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Article ; Online: Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning.

Ubaldi, Leonardo / Saponaro, Sara / Giuliano, Alessia / Talamonti, Cinzia / Retico, Alessandra

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

2023 Volume 107, Page(s) 102538

Abstract: Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and ... ...

Abstract	Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas. Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings. Results: The results show that using MRI-reliable features improves the performance in glioma grade classification (AUC=0.93±0.05) with respect to the use of raw (AUC=0.88±0.08) and robust features (AUC=0.83±0.08), defined as those not depending on image normalization and intensity discretization. Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out.
MeSH term(s)	Humans ; Multiparametric Magnetic Resonance Imaging ; Glioma/diagnostic imaging ; Glioma/pathology ; Brain Neoplasms/diagnostic imaging ; Brain Neoplasms/pathology ; Machine Learning ; Magnetic Resonance Imaging/methods ; Retrospective Studies
Language	English
Publishing date	2023-02-14
Publishing country	Italy
Document type	Journal Article
ZDB-ID	1122650-x
ISSN	1724-191X ; 1120-1797
ISSN (online)	1724-191X
ISSN	1120-1797
DOI	10.1016/j.ejmp.2023.102538
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Article: Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders.

Saponaro, Sara / Lizzi, Francesca / Serra, Giacomo / Mainas, Francesca / Oliva, Piernicola / Giuliano, Alessia / Calderoni, Sara / Retico, Alessandra

Brain informatics

2024 Volume 11, Issue 1, Page(s) 2

Abstract: Background: The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the ... ...

Abstract	Background: The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD). Material and methods: We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework. Results: The AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain. Conclusions: Our results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.
Language	English
Publishing date	2024-01-09
Publishing country	Germany
Document type	Journal Article
ISSN	2198-4018
ISSN	2198-4018
DOI	10.1186/s40708-023-00217-4
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Article ; Online: Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study.

Scapicchio, Camilla / Imbriani, Manuela / Lizzi, Francesca / Quattrocchi, Mariagrazia / Retico, Alessandra / Saponaro, Sara / Tenerani, Maria Irene / Tofani, Alessandro / Zafaranchi, Arman / Fantacci, Maria Evelina

Biomedical physics & engineering express

2024 Volume 10, Issue 4

Abstract: ... ...

Abstract	Objective
MeSH term(s)	Phantoms, Imaging ; Humans ; Algorithms ; Tomography, X-Ray Computed/methods ; Image Processing, Computer-Assisted/methods ; Neural Networks, Computer ; Radiomics
Language	English
Publishing date	2024-05-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2057-1976
ISSN (online)	2057-1976
DOI	10.1088/2057-1976/ad41e7
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Article ; Online: Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset.

Saponaro, Sara / Giuliano, Alessia / Bellotti, Roberto / Lombardi, Angela / Tangaro, Sabina / Oliva, Piernicola / Calderoni, Sara / Retico, Alessandra

NeuroImage. Clinical

2022 Volume 35, Page(s) 103082

Abstract: Machine Learning (ML) techniques have been widely used in Neuroimaging studies of Autism Spectrum Disorders (ASD) both to identify possible brain alterations related to this condition and to evaluate the predictive power of brain imaging modalities. The ... ...

Abstract	Machine Learning (ML) techniques have been widely used in Neuroimaging studies of Autism Spectrum Disorders (ASD) both to identify possible brain alterations related to this condition and to evaluate the predictive power of brain imaging modalities. The collection and public sharing of large imaging samples has favored an even greater diffusion of the use of ML-based analyses. However, multi-center data collections may suffer the batch effect, which, especially in case of Magnetic Resonance Imaging (MRI) studies, should be curated to avoid confounding effects for ML classifiers and masking biases. This is particularly important in the study of barely separable populations according to MRI data, such as subjects with ASD compared to controls with typical development (TD). Here, we show how the implementation of a harmo- nization protocol on brain structural features unlocks the case-control ML separation capability in the analysis of a multi-center MRI dataset. This effect is demonstrated on the ABIDE data collection, involving subjects encompassing a wide age range. After data harmonization, the overall ASD vs. TD discrimination capability by a Random Forest (RF) classifier improves from a very low performance (AUC = 0.58 ± 0.04) to a still low, but reasonably significant AUC = 0.67 ± 0.03. The performances of the RF classifier have been evaluated also in the age-specific subgroups of children, adolescents and adults, obtaining AUC = 0.62 ± 0.02, AUC = 0.65 ± 0.03 and AUC = 0.69 ± 0.06, respectively. Specific and consistent patterns of anatomical differences related to the ASD condition have been identified for the three different age subgroups.
MeSH term(s)	Adolescent ; Adult ; Autism Spectrum Disorder/diagnostic imaging ; Autism Spectrum Disorder/pathology ; Brain/diagnostic imaging ; Brain/pathology ; Child ; Humans ; Machine Learning ; Magnetic Resonance Imaging/methods ; Neuroimaging
Language	English
Publishing date	2022-06-08
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2701571-3
ISSN	2213-1582 ; 2213-1582
ISSN (online)	2213-1582
ISSN	2213-1582
DOI	10.1016/j.nicl.2022.103082
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Article ; Online: Distinct DNA Methylation Profiles in Ovarian Tumors: Opportunities for Novel Biomarkers.

Losi, Lorena / Fonda, Sergio / Saponaro, Sara / Chelbi, Sonia T / Lancellotti, Cesare / Gozzi, Gaia / Alberti, Loredana / Fabbiani, Luca / Botticelli, Laura / Benhattar, Jean

International journal of molecular sciences

2018 Volume 19, Issue 6

Abstract: Aberrant methylation of multiple promoter CpG islands could be related to the biology of ovarian tumors and its determination could help to improve treatment strategies. DNA methylation profiling was performed using the Methylation Ligation-dependent ... ...

Abstract	Aberrant methylation of multiple promoter CpG islands could be related to the biology of ovarian tumors and its determination could help to improve treatment strategies. DNA methylation profiling was performed using the Methylation Ligation-dependent Macroarray (MLM), an array-based analysis. Promoter regions of 41 genes were analyzed in 102 ovarian tumors and 17 normal ovarian samples. An average of 29% of hypermethylated promoter genes was observed in normal ovarian tissues. This percentage increased slightly in serous, endometrioid, and mucinous carcinomas (32%, 34%, and 45%, respectively), but decreased in germ cell tumors (20%). Ovarian tumors had methylation profiles that were more heterogeneous than other epithelial cancers. Unsupervised hierarchical clustering identified four groups that are very close to the histological subtypes of ovarian tumors. Aberrant methylation of three genes (
MeSH term(s)	Biomarkers, Tumor/metabolism ; Cluster Analysis ; DNA Methylation/genetics ; Female ; Humans ; Kaplan-Meier Estimate ; Ovarian Neoplasms/genetics ; Promoter Regions, Genetic
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2018-05-24
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms19061559
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Article: Long-term exposure to dehydroepiandrosterone affects the transcriptional activity of the glucocorticoid receptor.

Saponaro, Sara / Guarnieri, Vincenzo / Pescarmona, Gian Piero / Silvagno, Francesca

The Journal of steroid biochemistry and molecular biology

2007 Volume 103, Issue 2, Page(s) 129–136

Abstract: Although the antiglucocorticoid effects of dehydroepiandrosterone (DHEA) have been demonstrated in vivo in many systems, controversial results have been reported by in vitro studies. In order to elucidate the long-term antiglucocorticoid effects of DHEA ... ...

Abstract	Although the antiglucocorticoid effects of dehydroepiandrosterone (DHEA) have been demonstrated in vivo in many systems, controversial results have been reported by in vitro studies. In order to elucidate the long-term antiglucocorticoid effects of DHEA in vitro in a context more physiological than what proposed by previous works, we set up a system consisting of a carcinoma cell line relying on endogenously produced glucocorticoid receptor (GR) and stably expressing a reporter gene ErbB-2 under the control of a GR-dependent MMTV promoter. These cells grown in presence of low levels of serum glucocorticoids (GC) showed a basal translocation and activity of endogenous GR. The cells reacted to high concentrations of dexamethasone increasing GR nuclear import, although down-regulating receptor expression, and enhancing GR-dependent transcriptional activity, as shown by EMSA assay and expression of the reporter gene ErbB-2. The response to GC was also functional since the increase of ErbB-2 boosted cellular growth. On the contrary, 72h of incubation with DHEA diminished basal GR-dependent reporter expression and abated cellular proliferation. Analysing molecular mechanisms responsible for this failed transcriptional activity, upon prolonged treatment with DHEA we observed a slow nuclear import of GR not followed by its recruitment to DNA. These data add novel information about the long-term effects of DHEA in vitro.
MeSH term(s)	Animals ; DNA-Binding Proteins/metabolism ; Dehydroepiandrosterone/pharmacology ; Mice ; Models, Biological ; Protein Transport/drug effects ; Receptors, Glucocorticoid/metabolism ; Time ; Transcriptional Activation/drug effects ; Tumor Cells, Cultured
Chemical Substances	DNA-Binding Proteins ; Receptors, Glucocorticoid ; Dehydroepiandrosterone (459AG36T1B)
Language	English
Publishing date	2007-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1049188-0
ISSN	1879-1220 ; 0960-0760
ISSN (online)	1879-1220
ISSN	0960-0760
DOI	10.1016/j.jsbmb.2006.08.003
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Article: Promoter methylation and downregulated expression of the

Gozzi, Gaia / Chelbi, Sonia T / Manni, Paola / Alberti, Loredana / Fonda, Sergio / Saponaro, Sara / Fabbiani, Luca / Rivasi, Francesco / Benhattar, Jean / Losi, Lorena

Oncology letters

2016 Volume 12, Issue 4, Page(s) 2811–2819

Abstract: ... ...

Abstract	TBX15
Language	English
Publishing date	2016-08-16
Publishing country	Greece
Document type	Journal Article
ZDB-ID	2573196-8
ISSN	1792-1082 ; 1792-1074
ISSN (online)	1792-1082
ISSN	1792-1074
DOI	10.3892/ol.2016.5019
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Article ; Online: DNA methylation profiling of esophageal adenocarcinoma using Methylation Ligation-dependent Macroarray (MLM).

Guilleret, Isabelle / Losi, Lorena / Chelbi, Sonia T / Fonda, Sergio / Bougel, Stéphanie / Saponaro, Sara / Gozzi, Gaia / Alberti, Loredana / Braunschweig, Richard / Benhattar, Jean

Biochemical and biophysical research communications

2016 Volume 479, Issue 2, Page(s) 231–237

Abstract: Most types of cancer cells are characterized by aberrant methylation of promoter genes. In this study, we described a rapid, reproducible, and relatively inexpensive approach allowing the detection of multiple human methylated promoter genes from many ... ...

Abstract	Most types of cancer cells are characterized by aberrant methylation of promoter genes. In this study, we described a rapid, reproducible, and relatively inexpensive approach allowing the detection of multiple human methylated promoter genes from many tissue samples, without the need of bisulfite conversion. The Methylation Ligation-dependent Macroarray (MLM), an array-based analysis, was designed in order to measure methylation levels of 58 genes previously described as putative biomarkers of cancer. The performance of the design was proven by screening the methylation profile of DNA from esophageal cell lines, as well as microdissected formalin-fixed and paraffin-embedded (FFPE) tissues from esophageal adenocarcinoma (EAC). Using the MLM approach, we identified 32 (55%) hypermethylated promoters in EAC, and not or rarely methylated in normal tissues. Among them, 21promoters were found aberrantly methylated in more than half of tumors. Moreover, seven of them (ADAMTS18, APC, DKK2, FOXL2, GPX3, TIMP3 and WIF1) were found aberrantly methylated in all or almost all the tumor samples, suggesting an important role for these genes in EAC. In addition, dysregulation of the Wnt pathway with hypermethylation of several Wnt antagonist genes was frequently observed. MLM revealed a homogeneous pattern of methylation for a majority of tumors which were associated with an advanced stage at presentation and a poor prognosis. Interestingly, the few tumors presenting less methylation changes had a lower pathological stage. In conclusion, this study demonstrated the feasibility and accuracy of MLM for DNA methylation profiling of FFPE tissue samples.
MeSH term(s)	Adenocarcinoma/genetics ; Adenocarcinoma/pathology ; Biomarkers, Tumor/genetics ; Cell Line, Tumor ; DNA Methylation ; DNA, Neoplasm/chemistry ; DNA, Neoplasm/genetics ; Esophageal Neoplasms/genetics ; Esophageal Neoplasms/pathology ; Feasibility Studies ; Fixatives/chemistry ; Formaldehyde/chemistry ; Humans ; Microarray Analysis/methods ; Paraffin Embedding ; Polymerase Chain Reaction/methods ; Promoter Regions, Genetic/genetics ; Reproducibility of Results ; Sequence Analysis, DNA/methods ; Tissue Fixation ; Wnt Signaling Pathway/genetics
Chemical Substances	Biomarkers, Tumor ; DNA, Neoplasm ; Fixatives ; Formaldehyde (1HG84L3525)
Language	English
Publishing date	2016-10-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	205723-2
ISSN	1090-2104 ; 0006-291X ; 0006-291X
ISSN (online)	1090-2104 ; 0006-291X
ISSN	0006-291X
DOI	10.1016/j.bbrc.2016.09.049
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Article: A pilot study evaluating serum pro-prostate-specific antigen in patients with rising PSA following radical prostatectomy.

Sottile, Antonino / Ortega, Cinzia / Berruti, Alfredo / Mangioni, Monica / Saponaro, Sara / Polo, Alessandra / Prati, Veronica / Muto, Giovanni / Aglietta, Massimo / Montemurro, Filippo

Oncology letters

2012 Volume 3, Issue 4, Page(s) 819–824

Abstract: 2]pro-prostate-specific antigen (2pPSA), a proform of PSA, is a new marker in patients at risk of prostate cancer. We explored the potential role of 2pPSA in the identification of patients with metastatic progression following radical prostatectomy for ...

Abstract	[-2]pro-prostate-specific antigen (2pPSA), a proform of PSA, is a new marker in patients at risk of prostate cancer. We explored the potential role of 2pPSA in the identification of patients with metastatic progression following radical prostatectomy for prostate cancer. Seventy-six patients with biochemical (PSA) recurrence following radical prostatectomy were studied retrospectively. Diagnostic imaging performed at the time of biochemical recurrence confirmed metastatic disease in 31 of the 76 patients. Serum samples were collected and stored at the time of imaging-confirmed metastatic progression or at the most recent procedure for patients with negative imaging. Median values of PSA, free PSA (fPSA), %fPSA, 2pPSA and prostate health index (PHI) were compared between metastatic and non-metastatic patients by the Mann-Whitney U test. The results of each test were then correlated with metastatic status by univariate and multivariate logistic regression analysis. PSA, fPSA, %fPSA, 2pPSA serum concentrations and PHI values were statistically significantly higher in patients with metastatic disease. Results of the multivariate analysis revealed that 2pPSA remained a statistically significant predictor of imaging-proven metastatic prostate cancer among patients with biochemical recurrence. At a cut-off value of 12.25 pg/ml, 2pPSA outperformed the other markers in terms of sensitivity and specificity (97 and 80%, respectively) with respect to imaging-confirmed metastatic progression. This is the first study suggesting that 2pPSA predicts diagnostic imaging-proven metastatic disease in previously resected prostate cancer patients with biochemical recurrence. Our results merit validation in a prospective study.
Language	English
Publishing date	2012-01-16
Publishing country	Greece
Document type	Journal Article
ZDB-ID	2573196-8
ISSN	1792-1082 ; 1792-1074
ISSN (online)	1792-1082
ISSN	1792-1074
DOI	10.3892/ol.2012.570
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