2023
AI in cancer research: from histology to personalized medicine
KOVAČOVICOVÁ, Petra a Petr VAŇHARAZákladní údaje
Originální název
AI in cancer research: from histology to personalized medicine
Autoři
KOVAČOVICOVÁ, Petra (703 Slovensko) a Petr VAŇHARA (203 Česká republika, domácí)
Vydání
BIOCEV Regeneration III, 2023
Další údaje
Jazyk
angličtina
Typ výsledku
Vyžádané přednášky
Obor
30402 Technologies involving the manipulation of cells, tissues, organs or the whole organism
Stát vydavatele
Česká republika
Utajení
není předmětem státního či obchodního tajemství
Kód RIV
RIV/00216224:14110/23:00134676
Organizační jednotka
Lékařská fakulta
Klíčová slova anglicky
artificial intelligence; miachine learning; pancreatic cancer; biotechnology
Změněno: 24. 11. 2023 07:39, doc. RNDr. Petr Vaňhara, Ph.D.
Anotace
V originále
Artificial intelligence (AI) is becoming an inevitable part of a modern world medicine and biomedical industry that is becoming more and more digital. AI can analyze huge amounts of data, uncovering invisible patterns, speeding up processes and improving performance in many areas. Moreover, thanks to the process of continuous learning and thanks to updates, AI has an amazing capacity for continuous improvement. In histopathology, AI methods are mainly used in diagnosing diseases and designing treatment plans. They can be used, for example, for automatic classification in diagnosis, prediction of patient survival and response to treatment, segmentation, object detection, and analysis of microscope images[1]. AI methods are often used as a helpful screening tool, predicting the presence or absence of cancer. For example, Wang et al. developed a model that can discriminate between patients with pancreatic ductal adenocarcinoma (PDAC) and healthy controls with 86.74% accuracy[2]. Sohn et al. developed a different AI algorithm that can classify these patients with accuracy 91.00%[3]. Lee et al. used AI to create a model for predicting postoperative survival in PDAC patients. The AUC of the model for predicting 2-year overall survival in the test dataset was 0.76 and 1-year recurrence-free survival 0.74[4]. These results show that the use of AI in histology is promising and can significantly improve the diagnosis and prediction of patient survival. Supported by Ministry of Health of the Czech Republic, grant nr. NU23-08-00241 and by Masaryk University, project nr. MUNI/A/1301/2022. [1] S. Försch, F. Klauschen, P. Hufnagl, and W. Roth, “Artificial Intelligence in Pathology,” Dtsch Arztebl Int, vol. 118, no. 12, pp. 194–204, Mar. 2021, doi: 10.3238/arztebl.m2021.0011. [2] G. Wang et al., “Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics,” Sci Adv, vol. 7, no. 52, p. eabh2724, doi: 10.1126/sciadv.abh2724. [3] A. Sohn et al., “A deep learning model to triage and predict adenocarcinoma on pancreas cytology whole slide imaging,” Sci Rep, vol. 13, p. 16517, Oct. 2023, doi: 10.1038/s41598-023-42045-w. [4] W. Lee et al., “Preoperative data-based deep learning model for predicting postoperative survival in pancreatic cancer patients,” Int J Surg, vol. 105, p. 106851, Sep. 2022, doi: 10.1016/j.ijsu.2022.106851.
Návaznosti
| MUNI/A/1301/2022, interní kód MU |
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| NU23-08-00241, projekt VaV |
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