Maria Picchio

Breast cancer (BC) requires the evaluation of tumor aggressiveness features to guide treatment decisions. Biopsy-derived prognostic information may differ from surgical histopathology due to tumor heterogeneity. Hybrid PET/MRI can provide additional information for tumor characterization, supporting initial therapy planning and prognosis. In this work, we acquired 157 BC patients using a hybrid PET/MRI scanner. The PET data were combined with ADC and semi-quantitative DCE-MRI metrics to derive “hybrid PET/MRI parameters.” Pathological data such as tumor grade, hormone receptors, proliferation index (Ki67), and surrogate molecular subtype were collected, and we evaluated their associations with hybrid imaging, also comparing with the PET and MRI data analyzed separately. Ki67 showed moderate correlations with PET, ADCmin, and most hybrid parameters. The PET and hybrid data differentiate histopathological factors, while ADCmin differentiates G1 vs. G2 and luminal A vs. luminal B. In the ROC analysis, hybrid SUVmax/ADCmin shows better performance to predict luminal B from luminal A (AUC 0.720, sensitivity 73.1%, specificity 63.2%, PPV 54.3%, NPV 79.7%) than SUVmean alone. Our findings suggest that these novel hybrid PET/MRI parameters may help the characterization of tumor tissue in IDC. However, a multivariate analysis is needed to confirm our preliminary results.

Background/Objectives: Early and accurate characterization of endometrial cancer (EC) is crucial for patient management, but current imaging modalities lack in diagnostic accuracy and ability to assess molecular profiles. The aim of this study is to evaluate hybrid [¹⁸F]FDG PET/MRI’s diagnostic accuracy in EC staging and role in predicting tumor aggressiveness, molecular characterization, and recurrence. Methods: A prospective study (ClinicalTrials.gov, ID:NCT04212910) evaluating EC patients undergoing [¹⁸F]FDG PET/MRI before surgery (2018–2024). Histology, immunohistochemistry, and patients’ follow-up (mean FU time: 3.13y) were used as the reference standard. [¹⁸F]FDG PET/MRI, PET only, and MRI only were independently reviewed to assess the diagnostic accuracy (ACC), sensitivity (SN), specificity (SP), and positive/negative predictive value (PPV, NPV). Imaging parameters were extracted from [¹⁸F]FDG PET and pcT1w, T2w, DWI, and DCE MRI. Spearman’s correlations, Fisher’s exact test, ROC-AUC analysis, Kaplan–Meier survival curves, log-rank tests and Cox proportional hazards models were applied. Results: Eighty participants with primary EC (median age 63 ± 12 years) were enrolled, with 17% showing LN involvement. [¹⁸F]FDG PET/MRI provided ACC = 98.75%, SN = 98.75%, and PPV = 100% for primary tumor detection, and ACC = 92.41%, SN = 84.62%, SP = 93.94%, PPV = 73.33%, and NPV = 96.88% for LN detection. PET/MRI parameters predicted LN involvement (AUC = 79.49%), deep myometrial invasion (79.78%), lymphovascular space invasion (82.00%), p53abn (71.47%), MMRd (74.51%), relapse (82.00%), and postoperative administration of adjuvant therapy (79.64%). Patients with a tumor cranio-caudal diameter ≥ 43 mm and MTV ≥ 13.5 cm³ showed increased probabilities of recurrence (p < 0.001). Conclusions: [¹⁸F]FDG PET/MR showed exceptional accuracy in EC primary tumor and LN detection. Derived parameters demonstrated potential ability in defining features of aggressiveness, molecular alterations, and tumor recurrence.

The primary aim of this study was to compare the diagnostic accuracy of [⁶⁸Ga]Ga-PSMA-11 PET, [⁶⁸Ga]Ga-RM2 PET, and multiparametric MRI (mpMRI) for the detection of primary prostate cancer (PCa) using histopathology as the reference. The secondary aims of the study were to assess the agreement among imaging modalities and identify noninvasive biomarkers for the diagnosis and risk stratification of patients. Methods: Forty-two patients with biopsy-confirmed, high-risk PCa were enrolled in this single-center, prospective, phase 2 clinical trial between September 2020 and May 2023 at San Raffaele hospital. All patients underwent [⁶⁸Ga]Ga-PSMA-11 PET/MRI with mpMRI, and 36 had additional imaging with [⁶⁸Ga]Ga-RM2 PET/MRI. All patients were included in the patient-level T staging analysis. Twenty-five patients were treated with radical prostatectomy with extended lymphadenectomy and considered for N staging analysis. Sixteen patients underwent all imaging and surgical procedures needed for coregistration between imaging and histology and were included in the lesion-based analysis for T staging. Two expert nuclear medicine physicians reviewed [⁶⁸Ga]Ga-PSMA-11 and [⁶⁸Ga]Ga-RM2 PET images with knowledge of the patients' available clinical and imaging information. mpMRI was interpreted as the standard of care by 2 expert radiologists using Prostate Imaging Reporting and Data System, version 2, criteria. Peripheral whole-blood samples were collected at the time of patient's enrollment to assess their association with lymph node involvement on histology. Results: In the patient-based analysis, [⁶⁸Ga]Ga-PSMA-11 PET and mpMRI identified at least 1 intraprostatic lesion in all patients, whereas [⁶⁸Ga]Ga-RM2 PET results were negative in 3 of 36 patients. The lesion-level analysis performed in 16 patients showed that, in this cohort, the dominant intraprostatic lesion was always detected by [⁶⁸Ga]Ga-RM2 PET, whereas both [⁶⁸Ga]Ga-PSMA-11 PET and mpMRI missed it, reporting a false-positive finding elsewhere. For N staging analysis, [⁶⁸Ga]Ga-PSMA-11 PET had the highest sensitivity among the investigated imaging modalities (sensitivity, 0.375). Blood analysis showed that a higher fraction of polymorphonuclear-myeloid-derived suppressor cells (MDSCs) over monocytic MDSCs was significantly associated patients with lymph node involvement on histology (P = 0.0285). Conclusion: All imaging modalities showed high sensitivity for the preoperative detection of primary PCa, but only [⁶⁸Ga]Ga-RM2 PET correctly identified the dominant lesion in all patients who underwent lesion-based subanalysis. The identification of lymph node involvement remains challenging, with [⁶⁸Ga]Ga-PSMA-11 PET reaching a sensitivity of only 0.375. In this regard, the polymorphonuclear MDSC-to-monocytic MDSC ratio may represent a valuable biologic marker of lymph node involvement in patients with high-risk PCa and warrants further investigation.

A data-driven head Motion Correction (MoCo) method was tested on 11C-Methionine brain PET images in a cohort of 44 pediatric patients with brain tumors referred to a PET/MR study. Its impact was investigated both qualitatively and quantitatively. For each patient, PET images were reconstructed offline both with (PETddMoCo) and without (PETnoMoCo) MoCo algorithm. An expert Nuclear Medicine physician qualitatively evaluated PET images, and segmented PET positive lesions in both datasets, extracting the following PET parameters: maximum and mean Standardized Uptake Value (SUVmax and SUVmean, respectively), and Metabolic Tumor Volume (MTV). PET parameters before and after MoCo were compared and their absolute percentage difference was calculated (%). Contrast-to-Noise (CNR) and its difference () before and after MoCo were calculated. Thirty-one patients had a “low” level of motion, 8 patients “medium” and 5 patients “high”. Twenty-one patients out of forty-four had positive 11C-Methionine uptake (26 lesions). Qualitatively, no difference was evident in negative patients, while two PET positive lesions could be better defined after motion correction. Quantitatively, CNR increased significantly after motion correction for “medium+high” lesions, while none of the PET parameters showed significant difference. Increasing the sample of patients might confirm these results.

Background/Objectives: Pancreatic cancer is a very aggressive disease with a poor prognosis, even when diagnosed at an early stage. This study aimed to validate and refine a radiomic-based [¹⁸F]FDG-PET model to predict distant relapse-free survival (DRFS) in patients with unresectable locally advanced pancreatic cancer (LAPC). Methods: A Cox regression model incorporating two radiomic features (RFs) and cancer stage (III vs. IV) was temporally validated using a larger cohort (215 patients treated between 2005–2022). Patients received concurrent chemoradiotherapy with capecitabine and hypo-fractionated Intensity Modulated Radiotherapy (IMRT). Data were split into training (145 patients, 2005–2017) and validation (70 patients, 2017–2022) groups. Seventy-eight RFs were extracted, harmonized, and analyzed using machine learning to develop refined models. Results: The model incorporating Statistical-Percentile10, Morphological-ComShift, and stage demonstrated moderate predictive accuracy (training: C-index = 0.632; validation: C-index = 0.590). When simplified to include only Statistical-Percentile10, performance improved slightly in the validation group (C-index = 0.601). Adding GLSZM3D-grayLevelVariance to Statistical-Percentile10, while excluding Morphological-ComShift, further enhanced accuracy (training: C-index = 0.654; validation: C-index = 0.623). Despite these refinements, all versions showed similar moderate ability to stratify patients into risk classes. Conclusions: [¹⁸F]FDG-PET radiomic features are robust predictors of DRFS after chemoradiotherapy in LAPC. Despite moderate performance, these models hold promise for patient risk stratification. Further validation with external cohorts is ongoing.

Background: PSMA PET radiomics is a promising tool for primary prostate cancer (PCa) characterisation. However, small single-centre studies and lack of external validation hinder definitive conclusions on the potential of PSMA PET radiomics in the initial workup of PCa. We aimed to validate a radiomics signature in a larger internal cohort and in an external cohort from a separate centre. Methods: One hundred and twenty-seven PCa patients were retrospectively enrolled across two independent hospitals. The first centre (IRCCS San Raffaele Scientific Institute, Centre 1) contributed 62 [⁶⁸Ga]Ga-PSMA-11 PET scans, 20 patients classified as low-grade (ISUP grade < 4), and 42 as high-grade (ISUP grade ≥ 4). The second centre (Stanford University Hospital, Centre 2) provided 65 [⁶⁸Ga]Ga-PSMA-11 PET scans, and 49 low-grade and 16 high-grade patients. A radiomics model previously generated in Centre 1 was tested on the two cohorts separately and afterward on the entire dataset. Then, we evaluated whether the radiomics features selected in the previous investigation could generalise to new data. Several machine learning (ML) models underwent training and testing using 100-fold Monte Carlo cross-validation, independently at both Centre 1 and Centre 2, with a 70–30% train–test split. Additionally, models were trained in one centre and tested in the other, and vice versa. Furthermore, data from both centres were combined for training and testing using Monte Carlo cross-validation. Finally, a new radiomics signature built on this bicentric dataset was proposed. Several performance metrics were computed. Results: The previously generated radiomics signature resulted in an area under the receiver operating characteristic curve (AUC) of 80.4% when tested on Centre 1, while it generalised poorly to Centre 2, where it reached an AUC of 62.7%. When the whole cohort was considered, AUC was 72.5%. Similarly, new ML models trained on the previously selected features yielded, at best, an AUC of 80.9% for Centre 1 and performed at chance for Centre 2 (AUC of 49.3%). A new signature built on this bicentric dataset reached, at best, an average AUC of 91.4% in the test set. Conclusions: The satisfying performance of radiomics models when used in the original development settings, paired with the poor performance otherwise observed, emphasises the need to consider centre-specific factors and dataset characteristics when developing radiomics models. Combining radiomics datasets is a viable strategy to reduce such centre-specific biases, but external validation is still needed.

Neuroendocrine neoplasms (NENs) are a diverse group of tumors with varying clinical behaviors. Their incidence has risen due to increased awareness, improved diagnostics, and aging populations. The 2019 World Health Organization classification emphasizes integrating radiology and histopathology to characterize NENs and create personalized treatment plans. Imaging methods like CT, MRI, and PET/CT are crucial for detection, staging, treatment planning, and monitoring, but each of them poses different interpretative challenges and none are immune to pitfalls. Treatment options include surgery, targeted therapies, and chemotherapy, based on the tumor type, stage, and patient-specific factors. This review aims to provide insights into the latest developments and challenges in NEN imaging, diagnosis, and management.