À l’ère du tout connecté (l’internet des objets), le patient lui-même devient un «objet connecté» dans un écosystème caractérisé par le cercle vertueux des «big data» et de l’intelligence artificielle. Les algorithmes s’améliorent effectivement continuellement par le flux de données ininterrompu, ce qui permet ainsi une analyse de plus en plus adéquate, et donc de meilleures décisions.

Cet écosystème est uniquement possible dans le secteur des soins, si nous acceptons massivement la digitalisation des données. Compte tenu du déséquilibre entre l’offre et la demande de soins, les soignants se voient dans l’obligation de faire de plus en plus appel à la digitalisation et à l’automatisation des processus de soins et des décisions thérapeutiques. C’est bien entendu aussi le cas en oncologie. Nous allons illustrer ces concepts dans quelques domaines choisis.

À maintes reprises, nous avons déjà pu évoquer l’importance de la digitalisation dans tous les domaines d’activité, y compris dans le secteur des soins. L’oncologie au sens large, regroupant toutes les différentes sous-spécialités, n’échappera bien entendu pas à cette mouvance universelle. Aujourd’hui, Il est déjà impossible de faire la revue exhaustive des applications et impacts possibles, tellement l’évolution est rapide, à vrai dire quasi exponentielle. Pour sensibiliser les soignants actifs dans le secteur de la cancérologie à ce nouvel écosystème, nous nous contenterons volontairement de quelques exemples parlants dans les domaines du diagnostic (imagerie médicale et anatomopathologie), du suivi digital à distance, de la médecine de précision en oncologie, des changements en matière d’essais cliniques et du concept de real world evidence.

> Lisez l'article complet ici.

• Professeur et Chef de Service, Service de Radiothérapie, CHU de Liège

• 1.    Coucke P. Médecine du futur. L’intelligence artificielle au chevet des patients. Éditions Mardaga 2020, Bruxelles - Belgique. ISBN 9782804708160.

  2.    Coucke P. COVID-19: de la crise aux opportunités; le coronavirus, un accélérateur de changement qui force l’innovation? Éditions Mardaga 2021, Bruxelles – Belgique. ISBN 9782804709518. Version numérique ISBN9782804709525.

  3.    Adams K. Ezra earns FDA clearance for AI tool that makes MRI’s faster and cheaper. MedCityNews, juin 2023. https://medcitynews.com/2023/06/fda-artificial-intelligence-ai-mri-cancer-healthcare. Dernière consultation en ligne le 10 novembre 2023.

  4.    Sundar LKS, Yu J, Muzik O, et al. Fully automated, semantic segmentation of whole body ¹⁸F-FDG PET/CT images based on data-centric artificial intelligence. J Nucl Med 2022;63(12):1941-8.

  5.    Alhuseinalkhudhur A, Lindman H, Liss P, et al. Human epidermal growth factor receptor 2 targeting (⁶⁸Ga)Ga-ABY-025 PET/CT predicts early metabolic response in metastatic breast cancer. J Nucl Med 2023;64(9):1364-70.

  6.    McGale J, Khurana S, Huang A, et al. PET/CT and SPECT/CT imaging of HER-2 positive breast cancer. J Clin Med 2023;12(15):4882.

  7.    Chen Y, Taib AG, Darker IT, James JJ. Performance of breast cancer detection AI algorithm using the personal performance in mammographic screening scheme. Radiol 2023;308(3):e222399.

  8.    Dembrower K, Crippa A, Colón E, et al. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-redaer, non-inferiority study. Lancet Digit health 2023;5(10):e703-e711.

  9.    Larsen M, Aglen CF, Lee CI, et al. Artificial intelligence evaluation of 122,969 mammography examinations from a population-based screening program. Radiology 2022;303(3):502-11.

  10.   Freeman K, Geppert J, Stinton C, et al. Use of artificial intelligence for image analysis in breast cancer screening programs: systematic review of test accuracy. BMJ 2021; 374:n1872.

  11.   Ang A. Lunit lends hands to Sweden’s national cancer screening program. Mobihealthnews 2023. https://www.mobihealthnews.com/news/emea/lunit-lends-hands-swedens-national-cancer-screening-programme Dernière consultation en ligne, le 10 novembre 2023.

  12.   Dratsch T, Chen X, Mehrizi M, et al. Automation bias in mammography: the impact of artificial intelligence BI-RADS suggestions on reader performance. Radiology 2023;307(4). https://doi.org/10.1148/radiol.222176

  13.   Nam JG, Hwang EJ, Kim J, et al. AI improves nodule detection on chest radiographs in a health screening population: a randomized controlled trial. Radiology 2023;307(2):e221894.

  14.   Tiu E, Talius E, Patel P, et al. Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning. Nat Biomed Eng 2022;6:1399-406.

  15.   Causey JL, Zhang J, Ma S, et al. Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep 2018;8:9286.

  16.   Jacobs C, Setio AAA, Scholten ET, et al. Deep learning for lung cancer detection on screening CT scans: results of a large-scale public competition and an observer study with 11 radiologists. Radiol Artif Intell 2021;3(6):e210027.

  17.   Mikhael PG, Wohlwend J, Yala A, et al. Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. J Clin Oncol 2023;41(12):2191-200.

  18.   Xu K, Khan MS, Li TZ, et al. AI body composition in lung cancer screening: added value beyond lung cancer detection. Radiol 2023;308(1). https://doi.org/10.1148/radiol.222937.

  19.   Sverzellati N, Marrochio C. Deep learning based radiomics: pacing immunotherapy in lung cancer. Lancet Dig Health 2023;5(7):e396-e397.

  20.   Saad MB, Hong L, Aminu M, et al. Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study. Lancet Dig Health 2023;5(7):e404-e420.

  21.   Chung J. A more predictive and preventive cardiothoracic imaging interpretation model to avoid delayed diagnoses. MedCityNews August 2023. https://medcitynews.com/2023/08/a-more-predictive-and-preventive-cardiothoracic-imaging-interpretation-model-to-avoid-delayed-diagnoses/ Dernière consultation en ligne le 10 novembre 2023.

  22.   Bychkov A, Schubert M. Constant demand, patchy supply. https://thepathologist.com/outside-the-lab/constant-demand-patchy-supply Dernière consultation en ligne, le 10 novembre 2023.

  23.   Mageit S. UK rolls out AI-based cancer detection for NHS patients. Healthcare IT News 2020, https://www.healthcareitnews.com/news/emea/uk-rolls-out-ai-based-cancer-detection-nhs-patients Dernière consultation en ligne, le 10 novembre 2023.

  24.   Häger S. Will not using image analysis in pathology be perceived as irresponsible? Linkedin 2017 https://www.linkedin.com/pulse/using-image-analysis-pathology-perceived-simon-hager/ Dernière consultation en ligne, le 10 novembre 2023.

  25.   Cheikh JN, Nikulina B, Ma N, et al. (2023). Mapping the spatial proteome of head and neck tumors: key immune mediators and metabolic determinants in the tumor microenvironment. GEN Biotechnology 2023;2:418-34.

  26.   Dolgalev I, Zhou H, Murrell N, et al. Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma. Nat Communic 2023;14(1):6764.

  27.   Pantanowitz L, Quiroga-Garza GM, Bien L, et al. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsy: a blinded clinical validation and deployment study. Lancet Digit Health 2020;2(8):e407-e416.

  28.   Reuter E. Memorial Sloan Kettering spinout gets first FDA clearance for AI to detect prostate cancer. MedCityNews 2021, https://medcitynews.com/2021/09/memorial-sloan-kettering-spinout-gets-first-fda-clearance-for-ai-to-detect-prostate-cancer  Dernière consultation en ligne le 10 novembre 2023.

  29.   Ang A. 5 South Korean hospitals to try out Deep Bio’s AI support tool for prostate cancer diagnosis. Healthcare IT News 2022, https://www.healthcareitnews.com/news/asia/5-south-korean-hospitals-try-out-deep-bios-ai-support-tool-prostate-cancer-diagnosis  Dernière consultation en ligne le 10 novembre 2023.

  30.   Marin J. Owkin lance un consortium pour améliorer le traitement contre les cancers grâce à l’IA. L’usine Digitale 2023 ; https://www.usine-digitale.fr/article/owkin-lance-un-consortium-pour-ameliorer-le-traitement-contre-les-cancers-grace-a-l-ia.N2116756  Dernière consultation en ligne le 10 novembre 2023.

  31.   Tsai PC, Lee TH, Kuo KC, et al. Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nat Commun 2023;14(1):2102.

  32.   Wagner SJ, Reisenbüchler D, west NP, et al. Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study. Cancer Cell 2023;41(9):1650-61.

  33.   Hollon T, Jiang C, Chowdury A, et al. Artificial-intelligence-based molecular classification of diffuse gliomas using rapid label-free optical imaging. Nat Med 2023;29(4):828-32.

  34.   Patel KB, Tabriz AA, Turner K, et al. Telemedicine adoption in an NCI-designated cancer center during the COVID-19 pandemic: a report on patient experience of care. J Natl Comp Canc Netw 2023;21(5):496-502.

  35.   Topol E. Landmark digital medicine trial: patient-generated data improve cancer survival. Medscape Oncology 2017. https://www.medscape.com/viewarticle/881243  dernière consultation en ligne le 10 novembre 2023.

  36.   Mulcahy N. Web app ups lung cancer survival by 7 months. Medscape 2016. https://www.medscape.com/viewarticle/864564?form=fpf dernière consultation en ligne, le 10 novembre 2023.

  37.   Mesltrom LG, Rodin AS, Rossi LA, et al. Patient generated health data and electronic health record integration in oncologic surgery. A call for artificial intelligence and machine learning. J Surg Oncol 2021;123(1):52-60.

  38.   Vitard A. La start-up français DirectoSanté lance une solution de télésuivi pour les malades du cancer avec Bayer. L’Usine Digitale 2020. https://www.usine-digitale.fr/article/la-start-up-francaise-directosante-lance-une-solution-de-telesuivi-pour-les-malades-du-cancer-avec-bayer.N998829  Dernière consultation en ligne le 10 novembre 2023.

  39.   Vercell A, Gasteiger N, Yorke J, Dowding D. Patient-facing mobile apps that enable patient reported outcome data to be collected: a systematic review of content, functionality, quality, and ability to integrate with electronic health records. Int J Med Inform 2023, 170: 104931.

  40.   Patel KB, Turner K, Tabriz AA, et al. Estimated indirect cost savings of using telehealth among nonelderly patients with cancer. JAMA Netw Open 2023;6(1):e2250211.

  41.   Gresham W, Hendifart AE, Spiegel B, et al. Wearable activity monitors to assess performance status and predict clinical outcomes in advanced cancer patients. NPJ Digit Med 2018;5(1):27.

  42.   Vaidya A. Mayo Clinic strikes remote patient monitoring partnership with NXgenPort. MedCityNews 2021; https://medcitynews.com/2021/05/mayo-clinic-strikes-remote-patient-monitoring-partnership-with-nxgenport/  Dernière consultation en ligne, le 10 novembre 2023.

  43.   Medgadget editors. Siemens epoc NXS Host Mobile Computer cleared to help point of care blood testing. Medgadget 2020.  https://www.medgadget.com/2020/12/siemens-epoc-nxs-host-mobile-computer-cleared-to-help-with-point-of-care-blood-testing.html Dernière consultation en ligne, le 10 novembre 2023.

  44.   Adams K. Withings unveils urine analysis device that sits in your toilet bowl. MedCityNews 2023. https://medcitynews.com/2023/01/withings-unveils-urine-analysis-device-that-sits-in-your-toilet-bowl/  Dernière consultation en ligne, le 10 novembre 2023.

  45.   Vitard A. La Haute Autorité de santé prépare la généralisation du remboursement de la télésurveillance médicale. L’usine Digitale 2022. https://www.usine-digitale.fr/article/la-haute-autorite-de-sante-prepare-la-generalisation-du-remboursement-de-la-telesurveillance-medicale.N1778282  Dernière consultation en ligne, le 10 novembre 2023.

  46.   COVID-19 Le télémonitoring des patients est envisageable à l’avenir mais doit être peaufiné (KCE) MediQuality Medscape 2022. https://www.mediquality.net/be-fr/news/article/B1100378549_B1/covid-19-le-telemonitoring-des-patients-est-envisageable-a-l-avenir-mais-doit-etre-peaufine-kce  Dernière consultation en ligne, le 10 novembre 2023.

  47.   Lath G, Rhee R, Nikam K. Digital therapeutics in oncology: a better outlook for cancer patients in the future. J Artificial Intelligence and Big Data 2022;2(1):1-8.

  48.   Gussoni G, Ravot E, Zecchina M, et al. Digital therapeutics in oncology: findings barriers and prospects. A narrative review. Annals Res Oncol 2022;2(1):55-69.

  49.   Aapro M, Bossi P, Dasari A, et al. Digital health for optimal supportive care in oncology.: benefits, limits and future perspectives. Supportive Care in cancer 2020;28:4589-612.

  50.   Ning Y, Jia Z, Zhu R, et al. Effect and feasibility of gamification interventions for improving physical activity and health-related outcomes in cancer survivors: an early systemic review and meta-analysis. Support care Cancer 2022;31(1):92.

  51.   Sammut S-J, Crispin-Ortuzar M, Chin S-F, et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature 2022;601:623-9.

  52.   Johansson HJ, Socciarelli F, Vacanti NM, et al. Breast Cancer quantitative proteome and proteogenomic landscape. Nat Communic 2019;10(1):1600.

  53.   Rossi C, Cicalini I, Cufaro MC, et al. Breast cancer in the era of integrating “omics” approaches. Oncogenesis 2022;11(1):017.

  54.   Zitvogel L, Ma Y, Raoult D, Kroemer G, Gajewski TF. The microbiome in cancer immunotherapy: diagnostic tools and therapeutic strategies. Science 2018;359(6382):1366-70.

  55.   Park JS, Gazzaniga FS, Wu M, et al. Targeting PD-L2-RGMb overcomes microbiome-related immunotherapy resistance. Nature 2023;617(7960):377-85.

  56.   Soto-Pantoja DR, Gaber M, Arnone AA, et al. Diet alters entero-mammary signaling to regulate the breast microbiome and tumorigenesis. Cancer Res 2021;81(14):3890-904.

  57.   Davar D, Dzutsev AK, McCulloch JA, et al. Fecal microbiota transplant overcomes resistance to anti-PD1 therapy in melanoma patients. Science 2021;371(6529):595-602.

  58.   Vogtman E, Hua X, Yu G, et al. The oral microbiome and lung cancer risk: an analysis of 3 prospective cohort studies. J Natl Cancer Inst 2022;114(11):1501-10.

  59.   Stasiewicz M, Karpiński TM. The oral microbiome and its role in carcinogenesis. Semin cancer biol 2022;86(Pt3):633-42.

  60.   Comstock J. Precision medicine will require a lot more than just sequencing. MobiHealthNews 2016; https://www.mobihealthnews.com/content/precision-medicine-will-require-lot-more-just-sequencing  Dernière consultation en ligne, le 13 novembre 2023.

  61.   Deutsch I, Vorobiof D. Leveraging aggregated data from patient communities to improve cancer care. MedCityNews 2022. https://medcitynews.com/2022/02/leveraging-aggregated-data-from-patient-communities-to-improve-cancer-care/  Dernière consultation en ligne, le 13 novembre 2023.

  62.   Long S. How a pandemic shines a brighter light on the need for SDoH innovation. MedCityNews 2021. https://medcitynews.com/2021/11/how-a-pandemic-shines-a-brighter-light-on-the-need-for-sdoh-innovation/ Dernière consultation en ligne, le 13 novembre 2023.

  63.   Lone N, McPeake J, Stewart NI, et al. Influence of socioeconomic deprivation on interventions and outcomes for patients admitted with COVID-19 to critical care units in Scotland: a national cohort study. Lancet Reg Health Eur 2021;1:100005.

  64.   Wang F, Gao Y, Yu Y, et al. A systematic review and meta-analysis of 90 cohort studies of social isolation, loneliness and mortality. Nature Human Behaviour 2023;7:1307-19.

  65.   Boen CE, Barrow DA, Bensen JT, et al. Social relationship, inflammation and cancer survival. Cancer Epidemiol biomarkers Prev 2018;27(5):541-9.

  66.   National Institute of Health News Releases 2023. Pragmatica-Lung Study a streamlined model for future cancer clinical trials, begins enrolling patients. NIH News releases 2023; https://www.nih.gov/news-events/news-releases/pragmatica-lung-study-streamlined-model-future-cancer-clinical-trials-begins-enrolling-patients Dernière consultation en ligne, le 13 novembre 2023