Imaging personalizzato e rischio oncologico nei tumori femminili

Team Leader: Daniele La Forgia (SSD Radiodiagnostica Senologica)

Team members: Antonella Daniele, Maria Digennaro, Margherita Patruno (SC Oncologia Sperimentale e Gestione Biobanca), Porzia Casamassima, Vito Michele Garrisi, Giancarlo Sciortino (SSD Patologia Clinica), Gennaro Cormio, Francesca Arezzo, Anila Kardashi, Ambrogio Cazzolla (SSD Ginecologia), Lucia Rinaldi (Oncologia per la presa in carico del paziente oncologico), Annarita Fanizzi, Miria Lafranceschina, Rahel Signorile e Raffaella Massafra (Direzione Scientifico)

Work program
The Team “PERSONALIZED IMAGING and ONCOLOGICAL RISK IN FEMALE SPHERE CANCER” studies the correlations among imaging characteristics, phenotype, body constitution and nutrition in the incidence of neoplasms of the female sphere (breast and ovary cancers).
Recent research indicates that personalized risk has become an important parameter to define effective strategies for the prevention and treatment of neoplasms. From this point of view some oncological risk scores such as Tyrer Cuzick, Boodicea, Gail and so on, are more and more employed. Those, however, do not consider several important parameters and risk factors such as mammographic density, BMI, metabolism or eating habits.

The co-occurrence of chronic diseases in individuals, such as cardio-metabolic diseases and cancer, defined as multimorbidity, is also becoming more and more common and requires an ever more precise and personalized study.
A primary question is whether multimorbidity is due to shored risk factors or whether metabolic diseases increase the risk of subsequent cancer.
Another area of research is to precisely define the role of constitutional factors and structural-body composition in people with cancer. Thus, the group intends to make use of data deriving from the equipment present in our Institute (bioimpedance analysis precision scale and radiological imaging) also using automated software for the detection of particular characteristics (Volpara, software for the quantification of mammographic density) and appIying machine learning, deep learning and radiomics.
The research findings will contribute to public Health recommendations that include people living with metabolic diseases and cancer through targeted interventions.


Key networks: Alleanza contro il Cancro (ACC), Società italiana di Radiologia Medica (SIRM), Alleanza contro il tumore ovarico (ACTO)
Key Funding: Ricerca Corrente RC2022, Alleanza contro il Cancro (ACC), ACTO programs:

Key publications:

  • Daniele A, Guarini A, De Summa S et al. Body composition change. unhealthy lifestyles and steroid treatment as predictor of metabolic risk in non-hodgkin’s lymphoma survivors. Journal of Personalized Medicine, 2021, 11(3), 215

  • Daniele A, Divella R, Pilato B et al. Can harmful lifestyle, obesity and weight changes increase the risk of breast cancer in BRCA 1 and BRCA 2 mutation carriers? A Mini review. Hereditary Concer in Clinical Practice, 2021, 19(1), 45

  • Oliverio A, Radice P, Colombo M, Tommasi S, Daniele A et al. The impact of Mediterranean Dietary Intervention an Metabolic and Hormonal Parameters According to BRCA1/2 variant Type. Frontiers in Genetics, 2022, 13, 820878.

  • Oliverio A, Bruno E, Colombo M, Paradiso A, Tommosi S, Doniele A et al. BRCA1/2 variants and metabolic factors: Results from a cohort of italian female carriers. Cancers, 2020, 12(12), pp. 1-12, 3584

  • Bruno E, Oliverio A, Paradiso AV, Daniele A, Tommasi S, Digennaro M et al. Lifestyle Characteristics in Women Carriers of BRCA Mutations. Results From an Italian Trial Cohort. Clinical Breast Cancer, 2021, 21(3), pp. e168—e176

  • Daniele A, Paradiso AV, Divella R et al,The Role of Circulating Adiponectin and SNP276G> T at ADIPOQ Gene in BRCA-mutant Women. Cancer Genomics and Proteomics, 2020, 17(3), pp. 30)—307

  • Arezzo F, Loizzi V, Lo Forgia D........ Cormio G. Radiomics analysis in ovarian Cancer: A narrative review. Applied Sciences (Switzerland), 2021, 11(J 7), 7833.

  • Arezzp F, Cormio G, Lo Forgia D et al. A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovorion cancer patients.Archives of Gynecology ond Obstetrics, 2022

  • Petrillo A, Fusco R, Di Bernardo......... and La Forgia D. Prediction of Breast cancer Histological Outcome by Rodiomics and Artficial Intelligence Analysis in Contrast-Enhanced Mammography. Cancers, 2022, 14(9), 2232

  • Massafra P, Bove S, Lorusso V.......... and La Forgia D. Rodiomic feature reduction approach to predict breast cancer by contrast-enhanced spectral mammography images. Diagnostics, 2021, 11(4), 684.

  • Sardu C, Gotta G, Pieretti G, La Forgia D. et ol. Pre-MenopausaI Breast Fat Density Might Predict MACE During 10 Years of Follow-Up.' The BRECARD Study. JACC: Cordiovascular Imaging, 2021, 14(2), pp. 426—438

  • La Forgia D, Fanizzi A, Campobasso F et al.Rodiomic analysis in contrast-enhanced spectral mammography for predicting breast cancer histological outcome. Diagnostics, 2020, 10(9),708

  • Fanizzi A, Basile TMA, Losurdo L... and La Forgia D. A machine learning approach on multiscale texture analysis for breost microcalcification diagnosis,BMC Bioinformatics, 2020, 21, 91

  • Losurdo L, Fonizz/i A, Basile TMA. ........ ond La Forgio D. Padiomics analysis on contrast-enhanced​ spectral mammography images for breast cancer diagnosis. A pilot study. Entropy, 2019, 21(11), 1110.

  • La Forgia D, Vestito A, Lasciarrea M....and Fanizzi A. Response predictivity to neoodjuvant therapies in breast cancer: A qualitative analysis of background parenchymal enhancement in dce-mri.Journal of Personalized Medicine, 2021, 11(4), 256

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