Roberta Castriconi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Roberta Castriconi has a total of 161 co-authors. Here are the top co-authors by number of shared publications:
- Claudio Fiorino
Medical Physics, IRCCS San Raffaele Scientific Institute, Milan, Italy
11 shared publications - Alessia Tudda
Medical Physics Department, IRCCS San Raffaele Scientific Institute, 20132 Milano, Italy
9 shared publications Antonella del Vecchio
Medical Physics, IRCCS San Raffaele Scientific Institute, Milan, Italy
9 shared publications- Aldo Mazzilli
Medical Physics Dept, University Hospital of Parma AOUP, Italy
7 shared publications Paola Mangili
Medical Physics, IRCCS San Raffaele Scientific Institute, Milano, Italy
7 shared publications- Andrei Fodor
IRCCS San Raffaele Scientific Institute, Department of Radiation Oncology, Milan, Italy
6 shared publications
Latest Publications
- 3 December 2025
- Published by Elsevier in Physica Medica
- Vol. 140, 105678
AIFM young group focus on the medical physics residents in Italy: A survey results
- 3 December 2025
- Published by Elsevier in Physica Medica
- Vol. 140, 105678
AIFM young group focus on the medical physics residents in Italy: A survey results
- Open Access
An International Inter-Consortium Validation of Knowledge-Based Plan Prediction Modeling for Whole Breast Radiotherapy Treatment
Background: Knowledge-based (KB) planning is a promising approach to model prior planning experience and optimize radiotherapy. To enable the sharing of models across institutions, their transferability must be evaluated. This study aimed to validate KB prediction models developed by a national consortium using data from another multi-institutional consortium in a different country. Methods: Ten right whole breast tangential field (RWB-TF) models were built within the national consortium. A cohort of 20 patients from the external consortium was used for testing. Transferability was defined when the ipsilateral (IPSI) lung first principal component (PC1) was within the 10th–90th percentile of the training set. Predicted dose–volume parameters were compared with clinical dose–volume histograms (cDVHs). Results: Planning target volume (PTV) coverage strategies were comparable between the two consortia, even though significant volume differences were observed for the PTV and contralateral breast (p = 0.002 and p = 0.02, respectively). For the IPSI lung, the standard deviation of predicted mean dose/V20 Gy was 1.13 Gy/2.9% in the external consortium versus 0.55 Gy/1.6% in the training consortium. Differences between the cDVH and the predicted IPSI lung mean dose and the volume receiving more than 20 Gy (V20 Gy) were <2 Gy and <5% in 88.7% and 92.3% of cases, respectively. PC1 values fell within the 10th–90th percentile for ≥90% of patients in 6/10 models and 65–85% for the remaining 4. Conclusions: This study demonstrates the feasibility of applying RWB-TF KB models beyond the consortium in which they were developed, supporting broader clinical implementation. This retrospective study was supported by AIRC (Associazione Italiana per la Ricerca sul Cancro) and registered on ClinicalTrials.gov (NCT06317948, 12 March 2024).
Open AccessAn International Inter-Consortium Validation of Knowledge-Based Plan Prediction Modeling for Whole Breast Radiotherapy Treatment
Background: Knowledge-based (KB) planning is a promising approach to model prior planning experience and optimize radiotherapy. To enable the sharing of models across institutions, their transferability must be evaluated. This study aimed to validate KB prediction models developed by a national consortium using data from another multi-institutional consortium in a different country. Methods: Ten right whole breast tangential field (RWB-TF) models were built within the national consortium. A cohort of 20 patients from the external consortium was used for testing. Transferability was defined when the ipsilateral (IPSI) lung first principal component (PC1) was within the 10th–90th percentile of the training set. Predicted dose–volume parameters were compared with clinical dose–volume histograms (cDVHs). Results: Planning target volume (PTV) coverage strategies were comparable between the two consortia, even though significant volume differences were observed for the PTV and contralateral breast (p = 0.002 and p = 0.02, respectively). For the IPSI lung, the standard deviation of predicted mean dose/V20 Gy was 1.13 Gy/2.9% in the external consortium versus 0.55 Gy/1.6% in the training consortium. Differences between the cDVH and the predicted IPSI lung mean dose and the volume receiving more than 20 Gy (V20 Gy) were <2 Gy and <5% in 88.7% and 92.3% of cases, respectively. PC1 values fell within the 10th–90th percentile for ≥90% of patients in 6/10 models and 65–85% for the remaining 4. Conclusions: This study demonstrates the feasibility of applying RWB-TF KB models beyond the consortium in which they were developed, supporting broader clinical implementation. This retrospective study was supported by AIRC (Associazione Italiana per la Ricerca sul Cancro) and registered on ClinicalTrials.gov (NCT06317948, 12 March 2024).
- Open Access
- 28 March 2025
- Published by Elsevier in Physics and Imaging in Radiation Oncology
- Vol. 34, 100749
Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy
- 28 March 2025
- Published by Elsevier in Physics and Imaging in Radiation Oncology
- Vol. 34, 100749
Open AccessDevelopment and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy
- 16 January 2025
- Published by Elsevier in Physica Medica
- Vol. 130, 104889
Multi-institutional Knowledge-Based (KB) plan prediction benchmark models for whole breast irradiation
- 16 January 2025
- Published by Elsevier in Physica Medica
- Vol. 130, 104889
Multi-institutional Knowledge-Based (KB) plan prediction benchmark models for whole breast irradiation
- 31 December 2024
- Published by Elsevier in Physica Medica
- Vol. 129, 104887
Multicenter approach to predict plan quality of robotic intracranial SRS/SRT
- 31 December 2024
- Published by Elsevier in Physica Medica
- Vol. 129, 104887
Multicenter approach to predict plan quality of robotic intracranial SRS/SRT
- 13 March 2024
- Published by Elsevier in Physica Medica
- Vol. 120, 103331
Inter-institutional variability of knowledge-based plan prediction of left whole breast irradiation
- 13 March 2024
- Published by Elsevier in Physica Medica
- Vol. 120, 103331
Inter-institutional variability of knowledge-based plan prediction of left whole breast irradiation
- 8 November 2023
- Published by Elsevier in Physica Medica
- Vol. 115, 102970
SURVEY ON THE INTEREST AND COMMITMENT OF AIFM MEMBERS TO SCIENTIFIC ACTIVITIES (SICAS) - THE INITIATIVE OF THE FUTURUS WORKING GROUP
- 8 November 2023
- Published by Elsevier in Physica Medica
- Vol. 115, 102970
SURVEY ON THE INTEREST AND COMMITMENT OF AIFM MEMBERS TO SCIENTIFIC ACTIVITIES (SICAS) - THE INITIATIVE OF THE FUTURUS WORKING GROUP
- 8 November 2023
- Published by Elsevier in Physica Medica
- Vol. 115, 102896
SELECTIVE SPARING OF RECTUM/BLADDER SUB-REGIONS ASSOCIATED TO INCREASED RISK OF LATE TOXICITY AFTER PROSTATE CANCER RADIOTHERAPY BY MULTI-CRITERIA OPTIMIZATION
- 8 November 2023
- Published by Elsevier in Physica Medica
- Vol. 115, 102896
SELECTIVE SPARING OF RECTUM/BLADDER SUB-REGIONS ASSOCIATED TO INCREASED RISK OF LATE TOXICITY AFTER PROSTATE CANCER RADIOTHERAPY BY MULTI-CRITERIA OPTIMIZATION
- Open Access
- 13 October 2023
- Published by Elsevier in Physics and Imaging in Radiation Oncology
- Vol. 28, 100501
Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning
- 13 October 2023
- Published by Elsevier in Physics and Imaging in Radiation Oncology
- Vol. 28, 100501
Open AccessReal-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning
- Open Access
- 28 August 2023
- Published by Elsevier in Physics and Imaging in Radiation Oncology
- Vol. 28, 100488
Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization
- 28 August 2023
- Published by Elsevier in Physics and Imaging in Radiation Oncology
- Vol. 28, 100488
Open AccessSelective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization