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626 results on '"Mancosu, Pietro"'

1. Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors

Author

Brioso, Ricardo Coimbra, Dei, Damiano, Lambri, Nicola, Mancosu, Pietro, Scorsetti, Marta, and Loiacono, Daniele

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

Radiotherapy requires precise segmentation of organs at risk (OARs) and of the Clinical Target Volume (CTV) to maximize treatment efficacy and minimize toxicity. While deep learning (DL) has significantly advanced automatic contouring, complex targets like CTVs remain challenging. This study explores the use of simpler, well-segmented structures (e.g., OARs) as Anatomical Prior (AP) information to improve CTV segmentation. We investigate gender bias in segmentation models and the mitigation effect of the prior information. Findings indicate that incorporating prior knowledge with the discussed strategies enhances segmentation quality in female patients and reduces gender bias, particularly in the abdomen region. This research provides a comparative analysis of new encoding strategies and highlights the potential of using AP to achieve fairer segmentation outcomes.

Published
2024

2. Leveraging Multimodal CycleGAN for the Generation of Anatomically Accurate Synthetic CT Scans from MRIs

Author

Crespi, Leonardo, Camnasio, Samuele, Dei, Damiano, Lambri, Nicola, Mancosu, Pietro, Scorsetti, Marta, and Loiacono, Daniele

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

In many clinical settings, the use of both Computed Tomography (CT) and Magnetic Resonance (MRI) is necessary to pursue a thorough understanding of the patient's anatomy and to plan a suitable therapeutical strategy; this is often the case in MRI-based radiotherapy, where CT is always necessary to prepare the dose delivery, as it provides the essential information about the radiation absorption properties of the tissues. Sometimes, MRI is preferred to contour the target volumes. However, this approach is often not the most efficient, as it is more expensive, time-consuming and, most importantly, stressful for the patients. To overcome this issue, in this work, we analyse the capabilities of different configurations of Deep Learning models to generate synthetic CT scans from MRI, leveraging the power of Generative Adversarial Networks (GANs) and, in particular, the CycleGAN architecture, capable of working in an unsupervised manner and without paired images, which were not available. Several CycleGAN models were trained unsupervised to generate CT scans from different MRI modalities with and without contrast agents. To overcome the problem of not having a ground truth, distribution-based metrics were used to assess the model's performance quantitatively, together with a qualitative evaluation where physicians were asked to differentiate between real and synthetic images to understand how realistic the generated images were. The results show how, depending on the input modalities, the models can have very different performances; however, models with the best quantitative results, according to the distribution-based metrics used, can generate very difficult images to distinguish from the real ones, even for physicians, demonstrating the approach's potential., Comment: Currently submitted to: Scientific Reports

Published
2024

3. Deep Learning-Based Auto-Segmentation of Planning Target Volume for Total Marrow and Lymph Node Irradiation

Author

Brioso, Ricardo Coimbra, Dei, Damiano, Lambri, Nicola, Loiacono, Daniele, Mancosu, Pietro, and Scorsetti, Marta

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

In order to optimize the radiotherapy delivery for cancer treatment, especially when dealing with complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI), the accurate contouring of the Planning Target Volume (PTV) is crucial. Unfortunately, relying on manual contouring for such treatments is time-consuming and prone to errors. In this paper, we investigate the application of Deep Learning (DL) to automate the segmentation of the PTV in TMLI treatment, building upon previous work that introduced a solution to this problem based on a 2D U-Net model. We extend the previous research (i) by employing the nnU-Net framework to develop both 2D and 3D U-Net models and (ii) by evaluating the trained models on the PTV with the exclusion of bones, which consist mainly of lymp-nodes and represent the most challenging region of the target volume to segment. Our result show that the introduction of nnU-NET framework led to statistically significant improvement in the segmentation performance. In addition, the analysis on the PTV after the exclusion of bones showed that the models are quite robust also on the most challenging areas of the target volume. Overall, our study is a significant step forward in the application of DL in a complex radiotherapy treatment such as TMLI, offering a viable and scalable solution to increase the number of patients who can benefit from this treatment., Comment: arXiv admin note: text overlap with arXiv:2304.02353

Published
2024

4. Investigating Gender Bias in Lymph-Node Segmentation with Anatomical Priors

Author

Brioso, Ricardo Coimbra, Dei, Damiano, Lambri, Nicola, Mancosu, Pietro, Scorsetti, Marta, Loiacono, Daniele, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol-Antón, Esther, editor, Zamzmi, Ghada, editor, Feragen, Aasa, editor, King, Andrew P., editor, Cheplygina, Veronika, editor, Ganz-Benjaminsen, Melanie, editor, Ferrante, Enzo, editor, Glocker, Ben, editor, Petersen, Eike, editor, Baxter, John S. H., editor, Rekik, Islem, editor, and Eagleson, Roy, editor

Published
2025
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6. Segmentation of Planning Target Volume in CT Series for Total Marrow Irradiation Using U-Net

Author

Brioso, Ricardo Coimbra, Dei, Damiano, Franzese, Ciro, Lambri, Nicola, Loiacono, Daniele, Mancosu, Pietro, and Scorsetti, Marta

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Radiotherapy (RT) is a key component in the treatment of various cancers, including Acute Lymphocytic Leukemia (ALL) and Acute Myelogenous Leukemia (AML). Precise delineation of organs at risk (OARs) and target areas is essential for effective treatment planning. Intensity Modulated Radiotherapy (IMRT) techniques, such as Total Marrow Irradiation (TMI) and Total Marrow and Lymph node Irradiation (TMLI), provide more precise radiation delivery compared to Total Body Irradiation (TBI). However, these techniques require time-consuming manual segmentation of structures in Computerized Tomography (CT) scans by the Radiation Oncologist (RO). In this paper, we present a deep learning-based auto-contouring method for segmenting Planning Target Volume (PTV) for TMLI treatment using the U-Net architecture. We trained and compared two segmentation models with two different loss functions on a dataset of 100 patients treated with TMLI at the Humanitas Research Hospital between 2011 and 2021. Despite challenges in lymph node areas, the best model achieved an average Dice score of 0.816 for PTV segmentation. Our findings are a preliminary but significant step towards developing a segmentation model that has the potential to save radiation oncologists a considerable amount of time. This could allow for the treatment of more patients, resulting in improved clinical practice efficiency and more reproducible contours.

Published
2023
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7. Ensemble Methods for Multi-Organ Segmentation in CT Series

Author

Crespi, Leonardo, Roncaglioni, Paolo, Dei, Damiano, Franzese, Ciro, Lambri, Nicola, Loiacono, Daniele, Mancosu, Pietro, and Scorsetti, Marta

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

In the medical images field, semantic segmentation is one of the most important, yet difficult and time-consuming tasks to be performed by physicians. Thanks to the recent advancement in the Deep Learning models regarding Computer Vision, the promise to automate this kind of task is getting more and more realistic. However, many problems are still to be solved, like the scarce availability of data and the difficulty to extend the efficiency of highly specialised models to general scenarios. Organs at risk segmentation for radiotherapy treatment planning falls in this category, as the limited data available negatively affects the possibility to develop general-purpose models; in this work, we focus on the possibility to solve this problem by presenting three types of ensembles of single-organ models able to produce multi-organ masks exploiting the different specialisations of their components. The results obtained are promising and prove that this is a possible solution to finding efficient multi-organ segmentation methods.

Published
2023
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8. Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practice

Author

Maddalo, Michele, Fanizzi, Annarita, Lambri, Nicola, Loi, Emiliano, Branchini, Marco, Lorenzon, Leda, Giuliano, Alessia, Ubaldi, Leonardo, Saponaro, Sara, Signoriello, Michele, Fadda, Federico, Belmonte, Gina, Giannelli, Marco, Talamonti, Cinzia, Iori, Mauro, Tangaro, Sabina, Massafra, Raffaella, Mancosu, Pietro, and Avanzo, Michele

Published
2024
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11. Machine learning and lean six sigma for targeted patient-specific quality assurance of volumetric modulated arc therapy plans

Author

Lambri, Nicola, Dei, Damiano, Goretti, Giulia, Crespi, Leonardo, Brioso, Ricardo Coimbra, Pelizzoli, Marco, Parabicoli, Sara, Bresolin, Andrea, Gallo, Pasqualina, La Fauci, Francesco, Lobefalo, Francesca, Paganini, Lucia, Reggiori, Giacomo, Loiacono, Daniele, Franzese, Ciro, Tomatis, Stefano, Scorsetti, Marta, and Mancosu, Pietro

Published
2024
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17. Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.

Author

Fanizzi, Annarita, Fadda, Federico, Maddalo, Michele, Saponaro, Sara, Lorenzon, Leda, Ubaldi, Leonardo, Lambri, Nicola, Giuliano, Alessia, Loi, Emiliano, Signoriello, Michele, Branchini, Marco, Belmonte, Gina, Giannelli, Marco, Mancosu, Pietro, Talamonti, Cinzia, Iori, Mauro, Tangaro, Sabina, Avanzo, Michele, and Massafra, Raffaella

Subjects
MACHINE learning, ARTIFICIAL intelligence, COMPUTER-assisted image analysis (Medicine), CLASSIFICATION algorithms, COMPUTED tomography
Abstract

Background: To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status as autonomous entities. We postulated that different algorithms which have been proposed in the literature to address the same diagnostic task, can be aggregated to enhance classification performance. We suggested a proof of concept to define an ensemble approach useful for integrating different algorithms proposed to solve the same clinical task. Methods: The proposed approach was developed starting from a public database consisting of radiomic features extracted from CT images relating to 535 patients suffering from lung cancer. Seven algorithms were trained independently by participants in the AI4MP working group on Artificial Intelligence of the Italian Association of Physics in Medicine to discriminate metastatic from non-metastatic patients. The classification scores generated by these algorithms are used to train SVM classifier. The Explainable Artificial Intelligence approach is applied to the final model. The ensemble model was validated following an 80–20 hold-out and leave-one-out scheme on the training set. Results: Compared to individual algorithms, a more accurate result was achieved. On the independent test the ensemble model achieved an accuracy of 0.78, a F1-score of 0.57 and a log-loss of 0.49. Shapley values representing the contribution of each algorithm to the final classification result of the ensemble model were calculated. This information represents an added value for the end user useful for evaluating the appropriateness of the classification result on a particular case. It also allows us to evaluate on a global level which methodological approaches of the individual algorithms are likely to have the most impact. Conclusion: Our proposal represents an innovative approach useful for integrating different algorithms that populate the literature and which lays the foundations for future evaluations in broader application scenarios. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Predictive Factors for Long-Term Disease Control in Systemic Treatment-Naïve Oligorecurrent Renal Cell Carcinoma Treated with Up-Front Stereotactic Ablative Radiotherapy (SABR).

Author

Franzese, Ciro, Vernier, Veronica, Badalamenti, Marco, Lucchini, Raffaella, Stefanini, Sara, Bertolini, Anna, Ilieva, Maryia, Di Cristina, Luciana, Marini, Beatrice, Franceschini, Davide, Comito, Tiziana, Spoto, Ruggero, Dominici, Luca, Galdieri, Carmela, Mancosu, Pietro, Tomatis, Stefano, and Scorsetti, Marta

Subjects
RADIOSURGERY, TREATMENT effectiveness, DESCRIPTIVE statistics, METASTASIS, RENAL cell carcinoma, LUNG tumors, PROGRESSION-free survival, OVERALL survival, EVALUATION
Abstract

Simple Summary: Metastatic renal cell carcinoma (RCC) represents a significant challenge in oncology, and systemic therapy remains the primary treatment modality. Radiation therapy (RT) offers a non-invasive ablative approach to the management of oligometastatic disease, and the use of Stereotactic Ablative Radiotherapy (SABR) has demonstrated remarkably high disease control. Within the broad spectrum of oligometastatic disease, we evaluated the efficacy of SABR on oligorecurrent RCC systemic treatment-naïve, and we aimed to identify predictive factors for long-term disease control. We demonstrated the potential of SABR to delay the need for systemic therapy and confirmed the long-term efficacy of SABR for oligometastatic RCC patients, with a superior benefit for patients with single lung metastasis, potentially refining patient selection processes to tailor treatments. Background: Stereotactic ablative radiotherapy (SABR) is emerging as a potential local treatment option for oligometastatic RCC. This study aims to evaluate the efficacy of SABR in patients with oligorecurrent RCC. Methods: A total of 50 patients with histologically confirmed RCC underwent SABR for oligorecurrence between 2006 and 2022. Eligible patients had up to five extracranial metastases and were systemic treatment-naïve at the time of irradiation. The primary endpoints of the analysis were overall survival (OS), local control (LC), distant metastasis–free survival (DMFS), and time to systemic therapy initiation. Results: The median OS was not reached, with 1- and 3-year OS rates of 93.8% and 77.5%, respectively. LC rates at one and three years were 95.8% and 86.5%, respectively. The median time to systemic therapy initiation was 63.8 months, and the median DMFS was 17.9 months, with one- and three-year rates of 63.4% and 36.6%, respectively. Multiple metastases were a negative predictive factor for DMFS (HR 2.39, p = 0.023), whereas lung metastases were associated with a more favorable outcome (HR 0.38, p = 0.011). Conclusions: SABR offers a valuable treatment option for oligometastatic RCC, demonstrating significant potential for achieving long-term disease control and delaying the need for systemic therapy. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Optimization of Replanning Processes for Volumetric Modulated Arc Therapy Plans at Risk of QA Failure Predicted by a Machine Learning Model.

Author

Lambri, Nicola, Zaccone, Caterina, Bianchi, Monica, Bresolin, Andrea, Dei, Damiano, Gallo, Pasqualina, La Fauci, Francesco, Lobefalo, Francesca, Paganini, Lucia, Pelizzoli, Marco, Reggiori, Giacomo, Tomatis, Stefano, Scorsetti, Marta, Lenardi, Cristina, and Mancosu, Pietro

Subjects
MACHINE learning, VOLUMETRIC-modulated arc therapy, MEDICAL dosimetry, TREATMENT delay (Medicine), QUALITY assurance
Abstract

Featured Application: Automatable replanning strategies can improve patient-specific quality assurance outcomes of radiotherapy plans identified at risk by machine learning models without compromising the dosimetric quality. Patient-specific quality assurance (PSQA) procedures ensure the safe delivery of volumetric modulated arc therapy (VMAT) plans. PSQA requires extensive time and resources and may cause treatment delays if replanning is needed due to failures. Recently, our group developed a machine learning (ML) model predicting gamma passing rate (GPR) for VMAT arcs. This study explores automatable replanning strategies for plans identified at risk of failure, aiming to improve deliverability while maintaining dosimetric quality. Between 2022 and 2023, our ML model analyzed 1252 VMAT plans. Ten patients having a predicted GPR (pGPR) <95% were selected. Replanning strategies consisted of limiting monitor units (MUlimit) and employing the aperture shape controller (ASC) tool. Re-optimized plans were compared with the originals in terms of dose volume constraints (DVCs) for the target and organs-at-risk (OARs), and deliverability using the modulation complexity score (MCS), pGPR, and measured GPR (mGPR). Forty-five re-optimizations were performed. Replanning led to an increase in DVCs for OARs and a reduction for the target. Complexity decreased, reflected by the increase in the MCS from 0.17 to 0.21 (MUlimit) and 0.20 (ASC). The deliverability improved, with the pGPR increasing from 93.3% to 94.4% (MUlimit) and 95.1% (ASC), and the mGPR from 99.3% to 99.7% (MUlimit) and 99.8% (ASC). Limiting the MUs or utilizing the ASC reduced the complexity of plans and improved the GPR without compromising the dosimetric quality. These strategies can be used to automate replanning procedures, reduce the workload related to PSQA, and improve patient safety. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Deep learning‐based optimization of field geometry for total marrow irradiation delivered with volumetric modulated arc therapy.

Author

Lambri, Nicola, Longari, Giorgio, Loiacono, Daniele, Brioso, Ricardo Coimbra, Crespi, Leonardo, Galdieri, Carmela, Lobefalo, Francesca, Reggiori, Giacomo, Rusconi, Roberto, Tomatis, Stefano, Bellu, Luisa, Bramanti, Stefania, Clerici, Elena, De Philippis, Chiara, Dei, Damiano, Navarria, Pierina, Carlo‐Stella, Carmelo, Franzese, Ciro, Scorsetti, Marta, and Mancosu, Pietro

Subjects
DEEP learning, VOLUMETRIC-modulated arc therapy, HEMATOPOIETIC stem cell transplantation, MEDIAN (Mathematics), STANDARD deviations, BONE marrow, LUNGS
Abstract

Background: Total marrow (lymphoid) irradiation (TMI/TMLI) is a radiotherapy treatment used to selectively target the bone marrow and lymph nodes in conditioning regimens for allogeneic hematopoietic stem cell transplantation. A complex field geometry is needed to cover the large planning target volume (PTV) of TMI/TMLI with volumetric modulated arc therapy (VMAT). Five isocenters and ten overlapping fields are needed for the upper body, while, for patients with large anatomical conformation, two specific isocenters are placed on the arms. The creation of a field geometry is clinically challenging and is performed by a medical physicist (MP) specialized in TMI/TMLI. Purpose: To develop convolutional neural networks (CNNs) for automatically generating the field geometry of TMI/TMLI. Methods: The dataset comprised 117 patients treated with TMI/TMLI between 2011 and 2023 at our Institute. The CNN input image consisted of three channels, obtained by projecting along the sagittal plane: (1) average CT pixel intensity within the PTV; (2) PTV mask; (3) brain, lungs, liver, bowel, and bladder masks. This "averaged" frontal view combined the information analyzed by the MP when setting the field geometry in the treatment planning system (TPS). Two CNNs were trained to predict the isocenters coordinates and jaws apertures for patients with (CNN‐1) and without (CNN‐2) isocenters on the arms. Local optimization methods were used to refine the models output based on the anatomy of the patient. Model evaluation was performed on a test set of 15 patients in two ways: (1) by computing the root mean squared error (RMSE) between the CNN output and ground truth; (2) with a qualitative assessment of manual and generated field geometries—scale: 1 = not adequate, 4 = adequate—carried out in blind mode by three MPs with different expertise in TMI/TMLI. The Wilcoxon signed‐rank test was used to evaluate the independence of the given scores between manual and generated configurations (p < 0.05 significant). Results: The average and standard deviation values of RMSE for CNN‐1 and CNN‐2 before/after local optimization were 15 ± 2/13 ± 3 mm and 16 ± 2/18 ± 4 mm, respectively. The CNNs were integrated into a planning automation software for TMI/TMLI such that the MPs could analyze in detail the proposed field geometries directly in the TPS. The selection of the CNN model to create the field geometry was based on the PTV width to approximate the decision process of an experienced MP and provide a single option of field configuration. We found no significant differences between the manual and generated field geometries for any MP, with median values of 4 versus 4 (p = 0.92), 3 versus 3 (p = 0.78), 4 versus 3 (p = 0.48), respectively. Starting from October 2023, the generated field geometry has been introduced in our clinical practice for prospective patients. Conclusions: The generated field geometries were clinically acceptable and adequate, even for an MP with high level of expertise in TMI/TMLI. Incorporating the knowledge of the MPs into the development cycle was crucial for optimizing the models, especially in this scenario with limited data. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Considerations for intensity modulated total body or total marrow and lymphoid irradiation

Author

Parsons, David, primary, Lim, Tze Yee, additional, Teruel, Jose R., additional, Galavis, Paulina, additional, Agostinelli, Stefano, additional, Liang, Jieming, additional, Mancosu, Pietro, additional, Cherpak, Amanda, additional, Stanley, Dennis N., additional, Ahn, Kang-Hyun, additional, Guo, Bingqi, additional, Gonzalez, Yesenia, additional, Burmeister, Jay, additional, Wong, Jeffrey Y.C., additional, Gu, Xuejun, additional, and Kim, Grace Gwe-Ya, additional

Published
2023
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