and Jereczek-Fossa, Barbara Alicja

Published
2022
Full Text
View/download PDF

20. Single fraction ablative preoperative radiation treatment for early-stage breast cancer: the CRYSTAL study – a phase I/II clinical trial protocol

Author

Zerella, Maria Alessia, Zaffaroni, Mattia, Ronci, Giuseppe, Dicuonzo, Samantha, Rojas, Damaris Patricia, Morra, Anna, Fodor, Cristiana, Rondi, Elena, Vigorito, Sabrina, Botta, Francesca, Cremonesi, Marta, Garibaldi, Cristina, Penco, Silvia, Galimberti, Viviana Enrica, Intra, Mattia, Gandini, Sara, Barberis, Massimo, Renne, Giuseppe, Cattani, Federica, Veronesi, Paolo, Orecchia, Roberto, Jereczek-Fossa, Barbara Alicja, and Leonardi, Maria Cristina

Published
2022
Full Text
View/download PDF

25. Towards a Radio-guided Surgery with $\beta^{-}$ Decays: Uptake of a somatostatin analogue (DOTATOC) in Meningioma and High Grade Glioma

Author

Collamati, Francesco, Pepe, Alessandra, Bellini, Fabio, Bocci, Valerio, Cremonesi, Marta, De Lucia, Erika, Ferrari, Mahila, Frallicciardi, Paola M., Grana, Chiara M., Marafini, Michela, Mattei, Ilaria, Morganti, Silvio, Patera, Vincenzo, Piersanti, Luca, Recchia, Luigi, Russomando, Andrea, Sarti, Alessio, Sciubba, Adalberto, Senzacqua, Martina, Camillocci, Elena Solfaroli, Voena, Cecilia, and Faccini, Riccardo

Subjects
Physics - Medical Physics, Physics - Instrumentation and Detectors, Quantitative Biology - Tissues and Organs
Abstract

A novel radio guided surgery (RGS) technique for cerebral tumors using $\beta^{-}$ radiation is being developed. Checking the availability of a radio-tracer that can deliver a $\beta^{-}$ emitter to the tumor is a fundamental step in the deployment of such technique. This paper reports a study of the uptake of 90Y labeled (DOTATOC) in the meningioma and the high grade glioma (HGG) and a feasibility study of the RGS technique in these cases., Comment: 21 pages, 5 figures

Published
2014
Full Text
View/download PDF

26. MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218)

Author

Gugliandolo, Simone Giovanni, Pepa, Matteo, Isaksson, Lars Johannes, Marvaso, Giulia, Raimondi, Sara, Botta, Francesca, Gandini, Sara, Ciardo, Delia, Volpe, Stefania, Riva, Giulia, Rojas, Damari Patricia, Zerini, Dario, Pricolo, Paola, Alessi, Sarah, Petralia, Giuseppe, Summers, Paul Eugene, Mistretta, Frnacesco Alessandro, Luzzago, Stefano, Cattani, Federica, De Cobelli, Ottavio, Cassano, Enrico, Cremonesi, Marta, Bellomi, Massimo, Orecchia, Roberto, and Jereczek-Fossa, Barbara Alicja

Published
2021
Full Text
View/download PDF

30. Dosimetry in PRRT

Author

Cremonesi, Marta, Ferrari, Mahila, Botta, Francesca, Bombardieri, Emilio, editor, Seregni, Ettore, editor, Evangelista, Laura, editor, Chiesa, Carlo, editor, and Chiti, Arturo, editor

Published
2018
Full Text
View/download PDF

31. Technical note: Impact of dose voxel kernel (DVK) values on dosimetry estimates in 177Lu and 90Y radiopharmaceutical therapy (RPT) applications.

Author

Danieli, Rachele, Pistone, Daniele, Tranel, Jonathan, Botta, Francesca, Uribe‐Munoz, Carlos, Raspanti, Davide, Salvat, Francesc, Wilderman, Scott J, Bardiès, Manuel, Amato, Ernesto, Dewaraja, Yuni K, and Cremonesi, Marta

Subjects
ABSORBED dose, RADIOPHARMACEUTICALS, POSITRON emission tomography, FREEWARE (Computer software), CARTOGRAPHY software, SOFTWARE development tools
Abstract

Background: Radiopharmaceutical therapy (RPT) is an increasingly adopted modality for treating cancer. There is evidence that the optimization of the treatment based on dosimetry can improve outcomes. However, standardization of the clinical dosimetry workflow still represents a major effort. Among the many sources of variability, the impact of using different Dose Voxel Kernels (DVKs) to generate absorbed dose (AD) maps by convolution with the time‐integrated activity (TIA) distribution has not been systematically investigated. Purpose: This study aims to compare DVKs and assess the differences in the ADs when convolving the same TIA map with different DVKs. Methods: DVKs of 3 × 3 × 3 mm3 sampling—nine for 177Lu, nine for 90Y—were selected from those most used in commercial/free software or presented in prior publications. For each voxel within a 11 × 11 × 11 matrix, the coefficient of variation (CoV) and the percentage difference between maximum and minimum values (% maximum difference) were calculated. The total absorbed dose per decay (SUM), calculated as the sum of all the voxel values in each kernel, was also compared. Publicly available quantitative SPECT images for two patients treated with 177Lu‐DOTATATE and PET images for two patients treated with 90Y‐microspheres were used, including organs at risk (177Lu: kidneys; 90Y: liver and healthy liver) and tumors' segmentations. For each patient, the mean AD to the volumes of interest (VOIs) was calculated using the different DVKs, the same TIA map and the same software tool for dose convolution, thereby focusing on the DVK impact. For each VOI, the % maximum difference of the mean AD between maximum and minimum values was computed. Results: The CoV (% maximum difference) in voxels of normalized coordinates [0,0,0], [0,1,0], and [0,1,1] were 5%(21%), 9%(35%), and 10%(46%) for the 177Lu DVKs. For the case of 90Y, these values were 2%(9%), 4%(14%), and 4%(16%). The CoV (% maximum difference) for SUM was 9%(33%) for 177Lu, and 4%(15%) for 90Y. The variability of the mean tumor and organ AD was up to 19% and 15% in 177Lu‐DOTATATE and 90Y‐microspheres patients, respectively. Conclusions: This study showed a considerable AD variability due exclusively to the use of different DVKs. A concerted effort by the scientific community would contribute to decrease these discrepancies, strengthening the consistency of AD calculation in RPT. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

32. Technical note: Impact of dose voxel kernel (DVK) values on dosimetry estimates in 177Lu and 90Y radiopharmaceutical therapy (RPT) applications

Author

Danieli, Rachele, primary, Pistone, Daniele, additional, Tranel, Jonathan, additional, Botta, Francesca, additional, Uribe‐Munoz, Carlos, additional, Raspanti, Davide, additional, Salvat, Francesc, additional, Wilderman, Scott J, additional, Bardiès, Manuel, additional, Amato, Ernesto, additional, Dewaraja, Yuni K, additional, and Cremonesi, Marta, additional

Published
2023
Full Text
View/download PDF

39. Blood- and Imaging-Derived Biomarkers for Oncological Outcome Modelling in Oropharyngeal Cancer: Exploring the Low-Hanging Fruit

Author

Volpe, Stefania, primary, Gaeta, Aurora, additional, Colombo, Francesca, additional, Zaffaroni, Mattia, additional, Mastroleo, Federico, additional, Vincini, Maria Giulia, additional, Pepa, Matteo, additional, Isaksson, Lars Johannes, additional, Turturici, Irene, additional, Marvaso, Giulia, additional, Ferrari, Annamaria, additional, Cammarata, Giulio, additional, Santamaria, Riccardo, additional, Franzetti, Jessica, additional, Raimondi, Sara, additional, Botta, Francesca, additional, Ansarin, Mohssen, additional, Gandini, Sara, additional, Cremonesi, Marta, additional, Orecchia, Roberto, additional, Alterio, Daniela, additional, and Jereczek-Fossa, Barbara Alicja, additional

Published
2023
Full Text
View/download PDF

40. The role of radiomics in tongue cancer: A new tool for prognosis prediction

Author

Mossinelli, Chiara, primary, Tagliabue, Marta, additional, Ruju, Francesca, additional, Cammarata, Giulio, additional, Volpe, Stefania, additional, Raimondi, Sara, additional, Zaffaroni, Mattia, additional, Isaksson, Johannes Lars, additional, Garibaldi, Cristina, additional, Cremonesi, Marta, additional, Corso, Federica, additional, Gaeta, Aurora, additional, Emili, Ilaria, additional, Zorzi, Stefano, additional, Alterio, Daniela, additional, Marvaso, Giulia, additional, Pepa, Matteo, additional, De Fiori, Elvio, additional, Maffini, Fausto, additional, Preda, Lorenzo, additional, Benazzo, Marco, additional, Jereczek‐Fossa, Barbara Alicja, additional, and Ansarin, Mohssen, additional

Published
2023
Full Text
View/download PDF

41. Brain metastases from NSCLC treated with stereotactic radiotherapy: prediction mismatch between two different radiomic platforms

Author

Carloni, Gianluca, primary, Garibaldi, Cristina, additional, Marvaso, Giulia, additional, Volpe, Stefania, additional, Zaffaroni, Mattia, additional, Pepa, Matteo, additional, Isaksson, Lars Johannes, additional, Colombo, Francesca, additional, Durante, Stefano, additional, Lo Presti, Giuliana, additional, Raimondi, Sara, additional, Spaggiari, Lorenzo, additional, de Marinis, Filippo, additional, Piperno, Gaia, additional, Vigorito, Sabrina, additional, Gandini, Sara, additional, Cremonesi, Marta, additional, Positano, Vincenzo, additional, and Jereczek-Fossa, Barbara Alicja, additional

Published
2023
Full Text
View/download PDF

42. Role of interim 18F-FDG-PET/CT for the early prediction of clinical outcomes of Non-Small Cell Lung Cancer (NSCLC) during radiotherapy or chemo-radiotherapy. A systematic review

Author

Cremonesi, Marta, Gilardi, Laura, Ferrari, Mahila Esmeralda, Piperno, Gaia, Travaini, Laura Lavinia, Timmerman, Robert, Botta, Francesca, Baroni, Guido, Grana, Chiara Maria, Ronchi, Sara, Ciardo, Delia, Jereczek-Fossa, Barbara Alicja, Garibaldi, Cristina, and Orecchia, Roberto

Published
2017
Full Text
View/download PDF

44. EANM procedure guideline for the treatment of liver cancer and liver metastases with intra-arterial radioactive compounds

Author

Weber, Manuel, Lam, Marnix, Chiesa, Carlo, Konijnenberg, Mark W., Cremonesi, Marta, Flamen, Patrick B., Gnesin, Silvano, Bodei, Lisa, Kracmerova, T., Luster, Markus, Garin, Etienne, and Herrmann, Ken

Subjects
Medizin, Radiology, Nuclear Medicine and imaging, General Medicine
Abstract

Abstract Primary liver tumours (i.e. hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICC)) are among the most frequent cancers worldwide. However, only 10–20% of patients are amenable to curative treatment, such as resection or transplant. Liver metastases are most frequently caused by colorectal cancer, which accounts for the second most cancer-related deaths in Europe. In both primary and secondary tumours, radioembolization has been shown to be a safe and effective treatment option. The vast potential of personalized dosimetry has also been shown, resulting in markedly increased response rates and overall survival. In a rapidly evolving therapeutic landscape, the role of radioembolization will be subject to changes. Therefore, the decision for radioembolization should be taken by a multidisciplinary tumour board in accordance with the current clinical guidelines. The purpose of this procedure guideline is to assist the nuclear medicine physician in treating and managing patients undergoing radioembolization treatment. Preamble The European Association of Nuclear Medicine (EANM) is a professional non-profit medical association that facilitates communication worldwide among individuals pursuing clinical and research excellence in nuclear medicine. The EANM was founded in 1985. These guidelines are intended to assist practitioners in providing appropriate nuclear medicine care for patients. They are not inflexible rules or requirements of practice and are not intended, nor should they be used, to establish a legal standard of care. The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by medical professionals taking into account the unique circumstances of each case. Thus, there is no implication that an approach differing from the guidelines, standing alone, is below the standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set out in the guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources or advances in knowledge or technology subsequent to publication of the guidelines. The practice of medicine involves not only the science but also the art of dealing with the prevention, diagnosis, alleviation and treatment of disease. The variety and complexity of human conditions make it impossible to always reach the most appropriate diagnosis or to predict with certainty a particular response to treatment. Therefore, it should be recognised that adherence to these guidelines will not ensure an accurate diagnosis or a successful outcome. All that should be expected is that the practitioner will follow a reasonable course of action based on current knowledge, available resources and the needs of the patient to deliver effective and safe medical care. The sole purpose of these guidelines is to assist practitioners in achieving this objective.

Published
2022

45. Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models

Author

Ferrante, Matteo, primary, Rinaldi, Lisa, additional, Botta, Francesca, additional, Hu, Xiaobin, additional, Dolp, Andreas, additional, Minotti, Marta, additional, De Piano, Francesca, additional, Funicelli, Gianluigi, additional, Volpe, Stefania, additional, Bellerba, Federica, additional, De Marco, Paolo, additional, Raimondi, Sara, additional, Rizzo, Stefania, additional, Shi, Kuangyu, additional, Cremonesi, Marta, additional, Jereczek-Fossa, Barbara A., additional, Spaggiari, Lorenzo, additional, De Marinis, Filippo, additional, Orecchia, Roberto, additional, and Origgi, Daniela, additional

Published
2022
Full Text
View/download PDF

46. Abstract PR004: Radiogenomics for predicting underestimation of invasiveness in ductal carcinoma in situ (DCIS) diagnosed with vacuum assisted breast biopsy: study rationale and design

Author

Lazzeroni, Matteo, primary, Nicosia, Luca, additional, Rocco, Elena Guerini, additional, Gandini, Sara, additional, Raimondi, Sara, additional, Barberis, Massimo, additional, Fusco, Nicola, additional, Cremonesi, Marta, additional, Origgi, Daniela, additional, Cassano, Enrico, additional, Gonzaga, Aliana Guerrieri, additional, Serrano, Davide, additional, Macis, Debora, additional, Johansson, Harriet, additional, and Bonanni, Bernardo, additional

Published
2022
Full Text
View/download PDF

47. Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Author

Volpe, Stefania, primary, Isaksson, Lars Johannes, additional, Zaffaroni, Mattia, additional, Pepa, Matteo, additional, Raimondi, Sara, additional, Botta, Francesca, additional, Presti, Giuliana Lo, additional, Vincini, Maria Giulia, additional, Rampinelli, Cristiano, additional, Cremonesi, Marta, additional, de Marinis, Filippo, additional, Spaggiari, Lorenzo, additional, Gandini, Sara, additional, Guckenberger, Matthias, additional, Orecchia, Roberto, additional, and Jereczek-Fossa, Barbara Alicja, additional

Published
2022
Full Text
View/download PDF

49. Dosimetry Using SPECT-CT

Author

Basile, Chiara, Botta, Francesca, Cremonesi, Marta, De Cicco, Concetta, Di Dia, Amalia, Mango, Lucio, Pacilio, Massimiliano, Paganelli, Giovanni, Fanti, Stefano, Farsad, Mohsen, and Mansi, Luigi

Published
2011
Full Text
View/download PDF

50. Comparison of automated segmentation techniques for magnetic resonance images of the prostate

Author

Isaksson, Lars Johannes, primary, Pepa, Matteo, additional, Summers, Paul, additional, Zaffaroni, Mattia, additional, Vincini, Maria Giulia, additional, Corrao, Giulia, additional, Mazzola, Giovanni Carlo, additional, Rotondi, Marco, additional, Presti, Giuliana Lo, additional, Raimondi, Sara, additional, Mistretta, Sara, additional, Luzzago, Stefano, additional, Cattani, Federica, additional, Musi, Gennaro, additional, De Cobelli, Ottavio, additional, Cremonesi, Marta, additional, Orecchia, Roberto, additional, Marvaso, Giulia, additional, Petralia, Giuseppe, additional, and Jereczek-Fossa, Barbara Alicja, additional

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results