Gayanè Aghakhanyan

MRI-Based Radiomics to Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer: A Retrospective Study

Ilaria Ambrosini, Roberto Francischello, Salvatore Claudio Fanni, Lorenzo Faggioni et autres

Background: Response to neoadjuvant therapy in locally advanced rectal cancer (LARC) is heterogeneous, and early identification of non-responders may help optimize treatment strategies and reduce unnecessary toxicity. This study aimed to develop and internally validate a machine learning model based on radiomic …

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Radiogenomics in Lymphoma and Multiple Myeloma: A Systematic Review of Current Evidence and Future Directions

Valentina Formica, Gayanè Aghakhanyan, Valentina Baccolini, Francesca Pia Caputo et autres

Background/Objectives: Radiogenomics integrates quantitative imaging features with genomic and molecular data to better characterize tumor biology and support precision oncology. While extensively investigated in solid tumors, its application to hematologic malignancies remains relatively unexplored despite the widespread use of advanced imaging in …

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Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer

Ilaria Ambrosini, Roberto Francischello, Salvatore Claudio Fanni, Lorenzo Faggioni et autres

Purpose: magnetic resonance imaging (MRI)-based radiomics has emerged as a promising approach for non-invasive prediction of treatment response in rectal cancer. This study aimed to develop and validate a machine learning model based on radiomic features extracted from restaging MRI after neoadjuvant …

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IP53-28 PROSPECTIVE COMPARISON OF PSMA-FAD VS PI-FAB IN PROSTATE CANCER PATIENTS UNDERGOING FOCAL THERAPY

Egesta Lopci, Massimo Lazzeri, Vittorio Fasulo, Gayanè Aghakhanyan et autres

The influence of annotators' experience on radiomics-based machine learning performance in colorectal liver metastases characterization: Impact and mitigation strategy

Roberto Francischello, Salvatore Claudio Fanni, Francesca Pia Caputo, Gayanè Aghakhanyan et autres

Background Segmentation variability is a major source of bias in radiomics, yet its quantitative impact on downstream model performance remains poorly defined. This study aimed to assess how annotator expertise influences model generalization and to test a mitigation strategy based on a …

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Radiomics on 177 Lu-DOTATATE Posttreatment Scans: Feasibility, Preprocessing Optimization, and Planar–SPECT Comparison

Flavio Montanini, Alessio Imperiale, Alice Monaci, Samuele Valente et autres

177Lu-DOTATATE peptide receptor radionuclide therapy (PRRT) is an established therapeutic option for patients with neuroendocrine tumors (NETs). Although radiomics has been increasingly applied in NET research, it was developed almost exclusively using 68Ga-DOTATOC PET imaging, whereas PRRT-related scintigraphic acquisitions remain largely unexplored. …

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MRI-Based Radiomics to Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer: A Retrospective Study

Ilaria Ambrosini, Roberto Francischello, Salvatore Claudio Fanni, Lorenzo Faggioni et autres

Background: Response to neoadjuvant therapy in locally advanced rectal cancer (LARC) is heterogeneous and early identification of non-responders may help optimize treatment strategies and reduce unnecessary toxicity. This study aimed to develop and internally validate a machine learning model based on radiomic …

it (code pays fourni par la source)

Machine learning for automated differentiation of parkinson’s disease and its mimics using ¹²³I-mIBG scintigraphy: insights from a multicentre real-world cohort (ITA-mIBG study)

Luca Filippi, F Bianconi, Viviana Frantellizzi, Cristina Ferrari et autres

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Discriminating between proposed Brain-First and Body-First Parkinson’s disease using conventional and radiomics-enhanced dopamine transporter SPECT image analysis

Giovanni Palermo, Gayanè Aghakhanyan, Gabriele Bellini, Sara Giannoni et autres

Abstract Two Parkinson’s disease subtypes—“Brain-First” and “Body-First”—have been proposed based on putative sites of onset. We examined whether “Body-First” markers relate to more symmetric striatal [ 123 I]-FP-CIT uptake and whether imaging could discriminate the subtypes. In a retrospective cohort of 158 …

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NAVIGATOR: A regional multimodal imaging biobank initiative powered by AI tools for precision medicine in oncology

Gayanè Aghakhanyan, Andrea Barucci, Maria Antonietta Pascali, Massimiliano Assante et autres

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Synergizing Liquid Biopsy and Hybrid PET Imaging for Prognostic Assessment in Prostate Cancer: A Focus Review

Federica Stracuzzi, Sara Dall’Armellina, Gayanè Aghakhanyan, Salvatore Claudio Fanni et autres

Positron emission tomography (PET) and liquid biopsy have independently transformed prostate cancer management. This review explores the complementary roles of PET imaging and liquid biopsy in prostate cancer, focusing on their combined diagnostic, monitoring, and prognostic potential. A systematic search of PubMed, …

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PSMA-targeted PET imaging for brain metastases from non-prostatic solid tumors: a systematic review

Sara Dall’ Armellina, Gayanè Aghakhanyan, Alessio Rizzo, Salvatore Claudio Fanni et autres

Introduction: Prostate-Specific Membrane Antigen (PSMA) is a transmembrane glycoprotein initially identified in prostate cancer (PCa) but also expressed in the neovasculature of various solid tumors. Recently, PSMA PET has emerged as a promising tool for detecting brain metastases (BMs) from non-prostatic cancers, …

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