Current Situation of Proton Therapy for Hodgkin Lymphoma: From Expectations to Evidence

Loap, Pierre; Mirandola, A.; Marzi, Ludovic De; Dendale, R.; Iannalfi, Alberto; Vitolo, Viviana; Barcellini, Amelia; Filippi, Andrea Riccardo; Jereczek‐Fossa, Barbara Alicja; Kirova, Youlia; Orlandi, Ester

doi:10.3390/cancers13153746

Cited by 15 publications

(15 citation statements)

References 67 publications

(87 reference statements)

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“…The only DDA reported by DisGeNET not present in the top 100 edges from SicknessMiner was rheumatoid arthritis that was assessed as associated with both leukemia and multiple myeloma. However, SicknessMiner also retrieved AutoImmune Diseases (AID) MeSH term, for which rheumatoid arthritis in one [ 28 ], in the top co-mentions. Finally, it is noteworthy to mention that the top 30% results retrieved from DisGeNET are some form or variation of the original query, as “childhood leukemia” or “adult lymphoma” whereas SicknessMiner, comparatively, retrieves less of those variations of the original query, highlighting “genetic diseases, inborn”, “epstein-barr virus infections”, and “myelodysplastic syndromes” in the top 30 edges.…”

Section: Discussionmentioning

confidence: 99%

SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations

et al. 2021

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Background Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.

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Section: Discussionmentioning

confidence: 99%

SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations

et al. 2021

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“…Proton beams deliver most of the energy towards the end of their range, at the well‐known “Bragg peak.” This intrinsic characteristic allows for a significant decrease in the distant‐to‐target dose deposition compared with conventional photon beam RT, 46 leading to a dosimetric benefit in terms of Organ At Risks (OARs) sparing. For these reasons, to minimize the late toxicities, proton therapy has recently been proposed for HL, especially in the case of mediastinal localization.…”

Section: The Lesson Learned: Treatment Related‐toxicitiesmentioning

confidence: 99%

Future perspectives of radiation therapy for Hodgkin Lymphoma: Risk‐adapted, response‐adapted, and safer than before

Saddi,

Barcellini,

Gotti

et al. 2024

Hematological Oncology

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Classical Hodgkin lymphoma is a lymphoproliferative disease with a good prognosis mainly seen in young people. Nevertheless secondary malignancy, cardiac disease and infertility may affect the long survivors with significant impact on quality of life, morbidity and overall survival. In the last decades several treatment strategies were evaluated to reduce the toxicity of first line treatment such as avoiding radiotherapy or its reduction in terms of dosage and extension. Many trials including interim Positron Emission Tomography evaluation fail to compare efficacy between combined modality treatment versus chemotherapy alone in particular in early stage disease. In this review we analyze which subset of patients could take advantage from proton therapy in terms of toxicity and cost effectiveness.

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“…Next to the historical brain and head and neck cancers, protons are currently also considered for e.g. breast cancer (Kowalchuk et al 2022), prostate (Poon et al 2022), and lymphoma treatments (Loap et al 2021), with clinical trials already on-going. Together with paediatric patients, this raises specific concerns regarding the risk of late radiation effects for patients having a good survival probability and long-life expectancy.…”

Section: Introductionmentioning

confidence: 99%

Modelling the HPRT-gene mutation induction of particle beams: systematic in vitro data collection, analysis and microdosimetric kinetic model implementation

Attili¹,

Scifoni²,

Tommasino³

2022

Phys. Med. Biol.

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Objective: Since the early years, particle therapy treatments have been associated with concerns for late toxicities, especially secondary cancer risk (SCR). Nowadays, this concern is related to patients for whom long-term survival is expected (e.g., breast cancer, lymphoma, paediatrics). In the aim to contribute to this research, we present a dedicated statistical and modelling analysis aiming at improving our understanding of the RBE for mutation induction (RBE_M ̃) for different particle species. Approach: We built a new database based on a systematic collection of RBE data for mutation assays of the gene encoding for the purine salvage enzyme hypoxanthine-guanine phosphoribosyltransferase (HPRT) from literature (105 entries, distributed among 3 cell lines and 16 particle species). The data were employed to perform statistical and modelling analysis. For the latter, we adapted the microdosimetric kinetic model (MKM) to describe the mutagenesis in analogy to lethal lesion induction. Main Results: Correlation analysis between RBE for survival (RBE_S) and RBE_M ̃ reveals significant correlation between these two quantities (ρ=0.86, p<0.05). The correlation gets stronger when looking at subsets of data based on cell line and particle species. We also show that the MKM can be successfully employed to describe RBE_M ̃, obtaining comparably good agreement with the experimental data. Remarkably, to improve the agreement with experimental data the MKM requires, consistently in all the analysed cases, a reduced domain size for the description of mutation induction compared to that adopted for survival. Significance: We were able to show that RBE_S and RBE_M ̃ are strongly related quantities. We also showed for the first time that the MKM could be successfully applied to the description of mutation induction, representing an endpoint different from the more traditional cell killing. In analogy to the RBE_S, RBE_M ̃ can be implemented into treatment planning evaluations.

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Current Situation of Proton Therapy for Hodgkin Lymphoma: From Expectations to Evidence

Cited by 15 publications

References 67 publications

SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations

SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations

Future perspectives of radiation therapy for Hodgkin Lymphoma: Risk‐adapted, response‐adapted, and safer than before

Modelling the HPRT-gene mutation induction of particle beams: systematic in vitro data collection, analysis and microdosimetric kinetic model implementation

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