Radiomics model based on contrast-enhanced computed tomography imaging for early recurrence monitoring after radical resection of AFP-negative hepatocellular carcinoma
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| הוצא לאור ב: | BMC Cancer vol. 24 (2024), p. 1 |
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| מחבר ראשי: | |
| מחברים אחרים: | , , , , , , , |
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Springer Nature B.V.
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| גישה מקוונת: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 001 | 3066880887 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1471-2407 | ||
| 024 | 7 | |a 10.1186/s12885-024-12436-x |2 doi | |
| 035 | |a 3066880887 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 58465 |2 nlm | ||
| 100 | 1 | |a Xuanzhi Yan | |
| 245 | 1 | |a Radiomics model based on contrast-enhanced computed tomography imaging for early recurrence monitoring after radical resection of AFP-negative hepatocellular carcinoma | |
| 260 | |b Springer Nature B.V. |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundAlthough radical surgical resection is the most effective treatment for hepatocellular carcinoma (HCC), the high rate of postoperative recurrence remains a major challenge, especially in patients with alpha-fetoprotein (AFP)-negative HCC who lack effective biomarkers for postoperative recurrence surveillance. Emerging radiomics can reveal subtle structural changes in tumors by analyzing preoperative contrast-enhanced computer tomography (CECT) imaging data and may provide new ways to predict early recurrence (recurrence within 2 years) in AFP-negative HCC. In this study, we propose to develop a radiomics model based on preoperative CECT to predict the risk of early recurrence after surgery in AFP-negative HCC.Patients and methodsPatients with AFP-negative HCC who underwent radical resection were included in this study. A computerized tool was used to extract radiomic features from the tumor region of interest (ROI), select the best radiographic features associated with patient’s postoperative recurrence, and use them to construct the radiomics score (RadScore), which was then combined with clinical and follow-up information to comprehensively evaluate the reliability of the model.ResultsA total of 148 patients with AFP-negative HCC were enrolled in this study, and 1,977 radiographic features were extracted from CECT, 2 of which were the features most associated with recurrence in AFP-negative HCC. They had good predictive ability in both the training and validation cohorts, with an area under the ROC curve (AUC) of 0.709 and 0.764, respectively. Tumor number, microvascular invasion (MVI), AGPR and radiomic features were independent risk factors for early postoperative recurrence in patients with AFP-negative HCC. The AUCs of the integrated model in the training and validation cohorts were 0.793 and 0.791, respectively. The integrated model possessed the clinical value of predicting early postoperative recurrence in patients with AFP-negative HCC according to decision curve analysis, which allowed the classification of patients into subgroups of high-risk and low-risk for early recurrence.ConclusionThe nomogram constructed by combining clinical and imaging features has favorable performance in predicting the probability of early postoperative recurrence in AFP-negative HCC patients, which can help optimize the therapeutic decision-making and prognostic assessment of AFP-negative HCC patients. | |
| 653 | |a Radiomics | ||
| 653 | |a Patients | ||
| 653 | |a α-Fetoprotein | ||
| 653 | |a Tomography | ||
| 653 | |a Medical prognosis | ||
| 653 | |a Hepatocellular carcinoma | ||
| 653 | |a Blood diseases | ||
| 653 | |a Surgery | ||
| 653 | |a Computed tomography | ||
| 653 | |a Risk factors | ||
| 653 | |a Biomarkers | ||
| 653 | |a Microvasculature | ||
| 653 | |a Image processing | ||
| 653 | |a Tumors | ||
| 653 | |a Liver cancer | ||
| 653 | |a Medical imaging | ||
| 653 | |a Ultrasonic imaging | ||
| 653 | |a Decision making | ||
| 700 | 1 | |a Li, Yicheng | |
| 700 | 1 | |a Wanying Qin | |
| 700 | 1 | |a Liao, Jiayi | |
| 700 | 1 | |a Fan, Jiaxing | |
| 700 | 1 | |a Xie, Yujin | |
| 700 | 1 | |a Wang, Zewen | |
| 700 | 1 | |a Li, Siming | |
| 700 | 1 | |a Liao, Weijia | |
| 773 | 0 | |t BMC Cancer |g vol. 24 (2024), p. 1 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3066880887/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3066880887/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3066880887/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |