Barbara Alicja Jereczek-Fossa

Prof. Barbara Alicja Jereczek-Fossa

Division of Radiation Oncology, European Institute of Oncology (IEO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), 20141 Milan, Italy
orcid

Research Focus & Metrics

Academic Career Length

24Years (2003-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2012 in International Journal of Radiation Oncology, Biology, Physics
Verified
Journal Article
Closed access
BJBarbara Alicja Jereczek-Fossa
Barbara Alicja Jereczek-Fossa
GBGiancarlo Beltramo
Giancarlo Beltramo
LFLaura Fariselli
Laura Fariselli
CFCristiana Fodor
Cristiana Fodor
LSLuigi Santoro
Luigi Santoro
RORoberto Orecchia
Roberto Orecchia


AKAida Kusiak
Aida Kusiak
BJBarbara Alicja Jereczek-Fossa
Barbara Alicja Jereczek-Fossa
DCDominika Cichońska
Dominika Cichońska
DADaniela Alterio
Daniela Alterio
Oral mucositis is a toxic side effect of non-surgical cancer treatments: chemotherapy and radiotherapy, which strongly impair quality of life and can not only cause strong pain, but also lead to problems with basic physiological needs as eating and swallowing. Development of oral mucositis is associated with type, dosage, and schedule of radiation or chemotherapy and other factors related to patients. Management of oral mucositis is a valid problem, requiring topical application of anesthetics, coating agents, cryotherapy, low level laser therapy, pharmacological methods as usage of keratinocyte growth factors, supplementation of vitamins, and a proper diet. Another approach to oral mucositis measurement includesphotobiomodulation, which brings analgesic and anti-inflammatory effects.Although oral mucositis is a general health issues, the role of proper dental care is essential. It should include elimination of all potential sources of mucosal injury and microorganisms inhabiting theoral cavity through oral hygiene education, professional management ofdental plaque, and treatment of the caries and periodontium, which are necessary to reduce the risk of inflammation in the oral cavity. This paper describes the possibilities of monitoring oral mucositis, taking into account the latest therapeutic achievements.

3 June 2024 in Radiotherapy and Oncology
Verified
Journal Article
Open access
CHCoen Hurkmans
Coen Hurkmans
Jean-Emmanuel BibaultJean-Emmanuel Bibault
Jean-Emmanuel Bibault
KBKristy K. Brock
Kristy K. Brock
WEWouter van Elmpt
Wouter van Elmpt
MFMary Feng
Mary Feng
MVMichael Velec
Michael Velec
Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap. A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended. The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated. A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

7 July 2023 in Diagnostics
Verified
Journal Article
Open access
PDF file
MFMatteo Ferro
Matteo Ferro
UFUgo Falagario
Ugo Falagario
Biagio BaroneBiagio Barone
Biagio Barone
Martina MaggiMartina Maggi
Martina Maggi
FCFelice Crocetto
Felice Crocetto
Octavian Sabin TataruOctavian Sabin Tataru
Octavian Sabin Tataru
Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.

25 June 2021 in Current Oncology
Verified
Journal Article
Open access
PDF file
Filippo PesapaneFilippo Pesapane
Filippo Pesapane
Anna RotiliAnna Rotili
Anna Rotili
GAGiorgio Maria Agazzi
Giorgio Maria Agazzi
FBFrancesca Botta
Francesca Botta
SRSara Raimondi
Sara Raimondi
ECEnrico Cassano
Enrico Cassano
Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.