IRCCS San Raffaele Scientific Institute Showcase

Scaled input dataset for training the Long Short-Term Memory (LSTM) recurrent neural networks for prediction of gestational age at birth developed and included in the RF-2016-02364081 project titled "A generalized prediction framework of preterm birth: The combination of maternal risk factors, fetal and newborn functional and structural brain connectivity for predicting neurodevelopmental outcome".

Raw data to reprlicate the analyses conducted in "68Ga-PSMA and 68Ga-DOTA-RM2 PET/MRI in Recurrent Prostate Cancer: Diagnostic Performance and Association with Clinical and Histopathological Data"

Raw data, not included in the pubblished supplemental materials, that generated the figures of the manuscript by Raia et al. "Ceruloplasmin-deficient mice show dysregulation of lipid metabolism in liver and adipose tissue reduced by a protein replacement" International Journal of Molecular Sciences, 2023, 24, 1150 (https://doi.org/10.3390/ijms24021150).

Purpose: To explore the role of fully hybrid 68Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine tumours (PanNETs) undergoing surgery. Methods: One hundred eighty-seven consecutive 68Ga-DOTATOC PET/MRI scans (March 2018-June 2020) performed for gastroenteropancreatic neuroendocrine tumour were retrospectively evaluated; 16/187 patients met the eligibility criteria (68Ga-DOTATOC PET/MRI for preoperative staging of PanNET and availability of histological data). PET/MR scans were qualitatively and quantitatively interpreted, and the following imaging parameters were derived: PET-derived SUVmax, SUVmean, somatostatin receptor density (SRD), total lesion somatostatin receptor density (TLSRD), and MRI-derived apparent diffusion coefficient (ADC), arterial and late enhancement, necrosis, cystic degeneration, and maximum diameter. Additionally, first-, second-, and higher-order radiomic parameters were extracted from both PET and MRI scans. Correlations with several PanNETs' histopathological prognostic factors were evaluated using Spearman's coefficient, while the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate parameters' predictive performance. Results: Primary tumour was detected in all 16 patients (15/16 by 68Ga-DOTATOC PET and 16/16 by MRI). SUVmax and SUVmean resulted good predictors of lymphnodal (LN) involvement (AUC of 0.850 and 0.783, respectively). Second-order radiomic parameters GrayLevelVariance and HighGrayLevelZoneEmphasis extracted from T2 MRI demonstrated significant correlations with LN involvement (adjusted p = 0.009), also showing good predictive performance (AUC = 0.992). Conclusion: This study demonstrates the role of the fully hybrid PET/MRI tool for the synergic function of imaging parameters extracted by the two modalities and highlights the potentiality of imaging and radiomic parameters in assessing histopathological features of PanNET aggressiveness.

Purpose: to investigate the preoperative role of ML-based classification using conventional 18F-FDG PET parameters and clinical data in predicting features of EC aggressiveness. Methods: retrospective study, including 123 EC patients who underwent 18F-FDG PET (2009-2021) for preoperative staging. Maximum standardized uptake value (SUVmax), SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were computed on the primary tumour. Age and BMI were collected. Histotype, myometrial invasion (MI), risk group, lymph-nodal involvement (LN), and p53 expression were retrieved from histology. The population was split into a train and a validation set (80-20%). The train set was used to select relevant parameters (Mann-Whitney U test; ROC analysis) and implement ML models, while the validation set was used to test prediction abilities. Results: on the validation set, the best accuracies obtained with individual parameters and ML were: 61% (TLG) and 87% (ML) for MI; 71% (SUVmax) and 79% (ML) for risk groups; 72% (TLG) and 83% (ML) for LN; 45% (SUVmax; SUVmean) and 73% (ML) for p53 expression. Conclusions: ML-based classification using conventional 18F-FDG PET parameters and clinical data demonstrated ability to characterize the investigated features of EC aggressiveness, providing a non-invasive way to support preoperative stratification of EC patients.

Purpose: The aim of this study was to investigate the role of 18F-FDG PET/CT in predicting pathological prognostic factors, including tumor type and International Federation of Gynecology and Obstetrics (FIGO) score, in gestational trophoblastic disease (GTD). Methods: Retrospective monocentric study including 24 consecutive patients who underwent to 18F-FDG PET/CT from May 2005 to March 2021 for GTD staging purpose. The following semiquantitative PET parameters were measured from the primary tumor and used for the analysis: maximum standardized uptake value (SUVmax), SUVmean, metabolic tumor volume (MTV) and total lesion glycolisis (TLG). Statistical analysis included Spearman correlation coefficient to evaluate the correlations between imaging parameters and tumor type (nonmolar trophoblastic vs postmolar trophoblastic tumors) and risk groups (high vs low, defined according to the FIGO score), whereas area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the predictive value of the PET parameters. Mann-Whitney U test was used to further describe the parameter's potential in differentiating the populations. Results: SUVmax and SUVmean resulted fair (AUC, 0.783; 95% confidence interval [CI], 0.56-0.95) and good (AUC, 0.811; 95% CI, 0.59-0.97) predictors of tumor type, respectively, showing a low (ρ = 0.489, adjusted P = 0.030) and moderate (ρ = 0.538, adjusted P = 0.027) correlation. According to FIGO score, TLG was instead a fair predictor (AUC, 0.770; 95% CI, 0.50-0.99) for patient risk stratification. Conclusions: 18F-FDG PET parameters have a role in predicting GTD pathological prognostic factors, with SUVmax and SUVmean being predictive for tumor type and TLG for risk stratification.

Raw data that generated the graphs in the figures of the paper "Zanardi A, Barbariga M, Conti A, Vegliani F, Curnis F, Alessio M. Oxidized/deamidated-ceruloplasmin dysregulates choroid plexus epithelial cells functionality and barrier properties via RGD-recognizing integrin binding. Neurobiol Dis, 158: 105475, 2021" (DOI: 10.1016/j.nbd.2021.105474 ). These data were not included in the Supplementary Data associated to the pubblication in the web site of the journal.

In the manuscript "Combined plasma levels of IL-10 and testosterone, but not soluble HLA-G5, predict the risk of death in COVID-19 patients" (doi: 10.1111/andr.13334), we reported that the combined evaluation of IL-10 and testosterone predicts the risk of death in men with COVID-19. The two datasets here loaded (Raw_data_1 and Raw_data_2) contain all the clinical and immunological data, supporting our conclusions, that have been used to build the manuscript. An additional file (raw_data_description) contains the main guidelines to correctly associate the raw data with the corresponding figure/table in the manuscript.

Detection and removal of bladder cancer lesions at an early stage is crucial for preventing tumor relapse and progression. Photoacoustic imaging of targeted gold nanorods bound to tumor cells allowed for the detection of neoplastic lesions smaller than 0.5 mm that were undetectable by ultrasound imaging and bioluminescence. We here provide raw data that were used to generate information reported in the article with DOI: 10.1016/j.pacs.2022.100400 - Photoshop files can be opened with ImageJ, which is free - Prism files can only be opened with Prism, and those who do not have it can use the demo version which is free for 30 days (and then transfer the data in excel) - TIFF images that can be opened with any image program, including ImageJ

Raw data from the main figures of the article by Ferrari, Jacob, Cesana et al, Cell Stem Cell (https://doi.org/10.1016/j.stem.2022.09.001).