A virtual biopsy of liver parenchyma to predict the outcome of liver resection
The preoperative risk assessment of liver resections (LR) is still an open issue. Liver parenchyma characteristics influence the outcome but cannot be adequately evaluated in the preoperative setting. The present study aims to elucidate the contribution of the radiomic analysis of non-tumoral parenchyma to the prediction of complications after elective LR. All consecutive patients undergoing LR between 2017 and 2021 having a preoperative computed tomography (CT) were included. Patients with associated biliary/colorectal resection were excluded. Radiomic features were extracted from a virtual biopsy of non-tumoral liver parenchyma (a 2 mL cylinder) outlined in the portal phase of preoperative CT. Data were internally validated. Overall, 378 patients were analyzed (245 males/133 females—median age 67 years—39 cirrhotics). Radiomics increased the performances of the preoperative clinical models for both liver dysfunction (at internal validaton, AUC = 0.727 vs. 0.678) and bile leak (AUC = 0.744 vs. 0.614). The final predictive model combined clinical and radiomic variables: for bile leak, segment 1 resection, exposure of Glissonean pedicles, HU-related indices, NGLDM_Contrast, GLRLM indices, and GLZLM_ZLNU; for liver dysfunction, cirrhosis, liver function tests, major hepatectomy, segment 1 resection, and NGLDM_Contrast. The combined clinical-radiomic model for bile leak based on preoperative data performed even better than the model including the intraoperative data (AUC = 0.629). The textural features extracted from a virtual biopsy of non-tumoral liver parenchyma improved the prediction of postoperative liver dysfunction and bile leak, implementing information given by standard clinical data. Radiomics should become part of the preoperative assessment of candidates to LR. Graphical abstract.
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:75 |
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| Enthalten in: |
Updates in surgery - 75(2023), 6 vom: 05. Apr., Seite 1519-1531 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Laino, Maria Elena [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
© Italian Society of Surgery (SIC) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s13304-023-01495-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a The preoperative risk assessment of liver resections (LR) is still an open issue. Liver parenchyma characteristics influence the outcome but cannot be adequately evaluated in the preoperative setting. The present study aims to elucidate the contribution of the radiomic analysis of non-tumoral parenchyma to the prediction of complications after elective LR. All consecutive patients undergoing LR between 2017 and 2021 having a preoperative computed tomography (CT) were included. Patients with associated biliary/colorectal resection were excluded. Radiomic features were extracted from a virtual biopsy of non-tumoral liver parenchyma (a 2 mL cylinder) outlined in the portal phase of preoperative CT. Data were internally validated. Overall, 378 patients were analyzed (245 males/133 females—median age 67 years—39 cirrhotics). Radiomics increased the performances of the preoperative clinical models for both liver dysfunction (at internal validaton, AUC = 0.727 vs. 0.678) and bile leak (AUC = 0.744 vs. 0.614). The final predictive model combined clinical and radiomic variables: for bile leak, segment 1 resection, exposure of Glissonean pedicles, HU-related indices, NGLDM_Contrast, GLRLM indices, and GLZLM_ZLNU; for liver dysfunction, cirrhosis, liver function tests, major hepatectomy, segment 1 resection, and NGLDM_Contrast. The combined clinical-radiomic model for bile leak based on preoperative data performed even better than the model including the intraoperative data (AUC = 0.629). The textural features extracted from a virtual biopsy of non-tumoral liver parenchyma improved the prediction of postoperative liver dysfunction and bile leak, implementing information given by standard clinical data. Radiomics should become part of the preoperative assessment of candidates to LR. Graphical abstract | ||
| 650 | 4 | |a Liver resection | |
| 650 | 4 | |a Liver surgery | |
| 650 | 4 | |a Postoperative liver failure | |
| 650 | 4 | |a Bile leak | |
| 650 | 4 | |a Radiomics and texture analysis | |
| 650 | 4 | |a Artificial intelligence | |
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| 700 | 1 | |a Gionso, Matteo |4 aut | |
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| 700 | 1 | |a Sollini, Martina |4 aut | |
| 700 | 1 | |a Chiti, Arturo |4 aut | |
| 700 | 1 | |a Savevsky, Victor |4 aut | |
| 700 | 1 | |a Torzilli, Guido |4 aut | |
| 700 | 1 | |a Viganò, Luca |0 (orcid)0000-0002-4108-4832 |4 aut | |
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