{"id":1473,"date":"2025-01-30T14:12:46","date_gmt":"2025-01-30T14:12:46","guid":{"rendered":"https:\/\/cfiz.uoradea.ro\/?page_id=1473"},"modified":"2025-11-25T14:21:49","modified_gmt":"2025-11-25T14:21:49","slug":"pn-iv-p1-pce-2023-0042","status":"publish","type":"page","link":"https:\/\/cfiz.uoradea.ro\/?page_id=1473","title":{"rendered":"PN-IV-P1-PCE-2023-0042"},"content":{"rendered":"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
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I. PROJECT SUMMARY<\/strong><\/p>\r\n

ID:<\/strong> PN-IV-P1-PCE-2023-0042<\/strong><\/p>\r\n

Title<\/strong>:<\/strong> Delta-radiomics for personalised treatment of head and neck cancer using artificial intelligence (DeltaRadHNC)<\/strong><\/p>\r\n

Duration: Jan 2025 – Dec 2027<\/strong><\/p>\r\n

The challenge<\/strong>: Head and neck cancer (HNC) is a complex disease with incidence on the rise worldwide requiring continuous research for treatment optimisation and personalisation. HNC presents an exclusive set of diagnostic and therapeutic challenges due to its complex radiobiological behaviour and tumour heterogeneity. To advance HNC management there is need for a more in-depth evaluation of tumour features responsible for local recurrence and\/or second cancers, using radiomic and holomic approaches.<\/p>\r\n

The goal<\/strong>: The employment of radiomic analysis and holomic data to predict treatment outcome in HNC patients, risk of recurrence\/second cancer as well as timeline of recurrence. The radiomic features augmented with additional clinical information are used as input for deep learning networks trained and validated on retrospective data from a cohort of over 1,000 HNC patients. To achieve this goal, highly detailed and robust tumour imaging features (radiomic features) will be identified using deep learning, and further augmented with non-imaging data for a complex patient characterization (holomic features).<\/p>\r\n

The outcome<\/strong>: The development of a software system to offer clinicians a risk evaluation tool for recurrence\/second cancer with every follow-up image acquired (delta-radiomics), allowing for close monitoring of patients at risk. The research will also provide the clinician a rich set of data to enable treatment intensity adjustment for a more personalised and optimised therapy.<\/p>\r\n

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I. REZUMATUL PROIECTULUI<\/strong><\/p>\r\n

ID:<\/strong> PN-IV-P1-PCE-2023-0042<\/strong><\/p>\r\n

Titlu<\/strong>:<\/strong> Aplicarea delta-radiomicii utiliz\u00e2nd inteligen\u021ba artificial\u0103 pentru personalizarea tratamentului \u00een oncologia ORL (DeltaRadHNC)<\/strong><\/p>\r\n

Durata: Ian 2025 – Dec 2027<\/strong><\/p>\r\n

Provocarea<\/strong>: Cancerul ORL este o boal\u0103 complex\u0103 cu o inciden\u021b\u0103 \u00een cre\u0219tere, necesit\u00e2nd cercet\u0103ri continue pentru optimizarea tratamentului. Acesta prezint\u0103 un set exclusiv de provoc\u0103ri privind diagnosticul \u0219i tratamentul datorit\u0103 complexit\u0103\u021bii radiobiologice \u0219i a eterogeneit\u0103\u021bii tumorale. Optimizarea rezultatelor clinice necesit\u0103 o evaluare mai profund\u0103 a caracteristicilor tumorale responsabile de recuren\u021b\u0103 \u0219i\/sau de cancere secundare, aplic\u00e2nd abordarea radiomic\u0103 \u0219i holomic\u0103.<\/p>\r\n

Obiectivul<\/strong>: Utilizarea analizei radiomice \u0219i a datelor holomice pentru a prezice rezultatul tratamentului, riscul de recuren\u021b\u0103 precum \u0219i timpul probabil de apari\u021bie a recuren\u021bei. Caracteristicile radiomice \u00eembog\u0103\u021bite cu informa\u021bii clinice vor fi utilizate ca input pentru re\u021belele de \u00eenv\u0103\u021bare profund\u0103 instruite \u0219i validate pe date retrospective de la o cohort\u0103 de peste 1.000 pacien\u021bi ORL. Pentru atingerea obiectivului vor fi identificate caracteristici imagistice tumorale detaliate \u0219i robuste (radiomica) folosind \u00eenv\u0103\u021barea profund\u0103, suplimentate cu date non-imagistice pentru caracterizarea complex\u0103 a pacientului (holomica).<\/p>\r\n

Rezultatul<\/strong>: Dezvoltarea unui sistem software pentru a oferi medicilor un instrument de evaluare a riscului de recuren\u021b\u0103\/cancer secundar la fiecare imagine post-tratament (delta-radiomic\u0103), permi\u021b\u00e2nd monitorizarea atent\u0103 a pacien\u021bilor cu risc. Adi\u021bional, proiectul va furniza un set bogat de date care s\u0103 permit\u0103 ajustarea intensit\u0103\u021bii tratamentului pentru o terapie optim\u0103, personalizat\u0103.<\/p>\r\n<\/td>\r\n<\/tr>\r\n\r\n

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II. MAIN OBJECTIVE<\/strong><\/p>\r\n

The employment of radiomic analysis and clinical (holomic) data to predict treatment outcome and evaluate risk as well as timeline of possible recurrence in HNC patients. The radiomic features augmented with additional clinical information to be used as input for a deep learning network trained and validated on retrospective data from a cohort of over 1,000 HNC patients treated at the Amethyst Radiotherapy Centre, Cluj-Napoca.<\/span><\/p>\r\n

The specific objectives of the project are the following<\/strong>:<\/p>\r\n

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  • To connect a set of radiomic features to specific biological parameters (such as hypoxia, proliferative status, intrinsic radioresistance) in order to assist with treatment adaptation and optimisation of HNC patients.<\/li>\r\n
  • To develop a deep learning network to be trained and validated on radiomic features extracted from retrospective CT images of HNC patients. Enrich the radiomic information with patient-specific clinical data for a holomic<\/em> approach.<\/li>\r\n
  • To use the trained deep learning network to predict treatment outcome for individual patients.<\/li>\r\n
  • To develop a secondary deep learning network trained to detect patients with suboptimal post-treatment CT images that require more specific imaging (PET, MRI).<\/li>\r\n
  • To perform an early detection of patients with high risk of recurrence \/ secondary cancer using the variation in radiomic features between sequential CT images taken at different time points (delta-radiomics<\/em>).<\/li>\r\n<\/ul>\r\n<\/td>\r\n
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II. OBIECTIVUL PRINCIPAL<\/strong><\/p>\r\n

Utilizarea analizei radiomice \u0219i a datelor clinice (holomice) pentru a prezice rezultatul tratamentului \u0219i a evalua riscul, precum \u0219i posibilitatea recuren\u021bei la pacien\u021bii cu cancere din sfera ORL. Caracteristicile radiomice completate cu informa\u021bii clinice suplimentare, vor fi utilizate ca date de intrare pentru o re\u021bea de \u00eenv\u0103\u021bare profund\u0103, antrenat\u0103 \u0219i validat\u0103 pe date retrospective de la o cohort\u0103 de peste 1.000 de pacien\u021bi cu cancer din sfera ORL trata\u021bi la Centrul de Radioterapie Amethyst, Cluj-Napoca.<\/span><\/p>\r\n

Obiectivele specifice ale proiectului sunt urm\u0103toarele<\/strong>:<\/p>\r\n

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  • Conectarea unui set de caracteristici radiomice la parametri biologici specifici (cum ar fi hipoxia, statusul proliferativ, radiorezisten\u021ba intrinsec\u0103) pentru a ajuta la adaptarea tratamentului \u0219i optimizarea pacien\u021bilor cu cancer din sfera ORL.<\/li>\r\n
  • Dezvoltarea unei re\u021bele de \u00eenv\u0103\u021bare profund\u0103 care s\u0103 fie antrenat\u0103 \u0219i validat\u0103 pe baza caracteristicilor radiomice extrase din imaginile CT retrospective ale pacien\u021bilor cu cancer ORL. \u00cembog\u0103\u021birea informa\u021biilor radiomice cu date clinice specifice pacientului pentru o abordare holomic\u0103<\/em>.<\/li>\r\n
  • Utilizarea re\u021belei de \u00eenv\u0103\u021bare profund\u0103 pentru a prezice rezultatul tratamentului pentru fiecare pacient, \u00een mod personalizat.<\/li>\r\n
  • Dezvoltarea unei re\u021bele secundare de \u00eenv\u0103\u021bare profund\u0103, antrenat\u0103 pentru identificarea pacien\u021bilor cu imagini CT post-tratament suboptimale care necesit\u0103 imagistic\u0103 mai specific\u0103 (PET, IRM).<\/li>\r\n
  • Realizarea unei detect\u0103ri precoce a pacien\u021bilor cu risc crescut de recuren\u021b\u0103 \/ cancer secundar utiliz\u00e2nd varia\u021bia caracteristicilor radiomice \u00eentre imaginile CT secven\u021biale realizate la momente diferite (delta-radiomic\u0103<\/em>).<\/li>\r\n<\/ul>\r\n<\/td>\r\n<\/tr>\r\n\r\n
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III. THE RESEARCH TEAM<\/strong><\/p>\r\n

PROJECT LEADER<\/strong>:<\/strong><\/p>\r\n<\/td>\r\n

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III. ECHIPA DE CERCETARE<\/strong><\/p>\r\n

DIRECTOR PROIECT<\/strong>:<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n

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Loredana Gabriela MARCU \u00a0 (https:\/\/www.scopus.com\/authid\/detail.uri?authorId=7006835272<\/a>)<\/p>\r\n<\/td>\r\n<\/tr>\r\n

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MEMBERS<\/strong>:<\/strong><\/p>\r\n<\/td>\r\n

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MEMBRI<\/strong>:<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n

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Oreste STRACIUC \u00a0 (https:\/\/www.scopus.com\/authid\/detail.uri?authorId=35191261800<\/a>)<\/p>\r\n

David MARCU \u00a0 (https:\/\/www.scopus.com\/authid\/detail.uri?authorId=57188819985<\/a>)<\/p>\r\n

Renata ZAHU \u00a0 (https:\/\/www.scopus.com\/authid\/detail.uri?authorId=57880563900<\/a>)<\/p>\r\n

Ioana-Claudia COSTIN \u00a0 (https:\/\/www.scopus.com\/authid\/detail.uri?authorId=57860742300<\/a>)<\/p>\r\n

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IV. PREVIOUS PUBLICATIONS RELATED TO THE PROJECT<\/strong><\/p>\r\n

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IV. PUBLICA\u021aII ANTERIOARE ALE DIRECTORULUI DE PROIECT LEGATE DE TEMATICA PROIECTULUI<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n\r\n

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  1. L. Marcu, D. Marcu<\/li>\r\n<\/ol>\r\n

    Pharmacogenomics and Big Data in medical oncology: developments and challenges<\/em>
    Therapeutic Advances Med Oncol 16:1-18 (2024)<\/p>\r\n

      \r\n
    1. D. Marcu, C. Grava, L. Marcu<\/li>\r\n<\/ol>\r\n

      Current role of delta-radiomics in head and neck oncology<\/em>
      Int J Molecular Sciences 24(3):2214 (2023)<\/p>\r\n

        \r\n
      1. L. Marcu, D. Marcu<\/li>\r\n<\/ol>\r\n

        Current omics trends in personalised head and neck cancer chemoradiotherapy<\/em>
        Journal of Personalized Medicine 11(11):1094 (2021)<\/p>\r\n

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        1. L. Marcu, D. Marcu<\/li>\r\n<\/ol>\r\n

          Points of view on Artificial Intelligence in radiology – one good, one bad and one fuzzy<\/em>
          Health and Technology 11:17-22 (2021)<\/p>\r\n

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          1. L. Marcu, C. Boyd, E. Bezak<\/li>\r\n<\/ol>\r\n

            Current issues regarding artificial intelligence in cancer and health care. Implications for medical physicists and biomedical engineers.<\/em>
            Health and Technology 9(4):375-381 (2019)<\/p>\r\n

              \r\n
            1. L. Marcu, J. Forster, E. Bezak<\/li>\r\n<\/ol>\r\n

              The potential role of radiomics and radiogenomics in patient stratification by tumor hypoxia status<\/em>
              J American College Radiol 16(9PB):1330-1338 (2019)<\/p>\r\n

                \r\n
              1. L. Marcu, C. Boyd, E. Bezak<\/li>\r\n<\/ol>\r\n

                Feeding the data monster: data science in head and neck cancer for personalized therapy<\/em>
                J American College Radiol 16(12):1695-1701 (2019)<\/p>\r\n

                  \r\n
                1. L. Marcu, P. Reid, E. Bezak<\/li>\r\n<\/ol>\r\n

                  The promise of novel biomarkers for head and neck cancer from an\u00a0imaging perspective<\/em>
                  Int J Molec Sci 19(9):2511 (2018)<\/p>\r\n

                    \r\n
                  1. L. Marcu, L. Moghaddasi, E. Bezak<\/li>\r\n<\/ol>\r\n

                    Imaging of tumour characteristics and molecular pathways with PET: developments over the last decade towards personalised cancer therapy<\/em>
                    Int J Radiat Oncol Biol Phys 102(4):1165-1182 (2018)<\/p>\r\n

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                    1. L. Marcu, D. Marcu<\/li>\r\n<\/ol>\r\n

                      In silico modelling of radiation effects towards personalised treatment in radiotherapy<\/em>
                      AIP Conference Proceedings 1916(1): 040001 (2017)<\/p>\r\n

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V. DISSEMINATION OF RESULTS – IMPLEMENTATION YEAR 2025<\/strong><\/p>\r\n

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V. DISEMINAREA REZULTATELOR – ANUL DE IMPLEMENTARE 2025<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n\r\n

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    \r\n
  1. L. Marcu, D. Marcu, I.C. Costin, R. Zahu, O. Straciuc<\/li>\r\n<\/ol>\r\n

    From immunohistochemistry to machine learning-based patient stratification by tumour proliferation characteristics in head and neck cancer.<\/em>
    Critical Reviews in Oncology\/Hematology 216:104987 (2025)
    DOI: 10.1016\/j.critrevonc.2025.104987
    Q1 (IF, AIS); AIS = 1,483<\/p>\r\n

      \r\n
    1. C. Costin, L. Marcu, D. Marcu, R. Zahu, O. Straciuc<\/li>\r\n<\/ol>\r\n

      Is the use of machine learning in head and neck cancer radiotherapy supported by clinical trials?<\/em>
      Physica Medica 136:105036 (2025)
      DOI: 10.1016\/j.ejmp.2025.105036
      Q2 (IF, AIS); AIS = 0,715<\/p>\r\n

        \r\n
      1. L. Marcu, D. Marcu<\/li>\r\n<\/ol>\r\n

        Examining the role of AI in cancer imaging through the lens of clinical studies.<\/em>
        Health & Technology (2025)
        DOI: https:\/\/doi.org\/10.1007\/s12553-025-01019-w
        Q3 (IF), Q4 (AIS); AIS = 0,507<\/p>\r\n

          \r\n

           <\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/table>\r\n\r\n\r\n","protected":false},"excerpt":{"rendered":"

          I. PROJECT SUMMARY ID: PN-IV-P1-PCE-2023-0042 Title: Delta-radiomics for personalised treatment of head and neck cancer using artificial intelligence (DeltaRadHNC) Duration: Jan 2025 – Dec 2027 The challenge: Head and neck cancer (HNC) is a complex disease with incidence on the Read More …<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1473","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cfiz.uoradea.ro\/index.php?rest_route=\/wp\/v2\/pages\/1473","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cfiz.uoradea.ro\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cfiz.uoradea.ro\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cfiz.uoradea.ro\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/cfiz.uoradea.ro\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1473"}],"version-history":[{"count":29,"href":"https:\/\/cfiz.uoradea.ro\/index.php?rest_route=\/wp\/v2\/pages\/1473\/revisions"}],"predecessor-version":[{"id":1507,"href":"https:\/\/cfiz.uoradea.ro\/index.php?rest_route=\/wp\/v2\/pages\/1473\/revisions\/1507"}],"wp:attachment":[{"href":"https:\/\/cfiz.uoradea.ro\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1473"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}